From eadbc968f86e4230a0c89958a39bbe01ae613598 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Thu, 2 Feb 2023 06:25:14 +0100 Subject: [PATCH 001/159] Revert reverting package installation during CI This reverts commit 790afef6238f9450744b9347b1264c1296615a82 so that the code can be merged back into dev, once issue #589 is fixed. --- .github/workflows/ci.yaml | 4 ++++ noxfile.py | 9 +++++++++ 2 files changed, 13 insertions(+) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 83781f12e..4f2eb32f3 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -52,3 +52,7 @@ jobs: run: "python -m nox -s build" env: PLATFORM: ${{ matrix.os.image }} + - name: "Install the package" + run: "python -m nox -s install" + env: + PLATFORM: ${{ matrix.os.image }} diff --git a/noxfile.py b/noxfile.py index 98f0c9eb6..7c12709ae 100644 --- a/noxfile.py +++ b/noxfile.py @@ -1,3 +1,4 @@ +from pathlib import Path from pprint import pformat import nox @@ -58,3 +59,11 @@ def build(session): session.install("twine") session.run("python", "setup.py", "bdist", "bdist_wheel") session.run("twine", "check", "dist/eTraGo*") + + +@nox.session(python=["3", "3.8", "3.9", "3.10", "3.11"]) +def install(session): + """Install the package.""" + setdefaults(session) + session.run("python", "-mpip", "install", "--upgrade", "pip") + session.run("python", "-mpip", "install", *Path("dist").glob("*.whl")) From dc8a7ed2469cafd84514d0f040d77f0d7673214e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Sat, 4 Feb 2023 00:56:57 +0100 Subject: [PATCH 002/159] Force an error on "sklearn" installation attempts According to "sklearn"'s [description][0], installing the package doesn't always raise an error, but only according to a specific schedule. Obviously this doesn't work for tests. Fortunately setting the `SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL` environment variable to `False` always forces the error. [0]: https://pypi.org/project/sklearn/#description --- noxfile.py | 1 + 1 file changed, 1 insertion(+) diff --git a/noxfile.py b/noxfile.py index 7c12709ae..9764d83de 100644 --- a/noxfile.py +++ b/noxfile.py @@ -65,5 +65,6 @@ def build(session): def install(session): """Install the package.""" setdefaults(session) + session.env["SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL"] = "False" session.run("python", "-mpip", "install", "--upgrade", "pip") session.run("python", "-mpip", "install", *Path("dist").glob("*.whl")) From 87461e99f604399b08bbb4a9d427e01a7c2e8bc1 Mon Sep 17 00:00:00 2001 From: Tetens Date: Tue, 14 Mar 2023 16:39:04 +0100 Subject: [PATCH 003/159] updated gas.py, electrical.py and gas.py --- etrago/cluster/electrical.py | 265 ++++++++++++++++++++++++++++------- etrago/cluster/gas.py | 140 +++++++++++++++--- etrago/cluster/spatial.py | 121 ++++++++++------ 3 files changed, 411 insertions(+), 115 deletions(-) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index 2e0fac24e..0555d5fa9 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -63,7 +63,21 @@ def _leading(busmap, df): - """ """ + """ + Returns a function that computes the leading bus_id for a given mapped list of buses. + + Parameters + ----------- + busmap : dict + A dictionary that maps old bus_ids to new bus_ids. + df : pandas.DataFrame + A DataFrame containing network.buses data. Each row corresponds to a unique bus + + Returns + -------- + leader : function + A function that returns the leading bus_id for the argument `x`. + """ def leader(x): ix = busmap[x.index[0]] @@ -73,6 +87,27 @@ def leader(x): def adjust_no_electric_network(etrago, busmap, cluster_met): + """ + Adjusts the non-electric network based on the electrical network (esp. eHV network), + adds the gas buses to the busmap, and creates the new buses for the non-electric network. + + Parameters + ---------- + etrago : Etrago + An instance of the Etrago class. + busmap : dict + A dictionary that maps old bus_ids to new bus_ids. + cluster_met : str + A string indicating the clustering method to be used. + + Returns + ------- + network : pypsa.Network + Container for all network components of the clustered network. + + busmap : dict + Maps old bus_ids to new bus_ids including all sectors. + """ network = etrago.network # network2 is supposed to contain all the not electrical or gas buses and links network2 = network.copy(with_time=False) @@ -215,18 +250,17 @@ def adjust_no_electric_network(etrago, busmap, cluster_met): def cluster_on_extra_high_voltage(etrago, busmap, with_time=True): - """Main function of the EHV-Clustering approach. Creates a new clustered + """ + Main function of the EHV-Clustering approach. Creates a new clustered pypsa.Network given a busmap mapping all bus_ids to other bus_ids of the same network. Parameters ---------- - network : pypsa.Network - Container for all network components. - + etrago : Etrago + An instance of the Etrago class busmap : dict Maps old bus_ids to new bus_ids. - with_time : bool If true time-varying data will also be aggregated. @@ -234,7 +268,6 @@ def cluster_on_extra_high_voltage(etrago, busmap, with_time=True): ------- network : pypsa.Network Container for all network components of the clustered network. - busmap : dict Maps old bus_ids to new bus_ids including all sectors. """ @@ -346,9 +379,11 @@ def delete_ehv_buses_no_lines(network): to make possible the creation of busmaps based on electrical connections and other purposes. Additionally, it throws a warning to inform the user in case that any correction should be done. + Parameters ---------- network : pypsa.network + Returns ------- None @@ -403,6 +438,22 @@ def delete_ehv_buses_no_lines(network): def ehv_clustering(self): + """ + Cluster the network based on Extra High Voltage (EHV) grid. + + If `network_clustering_ehv` argument is True, the function clusters the network based on the EHV grid. + If `network_clustering` argument is not active, it calls the `load_shedding` function to apply load shedding. + + Parameters + ---------- + self: Etrago object pointer + The object pointer for an Etrago object. + + Returns + ------- + None + """ + if self.args["network_clustering_ehv"]: logger.info("Start ehv clustering") @@ -426,6 +477,22 @@ def ehv_clustering(self): def select_elec_network(etrago): + """ + Selects the electric network based on the clustering settings specified in the Etrago object. + + Parameters + ---------- + etrago : Etrago + An instance of the Etrago class + + Returns + ------- + Tuple containing: + elec_network : pypsa.Network + Contains the electric network + n_clusters : int + number of clusters used in the clustering process. + """ elec_network = etrago.network.copy() settings = etrago.args["network_clustering"] if settings["cluster_foreign_AC"]: @@ -509,56 +576,93 @@ def select_elec_network(etrago): return elec_network, n_clusters -def preprocessing(etrago): - def unify_foreign_buses(etrago): - network = etrago.network.copy(with_time=False) +def unify_foreign_buses(etrago): + """ + Unifies foreign AC buses into clusters using the k-medoids algorithm with + Dijkstra distance as a similarity measure. - foreign_buses = network.buses[ - (network.buses.country != "DE") & (network.buses.carrier == "AC") - ] - foreign_buses_load = foreign_buses[ - (foreign_buses.index.isin(network.loads.bus)) - & (foreign_buses.carrier == "AC") - ] + Parameters + ---------- + etrago : Etrago + An instance of the Etrago class - lines_col = network.lines.columns - # The Dijkstra clustering works using the shortest electrical path between - # buses. In some cases, a bus has just DC connections, which are considered - # links. Therefore it is necessary to include temporarily the DC links - # into the lines table. - dc = network.links[network.links.carrier == "DC"] - str1 = "DC_" - dc.index = f"{str1}" + dc.index - lines_plus_dc = lines_plus_dc = pd.concat([network.lines, dc]) - lines_plus_dc = lines_plus_dc[lines_col] - lines_plus_dc["carrier"] = "AC" + Returns + ------- + busmap_foreign : pd.Series + A pandas series that maps the foreign buses to their respective + clusters. The series index is the bus ID and the values are the + corresponding cluster medoid IDs. + """ + network = etrago.network.copy(with_time=False) - busmap_foreign = pd.Series(dtype=str) + foreign_buses = network.buses[ + (network.buses.country != "DE") & (network.buses.carrier == "AC") + ] + foreign_buses_load = foreign_buses[ + (foreign_buses.index.isin(network.loads.bus)) + & (foreign_buses.carrier == "AC") + ] - for country, df in foreign_buses.groupby(by="country"): - weight = df.apply( - lambda x: 1 if x.name in foreign_buses_load.index else 0, - axis=1, + lines_col = network.lines.columns + # The Dijkstra clustering works using the shortest electrical path between + # buses. In some cases, a bus has just DC connections, which are considered + # links. Therefore it is necessary to include temporarily the DC links + # into the lines table. + dc = network.links[network.links.carrier == "DC"] + str1 = "DC_" + dc.index = f"{str1}" + dc.index + lines_plus_dc = lines_plus_dc = pd.concat([network.lines, dc]) + lines_plus_dc = lines_plus_dc[lines_col] + lines_plus_dc["carrier"] = "AC" + + busmap_foreign = pd.Series(dtype=str) + + for country, df in foreign_buses.groupby(by="country"): + weight = df.apply( + lambda x: 1 if x.name in foreign_buses_load.index else 0, + axis=1, + ) + n_clusters = (foreign_buses_load.country == country).sum() + if n_clusters < len(df): + ( + busmap_country, + medoid_idx_country, + ) = kmedoids_dijkstra_clustering( + etrago, df, lines_plus_dc, weight, n_clusters ) - n_clusters = (foreign_buses_load.country == country).sum() - if n_clusters < len(df): - ( - busmap_country, - medoid_idx_country, - ) = kmedoids_dijkstra_clustering( - etrago, df, lines_plus_dc, weight, n_clusters - ) - medoid_idx_country.index = medoid_idx_country.index.astype(str) - busmap_country = busmap_country.map(medoid_idx_country) - busmap_foreign = pd.concat([busmap_foreign, busmap_country]) - else: - for bus in df.index: - busmap_foreign[bus] = bus + medoid_idx_country.index = medoid_idx_country.index.astype(str) + busmap_country = busmap_country.map(medoid_idx_country) + busmap_foreign = pd.concat([busmap_foreign, busmap_country]) + else: + for bus in df.index: + busmap_foreign[bus] = bus + + busmap_foreign.name = "foreign" + busmap_foreign.index.name = "bus" - busmap_foreign.name = "foreign" - busmap_foreign.index.name = "bus" + return busmap_foreign - return busmap_foreign + +def preprocessing(etrago): + """ + Preprocesses an Etrago object to prepare it for network clustering. + + Parameters + ---------- + etrago : Etrago + An instance of the Etrago class + + Returns + ------- + network_elec : pypsa.Network + Container for all network components of the electrical network. + weight : pandas.Series + A pandas.Series with the bus weighting data. + n_clusters : int + The number of clusters to use for network clustering. + busmap_foreign : pandas.Series + The Series object with the foreign bus mapping data. + """ network = etrago.network settings = etrago.args["network_clustering"] @@ -686,6 +790,28 @@ def unify_foreign_buses(etrago): def postprocessing(etrago, busmap, busmap_foreign, medoid_idx=None): + """ + Postprocessing function for network clustering. + + Parameters + ---------- + etrago : Etrago + An instance of the Etrago class + busmap : pandas.Series + mapping between buses and clusters + busmap_foreign : pandas.DataFrame + mapping between foreign buses and clusters + medoid_idx : pandas.DataFrame + mapping between cluster indices and medoids + + Returns + ------- + Tuple containing: + clustering : pypsa.network + Network object containing the clustered network + busmap : pandas.Series + Updated mapping between buses and clusters + """ settings = etrago.args["network_clustering"] method = settings["method"] num_clusters = settings["n_clusters_AC"] @@ -801,6 +927,32 @@ def weighting_for_scenario(network, save=None): """ def calc_availability_factor(gen): + + """ + Calculate the availability factor for a given generator. + + Parameters + ----------- + gen : pandas.DataFrame + A `pypsa.Network.generators` DataFrame. + + Returns + ------- + cf : float + The availability factor of the generator. + + Notes + ----- + Availability factor is defined as the ratio of the average power output of the generator + over the maximum power output capacity of the generator. + If the generator is time-dependent, its average power output is calculated using the + `network.generators_t` DataFrame. Otherwise, its availability factor is obtained from + the `fixed_capacity_fac` dictionary, which contains pre-defined factors for fixed + capacity generators. If the generator's availability factor cannot be found in the + dictionary, it is assumed to be 1. + + """ + if gen["carrier"] in time_dependent: cf = network.generators_t["p_max_pu"].loc[:, gen.name].mean() else: @@ -871,6 +1023,19 @@ def calc_availability_factor(gen): def run_spatial_clustering(self): + """ + Main method for running spatial clustering on the electrical network. + Allows for clustering based on k-means and k-medoids dijkstra. + + Parameters + ----------- + self + The object pointer for an Etrago object containing all relevant parameters and data + + Returns + ------- + None + """ if self.args["network_clustering"]["active"]: self.network.generators.control = "PV" diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 74f2518f6..4a0d05bcb 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -27,18 +27,24 @@ def preprocessing(etrago): """ - Create a bus map from the clustering of buses in space with a weighting. + Preprocesses the gas network data from the given Etrago object for the spatial + clustering process of the CH4 grid. Parameters ---------- - network : pypsa.Network - The buses must have coordinates x,y. + etrago : Etrago + An instance of the Etrago class + Returns ------- - busmap : pandas.Series - Mapping of network.buses to k-means clusters (indexed by - non-negative integers). + None + + Raises + ------ + ValueError + If `settings["n_clusters_gas"]` is less than or equal to the number of neighboring country gas buses. """ + # Create network_ch4 (grid nodes in order to create the busmap basis) network_ch4 = Network() @@ -90,8 +96,9 @@ def weighting_for_scenario(ch4_buses, save=None): ---------- ch4_buses : pandas.DataFrame Dataframe with CH4 etrago.network.buses to weight. - save: path + save : str or bool Path to save weightings to as .csv + Returns ------- weightings : pandas.Series @@ -171,6 +178,27 @@ def weighting_for_scenario(ch4_buses, save=None): def kmean_clustering_gas(etrago, network_ch4, weight, n_clusters): + """ + Performs K-means clustering on the gas network data in the given `network_ch4` pypsa.Network object. + + Parameters + ---------- + etrago : Etrago + An instance of the Etrago class + network_ch4 : pypsa.Network + A Network object containing the gas network data. + weight : str or None + The name of the bus weighting column to use for clustering. If None, unweighted clustering is performed. + n_clusters : int + The number of clusters to create. + + Returns + ------- + busmap : pandas.Series + A pandas.Series object mapping each bus in the CH4 network to its corresponding cluster ID + None + None is returned because k-means clustering makes no use of medoids + """ settings = etrago.args["network_clustering"] busmap = busmap_by_kmeans( @@ -187,6 +215,21 @@ def kmean_clustering_gas(etrago, network_ch4, weight, n_clusters): def get_h2_clusters(etrago, busmap_ch4): + """ + Maps H2 buses to CH4 cluster IDds and creates unique H2 cluster IDs. + + Parameters + ---------- + etrago : Etrago + An instance of the Etrago class + busmap_ch4 : pd.Series + A Pandas Series mapping each bus in the CH4 network to its corresponding cluster ID. + + Returns + ------- + busmap : pd.Series + A Pandas Series mapping each bus in the combined CH4 and H2 network to its corresponding cluster ID. + """ # Mapping of H2 buses to new CH4 cluster IDs busmap_h2 = pd.Series( busmap_ch4.loc[etrago.ch4_h2_mapping.index].values, @@ -205,6 +248,27 @@ def get_h2_clusters(etrago, busmap_ch4): def gas_postprocessing(etrago, busmap, medoid_idx=None): + """ + Performs the postprocessing for the gas grid clustering based on the provided busmap + and returns the clustered network. + + Parameters + ---------- + etrago : Etrago + An instance of the Etrago class + busmap : pd.Series + A Pandas Series mapping each bus to its corresponding cluster ID. + medoid_idx : pd.Series + A pandas.Series object containing the medoid indices for the gas network. + + Returns + ------- + Tuple containing: + network_gasgrid_c : pypsa.Network + A pypsa.Network containing the clustered network. + busmap : pd.Series + A Pandas Series mapping each bus to its corresponding cluster ID. + """ settings = etrago.args["network_clustering"] if settings["k_gas_busmap"] == False: @@ -408,12 +472,12 @@ def highestInteger(potentially_numbers): Parameters ---------- - potentially_numbers : pandas.core.series.Series + potentially_numbers : pandas.Series Series with mixed dtypes, potentially containing numbers. Returns ------- - int + highest : int Highest integer found in series. """ highest = 0 @@ -430,7 +494,8 @@ def highestInteger(potentially_numbers): def simultaneous_sector_coupling( network, busmap, carrier_based, carrier_to_cluster ): - """Cluster sector coupling technology based on multiple connected carriers. + """ + Cluster sector coupling technology based on multiple connected carriers. The topology of the sector coupling technology must be in a way, that the links connected to other sectors do only point inwards. E.g. for the heat @@ -527,7 +592,8 @@ def simultaneous_sector_coupling( def consecutive_sector_coupling( network, busmap, carrier_based, carrier_to_cluster ): - """Cluster sector coupling technology based on single connected carriers. + """ + Cluster sector coupling technology based on single connected carriers. The topology of the sector coupling technology must be in a way, that the links connected to other sectors do only point inwards. E.g. for the heat @@ -547,8 +613,8 @@ def consecutive_sector_coupling( Returns ------- - dict - Busmap for the sector coupling cluster. + busmap_sc : dict + Busmap for the sector coupled cluster. """ next_bus_id = highestInteger(busmap.values) + 1 buses_to_skip = network.buses[ @@ -639,7 +705,8 @@ def consecutive_sector_coupling( def sc_multi_carrier_based(buses_to_cluster, connected_links): - """Create busmap for sector coupled carrier based on multiple other carriers. + """ + Create busmap for sector coupled carrier based on multiple other carriers. Parameters ---------- @@ -652,8 +719,8 @@ def sc_multi_carrier_based(buses_to_cluster, connected_links): Returns ------- - dict - Busmap for the sector cupled carrier. + busmap : dict + Busmap for the sector coupled carrier. """ clusters = pd.Series() for bus_id in buses_to_cluster.index: @@ -676,7 +743,8 @@ def sc_multi_carrier_based(buses_to_cluster, connected_links): def sc_single_carrier_based(connected_links): - """Create busmap for sector coupled carrier based on single other carrier. + """ + Create busmap for sector coupled carrier based on single other carrier. Parameters ---------- @@ -686,8 +754,8 @@ def sc_single_carrier_based(connected_links): Returns ------- - dict - Busmap for the sector cupled carrier. + busmap : dict + Busmap for the sector coupled carrier. """ busmap = {} clusters = connected_links["bus0_clustered"].unique() @@ -707,6 +775,29 @@ def get_clustering_from_busmap( bus_strategies=dict(), one_port_strategies=dict(), ): + """ + Aggregates components of the given network based on a bus mapping and returns a clustered gas grid pypsa.Network. + + Parameters + ---------- + network : pypsa.Network + The input pypsa.Network object + busmap : pandas.Sereies : + A mapping of buses to clusters + line_length_factor : float + A factor used to adjust the length of new links created during aggregation. Default is 1.0. + with_time : bool + Determines whether to copy the time-dependent properties of the input network to the output network. Default is True. + bus_strategies : dict + A dictionary of custom strategies to use during the aggregation step. Default is an empty dictionary. + one_port_strategies : dict + A dictionary of custom strategies to use during the one-port component aggregation step. Default is an empty dictionary. + + Returns + ------- + network_gasgrid_c : pypsa.Network + A new gas grid pypsa.Network object with aggregated components based on the bus mapping. + """ network_gasgrid_c = Network() # Aggregate buses @@ -779,6 +870,17 @@ def get_clustering_from_busmap( def run_spatial_clustering_gas(self): + """ + Performs spatial clustering on the gas network using either K-means or K-medoids-Dijkstra algorithm. Updates the + network topology by aggregating buses and links, and then performs postprocessing to finalize the changes. + + Returns + None + + Raises + ValueError: If the selected method is not "kmeans" or "kmedoids-dijkstra". + + """ settings = self.args["network_clustering"] if settings["active"]: diff --git a/etrago/cluster/spatial.py b/etrago/cluster/spatial.py index 9b7197fd5..c9802e77d 100755 --- a/etrago/cluster/spatial.py +++ b/etrago/cluster/spatial.py @@ -59,6 +59,21 @@ def _make_consense_links(x): + """ + Ensure that all elements in the input Series `x` are identical, or that they + are all NaN. + + Parameters + ---------- + x : pandas.Series + A Series containing the values to be checked for consensus. + + Returns + ------- + object + The value of the first element in the Series `x`. + """ + v = x.iat[0] assert ( x == v @@ -225,7 +240,8 @@ def normed_or_uniform(x): def graph_from_edges(edges): - """Constructs an undirected multigraph from a list containing data on + """ + Constructs an undirected multigraph from a list containing data on weighted edges. Parameters @@ -252,16 +268,15 @@ def graph_from_edges(edges): def gen(nodes, n, graph): # TODO There could be a more convenient way of doing this. This generators # single purpose is to prepare data for multiprocessing's starmap function. - """Generator for applying multiprocessing. + """ + Generator for applying multiprocessing. Parameters ---------- nodes : list List of nodes in the system. - n : int Number of desired multiprocessing units. - graph : :class:`networkx.classes.multigraph.MultiGraph Graph representation of an electrical grid. @@ -277,13 +292,13 @@ def gen(nodes, n, graph): def shortest_path(paths, graph): - """Finds the minimum path lengths between node pairs defined in paths. + """ + Finds the minimum path lengths between node pairs defined in paths. Parameters ---------- paths : list List of pairs containing a source and a target node - graph : :class:`networkx.classes.multigraph.MultiGraph Graph representation of an electrical grid. @@ -316,7 +331,8 @@ def shortest_path(paths, graph): def busmap_by_shortest_path(etrago, scn_name, fromlvl, tolvl, cpu_cores=4): - """Creates a busmap for the EHV-Clustering between voltage levels based + """ + Creates a busmap for the EHV-Clustering between voltage levels based on dijkstra shortest path. The result is automatically written to the `model_draft` on the [www.openenergy-platform.org] database with the name `ego_grid_pf_hv_busmap` and the attributes scn_name @@ -327,21 +343,16 @@ def busmap_by_shortest_path(etrago, scn_name, fromlvl, tolvl, cpu_cores=4): Parameters ---------- - network : pypsa.Network object + network : pypsa.Network Container for all network components. - session : sqlalchemy.orm.session.Session object Establishes interactions with the database. - scn_name : str Name of the scenario. - fromlvl : list List of voltage-levels to cluster. - tolvl : list List of voltage-levels to remain. - cpu_cores : int Number of CPU-cores. @@ -448,18 +459,17 @@ def busmap_by_shortest_path(etrago, scn_name, fromlvl, tolvl, cpu_cores=4): def busmap_from_psql(etrago): - """Retrieves busmap from `model_draft.ego_grid_pf_hv_busmap` on the + """ + Retrieves busmap from `model_draft.ego_grid_pf_hv_busmap` on the [www.openenergy-platform.org] by a given scenario name. If this busmap does not exist, it is created with default values. Parameters ---------- - network : pypsa.Network object + network : pypsa.Network Container for all network components. - session : sqlalchemy.orm.session.Session object Establishes interactions with the database. - scn_name : str Name of the scenario. @@ -520,41 +530,35 @@ def fetch(): def kmean_clustering(etrago, selected_network, weight, n_clusters): - """Main function of the k-mean clustering approach. Maps an original + """ + Main function of the k-mean clustering approach. Maps an original network to a new one with adjustable number of nodes and new coordinates. Parameters ---------- - network : :class:`pypsa.Network + network : pypsa.Network Container for all network components. - n_clusters : int Desired number of clusters. - load_cluster : boolean Loads cluster coordinates from a former calculation. - line_length_factor : float Factor to multiply the crow-flies distance between new buses in order to get new line lengths. - remove_stubs: boolean Removes stubs and stubby trees (i.e. sequentially reducing dead-ends). - use_reduced_coordinates: boolean If True, do not average cluster coordinates, but take from busmap. - bus_weight_tocsv : str Creates a bus weighting based on conventional generation and load and save it to a csv file. - bus_weight_fromcsv : str Loads a bus weighting from a csv file to apply it to the clustering algorithm. Returns ------- - network : pypsa.Network object + network : pypsa.Network Container for all network components. """ network = etrago.network @@ -607,25 +611,26 @@ def kmean_clustering(etrago, selected_network, weight, n_clusters): def dijkstras_algorithm(buses, connections, medoid_idx, cpu_cores): - """Function for combination of k-medoids Clustering and Dijkstra's algorithm. - Creates a busmap assigning the nodes of a original network - to the nodes of a clustered network - considering the electrical distances based on Dijkstra's shortest path. - Parameters - centers - ---------- - network : pypsa.Network object - Container for all network components. - - medoid_idx : pd.Series - Indices of k-medoids - busmap_kmedoid: pd.Series - Busmap based on k-medoids clustering - cpu_cores: string - numbers of cores used during multiprocessing - Returns - ------- - busmap (format: with labels) + """ + Function for combination of k-medoids Clustering and Dijkstra's algorithm. + Creates a busmap assigning the nodes of a original network to the nodes of a clustered network + considering the electrical distances based on Dijkstra's shortest path. + + Parameters + ---------- + network : pypsa.Network + Container for all network components. + medoid_idx : pandas.Series + Indices of k-medoids + busmap_kmedoid: pandas.Series + Busmap based on k-medoids clustering + cpu_cores: string + numbers of cores used during multiprocessing + + Returns + ------- + busmap : pandas.Series + Mapping from bus ids to medoids ids """ # original data @@ -686,6 +691,30 @@ def dijkstras_algorithm(buses, connections, medoid_idx, cpu_cores): def kmedoids_dijkstra_clustering( etrago, buses, connections, weight, n_clusters ): + """ + Applies k-medoids clustering to the given gas network using Dijkstra's algorithm. + + Parameters + ---------- + etrago : Etrago + An instance of the Etrago class + buses : pandas.DataFrame + DataFrame with information about the buses of the network. + connections : pandas.DataFrame + DataFrame with information about the connections of the network (links or lines). + weight : pandas.Series + Series with the weight for each bus. + n_clusters : int + The number of clusters to create. + + Returns + ------- + Tuple containing: + busmap : pandas.Series + Series containing the mapping of buses to their resp. medoids + medoid_idx : pandas.Series + Series containing the medoid indeces + """ settings = etrago.args["network_clustering"] From 720f86185755be3c626515b31de0370ed3e3bc28 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 14:41:48 +0100 Subject: [PATCH 004/159] docstrings filter_links_by_carrier --- etrago/tools/utilities.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 36cedb51c..806c92a4b 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -52,6 +52,25 @@ def filter_links_by_carrier(self, carrier, like=True): + """ + + Parameters + ---------- + carrier : list or str + name of the carriers of interest. Can be a list of carriers or single + sting. + like : bool, optional + When like set to True, the links with carrier names that includes the + carrier(s) supplied are returned, Not just exact matches. + The default is True. + + Returns + ------- + df : pandas.DataFrame object + Dataframe that contains just links with carriers of the types given + in the argument carrier. + + """ if isinstance(carrier, str): if like: df = self.network.links[ From 816065c7d7e34cf5cb7b68fc54dab8a4bcc5a4a2 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 15:04:34 +0100 Subject: [PATCH 005/159] docstrings loading_minimization --- etrago/tools/utilities.py | 23 ++++++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 806c92a4b..b7c01c459 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -634,7 +634,9 @@ def data_manipulation_sh(network): network : :class:`pypsa.Network Overall container of PyPSA - + Returns + ------- + None """ from geoalchemy2.shape import from_shape, to_shape @@ -731,6 +733,9 @@ def export_to_csv(self, path): path: str or False or None Choose path for csv-files. Specify `""`, `False` or `None` to not do anything. + Returns + ------- + None """ if not path: @@ -781,6 +786,22 @@ def export_to_csv(self, path): def loading_minimization(network, snapshots): + """ + Minimizes the sum of the products of each element in the passive_branches + of the model. + + Parameters + ---------- + network : :class:`pypsa.Network + Overall container of PyPSA + snapshots : 'pandas.core.indexes.datetimes.DatetimeIndex' + snapshots to perform the minimization + + Returns + ------- + None + + """ network.model.number1 = Var( network.model.passive_branch_p_index, within=PositiveReals ) From eca667d6ef3995a4178cde18a1845cbcb78352a0 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 15:13:07 +0100 Subject: [PATCH 006/159] docstrings _make_consense --- etrago/tools/utilities.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index b7c01c459..75badd29c 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -826,6 +826,29 @@ def cRule(model, c, l, t): def _make_consense(component, attr): + """ + Returns a function `consense` that will be used to generate a consensus + value for the attribute `attr` of the given `component`. This consensus + value is derived from the input DataFrame `x`. If all values in the + DataFrame are equal, the consensus value will be that common value. + If all values are missing (NaN), the consensus value will be NaN. Otherwise, + an assertion error will be raised. + + Parameters + ---------- + component : str + specify the name of the component being clustered. + attr : str + specify the name of the attribute of the commponent being considered. + + + Returns + ------- + function + A function that takes a DataFrame as input and returns a single value + as output when all the elements of the commponent attribute are the same. + + """ def consense(x): v = x.iat[0] assert (x == v).all() or x.isnull().all(), ( From dce4be1e2236d7b62e949d184642f8afa426831e Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 15:21:08 +0100 Subject: [PATCH 007/159] docstrings _normed --- etrago/tools/utilities.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 75badd29c..fd827e274 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -861,6 +861,22 @@ def consense(x): def _normed(s): + """ + Given a pandas Series `s`, normalizes the series by dividing each element + by the sum of the series. If the sum of the series is zero, returns 1.0 to + avoid division by zero errors. + + Parameters + ---------- + s : pandas.Series + A pandas Series. + + Returns + ------- + pandas.Series + A normalized pandas Series. + + """ tot = s.sum() if tot == 0: return 1.0 From 59b59d6c4d3ea276146ed06b8df3289bb3e5b48c Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 15:29:23 +0100 Subject: [PATCH 008/159] docstrings agg_series_lines --- etrago/tools/utilities.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index fd827e274..b5649a60f 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -885,6 +885,25 @@ def _normed(s): def agg_series_lines(l, network): + """ + Given a pandas DataFrame `l` containing information about lines in a + network and a network object, aggregates the data in `l` for all its + attributes. Returns a pandas Series containing the aggregated data. + + + Parameters + ---------- + l : pandas.DataFrame + contain information about lines in a network. + network : :class:`pypsa.Network + Overall container of PyPSA + + Returns + ------- + pandas.Series + A pandas Series containing aggregated data for the lines in the network. + + """ attrs = network.components["Line"]["attrs"] columns = set( attrs.index[attrs.static & attrs.status.str.startswith("Input")] From b37f3ef2fc5bc037076a04ca04b02af595c7c191 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 15:32:41 +0100 Subject: [PATCH 009/159] docstrings merge_dicts --- etrago/tools/utilities.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index b5649a60f..d115055da 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -1785,7 +1785,22 @@ def get_args_setting(self, jsonpath="scenario_setting.json"): def merge_dicts(dict1, dict2): - """Return a new dictionary by merging two dictionaries recursively.""" + """ + Return a new dictionary by merging two dictionaries recursively. + + Parameters + ---------- + dict1 : dict + dictionary 1. + dict2 : dict + dictionary 2. + + Returns + ------- + result : dict + Union of dict1 and dict2 + + """ result = deepcopy(dict1) From fee8882eeb0da09da85796ede438ebeda50835aa Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 15:37:25 +0100 Subject: [PATCH 010/159] docstrings crossborder_capacity_tyndp2020 --- etrago/tools/utilities.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index d115055da..091fd9623 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -1821,6 +1821,9 @@ def get_clustering_data(self, path): ---------- path : str Name of folder from which to import CSVs of network data. + Returns + None + ------- """ if (self.args["network_clustering_ehv"]) | ( @@ -1988,6 +1991,20 @@ def set_line_country_tags(network): def crossborder_capacity_tyndp2020(): + """ + This function downloads and extracts a scenario datafile for the TYNDP 2020 + (Ten-Year Network Development Plan), reads a specific sheet from the file, + filters it based on certain criteria, and then calculates the minimum + cross-border capacities for a list of European countries. The minimum + cross-border capacity is the minimum of the export and import capacities + between two countries. + + Returns + ------- + dict + Dictionary with cossborder capacities. + + """ from urllib.request import urlretrieve import zipfile From 9e75d822c8ec11d66d627c58a2a6fe5048b79f75 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 15:42:22 +0100 Subject: [PATCH 011/159] add new authors utilities.py --- etrago/tools/utilities.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 091fd9623..911f708a3 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -48,7 +48,9 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "ulfmueller, s3pp, wolfbunke, mariusves, lukasol" +__author__ = """ulfmueller, s3pp, wolfbunke, mariusves, lukasol, ClaraBuettner, +CarlosEpia, gnn, pieterhexen, fwitte, KathiEsterl, MGlauer, birgits, AmeliaNadal, +MarlonSchlemminger, wheitkoetter, jankaeh""" def filter_links_by_carrier(self, carrier, like=True): From 792b0d3584a603ec9f28a8b8dc84222c0ac8f492 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 16:04:43 +0100 Subject: [PATCH 012/159] docstrings coloring --- etrago/tools/plot.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index 5d12a008c..3bb330127 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -117,6 +117,16 @@ def plot_osm(x, y, zoom, alpha=0.4): def coloring(): + """ + Return a dictionary with a color assign to each kind of carrier used in + etrago.network. This is used for plotting porpuses. + + Returns + ------- + colors : dict + Color for each kind of carrier. + + """ colors = { "load": "red", From 0eea7a8ca33591bb51b23a396b1d17bc924429e3 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 16:16:42 +0100 Subject: [PATCH 013/159] include return None to some functions --- etrago/tools/plot.py | 32 +++++++++++++++++++++++++------- 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index 3bb330127..c99df404a 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -420,6 +420,10 @@ def plot_residual_load(network): Parameters ---------- network : PyPSA network containter + + Returns + ------- + Plot """ renewables = network.generators[ @@ -590,7 +594,7 @@ def gen_by_c(network): pd.concat( [ network.generators_t.p.mul( - etwork.snapshot_weightings, axis=0 + network.snapshot_weightings, axis=0 )[ network.generators[ network.generators.control != "Slack" @@ -924,6 +928,10 @@ def gen_dist_diff( filename : str Specify filename If not given, figure will be show directly + + Returns + ------- + None. """ if techs is None: techs = networkA.generators.carrier.unique() @@ -1037,6 +1045,10 @@ def nodal_gen_dispatch( 'x': array of two floats, x axis boundaries (lat) 'y': array of two floats, y axis boundaries (long) 'zoom' : resolution of osm + + Returns + ------- + None. """ if osm != False: @@ -1201,6 +1213,10 @@ def storage_p_soc(network, mean="1H", filename=None): mean : str Defines over how many snapshots the p and soc values will averaged. filename : path to folder + + Returns + ------- + None. """ @@ -1335,6 +1351,10 @@ def storage_soc_sorted(network, filename=None): Holds topology of grid including results from powerflow analysis filename : path to folder + + Returns + ------- + None. """ sbatt = network.storage_units.index[ @@ -1695,7 +1715,7 @@ def demand_side_management(self, buses, snapshots, agg="5h", used=False): Returns ------- - potential : pandas.DataFrame + df : pandas.DataFrame Shifting potential (and usage) of power (MW) and energy (MWh) """ @@ -1775,7 +1795,7 @@ def bev_flexibility_potential( Returns ------- - potential : pandas.DataFrame + df : pandas.DataFrame Shifting potential (and usage) of power (MW) and energy (MWh) """ @@ -1882,7 +1902,7 @@ def heat_stores( Returns ------- - potential : pandas.DataFrame + df : pandas.DataFrame Shifting potential (and usage) of power (MW) and energy (MWh) """ @@ -1962,7 +1982,7 @@ def hydrogen_stores( Returns ------- - potential : pandas.DataFrame + df : pandas.DataFrame Shifting potential (and usage) of power (MW) and energy (MWh) """ @@ -2575,8 +2595,6 @@ def plot_clusters( ------- None. """ - # TODO: Make this function available for other carriers - # Create geometries new_geom = self.network.buses[ [ "carrier", From 7f26e8076014b349e36d8cd783d361a013bfe31b Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 16:24:29 +0100 Subject: [PATCH 014/159] add authors to plot.py --- etrago/tools/plot.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index c99df404a..c6a6187cf 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -57,7 +57,8 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "ulfmueller, MarlonSchlemminger, mariusves, lukasol" +__author__ = """ulfmueller, MarlonSchlemminger, mariusves, lukasol, ClaraBuettner, +CarlosEpia, pieterhexen, gnn, fwitte, lukasol, KathiEsterl, BartelsJ""" def set_epsg_network(network): From ec261973bfc1916b028332f834fa6951266b09bc Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 16:30:51 +0100 Subject: [PATCH 015/159] update descriptions in network.py --- etrago/tools/network.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/etrago/tools/network.py b/etrago/tools/network.py index fa215352e..a22971aa1 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -92,13 +92,13 @@ class Etrago: """ - Object containing pypsa.Network including the transmission grid, - input parameters and optimization results. + Object containing pypsa.Network including the transmission grid, not + electric sectors, input parameters and optimization results. Parameters ---------- args : dict - Dictionary including all inpu parameters. + Dictionary including all input parameters. csv_folder_name : string Name of folder from which to import CSVs of network data. name : string, default "" From 133a3cd7e05a43fd12cfc020df59f9ad62c00c4a Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 15 Mar 2023 16:43:22 +0100 Subject: [PATCH 016/159] include copyright, license and authors to network.py --- etrago/tools/network.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/etrago/tools/network.py b/etrago/tools/network.py index a22971aa1..a99ddf38b 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -89,6 +89,16 @@ logger = logging.getLogger(__name__) +__copyright__ = ( + "Flensburg University of Applied Sciences, " + "Europa-Universität Flensburg, " + "Centre for Sustainable Energy Systems, " + "DLR-Institute for Networked Energy Systems" +) +__license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" +__author__ = """AmeliaNadal, CarlosEpia, ClaraBuettner, KathiEsterl, gnn, fwitte, + ulfmueller, pieterhexen""" + class Etrago: """ From a7916cb127ab97b56da0848d58a8a8d1e0a5f26c Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Thu, 16 Mar 2023 09:02:49 +0100 Subject: [PATCH 017/159] docstrings snapshot_clustering_daily_bounds --- etrago/tools/constraints.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index 2fe25f2ef..5876863a0 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -1423,7 +1423,21 @@ def add_ch4_constraints_nmp(self, network, snapshots): def snapshot_clustering_daily_bounds(self, network, snapshots): - # This will bound the storage level to 0.5 max_level every 24th hour. + """ + Bound the storage level to 0.5 max_level every 24th hour. + + Parameters + ---------- + network : :class:`pypsa.Network + Overall container of PyPSA + snapshots : pandas.DatetimeIndex + List of timesteps that will be constrained + + Returns + ------- + None + + """ sus = network.storage_units # take every first hour of the clustered days network.model.period_starts = network.snapshot_weightings.index[0::24] From 4e185416832c97fdb042935ce1e9118e66c921ba Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Thu, 16 Mar 2023 09:04:22 +0100 Subject: [PATCH 018/159] docstrings snapshot_clustering_daily_bounds_nmp --- etrago/tools/constraints.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index 5876863a0..3d8fd7d51 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -1461,7 +1461,21 @@ def day_rule(m, s, p): def snapshot_clustering_daily_bounds_nmp(self, network, snapshots): + """ + Bound the storage level to 0.5 max_level every 24th hour. + + Parameters + ---------- + network : :class:`pypsa.Network + Overall container of PyPSA + snapshots : pandas.DatetimeIndex + List of timesteps that will be constrained + Returns + ------- + None + + """ c = "StorageUnit" period_starts = snapshots[0::24] From 2e6dffe342ef0fc8c1cf22fef61832ff50de1a05 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Thu, 16 Mar 2023 09:40:18 +0100 Subject: [PATCH 019/159] Adapt python version --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 7b9c0663e..098db7417 100644 --- a/README.rst +++ b/README.rst @@ -88,7 +88,7 @@ you create a virtual environment (where you like it) and activate it: .. code-block:: bash - $ virtualenv venv --clear -p python3.7 + $ virtualenv venv --clear -p python3.8 $ source venv/bin/activate $ cd venv From b7418a21271eba051c97e24a4859ae965c56c576 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Thu, 16 Mar 2023 09:40:51 +0100 Subject: [PATCH 020/159] Delete description of argument which is not used anymore --- etrago/appl.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index 7693bf8d1..9f5d475c3 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -362,8 +362,6 @@ def run_etrago(args, json_path): The other options affect the kmeans algorithm and should only be changed carefully, documentation and possible settings are described in sklearn-package (sklearn/cluster/k_means_.py). - This function doesn't work together with - ``'network_clustering_kmedoids_dijkstra`` and ``'line_grouping = True'``. sector_coupled_clustering : nested dict {'active': True, 'carrier_data': { From bfd9f82872e7246b13d3c22ec9da17f6e3729c33 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Thu, 16 Mar 2023 09:41:12 +0100 Subject: [PATCH 021/159] Add authors --- setup.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 4ea9b7ab6..4cde561bc 100755 --- a/setup.py +++ b/setup.py @@ -14,7 +14,8 @@ __author__ = ( "ulfmueller, wolfbunke, BartelsJ, ClaraBuettner, gnn, " "simnh, lukasol, s3pp, MGlauer, kimvk, MarlonSchlemminger, " - "mariusves" + "mariusves", "CarlosEpia", "KathiEsterl", "pieterhexen", + "fwitte", "AmeliaNadal" ) From b8a5b8034e716fc8bf8675c0322cfaef679fb981 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Thu, 16 Mar 2023 09:57:30 +0100 Subject: [PATCH 022/159] docstrings snapshot_clustering_seasonal_storage --- etrago/tools/constraints.py | 86 +++++++++++++++++++++++++++++++++++++ 1 file changed, 86 insertions(+) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index 3d8fd7d51..3700da9e9 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -1516,7 +1516,30 @@ def masked_term(coeff, var, cols): def snapshot_clustering_seasonal_storage(self, network, snapshots, simplified=False): + """ + Depicts intertemporal dependencies of storage units and stores when using + snapshot clustering to typical periods for temporal complexity reduction. + + According to: + L. Kotzur et al: 'Time series aggregation for energy + system design: + Modeling seasonal storage', 2018 + + Parameters + ---------- + network : :class:`pypsa.Network + Overall container of PyPSA + snapshots : list + A list of datetime objects representing the timestamps of the snapshots + to be clustered. + simplified : bool, optional + A flag indicating whether to use a simplified version of the model that + does not include intra-temporal constraints and variables. + Returns + ------- + None + """ sus = network.storage_units sto = network.stores @@ -2245,7 +2268,27 @@ def cyclic_state_of_charge_store(m, s): def snapshot_clustering_seasonal_storage_hourly(self, network, snapshots): + """ + Depicts intertemporal dependencies of storage units and stores when using + snapshot clustering to typical periods for temporal complexity reduction. + According to: + L. Kotzur et al: 'Time series aggregation for energy + system design: + Modeling seasonal storage', 2018 + + Parameters + ---------- + network : :class:`pypsa.Network + Overall container of PyPSA + snapshots : list + A list of datetime objects representing the timestamps of the snapshots + to be clustered. + + Returns + ------- + None + """ # TODO: updaten mit stores (Sektorkopplung) network.model.del_component("state_of_charge_all") @@ -2330,7 +2373,30 @@ def state_of_charge_upper(m, s, h): def snapshot_clustering_seasonal_storage_nmp(self, n, sns, simplified=False): + """ + Depicts intertemporal dependencies of storage units and stores when using + snapshot clustering to typical periods for temporal complexity reduction. + + According to: + L. Kotzur et al: 'Time series aggregation for energy + system design: + Modeling seasonal storage', 2018 + + Parameters + ---------- + n : :class:`pypsa.Network + Overall container of PyPSA + sns : list + A list of datetime objects representing the timestamps of the snapshots + to be clustered. + simplified : bool, optional + A flag indicating whether to use a simplified version of the model that + does not include intra-temporal constraints and variables. + Returns + ------- + None + """ # TODO: so noch nicht korrekt... # TODO: updaten mit stores (Sektorkopplung) # TODO: simplified ergänzen @@ -2403,7 +2469,27 @@ def snapshot_clustering_seasonal_storage_nmp(self, n, sns, simplified=False): def snapshot_clustering_seasonal_storage_hourly_nmp(self, n, sns): + """ + Depicts intertemporal dependencies of storage units and stores when using + snapshot clustering to typical periods for temporal complexity reduction. + According to: + L. Kotzur et al: 'Time series aggregation for energy + system design: + Modeling seasonal storage', 2018 + + Parameters + ---------- + n : :class:`pypsa.Network + Overall container of PyPSA + sns : list + A list of datetime objects representing the timestamps of the snapshots + to be clustered. + + Returns + ------- + None + """ print("TODO") # TODO: implementieren From 30091abe87059c2ad889599b207d3a099461e615 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Thu, 16 Mar 2023 10:53:47 +0100 Subject: [PATCH 023/159] add new authors to constraints.py --- etrago/tools/constraints.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index 3700da9e9..46cf8cc91 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -41,7 +41,8 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "ulfmueller, s3pp, wolfbunke, mariusves, lukasol" +__author__ = """ulfmueller, s3pp, wolfbunke, mariusves, lukasol, AmeliaNadal, +CarlosEpia, ClaraBuettner, KathiEsterl""" def _get_crossborder_components(network, cntr="all"): From 0f0e1aa9417630caae51c23fd85ad19ed136727c Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Thu, 16 Mar 2023 10:56:49 +0100 Subject: [PATCH 024/159] use black and isort --- etrago/tools/constraints.py | 532 +++++++++++++++++++++++------------- etrago/tools/plot.py | 20 +- etrago/tools/utilities.py | 5 +- 3 files changed, 355 insertions(+), 202 deletions(-) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index 46cf8cc91..d583682b9 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -23,14 +23,14 @@ """ import logging -import numpy as np -import pandas as pd -import pyomo.environ as po from egoio.tools import db from pyomo.environ import Constraint from pypsa.descriptors import expand_series from pypsa.linopt import define_constraints, define_variables, get_var, linexpr from pypsa.pf import get_switchable_as_dense as get_as_dense +import numpy as np +import pandas as pd +import pyomo.environ as po logger = logging.getLogger(__name__) @@ -81,20 +81,26 @@ def _get_crossborder_components(network, cntr="all"): buses_for = network.buses.index[network.buses.country == cntr] cb0 = network.lines.index[ - (network.lines.bus0.isin(buses_for)) & (network.lines.bus1.isin(buses_de)) + (network.lines.bus0.isin(buses_for)) + & (network.lines.bus1.isin(buses_de)) ] cb1 = network.lines.index[ - (network.lines.bus1.isin(buses_for)) & (network.lines.bus0.isin(buses_de)) + (network.lines.bus1.isin(buses_for)) + & (network.lines.bus0.isin(buses_de)) ] cb0_link = network.links.index[ - (network.links.bus0.isin(buses_for)) & (network.links.bus1.isin(buses_de)) - & (network.links.carrier=='DC')] + (network.links.bus0.isin(buses_for)) + & (network.links.bus1.isin(buses_de)) + & (network.links.carrier == "DC") + ] cb1_link = network.links.index[ - (network.links.bus0.isin(buses_de)) & (network.links.bus1.isin(buses_for)) - & (network.links.carrier=='DC')] + (network.links.bus0.isin(buses_de)) + & (network.links.bus1.isin(buses_for)) + & (network.links.carrier == "DC") + ] return buses_de, buses_for, cb0, cb1, cb0_link, cb1_link @@ -119,21 +125,21 @@ def _max_line_ext(self, network, snapshots): lines_snom = network.lines.s_nom_min.sum() - links_elec = network.links[network.links.carrier=='DC'] + links_elec = network.links[network.links.carrier == "DC"] links_index = links_elec.index links_pnom = links_elec.p_nom_min.sum() def _rule(m): - lines_opt = sum( - m.passive_branch_s_nom[index] for index in m.passive_branch_s_nom_index + m.passive_branch_s_nom[index] + for index in m.passive_branch_s_nom_index ) links_opt = sum(m.link_p_nom[index] for index in links_index) - return (lines_opt + links_opt) <= (lines_snom + links_pnom) * self.args[ - "extra_functionality" - ]["max_line_ext"] + return (lines_opt + links_opt) <= ( + lines_snom + links_pnom + ) * self.args["extra_functionality"]["max_line_ext"] network.model.max_line_ext = Constraint(rule=_rule) @@ -158,7 +164,7 @@ def _max_line_ext_nmp(self, network, snapshots): lines_snom = network.lines.s_nom.sum() - links_elec = network.links[network.links.carrier=='DC'] + links_elec = network.links[network.links.carrier == "DC"] links_index = links_elec.index links_pnom = links_elec.p_nom_min.sum() @@ -166,14 +172,15 @@ def _max_line_ext_nmp(self, network, snapshots): def _rule(m): lines_opt = sum( - m.passive_branch_s_nom[index] for index in m.passive_branch_s_nom_index + m.passive_branch_s_nom[index] + for index in m.passive_branch_s_nom_index ) links_opt = sum(m.link_p_nom[index] for index in links_index) - return (lines_opt + links_opt) <= (lines_snom + links_pnom) * self.args[ - "extra_functionality" - ]["max_line_ext"] + return (lines_opt + links_opt) <= ( + lines_snom + links_pnom + ) * self.args["extra_functionality"]["max_line_ext"] network.model.max_line_ext = Constraint(rule=_rule) @@ -197,10 +204,20 @@ def _min_renewable_share_nmp(self, network, snapshots): """ - renewables = ['biomass', 'central_biomass_CHP', 'industrial_biomass_CHP', - 'solar', 'solar_rooftop', 'wind_offshore', 'wind_onshore', - 'run_of_river', 'other_renewable', - 'central_biomass_CHP_heat', 'solar_thermal_collector', 'geo_thermal'] + renewables = [ + "biomass", + "central_biomass_CHP", + "industrial_biomass_CHP", + "solar", + "solar_rooftop", + "wind_offshore", + "wind_onshore", + "run_of_river", + "other_renewable", + "central_biomass_CHP_heat", + "solar_thermal_collector", + "geo_thermal", + ] res = network.generators.index[network.generators.carrier.isin(renewables)] @@ -215,7 +232,9 @@ def _min_renewable_share_nmp(self, network, snapshots): renew_production = linexpr((1, renew)).sum().sum() total_production = ( - linexpr((-self.args["extra_functionality"]["min_renewable_share"], total)) + linexpr( + (-self.args["extra_functionality"]["min_renewable_share"], total) + ) .sum() .sum() ) @@ -244,17 +263,28 @@ def _min_renewable_share(self, network, snapshots): """ - renewables = ['biomass', 'central_biomass_CHP', 'industrial_biomass_CHP', - 'solar', 'solar_rooftop', 'wind_offshore', 'wind_onshore', - 'run_of_river', 'other_renewable', - 'central_biomass_CHP_heat', 'solar_thermal_collector', 'geo_thermal'] + renewables = [ + "biomass", + "central_biomass_CHP", + "industrial_biomass_CHP", + "solar", + "solar_rooftop", + "wind_offshore", + "wind_onshore", + "run_of_river", + "other_renewable", + "central_biomass_CHP_heat", + "solar_thermal_collector", + "geo_thermal", + ] - res = list(network.generators.index[network.generators.carrier.isin(renewables)]) + res = list( + network.generators.index[network.generators.carrier.isin(renewables)] + ) total = list(network.generators.index) def _rule(m): - renewable_production = sum( m.generator_p[gen, sn] * network.snapshot_weightings.generators[sn] for gen in res @@ -291,12 +321,17 @@ def _cross_border_flow(self, network, snapshots): """ - buses_de, buses_for, cb0, cb1, cb0_link, cb1_link = _get_crossborder_components( - network - ) - - export = ( - pd.Series(data=self.args["extra_functionality"]["cross_border_flow"]) + ( + buses_de, + buses_for, + cb0, + cb1, + cb0_link, + cb1_link, + ) = _get_crossborder_components(network) + + export = pd.Series( + data=self.args["extra_functionality"]["cross_border_flow"] ) def _rule_min(m): @@ -373,12 +408,17 @@ def _cross_border_flow_nmp(self, network, snapshots): """ - buses_de, buses_for, cb0, cb1, cb0_link, cb1_link = _get_crossborder_components( - network - ) - - export = ( - pd.Series(data=self.args["extra_functionality"]["cross_border_flow"]) + ( + buses_de, + buses_for, + cb0, + cb1, + cb0_link, + cb1_link, + ) = _get_crossborder_components(network) + + export = pd.Series( + data=self.args["extra_functionality"]["cross_border_flow"] ) cb0_flow = ( @@ -438,11 +478,9 @@ def _cross_border_flow_per_country_nmp(self, network, snapshots): countries = network.buses.country.unique() - export_per_country = ( - pd.DataFrame( - data=self.args["extra_functionality"]["cross_border_flow_per_country"] - ).transpose() - ) + export_per_country = pd.DataFrame( + data=self.args["extra_functionality"]["cross_border_flow_per_country"] + ).transpose() for cntr in export_per_country.index: if cntr in countries: @@ -526,11 +564,9 @@ def _cross_border_flow_per_country(self, network, snapshots): countries = network.buses.country.unique() - export_per_country = ( - pd.DataFrame( - data=self.args["extra_functionality"]["cross_border_flow_per_country"] - ).transpose() - ) + export_per_country = pd.DataFrame( + data=self.args["extra_functionality"]["cross_border_flow_per_country"] + ).transpose() for cntr in export_per_country.index: if cntr in countries: @@ -558,12 +594,14 @@ def _rule_min(m): for sn in snapshots ) - sum( - m.link_p[link, sn] * network.snapshot_weightings.objective[sn] + m.link_p[link, sn] + * network.snapshot_weightings.objective[sn] for link in cb0_link for sn in snapshots ) + sum( - m.link_p[link, sn] * network.snapshot_weightings.objective[sn] + m.link_p[link, sn] + * network.snapshot_weightings.objective[sn] for link in cb1_link for sn in snapshots ) @@ -591,12 +629,14 @@ def _rule_max(m): for sn in snapshots ) - sum( - m.link_p[link, sn] * network.snapshot_weightings.objective[sn] + m.link_p[link, sn] + * network.snapshot_weightings.objective[sn] for link in cb0_link for sn in snapshots ) + sum( - m.link_p[link, sn] * network.snapshot_weightings.objective[sn] + m.link_p[link, sn] + * network.snapshot_weightings.objective[sn] for link in cb1_link for sn in snapshots ) @@ -692,9 +732,9 @@ def _capacity_factor(self, network, snapshots): gens, potential = _generation_potential(network, c, cntr="all") def _rule_max(m): - dispatch = sum( - m.generator_p[gen, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[gen, sn] + * network.snapshot_weightings.generators[sn] for gen in gens for sn in snapshots ) @@ -704,9 +744,9 @@ def _rule_max(m): setattr(network.model, "max_flh_" + c, Constraint(rule=_rule_max)) def _rule_min(m): - dispatch = sum( - m.generator_p[gen, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[gen, sn] + * network.snapshot_weightings.generators[sn] for gen in gens for sn in snapshots ) @@ -800,9 +840,9 @@ def _capacity_factor_per_cntr(self, network, snapshots): if len(gens) > 0: def _rule_max(m): - dispatch = sum( - m.generator_p[gen, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[gen, sn] + * network.snapshot_weightings.generators[sn] for gen in gens for sn in snapshots ) @@ -810,13 +850,15 @@ def _rule_max(m): return dispatch <= factor[1] * potential setattr( - network.model, "max_flh_" + cntr + "_" + c, Constraint(rule=_rule_max) + network.model, + "max_flh_" + cntr + "_" + c, + Constraint(rule=_rule_max), ) def _rule_min(m): - dispatch = sum( - m.generator_p[gen, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[gen, sn] + * network.snapshot_weightings.generators[sn] for gen in gens for sn in snapshots ) @@ -824,11 +866,19 @@ def _rule_min(m): return dispatch >= factor[0] * potential setattr( - network.model, "min_flh_" + cntr + "_" + c, Constraint(rule=_rule_min) + network.model, + "min_flh_" + cntr + "_" + c, + Constraint(rule=_rule_min), ) else: - print('Carrier '+c+' is not available in '+cntr+'. Skipping this constraint.') + print( + "Carrier " + + c + + " is not available in " + + cntr + + ". Skipping this constraint." + ) def _capacity_factor_per_cntr_nmp(self, network, snapshots): @@ -861,7 +911,6 @@ def _capacity_factor_per_cntr_nmp(self, network, snapshots): gens, potential = _generation_potential(network, c, cntr) if len(gens) > 0: - generation = ( get_var(network, "Generator", "p") .loc[snapshots, gens] @@ -886,7 +935,13 @@ def _capacity_factor_per_cntr_nmp(self, network, snapshots): ) else: - print('Carrier '+c+' is not available in '+cntr+'. Skipping this constraint.') + print( + "Carrier " + + c + + " is not available in " + + cntr + + ". Skipping this constraint." + ) def _capacity_factor_per_gen(self, network, snapshots): @@ -936,9 +991,9 @@ def _capacity_factor_per_gen(self, network, snapshots): ) def _rule_max(m): - dispatch = sum( - m.generator_p[g, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[g, sn] + * network.snapshot_weightings.generators[sn] for sn in snapshots ) @@ -947,9 +1002,9 @@ def _rule_max(m): setattr(network.model, "max_flh_" + g, Constraint(rule=_rule_max)) def _rule_min(m): - dispatch = sum( - m.generator_p[g, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[g, sn] + * network.snapshot_weightings.generators[sn] for sn in snapshots ) @@ -1052,7 +1107,6 @@ def _capacity_factor_per_gen_cntr(self, network, snapshots): """ arg = self.args["extra_functionality"]["capacity_factor_per_gen_cntr"] for cntr in arg.keys(): - carrier = arg[cntr].keys() snapshots = network.snapshots for c in carrier: @@ -1067,14 +1121,14 @@ def _capacity_factor_per_gen_cntr(self, network, snapshots): ] if len(gens) > 0: - for g in gens: if c in ["wind_onshore", "wind_offshore", "solar"]: potential = ( ( network.generators.p_nom[g] * network.generators_t.p_max_pu[g].mul( - network.snapshot_weightings.generators, axis=0 + network.snapshot_weightings.generators, + axis=0, ) ) .sum() @@ -1087,9 +1141,9 @@ def _capacity_factor_per_gen_cntr(self, network, snapshots): ) def _rule_max(m): - dispatch = sum( - m.generator_p[g, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[g, sn] + * network.snapshot_weightings.generators[sn] for sn in snapshots ) return dispatch <= factor[1] * potential @@ -1101,9 +1155,9 @@ def _rule_max(m): ) def _rule_min(m): - dispatch = sum( - m.generator_p[g, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[g, sn] + * network.snapshot_weightings.generators[sn] for sn in snapshots ) return dispatch >= factor[0] * potential @@ -1115,7 +1169,13 @@ def _rule_min(m): ) else: - print('Carrier '+c+' is not available in '+cntr+'. Skipping this constraint.') + print( + "Carrier " + + c + + " is not available in " + + cntr + + ". Skipping this constraint." + ) def _capacity_factor_per_gen_cntr_nmp(self, network, snapshots): @@ -1143,7 +1203,6 @@ def _capacity_factor_per_gen_cntr_nmp(self, network, snapshots): """ arg = self.args["extra_functionality"]["capacity_factor_per_gen_cntr"] for cntr in arg.keys(): - carrier = arg[cntr].keys() for c in carrier: @@ -1157,14 +1216,14 @@ def _capacity_factor_per_gen_cntr_nmp(self, network, snapshots): ] if len(gens) > 0: - for g in gens: if c in ["wind_onshore", "wind_offshore", "solar"]: potential = ( ( network.generators.p_nom[g] * network.generators_t.p_max_pu[g].mul( - network.snapshot_weightings.generators, axis=0 + network.snapshot_weightings.generators, + axis=0, ) ) .sum() @@ -1200,7 +1259,13 @@ def _capacity_factor_per_gen_cntr_nmp(self, network, snapshots): ) else: - print('Carrier '+c+' is not available in '+cntr+'. Skipping this constraint.') + print( + "Carrier " + + c + + " is not available in " + + cntr + + ". Skipping this constraint." + ) def read_max_gas_generation(self): @@ -1293,16 +1358,18 @@ def add_ch4_constraints(self, network, snapshots): factor = arg[c] def _rule_max(m): - dispatch = sum( - m.generator_p[gen, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[gen, sn] + * network.snapshot_weightings.generators[sn] for gen in gens for sn in snapshots ) return dispatch <= factor * (n_snapshots / 8760) - setattr(network.model, "max_flh_DE_" + c, Constraint(rule=_rule_max)) + setattr( + network.model, "max_flh_DE_" + c, Constraint(rule=_rule_max) + ) # Add contraints for neigbouring countries gen_abroad = network.generators[ @@ -1318,9 +1385,9 @@ def _rule_max(m): factor = network.generators.e_nom_max[g] def _rule_max(m): - dispatch = sum( - m.generator_p[g, sn] * network.snapshot_weightings.generators[sn] + m.generator_p[g, sn] + * network.snapshot_weightings.generators[sn] for sn in snapshots ) @@ -1453,7 +1520,8 @@ def day_rule(m, s, p): soc of the last hour of the day (i.e. + 23 hours) """ return ( - m.state_of_charge[s, p] == m.state_of_charge[s, p + pd.Timedelta(hours=23)] + m.state_of_charge[s, p] + == m.state_of_charge[s, p + pd.Timedelta(hours=23)] ) network.model.period_bound = Constraint( @@ -1483,20 +1551,28 @@ def snapshot_clustering_daily_bounds_nmp(self, network, snapshots): period_ends = period_starts + pd.Timedelta(hours=23) eh = expand_series( - network.snapshot_weightings.objective[period_ends], network.storage_units.index + network.snapshot_weightings.objective[period_ends], + network.storage_units.index, ) # elapsed hours eff_stand = expand_series(1 - network.df(c).standing_loss, period_ends).T - eff_dispatch = expand_series(network.df(c).efficiency_dispatch, period_ends).T + eff_dispatch = expand_series( + network.df(c).efficiency_dispatch, period_ends + ).T eff_store = expand_series(network.df(c).efficiency_store, period_ends).T soc = get_var(network, c, "state_of_charge").loc[period_ends, :] - soc_peroid_start = get_var(network, c, "state_of_charge").loc[period_starts] + soc_peroid_start = get_var(network, c, "state_of_charge").loc[ + period_starts + ] coeff_var = [ (-1, soc), - (-1 / eff_dispatch * eh, get_var(network, c, "p_dispatch").loc[period_ends, :]), + ( + -1 / eff_dispatch * eh, + get_var(network, c, "p_dispatch").loc[period_ends, :], + ), (eff_store * eh, get_var(network, c, "p_store").loc[period_ends, :]), ] @@ -1509,14 +1585,18 @@ def masked_term(coeff, var, cols): .values ) - lhs += masked_term(eff_stand, soc_peroid_start, network.storage_units.index) + lhs += masked_term( + eff_stand, soc_peroid_start, network.storage_units.index + ) rhs = -get_as_dense(network, c, "inflow", period_ends).mul(eh) define_constraints(network, lhs, "==", rhs, "daily_bounds") -def snapshot_clustering_seasonal_storage(self, network, snapshots, simplified=False): +def snapshot_clustering_seasonal_storage( + self, network, snapshots, simplified=False +): """ Depicts intertemporal dependencies of storage units and stores when using snapshot clustering to typical periods for temporal complexity reduction. @@ -1560,13 +1640,15 @@ def snapshot_clustering_seasonal_storage(self, network, snapshots, simplified=Fa network.model.candidates = po.Set(initialize=candidates, ordered=True) if simplified == False: - # create intra soc variable for each storage/store and each hour - network.model.state_of_charge_intra = po.Var(sus.index, network.snapshots) - network.model.state_of_charge_intra_store = po.Var(sto.index, network.snapshots) + network.model.state_of_charge_intra = po.Var( + sus.index, network.snapshots + ) + network.model.state_of_charge_intra_store = po.Var( + sto.index, network.snapshots + ) else: - network.model.state_of_charge_intra_max = po.Var( sus.index, network.model.candidates ) @@ -1581,8 +1663,12 @@ def snapshot_clustering_seasonal_storage(self, network, snapshots, simplified=Fa ) # create intra soc variable for each storage and each hour - network.model.state_of_charge_intra = po.Var(sus.index, network.snapshots) - network.model.state_of_charge_intra_store = po.Var(sto.index, network.snapshots) + network.model.state_of_charge_intra = po.Var( + sus.index, network.snapshots + ) + network.model.state_of_charge_intra_store = po.Var( + sto.index, network.snapshots + ) def intra_max(model, st, h): cand = network.cluster_ts["Candidate_day"][h] @@ -1649,7 +1735,9 @@ def intra_soc_rule(m, s, h): and h in network.snapshot_weightings[0::720].index ): expr = m.state_of_charge_intra[s, h] == 0 - elif self.args["snapshot_clustering"]["how"] == "daily" and h.hour == 0: + elif ( + self.args["snapshot_clustering"]["how"] == "daily" and h.hour == 0 + ): expr = m.state_of_charge_intra[s, h] == 0 else: expr = m.state_of_charge_intra[s, h] == m.state_of_charge_intra[ @@ -1663,7 +1751,6 @@ def intra_soc_rule(m, s, h): return expr def intra_soc_rule_store(m, s, h): - if ( self.args["snapshot_clustering"]["how"] == "weekly" and h in network.snapshot_weightings[0::168].index @@ -1674,7 +1761,9 @@ def intra_soc_rule_store(m, s, h): and h in network.snapshot_weightings[0::720].index ): expr = m.state_of_charge_intra_store[s, h] == 0 - elif self.args["snapshot_clustering"]["how"] == "daily" and h.hour == 0: + elif ( + self.args["snapshot_clustering"]["how"] == "daily" and h.hour == 0 + ): expr = m.state_of_charge_intra_store[s, h] == 0 else: expr = ( @@ -1724,9 +1813,9 @@ def inter_storage_soc_rule(m, s, i): hrs = 720 else: hrs = 24 - expr = m.state_of_charge_inter[s, i + 1] == m.state_of_charge_inter[ - s, i - ] * ( + expr = m.state_of_charge_inter[ + s, i + 1 + ] == m.state_of_charge_inter[s, i] * ( 1 - network.storage_units.at[s, "standing_loss"] ) ** hrs + m.state_of_charge_intra[ s, last_hour @@ -1741,7 +1830,6 @@ def inter_storage_soc_rule(m, s, i): return expr def inter_store_soc_rule(m, s, i): - if i == network.model.candidates[-1]: last_hour = network.cluster["last_hour_RepresentativeDay"][i] expr = po.Constraint.Skip @@ -1795,15 +1883,18 @@ def total_state_of_charge(m, s, h): return ( m.state_of_charge[s, h] == m.state_of_charge_intra[s, h] - + m.state_of_charge_inter[s, network.cluster_ts["Candidate_day"][h]] + + m.state_of_charge_inter[ + s, network.cluster_ts["Candidate_day"][h] + ] ) def total_state_of_charge_store(m, s, h): - return ( m.store_e[s, h] == m.state_of_charge_intra_store[s, h] - + m.state_of_charge_inter_store[s, network.cluster_ts["Candidate_day"][h]] + + m.state_of_charge_inter_store[ + s, network.cluster_ts["Candidate_day"][h] + ] ) network.model.total_storage_constraint = po.Constraint( @@ -1837,7 +1928,9 @@ def state_of_charge_lower(m, s, h): if self.args["snapshot_clustering"]["how"] == "weekly": hrs = 168 candidate = network.cluster_ts["Candidate_day"][h] - last_hour = network.cluster.loc[candidate]["last_hour_RepresentativeDay"] + last_hour = network.cluster.loc[candidate][ + "last_hour_RepresentativeDay" + ] first_hour = last_hour - pd.DateOffset(hours=167) period_start = network.cluster_ts.index[0::168][candidate - 1] delta_t = h - period_start @@ -1845,7 +1938,9 @@ def state_of_charge_lower(m, s, h): elif self.args["snapshot_clustering"]["how"] == "monthly": hrs = 720 candidate = network.cluster_ts["Candidate_day"][h] - last_hour = network.cluster.loc[candidate]["last_hour_RepresentativeDay"] + last_hour = network.cluster.loc[candidate][ + "last_hour_RepresentativeDay" + ] first_hour = last_hour - pd.DateOffset(hours=719) period_start = network.cluster_ts.index[0::720][candidate - 1] delta_t = h - period_start @@ -1863,18 +1958,21 @@ def state_of_charge_lower(m, s, h): return ( m.state_of_charge_intra[s, intra_hour] - + m.state_of_charge_inter[s, network.cluster_ts["Candidate_day"][h]] + + m.state_of_charge_inter[ + s, network.cluster_ts["Candidate_day"][h] + ] * (1 - network.storage_units.at[s, "standing_loss"]) ** hrs ### >= 0 ) def state_of_charge_lower_store(m, s, h): - # Choose datetime of representive day if self.args["snapshot_clustering"]["how"] == "weekly": hrs = 168 candidate = network.cluster_ts["Candidate_day"][h] - last_hour = network.cluster.loc[candidate]["last_hour_RepresentativeDay"] + last_hour = network.cluster.loc[candidate][ + "last_hour_RepresentativeDay" + ] first_hour = last_hour - pd.DateOffset(hours=167) period_start = network.cluster_ts.index[0::168][candidate - 1] delta_t = h - period_start @@ -1882,7 +1980,9 @@ def state_of_charge_lower_store(m, s, h): elif self.args["snapshot_clustering"]["how"] == "monthly": hrs = 720 candidate = network.cluster_ts["Candidate_day"][h] - last_hour = network.cluster.loc[candidate]["last_hour_RepresentativeDay"] + last_hour = network.cluster.loc[candidate][ + "last_hour_RepresentativeDay" + ] first_hour = last_hour - pd.DateOffset(hours=719) period_start = network.cluster_ts.index[0::720][candidate - 1] delta_t = h - period_start @@ -1899,13 +1999,18 @@ def state_of_charge_lower_store(m, s, h): intra_hour = pd.to_datetime(date + " " + hour) if "DSM" in s: - low = network.stores.e_nom[s] * network.stores_t.e_min_pu.at[intra_hour, s] + low = ( + network.stores.e_nom[s] + * network.stores_t.e_min_pu.at[intra_hour, s] + ) else: low = 0 return ( m.state_of_charge_intra_store[s, intra_hour] - + m.state_of_charge_inter_store[s, network.cluster_ts["Candidate_day"][h]] + + m.state_of_charge_inter_store[ + s, network.cluster_ts["Candidate_day"][h] + ] * (1 - network.stores.at[s, "standing_loss"]) ** hrs ### >= low ) @@ -1927,14 +2032,17 @@ def state_of_charge_lower_simplified(m, s, h): hrs = 24 # 0 return ( - m.state_of_charge_intra_min[s, network.cluster_ts["Candidate_day"][h]] - + m.state_of_charge_inter[s, network.cluster_ts["Candidate_day"][h]] + m.state_of_charge_intra_min[ + s, network.cluster_ts["Candidate_day"][h] + ] + + m.state_of_charge_inter[ + s, network.cluster_ts["Candidate_day"][h] + ] * (1 - network.storage_units.at[s, "standing_loss"]) ** hrs ### >= 0 ) def state_of_charge_lower_store_simplified(m, s, h): - if self.args["snapshot_clustering"]["how"] == "weekly": hrs = 168 elif self.args["snapshot_clustering"]["how"] == "monthly": @@ -1970,20 +2078,29 @@ def state_of_charge_lower_store_simplified(m, s, h): ).split(" ")[0] hour = str(h).split(" ")[1] intra_hour = pd.to_datetime(date + " " + hour) - low = network.stores.e_nom[s] * network.stores_t.e_min_pu.at[intra_hour, s] + low = ( + network.stores.e_nom[s] + * network.stores_t.e_min_pu.at[intra_hour, s] + ) else: low = 0 return ( - m.state_of_charge_intra_store_min[s, network.cluster_ts["Candidate_day"][h]] - + m.state_of_charge_inter_store[s, network.cluster_ts["Candidate_day"][h]] + m.state_of_charge_intra_store_min[ + s, network.cluster_ts["Candidate_day"][h] + ] + + m.state_of_charge_inter_store[ + s, network.cluster_ts["Candidate_day"][h] + ] * (1 - network.stores.at[s, "standing_loss"]) ** hrs ### >= low ) if simplified: network.model.state_of_charge_lower = po.Constraint( - sus.index, network.cluster_ts.index, rule=state_of_charge_lower_simplified + sus.index, + network.cluster_ts.index, + rule=state_of_charge_lower_simplified, ) network.model.state_of_charge_lower_store = po.Constraint( sto.index, @@ -1996,7 +2113,9 @@ def state_of_charge_lower_store_simplified(m, s, h): sus.index, network.cluster_ts.index, rule=state_of_charge_lower ) network.model.state_of_charge_lower_store = po.Constraint( - sto.index, network.cluster_ts.index, rule=state_of_charge_lower_store + sto.index, + network.cluster_ts.index, + rule=state_of_charge_lower_store, ) network.model.del_component("state_of_charge_upper") @@ -2010,12 +2129,13 @@ def state_of_charge_lower_store_simplified(m, s, h): network.model.del_component("store_e_upper_index_1") def state_of_charge_upper(m, s, h): - # Choose datetime of representive day if self.args["snapshot_clustering"]["how"] == "weekly": hrs = 168 candidate = network.cluster_ts["Candidate_day"][h] - last_hour = network.cluster.loc[candidate]["last_hour_RepresentativeDay"] + last_hour = network.cluster.loc[candidate][ + "last_hour_RepresentativeDay" + ] first_hour = last_hour - pd.DateOffset(hours=167) period_start = network.cluster_ts.index[0::168][candidate - 1] delta_t = h - period_start @@ -2023,7 +2143,9 @@ def state_of_charge_upper(m, s, h): elif self.args["snapshot_clustering"]["how"] == "monthly": hrs = 720 candidate = network.cluster_ts["Candidate_day"][h] - last_hour = network.cluster.loc[candidate]["last_hour_RepresentativeDay"] + last_hour = network.cluster.loc[candidate][ + "last_hour_RepresentativeDay" + ] first_hour = last_hour - pd.DateOffset(hours=719) period_start = network.cluster_ts.index[0::720][candidate - 1] delta_t = h - period_start @@ -2046,18 +2168,21 @@ def state_of_charge_upper(m, s, h): return ( m.state_of_charge_intra[s, intra_hour] - + m.state_of_charge_inter[s, network.cluster_ts["Candidate_day"][h]] + + m.state_of_charge_inter[ + s, network.cluster_ts["Candidate_day"][h] + ] * (1 - network.storage_units.at[s, "standing_loss"]) ** hrs ### <= p_nom * network.storage_units.at[s, "max_hours"] ) def state_of_charge_upper_store(m, s, h): - # Choose datetime of representive day if self.args["snapshot_clustering"]["how"] == "weekly": hrs = 168 candidate = network.cluster_ts["Candidate_day"][h] - last_hour = network.cluster.loc[candidate]["last_hour_RepresentativeDay"] + last_hour = network.cluster.loc[candidate][ + "last_hour_RepresentativeDay" + ] first_hour = last_hour - pd.DateOffset(hours=167) period_start = network.cluster_ts.index[0::168][candidate - 1] delta_t = h - period_start @@ -2065,7 +2190,9 @@ def state_of_charge_upper_store(m, s, h): elif self.args["snapshot_clustering"]["how"] == "monthly": hrs = 720 candidate = network.cluster_ts["Candidate_day"][h] - last_hour = network.cluster.loc[candidate]["last_hour_RepresentativeDay"] + last_hour = network.cluster.loc[candidate][ + "last_hour_RepresentativeDay" + ] first_hour = last_hour - pd.DateOffset(hours=719) period_start = network.cluster_ts.index[0::720][candidate - 1] delta_t = h - period_start @@ -2094,13 +2221,14 @@ def state_of_charge_upper_store(m, s, h): return ( m.state_of_charge_intra_store[s, intra_hour] - + m.state_of_charge_inter_store[s, network.cluster_ts["Candidate_day"][h]] + + m.state_of_charge_inter_store[ + s, network.cluster_ts["Candidate_day"][h] + ] * (1 - network.stores.at[s, "standing_loss"]) ** hrs ### <= e_nom ) def state_of_charge_upper_simplified(m, s, h): - if self.args["snapshot_clustering"]["how"] == "weekly": hrs = 168 elif self.args["snapshot_clustering"]["how"] == "monthly": @@ -2114,14 +2242,17 @@ def state_of_charge_upper_simplified(m, s, h): p_nom = network.storage_units.p_nom[s] return ( - m.state_of_charge_intra_max[s, network.cluster_ts["Candidate_day"][h]] - + m.state_of_charge_inter[s, network.cluster_ts["Candidate_day"][h]] + m.state_of_charge_intra_max[ + s, network.cluster_ts["Candidate_day"][h] + ] + + m.state_of_charge_inter[ + s, network.cluster_ts["Candidate_day"][h] + ] * (1 - network.storage_units.at[s, "standing_loss"]) ** hrs ### <= p_nom * network.storage_units.at[s, "max_hours"] ) def state_of_charge_upper_store_simplified(m, s, h): - if self.args["snapshot_clustering"]["how"] == "weekly": hrs = 168 elif self.args["snapshot_clustering"]["how"] == "monthly": @@ -2139,7 +2270,9 @@ def state_of_charge_upper_store_simplified(m, s, h): "last_hour_RepresentativeDay" ] first_hour = last_hour - pd.DateOffset(hours=167) - period_start = network.cluster_ts.index[0::168][candidate - 1] + period_start = network.cluster_ts.index[0::168][ + candidate - 1 + ] delta_t = h - period_start intra_hour = first_hour + delta_t @@ -2149,7 +2282,9 @@ def state_of_charge_upper_store_simplified(m, s, h): "last_hour_RepresentativeDay" ] first_hour = last_hour - pd.DateOffset(hours=719) - period_start = network.cluster_ts.index[0::720][candidate - 1] + period_start = network.cluster_ts.index[0::720][ + candidate - 1 + ] delta_t = h - period_start intra_hour = first_hour + delta_t @@ -2157,7 +2292,9 @@ def state_of_charge_upper_store_simplified(m, s, h): date = str( network.snapshots[ network.snapshots.dayofyear - 1 - == network.cluster["RepresentativeDay"][h.dayofyear] + == network.cluster["RepresentativeDay"][ + h.dayofyear + ] ][0] ).split(" ")[0] hour = str(h).split(" ")[1] @@ -2171,16 +2308,21 @@ def state_of_charge_upper_store_simplified(m, s, h): e_nom = network.stores.e_nom[s] return ( - m.state_of_charge_intra_store_max[s, network.cluster_ts["Candidate_day"][h]] - + m.state_of_charge_inter_store[s, network.cluster_ts["Candidate_day"][h]] + m.state_of_charge_intra_store_max[ + s, network.cluster_ts["Candidate_day"][h] + ] + + m.state_of_charge_inter_store[ + s, network.cluster_ts["Candidate_day"][h] + ] * (1 - network.stores.at[s, "standing_loss"]) ** hrs ### <= e_nom ) if simplified: - network.model.state_of_charge_upper = po.Constraint( - sus.index, network.cluster_ts.index, rule=state_of_charge_upper_simplified + sus.index, + network.cluster_ts.index, + rule=state_of_charge_upper_simplified, ) network.model.state_of_charge_upper_store = po.Constraint( sto.index, @@ -2189,12 +2331,13 @@ def state_of_charge_upper_store_simplified(m, s, h): ) else: - network.model.state_of_charge_upper = po.Constraint( sus.index, network.cluster_ts.index, rule=state_of_charge_upper ) network.model.state_of_charge_upper_store = po.Constraint( - sto.index, network.cluster_ts.index, rule=state_of_charge_upper_store + sto.index, + network.cluster_ts.index, + rule=state_of_charge_upper_store, ) def cyclic_state_of_charge(m, s): @@ -2203,7 +2346,9 @@ def cyclic_state_of_charge(m, s): There are small differences to original results. """ last_day = network.cluster.index[-1] - last_calc_hour = network.cluster["last_hour_RepresentativeDay"][last_day] + last_calc_hour = network.cluster["last_hour_RepresentativeDay"][ + last_day + ] last_inter = m.state_of_charge_inter[s, last_day] last_intra = m.state_of_charge_intra[s, last_calc_hour] first_day = network.cluster.index[0] @@ -2233,9 +2378,10 @@ def cyclic_state_of_charge(m, s): ) def cyclic_state_of_charge_store(m, s): - last_day = network.cluster.index[-1] - last_calc_hour = network.cluster["last_hour_RepresentativeDay"][last_day] + last_calc_hour = network.cluster["last_hour_RepresentativeDay"][ + last_day + ] last_inter = m.state_of_charge_inter_store[s, last_day] last_intra = m.state_of_charge_intra_store[s, last_calc_hour] first_day = network.cluster.index[0] @@ -2254,7 +2400,8 @@ def cyclic_state_of_charge_store(m, s): first_intra = m.state_of_charge_intra_store[s, first_calc_hour] expr = first_intra + first_inter == ( - (last_intra + last_inter) * (1 - network.stores.at[s, "standing_loss"]) + (last_intra + last_inter) + * (1 - network.stores.at[s, "standing_loss"]) + m.store_p[s, last_calc_hour] ) @@ -2310,7 +2457,6 @@ def snapshot_clustering_seasonal_storage_hourly(self, network, snapshots): network.model.storages = network.storage_units.index def set_soc_all(m, s, h): - if h == self.args["start_snapshot"]: prev = ( network.cluster.index.get_level_values(0)[-1] @@ -2321,11 +2467,13 @@ def set_soc_all(m, s, h): else: prev = h - 1 - cluster_hour = network.cluster["Hour"][h + 1 - self.args["start_snapshot"]] + cluster_hour = network.cluster["Hour"][ + h + 1 - self.args["start_snapshot"] + ] - expr = m.state_of_charge_all[s, h] == m.state_of_charge_all[s, prev] * ( - 1 - network.storage_units.at[s, "standing_loss"] - ) - ( + expr = m.state_of_charge_all[s, h] == m.state_of_charge_all[ + s, prev + ] * (1 - network.storage_units.at[s, "standing_loss"]) - ( m.storage_p_dispatch[s, cluster_hour] / network.storage_units.at[s, "efficiency_dispatch"] - network.storage_units.at[s, "efficiency_store"] @@ -2340,7 +2488,6 @@ def set_soc_all(m, s, h): ) def soc_equals_soc_all(m, s, h): - hour = (h.dayofyear - 1) * 24 + h.hour return m.state_of_charge_all[s, hour] == m.state_of_charge[s, h] @@ -2355,7 +2502,6 @@ def soc_equals_soc_all(m, s, h): network.model.del_component("state_of_charge_upper_index_1") def state_of_charge_upper(m, s, h): - if network.storage_units.p_nom_extendable[s]: p_nom = m.storage_p_nom[s] else: @@ -2462,7 +2608,12 @@ def snapshot_clustering_seasonal_storage_nmp(self, n, sns, simplified=False): coeff_var = [ (-1, soc_total), (1, soc_intra), - (1, soc_inter.loc[n.cluster_ts.loc[sns, "Candidate_day"]].set_index(sns)), + ( + 1, + soc_inter.loc[n.cluster_ts.loc[sns, "Candidate_day"]].set_index( + sns + ), + ), ] lhs, *axes = linexpr(*coeff_var, return_axes=True) @@ -2525,7 +2676,9 @@ def functionality(self, network, snapshots): type(network.model) try: eval("_" + constraint + "(self, network, snapshots)") - logger.info("Added extra_functionality {}".format(constraint)) + logger.info( + "Added extra_functionality {}".format(constraint) + ) except: logger.warning( "Constraint {} not defined".format(constraint) @@ -2536,33 +2689,35 @@ def functionality(self, network, snapshots): try: eval("_" + constraint + "_nmp(self, network, snapshots)") logger.info( - "Added extra_functionality {} without pyomo".format(constraint) + "Added extra_functionality {} without pyomo".format( + constraint + ) ) except: - logger.warning("Constraint {} not defined".format(constraint)) + logger.warning( + "Constraint {} not defined".format(constraint) + ) if ( self.args["snapshot_clustering"]["active"] and self.args["snapshot_clustering"]["method"] == "typical_periods" ): - if ( self.args["snapshot_clustering"]["storage_constraints"] == "daily_bounds" ): - if self.args["method"]["pyomo"]: snapshot_clustering_daily_bounds(self, network, snapshots) else: - snapshot_clustering_daily_bounds_nmp(self, network, snapshots) + snapshot_clustering_daily_bounds_nmp( + self, network, snapshots + ) elif ( self.args["snapshot_clustering"]["storage_constraints"] == "soc_constraints" ): - if self.args["snapshot_clustering"]["how"] == "hourly": - if self.args["method"]["pyomo"]: snapshot_clustering_seasonal_storage_hourly( self, network, snapshots @@ -2572,9 +2727,10 @@ def functionality(self, network, snapshots): self, network, snapshots ) else: - if self.args["method"]["pyomo"]: - snapshot_clustering_seasonal_storage(self, network, snapshots) + snapshot_clustering_seasonal_storage( + self, network, snapshots + ) else: snapshot_clustering_seasonal_storage_nmp( self, network, snapshots @@ -2584,9 +2740,7 @@ def functionality(self, network, snapshots): self.args["snapshot_clustering"]["storage_constraints"] == "soc_constraints_simplified" ): - if self.args["snapshot_clustering"]["how"] == "hourly": - logger.info( "soc_constraints_simplified not possible while hourly clustering -> changed to soc_constraints" ) @@ -2652,7 +2806,6 @@ def add_chp_constraints_nmp(n): ].index.unique() for i in ch4_nodes_with_chp: - elec_chp = n.links[ (n.links.carrier == "central_gas_CHP") & (n.links.bus0 == i) ].index @@ -2665,16 +2818,20 @@ def add_chp_constraints_nmp(n): # backpressure lhs_1 = sum( - c_m * n.links.at[h_chp, "efficiency"] * link_p[h_chp] for h_chp in heat_chp + c_m * n.links.at[h_chp, "efficiency"] * link_p[h_chp] + for h_chp in heat_chp ) lhs_2 = sum( - n.links.at[e_chp, "efficiency"] * link_p[e_chp] for e_chp in elec_chp + n.links.at[e_chp, "efficiency"] * link_p[e_chp] + for e_chp in elec_chp ) lhs = linexpr((1, lhs_1), (1, lhs_2)) - define_constraints(n, lhs, "<=", 0, "chplink_" + str(i), "backpressure") + define_constraints( + n, lhs, "<=", 0, "chplink_" + str(i), "backpressure" + ) # top_iso_fuel_line lhs, *ax = linexpr( @@ -2733,9 +2890,9 @@ def add_chp_constraints(network, snapshots): ].index.unique() for i in ch4_nodes_with_chp: - elec_chp = network.links[ - (network.links.carrier == "central_gas_CHP") & (network.links.bus0 == i) + (network.links.carrier == "central_gas_CHP") + & (network.links.bus0 == i) ].index heat_chp = network.links[ @@ -2753,7 +2910,8 @@ def backpressure(model, snapshot): ) rhs = sum( - network.links.at[e_chp, "efficiency"] * model.link_p[e_chp, snapshot] + network.links.at[e_chp, "efficiency"] + * model.link_p[e_chp, snapshot] for e_chp in elec_chp ) @@ -2767,13 +2925,13 @@ def backpressure(model, snapshot): # Guarantees p_g1 +c_v p_b1 \leq p_g1_nom def top_iso_fuel_line(model, snapshot): - - lhs = sum(model.link_p[h_chp, snapshot] for h_chp in heat_chp) + sum( - model.link_p[e_chp, snapshot] for e_chp in elec_chp - ) + lhs = sum( + model.link_p[h_chp, snapshot] for h_chp in heat_chp + ) + sum(model.link_p[e_chp, snapshot] for e_chp in elec_chp) rhs = network.links[ - (network.links.carrier == "central_gas_CHP") & (network.links.bus0 == i) + (network.links.carrier == "central_gas_CHP") + & (network.links.bus0 == i) ].p_nom.sum() return lhs <= rhs diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index c6a6187cf..deb83e605 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -228,6 +228,7 @@ def plot_line_loading_diff(networkA, networkB, timestep=0, osm=False): set_epsg_network(networkA) set_epsg_network(networkB) plot_osm(osm["x"], osm["y"], osm["zoom"]) + # new colormap to make sure 0% difference has the same color in every plot def shiftedColorMap( cmap, start=0, midpoint=0.5, stop=1.0, name="shiftedcmap" @@ -421,7 +422,7 @@ def plot_residual_load(network): Parameters ---------- network : PyPSA network containter - + Returns ------- Plot @@ -929,7 +930,7 @@ def gen_dist_diff( filename : str Specify filename If not given, figure will be show directly - + Returns ------- None. @@ -1046,7 +1047,7 @@ def nodal_gen_dispatch( 'x': array of two floats, x axis boundaries (lat) 'y': array of two floats, y axis boundaries (long) 'zoom' : resolution of osm - + Returns ------- None. @@ -1214,7 +1215,7 @@ def storage_p_soc(network, mean="1H", filename=None): mean : str Defines over how many snapshots the p and soc values will averaged. filename : path to folder - + Returns ------- None. @@ -1253,7 +1254,6 @@ def storage_p_soc(network, mean="1H", filename=None): network.storage_units.p_nom_opt[sbatt].sum() > 1 and network.storage_units.p_nom_opt[shydr].sum() < 1 ): - ( network.storage_units_t.p[sbatt].resample(mean).mean().sum(axis=1) / network.storage_units.p_nom_opt[sbatt].sum() @@ -1352,7 +1352,7 @@ def storage_soc_sorted(network, filename=None): Holds topology of grid including results from powerflow analysis filename : path to folder - + Returns ------- None. @@ -1482,7 +1482,6 @@ def calc_ac_loading(network, timesteps): ) if not network.lines_t.q0.empty: - loading_lines = ( loading_lines**2 + mul_weighting(network, network.lines_t.q0) @@ -1523,7 +1522,6 @@ def calc_dc_loading(network, timesteps): & (network.links.length == row["length"]) ] ).empty: - l = network.links.index[ (network.links.bus0 == row["bus1"]) & (network.links.bus1 == row["bus0"]) @@ -1641,7 +1639,6 @@ def calc_network_expansion(network, method="abs", ext_min=0.1): network.links.p_nom_opt[i] = linked.p_nom_opt.values[0] if method == "rel": - extension_lines = ( 100 * (network.lines.s_nom_opt - network.lines.s_nom_min) @@ -2083,7 +2080,6 @@ def flexibility_usage( ) elif flexibility == "battery": - df = pd.DataFrame(index=self.network.snapshots[snapshots]) su = self.network.storage_units[ @@ -2170,13 +2166,11 @@ def plot_carrier(network, carrier_links=["AC"], carrier_buses=["AC"]): link_width = pd.Series(index=network.links.index, data=2) if len(carrier_links) > 0: - link_width.loc[~network.links.carrier.isin(carrier_links)] = 0 bus_sizes = pd.Series(index=network.buses.index, data=0.0005) if len(carrier_buses) > 0: - bus_sizes.loc[~network.buses.carrier.isin(carrier_buses)] = 0 link_colors = network.links.carrier.map(colors) @@ -3306,7 +3300,7 @@ def plot_heat_summary(self, t_resolution="20H", stacked=True, save_path=False): data.resample(t_resolution).mean().plot( ax=ax, label=i, legend=True ) - + heat_store_dispatch_hb.resample(t_resolution).mean().plot.line( ax=ax, legend=True, diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 911f708a3..ccabc12b6 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -851,6 +851,7 @@ def _make_consense(component, attr): as output when all the elements of the commponent attribute are the same. """ + def consense(x): v = x.iat[0] assert (x == v).all() or x.isnull().all(), ( @@ -867,7 +868,7 @@ def _normed(s): Given a pandas Series `s`, normalizes the series by dividing each element by the sum of the series. If the sum of the series is zero, returns 1.0 to avoid division by zero errors. - + Parameters ---------- s : pandas.Series @@ -891,7 +892,7 @@ def agg_series_lines(l, network): Given a pandas DataFrame `l` containing information about lines in a network and a network object, aggregates the data in `l` for all its attributes. Returns a pandas Series containing the aggregated data. - + Parameters ---------- From 1a6d77e06d556620eee092a74c15afe857dee590 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Thu, 16 Mar 2023 10:58:04 +0100 Subject: [PATCH 025/159] Documentation --- etrago/cluster/snapshot.py | 498 +++++++++++++++++++++---------------- 1 file changed, 285 insertions(+), 213 deletions(-) diff --git a/etrago/cluster/snapshot.py b/etrago/cluster/snapshot.py index 3d8f7a215..4cab1ad5c 100644 --- a/etrago/cluster/snapshot.py +++ b/etrago/cluster/snapshot.py @@ -40,11 +40,22 @@ "Europa-Universität Flensburg, " "Centre for Sustainable Energy Systems") __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "Simon Hilpert" +__author__ = "ClaraBuettner, ulfmueller, KathiEsterl, simnh, wheitkoetter, BartelsJ, AmeliaNadal" def snapshot_clustering(self): """ + Function to call the snapshot clustering function with the respecting method and settings. + + Raises + ------ + ValueError + When calling a non-available function. + + Returns + ------- + None. + """ if self.args['snapshot_clustering']['active'] == True: @@ -76,40 +87,45 @@ def tsam_cluster(timeseries_df, extremePeriodMethod = 'None', segmentation = False, segment_no = 10, - segm_hoursperperiod = 24): + segm_hoursperperiod = 24): """ + Conducts the clustering of the snapshots for temporal aggregation with the respecting method. + Parameters ---------- - df : pd.DataFrame - DataFrame with timeseries to cluster - typical_periods: Number of typical Periods (or clusters) - how: {'daily', 'weekly'} - extremePeriodMethod: {'None','append','new_cluster_center', - 'replace_cluster_center'}, default: 'None' - Method how to integrate extreme Periods into to the typical period profiles. - 'None': No integration at all. - 'append': append typical Periods to cluster centers - 'new_cluster_center': add the extreme period as additional cluster - center. It is checked then for all Periods if they fit better - to the this new center or their original cluster center. - 'replace_cluster_center': replaces the cluster center of the - cluster where the extreme period belongs to with the periodly - profile of the extreme period. (Worst case system design) - segmentation: Is given by the run-function, can be True or False - segment_no: Only used when segmentation is true, the number of segments - segm_hoursperperiod: Only used when segmentation is true, defines the length of a cluster period + timeseries_df : pd.DataFrame + Dataframe wit timeseries to cluster. + typical_periods : int, optional + Number of clusters for typical_periods. The default is 10. + how : {'daily', 'weekly', 'monthly'}, optional + Definition of period for typical_periods. The default is 'daily'. + extremePeriodMethod : {'None','append','new_cluster_center', + 'replace_cluster_center'}, optional + Method to consider extreme snapshots in reduced timeseries. The default is 'None'. + segmentation : boolean, optional + Argument to activate segmenation method. The default is False. + segment_no : int, optional + Number of segments for segmentation. The default is 10. + segm_hoursperperiod : int, optional + Only for segmentation, ensures to cluster to segments considering all snapshots. The default is 24. Returns ------- - df_cluster - cluster_weights - dates - hours - df_i_h + df_cluster : pd.DataFrame + Information on cluster after clustering to typical periods. + cluster_weights : dict + Weightings per cluster after clustering to typical periods. + dates : DatetimeIndex + Dates of clusters after clustering to typical periods. + hours : int + Hours per typical period. + df_i_h : pd.DataFrame + Information on cluster after clustering to typical periods. timeseries : pd.DataFrame - Clustered timeseries, only used if segmentation is True - """ + Information on segments after segmentation. + """ + if how == 'daily': hours = 24 period = ' days' @@ -128,6 +144,7 @@ def tsam_cluster(timeseries_df, if segmentation: hoursPerPeriod = segm_hoursperperiod + hours=1 else: hoursPerPeriod = hours @@ -188,7 +205,7 @@ def tsam_cluster(timeseries_df, #print(timeseries) cluster_weights = aggregation.clusterPeriodNoOccur - clusterOrder =aggregation.clusterOrder + clusterOrder = aggregation.clusterOrder clusterCenterIndices= aggregation.clusterCenterIndices if segmentation == True: @@ -268,180 +285,225 @@ def tsam_cluster(timeseries_df, return df_cluster, cluster_weights, dates, hours, df_i_h, timeseries def segmentation_extreme_periods(timeseries_df, timeseries, extremePeriodMethod): + """ + Function to consider extreme snapshots while using segmentation. - # find maximum / minimum value in residual load - maxi = timeseries_df['residual_load'].idxmax() - mini = timeseries_df['residual_load'].idxmin() - - # add timestep if it is not already calculated - if maxi not in timeseries.index.get_level_values('dates'): - - # identifiy timestep, adapt it to timeseries-df and add it - max_val = timeseries_df.loc[maxi].copy() - max_val['SegmentNo'] = len(timeseries) - max_val['SegmentDuration'] = 1 - max_val['dates'] = max_val.name - max_val = pd.DataFrame(max_val).transpose() + Parameters + ---------- + timeseries_df : pd.DataFrame + Dataframe wit timeseries to cluster. + timeseries : pd.DataFrame + Information on segments after segmentation. + extremePeriodMethod : {'None','append','new_cluster_center', + 'replace_cluster_center'}, optional + Method to consider extreme snapshots in reduced timeseries. The default is 'None'. - if extremePeriodMethod == 'append': + Raises + ------ + ValueError + When calling wrong method to consider extreme values. - max_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - timeseries = timeseries.append(max_val) - timeseries = timeseries.sort_values(by='dates') + Returns + ------- + timeseries : pd.DataFrame + Information on segments including extreme snapshots after segmentation. + """ - # split up segment in which the extreme timestep was added - i=-1 - for date in timeseries.index.get_level_values('dates'): - if date < maxi: - i = i+1 - else: - timeseries['SegmentDuration_Extreme']=timeseries.index.get_level_values('SegmentDuration') - old_row = timeseries.iloc[i].copy() - old_row = pd.DataFrame(old_row).transpose() - - delta_t = timeseries.index.get_level_values('dates')[i+1]-timeseries.index.get_level_values('dates')[i] - delta_t = delta_t.total_seconds()/3600 - timeseries['SegmentDuration_Extreme'].iloc[i]=delta_t - - timeseries_df['row_no']=range(0,len(timeseries_df)) - new_row = int(timeseries_df.loc[maxi]['row_no'])+1 - new_date = timeseries_df[timeseries_df.row_no==new_row].index - - if new_date.isin(timeseries.index.get_level_values('dates')): - timeseries['dates'] = timeseries.index.get_level_values('dates') - timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') - timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] - timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) - timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - break - else: - new_row = timeseries_df.iloc[new_row].copy() - new_row.drop('row_no', inplace=True) - new_row['SegmentNo'] = len(timeseries) - new_row['SegmentDuration'] = old_row['SegmentDuration_Extreme'][0] - delta_t - 1 - new_row['dates'] = new_row.name - new_row = pd.DataFrame(new_row).transpose() - new_row.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - for col in new_row.columns: - new_row[col][0] = old_row[col][0] - - timeseries['dates'] = timeseries.index.get_level_values('dates') - timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') - timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] - timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) - timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - timeseries = timeseries.append(new_row) - timeseries = timeseries.sort_values(by='dates') - break - - elif extremePeriodMethod == 'replace_cluster_center': - - # replace segment in which the extreme timestep was added - i=-1 - for date in timeseries.index.get_level_values('dates'): - if date < maxi: - i = i+1 + # find maximum / minimum value in residual load + maxi = timeseries_df['residual_load'].idxmax() + mini = timeseries_df['residual_load'].idxmin() + + # add timestep if it is not already calculated + if maxi not in timeseries.index.get_level_values('dates'): + + # identifiy timestep, adapt it to timeseries-df and add it + max_val = timeseries_df.loc[maxi].copy() + max_val['SegmentNo'] = len(timeseries) + max_val['SegmentDuration'] = 1 + max_val['dates'] = max_val.name + max_val = pd.DataFrame(max_val).transpose() + + if extremePeriodMethod == 'append': + + max_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + timeseries = timeseries.append(max_val) + timeseries = timeseries.sort_values(by='dates') + + # split up segment in which the extreme timestep was added + i=-1 + for date in timeseries.index.get_level_values('dates'): + if date < maxi: + i = i+1 + else: + timeseries['SegmentDuration_Extreme']=timeseries.index.get_level_values('SegmentDuration') + old_row = timeseries.iloc[i].copy() + old_row = pd.DataFrame(old_row).transpose() + + delta_t = timeseries.index.get_level_values('dates')[i+1]-timeseries.index.get_level_values('dates')[i] + delta_t = delta_t.total_seconds()/3600 + timeseries['SegmentDuration_Extreme'].iloc[i]=delta_t + + timeseries_df['row_no']=range(0,len(timeseries_df)) + new_row = int(timeseries_df.loc[maxi]['row_no'])+1 + new_date = timeseries_df[timeseries_df.row_no==new_row].index + + if new_date.isin(timeseries.index.get_level_values('dates')): + timeseries['dates'] = timeseries.index.get_level_values('dates') + timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') + timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] + timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) + timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + break else: - if i ==-1: - i=0 - max_val['SegmentDuration'] = timeseries.index.get_level_values('SegmentDuration')[i] - max_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - timeseries.drop(timeseries.index[i], inplace=True) - timeseries = timeseries.append(max_val) + new_row = timeseries_df.iloc[new_row].copy() + new_row.drop('row_no', inplace=True) + new_row['SegmentNo'] = len(timeseries) + new_row['SegmentDuration'] = old_row['SegmentDuration_Extreme'][0] - delta_t - 1 + new_row['dates'] = new_row.name + new_row = pd.DataFrame(new_row).transpose() + new_row.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + for col in new_row.columns: + new_row[col][0] = old_row[col][0] + + timeseries['dates'] = timeseries.index.get_level_values('dates') + timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') + timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] + timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) + timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + timeseries = timeseries.append(new_row) timeseries = timeseries.sort_values(by='dates') break - else: - raise ValueError("Choose 'append' or 'replace_cluster_center' for consideration of extreme periods with segmentation method") - - # add timestep if it is not already calculated - if mini not in timeseries.index.get_level_values('dates'): - - # identifiy timestep, adapt it to timeseries-df and add it - min_val = timeseries_df.loc[mini].copy() - min_val['SegmentNo'] = len(timeseries)+1 - min_val['SegmentDuration'] = 1 - min_val['dates'] = min_val.name - min_val = pd.DataFrame(min_val).transpose() - - if extremePeriodMethod == 'append': - - min_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - timeseries = timeseries.append(min_val) - timeseries = timeseries.sort_values(by='dates') - - # split up segment in which the extreme timestep was added - i=-1 - for date in timeseries.index.get_level_values('dates'): - if date < mini: - i = i+1 - else: - timeseries['SegmentDuration_Extreme']=timeseries.index.get_level_values('SegmentDuration') - old_row = timeseries.iloc[i].copy() - old_row = pd.DataFrame(old_row).transpose() - - delta_t = timeseries.index.get_level_values('dates')[i+1]-timeseries.index.get_level_values('dates')[i] - delta_t = delta_t.total_seconds()/3600 - timeseries['SegmentDuration_Extreme'].iloc[i]=delta_t - - timeseries_df['row_no']=range(0,len(timeseries_df)) - new_row = int(timeseries_df.loc[mini]['row_no'])+1 - new_date = timeseries_df[timeseries_df.row_no==new_row].index - - if new_date.isin(timeseries.index.get_level_values('dates')): - timeseries['dates'] = timeseries.index.get_level_values('dates') - timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') - timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] - timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) - timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - break - else: - new_row = timeseries_df.iloc[new_row].copy() - new_row.drop('row_no', inplace=True) - new_row['SegmentNo'] = len(timeseries)+1 - new_row['SegmentDuration'] = old_row['SegmentDuration_Extreme'][0] - delta_t - 1 - new_row['dates'] = new_row.name - new_row = pd.DataFrame(new_row).transpose() - new_row.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - for col in new_row.columns: - new_row[col][0] = old_row[col][0] - timeseries['dates'] = timeseries.index.get_level_values('dates') - timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') - timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] - timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) - timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - timeseries = timeseries.append(new_row) - timeseries = timeseries.sort_values(by='dates') + elif extremePeriodMethod == 'replace_cluster_center': + + # replace segment in which the extreme timestep was added + i=-1 + for date in timeseries.index.get_level_values('dates'): + if date < maxi: + i = i+1 + else: + if i ==-1: + i=0 + max_val['SegmentDuration'] = timeseries.index.get_level_values('SegmentDuration')[i] + max_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + timeseries.drop(timeseries.index[i], inplace=True) + timeseries = timeseries.append(max_val) + timeseries = timeseries.sort_values(by='dates') + break + + else: + raise ValueError("Choose 'append' or 'replace_cluster_center' for consideration of extreme periods with segmentation method") + + # add timestep if it is not already calculated + if mini not in timeseries.index.get_level_values('dates'): + + # identifiy timestep, adapt it to timeseries-df and add it + min_val = timeseries_df.loc[mini].copy() + min_val['SegmentNo'] = len(timeseries)+1 + min_val['SegmentDuration'] = 1 + min_val['dates'] = min_val.name + min_val = pd.DataFrame(min_val).transpose() + + if extremePeriodMethod == 'append': + + min_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + timeseries = timeseries.append(min_val) + timeseries = timeseries.sort_values(by='dates') + + # split up segment in which the extreme timestep was added + i=-1 + for date in timeseries.index.get_level_values('dates'): + if date < mini: + i = i+1 + else: + timeseries['SegmentDuration_Extreme']=timeseries.index.get_level_values('SegmentDuration') + old_row = timeseries.iloc[i].copy() + old_row = pd.DataFrame(old_row).transpose() + + delta_t = timeseries.index.get_level_values('dates')[i+1]-timeseries.index.get_level_values('dates')[i] + delta_t = delta_t.total_seconds()/3600 + timeseries['SegmentDuration_Extreme'].iloc[i]=delta_t + + timeseries_df['row_no']=range(0,len(timeseries_df)) + new_row = int(timeseries_df.loc[mini]['row_no'])+1 + new_date = timeseries_df[timeseries_df.row_no==new_row].index + + if new_date.isin(timeseries.index.get_level_values('dates')): + timeseries['dates'] = timeseries.index.get_level_values('dates') + timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') + timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] + timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) + timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) break - - elif extremePeriodMethod == 'replace_cluster_center': - - # replace segment in which the extreme timestep was added - i=-1 - for date in timeseries.index.get_level_values('dates'): - if date < mini: - i = i+1 else: - if i ==-1: - i=0 - min_val['SegmentDuration'] = timeseries.index.get_level_values('SegmentDuration')[i] - min_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) - timeseries.drop(timeseries.index[i], inplace=True) - timeseries = timeseries.append(min_val) + new_row = timeseries_df.iloc[new_row].copy() + new_row.drop('row_no', inplace=True) + new_row['SegmentNo'] = len(timeseries)+1 + new_row['SegmentDuration'] = old_row['SegmentDuration_Extreme'][0] - delta_t - 1 + new_row['dates'] = new_row.name + new_row = pd.DataFrame(new_row).transpose() + new_row.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + for col in new_row.columns: + new_row[col][0] = old_row[col][0] + timeseries['dates'] = timeseries.index.get_level_values('dates') + timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') + timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] + timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) + timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + timeseries = timeseries.append(new_row) timeseries = timeseries.sort_values(by='dates') - break + break + + elif extremePeriodMethod == 'replace_cluster_center': - else: - raise ValueError("Choose 'append' or 'replace_cluster_center' for consideration of extreme periods with segmentation method") + # replace segment in which the extreme timestep was added + i=-1 + for date in timeseries.index.get_level_values('dates'): + if date < mini: + i = i+1 + else: + if i ==-1: + i=0 + min_val['SegmentDuration'] = timeseries.index.get_level_values('SegmentDuration')[i] + min_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + timeseries.drop(timeseries.index[i], inplace=True) + timeseries = timeseries.append(min_val) + timeseries = timeseries.sort_values(by='dates') + break - if 'row_no' in timeseries.columns: - timeseries.drop('row_no', axis=1, inplace=True) + else: + raise ValueError("Choose 'append' or 'replace_cluster_center' for consideration of extreme periods with segmentation method") - return timeseries + if 'row_no' in timeseries.columns: + timeseries.drop('row_no', axis=1, inplace=True) + + return timeseries def run(network, n_clusters=None, how='daily', segmented_to=False, extreme_periods='None'): """ + Function to call the respecting snapshot clustering function and export the result to a csv-file. + + Parameters + ---------- + network : pypsa.Network + Network getting reduced in temporal dimension. + n_clusters : int, optional + Number of clusters for typical_periods. The default is None. + how : {'daily', 'weekly', 'monthly'}, optional + Definition of period for typical_periods. The default is 'daily'. + segmented_to : int, optional + Number of segments for segmentation. The default is False. + extremePeriodMethod : {'None','append','new_cluster_center', + 'replace_cluster_center'}, optional + Method to consider extreme snapshots in reduced timeseries. The default is 'None'. + + Returns + ------- + network : pypsa.Network + Network reduced in temporal dimension. + """ + if segmented_to is not False: segment_no = segmented_to segmentation = True @@ -488,8 +550,20 @@ def run(network, n_clusters=None, how='daily', segmented_to=False, extreme_perio return network -def prepare_pypsa_timeseries(network, normed=False): +def prepare_pypsa_timeseries(network): """ + Prepares timeseries and residual load timeseries for clustering. + + Parameters + ---------- + network : pypsa.Network + Network getting reduced in temporal dimension. + + Returns + ------- + df : pd.DataFrame + Timeseries to be considered when clustering. + """ loads = network.loads_t.p_set.copy() @@ -508,23 +582,32 @@ def prepare_pypsa_timeseries(network, normed=False): def update_data_frames(network, cluster_weights, dates, hours, timeseries, segmentation): - """ Updates the snapshots, snapshots weights and the dataframes based on + """ + Updates the snapshots, snapshot weightings and the dataframes based on the original data in the network and the medoids created by clustering these original data. Parameters - ----------- - network : pyPSA network object - cluster_weights: dictionary - dates: Datetimeindex - + ---------- + network : pypsa.Network + Network getting reduced in temporal dimension + cluster_weights : dict + Weightings per cluster after clustering to typical periods. + dates : DatetimeIndex + Dates of clusters after clustering to typical periods. + hours : int + Hours per typical period. + timeseries : pd.DataFrame + Information on segments after segmentation. + segmentation : boolean + Checks if segmentation of clustering to typical periods has been used. Returns ------- - network + network : pypsa.Network + Network reduced in temporal dimension """ - if segmentation: network.snapshots = timeseries.index.get_level_values(0) @@ -561,6 +644,14 @@ def update_data_frames(network, cluster_weights, dates, hours, timeseries, segme def skip_snapshots(self): + """ + Conducts the downsapling to every n-th snapshot. + + Returns + ------- + None. + + """ # save second network for optional dispatch disaggregation if self.args["dispatch_disaggregation"] == True and self.args['snapshot_clustering']['active'] == False: @@ -574,23 +665,4 @@ def skip_snapshots(self): self.network.snapshot_weightings['objective'] = n_skip self.network.snapshot_weightings['stores'] = n_skip - self.network.snapshot_weightings['generators'] = n_skip - -#################################### -def manipulate_storage_invest(network, costs=None, wacc=0.05, lifetime=15): - # default: 4500 € / MW, high 300 €/MW - crf = (1 / wacc) - (wacc / ((1 + wacc) ** lifetime)) - network.storage_units.capital_cost = costs / crf - - -def write_lpfile(network=None, path=None): - network.model.write(path, - io_options={'symbolic_solver_labels': True}) - - -def fix_storage_capacity(network, resultspath, n_clusters): # "network" added - path = resultspath.strip('daily') - values = pd.read_csv(path + 'storage_capacity.csv')[n_clusters].values - network.storage_units.p_nom_max = values - network.storage_units.p_nom_min = values - resultspath = 'compare-' + resultspath \ No newline at end of file + self.network.snapshot_weightings['generators'] = n_skip \ No newline at end of file From 6943d999c141429c334837b1a981644b670d88ca Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Thu, 16 Mar 2023 16:04:58 +0100 Subject: [PATCH 026/159] update description of the scripts --- etrago/tools/constraints.py | 2 +- etrago/tools/network.py | 2 +- etrago/tools/plot.py | 2 +- etrago/tools/utilities.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index d583682b9..e468d8acd 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2018 Flensburg University of Applied Sciences, +# Copyright 2016-2023 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems, # DLR-Institute for Networked Energy Systems diff --git a/etrago/tools/network.py b/etrago/tools/network.py index a99ddf38b..087fb3452 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2018 Flensburg University of Applied Sciences, +# Copyright 2016-2023 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems, # DLR-Institute for Networked Energy Systems diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index deb83e605..25dc73e7c 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2018 Flensburg University of Applied Sciences, +# Copyright 2016-2023 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems, # DLR-Institute for Networked Energy Systems diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index ccabc12b6..a06c3c37e 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2018 Flensburg University of Applied Sciences, +# Copyright 2016-2023 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems, # DLR-Institute for Networked Energy Systems From c5fcf3cb6e9ff6ad7c918128b267d23b43541296 Mon Sep 17 00:00:00 2001 From: Tetens Date: Fri, 17 Mar 2023 08:41:34 +0100 Subject: [PATCH 027/159] update --- etrago/cluster/electrical.py | 2 +- etrago/cluster/gas.py | 18 ++++++++++++++++++ etrago/cluster/spatial.py | 2 +- 3 files changed, 20 insertions(+), 2 deletions(-) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index 0555d5fa9..3e0f10103 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2018 Flensburg University of Applied Sciences, +# Copyright 2016-2023 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems, # DLR-Institute for Networked Energy Systems diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 4a0d05bcb..bca066da0 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -1,4 +1,22 @@ # -*- coding: utf-8 -*- +# Copyright 2016-2023 Flensburg University of Applied Sciences, +# Europa-Universität Flensburg, +# Centre for Sustainable Energy Systems, +# DLR-Institute for Networked Energy Systems + +# This program is free software; you can redistribute it and/or +# modify it under the terms of the GNU Affero General Public License as +# published by the Free Software Foundation; either version 3 of the +# License, or (at your option) any later version. + +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. + +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . + # File description for read-the-docs """ gas.py defines the methods to cluster gas grid networks spatially for applications within the tool eTraGo.""" diff --git a/etrago/cluster/spatial.py b/etrago/cluster/spatial.py index c9802e77d..b96537c0c 100755 --- a/etrago/cluster/spatial.py +++ b/etrago/cluster/spatial.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2018 Flensburg University of Applied Sciences, +# Copyright 2016-2023 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems, # DLR-Institute for Networked Energy Systems From b6bf85f1f1e08a32a902377b4bec330948636ee4 Mon Sep 17 00:00:00 2001 From: Tetens Date: Fri, 17 Mar 2023 08:49:27 +0100 Subject: [PATCH 028/159] black and isort --- etrago/cluster/electrical.py | 28 ++++++++++++++-------------- etrago/cluster/gas.py | 22 +++++++++++----------- etrago/cluster/spatial.py | 30 +++++++++++++++--------------- 3 files changed, 40 insertions(+), 40 deletions(-) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index 3e0f10103..4fa81393a 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -88,14 +88,14 @@ def leader(x): def adjust_no_electric_network(etrago, busmap, cluster_met): """ - Adjusts the non-electric network based on the electrical network (esp. eHV network), + Adjusts the non-electric network based on the electrical network (esp. eHV network), adds the gas buses to the busmap, and creates the new buses for the non-electric network. Parameters ---------- etrago : Etrago An instance of the Etrago class. - busmap : dict + busmap : dict A dictionary that maps old bus_ids to new bus_ids. cluster_met : str A string indicating the clustering method to be used. @@ -441,14 +441,14 @@ def ehv_clustering(self): """ Cluster the network based on Extra High Voltage (EHV) grid. - If `network_clustering_ehv` argument is True, the function clusters the network based on the EHV grid. + If `network_clustering_ehv` argument is True, the function clusters the network based on the EHV grid. If `network_clustering` argument is not active, it calls the `load_shedding` function to apply load shedding. Parameters ---------- self: Etrago object pointer The object pointer for an Etrago object. - + Returns ------- None @@ -490,7 +490,7 @@ def select_elec_network(etrago): Tuple containing: elec_network : pypsa.Network Contains the electric network - n_clusters : int + n_clusters : int number of clusters used in the clustering process. """ elec_network = etrago.network.copy() @@ -803,7 +803,7 @@ def postprocessing(etrago, busmap, busmap_foreign, medoid_idx=None): mapping between foreign buses and clusters medoid_idx : pandas.DataFrame mapping between cluster indices and medoids - + Returns ------- Tuple containing: @@ -943,12 +943,12 @@ def calc_availability_factor(gen): Notes ----- - Availability factor is defined as the ratio of the average power output of the generator - over the maximum power output capacity of the generator. - If the generator is time-dependent, its average power output is calculated using the - `network.generators_t` DataFrame. Otherwise, its availability factor is obtained from - the `fixed_capacity_fac` dictionary, which contains pre-defined factors for fixed - capacity generators. If the generator's availability factor cannot be found in the + Availability factor is defined as the ratio of the average power output of the generator + over the maximum power output capacity of the generator. + If the generator is time-dependent, its average power output is calculated using the + `network.generators_t` DataFrame. Otherwise, its availability factor is obtained from + the `fixed_capacity_fac` dictionary, which contains pre-defined factors for fixed + capacity generators. If the generator's availability factor cannot be found in the dictionary, it is assumed to be 1. """ @@ -1026,10 +1026,10 @@ def run_spatial_clustering(self): """ Main method for running spatial clustering on the electrical network. Allows for clustering based on k-means and k-medoids dijkstra. - + Parameters ----------- - self + self The object pointer for an Etrago object containing all relevant parameters and data Returns diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index bca066da0..69d98ecbc 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -24,9 +24,6 @@ import os if "READTHEDOCS" not in os.environ: - import numpy as np - import pandas as pd - import pypsa.io as io from pypsa import Network from pypsa.networkclustering import ( aggregatebuses, @@ -34,11 +31,14 @@ busmap_by_kmeans, ) from six import iteritems + import numpy as np + import pandas as pd + import pypsa.io as io from etrago.cluster.spatial import ( - sum_with_inf, group_links, kmedoids_dijkstra_clustering, + sum_with_inf, ) from etrago.tools.utilities import * @@ -212,7 +212,7 @@ def kmean_clustering_gas(etrago, network_ch4, weight, n_clusters): Returns ------- - busmap : pandas.Series + busmap : pandas.Series A pandas.Series object mapping each bus in the CH4 network to its corresponding cluster ID None None is returned because k-means clustering makes no use of medoids @@ -247,7 +247,7 @@ def get_h2_clusters(etrago, busmap_ch4): ------- busmap : pd.Series A Pandas Series mapping each bus in the combined CH4 and H2 network to its corresponding cluster ID. - """ + """ # Mapping of H2 buses to new CH4 cluster IDs busmap_h2 = pd.Series( busmap_ch4.loc[etrago.ch4_h2_mapping.index].values, @@ -268,8 +268,8 @@ def get_h2_clusters(etrago, busmap_ch4): def gas_postprocessing(etrago, busmap, medoid_idx=None): """ Performs the postprocessing for the gas grid clustering based on the provided busmap - and returns the clustered network. - + and returns the clustered network. + Parameters ---------- etrago : Etrago @@ -800,7 +800,7 @@ def get_clustering_from_busmap( ---------- network : pypsa.Network The input pypsa.Network object - busmap : pandas.Sereies : + busmap : pandas.Sereies : A mapping of buses to clusters line_length_factor : float A factor used to adjust the length of new links created during aggregation. Default is 1.0. @@ -808,7 +808,7 @@ def get_clustering_from_busmap( Determines whether to copy the time-dependent properties of the input network to the output network. Default is True. bus_strategies : dict A dictionary of custom strategies to use during the aggregation step. Default is an empty dictionary. - one_port_strategies : dict + one_port_strategies : dict A dictionary of custom strategies to use during the one-port component aggregation step. Default is an empty dictionary. Returns @@ -889,7 +889,7 @@ def get_clustering_from_busmap( def run_spatial_clustering_gas(self): """ - Performs spatial clustering on the gas network using either K-means or K-medoids-Dijkstra algorithm. Updates the + Performs spatial clustering on the gas network using either K-means or K-medoids-Dijkstra algorithm. Updates the network topology by aggregating buses and links, and then performs postprocessing to finalize the changes. Returns diff --git a/etrago/cluster/spatial.py b/etrago/cluster/spatial.py index b96537c0c..6b4650fdd 100755 --- a/etrago/cluster/spatial.py +++ b/etrago/cluster/spatial.py @@ -343,7 +343,7 @@ def busmap_by_shortest_path(etrago, scn_name, fromlvl, tolvl, cpu_cores=4): Parameters ---------- - network : pypsa.Network + network : pypsa.Network Container for all network components. session : sqlalchemy.orm.session.Session object Establishes interactions with the database. @@ -558,14 +558,14 @@ def kmean_clustering(etrago, selected_network, weight, n_clusters): Returns ------- - network : pypsa.Network + network : pypsa.Network Container for all network components. """ network = etrago.network kmean_settings = etrago.args["network_clustering"] with threadpool_limits(limits=kmean_settings["CPU_cores"], user_api=None): - + # remove stubs if kmean_settings["remove_stubs"]: network.determine_network_topology() @@ -618,7 +618,7 @@ def dijkstras_algorithm(buses, connections, medoid_idx, cpu_cores): Parameters ---------- - network : pypsa.Network + network : pypsa.Network Container for all network components. medoid_idx : pandas.Series Indices of k-medoids @@ -629,7 +629,7 @@ def dijkstras_algorithm(buses, connections, medoid_idx, cpu_cores): Returns ------- - busmap : pandas.Series + busmap : pandas.Series Mapping from bus ids to medoids ids """ @@ -721,20 +721,20 @@ def kmedoids_dijkstra_clustering( # n_jobs was deprecated for the function fit(). scikit-learn recommends # to use threadpool_limits: https://scikit-learn.org/stable/computing/parallelism.html with threadpool_limits(limits=settings["CPU_cores"], user_api=None): - + # remove stubs if settings["remove_stubs"]: - + logger.info( - "options remove_stubs and use_reduced_coordinates not reasonable for k-medoids Dijkstra Clustering" - ) + "options remove_stubs and use_reduced_coordinates not reasonable for k-medoids Dijkstra Clustering" + ) bus_weightings = pd.Series(weight) buses_i = buses.index points = buses.loc[buses_i, ["x", "y"]].values.repeat( bus_weightings.reindex(buses_i).astype(int), axis=0 ) - + kmeans = KMeans( init="k-means++", n_clusters=n_clusters, @@ -744,24 +744,24 @@ def kmedoids_dijkstra_clustering( random_state=settings["random_state"], ) kmeans.fit(points) - + busmap = pd.Series( data=kmeans.predict(buses.loc[buses_i, ["x", "y"]]), index=buses_i, dtype=object, ) - + # identify medoids per cluster -> k-medoids clustering - + distances = pd.DataFrame( data=kmeans.transform(buses.loc[buses_i, ["x", "y"]].values), index=buses_i, dtype=object, ) distances = distances.apply(pd.to_numeric) - + medoid_idx = distances.idxmin() - + # dijkstra's algorithm busmap = dijkstras_algorithm( buses, From d5b283585436b94d4f484a55a96fbeb94e9f0b18 Mon Sep 17 00:00:00 2001 From: Tetens Date: Fri, 17 Mar 2023 10:04:44 +0100 Subject: [PATCH 029/159] added authors --- etrago/cluster/electrical.py | 2 +- etrago/cluster/gas.py | 9 +++++++++ etrago/cluster/spatial.py | 2 +- 3 files changed, 11 insertions(+), 2 deletions(-) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index 4fa81393a..b4177ba75 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -57,7 +57,7 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "s3pp, wolfbunke, ulfmueller, lukasol" +__author__ = "MGlauer, MarlonSchlemminger, mariusves, BartelsJ, gnn, lukasoldi, ulfmueller, lukasol, ClaraBuettner, CarlosEpia, KathiEsterl, pieterhexen, fwitte, AmeliaNadal, cjbernal071421" # TODO: Workaround because of agg diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 69d98ecbc..9e0dab327 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -42,6 +42,15 @@ ) from etrago.tools.utilities import * +__copyright__ = ( + "Flensburg University of Applied Sciences, " + "Europa-Universität Flensburg, " + "Centre for Sustainable Energy Systems, " + "DLR-Institute for Networked Energy Systems" +) +__license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" +__author__ = "MGlauer, MarlonSchlemminger, mariusves, BartelsJ, gnn, lukasoldi, ulfmueller, lukasol, ClaraBuettner, CarlosEpia, KathiEsterl, pieterhexen, fwitte, AmeliaNadal, cjbernal071421" + def preprocessing(etrago): """ diff --git a/etrago/cluster/spatial.py b/etrago/cluster/spatial.py index 6b4650fdd..1bb97901b 100755 --- a/etrago/cluster/spatial.py +++ b/etrago/cluster/spatial.py @@ -53,7 +53,7 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "s3pp, wolfbunke, ulfmueller, lukasol" +__author__ = "MGlauer, MarlonSchlemminger, mariusves, BartelsJ, gnn, lukasoldi, ulfmueller, lukasol, ClaraBuettner, CarlosEpia, KathiEsterl, pieterhexen, fwitte, AmeliaNadal, cjbernal071421" # TODO: Workaround because of agg From 5a245342ddf6f14a74ea0c23833fc4d6d3122015 Mon Sep 17 00:00:00 2001 From: Tetens Date: Fri, 17 Mar 2023 11:20:12 +0100 Subject: [PATCH 030/159] updated installation read the docs --- doc/installation.rst | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/doc/installation.rst b/doc/installation.rst index 75128c3ca..33ec0c660 100644 --- a/doc/installation.rst +++ b/doc/installation.rst @@ -30,7 +30,7 @@ install eTraGo with the pip command, as previously explained. Linux and Ubuntu ================ -The Package eTraGo is tested with Ubuntu 16.04, 18.04 and 20.04 inside the virtual +The Package eTraGo is tested with Ubuntu 16.04, 18.04, 20.04 and 22.04 inside the virtual environments of `virtualenv `_. The installation is shown above. @@ -40,14 +40,15 @@ Windows or Mac OSX users ======================== For Windows and/or Mac OSX user we highly recommend to install and use Anaconda -for you Python3 installation. First install anaconda including python 3.5 or +for your Python3 installation. First install Conda including python 3.7 or higher version from https://www.anaconda.com/download/ and open an anaconda prompt as administrator and run: .. code-block:: bash $ conda config --add channels conda-forge - $ conda install shapely + $ conda create -n etrago_env python=3.8 + $ conda activate etrago_env $ pip install eTraGo @@ -73,7 +74,7 @@ Python package for the REST-API used by the OpenEnergy Platform (OEP). In order to connect eTraGo via the oedialect with the oedb you have to create an account at `openenergy-platform.org/login `_. -You can name the `'db' `_ +You can name the `'db' `_ argument of the 'args' of the :func:`etrago.appl.etrago` as you wish. Once the :func:`etrago.appl.etrago` is executed you will be asked to enter how you want to connect to which database. If you want to use @@ -89,7 +90,7 @@ oedialect, a local postgresql database or the old psycopg2 developer connection. Once you have created a connection (which is saved in the config.ini) you do not have to enter the connection parameter again. The software will take the connection parameter -which corresponds to the entry at the `'db' `_ argument. +which corresponds to the entry at the `'db' `_ argument. oedialect connection From 0817780633fa78877c99c428e5d9d57ad67eb2f5 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Fri, 17 Mar 2023 11:28:28 +0100 Subject: [PATCH 031/159] Documentation and black --- etrago/appl.py | 170 ++++++++++++++++++++++++------------------------- 1 file changed, 85 insertions(+), 85 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index 9f5d475c3..304309f05 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2018 Flensburg University of Applied Sciences, +# Copyright 2016-2013 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems, # DLR-Institute for Networked Energy Systems @@ -21,7 +21,7 @@ """ This is the application file for the tool eTraGo. Define your connection parameters and power flow settings before executing -the function etrago. +the function run_etrago. """ @@ -37,8 +37,10 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "ulfmueller, lukasol, wolfbunke, mariusves, s3pp" - +__author__ = ( + "ulfmueller, lukasol, wolfbunke, mariusves, s3pp, ClaraBuettner, " + "CarlosEpia, KathiEsterl, fwitte, gnn, pieterhexen, AmeliaNadal" +) if "READTHEDOCS" not in os.environ: # Sphinx does not run this code. @@ -53,13 +55,13 @@ "method": { # Choose method and settings for optimization "type": "lopf", # type of optimization, currently only 'lopf' "n_iter": 4, # abort criterion of iterative optimization, 'n_iter' or 'threshold' - "pyomo": True, - }, # set if pyomo is used for model building + "pyomo": True, # set if pyomo is used for model building + }, "pf_post_lopf": { - "active": True, # choose if perform a pf after a lopf simulation + "active": True, # choose if perform a pf after lopf "add_foreign_lopf": True, # keep results of lopf for foreign DC-links - "q_allocation": "p_nom", - }, # allocate reactive power via 'p_nom' or 'p' + "q_allocation": "p_nom", # allocate reactive power via 'p_nom' or 'p' + }, "start_snapshot": 1, "end_snapshot": 2, "solver": "gurobi", # glpk, cplex or gurobi @@ -72,7 +74,7 @@ "threads": 4, }, "model_formulation": "kirchhoff", # angles or kirchhoff - "scn_name": "eGon2035", # a scenario: eGon2035 or eGon100RE + "scn_name": "eGon2035", # scenario: eGon2035 or eGon100RE # Scenario variations: "scn_extension": None, # None or array of extension scenarios "scn_decommissioning": None, # None or decommissioning scenario @@ -101,8 +103,8 @@ "generator_noise": 789456, # apply generator noise, False or seed number "extra_functionality": {}, # Choose function name or {} # Spatial Complexity: + "network_clustering_ehv": False, # clustering of HV buses to EHV buses "network_clustering": { - "random_state": 42, # random state for replicability of kmeans results "active": True, # choose if clustering is activated "method": "kmedoids-dijkstra", # choose clustering method: kmeans or kmedoids-dijkstra "n_clusters_AC": 30, # total number of resulting AC nodes (DE+foreign) @@ -112,28 +114,28 @@ "cluster_foreign_gas": False, # take foreign CH4 buses into account, True or False "k_elec_busmap": False, # False or path/to/busmap.csv "k_gas_busmap": False, # False or path/to/ch4_busmap.csv - "line_length_factor": 1, # - "remove_stubs": False, # remove stubs bevore kmeans clustering - "use_reduced_coordinates": False, # "bus_weight_tocsv": None, # None or path/to/bus_weight.csv "bus_weight_fromcsv": None, # None or path/to/bus_weight.csv "gas_weight_tocsv": None, # None or path/to/gas_bus_weight.csv "gas_weight_fromcsv": None, # None or path/to/gas_bus_weight.csv + "line_length_factor": 1, # Factor to multiply distance between new buses for new line lengths + "remove_stubs": False, # remove stubs bevore kmeans clustering + "use_reduced_coordinates": False, # If True, do not average cluster coordinates + "random_state": 42, # random state for replicability of clustering results "n_init": 10, # affects clustering algorithm, only change when neccesary "max_iter": 100, # affects clustering algorithm, only change when neccesary "tol": 1e-6, # affects clustering algorithm, only change when neccesary - "CPU_cores": 4, # number of cores used during clustering. "max" for all cores available. + "CPU_cores": 4, # number of cores used during clustering, "max" for all cores available. }, "sector_coupled_clustering": { "active": True, # choose if clustering is activated "carrier_data": { # select carriers affected by sector coupling "central_heat": { "base": ["CH4", "AC"], - "strategy": "simultaneous", + "strategy": "simultaneous", # select strategy to cluster other sectors }, }, }, - "network_clustering_ehv": False, # clustering of HV buses to EHV buses. "disaggregation": "uniform", # None, 'mini' or 'uniform' # Temporal Complexity: "snapshot_clustering": { @@ -143,8 +145,8 @@ "how": "daily", # type of period, currently only 'daily' - only relevant for 'typical_periods' "storage_constraints": "soc_constraints", # additional constraints for storages - only relevant for 'typical_periods' "n_clusters": 5, # number of periods - only relevant for 'typical_periods' - "n_segments": 5, - }, # number of segments - only relevant for segmentation + "n_segments": 5, # number of segments - only relevant for segmentation + }, "skip_snapshots": 5, # False or number of snapshots to skip "dispatch_disaggregation": False, # choose if full complex dispatch optimization should be conducted # Simplifications: @@ -152,15 +154,14 @@ "load_shedding": False, # meet the demand at value of loss load cost "foreign_lines": { "carrier": "AC", # 'DC' for modeling foreign lines as links - "capacity": "osmTGmod", - }, # 'osmTGmod', 'tyndp2020', 'ntc_acer' or 'thermal_acer' + "capacity": "osmTGmod", # 'osmTGmod', 'tyndp2020', 'ntc_acer' or 'thermal_acer' + }, "comments": None, } def run_etrago(args, json_path): - """The etrago function works with following arguments: - + """Function to conduct optimization considering the following arguments. Parameters ---------- @@ -176,7 +177,7 @@ def run_etrago(args, json_path): (e.g. 'v0.4.6') for the grid schema. method : dict - {'type': 'lopf', 'n_iter': 5, 'pyomo': True}, + {'type': 'lopf', 'n_iter': 4, 'pyomo': True}, Choose 'lopf' for 'type'. In case of extendable lines, several lopfs have to be performed. Choose either 'n_init' and a fixed number of iterations or 'thershold' and a threashold of the objective function as @@ -184,7 +185,7 @@ def run_etrago(args, json_path): Set 'pyomo' to False for big optimization problems, currently only possible when solver is 'gurobi'. - pf_post_lopf :dict + pf_post_lopf : dict {'active': True, 'add_foreign_lopf': True, 'q_allocation': 'p_nom'}, Option to run a non-linear power flow (pf) directly after the linear optimal power flow (and thus the dispatch) has finished. @@ -199,29 +200,29 @@ def run_etrago(args, json_path): end_snapshot : int 2, End hour of the scenario year to be calculated. - If temporal clustering is used, the selected snapshots should cover - whole days. + If snapshot clusterung is used, the selected snapshots should cover + the number of periods / segments. solver : str 'glpk', Choose your preferred solver. Current options: 'glpk' (open-source), 'cplex' or 'gurobi'. - solver_options: dict + solver_options : dict Choose settings of solver to improve simulation time and result. - Options are described in documentation of choosen solver. + Options are described in documentation of chosen solver. - model_formulation: str - 'angles' + model_formulation : str + 'kirchoff', Choose formulation of pyomo-model. Current options: angles, cycles, kirchhoff, ptdf - scn_name : str + scn_name : str 'eGon2035', Choose your scenario. Currently, there are two different scenarios: 'eGon2035', 'eGon100RE'. - scn_extension : NoneType or list + scn_extension : NoneType or str None, Choose extension-scenarios which will be added to the existing network container. Data of the extension scenarios are located in @@ -235,8 +236,8 @@ def run_etrago(args, json_path): 'BE_NO_NEP 2035' includes planned lines to Belgium and Norway and adds BE and NO as electrical neighbours - scn_decommissioning : str - None, + scn_decommissioning : NoneType or str + None, Choose an extra scenario which includes lines you want to decommise from the existing network. Data of the decommissioning scenarios are located in extension-tables @@ -249,12 +250,12 @@ def run_etrago(args, json_path): 'nep2035_b2' includes all lines that will be replaced in NEP-scenario 2035 B2 - lpfile : obj + lpfile : bool or str False, State if and where you want to save pyomo's lp file. Options: False or '/path/tofile.lp' - csv_export : obj + csv_export : bool or str False, State if and where you want to save results as csv files.Options: False or '/path/tofolder'. @@ -287,10 +288,6 @@ def run_etrago(args, json_path): 'storages' / 'stores': allow to install extendable storages (unlimited in size) at each grid node in order to meet the flexibility demand. - 'overlay_network': set all components of the 'scn_extension' - extendable - 'network_preselection': set only preselected lines extendable, - method is chosen in function call Upper bounds for electrical grid expansion can be defined for lines in Germany relative to the existing capacity using 'grid_max_D'. Alternatively, absolute maximum capacities between two electrical buses @@ -303,9 +300,9 @@ def run_etrago(args, json_path): of each generator in order to prevent an optima plateau. To reproduce a noise, choose the same integer (seed number). - extra_functionality : dict or None + extra_functionality : dict None, - Choose extra functionalities and their parameters for PyPSA-model. + Choose extra functionalities and their parameters. Settings can be added in /tools/constraints.py. Current options are: 'max_line_ext': float @@ -333,44 +330,54 @@ def run_etrago(args, json_path): Limit overall energy production country-wise for each generator by carrier, set upper/lower limit in p.u. + network_clustering_ehv : bool + False, + Choose if you want to cluster the full HV/EHV dataset down to only the + EHV buses. In that case, all HV buses are assigned to their closest EHV + substation, taking into account the shortest distance on power lines. + network_clustering : dict {'active': True, method: 'kmedoids-dijkstra', 'n_clusters_AC': 30, - 'cluster_foreign_AC': False, method_gas: 'kmeans', - 'n_clusters_gas': 30, 'cluster_foreign_gas': False, - 'k_elec_busmap': False, 'k_ch4_busmap': False, 'line_length_factor': 1, - 'remove_stubs': False, 'use_reduced_coordinates': False, + 'cluster_foreign_AC': False, method_gas: 'kmeans', 'n_clusters_gas': 30, + 'cluster_foreign_gas': False, 'k_elec_busmap': False, 'k_gas_busmap': False, 'bus_weight_tocsv': None, 'bus_weight_fromcsv': None, - 'gas_weight_tocsv': None, 'gas_weight_fromcsv': None, 'n_init': 10, - 'max_iter': 100, 'tol': 1e-6}, + 'gas_weight_tocsv': None, 'gas_weight_fromcsv': None, ' + line_length_factor': 1, 'remove_stubs': False, 'use_reduced_coordinates': False, + 'random_state': 42, 'n_init': 10, 'max_iter': 100, 'tol': 1e-6}, State if you want to apply a clustering of all network buses. - When ``'active'`` is set to True, the AC buses are clustered down to - ``'n_clusters_AC'`` and ``'n_clusters_gas'``buses. If ``'cluster_foreign_AC'`` is set to False, - the AC buses outside Germany are not clustered, and the buses inside - Germany are clustered to complete ``'n_clusters'`` buses. - The weighting takes place considering generation and load at each node. CH-4 nodes also take - non-transport capacities into account. - ``'cluster_foreign_gas'`` controls whether gas buses of Germanies - neighboring countries are considered for clustering. + When ``'active'`` is set to True, the AC buses are clustered down to ``'n_clusters_AC'`` + and the gas buses are clustered down to``'n_clusters_gas'``. + If ``'cluster_foreign_AC'`` is set to False, the AC buses outside Germany are not + clustered and the buses inside Germany are clustered to complete ``'n_clusters'``. + ``'cluster_foreign_gas'`` controls whether gas buses of neighboring countries + are considered for clustering. With ``'method'`` you can choose between two clustering methods: k-means Clustering considering geopraphical locations of buses or k-medoids Dijkstra Clustering considering electrical distances between buses. - With ``'k_elec_busmap'`` or ``'k_ch4_busmap'``you can choose if you - want to load cluster coordinates from a previous run for the respecting carrier. - It should be considered that once this option is set to True, the - provided number of clusters will be ignored. - Option ``'remove_stubs'`` reduces the overestimating of line meshes. - The other options affect the kmeans algorithm and should only be + With ``'k_elec_busmap'`` or ``'k_ch4_busmap'``you can load cluster coordinates + from a previous run for the respecting carrier. It should be considered that + once this option is set to True, the provided number of clusters will be ignored. + In genral, the weighting takes place considering generation and load at each node. + CH4 nodes also take non-transport capacities into account. + With the ``'bus_weight_tocsv'`` you can load an own weighting for the buses + or store the weighting with ``'bus_weight_fromcsv'``. + ``'line_length_factor'`` defines the factor to multiply the crow-flies distance + between new buses in order to get new line lengths. + Option ``'remove_stubs'`` reduces the overestimating of line meshes and + ``'use_reduced_coordinates'`` does not average cluster coordinates, but take + from busmap; both only within the k-means Clustering. + The other options affect the clustering algorithm in both methods and should only be changed carefully, documentation and possible settings are described in sklearn-package (sklearn/cluster/k_means_.py). - sector_coupled_clustering : nested dict + sector_coupled_clustering : dict {'active': True, 'carrier_data': { 'central_heat': {'base': ['CH4', 'AC'], 'strategy': "simultaneous"}, } State if you want to apply clustering of sector coupled carriers, such as central_heat. The approach builds on already clustered - buses (e.g. CH4 and AC) and builds clusters around the topology of the - buses with carrier ``'base'`` for all buses of a specific carrier, e.g. + buses (AC and CH4) building clusters around the topology of those buses + with carrier ``'base'`` for all buses of a specific carrier, e.g. ``'central_heat'``. With ``'strategy'`` it is possible to apply either ``'consecutive'`` or ``'simultaneous'`` clustering. The consecutive strategy clusters around the buses of the first carrier in the list. @@ -383,18 +390,12 @@ def run_etrago(args, json_path): only form a cluster with other buses, if these have the same links to the same clusters of CH4 and AC. - network_clustering_ehv : bool - False, - Choose if you want to cluster the full HV/EHV dataset down to only the - EHV buses. In that case, all HV buses are assigned to their closest EHV - sub-station, taking into account the shortest distance on power lines. - snapshot_clustering : dict - {'active': False, 'method':'typical_periods', 'how': 'daily', - 'extreme_periods': None, 'storage_constraints': '', 'n_clusters': 5, 'n_segments': 5}, + {'active': False, 'method':'typical_periods', 'extreme_periods': None, 'how': 'daily', + 'storage_constraints': 'soc_constraints', 'n_clusters': 5, 'n_segments': 5}, State if you want to apply a temporal clustering and run the optimization only on a subset of snapshot periods. - You can choose between a method clustering to typical periods, e.g. days + You can choose between a method clustering to typical periods, e.g. days or weeks or a method clustering to segments of adjacent hours. With ``'extreme_periods'`` you define the consideration of timesteps with extreme residual load while temporal aggregation. @@ -409,7 +410,7 @@ def run_etrago(args, json_path): to reduce temporal complexity. dispatch_disaggregation : bool - State if you to apply a second lopf considering dispatch only + State if you want to apply a second lopf considering dispatch only to disaggregate the dispatch to the whole temporal complexity. branch_capacity_factor : dict @@ -435,8 +436,8 @@ def run_etrago(args, json_path): Returns ------- - network : `pandas.DataFrame` - eTraGo result network based on `PyPSA network + etrago : etrago object + eTraGo containing all network information and a PyPSA network `_ """ @@ -521,11 +522,10 @@ def run_etrago(args, json_path): etrago = run_etrago(args, json_path=None) print(datetime.datetime.now()) etrago.session.close() - # plots + # plots: more in tools/plot.py # make a line loading plot - # plot_line_loading(network) - # plot stacked sum of nominal power for each generator type and timestep - # plot_stacked_gen(network, resolution="MW") - # plot to show extendable storages - # storage_distribution(network) - # extension_overlay_network(network) + # etrago.plot_grid(line_colors='line_loading', bus_sizes=0.0001, timesteps=range(2)) + # network and storage + # etrago.plot_grid(line_colors='expansion_abs', bus_colors='storage_expansion', bus_sizes=0.0001) + # flexibility usage + # etrago.flexibility_usage('DSM') From eee09704b1cb46b31ced67c6d99d6ef07c3d0399 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Fri, 17 Mar 2023 11:28:50 +0100 Subject: [PATCH 032/159] Documentation --- etrago/tools/extendable.py | 105 ++++++++++++++++++++++++++----------- 1 file changed, 75 insertions(+), 30 deletions(-) diff --git a/etrago/tools/extendable.py b/etrago/tools/extendable.py index 9707ca341..d9dc96858 100644 --- a/etrago/tools/extendable.py +++ b/etrago/tools/extendable.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2018 Flensburg University of Applied Sciences, +# Copyright 2016-2023 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems, # DLR-Institute for Networked Energy Systems @@ -19,7 +19,7 @@ # File description """ -Extendable.py defines function to set PyPSA-components extendable. +Extendable.py defines function to set PyPSA components extendable. """ from etrago.tools.utilities import convert_capital_costs, find_snapshots @@ -39,7 +39,7 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "ulfmueller, s3pp, wolfbunke, mariusves, lukasol" +__author__ = "ulfmueller, s3pp, wolfbunke, mariusves, lukasol, ClaraBuettner, KathiEsterl, CarlosEpia" def extendable( @@ -54,30 +54,26 @@ def extendable( grid_max_foreign=4, grid_max_abs_foreign=None, ): - """ - Function that sets selected components extendable - - 'network' for all lines, links and transformers - 'german_network' for all lines, links and transformers located in Germany - 'foreign_network' for all foreign lines, links and transformers - 'transformers' for all transformers - 'storages' for extendable storages - 'overlay_network' for lines, links and trafos in extension scenerio(s) + Function that sets selected components extendable. Parameters ---------- - network : :class:`pypsa.Network - Overall container of PyPSA - args : dict - Arguments set in appl.py - + grid_max_D : int, optional + Upper bounds for electrical grid expansion relative to existing capacity. The default is None. + grid_max_abs_D : dict, optional + Absolute upper bounds for electrical grid expansion in Germany. + grid_max_foreign : int, optional + Upper bounds for expansion of electrical foreign lines relative to the existing capacity. The default is 4. + grid_max_abs_foreign : dict, optional + Absolute upper bounds for expansion of foreign electrical grid. The default is None. Returns ------- - network : :class:`pypsa.Network - Overall container of PyPSA + None. + """ + network = self.network extendable_settings = self.args["extendable"] @@ -449,10 +445,28 @@ def extendable( grid_max_foreign * network.transformers.s_nom ) - return network - def snommax(i=1020, u=380, wires=4, circuits=4): + """ + Function to calculate limitation for capacity expansion. + + Parameters + ---------- + i : int, optional + Current. The default is 1020. + u : int, optional + Voltage level. The default is 380. + wires : int, optional + Number of wires per line. The default is 4. + circuits : int, optional + Number of circuits. The default is 4. + + Returns + ------- + s_nom_max : float + Limitation for capacity expansion. + + """ s_nom_max = (i * u * sqrt(3) * wires * circuits) / 1000 return s_nom_max @@ -467,6 +481,23 @@ def line_max_abs( "dc": 0, }, ): + """ + Function to calculate limitation for capacity expansion of lines in network. + + Parameters + ---------- + network : pypsa.Network object + Container for all network components. + buses : pypsa.Network buses + Considered buses in network. + line_max_abs : dict, optional + Line parameters considered to calculate maximum capacity. + + Returns + ------- + None. + + """ # calculate the cables of the route between two buses cables = network.lines.groupby(["bus0", "bus1"]).cables.sum() cables2 = network.lines.groupby(["bus1", "bus0"]).cables.sum() @@ -547,6 +578,21 @@ def line_max_abs( def transformer_max_abs(network, buses): + """ + Function to calculate limitation for capacity expansion of transformers in network. + + Parameters + ---------- + network : pypsa.Network object + Container for all network components. + buses : pypsa.Network buses + Considered buses in network. + + Returns + ------- + None. + + """ # To determine the maximum extendable capacity of a transformer, the sum of # the maximum capacities of the lines connected to it is calculated for each @@ -603,10 +649,10 @@ def extension_preselection(etrago, method, days=3): Parameters ---------- - network : :class:`pypsa.Network - Overall container of PyPSA + network : pypsa.Network object + Container for all network components. args : dict - Arguments set in appl.py + Arguments set in appl.py. method: str Choose method of selection: 'extreme_situations' for remarkable timsteps @@ -617,8 +663,8 @@ def extension_preselection(etrago, method, days=3): Returns ------- - network : :class:`pypsa.Network - Overall container of PyPSA + network : pypsa.Network object + Container for all network components. """ network = etrago.network args = etrago.args @@ -694,13 +740,12 @@ def extension_preselection(etrago, method, days=3): def print_expansion_costs(network): - """Function that prints network and storage investment costs - + """Function that prints network and storage investment costs. Parameters ---------- - network : :class:`pypsa.Network - Overall container of PyPSA + network : pypsa.Network object + Container for all network components. Returns ------- From 60fcd5709de944c70e62c288fc77743712da7ab8 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Fri, 17 Mar 2023 11:29:24 +0100 Subject: [PATCH 033/159] More documentation --- etrago/cluster/snapshot.py | 40 +++++++++++++++++--------------------- 1 file changed, 18 insertions(+), 22 deletions(-) diff --git a/etrago/cluster/snapshot.py b/etrago/cluster/snapshot.py index 4cab1ad5c..9312aa24e 100644 --- a/etrago/cluster/snapshot.py +++ b/etrago/cluster/snapshot.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2018 Flensburg University of Applied Sciences, +# Copyright 2016-2013 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems @@ -18,16 +18,12 @@ # along with this program. If not, see . # File description for read-the-docs -""" This module contains functions for calculating representative days/weeks -based on a pyPSA network object. It is designed to be used for the `lopf` -method. Essentially the tsam package -( https://github.com/FZJ-IEK3-VSA/tsam ), which is developed by -Leander Kotzur is used. - -Remaining questions/tasks: - -- Does it makes sense to cluster normed values? -- Include scaling method for yearly sums +""" This module contains functions for reducing the complexity of a PyPSA network in temporal dimension by +a) downsampling to every n-th snapshot +b) clustering to typical periods (eg days, weeks) +c) clustering to segments of variable length +Essentially used is the tsam package +( https://github.com/FZJ-IEK3-VSA/tsam ) developed by Leander Kotzur et al. """ import pandas as pd @@ -485,8 +481,8 @@ def run(network, n_clusters=None, how='daily', segmented_to=False, extreme_perio Parameters ---------- - network : pypsa.Network - Network getting reduced in temporal dimension. + network : pypsa.Network object + Container for all network components. n_clusters : int, optional Number of clusters for typical_periods. The default is None. how : {'daily', 'weekly', 'monthly'}, optional @@ -499,8 +495,8 @@ def run(network, n_clusters=None, how='daily', segmented_to=False, extreme_perio Returns ------- - network : pypsa.Network - Network reduced in temporal dimension. + network : pypsa.Network object + Container for all network components. """ @@ -556,8 +552,8 @@ def prepare_pypsa_timeseries(network): Parameters ---------- - network : pypsa.Network - Network getting reduced in temporal dimension. + network : pypsa.Network object + Container for all network components. Returns ------- @@ -589,8 +585,8 @@ def update_data_frames(network, cluster_weights, dates, hours, timeseries, segme Parameters ---------- - network : pypsa.Network - Network getting reduced in temporal dimension + network : pypsa.Network object + Container for all network components. cluster_weights : dict Weightings per cluster after clustering to typical periods. dates : DatetimeIndex @@ -604,8 +600,8 @@ def update_data_frames(network, cluster_weights, dates, hours, timeseries, segme Returns ------- - network : pypsa.Network - Network reduced in temporal dimension + network : pypsa.Network object + Container for all network components. """ if segmentation: @@ -665,4 +661,4 @@ def skip_snapshots(self): self.network.snapshot_weightings['objective'] = n_skip self.network.snapshot_weightings['stores'] = n_skip - self.network.snapshot_weightings['generators'] = n_skip \ No newline at end of file + self.network.snapshot_weightings['generators'] = n_skip From b707499172e06e3e128c9dc5e7605f15291ac480 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Fri, 17 Mar 2023 11:30:16 +0100 Subject: [PATCH 034/159] Apply black --- etrago/cluster/snapshot.py | 654 +++++++++++++++++++++++-------------- etrago/tools/extendable.py | 79 +++-- 2 files changed, 455 insertions(+), 278 deletions(-) diff --git a/etrago/cluster/snapshot.py b/etrago/cluster/snapshot.py index 9312aa24e..17a323609 100644 --- a/etrago/cluster/snapshot.py +++ b/etrago/cluster/snapshot.py @@ -28,13 +28,16 @@ import pandas as pd import os -if 'READTHEDOCS' not in os.environ: + +if "READTHEDOCS" not in os.environ: import pyomo.environ as po import tsam.timeseriesaggregation as tsam -__copyright__ = ("Flensburg University of Applied Sciences, " - "Europa-Universität Flensburg, " - "Centre for Sustainable Energy Systems") +__copyright__ = ( + "Flensburg University of Applied Sciences, " + "Europa-Universität Flensburg, " + "Centre for Sustainable Energy Systems" +) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" __author__ = "ClaraBuettner, ulfmueller, KathiEsterl, simnh, wheitkoetter, BartelsJ, AmeliaNadal" @@ -53,37 +56,45 @@ def snapshot_clustering(self): None. """ - if self.args['snapshot_clustering']['active'] == True: - + if self.args["snapshot_clustering"]["active"] == True: # save second network for optional dispatch disaggregation if self.args["dispatch_disaggregation"] == True: self.network_tsa = self.network.copy() - if self.args['snapshot_clustering']['method'] == 'segmentation' : - - self.network = run(network=self.network.copy(), - n_clusters=1, - segmented_to = self.args['snapshot_clustering']['n_segments'], - extreme_periods = self.args['snapshot_clustering']['extreme_periods'] - ) - - elif self.args['snapshot_clustering']['method'] == 'typical_periods' : - - self.network = run(network=self.network.copy(), - n_clusters=self.args['snapshot_clustering']['n_clusters'], - how=self.args['snapshot_clustering']['how'], - extreme_periods = self.args['snapshot_clustering']['extreme_periods'] - ) - else : - raise ValueError("Type of clustering should be 'typical_periods' or 'segmentation'") - -def tsam_cluster(timeseries_df, - typical_periods=10, - how='daily', - extremePeriodMethod = 'None', - segmentation = False, - segment_no = 10, - segm_hoursperperiod = 24): + if self.args["snapshot_clustering"]["method"] == "segmentation": + self.network = run( + network=self.network.copy(), + n_clusters=1, + segmented_to=self.args["snapshot_clustering"]["n_segments"], + extreme_periods=self.args["snapshot_clustering"][ + "extreme_periods" + ], + ) + + elif self.args["snapshot_clustering"]["method"] == "typical_periods": + self.network = run( + network=self.network.copy(), + n_clusters=self.args["snapshot_clustering"]["n_clusters"], + how=self.args["snapshot_clustering"]["how"], + extreme_periods=self.args["snapshot_clustering"][ + "extreme_periods" + ], + ) + else: + raise ValueError( + "Type of clustering should be 'typical_periods' or 'segmentation'" + ) + + +def tsam_cluster( + timeseries_df, + typical_periods=10, + how="daily", + extremePeriodMethod="None", + segmentation=False, + segment_no=10, + segm_hoursperperiod=24, +): """ Conducts the clustering of the snapshots for temporal aggregation with the respecting method. @@ -121,26 +132,26 @@ def tsam_cluster(timeseries_df, Information on segments after segmentation. """ - - if how == 'daily': + + if how == "daily": hours = 24 - period = ' days' + period = " days" - elif how == 'weekly': + elif how == "weekly": hours = 168 - period = ' weeks' + period = " weeks" - elif how == 'monthly': + elif how == "monthly": hours = 720 - period = ' months' + period = " months" - elif how == 'hourly': + elif how == "hourly": hours = 1 - period = ' hours' + period = " hours" if segmentation: hoursPerPeriod = segm_hoursperperiod - hours=1 + hours = 1 else: hoursPerPeriod = hours @@ -148,81 +159,96 @@ def tsam_cluster(timeseries_df, # residual load should not impact cluster findings, # but only be the optional parameter to choose an extreme period weight = pd.Series(data=1, index=timeseries_df.columns) - weight['residual_load'] = 0 + weight["residual_load"] = 0 weight = weight.to_dict() aggregation = tsam.TimeSeriesAggregation( timeseries_df, noTypicalPeriods=typical_periods, - extremePeriodMethod = extremePeriodMethod, - addPeakMin = ['residual_load'], - addPeakMax = ['residual_load'], + extremePeriodMethod=extremePeriodMethod, + addPeakMin=["residual_load"], + addPeakMax=["residual_load"], rescaleClusterPeriods=False, hoursPerPeriod=hoursPerPeriod, - clusterMethod='hierarchical', - segmentation = segmentation, - noSegments = segment_no, - weightDict = weight) + clusterMethod="hierarchical", + segmentation=segmentation, + noSegments=segment_no, + weightDict=weight, + ) if segmentation: - print('Snapshot clustering to ' + str(segment_no) + ' segments' + '\n' + - 'Using extreme period method: ' + extremePeriodMethod) + print( + "Snapshot clustering to " + + str(segment_no) + + " segments" + + "\n" + + "Using extreme period method: " + + extremePeriodMethod + ) else: - print('Snapshot clustering to ' + str(typical_periods) + period + '\n' + - 'Using extreme period method: ' + extremePeriodMethod) + print( + "Snapshot clustering to " + + str(typical_periods) + + period + + "\n" + + "Using extreme period method: " + + extremePeriodMethod + ) timeseries_creator = aggregation.createTypicalPeriods() timeseries = timeseries_creator.copy() - #If Segmentation is True, insert 'Dates' and 'SegmentNo' column in timeseries + # If Segmentation is True, insert 'Dates' and 'SegmentNo' column in timeseries if segmentation == True: - - weights=timeseries.index.get_level_values(2) - dates_df= timeseries_df.index.get_level_values(0) - dates=[] - segmentno=[] - wcount=0 - count=0 + weights = timeseries.index.get_level_values(2) + dates_df = timeseries_df.index.get_level_values(0) + dates = [] + segmentno = [] + wcount = 0 + count = 0 for weight in weights: dates.append(dates_df[wcount]) wcount = wcount + weight segmentno.append(count) - count = count +1 + count = count + 1 timeseries.insert(0, "dates", dates, True) timeseries.insert(1, "SegmentNo", segmentno, True) timeseries.insert(2, "SegmentDuration", weights, True) - timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'], inplace=True) + timeseries.set_index( + ["dates", "SegmentNo", "SegmentDuration"], inplace=True + ) - if 'Unnamed: 0' in timeseries.columns: - del timeseries['Unnamed: 0'] - if 'Segment Step' in timeseries.columns: - del timeseries['Segment Step'] - #print(timeseries) + if "Unnamed: 0" in timeseries.columns: + del timeseries["Unnamed: 0"] + if "Segment Step" in timeseries.columns: + del timeseries["Segment Step"] + # print(timeseries) cluster_weights = aggregation.clusterPeriodNoOccur clusterOrder = aggregation.clusterOrder - clusterCenterIndices= aggregation.clusterCenterIndices + clusterCenterIndices = aggregation.clusterCenterIndices if segmentation == True: - - if extremePeriodMethod != 'None': - - timeseries = segmentation_extreme_periods(timeseries_df, timeseries, extremePeriodMethod) + if extremePeriodMethod != "None": + timeseries = segmentation_extreme_periods( + timeseries_df, timeseries, extremePeriodMethod + ) else: - - if extremePeriodMethod == 'new_cluster_center': + if extremePeriodMethod == "new_cluster_center": for i in aggregation.extremePeriods.keys(): clusterCenterIndices.insert( - aggregation.extremePeriods[i]['newClusterNo'], - aggregation.extremePeriods[i]['stepNo']) + aggregation.extremePeriods[i]["newClusterNo"], + aggregation.extremePeriods[i]["stepNo"], + ) - if extremePeriodMethod == 'append': + if extremePeriodMethod == "append": for i in aggregation.extremePeriods.keys(): clusterCenterIndices.insert( - aggregation.extremePeriods[i]['clusterNo'], - aggregation.extremePeriods[i]['stepNo']) + aggregation.extremePeriods[i]["clusterNo"], + aggregation.extremePeriods[i]["stepNo"], + ) # get all index for every hour of that day of the clusterCenterIndices start = [] @@ -243,44 +269,51 @@ def tsam_cluster(timeseries_df, # get the origial Datetimeindex dates = timeseries_df.iloc[nrhours].index - #get list of representative days - representative_day=[] + # get list of representative days + representative_day = [] - #cluster:medoid des jeweiligen Clusters + # cluster:medoid des jeweiligen Clusters dic_clusterCenterIndices = dict(enumerate(clusterCenterIndices)) for i in clusterOrder: representative_day.append(dic_clusterCenterIndices[i]) - #get list of last and first hour of representative days - last_hour_datetime=[] + # get list of last and first hour of representative days + last_hour_datetime = [] for i in representative_day: last_hour = i * hours + hours - 1 last_hour_datetime.append(timeseries_df.index[last_hour]) - #create a dataframe (index=nr. of day in a year/candidate) - df_cluster = pd.DataFrame({ - 'Cluster': clusterOrder, #Cluster of the day - 'RepresentativeDay': representative_day, #representative day of the cluster - 'last_hour_RepresentativeDay': last_hour_datetime}) #last hour of the cluster + # create a dataframe (index=nr. of day in a year/candidate) + df_cluster = pd.DataFrame( + { + "Cluster": clusterOrder, # Cluster of the day + "RepresentativeDay": representative_day, # representative day of the cluster + "last_hour_RepresentativeDay": last_hour_datetime, + } + ) # last hour of the cluster df_cluster.index = df_cluster.index + 1 - df_cluster.index.name = 'Candidate' + df_cluster.index.name = "Candidate" - #create a dataframe each timeseries (h) and its candiddate day (i) df_i_h + # create a dataframe each timeseries (h) and its candiddate day (i) df_i_h nr_day = [] - x = len(timeseries_df.index)/hours+1 + x = len(timeseries_df.index) / hours + 1 - for i in range(1,int(x)): - j=1 + for i in range(1, int(x)): + j = 1 while j <= hours: nr_day.append(i) - j=j+1 - df_i_h = pd.DataFrame({'Timeseries': timeseries_df.index, - 'Candidate_day': nr_day}) - df_i_h.set_index('Timeseries',inplace=True) + j = j + 1 + df_i_h = pd.DataFrame( + {"Timeseries": timeseries_df.index, "Candidate_day": nr_day} + ) + df_i_h.set_index("Timeseries", inplace=True) return df_cluster, cluster_weights, dates, hours, df_i_h, timeseries -def segmentation_extreme_periods(timeseries_df, timeseries, extremePeriodMethod): + +def segmentation_extreme_periods( + timeseries_df, timeseries, extremePeriodMethod +): """ Function to consider extreme snapshots while using segmentation. @@ -306,179 +339,268 @@ def segmentation_extreme_periods(timeseries_df, timeseries, extremePeriodMethod) """ # find maximum / minimum value in residual load - maxi = timeseries_df['residual_load'].idxmax() - mini = timeseries_df['residual_load'].idxmin() + maxi = timeseries_df["residual_load"].idxmax() + mini = timeseries_df["residual_load"].idxmin() # add timestep if it is not already calculated - if maxi not in timeseries.index.get_level_values('dates'): - + if maxi not in timeseries.index.get_level_values("dates"): # identifiy timestep, adapt it to timeseries-df and add it max_val = timeseries_df.loc[maxi].copy() - max_val['SegmentNo'] = len(timeseries) - max_val['SegmentDuration'] = 1 - max_val['dates'] = max_val.name + max_val["SegmentNo"] = len(timeseries) + max_val["SegmentDuration"] = 1 + max_val["dates"] = max_val.name max_val = pd.DataFrame(max_val).transpose() - if extremePeriodMethod == 'append': - - max_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + if extremePeriodMethod == "append": + max_val.set_index( + ["dates", "SegmentNo", "SegmentDuration"], inplace=True + ) timeseries = timeseries.append(max_val) - timeseries = timeseries.sort_values(by='dates') + timeseries = timeseries.sort_values(by="dates") # split up segment in which the extreme timestep was added - i=-1 - for date in timeseries.index.get_level_values('dates'): + i = -1 + for date in timeseries.index.get_level_values("dates"): if date < maxi: - i = i+1 + i = i + 1 else: - timeseries['SegmentDuration_Extreme']=timeseries.index.get_level_values('SegmentDuration') + timeseries[ + "SegmentDuration_Extreme" + ] = timeseries.index.get_level_values("SegmentDuration") old_row = timeseries.iloc[i].copy() old_row = pd.DataFrame(old_row).transpose() - delta_t = timeseries.index.get_level_values('dates')[i+1]-timeseries.index.get_level_values('dates')[i] - delta_t = delta_t.total_seconds()/3600 - timeseries['SegmentDuration_Extreme'].iloc[i]=delta_t - - timeseries_df['row_no']=range(0,len(timeseries_df)) - new_row = int(timeseries_df.loc[maxi]['row_no'])+1 - new_date = timeseries_df[timeseries_df.row_no==new_row].index - - if new_date.isin(timeseries.index.get_level_values('dates')): - timeseries['dates'] = timeseries.index.get_level_values('dates') - timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') - timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] - timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) - timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + delta_t = ( + timeseries.index.get_level_values("dates")[i + 1] + - timeseries.index.get_level_values("dates")[i] + ) + delta_t = delta_t.total_seconds() / 3600 + timeseries["SegmentDuration_Extreme"].iloc[i] = delta_t + + timeseries_df["row_no"] = range(0, len(timeseries_df)) + new_row = int(timeseries_df.loc[maxi]["row_no"]) + 1 + new_date = timeseries_df[ + timeseries_df.row_no == new_row + ].index + + if new_date.isin( + timeseries.index.get_level_values("dates") + ): + timeseries[ + "dates" + ] = timeseries.index.get_level_values("dates") + timeseries[ + "SegmentNo" + ] = timeseries.index.get_level_values("SegmentNo") + timeseries["SegmentDuration"] = timeseries[ + "SegmentDuration_Extreme" + ] + timeseries.drop( + "SegmentDuration_Extreme", axis=1, inplace=True + ) + timeseries.set_index( + ["dates", "SegmentNo", "SegmentDuration"], + inplace=True, + ) break else: new_row = timeseries_df.iloc[new_row].copy() - new_row.drop('row_no', inplace=True) - new_row['SegmentNo'] = len(timeseries) - new_row['SegmentDuration'] = old_row['SegmentDuration_Extreme'][0] - delta_t - 1 - new_row['dates'] = new_row.name + new_row.drop("row_no", inplace=True) + new_row["SegmentNo"] = len(timeseries) + new_row["SegmentDuration"] = ( + old_row["SegmentDuration_Extreme"][0] - delta_t - 1 + ) + new_row["dates"] = new_row.name new_row = pd.DataFrame(new_row).transpose() - new_row.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + new_row.set_index( + ["dates", "SegmentNo", "SegmentDuration"], + inplace=True, + ) for col in new_row.columns: new_row[col][0] = old_row[col][0] - timeseries['dates'] = timeseries.index.get_level_values('dates') - timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') - timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] - timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) - timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + timeseries[ + "dates" + ] = timeseries.index.get_level_values("dates") + timeseries[ + "SegmentNo" + ] = timeseries.index.get_level_values("SegmentNo") + timeseries["SegmentDuration"] = timeseries[ + "SegmentDuration_Extreme" + ] + timeseries.drop( + "SegmentDuration_Extreme", axis=1, inplace=True + ) + timeseries.set_index( + ["dates", "SegmentNo", "SegmentDuration"], + inplace=True, + ) timeseries = timeseries.append(new_row) - timeseries = timeseries.sort_values(by='dates') + timeseries = timeseries.sort_values(by="dates") break - elif extremePeriodMethod == 'replace_cluster_center': - + elif extremePeriodMethod == "replace_cluster_center": # replace segment in which the extreme timestep was added - i=-1 - for date in timeseries.index.get_level_values('dates'): + i = -1 + for date in timeseries.index.get_level_values("dates"): if date < maxi: - i = i+1 + i = i + 1 else: - if i ==-1: - i=0 - max_val['SegmentDuration'] = timeseries.index.get_level_values('SegmentDuration')[i] - max_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + if i == -1: + i = 0 + max_val[ + "SegmentDuration" + ] = timeseries.index.get_level_values("SegmentDuration")[i] + max_val.set_index( + ["dates", "SegmentNo", "SegmentDuration"], inplace=True + ) timeseries.drop(timeseries.index[i], inplace=True) timeseries = timeseries.append(max_val) - timeseries = timeseries.sort_values(by='dates') + timeseries = timeseries.sort_values(by="dates") break else: - raise ValueError("Choose 'append' or 'replace_cluster_center' for consideration of extreme periods with segmentation method") + raise ValueError( + "Choose 'append' or 'replace_cluster_center' for consideration of extreme periods with segmentation method" + ) # add timestep if it is not already calculated - if mini not in timeseries.index.get_level_values('dates'): - + if mini not in timeseries.index.get_level_values("dates"): # identifiy timestep, adapt it to timeseries-df and add it min_val = timeseries_df.loc[mini].copy() - min_val['SegmentNo'] = len(timeseries)+1 - min_val['SegmentDuration'] = 1 - min_val['dates'] = min_val.name + min_val["SegmentNo"] = len(timeseries) + 1 + min_val["SegmentDuration"] = 1 + min_val["dates"] = min_val.name min_val = pd.DataFrame(min_val).transpose() - if extremePeriodMethod == 'append': - - min_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + if extremePeriodMethod == "append": + min_val.set_index( + ["dates", "SegmentNo", "SegmentDuration"], inplace=True + ) timeseries = timeseries.append(min_val) - timeseries = timeseries.sort_values(by='dates') + timeseries = timeseries.sort_values(by="dates") # split up segment in which the extreme timestep was added - i=-1 - for date in timeseries.index.get_level_values('dates'): + i = -1 + for date in timeseries.index.get_level_values("dates"): if date < mini: - i = i+1 + i = i + 1 else: - timeseries['SegmentDuration_Extreme']=timeseries.index.get_level_values('SegmentDuration') + timeseries[ + "SegmentDuration_Extreme" + ] = timeseries.index.get_level_values("SegmentDuration") old_row = timeseries.iloc[i].copy() old_row = pd.DataFrame(old_row).transpose() - delta_t = timeseries.index.get_level_values('dates')[i+1]-timeseries.index.get_level_values('dates')[i] - delta_t = delta_t.total_seconds()/3600 - timeseries['SegmentDuration_Extreme'].iloc[i]=delta_t - - timeseries_df['row_no']=range(0,len(timeseries_df)) - new_row = int(timeseries_df.loc[mini]['row_no'])+1 - new_date = timeseries_df[timeseries_df.row_no==new_row].index - - if new_date.isin(timeseries.index.get_level_values('dates')): - timeseries['dates'] = timeseries.index.get_level_values('dates') - timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') - timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] - timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) - timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + delta_t = ( + timeseries.index.get_level_values("dates")[i + 1] + - timeseries.index.get_level_values("dates")[i] + ) + delta_t = delta_t.total_seconds() / 3600 + timeseries["SegmentDuration_Extreme"].iloc[i] = delta_t + + timeseries_df["row_no"] = range(0, len(timeseries_df)) + new_row = int(timeseries_df.loc[mini]["row_no"]) + 1 + new_date = timeseries_df[ + timeseries_df.row_no == new_row + ].index + + if new_date.isin( + timeseries.index.get_level_values("dates") + ): + timeseries[ + "dates" + ] = timeseries.index.get_level_values("dates") + timeseries[ + "SegmentNo" + ] = timeseries.index.get_level_values("SegmentNo") + timeseries["SegmentDuration"] = timeseries[ + "SegmentDuration_Extreme" + ] + timeseries.drop( + "SegmentDuration_Extreme", axis=1, inplace=True + ) + timeseries.set_index( + ["dates", "SegmentNo", "SegmentDuration"], + inplace=True, + ) break else: new_row = timeseries_df.iloc[new_row].copy() - new_row.drop('row_no', inplace=True) - new_row['SegmentNo'] = len(timeseries)+1 - new_row['SegmentDuration'] = old_row['SegmentDuration_Extreme'][0] - delta_t - 1 - new_row['dates'] = new_row.name + new_row.drop("row_no", inplace=True) + new_row["SegmentNo"] = len(timeseries) + 1 + new_row["SegmentDuration"] = ( + old_row["SegmentDuration_Extreme"][0] - delta_t - 1 + ) + new_row["dates"] = new_row.name new_row = pd.DataFrame(new_row).transpose() - new_row.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + new_row.set_index( + ["dates", "SegmentNo", "SegmentDuration"], + inplace=True, + ) for col in new_row.columns: new_row[col][0] = old_row[col][0] - timeseries['dates'] = timeseries.index.get_level_values('dates') - timeseries['SegmentNo'] = timeseries.index.get_level_values('SegmentNo') - timeseries['SegmentDuration'] = timeseries['SegmentDuration_Extreme'] - timeseries.drop('SegmentDuration_Extreme', axis=1, inplace=True) - timeseries.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + timeseries[ + "dates" + ] = timeseries.index.get_level_values("dates") + timeseries[ + "SegmentNo" + ] = timeseries.index.get_level_values("SegmentNo") + timeseries["SegmentDuration"] = timeseries[ + "SegmentDuration_Extreme" + ] + timeseries.drop( + "SegmentDuration_Extreme", axis=1, inplace=True + ) + timeseries.set_index( + ["dates", "SegmentNo", "SegmentDuration"], + inplace=True, + ) timeseries = timeseries.append(new_row) - timeseries = timeseries.sort_values(by='dates') + timeseries = timeseries.sort_values(by="dates") break - elif extremePeriodMethod == 'replace_cluster_center': - + elif extremePeriodMethod == "replace_cluster_center": # replace segment in which the extreme timestep was added - i=-1 - for date in timeseries.index.get_level_values('dates'): + i = -1 + for date in timeseries.index.get_level_values("dates"): if date < mini: - i = i+1 + i = i + 1 else: - if i ==-1: - i=0 - min_val['SegmentDuration'] = timeseries.index.get_level_values('SegmentDuration')[i] - min_val.set_index(['dates', 'SegmentNo', 'SegmentDuration'],inplace=True) + if i == -1: + i = 0 + min_val[ + "SegmentDuration" + ] = timeseries.index.get_level_values("SegmentDuration")[i] + min_val.set_index( + ["dates", "SegmentNo", "SegmentDuration"], inplace=True + ) timeseries.drop(timeseries.index[i], inplace=True) timeseries = timeseries.append(min_val) - timeseries = timeseries.sort_values(by='dates') + timeseries = timeseries.sort_values(by="dates") break else: - raise ValueError("Choose 'append' or 'replace_cluster_center' for consideration of extreme periods with segmentation method") + raise ValueError( + "Choose 'append' or 'replace_cluster_center' for consideration of extreme periods with segmentation method" + ) - if 'row_no' in timeseries.columns: - timeseries.drop('row_no', axis=1, inplace=True) + if "row_no" in timeseries.columns: + timeseries.drop("row_no", axis=1, inplace=True) return timeseries -def run(network, n_clusters=None, how='daily', segmented_to=False, extreme_periods='None'): + +def run( + network, + n_clusters=None, + how="daily", + segmented_to=False, + extreme_periods="None", +): """ Function to call the respecting snapshot clustering function and export the result to a csv-file. - + Parameters ---------- network : pypsa.Network object @@ -509,39 +631,53 @@ def run(network, n_clusters=None, how='daily', segmented_to=False, extreme_perio segmentation = False if not extreme_periods: - extreme_periods = 'None' + extreme_periods = "None" # calculate clusters - df_cluster, cluster_weights, dates, hours, df_i_h, timeseries = tsam_cluster( - prepare_pypsa_timeseries(network), - typical_periods = n_clusters, - how='daily', - extremePeriodMethod = extreme_periods, - segmentation = segmentation, - segment_no = segment_no, - segm_hoursperperiod = network.snapshots.size) + ( + df_cluster, + cluster_weights, + dates, + hours, + df_i_h, + timeseries, + ) = tsam_cluster( + prepare_pypsa_timeseries(network), + typical_periods=n_clusters, + how="daily", + extremePeriodMethod=extreme_periods, + segmentation=segmentation, + segment_no=segment_no, + segm_hoursperperiod=network.snapshots.size, + ) if segmentation != False: - timeseries.to_csv('timeseries_segmentation=' + str(segment_no) + '.csv') + timeseries.to_csv( + "timeseries_segmentation=" + str(segment_no) + ".csv" + ) else: - if how=='daily': - howie='days' - path='typical_days' - elif how=='weekly': - howie='weeks' - path='typical_weeks' - elif how=='monthly': - howie='months' - path='typical_months' - elif how=='hourly': - howie='hours' - path='typical_hours' - df_cluster.to_csv('cluster_typical-periods=' + str(n_clusters) + howie + '.csv') + if how == "daily": + howie = "days" + path = "typical_days" + elif how == "weekly": + howie = "weeks" + path = "typical_weeks" + elif how == "monthly": + howie = "months" + path = "typical_months" + elif how == "hourly": + howie = "hours" + path = "typical_hours" + df_cluster.to_csv( + "cluster_typical-periods=" + str(n_clusters) + howie + ".csv" + ) network.cluster = df_cluster network.cluster_ts = df_i_h - update_data_frames(network, cluster_weights, dates, hours, timeseries, segmentation) + update_data_frames( + network, cluster_weights, dates, hours, timeseries, segmentation + ) return network @@ -549,7 +685,7 @@ def run(network, n_clusters=None, how='daily', segmented_to=False, extreme_perio def prepare_pypsa_timeseries(network): """ Prepares timeseries and residual load timeseries for clustering. - + Parameters ---------- network : pypsa.Network object @@ -563,21 +699,23 @@ def prepare_pypsa_timeseries(network): """ loads = network.loads_t.p_set.copy() - loads.columns = 'L' + loads.columns + loads.columns = "L" + loads.columns renewables = network.generators_t.p_max_pu.mul( - network.generators.p_nom[ - network.generators_t.p_max_pu.columns], axis = 1).copy() - renewables.columns = 'G' + renewables.columns + network.generators.p_nom[network.generators_t.p_max_pu.columns], axis=1 + ).copy() + renewables.columns = "G" + renewables.columns - residual_load=pd.DataFrame() - residual_load['residual_load']=loads.sum(axis=1)-renewables.sum(axis=1) + residual_load = pd.DataFrame() + residual_load["residual_load"] = loads.sum(axis=1) - renewables.sum(axis=1) df = pd.concat([renewables, loads, residual_load], axis=1) return df -def update_data_frames(network, cluster_weights, dates, hours, timeseries, segmentation): +def update_data_frames( + network, cluster_weights, dates, hours, timeseries, segmentation +): """ Updates the snapshots, snapshot weightings and the dataframes based on the original data in the network and the medoids created by clustering @@ -605,17 +743,21 @@ def update_data_frames(network, cluster_weights, dates, hours, timeseries, segme """ if segmentation: - network.snapshots = timeseries.index.get_level_values(0) - network.snapshot_weightings['objective'] = pd.Series(data = timeseries.index.get_level_values(2).values, - index = timeseries.index.get_level_values(0)) - network.snapshot_weightings['stores'] = pd.Series(data = timeseries.index.get_level_values(2).values, - index = timeseries.index.get_level_values(0)) - network.snapshot_weightings['generators'] = pd.Series(data = timeseries.index.get_level_values(2).values, - index = timeseries.index.get_level_values(0)) + network.snapshot_weightings["objective"] = pd.Series( + data=timeseries.index.get_level_values(2).values, + index=timeseries.index.get_level_values(0), + ) + network.snapshot_weightings["stores"] = pd.Series( + data=timeseries.index.get_level_values(2).values, + index=timeseries.index.get_level_values(0), + ) + network.snapshot_weightings["generators"] = pd.Series( + data=timeseries.index.get_level_values(2).values, + index=timeseries.index.get_level_values(0), + ) else: - network.snapshots = dates network.snapshot_weightings = network.snapshot_weightings.loc[dates] @@ -626,9 +768,13 @@ def update_data_frames(network, cluster_weights, dates, hours, timeseries, segme snapshot_weightings.append(i) x += 1 for i in range(len(network.snapshot_weightings)): - network.snapshot_weightings['objective'][i] = snapshot_weightings[i] - network.snapshot_weightings['stores'][i] = snapshot_weightings[i] - network.snapshot_weightings['generators'][i] = snapshot_weightings[i] + network.snapshot_weightings["objective"][i] = snapshot_weightings[ + i + ] + network.snapshot_weightings["stores"][i] = snapshot_weightings[i] + network.snapshot_weightings["generators"][i] = snapshot_weightings[ + i + ] # put the snapshot in the right order network.snapshots.sort_values() @@ -650,15 +796,17 @@ def skip_snapshots(self): """ # save second network for optional dispatch disaggregation - if self.args["dispatch_disaggregation"] == True and self.args['snapshot_clustering']['active'] == False: + if ( + self.args["dispatch_disaggregation"] == True + and self.args["snapshot_clustering"]["active"] == False + ): self.network_tsa = self.network.copy() - n_skip = self.args['skip_snapshots'] + n_skip = self.args["skip_snapshots"] if n_skip: - self.network.snapshots = self.network.snapshots[::n_skip] - self.network.snapshot_weightings['objective'] = n_skip - self.network.snapshot_weightings['stores'] = n_skip - self.network.snapshot_weightings['generators'] = n_skip + self.network.snapshot_weightings["objective"] = n_skip + self.network.snapshot_weightings["stores"] = n_skip + self.network.snapshot_weightings["generators"] = n_skip diff --git a/etrago/tools/extendable.py b/etrago/tools/extendable.py index d9dc96858..6bb566585 100644 --- a/etrago/tools/extendable.py +++ b/etrago/tools/extendable.py @@ -71,7 +71,7 @@ def extendable( Returns ------- None. - + """ network = self.network @@ -95,11 +95,15 @@ def extendable( network.transformers.s_nom_max = float("inf") if not network.links.empty: - network.links.loc[network.links.carrier == "DC", "p_nom_extendable"] = True + network.links.loc[ + network.links.carrier == "DC", "p_nom_extendable" + ] = True network.links.loc[ network.links.carrier == "DC", "p_nom_min" ] = network.links.p_nom - network.links.loc[network.links.carrier == "DC", "p_nom_max"] = float("inf") + network.links.loc[ + network.links.carrier == "DC", "p_nom_max" + ] = float("inf") if "german_network" in extendable_settings["extendable_components"]: buses = network.buses[network.buses.country == "DE"] @@ -153,15 +157,18 @@ def extendable( if "foreign_network" in extendable_settings["extendable_components"]: buses = network.buses[network.buses.country != "DE"] network.lines.loc[ - network.lines.bus0.isin(buses.index) | network.lines.bus1.isin(buses.index), + network.lines.bus0.isin(buses.index) + | network.lines.bus1.isin(buses.index), "s_nom_extendable", ] = True network.lines.loc[ - network.lines.bus0.isin(buses.index) | network.lines.bus1.isin(buses.index), + network.lines.bus0.isin(buses.index) + | network.lines.bus1.isin(buses.index), "s_nom_min", ] = network.lines.s_nom network.lines.loc[ - network.lines.bus0.isin(buses.index) | network.lines.bus1.isin(buses.index), + network.lines.bus0.isin(buses.index) + | network.lines.bus1.isin(buses.index), "s_nom_max", ] = float("inf") @@ -268,14 +275,14 @@ def extendable( ): for c in ext_stores: network.stores.loc[ - (network.stores.carrier == c) & (network.stores.capital_cost == 0), + (network.stores.carrier == c) + & (network.stores.capital_cost == 0), "capital_cost", ] = network.stores.loc[ (network.stores.carrier == c), "capital_cost" ].max() if "foreign_storage" in extendable_settings["extendable_components"]: - foreign_battery = network.storage_units[ ( network.storage_units.bus.isin( @@ -300,9 +307,9 @@ def extendable( foreign_battery, "p_nom_max" ] = network.storage_units.loc[foreign_battery, "p_nom"] - network.storage_units.loc[foreign_battery, "p_nom"] = network.storage_units.loc[ - foreign_battery, "p_nom_min" - ] + network.storage_units.loc[ + foreign_battery, "p_nom" + ] = network.storage_units.loc[foreign_battery, "p_nom_min"] network.storage_units.loc[ foreign_battery, "capital_cost" @@ -326,7 +333,8 @@ def extendable( == ("extension_" + self.args["scn_extension"][i]), "s_nom_max", ] = network.lines.s_nom[ - network.lines.scn_name == ("extension_" + self.args["scn_extension"][i]) + network.lines.scn_name + == ("extension_" + self.args["scn_extension"][i]) ] network.links.loc[ @@ -617,8 +625,12 @@ def transformer_max_abs(network, buses): trafo_smax_0 = network.transformers.bus0.map(smax_bus["s_nom_max_bus"]) trafo_smax_1 = network.transformers.bus1.map(smax_bus["s_nom_max_bus"]) - trafo_pmax_0 = network.transformers.bus0.map(pmax_links_bus["p_nom_max_bus"]) / 2 - trafo_pmax_1 = network.transformers.bus1.map(pmax_links_bus["p_nom_max_bus"]) / 2 + trafo_pmax_0 = ( + network.transformers.bus0.map(pmax_links_bus["p_nom_max_bus"]) / 2 + ) + trafo_pmax_1 = ( + network.transformers.bus1.map(pmax_links_bus["p_nom_max_bus"]) / 2 + ) trafo_smax = pd.concat( [trafo_smax_0, trafo_smax_1, trafo_pmax_0, trafo_pmax_1], axis=1 ) @@ -642,7 +654,6 @@ def transformer_max_abs(network, buses): def extension_preselection(etrago, method, days=3): - """ Function that preselects lines which are extendend in snapshots leading to overloading to reduce nubmer of extension variables. @@ -696,19 +707,27 @@ def extension_preselection(etrago, method, days=3): network.transformers.loc[:, "s_nom_max"] = np.inf network = convert_capital_costs(network, 1, 1) - extended_lines = network.lines.index[network.lines.s_nom_opt > network.lines.s_nom] - extended_links = network.links.index[network.links.p_nom_opt > network.links.p_nom] + extended_lines = network.lines.index[ + network.lines.s_nom_opt > network.lines.s_nom + ] + extended_links = network.links.index[ + network.links.p_nom_opt > network.links.p_nom + ] x = time.time() for i in range(int(snapshots.value_counts().sum())): if i > 0: network.lopf(snapshots[i], solver_name=args["solver"]) extended_lines = extended_lines.append( - network.lines.index[network.lines.s_nom_opt > network.lines.s_nom] + network.lines.index[ + network.lines.s_nom_opt > network.lines.s_nom + ] ) extended_lines = extended_lines.drop_duplicates() extended_links = extended_links.append( - network.links.index[network.links.p_nom_opt > network.links.p_nom] + network.links.index[ + network.links.p_nom_opt > network.links.p_nom + ] ) extended_links = extended_links.drop_duplicates() @@ -717,13 +736,17 @@ def extension_preselection(etrago, method, days=3): network.lines.loc[ ~network.lines.index.isin(extended_lines), "s_nom_extendable" ] = False - network.lines.loc[network.lines.s_nom_extendable, "s_nom_min"] = network.lines.s_nom + network.lines.loc[ + network.lines.s_nom_extendable, "s_nom_min" + ] = network.lines.s_nom network.lines.loc[network.lines.s_nom_extendable, "s_nom_max"] = np.inf network.links.loc[ ~network.links.index.isin(extended_links), "p_nom_extendable" ] = False - network.links.loc[network.links.p_nom_extendable, "p_nom_min"] = network.links.p_nom + network.links.loc[ + network.links.p_nom_extendable, "p_nom_min" + ] = network.links.p_nom network.links.loc[network.links.p_nom_extendable, "p_nom_max"] = np.inf network.snapshot_weightings = weighting @@ -759,11 +782,16 @@ def print_expansion_costs(network): ext_trafos = network.transformers[network.transformers.s_nom_extendable] if not ext_storage.empty: - storage_costs = (ext_storage.p_nom_opt * ext_storage.capital_cost).sum() + storage_costs = ( + ext_storage.p_nom_opt * ext_storage.capital_cost + ).sum() if not ext_lines.empty: network_costs = ( - ((ext_lines.s_nom_opt - ext_lines.s_nom) * ext_lines.capital_cost).sum() + ( + (ext_lines.s_nom_opt - ext_lines.s_nom) + * ext_lines.capital_cost + ).sum() + (ext_links.p_nom_opt - ext_links.p_nom) * ext_links.capital_cost ).sum() @@ -771,7 +799,8 @@ def print_expansion_costs(network): network_costs = ( network_costs + ( - (ext_trafos.s_nom_opt - ext_trafos.s_nom) * ext_trafos.capital_cost + (ext_trafos.s_nom_opt - ext_trafos.s_nom) + * ext_trafos.capital_cost ).sum() ) @@ -785,4 +814,4 @@ def print_expansion_costs(network): print( "Investment costs for all lines and transformers in selected snapshots [EUR]:", round(network_costs, 2), - ) 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Assumptions on Data =================== -eTraGo fetches its necessary input data from the OpenEnergy Platform including -load, generation, grid and scenario-related data. More details can be found in -the `Data-Processing `_. +eTraGo fetches the input data from the `OpenEnergy Platform `_. The data includes electricity and gas grid topology as well as data on energy supply and load for the considered sectors (electricity, gas, heat and e-mobility) and data on additional electrical flexibilities such as Dynamic Line Rating or Demand Side Management. More details on the data model can be found in the documentaton of `eGon-data `_. + +At the moment, there are two scenarios available basing on scenario C2035 of the network expansion plan ([NEP]_), version 2021. The base one is called eGon2035. To analyse the effect of flexibility option, there is an eGon2035_lowflex scenario available which depicts a lower penetration of flexibilities. More scenarios are being developed. A eGon100RE scenario is being implemented which is characterised by a 100% renewable generation. Analog to the scenario above, a eGon100RE_lowflex scenario is available. + +You can see the modelling concepts of the scenarios in the figure below. The components marked green have exogenous capacity and endogenous dispatch whereas the components marked in red are optimized endogenously in capacity and dispatch. -As overview, the Open Source grid structure is developed by processing data -from `OpenStreetMap (OSM) `_ to obtain -geo-referenced locations of substations and links equal or above the 110 kV -voltage level. OSM also provides information about residential, retail, -industrial and agricultural areas which is used with standardized profiles to -obtain load data. Generation data of solar and wind rely on weather data from -[coastdat-2]_. Both, load and generation data, match the annual amount for the -year 2011. eTraGo enables the investigation of three scenarios - Status Quo, -NEP 2035 and eGo100. Status Quo corresponds to the actual grid, NEP2035 -follows assumptions for the year 2035 by [NEP2015]_ and eGo100 assumes to -operate the future energy system completely by renewables [ehighway2050]_. +.. figure:: images/eGon2035.pdf + :align: center + :scale: 75% + +.. figure:: images/eGon100RE.pdf + :align: center + :scale: 75% Methods -=========== - -PyPSA ------ -The power flow simulations are performed by the Open Source tool -`PyPSA `_ with a linear approximation for the -optimization of power flows in general. Expecting that eTraGo fulfills the -assumptions to perfom a LOPF (small voltage angle differences, branch -resistances negligible to their reactances, voltage magnitudes can be kept at -nominal values) since it focuses on the extra-high and high voltage levels. As -objective value of the optimization, the overall system costs are considered. - - -Clustering approaches ---------------- - -EHV-Clustering -^^^^^^^^^^^^^^ - -This method maps an input network to an output network with the nodes of the -extra-high voltage level. All nodes with a voltage level below the extra-high -voltage level are mapped to their nearest neighboring node in the extra-high -voltage level with the -`dijkstra algorithm `_ -(110 kV ---> 220,380 kV). - -K-Means Clustering -^^^^^^^^^^^^^^^^^^ - -This `method `_ maps an input -network to a new output network with an adjustable number of nodes and new -coordinates. The algorithm sets these coordinates randomly and minimizes a -certain parameter like for example the distances between old coordinates and -their nearest neighbor in the set of new coordinates. The method was -implemented by `Hoersch et al. `_ within -PyPSA. - -Snapshot skipping -^^^^^^^^^^^^^^^^^ -This method simplifies the simulation temporally by considering every n-th -snapshot of a given time series. The regarded snapshots are weighted by the -number of neglected snapshots to ensure a comparable calculation of costs. -This method assumes the chosen snapshots to be represenative for the next n-th -snapshots in the time series. - -Snapshot-Clustering -^^^^^^^^^^^^^^^^^^^ -This method aggregate given time series for various time intervals like i.e. -days using the `tsam `_ package. Contrary to -snapshot skipping, this approach averages a certain period of snapshots -instead of choosing a representative snapshot. - - -Storage expansion ------------------ -To evaluate the amount of storage units in future energy systems, the possible -installation of new storage units at every node in the network is allowed. The -size and operation of these storages are part of the optimization problem. - -Two types of storage technologies are considered - batteries and hydrogen in -underground caverns. Li-Ion battery storages as representatives for short-term -(several hours) technologies, which can be installed at every node. -Underground hydrogen storages represent long-term or seasonal (weeks) -technologies and can be build at every node with appropriate salt formations -in the underground. The storage parameters for both types are reached by -[Acatech2015]_, the information about salt formations are given by [BGR]_. - - -Grid expansion --------------- -The grid expansion is realized by extending the capacities of existing -lines and substations. These capacities are regarded as part of the -optimization problem, whereby the possible extension is unlimited. With respect -to the different voltage levels and lengths MVA-specific costs are considered -in the linear optimization of the power flow. Besides, several planned grid -expansion scenarios from the German grid development plan can be considered as -possible additional power lines by using the 'scn_extension' argument. +======= + + +Optimization with PyPSA +----------------------- + +Within eTraGo, the fetched data model is translated into a `PyPSA `_-network. The power flow simulations and optimizations are performed with a linear approximation aussuming eTraGo to fulfill the assumptions to perfom a LOPF (small voltage angle differences, branch resistances negligible to their reactances, voltage magnitudes can be kept at nominal values) since it focuses on the extra-high and high voltage levels. As objective value of the optimization, the overall system costs are considered. + +With the argument ‘pf_post_lopf’, after the LOPF a non-linear power flow simulation can be conducted. + + +Complexity Reduction +--------------------- + +The data model is characterised by a high spatial (abou 8,000 electrical and 600 gas nodes) and temporal resolution (8,760 timesteps). To reduce the complexity of the resulting optimization problem, several methods can be applied. + + +Reduction in spatial dimension: +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The **ehv clustering** maps all electrical nodes with a voltage level below the extra-high voltage level to their nearest neighboring node in the extra-high voltage level with the Dijkstra’s algorithm (110 kV —> 220 / 380 kV). + +The **k-means Clustering** reduces the electrical or gas network to an adjustable number of nodes by considering the geographical position of the respective nodes. This method has been implemented within PyPSA by [Hoersch]_. + +The **k-medoids Dijkstra Clustering** aggregates nodes considering the network topology. First, a k-medoids Clustering is used dividing the original nodes of the network into groups by their geographical positions while identifiying the geographical medoid nodes per cluster. Afterwards, the original nodes in the original network are assigned to the former identified medoids considering the original network’s topology applying a Dijkstra’s algorithm considering the line lengths. Afterall, the original nodes are represented by one aggregated node per cluster at the position of the former identified medoid node. + +In general, the clustering of the **sector-coupled system** is divided into two steps: +First, the electrical and gas grid are clustered independently using one of the methods described above. Afterwards, nodes of the other sectors (hydrogen, heat, e-mobility and DSM nodes) are mapped according to their connection to electricity or gas buses and aggregated to one node per carrier. + +After optimising the spatially reduced network, a **spatial disaggregation** can be conducted. + + +Reduction in temporal dimension: +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The method **Skip Snapshots** implies a downsampling to every nth time step. The considered snapshots are weighted respectively to account for the analysis of one whole year. + +By using the method called **Segmentation**, a hierarchical clustering of consecutive timesteps to segments with variable lengths is applied [Pineda]_. + +The **Snapshot Clustering on Typical Periods** implies a hierarchical clustering of time periods with a predefined length (e.g. days or weeks) to typical periods. Those typical periods are weighted according to the number of periods in their cluster. This method optionally includes the linkage of the typical periods in a second time layer to account for the intertemporal dependencies following [Kotzur]_. + +By applying a 2-level-approach, a **temporal disaggregation** can be conducted. This means optimising grid and storage expansion using the complexity-reduced time series in the first step and optimising dispatch using the fully complex time series in the second step. + + +Grid and Storage / Store expansion +----------------------------------- + +The grid expansion is realized by extending the capacities of existing lines and substations. These capacities are considered as part of the optimization problem whereby the possible extension is unlimited. With respect to the different voltage levels and lengths, MVA-specific costs are considered in the optimization. + +As shown in the figure above, several options to store energy are part of the modelling concept. Extendable batteries (modelled as storage units) are assigned to every node in the electrical grid. A minimum installed capacity is being considered to account for home batteries ([NEP]_). The expansion and operation is part of the optimization. Furthermore, two types of hydrogen stores (modelled as stores) are available. Overground stores are optimised in operation and dispatch without limitations whereas underground stores depicting saltcaverns are limited by geographical conditions ([BGR]_). Additionally, heat stores part of the optimisation in terms of power and energy without upper limits. Miscellaneous Features -------- -Several features were developed to enhance the functionality of eTraGo. As -appropriate computer setting, the 'solver_options' and a 'generator_noise' are -possible arguments. The latter adds a reproducible small random noise to the -marginal costs of each generator in order to prevent an optima plateau. The -specific solver options depend on the applied solver like for example Gurobi, -CPLEX or GLPK. Considering reproducibility, the 'load_cluster' argument -enables to load a former calculated clustered network. Besides, -'line_grouping' provides a grouping of lines which connect the same buses. -The 'branch_capacity_factor' adds a factor to adapt all line capacities in order -to consider (n-1) security. Because the average number of HV systems is much -smaller than the one of eHV lines, you can choose factors for 'HV' and 'eHV'. -The 'load_shedding' argument is used for debugging complex grids in order to avoid -infeasibilities. It introduces a very expensive generator at each bus to meet -the demand. When optimizing storage units and grid expansion without limiting -constraints, the need for load shedding should not be existent. The -'minimize_loading' argument forces to minimize the loading of the lines next -to the costs. 'Parallelization' provides the opportunity to devide the -optimization problem into a given number of sub-problems. For a group of -snapshots the problem will be solved separately. This functionality can -only be used for problems which do not have dependencies from one snapshot -to another. Therefore this option can not be used -with the optimization of storage units due to their state of charge. +Several features were developed to enhance the functionality of eTraGo. -References -========== +To customize computation settings, ‘solver_options’ and ‘generator_noise’ should be adapted. The latter adds a reproducible small random noise to the marginal costs of each generator in order to prevent an optima plateau. The specific solver options depend on the applied solver (e.g. Gurobi, CPLEX or GLPK). + +In ‚extendable‘ you can adapt the type of components you want to be optimized in capacity and set upper limits for gird expansion inside Germany and of lines to foreign countries. -.. [NEP2015] Übertragungsnetzbetreiber Deutschland. (2015).: - *Netzentwicklungsplan Strom 2025*, Version 2015, 1. Entwurf, 2015. - (``_) +The ‚extra_functionality‘-argument allows to consider extra constraints like limits for energy imort and export or minimal renewable shares in generation. -.. [coastdat-2] coastDat-2 (2017).: - Hindcast model ``_ +‘branch_capacity_factor’ adds a factor to adapt all line capacities in order to consider (n-1) security. Because the average number of HV systems is much smaller than the one of eHV lines, you can choose factors for ‘HV’ and ‘eHV’ separately. -.. [ehighway2050] e-Highway2050. (2015).: - e-HIGHWAY 2050 Modular Development Plan of the Pan-European Transmission - System 2050 - database per country. Retrieved from - (``_) +The ‘load_shedding’-argument is used for debugging complex grids in order to avoid infeasibilities. It introduces a very expensive generator at each bus to meet the demand. When optimizing storage units and grid expansion without limiting constraints, the need for load shedding should not be existent. -.. [Acatech2015] 'Flexibilitätskonzepte für die Stromversorgung 2050 - ``_' +With ‘foreign_lines‘ you can adapt the foreign lines to be modelled as DC-links (e.g. to avoid loop flows). + + +References +========== -.. [BGR] 'Salzstruktur in Norddeutschland <>'_. 2015.: - Data provided by the Federal Institute for Geosciences and Natural - Resources (Bundesanstalt für Geowissenschaften und Rohstoffe, BGR) +.. [NEP] Übertragungsnetzbetreiber Deutschland (2021): + *Netzentwicklungsplan Strom 2035*, Version 2021, 1. Entwurf. 2021. + +.. [Hoersch] Jonas Hoersch et al. (2017): + *The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios*. 2017. + ``_ + +.. [Pineda] Salvador Pineda et al. (2018): + *Chronological Time-Period Clustering for Optimal Capacity Expansion Planning With Storage*. 2018. + ``_ + +.. [Kotzur] Leander Kotzur et al. (2018): + *Time series aggregation for energy system design: Modeling seasonal storage*. 2018. + ``_ + +.. [BGR] Bundesanstalt fuer Geowissenschaften und Rohstoffe et al. 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zFyIux$Y6u1LEN0&UCkkG6aYvDD^P@3*qFlbSlD?UK2BC1c6J_K9xywIi-(H>4CZD9 zb8&O?aqxjSIeEDa*`!^a+?|2zR|gP10N!rC*5H)ErHdzWNfc6Os zvxx(92L=ZQgN+5u>Ngt8@i$Ca+{qnweZVgQ!2BpU{tyZb^ZRxD_Zj{?`%AEYv}0$} zuz}h`1f?XUq}bWP-0bY!z$dpj@a6;FTHL3?7EICPZ7kgMDPXFj2K)MJvVRu^Z0-L(y0|F-f;(9Omum$q(;w@G z#-{8HaTGO&q3Zg7bjGHmt4HDJZg2m`A9kQ~Wp}8(jU#Mr|G+f=KFZ$^OmTp?4*>VS zMFta-1hD9Un0NqL!9>Qx348ycgck1RkU#7G7AsKicU>?{+%U1j-mt!awttEp=#)be z5I?N1pTh#zF99(A=_U9%*g1eZFo*|0|Jm93nAkyd?Cf+5f4gKaSBNDVI|T<18c^Za zivoE5=j5WWr1)9J!3$ekil65w9RDbTeE|W#UD)eyWxOz$_wQvO9x#~mKkM;ugV}-Q z_0l~|5?Vv&B^;8Ww8E0z(26Rx8>kr=L7?HmVcD-a&W?|6aOd!>hb)?JUD=B z;RJp*|E$Li<^p~n|53)x^xdRLS&w4x@9RD#l4qgz5`#-N4DC7MPxd8Pz0b|WS z`{MMB+@Pke(4T4zjKu@uMWdmSRF*>fKRV()-~a#s From 51dfc717e8ae236e524991b5cff6ce0ce771dc05 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Fri, 17 Mar 2023 20:41:14 +0100 Subject: [PATCH 040/159] Adapt args --- etrago/args.json | 37 ++++++++++++------------------------- 1 file changed, 12 insertions(+), 25 deletions(-) diff --git a/etrago/args.json b/etrago/args.json index 6335c1218..1ebcf89a1 100644 --- a/etrago/args.json +++ b/etrago/args.json @@ -51,6 +51,7 @@ }, "generator_noise": 789456, "extra_functionality": {}, + "network_clustering_ehv": false, "network_clustering": { "active": true, "method": "kmedoids-dijkstra", @@ -58,15 +59,16 @@ "cluster_foreign_AC": false, "n_clusters_gas": 30, "cluster_foreign_gas": false, - "k_busmap": false, - "kmeans_gas_busmap": false, - "line_length_factor": 1, - "remove_stubs": false, - "use_reduced_coordinates": false, + "k_elec_busmap": false, + "k_gas_busmap": false, "bus_weight_tocsv": null, "bus_weight_fromcsv": null, "gas_weight_tocsv": null, "gas_weight_fromcsv": null, + "line_length_factor": 1, + "remove_stubs": false, + "use_reduced_coordinates": false, + "random_state":42, "n_init": 10, "max_iter": 100, "tol": 1e-06 @@ -74,39 +76,24 @@ "sector_coupled_clustering": { "active": true, "carrier_data": { - "H2_ind_load": { - "base": [ - "H2_grid" - ], - "strategy": "consecutive" - }, "central_heat": { "base": [ "CH4", "AC" ], - "strategy": "consecutive" + "strategy": "simultaneous" }, - "rural_heat": { - "base": [ - "CH4", - "AC" - ], - "strategy": "consecutive" - } - } }, - "network_clustering_ehv": false, - "disaggregation": "uniform", + "disaggregation": null, "snapshot_clustering": { "active": false, - "method": "typical_periods", + "method": "segmentation", "how": "daily", - "storage_constraints": "", + "storage_constraints": "soc_constraints", "n_clusters": 5, "n_segments": 5 }, - "skip_snapshots": 3, + "skip_snapshots": 5, "temporal_disaggregation": { "active": true, "no_slices": 8, From cb2d378071a6d314386a62194d10b9fe25a021bc Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Mon, 20 Mar 2023 08:55:47 +0100 Subject: [PATCH 041/159] update howToUse etrago documentation --- doc/howToUse.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/howToUse.rst b/doc/howToUse.rst index c1f782a4b..e4ada4d69 100644 --- a/doc/howToUse.rst +++ b/doc/howToUse.rst @@ -5,15 +5,15 @@ How to use eTraGo? After you installed eTraGo you would typically start optimization runs by executing the ‘appl.py’ which is situated in -``./eTrago/etrago/`` (e.g by ``python3 appl.py``). +``./eTrago/etrago/`` (e.g by ``python3 appl.py`` from the terminal). eTraGo doesn't have a graphical user interface, the ‘appl.py’ is used as a simple user interface which can be edited with the preferred python-editor. Here parameters, calculation methods and scenario settings are set in a python dictionary called 'args'. -To run the desired calculation, it is crucial to understand these parameters. -In addition, some of them contradict the usage of others. +To run the desired calculation, it is crucial to understand these parameters, +since for particular cases some of them contradict the usage of others. You find the documentation of all defined parameters from the 'args' here: :func:`etrago.appl.run_etrago`. @@ -35,7 +35,7 @@ For more specific or extensive changes you are highly invited to write code and add new functionalities. Once the calculation has finished the PyPSA network of the Etrago-object will -contain all results. Som main results (e.g. anuual system costs) are calculated +contain all results. Some main results (e.g. anuual system costs) are calculated by :meth:`etrago.calc_results` and can be accesed via 'etrago.results'. You can use several plotting functions from the :meth:`etrago.tools.plot` in order to visualize the results. For example From 0150641ca41872d88ca8894d29c25228cb9e1f9d Mon Sep 17 00:00:00 2001 From: Tetens Date: Mon, 20 Mar 2023 13:16:39 +0100 Subject: [PATCH 042/159] accounted for 80 characters per line --- etrago/cluster/electrical.py | 95 +++++++++++++++++++++--------------- etrago/cluster/gas.py | 80 ++++++++++++++++++++---------- etrago/cluster/spatial.py | 34 ++++++++----- 3 files changed, 130 insertions(+), 79 deletions(-) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index b4177ba75..943462fa2 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -57,21 +57,28 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "MGlauer, MarlonSchlemminger, mariusves, BartelsJ, gnn, lukasoldi, ulfmueller, lukasol, ClaraBuettner, CarlosEpia, KathiEsterl, pieterhexen, fwitte, AmeliaNadal, cjbernal071421" +__author__ = ( + "MGlauer, MarlonSchlemminger, mariusves, BartelsJ, gnn, lukasoldi, " + "ulfmueller, lukasol, ClaraBuettner, CarlosEpia, KathiEsterl, " + "pieterhexen, fwitte, AmeliaNadal, cjbernal071421" +) + # TODO: Workaround because of agg def _leading(busmap, df): """ - Returns a function that computes the leading bus_id for a given mapped list of buses. + Returns a function that computes the leading bus_id for a given mapped + list of buses. Parameters ----------- busmap : dict A dictionary that maps old bus_ids to new bus_ids. df : pandas.DataFrame - A DataFrame containing network.buses data. Each row corresponds to a unique bus + A DataFrame containing network.buses data. Each row corresponds + to a unique bus Returns -------- @@ -88,8 +95,9 @@ def leader(x): def adjust_no_electric_network(etrago, busmap, cluster_met): """ - Adjusts the non-electric network based on the electrical network (esp. eHV network), - adds the gas buses to the busmap, and creates the new buses for the non-electric network. + Adjusts the non-electric network based on the electrical network + (esp. eHV network), adds the gas buses to the busmap, and creates the + new buses for the non-electric network. Parameters ---------- @@ -109,7 +117,8 @@ def adjust_no_electric_network(etrago, busmap, cluster_met): Maps old bus_ids to new bus_ids including all sectors. """ network = etrago.network - # network2 is supposed to contain all the not electrical or gas buses and links + # network2 is supposed to contain all the not electrical or gas buses + # and links network2 = network.copy(with_time=False) network2.buses = network2.buses[ (network2.buses["carrier"] != "AC") @@ -138,7 +147,8 @@ def adjust_no_electric_network(etrago, busmap, cluster_met): # eHV network busmap2 = {} - # Map crossborder AC buses in case that they were not part of the k-mean clustering + # Map crossborder AC buses in case that they were not part of the + # k-mean clustering if not (etrago.args["network_clustering"]["cluster_foreign_AC"]) & ( cluster_met in ["kmeans", "kmedoids-dijkstra"] ): @@ -227,7 +237,8 @@ def adjust_no_electric_network(etrago, busmap, cluster_met): busmap = {**busmap, **busmap2, **busmap3} - # The new buses based on the eHV network for not electrical buses are created + # The new buses based on the eHV network for not electrical buses are + # created if cluster_met in ["kmeans", "kmedoids-dijkstra"]: network.madd( "Bus", @@ -375,10 +386,10 @@ def cluster_on_extra_high_voltage(etrago, busmap, with_time=True): def delete_ehv_buses_no_lines(network): """ - When there are AC buses totally isolated, this function deletes them in order - to make possible the creation of busmaps based on electrical connections - and other purposes. Additionally, it throws a warning to inform the user - in case that any correction should be done. + When there are AC buses totally isolated, this function deletes them in + order to make possible the creation of busmaps based on electrical + connections and other purposes. Additionally, it throws a warning to + inform the user in case that any correction should be done. Parameters ---------- @@ -410,13 +421,13 @@ def delete_ehv_buses_no_lines(network): logger.info( f""" - ----------------------- WARNING --------------------------- - THE FOLLOWING BUSES WERE DELETED BECAUSE THEY WERE ISOLATED: - {delete_buses.to_list()}. - IT IS POTENTIALLY A SIGN OF A PROBLEM IN THE DATASET - ----------------------- WARNING --------------------------- + ----------------------- WARNING --------------------------- + THE FOLLOWING BUSES WERE DELETED BECAUSE THEY WERE ISOLATED: + {delete_buses.to_list()}. + IT IS POTENTIALLY A SIGN OF A PROBLEM IN THE DATASET + ----------------------- WARNING --------------------------- - """ + """ ) network.mremove("Bus", delete_buses) @@ -441,8 +452,9 @@ def ehv_clustering(self): """ Cluster the network based on Extra High Voltage (EHV) grid. - If `network_clustering_ehv` argument is True, the function clusters the network based on the EHV grid. - If `network_clustering` argument is not active, it calls the `load_shedding` function to apply load shedding. + If `network_clustering_ehv` argument is True, the function clusters the + network based on the EHV grid. If `network_clustering` argument is not + active, it calls the `load_shedding` function to apply load shedding. Parameters ---------- @@ -478,7 +490,8 @@ def ehv_clustering(self): def select_elec_network(etrago): """ - Selects the electric network based on the clustering settings specified in the Etrago object. + Selects the electric network based on the clustering settings specified + in the Etrago object. Parameters ---------- @@ -739,14 +752,14 @@ def preprocessing(etrago): logger.info( f""" - ----------------------- WARNING --------------------------- - THE FOLLOWING BUSES HAVE NOT COUNTRY DATA: - {network.buses[network.buses.country.isna()].index.to_list()}. - THEY WILL BE ASSIGNED TO GERMANY, BUT IT IS POTENTIALLY A - SIGN OF A PROBLEM IN THE DATASET. - ----------------------- WARNING --------------------------- + ----------------------- WARNING --------------------------- + THE FOLLOWING BUSES HAVE NOT COUNTRY DATA: + {network.buses[network.buses.country.isna()].index.to_list()}. + THEY WILL BE ASSIGNED TO GERMANY, BUT IT IS POTENTIALLY A + SIGN OF A PROBLEM IN THE DATASET. + ----------------------- WARNING --------------------------- - """ + """ ) network.buses.country.loc[network.buses.country.isna()] = "DE" @@ -760,10 +773,10 @@ def preprocessing(etrago): if settings["method"] == "kmedoids-dijkstra": lines_col = network_elec.lines.columns - # The Dijkstra clustering works using the shortest electrical path between - # buses. In some cases, a bus has just DC connections, which are considered - # links. Therefore it is necessary to include temporarily the DC links - # into the lines table. + # The Dijkstra clustering works using the shortest electrical path + # between buses. In some cases, a bus has just DC connections, which + # are considered links. Therefore it is necessary to include + # temporarily the DC links into the lines table. dc = network.links[network.links.carrier == "DC"] str1 = "DC_" dc.index = f"{str1}" + dc.index @@ -943,13 +956,14 @@ def calc_availability_factor(gen): Notes ----- - Availability factor is defined as the ratio of the average power output of the generator - over the maximum power output capacity of the generator. - If the generator is time-dependent, its average power output is calculated using the - `network.generators_t` DataFrame. Otherwise, its availability factor is obtained from - the `fixed_capacity_fac` dictionary, which contains pre-defined factors for fixed - capacity generators. If the generator's availability factor cannot be found in the - dictionary, it is assumed to be 1. + Availability factor is defined as the ratio of the average power + output of the generator over the maximum power output capacity of + the generator. If the generator is time-dependent, its average power + output is calculated using the `network.generators_t` DataFrame. + Otherwise, its availability factor is obtained from the + `fixed_capacity_fac` dictionary, which contains pre-defined factors + for fixed capacity generators. If the generator's availability factor + cannot be found in the dictionary, it is assumed to be 1. """ @@ -1030,7 +1044,8 @@ def run_spatial_clustering(self): Parameters ----------- self - The object pointer for an Etrago object containing all relevant parameters and data + The object pointer for an Etrago object containing all relevant + parameters and data Returns ------- diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 9e0dab327..3130ff02b 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -49,13 +49,17 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "MGlauer, MarlonSchlemminger, mariusves, BartelsJ, gnn, lukasoldi, ulfmueller, lukasol, ClaraBuettner, CarlosEpia, KathiEsterl, pieterhexen, fwitte, AmeliaNadal, cjbernal071421" +__author__ = ( + "MGlauer, MarlonSchlemminger, mariusves, BartelsJ, gnn, lukasoldi, " + "ulfmueller, lukasol, ClaraBuettner, CarlosEpia, KathiEsterl, " + "pieterhexen, fwitte, AmeliaNadal, cjbernal071421" +) def preprocessing(etrago): """ - Preprocesses the gas network data from the given Etrago object for the spatial - clustering process of the CH4 grid. + Preprocesses the gas network data from the given Etrago object for the + spatial clustering process of the CH4 grid. Parameters ---------- @@ -69,7 +73,8 @@ def preprocessing(etrago): Raises ------ ValueError - If `settings["n_clusters_gas"]` is less than or equal to the number of neighboring country gas buses. + If `settings["n_clusters_gas"]` is less than or equal to the number of + neighboring country gas buses. """ # Create network_ch4 (grid nodes in order to create the busmap basis) @@ -89,7 +94,8 @@ def preprocessing(etrago): ch4_filter & (network_ch4.buses["country"] != "DE") ).sum() - # select buses dependent on whether they should be clustered in (only DE or DE+foreign) + # select buses dependent on whether they should be clustered in (only DE + # or DE+foreign) if not settings["cluster_foreign_gas"]: network_ch4.buses = network_ch4.buses.loc[ ch4_filter & (network_ch4.buses["country"].values == "DE") @@ -99,7 +105,8 @@ def preprocessing(etrago): msg = ( "The number of clusters for the gas sector (" + str(settings["n_clusters_gas"]) - + ") must be higher than the number of neighboring country gas buses (" + + ") must be higher than the number of neighboring country " + + "gas buses (" + str(num_neighboring_country) + ")." ) @@ -132,7 +139,8 @@ def weighting_for_scenario(ch4_buses, save=None): Integer weighting for each ch4_buses.index """ - MAX_WEIGHT = 1e5 # relevant only for foreign nodes with extra high CH4 generation capacity + MAX_WEIGHT = 1e5 # relevant only for foreign nodes with extra high + # CH4 generation capacity to_neglect = [ "CH4", @@ -206,7 +214,8 @@ def weighting_for_scenario(ch4_buses, save=None): def kmean_clustering_gas(etrago, network_ch4, weight, n_clusters): """ - Performs K-means clustering on the gas network data in the given `network_ch4` pypsa.Network object. + Performs K-means clustering on the gas network data in the given + `network_ch4` pypsa.Network object. Parameters ---------- @@ -215,14 +224,16 @@ def kmean_clustering_gas(etrago, network_ch4, weight, n_clusters): network_ch4 : pypsa.Network A Network object containing the gas network data. weight : str or None - The name of the bus weighting column to use for clustering. If None, unweighted clustering is performed. + The name of the bus weighting column to use for clustering. If None, + unweighted clustering is performed. n_clusters : int The number of clusters to create. Returns ------- busmap : pandas.Series - A pandas.Series object mapping each bus in the CH4 network to its corresponding cluster ID + A pandas.Series object mapping each bus in the CH4 network to its + corresponding cluster ID None None is returned because k-means clustering makes no use of medoids """ @@ -250,12 +261,14 @@ def get_h2_clusters(etrago, busmap_ch4): etrago : Etrago An instance of the Etrago class busmap_ch4 : pd.Series - A Pandas Series mapping each bus in the CH4 network to its corresponding cluster ID. + A Pandas Series mapping each bus in the CH4 network to its + corresponding cluster ID. Returns ------- busmap : pd.Series - A Pandas Series mapping each bus in the combined CH4 and H2 network to its corresponding cluster ID. + A Pandas Series mapping each bus in the combined CH4 and H2 network + to its corresponding cluster ID. """ # Mapping of H2 buses to new CH4 cluster IDs busmap_h2 = pd.Series( @@ -276,7 +289,8 @@ def get_h2_clusters(etrago, busmap_ch4): def gas_postprocessing(etrago, busmap, medoid_idx=None): """ - Performs the postprocessing for the gas grid clustering based on the provided busmap + Performs the postprocessing for the gas grid clustering based on the + provided busmap and returns the clustered network. Parameters @@ -286,7 +300,8 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None): busmap : pd.Series A Pandas Series mapping each bus to its corresponding cluster ID. medoid_idx : pd.Series - A pandas.Series object containing the medoid indices for the gas network. + A pandas.Series object containing the medoid indices for the gas + network. Returns ------- @@ -298,7 +313,7 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None): """ settings = etrago.args["network_clustering"] - if settings["k_gas_busmap"] == False: + if settings["k_gas_busmap"] is False: if settings["method_gas"] == "kmeans": busmap.index.name = "bus_id" busmap.name = "cluster" @@ -410,7 +425,8 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None): # Overwrite p_nom of links with carrier "H2_feedin" (eGon2035 only) if etrago.args["scn_name"] == "eGon2035": - H2_energy_share = 0.05053 # H2 energy share via volumetric share outsourced in a mixture of H2 and CH4 with 15 %vol share + H2_energy_share = 0.05053 # H2 energy share via volumetric share + # outsourced in a mixture of H2 and CH4 with 15 %vol share feed_in = network_gasgrid_c.links.loc[ network_gasgrid_c.links.carrier == "H2_feedin" ] @@ -425,7 +441,8 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None): | (pipeline_capacities["bus1"] == bus), "p_nom", ].sum() - # multiply total pipeline capacity with H2 energy share corresponding to volumetric share + # multiply total pipeline capacity with H2 energy share + # corresponding to volumetric share network_gasgrid_c.links.loc[ (network_gasgrid_c.links["bus1"].values == bus) & (network_gasgrid_c.links["carrier"].values == "H2_feedin"), @@ -803,7 +820,8 @@ def get_clustering_from_busmap( one_port_strategies=dict(), ): """ - Aggregates components of the given network based on a bus mapping and returns a clustered gas grid pypsa.Network. + Aggregates components of the given network based on a bus mapping and + returns a clustered gas grid pypsa.Network. Parameters ---------- @@ -812,18 +830,23 @@ def get_clustering_from_busmap( busmap : pandas.Sereies : A mapping of buses to clusters line_length_factor : float - A factor used to adjust the length of new links created during aggregation. Default is 1.0. + A factor used to adjust the length of new links created during + aggregation. Default is 1.0. with_time : bool - Determines whether to copy the time-dependent properties of the input network to the output network. Default is True. + Determines whether to copy the time-dependent properties of the input + network to the output network. Default is True. bus_strategies : dict - A dictionary of custom strategies to use during the aggregation step. Default is an empty dictionary. + A dictionary of custom strategies to use during the aggregation step. + Default is an empty dictionary. one_port_strategies : dict - A dictionary of custom strategies to use during the one-port component aggregation step. Default is an empty dictionary. + A dictionary of custom strategies to use during the one-port component + aggregation step. Default is an empty dictionary. Returns ------- network_gasgrid_c : pypsa.Network - A new gas grid pypsa.Network object with aggregated components based on the bus mapping. + A new gas grid pypsa.Network object with aggregated components based + on the bus mapping. """ network_gasgrid_c = Network() @@ -898,14 +921,16 @@ def get_clustering_from_busmap( def run_spatial_clustering_gas(self): """ - Performs spatial clustering on the gas network using either K-means or K-medoids-Dijkstra algorithm. Updates the - network topology by aggregating buses and links, and then performs postprocessing to finalize the changes. + Performs spatial clustering on the gas network using either K-means or + K-medoids-Dijkstra algorithm. Updates the network topology by aggregating + buses and links, and then performs postprocessing to finalize the changes. Returns None Raises - ValueError: If the selected method is not "kmeans" or "kmedoids-dijkstra". + ValueError: If the selected method is not "kmeans" or + "kmedoids-dijkstra". """ settings = self.args["network_clustering"] @@ -965,7 +990,8 @@ def run_spatial_clustering_gas(self): self.load_shedding() logger.info( - "GAS Network clustered to {} DE-buses and {} foreign buses with {} algorithm.".format( + "GAS Network clustered to {} DE-buses and {} foreign buses " + "with {} algorithm.".format( len( self.network.buses.loc[ (self.network.buses.carrier == "CH4") diff --git a/etrago/cluster/spatial.py b/etrago/cluster/spatial.py index 1bb97901b..ee5fadb7d 100755 --- a/etrago/cluster/spatial.py +++ b/etrago/cluster/spatial.py @@ -53,15 +53,19 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "MGlauer, MarlonSchlemminger, mariusves, BartelsJ, gnn, lukasoldi, ulfmueller, lukasol, ClaraBuettner, CarlosEpia, KathiEsterl, pieterhexen, fwitte, AmeliaNadal, cjbernal071421" +__author__ = ( + "MGlauer, MarlonSchlemminger, mariusves, BartelsJ, gnn, lukasoldi, " + "ulfmueller, lukasol, ClaraBuettner, CarlosEpia, KathiEsterl, " + "pieterhexen, fwitte, AmeliaNadal, cjbernal071421" +) # TODO: Workaround because of agg def _make_consense_links(x): """ - Ensure that all elements in the input Series `x` are identical, or that they - are all NaN. + Ensure that all elements in the input Series `x` are identical, or that + they are all NaN. Parameters ---------- @@ -175,7 +179,8 @@ def group_links(network, with_time=True, carriers=None, cus_strateg=dict()): with_time : bool says if the network object contains timedependent series. carriers : list of strings - Describe which typed of carriers should be aggregated. The default is None. + Describe which typed of carriers should be aggregated. The default is + None. strategies : dictionary custom strategies to perform the aggregation @@ -613,8 +618,9 @@ def kmean_clustering(etrago, selected_network, weight, n_clusters): def dijkstras_algorithm(buses, connections, medoid_idx, cpu_cores): """ Function for combination of k-medoids Clustering and Dijkstra's algorithm. - Creates a busmap assigning the nodes of a original network to the nodes of a clustered network - considering the electrical distances based on Dijkstra's shortest path. + Creates a busmap assigning the nodes of a original network to the nodes of + a clustered network considering the electrical distances based on + Dijkstra's shortest path. Parameters ---------- @@ -655,8 +661,8 @@ def dijkstras_algorithm(buses, connections, medoid_idx, cpu_cores): else: cpu_cores = int(cpu_cores) - # calculation of shortest path between original points and k-medoids centers - # using multiprocessing + # calculation of shortest path between original points and k-medoids + # centers using multiprocessing p = mp.Pool(cpu_cores) chunksize = ceil(len(ppathss) / cpu_cores) container = p.starmap(shortest_path, gen(ppathss, chunksize, M)) @@ -692,7 +698,8 @@ def kmedoids_dijkstra_clustering( etrago, buses, connections, weight, n_clusters ): """ - Applies k-medoids clustering to the given gas network using Dijkstra's algorithm. + Applies k-medoids clustering to the given gas network using Dijkstra's + algorithm. Parameters ---------- @@ -701,7 +708,8 @@ def kmedoids_dijkstra_clustering( buses : pandas.DataFrame DataFrame with information about the buses of the network. connections : pandas.DataFrame - DataFrame with information about the connections of the network (links or lines). + DataFrame with information about the connections of the network + (links or lines). weight : pandas.Series Series with the weight for each bus. n_clusters : int @@ -719,14 +727,16 @@ def kmedoids_dijkstra_clustering( settings = etrago.args["network_clustering"] # n_jobs was deprecated for the function fit(). scikit-learn recommends - # to use threadpool_limits: https://scikit-learn.org/stable/computing/parallelism.html + # to use threadpool_limits: + # https://scikit-learn.org/stable/computing/parallelism.html with threadpool_limits(limits=settings["CPU_cores"], user_api=None): # remove stubs if settings["remove_stubs"]: logger.info( - "options remove_stubs and use_reduced_coordinates not reasonable for k-medoids Dijkstra Clustering" + "options remove_stubs and use_reduced_coordinates not " + "reasonable for k-medoids Dijkstra Clustering" ) bus_weightings = pd.Series(weight) From 42a10874576496aaa2d84b47fcad0c6f01a3941c Mon Sep 17 00:00:00 2001 From: Tetens Date: Mon, 20 Mar 2023 13:24:40 +0100 Subject: [PATCH 043/159] adjusted to 80 character limit --- etrago/tools/network.py | 4 ++-- etrago/tools/utilities.py | 14 ++++++++------ 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/etrago/tools/network.py b/etrago/tools/network.py index 087fb3452..9d2492438 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -96,8 +96,8 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = """AmeliaNadal, CarlosEpia, ClaraBuettner, KathiEsterl, gnn, fwitte, - ulfmueller, pieterhexen""" +__author__ = """AmeliaNadal, CarlosEpia, ClaraBuettner, KathiEsterl, gnn, + fwitte, ulfmueller, pieterhexen""" class Etrago: diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 1b4e098bf..c4003a6bb 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -49,8 +49,8 @@ ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" __author__ = """ulfmueller, s3pp, wolfbunke, mariusves, lukasol, ClaraBuettner, -CarlosEpia, gnn, pieterhexen, fwitte, KathiEsterl, MGlauer, birgits, AmeliaNadal, -MarlonSchlemminger, wheitkoetter, jankaeh""" +CarlosEpia, gnn, pieterhexen, fwitte, KathiEsterl, MGlauer, birgits, + AmeliaNadal, MarlonSchlemminger, wheitkoetter, jankaeh""" def filter_links_by_carrier(self, carrier, like=True): @@ -838,8 +838,8 @@ def _make_consense(component, attr): value for the attribute `attr` of the given `component`. This consensus value is derived from the input DataFrame `x`. If all values in the DataFrame are equal, the consensus value will be that common value. - If all values are missing (NaN), the consensus value will be NaN. Otherwise, - an assertion error will be raised. + If all values are missing (NaN), the consensus value will be NaN. + Otherwise, an assertion error will be raised. Parameters ---------- @@ -853,7 +853,8 @@ def _make_consense(component, attr): ------- function A function that takes a DataFrame as input and returns a single value - as output when all the elements of the commponent attribute are the same. + as output when all the elements of the commponent attribute are the + same. """ @@ -909,7 +910,8 @@ def agg_series_lines(l, network): Returns ------- pandas.Series - A pandas Series containing aggregated data for the lines in the network. + A pandas Series containing aggregated data for the lines in the + network. """ attrs = network.components["Line"]["attrs"] From 9384508845cf88e4da6ec95f115b6188d7095f7a Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Mon, 20 Mar 2023 15:23:26 +0100 Subject: [PATCH 044/159] provide data about examples --- doc/howToUse.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/howToUse.rst b/doc/howToUse.rst index e4ada4d69..856ae3b91 100644 --- a/doc/howToUse.rst +++ b/doc/howToUse.rst @@ -54,5 +54,7 @@ Examples and tutorial notebooks .. toctree:: :maxdepth: 7 - + eTraGo version 0.5.1: OpenMod + eTraGo version 0.9: + Tutorial notebook will be available in the next release. From 9a3b96e0de030fc53981758cc3ad5c4048b6bbda Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 20 Mar 2023 15:45:12 +0100 Subject: [PATCH 045/159] Update 'About etrago' section --- doc/about.rst | 23 +- doc/images/eTraGo_model.png | Bin 132918 -> 0 bytes doc/images/ego_tools.svg | 679 ++++++++++++++++++++++++++++++++++++ 3 files changed, 692 insertions(+), 10 deletions(-) delete mode 100644 doc/images/eTraGo_model.png create mode 100644 doc/images/ego_tools.svg diff --git a/doc/about.rst b/doc/about.rst index 3bad066df..88ab7514a 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -15,15 +15,17 @@ energy storage and grid expansion measures. -The open_eGo project +Research projects ==================== -This software project is part of the research project +This software project was initially developed in the research project `open_eGo `_. +It is constantly further developed in different reserach projects, +e.g. `eGon `_ and `PoWerD `_ and login. @@ -44,7 +46,7 @@ Model overview -.. figure:: images/eTraGo_model.png +.. figure:: images/ego_tools.svg :align: center :scale: 75% @@ -63,17 +65,18 @@ eGo The python package eGo is a toolbox and application which connects the tool eTraGo (optimization of flexibility options at transmission grid level) and eDisGo (optimization of distribution grids). All those python -packages are part of the research project +packages were initially developed in the research project `open_eGo `_. `Learn more here `_. -Dataprocessing +Data model creation ============== +For the eGon project the python-tool `eGon-data `_ was implemented, which creates input data for the optimization tools `eTraGo `_, `ding0 `_ and `eDisGo `_ and delivers for example data on grid topologies, demands/demand curves and generation capacities in a high spatial resolution. The outputs of egon-data are published under open source and open data licenses. + +eGon-data is a further development of the `Data processing `_ developed in the former research project `open_eGo `_. It aims for an extensions of the data models as well as for a better replicability and manageability of the data preparation and processing. +The resulting data set serves as an input for the optimization tools `eTraGo `_, `ding0 `_ and `eDisGo `_ and delivers for example data on grid topologies, demands/demand curves and generation capacities in a high spatial resolution. The outputs of egon-data are published under open source and open data licenses. -For the open_eGo project several python packages are developed which are feeded -by the input data of the data processing. The Dataprocessing is writen in -SQL and Python. `Learn more here `_. ego.io ====== @@ -97,7 +100,7 @@ medium and low voltage power distribution grids based on open LICENSE ======= -© Copyright 2015-2018 +© Copyright 2015-2023 Flensburg University of Applied Sciences, Europa-Universität Flensburg, Centre for Sustainable Energy Systems and diff --git a/doc/images/eTraGo_model.png b/doc/images/eTraGo_model.png deleted file mode 100644 index 1be7f3a97c683ce5be4d070d241cf5277e651fc9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 132918 zcmYg%1yCGauyqpLgS%VM#a)8CySsY`?(Xg$+=9EqVvB2VcZbED$5-#KdVg*0*3{mb zsp+2X+ow;TiBOc6K!V4E|MKMvl9Z&V@|Q1=CSSfl;K4$F`SMkbqyzJF0nSlU%lXR} z|AP)6lB(poOyY$w>R5+?8yFt0 zsMrUFp$=C>#f14L|I>+-1Qg$8it$5H9HIE<@di1e)cd7ZT9MR)?PA#rN1R*IUg-T`eBZ&q#R+Q|knUz|v zrTu2N9v!N>NvLyM29u8aeVnqJ&f|FqT1(XhPxT(e>N*FYkY>X0RU7fEY6~hVs*>rd z!xW_99!%bD*rB=cf?!DVp~TLJdQe>q;e$pZUZEZ(RSlO=mawGps0~{&jqJe0TLJj- 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zAW^Tm7MomA?V|TtUM0x+e}L8Z%R1Lq<)V|%(H#!^+o7HzP!s90+r0}y;63oeQc4bE zJ=N8{?^@nP4%LB++lpql_gZP}3||)`K>TGSB~Gl(|B5sOPs2fG9q)1nlUR?}$5Wrp z;J{@Il?laPj|>)l)#dU|29B0HvRB++o! zrC`Xau3^9^F+eNq2BmzkYgw<=-F8&_McX|dJOEu+U;O$G+|eCV)KcmMnO1Y`NEZQ> zG-gc;JAKd>5pMjt4^`KrU5LHyMrE;mK7YWLwOv@$)t7o08NU`4*8wFeG=NN`hw$q%q^9Eq5bJ&RN~Z#8L9q)bpg zD&;))=oCQQ8Ljuf8g21gwWbn_`<BL7y_!VnN(x{R1hkM{*976|k0?~t#@JPpbF z*Cv0J=;X+GVFvYU?@(q5dqhs9gQI`;1rdPwga{atThF6OgO?V}8>wU+S zDI(IQ=qd{=|C`EL1QDF|&{W=+vmVq21kdY%BQ-&_EvY*cdru%TD3pW@BW)oh63357NxB0`FVZqZ46`dACvZ@O8~45T1nWv z+Q@bPGlC>g)x5ab?tqlehHbpDv(@i*0IgcAFl?gqGuj*AKu)^Qb{lJK&n9cFPTD4GF*fEVYprh2O{v8YVw+Nnu`Mow4%WQf_4> PTfk5Eq@i|!rhV97 Date: Mon, 20 Mar 2023 17:01:22 +0100 Subject: [PATCH 049/159] Update theoretical_background.rst Adapt figure of modelling concept --- doc/theoretical_background.rst | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/doc/theoretical_background.rst b/doc/theoretical_background.rst index dcb92bf46..ad9bbaaf2 100644 --- a/doc/theoretical_background.rst +++ b/doc/theoretical_background.rst @@ -19,11 +19,7 @@ At the moment, there are two scenarios available basing on scenario C2035 of the You can see the modelling concepts of the scenarios in the figure below. The components marked green have exogenous capacity and endogenous dispatch whereas the components marked in red are optimized endogenously in capacity and dispatch. -.. figure:: images/eGon2035.png - :align: center - :scale: 75% - -.. figure:: images/eGon100RE.png +.. figure:: images/modelling_conept.png :align: center :scale: 75% From 9b1e2cefa2f40a235217ff33530fd4c36e830e3e Mon Sep 17 00:00:00 2001 From: KathiEsterl <47177587+KathiEsterl@users.noreply.github.com> Date: Mon, 20 Mar 2023 17:03:28 +0100 Subject: [PATCH 050/159] Update theoretical_background.rst --- doc/theoretical_background.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/theoretical_background.rst b/doc/theoretical_background.rst index ad9bbaaf2..4eb25dfcb 100644 --- a/doc/theoretical_background.rst +++ b/doc/theoretical_background.rst @@ -19,7 +19,7 @@ At the moment, there are two scenarios available basing on scenario C2035 of the You can see the modelling concepts of the scenarios in the figure below. The components marked green have exogenous capacity and endogenous dispatch whereas the components marked in red are optimized endogenously in capacity and dispatch. -.. figure:: images/modelling_conept.png +.. figure:: images/modelling_concept.png :align: center :scale: 75% From fe16ed1d19d1ae3aa1b9bd6b7b5e3639dac7c325 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 20 Mar 2023 17:14:44 +0100 Subject: [PATCH 051/159] Update "About eTraGo" --- doc/about.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/about.rst b/doc/about.rst index 88ab7514a..59b9652a2 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -20,7 +20,7 @@ Research projects This software project was initially developed in the research project `open_eGo `_. It is constantly further developed in different reserach projects, -e.g. `eGon `_ and `PoWerD `_ and `PoWerD `_. The OpenEnergy Platform @@ -71,7 +71,7 @@ packages were initially developed in the research project Data model creation -============== +=================== For the eGon project the python-tool `eGon-data `_ was implemented, which creates input data for the optimization tools `eTraGo `_, `ding0 `_ and `eDisGo `_ and delivers for example data on grid topologies, demands/demand curves and generation capacities in a high spatial resolution. The outputs of egon-data are published under open source and open data licenses. eGon-data is a further development of the `Data processing `_ developed in the former research project `open_eGo `_. It aims for an extensions of the data models as well as for a better replicability and manageability of the data preparation and processing. From b4e2b595d790f3a0947c999a7b5f5361d6191310 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 20 Mar 2023 17:15:01 +0100 Subject: [PATCH 052/159] Update list of authors --- etrago/tools/io.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/tools/io.py b/etrago/tools/io.py index bc5109ed8..2df92479c 100644 --- a/etrago/tools/io.py +++ b/etrago/tools/io.py @@ -48,7 +48,7 @@ "DLR-Institute for Networked Energy Systems" ) __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" -__author__ = "ulfmueller, mariusves" +__author__ = "ulfmueller, mariusves, pieterhexen, ClaraBuettner" import pypsa from importlib import import_module From aa636b1cb483b326bf2841289b073392f4a1b3af Mon Sep 17 00:00:00 2001 From: KathiEsterl <47177587+KathiEsterl@users.noreply.github.com> Date: Wed, 22 Mar 2023 19:24:22 +0100 Subject: [PATCH 053/159] Correct typos --- doc/theoretical_background.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/theoretical_background.rst b/doc/theoretical_background.rst index 4eb25dfcb..7663224d1 100644 --- a/doc/theoretical_background.rst +++ b/doc/theoretical_background.rst @@ -15,7 +15,7 @@ Assumptions on Data eTraGo fetches the input data from the `OpenEnergy Platform `_. The data includes electricity and gas grid topology as well as data on energy supply and load for the considered sectors (electricity, gas, heat and e-mobility) and data on additional electrical flexibilities such as Dynamic Line Rating or Demand Side Management. More details on the data model can be found in the documentaton of `eGon-data `_. -At the moment, there are two scenarios available basing on scenario C2035 of the network expansion plan ([NEP]_), version 2021. The base one is called eGon2035. To analyse the effect of flexibility option, there is an eGon2035_lowflex scenario available which depicts a lower penetration of flexibilities. More scenarios are being developed. A eGon100RE scenario is being implemented which is characterised by a 100% renewable generation. Analog to the scenario above, a eGon100RE_lowflex scenario is available. +At the moment, there are two scenarios available basing on scenario C2035 of the network expansion plan ([NEP]_), version 2021. The base one is called eGon2035. To analyse the effect of flexibility options, there is an eGon2035_lowflex scenario available which depicts a lower penetration of flexibilities. More scenarios are being developed. The eGon100RE scenario is being implemented which is characterised by a 100% renewable generation. Analog to the scenario above, a eGon100RE_lowflex scenario is available. You can see the modelling concepts of the scenarios in the figure below. The components marked green have exogenous capacity and endogenous dispatch whereas the components marked in red are optimized endogenously in capacity and dispatch. @@ -31,7 +31,7 @@ Methods Optimization with PyPSA ----------------------- -Within eTraGo, the fetched data model is translated into a `PyPSA `_-network. The power flow simulations and optimizations are performed with a linear approximation aussuming eTraGo to fulfill the assumptions to perfom a LOPF (small voltage angle differences, branch resistances negligible to their reactances, voltage magnitudes can be kept at nominal values) since it focuses on the extra-high and high voltage levels. As objective value of the optimization, the overall system costs are considered. +Within eTraGo, the fetched data model is translated into a `PyPSA `_-network. The power flow simulations and optimizations are performed with a linear approximation assuming eTraGo to fulfill the assumptions to perfom a LOPF (as those are small voltage angle differences, branch resistances negligible to their reactances, voltage magnitudes can be kept at nominal values) since it focuses on the extra-high and high voltage levels. As objective value of the optimization, the overall system costs are considered. With the argument ‘pf_post_lopf’, after the LOPF a non-linear power flow simulation can be conducted. @@ -66,7 +66,7 @@ By using the method called **Segmentation**, a hierarchical clustering of consec The **Snapshot Clustering on Typical Periods** implies a hierarchical clustering of time periods with a predefined length (e.g. days or weeks) to typical periods. Those typical periods are weighted according to the number of periods in their cluster. This method optionally includes the linkage of the typical periods in a second time layer to account for the intertemporal dependencies following [Kotzur]_. -By applying a 2-level-approach, a **temporal disaggregation** can be conducted. This means optimising grid and storage expansion using the complexity-reduced time series in the first step and optimising dispatch using the fully complex time series in the second step. +By applying a 2-level-approach, a **temporal disaggregation** can be conducted. This means optimising dispatch using the fullcomplex time series in the second step after having optimised grid and storage expansion using the complexity-reduced time series in the first step. Grid and Storage / Store expansion From 0a17cac0b07f24030a289f11063537e2be041127 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Wed, 22 Mar 2023 19:08:27 +0100 Subject: [PATCH 054/159] Call `UniformDisaggregation` with named arguments Doesn't change the behaviour of the code but makes it easier to understand. --- etrago/cluster/disaggregation.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 0b4378836..b7531d551 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -733,9 +733,9 @@ def run_disaggregation(self): ) elif disagg == "uniform": disaggregation = UniformDisaggregation( - self.disaggregated_network, - self.network, - self.clustering, + original_network=self.disaggregated_network, + clustered_network=self.network, + clustering=self.clustering, skip=skip, ) From db8232c1b4aca4dc60794860dc4fbaaa79f21dfe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Wed, 22 Mar 2023 12:16:36 +0100 Subject: [PATCH 055/159] Replace `print`s with proper logging Use `loguru` for the logging. Get rid of the `"---"` lines in logging, because log messages should contain some explicit information. --- etrago/cluster/disaggregation.py | 37 ++++++++++++++++---------------- setup.py | 1 + 2 files changed, 20 insertions(+), 18 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index b7531d551..0371de296 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -4,6 +4,7 @@ import cProfile import time +from loguru import logger as log from pyomo.environ import Constraint from pypsa import Network import pandas as pd @@ -275,8 +276,7 @@ def solve(self, scenario, solver): } profile = cProfile.Profile() for i, cluster in enumerate(sorted(clusters)): - print("---") - print("Decompose cluster %s (%d/%d)" % (cluster, i + 1, n)) + log.info("Decompose cluster %s (%d/%d)" % (cluster, i + 1, n)) profile.enable() t = time.time() partial_network, externals = self.construct_partial_network( @@ -284,9 +284,9 @@ def solve(self, scenario, solver): ) profile.disable() self.stats["clusters"].loc[cluster, "decompose"] = time.time() - t - print( - "Decomposed in ", - self.stats["clusters"].loc[cluster, "decompose"], + log.info( + "Decomposed in " + f'{self.stats["clusters"].loc[cluster, "decompose"]}' ) t = time.time() profile.enable() @@ -295,32 +295,33 @@ def solve(self, scenario, solver): ) profile.disable() self.stats["clusters"].loc[cluster, "spread"] = time.time() - t - print( - "Result distributed in ", - self.stats["clusters"].loc[cluster, "spread"], + log.info( + "Result distributed in " + f'{self.stats["clusters"].loc[cluster, "spread"]}' ) profile.enable() t = time.time() self.transfer_results(partial_network, externals) profile.disable() self.stats["clusters"].loc[cluster, "transfer"] = time.time() - t - print( - "Results transferred in ", - self.stats["clusters"].loc[cluster, "transfer"], + log.info( + "Results transferred in " + f'{self.stats["clusters"].loc[cluster, "transfer"]}' ) profile.enable() t = time.time() - print("---") fs = (mc("sum"), mc("sum")) for bt, ts in ( ("generators", {"p": fs, "q": fs}), ("storage_units", {"p": fs, "state_of_charge": fs, "q": fs}), ): - print("Attribute sums, {}, clustered - disaggregated:".format(bt)) + log.info( + "Attribute sums, {}, clustered - disaggregated:".format(bt) + ) cnb = getattr(self.clustered_network, bt) onb = getattr(self.original_network, bt) - print( + log.info( "{:>{}}: {}".format( "p_nom_opt", 4 + len("state_of_charge"), @@ -329,11 +330,11 @@ def solve(self, scenario, solver): ) ) - print("Series sums, {}, clustered - disaggregated:".format(bt)) + log.info("Series sums, {}, clustered - disaggregated:".format(bt)) cnb = getattr(self.clustered_network, bt + "_t") onb = getattr(self.original_network, bt + "_t") for s in ts: - print( + log.info( "{:>{}}: {}".format( s, 4 + len("state_of_charge"), @@ -343,7 +344,7 @@ def solve(self, scenario, solver): ) profile.disable() self.stats["check"] = time.time() - t - print("Checks computed in ", self.stats["check"]) + log.info("Checks computed in {self.stats['check']}") # profile.print_stats(sort='cumtime') @@ -749,7 +750,7 @@ def run_disaggregation(self): self.disaggregated_network.generators_t.q.fillna(0, inplace=True) self.disaggregated_network.results = self.network.results - print( + log.info( "Time for overall desaggregation [min]: {:.2}".format( (time.time() - t) / 60 ) diff --git a/setup.py b/setup.py index 4ea9b7ab6..c9fd3aaa1 100755 --- a/setup.py +++ b/setup.py @@ -46,6 +46,7 @@ def read(*names, **kwargs): "egoio == 0.4.7", "geoalchemy2 >= 0.3.0", "geopandas", + "loguru", "matplotlib >= 3.0.3", "oedialect", # PyPSA uses a deprecated import that errors with Pyomo 6.4.3. From 4716abd94daa5b39759ec9088896893aafd05798 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Wed, 22 Mar 2023 18:44:14 +0100 Subject: [PATCH 056/159] Log more stuff --- etrago/cluster/disaggregation.py | 3 +++ etrago/tools/utilities.py | 1 + 2 files changed, 4 insertions(+) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 0371de296..e9281cedc 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -520,6 +520,7 @@ class UniformDisaggregation(Disaggregation): def solve_partial_network( self, cluster, partial_network, scenario, solver=None ): + log.debug("Solving partial network.") bustypes = { "generators": {"group_by": ("carrier",), "series": ("p", "q")}, "storage_units": { @@ -541,6 +542,7 @@ def solve_partial_network( } filters = {"q": lambda o: o.control == "PV"} for bustype in bustypes: + log.debug(f"Decomposing {bustype}.") pn_t = getattr(partial_network, bustype + "_t") cl_t = getattr(self.clustered_network, bustype + "_t") pn_buses = getattr(partial_network, bustype) @@ -720,6 +722,7 @@ def update_constraints(network, externals): def run_disaggregation(self): + log.debug("Running disaggregation.") if self.clustering: disagg = self.args.get("disaggregation") skip = () if self.args["pf_post_lopf"]["active"] else ("q",) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index fddaab0eb..1524e19d8 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -571,6 +571,7 @@ def load_shedding(self, temporal_disaggregation=False, **kwargs): ------- """ + logger.debug("Shedding the load.") if self.args["load_shedding"]: if temporal_disaggregation: network = self.network_tsa From afdab331cdf482cd41eae10d9393600b405e4c08 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Thu, 23 Mar 2023 11:29:55 +0100 Subject: [PATCH 057/159] Fix temporal disaggregation to work with no_slices=1 --- etrago/tools/constraints.py | 8 ++++++-- etrago/tools/execute.py | 5 +++-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index ab3650bca..33fde3b6f 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -2550,6 +2550,10 @@ def split_dispatch_disaggregation_constraints(self, n, sns): None. """ tsa_hour = sns[sns.isin(self.conduct_dispatch_disaggregation.index)] + if len(tsa_hour) > 1: + tsa_hour = tsa_hour[-1] + else: + tsa_hour = tsa_hour[0] n.model.soc_values = self.conduct_dispatch_disaggregation.loc[tsa_hour] sus = n.storage_units.index @@ -2563,7 +2567,7 @@ def disaggregation_sus_soc(m, s, h): Sets soc at the end of the time slice in disptach_disaggregation to value calculated in temporally reduced lopf without slices. """ - return m.state_of_charge[s, h] == m.soc_values[s].values[0] + return m.state_of_charge[s, h] == m.soc_values[s] n.model.split_dispatch_sus_soc = po.Constraint( sus, sns[-1:], rule=disaggregation_sus_soc @@ -2574,7 +2578,7 @@ def disaggregation_sto_soc(m, s, h): Sets soc at the end of the time slice in disptach_disaggregation to value calculated in temporally reduced lopf without slices. """ - return m.store_e[s, h] == m.soc_values[s].values[0] + return m.store_e[s, h] == m.soc_values[s] n.model.split_dispatch_sto_soc = po.Constraint( sto, sns[-1:], rule=disaggregation_sto_soc diff --git a/etrago/tools/execute.py b/etrago/tools/execute.py index 81ee64cbc..2ee156617 100755 --- a/etrago/tools/execute.py +++ b/etrago/tools/execute.py @@ -164,7 +164,7 @@ def run_lopf(etrago, extra_functionality, method): end = transits[i] + (skipped - 1) if i == 0: start = 0 - elif i == no_slices - 1: + if i == no_slices - 1: end = len(etrago.network_tsa.snapshots) etrago.network_tsa.lopf( @@ -427,7 +427,8 @@ def dispatch_disaggregation(self): slice_len = int(len(self.network.snapshots) / no_slices) # transition snapshots defining start and end of slices transits = self.network.snapshots[0::slice_len] - transits = transits[1:] + if len(transits) > 1: + transits = transits[1:] if transits[-1] != self.network_tsa.snapshots[-1]: transits = transits.insert( (len(transits)), self.network.snapshots[-1] From 48dacc8052cce4882f959a0c64a8b487f83d512c Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Thu, 23 Mar 2023 17:23:11 +0100 Subject: [PATCH 058/159] Remove fixed version for tsam --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 4ea9b7ab6..997e1ddd7 100755 --- a/setup.py +++ b/setup.py @@ -59,7 +59,7 @@ def read(*names, **kwargs): "shapely", "sqlalchemy < 2", "tilemapbase == 0.4.5", - "tsam == 1.1.0", + "tsam", ], extras_require={ "docs": ["sphinx >= 1.4", "sphinx_rtd_theme"], From d06926b08452903b32885cf4d9e66d3561e06c22 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Thu, 23 Mar 2023 18:13:04 +0100 Subject: [PATCH 059/159] Make local variable `busflags` an argument When closing over local variables, changing the outer variable also changes the value inside the function. Making the local variable an argument with default value prevents that and it's also a tiny bit more efficient. --- etrago/cluster/disaggregation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index e9281cedc..58deb414d 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -77,7 +77,7 @@ def construct_partial_network(self, cluster, scenario): # find all lines that have at least one bus inside the cluster busflags = self.buses["cluster"] == cluster - def is_bus_in_cluster(conn): + def is_bus_in_cluster(conn, busflags=busflags): return busflags[conn] # Copy configurations to new network From a8c24ee3164d89dd6bb8771ca060f0160a30a7a9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Thu, 23 Mar 2023 18:31:27 +0100 Subject: [PATCH 060/159] Factor `rows` out into a local variable And put a type annotation on the local variable. Helps me to figure out what values are getting passed around, i.e. it makes the code more readable for me. Also, actually makes the code shorter. --- etrago/cluster/disaggregation.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 58deb414d..b052ba45b 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -93,14 +93,12 @@ def is_bus_in_cluster(conn, busflags=busflags): line_types = ["lines", "links", "transformers"] for line_type in line_types: + rows: pd.DataFrame = getattr(self.original_network, line_type) # Copy all lines that reside entirely inside the cluster ... setattr( partial_network, line_type, - filter_internal_connector( - getattr(self.original_network, line_type), - is_bus_in_cluster, - ), + filter_internal_connector(rows, is_bus_in_cluster), ) # ... and their time series @@ -115,7 +113,7 @@ def is_bus_in_cluster(conn, busflags=busflags): # Copy all lines whose `bus0` lies within the cluster left_external_connectors = filter_left_external_connector( - getattr(self.original_network, line_type), is_bus_in_cluster + rows, is_bus_in_cluster ) def from_busmap(x): @@ -134,7 +132,7 @@ def from_busmap(x): # Copy all lines whose `bus1` lies within the cluster right_external_connectors = filter_right_external_connector( - getattr(self.original_network, line_type), is_bus_in_cluster + rows, is_bus_in_cluster ) if not right_external_connectors.empty: ca_option = pd.get_option("mode.chained_assignment") From 7f61dbe562d5f41b471bc6d3d312bca9976c7651 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Thu, 23 Mar 2023 18:38:49 +0100 Subject: [PATCH 061/159] Factor `timeseries` out into a local variable Same reasons as in the previous commit. --- etrago/cluster/disaggregation.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index b052ba45b..93d4be02f 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -94,6 +94,9 @@ def is_bus_in_cluster(conn, busflags=busflags): line_types = ["lines", "links", "transformers"] for line_type in line_types: rows: pd.DataFrame = getattr(self.original_network, line_type) + timeseries: dict[str, pd.DataFrame] = getattr( + self.original_network, line_type + "_t" + ) # Copy all lines that reside entirely inside the cluster ... setattr( partial_network, @@ -105,11 +108,7 @@ def is_bus_in_cluster(conn, busflags=busflags): # TODO: These are all time series, not just the ones from lines # residing entirely in side the cluster. # Is this a problem? - setattr( - partial_network, - line_type + "_t", - getattr(self.original_network, line_type + "_t"), - ) + setattr(partial_network, line_type + "_t", timeseries) # Copy all lines whose `bus0` lies within the cluster left_external_connectors = filter_left_external_connector( From ff9c359b27c807a89724ea676bf4976469814642 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Wed, 22 Mar 2023 05:59:58 +0100 Subject: [PATCH 062/159] Use implicit string joining instead of `+` And move separating spaces to the beginning of the line while at it. --- etrago/cluster/disaggregation.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 93d4be02f..50095e4f8 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -606,9 +606,9 @@ def solve_partial_network( pnb.loc[:, "p_nom_extendable"] == clb.iloc[0].at["p_nom_extendable"] ).all(), ( - "The `'p_nom_extendable'` flag for the current " - + "cluster's bus does not have the same value " - + "it has on the buses of it's partial network." + "The `'p_nom_extendable'` flag for the current" + " cluster's bus does not have the same value" + " it has on the buses of it's partial network." ) if clb.iloc[0].at["p_nom_extendable"]: From 4848efe77af4cdbbf8ba22416c9d390cd460728e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 00:12:49 +0100 Subject: [PATCH 063/159] Fix typo in comment: "in side" -> "inside" --- etrago/cluster/disaggregation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 50095e4f8..23a44d67e 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -106,7 +106,7 @@ def is_bus_in_cluster(conn, busflags=busflags): # ... and their time series # TODO: These are all time series, not just the ones from lines - # residing entirely in side the cluster. + # residing entirely inside the cluster. # Is this a problem? setattr(partial_network, line_type + "_t", timeseries) From aaa04359088142c5cdc8b19ec0e882871da98e54 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 00:43:07 +0100 Subject: [PATCH 064/159] Update comment about unfiltered time series --- etrago/cluster/disaggregation.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 23a44d67e..b98428e34 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -108,6 +108,10 @@ def is_bus_in_cluster(conn, busflags=busflags): # TODO: These are all time series, not just the ones from lines # residing entirely inside the cluster. # Is this a problem? + # I hope not, because neither is `rows.index` a subset + # of the columns of one of the values of `timeseries`, + # nor the other way around, so it's not clear how to + # align both. setattr(partial_network, line_type + "_t", timeseries) # Copy all lines whose `bus0` lies within the cluster From 096f9d3c471807767d669374f6fb4d03e9f5b20f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 00:53:58 +0100 Subject: [PATCH 065/159] Replace `DataFrame.append` with `pandas.concat` The `DataFrame.append` method is deprecated in favour of `pandas.concat` as of `pandas` version 1.4.0. --- etrago/cluster/disaggregation.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index b98428e34..1904d6414 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -181,8 +181,8 @@ def from_busmap(x): self.reindex_with_prefix(externals_to_insert) # .. and insert them as well as their time series - partial_network.buses = partial_network.buses.append( - externals_to_insert + partial_network.buses = pd.concat( + [partial_network.buses, externals_to_insert] ) partial_network.buses_t = self.original_network.buses_t @@ -214,7 +214,9 @@ def from_busmap(x): setattr( partial_network, bustype, - getattr(partial_network, bustype).append(buses_to_insert), + pd.concat( + [getattr(partial_network, bustype), buses_to_insert] + ), ) # Also copy their time series From 6ae2a45fb8856348b7a2629afca7329bf430cbdc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 01:15:14 +0100 Subject: [PATCH 066/159] Improve sanity check's comprehensibility Use descriptive variable names for intermediate values to make the code shorter and basically read as what it does: assert that the `DataFrame` of rows which don't pass the sanity check, i.e. are "not sane", or `~sane`, is empty. --- etrago/cluster/disaggregation.py | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 1904d6414..b976b123e 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -246,16 +246,13 @@ def from_busmap(x): # Just a simple sanity check # TODO: Remove when sure that disaggregation will not go insane anymore for line_type in line_types: - assert ( - getattr(partial_network, line_type) - .bus0.isin(partial_network.buses.index) - .all() - ) - assert ( - getattr(partial_network, line_type) - .bus1.isin(partial_network.buses.index) - .all() - ) + rows = getattr(partial_network, line_type) + + sane = rows.bus0.isin(partial_network.buses.index) + assert rows.loc[~sane, :].empty + + sane = rows.bus1.isin(partial_network.buses.index) + assert rows.loc[~sane, :].empty return partial_network, external_buses From 4339ebedd22cb038518af0ed3009073982cb9c20 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 01:23:51 +0100 Subject: [PATCH 067/159] Add an error message to sanity checks --- etrago/cluster/disaggregation.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index b976b123e..4fe0b798e 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -249,10 +249,18 @@ def from_busmap(x): rows = getattr(partial_network, line_type) sane = rows.bus0.isin(partial_network.buses.index) - assert rows.loc[~sane, :].empty + assert rows.loc[~sane, :].empty, ( + f"Not all `partial_network.{line_type}.bus0` entries are" + f" contained in `partial_network.buses.index`." + f" Spurious additional rows:\nf{rows.loc[~sane, :]}" + ) sane = rows.bus1.isin(partial_network.buses.index) - assert rows.loc[~sane, :].empty + assert rows.loc[~sane, :].empty, ( + f"Not all `partial_network.{line_type}.bus1` entries are" + f" contained in `partial_network.buses.index`." + f" Spurious additional rows:\nf{rows.loc[~sane, :]}" + ) return partial_network, external_buses From 021d83bb99b73c1dc71655f9d5233ae68943e0b9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 01:26:33 +0100 Subject: [PATCH 068/159] Bulk insert disaggregated columns Instead of using a for loop to append them one at a time. Doing so triggered a few warnings about the `DataFrame` being fragmented. The new code should prevent that, while still inserting the same values. Additionally, no duplicate columns are created. Instead the data in already existing columns is overwritten, which is a plus. --- etrago/cluster/disaggregation.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 4fe0b798e..4d6deb4fa 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -680,9 +680,13 @@ def solve_partial_network( else () ) ws = weight.sum(axis=len(loc)) - for bus_id in filtered.index: - values = clt * weight.loc[loc + (bus_id,)] / ws - pn_t[s].insert(len(pn_t[s].columns), bus_id, values) + new_columns = pd.DataFrame( + { + bus_id: clt * weight.loc[loc + (bus_id,)] / ws + for bus_id in filtered.index + } + ) + pn_t[s].loc[:, new_columns.columns] = new_columns def transfer_results(self, *args, **kwargs): kwargs["bustypes"] = ["generators", "storage_units"] From f00d49aa9c6dd0a89364cc30fd541c2ef257d387 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 01:45:17 +0100 Subject: [PATCH 069/159] Add some information to "etrago/tools/__init__.py" A short docstring along with author, copyright and license information. --- etrago/tools/__init__.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/etrago/tools/__init__.py b/etrago/tools/__init__.py index 497ec4c5a..df40335b9 100644 --- a/etrago/tools/__init__.py +++ b/etrago/tools/__init__.py @@ -1,8 +1,12 @@ -""" +"""Multi purpose tools that don't fit anywhere else in eTraGo. """ -__copyright__ = "tba" -__license__ = "tba" -__author__ = "tba" +__copyright__ = ( + "Copyright (C) 2023" + " Otto-von-Guericke-University Magdeburg," + " Research group for theoretical computer science" +) +__license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" +__author__ = "gnn " From 4ecd568dfc0de05d1b8a9832f1c63a9864708c19 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 03:52:00 +0100 Subject: [PATCH 070/159] Add various ways of doing nothing --- etrago/tools/__init__.py | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/etrago/tools/__init__.py b/etrago/tools/__init__.py index df40335b9..170b87144 100644 --- a/etrago/tools/__init__.py +++ b/etrago/tools/__init__.py @@ -10,3 +10,29 @@ __author__ = "gnn " +def noop(*ignored_arguments, **ignored_keyword_arguments): + """Do nothing. + + Accept all kinds of arguments, ignore them and do nothing. + """ + pass + + +class Noops: + """Provide arbitrarily named methods that do nothing. + + Any attribute access will return a method that does nothing, i.e. + all methods of this object are :py:func:`noop`s. Normally you don't + need to instantiate this class. All instances behave the same, so + the containing module provides one called :py:obj:`noops` which you + can import and use. + """ + + @classmethod + def __getattribute__(cls, ignored_name): + return noop + + +noops = Noops() +"""A default :py:class:`Noops` instance so you don't have to create one. +""" From 17c5e631b6f11d3f05efdc0eb69cd43c6fbfac1c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 03:56:41 +0100 Subject: [PATCH 071/159] Disable profiling more thorough Using `noops` not only disables the profiling report, but also makes `profile.enable()` and `profile.disable()` calls do nothing with as little overhead as possible. --- etrago/cluster/disaggregation.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 4d6deb4fa..cd0ef2995 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -9,6 +9,7 @@ from pypsa import Network import pandas as pd +from etrago.tools import noops from etrago.tools.utilities import residual_load @@ -283,6 +284,7 @@ def solve(self, scenario, solver): ) } profile = cProfile.Profile() + profile = noops for i, cluster in enumerate(sorted(clusters)): log.info("Decompose cluster %s (%d/%d)" % (cluster, i + 1, n)) profile.enable() @@ -354,7 +356,7 @@ def solve(self, scenario, solver): self.stats["check"] = time.time() - t log.info("Checks computed in {self.stats['check']}") - # profile.print_stats(sort='cumtime') + profile.print_stats(sort="cumtime") def transfer_results( self, From c350ae4f7b26e9c09ddd243cff4fe713cb399092 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Thu, 12 Jan 2023 09:53:08 +0100 Subject: [PATCH 072/159] Don't create `disaggregated_network.results` Results are stored directly in `disaggregated_network`'s attributes. --- etrago/cluster/disaggregation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index cd0ef2995..ecfa7f8dd 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -766,7 +766,6 @@ def run_disaggregation(self): self.disaggregated_network.generators_t.p.fillna(0, inplace=True) self.disaggregated_network.generators_t.q.fillna(0, inplace=True) - self.disaggregated_network.results = self.network.results log.info( "Time for overall desaggregation [min]: {:.2}".format( (time.time() - t) / 60 From 9db0c2d67163ad99a65119f74b0d89c5a4806c9b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 3 Mar 2023 16:51:19 +0100 Subject: [PATCH 073/159] Use a shorter way of getting the first index value That's what `next(clb.itertuples()).Index` does. It just gets the index of the first row of `clb`, so `clb.index[0]` should be equivalent but way clearer and more readable. Also note that `clb` should only have one row anyway. --- etrago/cluster/disaggregation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index ecfa7f8dd..2f45e9d0f 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -665,7 +665,7 @@ def solve_partial_network( if s in self.skip: continue filtered = pnb.loc[filters.get(s, slice(None))] - clt = cl_t[s].loc[:, next(clb.itertuples()).Index] + clt = cl_t[s].loc[:, clb.index[0]] weight = reduce( multiply, ( From 57352b106e6b193f98be44c8935003b2ca00f7ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Wed, 22 Mar 2023 06:04:34 +0100 Subject: [PATCH 074/159] Replace older string interpolation with f-strings --- etrago/cluster/disaggregation.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 2f45e9d0f..79e614140 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -286,7 +286,7 @@ def solve(self, scenario, solver): profile = cProfile.Profile() profile = noops for i, cluster in enumerate(sorted(clusters)): - log.info("Decompose cluster %s (%d/%d)" % (cluster, i + 1, n)) + log.info(f"Decompose {cluster=} ({i + 1}/{n})") profile.enable() t = time.time() partial_network, externals = self.construct_partial_network( @@ -326,9 +326,7 @@ def solve(self, scenario, solver): ("generators", {"p": fs, "q": fs}), ("storage_units", {"p": fs, "state_of_charge": fs, "q": fs}), ): - log.info( - "Attribute sums, {}, clustered - disaggregated:".format(bt) - ) + log.info(f"Attribute sums, {bt}, clustered - disaggregated:") cnb = getattr(self.clustered_network, bt) onb = getattr(self.original_network, bt) log.info( @@ -340,7 +338,7 @@ def solve(self, scenario, solver): ) ) - log.info("Series sums, {}, clustered - disaggregated:".format(bt)) + log.info(f"Series sums, {bt}, clustered - disaggregated:") cnb = getattr(self.clustered_network, bt + "_t") onb = getattr(self.original_network, bt + "_t") for s in ts: @@ -575,10 +573,8 @@ def solve_partial_network( if len(clb) == 0: continue assert len(clb) == 1, ( - "Cluster {} has {} buses for group {}.\n".format( - cluster, len(clb), group - ) - + "Should be exactly one." + f"Cluster {cluster} has {len(clb)} buses for {group=}." + "\nShould be exactly one." ) # Remove buses not belonging to the partial network pnb = pn_buses.iloc[ From 988d19cb95e8811d4e6e425f89952d58eb992468 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 24 Mar 2023 05:33:50 +0100 Subject: [PATCH 075/159] Disaggregate "p0" and "p1" of "links" Instead of only retaining connecting components, i.e. "lines", "links" and "transformers", which are completely contained in a cluster, retain all components which have at least one endpoint inside the cluster. This is what the switch from `&` to `|` does. That way, these components are available as `cl_buses` when running `solve_partial_network`. Then, when solving the partial network, only select those components for disaggregation which have the left endpoint, i.e. `bus0` in the cluster and group them by right endpoint, i.e. `bus1`. Adjust sanity checks accordingly, as now a component doesn't have to have both of its endpoints inside the cluster, but only needs to have one. --- etrago/cluster/disaggregation.py | 36 +++++++++++++++++++------------- 1 file changed, 22 insertions(+), 14 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 79e614140..6d5a89b5c 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -249,18 +249,13 @@ def from_busmap(x): for line_type in line_types: rows = getattr(partial_network, line_type) - sane = rows.bus0.isin(partial_network.buses.index) - assert rows.loc[~sane, :].empty, ( - f"Not all `partial_network.{line_type}.bus0` entries are" + left = rows.bus0.isin(partial_network.buses.index) + right = rows.bus1.isin(partial_network.buses.index) + assert rows.loc[~(left | right), :].empty, ( + f"Not all `partial_network.{line_type}` have an endpoint," + " i.e. `bus0` or `bus1`," f" contained in `partial_network.buses.index`." - f" Spurious additional rows:\nf{rows.loc[~sane, :]}" - ) - - sane = rows.bus1.isin(partial_network.buses.index) - assert rows.loc[~sane, :].empty, ( - f"Not all `partial_network.{line_type}.bus1` entries are" - f" contained in `partial_network.buses.index`." - f" Spurious additional rows:\nf{rows.loc[~sane, :]}" + f" Spurious additional rows:\nf{rows.loc[~(left | right), :]}" ) return partial_network, external_buses @@ -530,6 +525,10 @@ def solve_partial_network( ): log.debug("Solving partial network.") bustypes = { + "links": { + "group_by": ("carrier", "bus1"), + "series": ("p0", "p1"), + }, "generators": {"group_by": ("carrier",), "series": ("p", "q")}, "storage_units": { "group_by": ("carrier", "max_hours"), @@ -546,6 +545,8 @@ def solve_partial_network( ) else ("p_nom_opt", "p_max_pu") ), + "p0": ("p_nom_opt",), + "p1": ("p_nom_opt",), "state_of_charge": ("p_nom_opt",), } filters = {"q": lambda o: o.control == "PV"} @@ -565,7 +566,11 @@ def solve_partial_network( ] ) for group in groups: - clb = cl_buses[cl_buses.bus == cluster] + clb = ( + cl_buses[cl_buses.bus == cluster] + if "bus" in cl_buses.columns + else cl_buses[cl_buses.bus0 == cluster] + ) query = " & ".join( ["({key} == {value!r})".format(**axis) for axis in group] ) @@ -581,7 +586,10 @@ def solve_partial_network( [ i for i, row in enumerate(pn_buses.itertuples()) - if not row.bus.startswith(self.idx_prefix) + for bus in [ + row.bus if hasattr(row, "bus") else row.bus1 + ] + if not bus.startswith(self.idx_prefix) ] ] pnb = pnb.query(query) @@ -701,7 +709,7 @@ def swap_series(s): def filter_internal_connector(conn, is_bus_in_cluster): return conn[ - conn.bus0.apply(is_bus_in_cluster) & conn.bus1.apply(is_bus_in_cluster) + conn.bus0.apply(is_bus_in_cluster) | conn.bus1.apply(is_bus_in_cluster) ] From b6f42cd6b4f7f2e6fd343fb7d9537a9d908bcba2 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Wed, 11 Jan 2023 18:33:23 +0100 Subject: [PATCH 076/159] Disaggregate "stores" Differentiate between the different columns of stores and other components, as stores use "e_.*" where other components use "p_.*". --- etrago/cluster/disaggregation.py | 49 +++++++++++++++++++++++--------- 1 file changed, 35 insertions(+), 14 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 6d5a89b5c..fe856b3aa 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -534,6 +534,10 @@ def solve_partial_network( "group_by": ("carrier", "max_hours"), "series": ("p", "state_of_charge", "q"), }, + "stores": { + "group_by": ("carrier",), + "series": ("e", "p"), + }, } weights = { "p": ("p_nom_opt", "p_max_pu"), @@ -548,14 +552,31 @@ def solve_partial_network( "p0": ("p_nom_opt",), "p1": ("p_nom_opt",), "state_of_charge": ("p_nom_opt",), + "e": ("e_nom_opt",), } filters = {"q": lambda o: o.control == "PV"} + for bustype in bustypes: + # Define attributeof components which are available + if bustype == "stores": + extendable_flag = "e_nom_extendable" + nominal_capacity = "e_nom" + optimal_capacity = "e_nom_opt" + maximal_capacity = "e_nom_max" + weights["p"] = ("e_nom_opt", "e_max_pu") + else: + extendable_flag = "p_nom_extendable" + nominal_capacity = "p_nom" + optimal_capacity = "p_nom_opt" + maximal_capacity = "p_nom_max" + weights["p"] = ("p_nom_opt", "p_max_pu") + log.debug(f"Decomposing {bustype}.") pn_t = getattr(partial_network, bustype + "_t") cl_t = getattr(self.clustered_network, bustype + "_t") pn_buses = getattr(partial_network, bustype) cl_buses = getattr(self.clustered_network, bustype) + groups = product( *[ [ @@ -603,8 +624,8 @@ def solve_partial_network( ) if not ( - pnb.loc[:, "p_nom_extendable"].all() - or not pnb.loc[:, "p_nom_extendable"].any() + pnb.loc[:, extendable_flag].all() + or not pnb.loc[:, extendable_flag].any() ): raise NotImplementedError( "The `'p_nom_extendable'` flag for buses in the" @@ -620,41 +641,41 @@ def solve_partial_network( ) else: assert ( - pnb.loc[:, "p_nom_extendable"] - == clb.iloc[0].at["p_nom_extendable"] + pnb.loc[:, extendable_flag] + == clb.iloc[0].at[extendable_flag] ).all(), ( "The `'p_nom_extendable'` flag for the current" " cluster's bus does not have the same value" " it has on the buses of it's partial network." ) - if clb.iloc[0].at["p_nom_extendable"]: + if clb.iloc[0].at[extendable_flag]: # That means, `p_nom` got computed via optimization and we # have to distribute it into the subnetwork first. - pnb_p_nom_max = pnb.loc[:, "p_nom_max"] + pnb_p_nom_max = pnb.loc[:, maximal_capacity] p_nom_max_global = pnb_p_nom_max.sum(axis="index") - pnb.loc[:, "p_nom_opt"] = ( - clb.iloc[0].at["p_nom_opt"] + pnb.loc[:, optimal_capacity] = ( + clb.iloc[0].at[optimal_capacity] * pnb_p_nom_max / p_nom_max_global ) getattr(self.original_network, bustype).loc[ - pnb.index, "p_nom_opt" - ] = pnb.loc[:, "p_nom_opt"] - pnb.loc[:, "p_nom"] = pnb.loc[:, "p_nom_opt"] + pnb.index, optimal_capacity + ] = pnb.loc[:, optimal_capacity] + pnb.loc[:, nominal_capacity] = pnb.loc[:, optimal_capacity] else: # That means 'p_nom_opt' didn't get computed and is # potentially not present in the dataframe. But we want to # always use 'p_nom_opt' in the remaining code, so save a # view of the computed 'p_nom' values under 'p_nom_opt'. - pnb.loc[:, "p_nom_opt"] = pnb.loc[:, "p_nom"] + pnb.loc[:, optimal_capacity] = pnb.loc[:, nominal_capacity] # This probably shouldn't be here, but rather in # `transfer_results`, but it's easier to do it this way right # now. getattr(self.original_network, bustype).loc[ - pnb.index, "p_nom_opt" - ] = pnb.loc[:, "p_nom_opt"] + pnb.index, optimal_capacity + ] = pnb.loc[:, optimal_capacity] timed = ( lambda key, series=set( s From f109cec35b0e4713564ab0ebcf960e98b79b4bf2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Mon, 16 Jan 2023 17:20:48 +0100 Subject: [PATCH 077/159] Transfer `"links"`' and `"stores"`' results --- etrago/cluster/disaggregation.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index fe856b3aa..f41453b3f 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -716,10 +716,12 @@ def solve_partial_network( pn_t[s].loc[:, new_columns.columns] = new_columns def transfer_results(self, *args, **kwargs): - kwargs["bustypes"] = ["generators", "storage_units"] + kwargs["bustypes"] = ["generators", "links", "storage_units", "stores"] kwargs["series"] = { "generators": {"p"}, + "links": {"p0", "p1"}, "storage_units": {"p", "state_of_charge"}, + "stores": {"e", "p"}, } return super().transfer_results(*args, **kwargs) From 886211bf49cc34b96108ec63bc0c40b455b66adc Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 6 Feb 2023 14:52:14 +0100 Subject: [PATCH 078/159] Replace infinite values with a large constant Use a large constant to avoid NaNs as disaggregation weights. --- etrago/cluster/disaggregation.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index f41453b3f..78f5cc755 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -653,7 +653,13 @@ def solve_partial_network( # That means, `p_nom` got computed via optimization and we # have to distribute it into the subnetwork first. pnb_p_nom_max = pnb.loc[:, maximal_capacity] + + # If upper limit is infinite, replace it by a very large + # number to avoid NaN values in the calculation + pnb_p_nom_max.replace(float("inf"), 10000000, inplace=True) + p_nom_max_global = pnb_p_nom_max.sum(axis="index") + pnb.loc[:, optimal_capacity] = ( clb.iloc[0].at[optimal_capacity] * pnb_p_nom_max From 854efd9814d7e028ee9d42984d40ca5b4b1006f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Wed, 8 Mar 2023 16:36:14 +0100 Subject: [PATCH 079/159] Reformat the definition of `timed` Just putting `key in series` in parenthesis prevents `black` from spreading it over multiple lines, which means that the whole `lambda` expression doesn't have to be put in parenthesis, making the whole thing less indented and a bit more readable IMHO. Also note that it uses `{}` instead of `set()` now. Last but not least, use a `# noqa` comment do pacify linters complaining about an assigned lambda. A `def` would make these lines less compact and thus slightly less readable, IMHO. --- etrago/cluster/disaggregation.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 78f5cc755..2c18601c7 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -682,15 +682,12 @@ def solve_partial_network( getattr(self.original_network, bustype).loc[ pnb.index, optimal_capacity ] = pnb.loc[:, optimal_capacity] - timed = ( - lambda key, series=set( - s - for s in cl_t - if not cl_t[s].empty - if not pn_t[s].columns.intersection(pnb.index).empty - ): key - in series - ) + timed = lambda key, series={ # noqa: 731 + s + for s in cl_t + if not cl_t[s].empty + if not pn_t[s].columns.intersection(pnb.index).empty + }: (key in series) for s in bustypes[bustype]["series"]: if s in self.skip: From df4a12d98ea9a0d30605b8238ed6de8b04d6e49b Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Fri, 24 Mar 2023 12:16:08 +0100 Subject: [PATCH 080/159] Call load_shedding function in adjust_network again --- etrago/cluster/electrical.py | 3 --- etrago/cluster/gas.py | 1 - etrago/tools/network.py | 6 +----- 3 files changed, 1 insertion(+), 9 deletions(-) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index 1bec60682..464414061 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -419,9 +419,6 @@ def ehv_clustering(self): self.update_busmap(busmap) self.buses_by_country() - if not (self.args["network_clustering"]["active"]): - self.load_shedding() - logger.info("Network clustered to EHV-grid") diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 1430710b4..d8c0d82ff 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -834,7 +834,6 @@ def run_spatial_clustering_gas(self): self.network, busmap = gas_postprocessing(self, busmap, medoid_idx) self.update_busmap(busmap) - self.load_shedding() logger.info( "GAS Network clustered to {} DE-buses and {} foreign buses with {} algorithm.".format( diff --git a/etrago/tools/network.py b/etrago/tools/network.py index fa215352e..d0d2a1330 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -328,11 +328,7 @@ def adjust_network(self): self.geolocation_buses() - if not ( - self.args["network_clustering_ehv"] - | self.args["network_clustering"]["active"] - ): - self.load_shedding() + self.load_shedding() self.adjust_CH4_gen_carriers() From 6afed5efe0a960428382c916fc6ab3b45dbc0465 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Fri, 24 Mar 2023 12:16:43 +0100 Subject: [PATCH 081/159] Remove 'load shedding' generators from weighting for clustering --- etrago/cluster/electrical.py | 2 +- etrago/cluster/gas.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index 464414061..7a29b8f9d 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -834,7 +834,7 @@ def calc_availability_factor(gen): "nuclear": 1, } - gen = network.generators[["bus", "carrier", "p_nom"]].copy() + gen = network.generators[network.generators.carrier!="load shedding"][["bus", "carrier", "p_nom"]].copy() gen["cf"] = gen.apply(calc_availability_factor, axis=1) gen["weight"] = gen["p_nom"] * gen["cf"] diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index d8c0d82ff..473448c71 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -119,8 +119,8 @@ def weighting_for_scenario(ch4_buses, save=None): ].index # get all generators and loads related to ch4_buses generators_ = pd.Series( - etrago.network.generators.index, - index=etrago.network.generators.bus, + etrago.network.generators[etrago.network.generators.carrier!="load shedding"].index, + index=etrago.network.generators[etrago.network.generators.carrier!="load shedding"].bus, ) buses_CH4_gen = generators_.index.intersection(rel_links.keys()) loads_ = pd.Series( From 689d59af9184a580b09b9046377394cb883566b4 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Fri, 24 Mar 2023 12:18:18 +0100 Subject: [PATCH 082/159] Apply black --- etrago/cluster/electrical.py | 4 +++- etrago/cluster/gas.py | 8 ++++++-- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index 7a29b8f9d..d6259d3f1 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -834,7 +834,9 @@ def calc_availability_factor(gen): "nuclear": 1, } - gen = network.generators[network.generators.carrier!="load shedding"][["bus", "carrier", "p_nom"]].copy() + gen = network.generators[network.generators.carrier != "load shedding"][ + ["bus", "carrier", "p_nom"] + ].copy() gen["cf"] = gen.apply(calc_availability_factor, axis=1) gen["weight"] = gen["p_nom"] * gen["cf"] diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 473448c71..05534ab3f 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -119,8 +119,12 @@ def weighting_for_scenario(ch4_buses, save=None): ].index # get all generators and loads related to ch4_buses generators_ = pd.Series( - etrago.network.generators[etrago.network.generators.carrier!="load shedding"].index, - index=etrago.network.generators[etrago.network.generators.carrier!="load shedding"].bus, + etrago.network.generators[ + etrago.network.generators.carrier != "load shedding" + ].index, + index=etrago.network.generators[ + etrago.network.generators.carrier != "load shedding" + ].bus, ) buses_CH4_gen = generators_.index.intersection(rel_links.keys()) loads_ = pd.Series( From c6d97d9b1c78c531d7d5892f626b2a2643795cca Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Fri, 24 Mar 2023 16:28:12 +0100 Subject: [PATCH 083/159] Filter load shedding generators in delete_dispensable_ac_buses --- etrago/tools/utilities.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index e1c266e13..79b4c3069 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -1018,7 +1018,9 @@ def count(bus): b_trafo = pd.concat( [network.transformers.bus0, network.transformers.bus1] ).unique() - b_gen = network.generators.bus.unique() + b_gen = network.generators[ + network.generators.carrier != "load shedding" + ].bus.unique() b_load = network.loads.bus.unique() b_store = network.stores[network.stores.e_nom > 0].bus.unique() b_store_unit = network.storage_units[ From 6f15eee1aca51716dd32f1e826e640c6e5e4f256 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Fri, 24 Mar 2023 18:19:27 +0100 Subject: [PATCH 084/159] Delete symbolic_solver-option because of bug when exporting lp-file --- etrago/tools/execute.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/tools/execute.py b/etrago/tools/execute.py index 2ee156617..ebc9ead0b 100755 --- a/etrago/tools/execute.py +++ b/etrago/tools/execute.py @@ -361,7 +361,7 @@ def iterate_lopf( if not args["lpfile"] is False: network.model.write( - lp_path, io_options={"symbolic_solver_labels": True} + lp_path ) return network From 73b9e3b356ef1a6df923204d424cc7a9792e6e46 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 27 Mar 2023 09:39:52 +0200 Subject: [PATCH 085/159] Add busmap as type pandas.Series --- etrago/cluster/disaggregation.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 2c18601c7..c727fc479 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -26,10 +26,11 @@ def __init__( self.original_network = original_network self.clustered_network = clustered_network self.clustering = clustering + self.busmap = pd.Series(clustering.busmap) self.buses = pd.merge( original_network.buses, - clustering.busmap.to_frame(name="cluster"), + self.busmap.to_frame(name="cluster"), left_index=True, right_index=True, ) @@ -121,7 +122,7 @@ def is_bus_in_cluster(conn, busflags=busflags): ) def from_busmap(x): - return self.idx_prefix + self.clustering.busmap.loc[x] + return self.idx_prefix + self.busmap.loc[x] if not left_external_connectors.empty: ca_option = pd.get_option("mode.chained_assignment") @@ -270,7 +271,7 @@ def solve(self, scenario, solver): :param scenario: :param solver: Solver that may be used to optimize partial networks """ - clusters = set(self.clustering.busmap.values) + clusters = set(self.busmap.values) n = len(clusters) self.stats = { "clusters": pd.DataFrame( @@ -424,7 +425,7 @@ def _validate_disaggregation_generators(self, cluster, f): def extra_functionality(network, snapshots): f(network, snapshots) generators = self.original_network.generators.assign( - bus=lambda df: df.bus.map(self.clustering.busmap) + bus=lambda df: df.bus.map(self.busmap) ) def construct_constraint(model, snapshot, carrier): @@ -476,7 +477,7 @@ def extra_functionality(network, snapshots): ]: generators = getattr( self.original_network, bustype_pypsa - ).assign(bus=lambda df: df.bus.map(self.clustering.busmap)) + ).assign(bus=lambda df: df.bus.map(self.busmap)) for suffix in suffixes: def construct_constraint(model, snapshot): From d806f8b556cd082d18097aaa0d51a213e2561d4f Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 27 Mar 2023 09:40:29 +0200 Subject: [PATCH 086/159] Un-comment spatial disaggregation --- etrago/appl.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index da10a4e3d..ee851d3a6 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -134,7 +134,7 @@ }, }, "network_clustering_ehv": False, # clustering of HV buses to EHV buses. - "disaggregation": None, # None, 'mini' or 'uniform' + "disaggregation": "uniform", # None, 'mini' or 'uniform' # Temporal Complexity: "snapshot_clustering": { "active": False, # choose if clustering is activated @@ -517,7 +517,7 @@ def run_etrago(args, json_path): # spatial disaggregation # needs to be adjusted for new sectors - # etrago.disaggregation() + etrago.disaggregation() # calculate central etrago results etrago.calc_results() From a40fec9cdf7c4ca8acebcb368321c7f5b34f67aa Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 28 Mar 2023 09:27:59 +0200 Subject: [PATCH 087/159] Fix delete_dispensable_ac_buses function --- etrago/tools/utilities.py | 29 +++++++++++++++++++++++++---- 1 file changed, 25 insertions(+), 4 deletions(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 79b4c3069..c5f0c0a9a 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -984,7 +984,16 @@ def delete_buses(delete_buses, network): (network.storage_units.bus.isin(drop_buses)) ].to_list() network.storage_units.drop(drop_storage_units, inplace=True) - return (network.buses, network.lines, network.storage_units) + drop_generators = network.generators.index[ + (network.generators.bus.isin(drop_buses)) + ].to_list() + network.generators.drop(drop_generators, inplace=True) + return ( + network.buses, + network.lines, + network.storage_units, + network.generators, + ) def count_lines(lines): buses_in_lines = lines[["bus0", "bus1"]].drop_duplicates() @@ -1159,9 +1168,12 @@ def count(bus): new_lines_df.loc[l_new.name] = l_new # Delete all the dispensable buses - (network.buses, network.lines, network.storage_units) = delete_buses( - ac_buses, network - ) + ( + network.buses, + network.lines, + network.storage_units, + network.generators, + ) = delete_buses(ac_buses, network) # exclude from the new lines the ones connected to deleted buses new_lines_df = new_lines_df[ @@ -1171,6 +1183,15 @@ def count(bus): etrago.network.lines = pd.concat([etrago.network.lines, new_lines_df]) + # Drop s_max_pu timeseries for deleted lines + etrago.network.lines_t.s_max_pu = ( + etrago.network.lines_t.s_max_pu.transpose()[ + etrago.network.lines_t.s_max_pu.columns.isin( + etrago.network.lines.index + ) + ].transpose() + ) + return From 1f511e84d54c36181f319265089209657e8129f0 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 28 Mar 2023 10:52:06 +0200 Subject: [PATCH 088/159] Add updated carrier names of e mobility --- etrago/cluster/electrical.py | 1 + etrago/tools/plot.py | 1 + 2 files changed, 2 insertions(+) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index d6259d3f1..fcd9694f8 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -88,6 +88,7 @@ def adjust_no_electric_network(etrago, busmap, cluster_met): "H2_saltcavern": "power_to_H2", "dsm": "dsm", "Li ion": "BEV charger", + "Li_ion": "BEV_charger", "rural_heat": "rural_heat_pump", } diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index 5d12a008c..57c3fc9c1 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -179,6 +179,7 @@ def coloring(): "battery": "blue", "pumped_hydro": "indigo", "BEV charger": "indigo", + "BEV_charger": "indigo", } return colors From 15bd596b4cabb0e4e3368b3d622b1f528bdfbc67 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 28 Mar 2023 10:53:05 +0200 Subject: [PATCH 089/159] Add pypsa default values to empty series --- etrago/tools/io.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/etrago/tools/io.py b/etrago/tools/io.py index bc5109ed8..18413cc95 100644 --- a/etrago/tools/io.py +++ b/etrago/tools/io.py @@ -318,14 +318,18 @@ def series_fetch_by_relname(self, network, name, pypsa_name): df_all.set_index(index_col, inplace=True) df_all.index = df_all.index.astype(str) - + if not df_all.isnull().all().all(): + # Fill empty lists with default values from pypsa if col in network.component_attrs[pypsa_name].index: - df_all.fillna( - network.component_attrs[pypsa_name].default[col], - inplace=True, - ) + + df_all.loc[df_all.anon_1.isnull(), "anon_1"] = df_all.loc[ + df_all.anon_1.isnull(), "anon_1"].apply(lambda x: + + [float(network.component_attrs[pypsa_name].default[col]) + ]*len(network.snapshots) + ) df = df_all.anon_1.apply(pd.Series).transpose() From c2aae166042c4f3936c9767836cd87eeec551bf5 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 28 Mar 2023 10:56:47 +0200 Subject: [PATCH 090/159] Move manual fixes of the datamodel to function --- etrago/appl.py | 36 +----------------------------------- etrago/tools/network.py | 5 +++++ etrago/tools/utilities.py | 39 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 45 insertions(+), 35 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index 003d31336..61108cf41 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -456,41 +456,7 @@ def run_etrago(args, json_path): # import network from database etrago.build_network_from_db() - etrago.network.lines.type = "" - etrago.network.storage_units.lifetime = np.inf - etrago.network.transformers.lifetime = 40 # only temporal fix - etrago.network.lines.lifetime = 40 # only temporal fix until either the - # PyPSA network clustering function - # is changed (taking the mean) or our - # data model is altered, which will - # happen in the next data creation run - - etrago.network.lines_t.s_max_pu = ( - etrago.network.lines_t.s_max_pu.transpose()[ - etrago.network.lines_t.s_max_pu.columns.isin( - etrago.network.lines.index - ) - ].transpose() - ) - - # Set gas grid links bidirectional - etrago.network.links.loc[ - etrago.network.links[etrago.network.links.carrier == "CH4"].index, - "p_min_pu", - ] = -1.0 - - # Set efficiences of CHP - etrago.network.links.loc[ - etrago.network.links[ - etrago.network.links.carrier.str.contains("CHP") - ].index, - "efficiency", - ] = 0.43 - - etrago.network.links_t.p_min_pu.fillna(0.0, inplace=True) - etrago.network.links_t.p_max_pu.fillna(1.0, inplace=True) - etrago.network.links_t.efficiency.fillna(1.0, inplace=True) - + # adjust network regarding eTraGo setting etrago.adjust_network() # ehv network clustering diff --git a/etrago/tools/network.py b/etrago/tools/network.py index d0d2a1330..34eb37545 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -78,6 +78,7 @@ get_args_setting, get_clustering_data, load_shedding, + manual_fixes_datamodel, set_branch_capacity, set_line_costs, set_q_foreign_loads, @@ -284,6 +285,8 @@ def __init__( get_clustering_data = get_clustering_data adjust_CH4_gen_carriers = adjust_CH4_gen_carriers + + manual_fixes_datamodel = manual_fixes_datamodel def dc_lines(self): return self.filter_links_by_carrier("DC", like=False) @@ -325,6 +328,8 @@ def adjust_network(self): None. """ + + self.manual_fixes_datamodel() self.geolocation_buses() diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index c5f0c0a9a..1eb5a7c8b 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -2612,3 +2612,42 @@ def residual_load(network, sector="electricity"): ) return loads_per_bus - renewable_dispatch + +def manual_fixes_datamodel(etrago): + """Apply temporal fixes to the data model until a new egon-data run is there + + Parameters + ---------- + etrago : :class:`Etrago + Overall container of Etrago + + Returns + ------- + None. + + """ + # Set line type + etrago.network.lines.type = "" + + # Set life time of storage_units, transformers and lines + etrago.network.storage_units.lifetime = 27.5 + etrago.network.transformers.lifetime = 40 + etrago.network.lines.lifetime = 40 + + # Set efficiences of CHP + etrago.network.links.loc[ + etrago.network.links[ + etrago.network.links.carrier.str.contains("CHP") + ].index, + "efficiency", + ] = 0.43 + + # Enlarge gas boilers as backup heat supply + etrago.network.links.loc[etrago.network.links[ + etrago.network.links.carrier.str.contains('gas_boiler')].index, 'p_nom'] *= 1000 + + # Set p_max_pu for run of river and reservoir + etrago.network.generators.loc[etrago.network.generators[ + etrago.network.generators.carrier.isin(["run_of_river", "reservoir"])].index, 'p_max_pu'] = 0.65 + + From 55f9dbdf6af7d9d12eec4094812c60e1746db92d Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 28 Mar 2023 10:59:24 +0200 Subject: [PATCH 091/159] Black and isort --- etrago/tools/io.py | 51 +++++++++++++++++++-------------------- etrago/tools/network.py | 4 +-- etrago/tools/utilities.py | 25 +++++++++++++------ 3 files changed, 44 insertions(+), 36 deletions(-) diff --git a/etrago/tools/io.py b/etrago/tools/io.py index 18413cc95..2f4aaa59d 100644 --- a/etrago/tools/io.py +++ b/etrago/tools/io.py @@ -50,20 +50,22 @@ __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" __author__ = "ulfmueller, mariusves" -import pypsa -from importlib import import_module -import pandas as pd from collections import OrderedDict +from importlib import import_module import json import os + import numpy as np +import pandas as pd +import pypsa if "READTHEDOCS" not in os.environ: + import logging + from geoalchemy2.shape import to_shape + from sqlalchemy import and_, create_engine, func, or_ from sqlalchemy.orm.exc import NoResultFound - from sqlalchemy import and_, func, or_, create_engine import saio - import logging logger = logging.getLogger(__name__) @@ -84,7 +86,6 @@ class ScenarioBase: """ def __init__(self, engine, session, version=None): - global carr_ormclass saio.register_schema("grid", engine) @@ -120,7 +121,6 @@ def __init__( temp_id=1, **kwargs, ): - self.scn_name = scn_name self.start_snapshot = start_snapshot self.end_snapshot = end_snapshot @@ -181,7 +181,6 @@ def configure_timeindex(self): Index of snapshots or timesteps. """ def id_to_source(self): - ormclass = self._mapped["Source"] query = self.session.query(ormclass) @@ -207,7 +206,6 @@ def fetch_by_relname(self, name): from saio.grid import ( egon_etrago_bus, egon_etrago_generator, - egon_etrago_load, egon_etrago_line, egon_etrago_link, egon_etrago_load, @@ -263,7 +261,6 @@ def series_fetch_by_relname(self, network, name, pypsa_name): from saio.grid import ( egon_etrago_bus_timeseries, egon_etrago_generator_timeseries, - egon_etrago_load_timeseries, egon_etrago_line_timeseries, egon_etrago_link_timeseries, egon_etrago_load_timeseries, @@ -318,18 +315,22 @@ def series_fetch_by_relname(self, network, name, pypsa_name): df_all.set_index(index_col, inplace=True) df_all.index = df_all.index.astype(str) - - if not df_all.isnull().all().all(): + if not df_all.isnull().all().all(): # Fill empty lists with default values from pypsa if col in network.component_attrs[pypsa_name].index: - df_all.loc[df_all.anon_1.isnull(), "anon_1"] = df_all.loc[ - df_all.anon_1.isnull(), "anon_1"].apply(lambda x: - - [float(network.component_attrs[pypsa_name].default[col]) - ]*len(network.snapshots) - ) + df_all.anon_1.isnull(), "anon_1" + ].apply( + lambda x: [ + float( + network.component_attrs[pypsa_name].default[ + col + ] + ) + ] + * len(network.snapshots) + ) df = df_all.anon_1.apply(pd.Series).transpose() @@ -360,7 +361,6 @@ def build_network(self, network=None, *args, **kwargs): "Storage", "Store", ]: - pypsa_comp = "StorageUnit" if comp == "Storage" else comp if comp[-1] == "s": @@ -404,17 +404,17 @@ def clear_results_db(session): from egoio.db_tables.model_draft import ( EgoGridPfHvResultBus as BusResult, EgoGridPfHvResultBusT as BusTResult, - EgoGridPfHvResultStorage as StorageResult, - EgoGridPfHvResultStorageT as StorageTResult, EgoGridPfHvResultGenerator as GeneratorResult, EgoGridPfHvResultGeneratorT as GeneratorTResult, EgoGridPfHvResultLine as LineResult, EgoGridPfHvResultLineT as LineTResult, EgoGridPfHvResultLoad as LoadResult, EgoGridPfHvResultLoadT as LoadTResult, + EgoGridPfHvResultMeta as ResultMeta, + EgoGridPfHvResultStorage as StorageResult, + EgoGridPfHvResultStorageT as StorageTResult, EgoGridPfHvResultTransformer as TransformerResult, EgoGridPfHvResultTransformerT as TransformerTResult, - EgoGridPfHvResultMeta as ResultMeta, ) print("Are you sure that you want to clear all results in the OEDB?") @@ -489,17 +489,17 @@ def results_to_oedb(session, network, args, grid="hv", safe_results=False): from egoio.db_tables.model_draft import ( EgoGridPfHvResultBus as BusResult, EgoGridPfHvResultBusT as BusTResult, - EgoGridPfHvResultStorage as StorageResult, - EgoGridPfHvResultStorageT as StorageTResult, EgoGridPfHvResultGenerator as GeneratorResult, EgoGridPfHvResultGeneratorT as GeneratorTResult, EgoGridPfHvResultLine as LineResult, EgoGridPfHvResultLineT as LineTResult, EgoGridPfHvResultLoad as LoadResult, EgoGridPfHvResultLoadT as LoadTResult, + EgoGridPfHvResultMeta as ResultMeta, + EgoGridPfHvResultStorage as StorageResult, + EgoGridPfHvResultStorageT as StorageTResult, EgoGridPfHvResultTransformer as TransformerResult, EgoGridPfHvResultTransformerT as TransformerTResult, - EgoGridPfHvResultMeta as ResultMeta, EgoGridPfHvSource as Source, ) else: @@ -793,7 +793,6 @@ def extension(self, **kwargs): """ if self.args["scn_extension"] is not None: - if self.args["gridversion"] is None: ormcls_prefix = "EgoGridPfHvExtension" else: diff --git a/etrago/tools/network.py b/etrago/tools/network.py index 34eb37545..f1bb63c95 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -285,7 +285,7 @@ def __init__( get_clustering_data = get_clustering_data adjust_CH4_gen_carriers = adjust_CH4_gen_carriers - + manual_fixes_datamodel = manual_fixes_datamodel def dc_lines(self): @@ -328,7 +328,7 @@ def adjust_network(self): None. """ - + self.manual_fixes_datamodel() self.geolocation_buses() diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 1eb5a7c8b..934c7d69d 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -2613,6 +2613,7 @@ def residual_load(network, sector="electricity"): return loads_per_bus - renewable_dispatch + def manual_fixes_datamodel(etrago): """Apply temporal fixes to the data model until a new egon-data run is there @@ -2628,11 +2629,11 @@ def manual_fixes_datamodel(etrago): """ # Set line type etrago.network.lines.type = "" - + # Set life time of storage_units, transformers and lines etrago.network.storage_units.lifetime = 27.5 etrago.network.transformers.lifetime = 40 - etrago.network.lines.lifetime = 40 + etrago.network.lines.lifetime = 40 # Set efficiences of CHP etrago.network.links.loc[ @@ -2643,11 +2644,19 @@ def manual_fixes_datamodel(etrago): ] = 0.43 # Enlarge gas boilers as backup heat supply - etrago.network.links.loc[etrago.network.links[ - etrago.network.links.carrier.str.contains('gas_boiler')].index, 'p_nom'] *= 1000 + etrago.network.links.loc[ + etrago.network.links[ + etrago.network.links.carrier.str.contains("gas_boiler") + ].index, + "p_nom", + ] *= 1000 # Set p_max_pu for run of river and reservoir - etrago.network.generators.loc[etrago.network.generators[ - etrago.network.generators.carrier.isin(["run_of_river", "reservoir"])].index, 'p_max_pu'] = 0.65 - - + etrago.network.generators.loc[ + etrago.network.generators[ + etrago.network.generators.carrier.isin( + ["run_of_river", "reservoir"] + ) + ].index, + "p_max_pu", + ] = 0.65 From 81e2b4e82085e9a7e57316d1a51545c561e839e9 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 28 Mar 2023 11:07:42 +0200 Subject: [PATCH 092/159] Fix some flake8 issues --- etrago/tools/io.py | 48 ++++++++++++++++++++++++++++------------------ 1 file changed, 29 insertions(+), 19 deletions(-) diff --git a/etrago/tools/io.py b/etrago/tools/io.py index 2f4aaa59d..b8fc68047 100644 --- a/etrago/tools/io.py +++ b/etrago/tools/io.py @@ -50,9 +50,7 @@ __license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)" __author__ = "ulfmueller, mariusves" -from collections import OrderedDict from importlib import import_module -import json import os import numpy as np @@ -62,8 +60,6 @@ if "READTHEDOCS" not in os.environ: import logging - from geoalchemy2.shape import to_shape - from sqlalchemy import and_, create_engine, func, or_ from sqlalchemy.orm.exc import NoResultFound import saio @@ -139,7 +135,9 @@ def __repr__(self): r = "NetworkScenario: %s" % self.scn_name if not self.network: - r += "\nTo create a PyPSA network call .build_network()." + r += """ + \nTo create a PyPSA network call .build_network(). + """ return r @@ -344,7 +342,7 @@ def series_fetch_by_relname(self, network, name, pypsa_name): def build_network(self, network=None, *args, **kwargs): """Core method to construct PyPSA Network object.""" - if network != None: + if network is not None: network = network else: @@ -468,7 +466,7 @@ def results_to_oedb(session, network, args, grid="hv", safe_results=False): """ # Update generator_ids when k_means clustering to get integer ids - if args["network_clustering_kmeans"] != False: + if args["network_clustering_kmeans"]: new_index = pd.DataFrame(index=network.generators.index) new_index["new"] = range(len(network.generators)) @@ -765,26 +763,35 @@ def run_sql_script(conn, scriptname="results_md2grid.sql"): def extension(self, **kwargs): """ Function that adds an additional network to the existing network container. - The new network can include every PyPSA-component (e.g. buses, lines, links). + The new network can include every PyPSA-component (e.g. buses, lines, + links). To connect it to the existing network, transformers are needed. - All components and its timeseries of the additional scenario need to be inserted in the fitting 'model_draft.ego_grid_pf_hv_extension_' table. - The scn_name in the tables have to be labled with 'extension_' + scn_name (e.g. 'extension_nep2035'). + All components and its timeseries of the additional scenario need to be + inserted in the fitting 'model_draft.ego_grid_pf_hv_extension_' table. + The scn_name in the tables have to be labled with 'extension_' + scn_name + (e.g. 'extension_nep2035'). Until now, the tables include three additional scenarios: - 'nep2035_confirmed': all new lines and needed transformers planed in the 'Netzentwicklungsplan 2035' (NEP2035) that have been confirmed by the Bundesnetzagentur (BNetzA) + 'nep2035_confirmed': all new lines and needed transformers planed in the + 'Netzentwicklungsplan 2035' (NEP2035) that have been confirmed by the + Bundesnetzagentur (BNetzA) - 'nep2035_b2': all new lines and needed transformers planned in the NEP 2035 in the scenario 2035 B2 + 'nep2035_b2': all new lines and needed transformers planned in the NEP 2035 + in the scenario 2035 B2 - 'BE_NO_NEP 2035': DC-lines and transformers to connect the upcomming electrical-neighbours Belgium and Norway - Generation, loads and its timeseries in Belgium and Norway for scenario 'NEP 2035' + 'BE_NO_NEP 2035': DC-lines and transformers to connect the upcomming + electrical-neighbours Belgium and Norway + Generation, loads and its timeseries in Belgium and Norway for scenario + 'NEP 2035' Parameters ----- network : The existing network container (e.g. scenario 'NEP 2035') session : session-data - overlay_scn_name : Name of the additional scenario (WITHOUT 'extension_') + overlay_scn_name : Name of the additional scenario + (WITHOUT 'extension_') start_snapshot, end_snapshot: Simulation time Returns @@ -844,7 +851,7 @@ def decommissioning(self, **kwargs): """ if self.args["scn_decommissioning"] is not None: - if self.args["gridversion"] == None: + if self.args["gridversion"] is None: ormclass = getattr( import_module("egoio.db_tables.model_draft"), "EgoGridPfHvExtensionLine", @@ -878,7 +885,7 @@ def decommissioning(self, **kwargs): self.network.lines.index == idx ] = self.network.lines.s_nom_min - ### Drop decommissioning-lines from existing network + # Drop decommissioning-lines from existing network self.network.lines = self.network.lines[ ~self.network.lines.index.isin(df_decommisionning.index) ] @@ -912,7 +919,8 @@ def distance(x0, x1, y0, y1): def calc_nearest_point(bus1, network): """ - Function that finds the geographical nearest point in a network from a given bus. + Function that finds the geographical nearest point in a network from a + given bus. Parameters @@ -989,7 +997,9 @@ def add_ch4_h2_correspondence(self): """ - sql = f"""SELECT "bus_H2", "bus_CH4", scn_name FROM grid.egon_etrago_ch4_h2;""" + sql = """ + SELECT "bus_H2", "bus_CH4", scn_name FROM grid.egon_etrago_ch4_h2; + """ table = pd.read_sql(sql, self.engine) From 3ea43e700e65388304899c0dc8b2ee79ebfc5f00 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 28 Mar 2023 11:12:20 +0200 Subject: [PATCH 093/159] Fix flake8 issue --- etrago/tools/utilities.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 934c7d69d..3202b89c9 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -2615,7 +2615,8 @@ def residual_load(network, sector="electricity"): def manual_fixes_datamodel(etrago): - """Apply temporal fixes to the data model until a new egon-data run is there + """Apply temporal fixes to the data model until a new egon-data run + is there Parameters ---------- From e273fad6b230ad68e6f1a6ad8872d62b1ac5571c Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 28 Mar 2023 11:37:27 +0200 Subject: [PATCH 094/159] fix tutorial problem try1 --- doc/howToUse.rst | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/doc/howToUse.rst b/doc/howToUse.rst index 856ae3b91..629422367 100644 --- a/doc/howToUse.rst +++ b/doc/howToUse.rst @@ -52,9 +52,7 @@ Examples and tutorial notebooks -.. toctree:: - :maxdepth: 7 - eTraGo version 0.5.1: - OpenMod - eTraGo version 0.9: - Tutorial notebook will be available in the next release. +eTraGo version 0.5.1: +OpenMod +eTraGo version 0.9: +Tutorial notebook will be available in the next release. From d7ebfbc35ab262794ad6e012726903c6e427206e Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 28 Mar 2023 12:07:28 +0200 Subject: [PATCH 095/159] fix tutorial problem try2 --- doc/howToUse.rst | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/doc/howToUse.rst b/doc/howToUse.rst index 629422367..6f375e610 100644 --- a/doc/howToUse.rst +++ b/doc/howToUse.rst @@ -52,7 +52,8 @@ Examples and tutorial notebooks -eTraGo version 0.5.1: -OpenMod -eTraGo version 0.9: +**eTraGo version 0.5.1:** +`etrago_OpenMod_Zuerich18.ipynb ` + +**eTraGo version 0.9:** Tutorial notebook will be available in the next release. From 1ac1ebbfa9e1c6b12453a7a637fd47a2e321b149 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 28 Mar 2023 12:10:47 +0200 Subject: [PATCH 096/159] fix tutorial problem try3 --- doc/howToUse.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/howToUse.rst b/doc/howToUse.rst index 6f375e610..063266487 100644 --- a/doc/howToUse.rst +++ b/doc/howToUse.rst @@ -53,7 +53,7 @@ Examples and tutorial notebooks **eTraGo version 0.5.1:** -`etrago_OpenMod_Zuerich18.ipynb ` +etrago_OpenMod_Zuerich18.ipynb **eTraGo version 0.9:** Tutorial notebook will be available in the next release. From e50123617a5df2f44a1584f16bcab39aed860702 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 28 Mar 2023 12:13:05 +0200 Subject: [PATCH 097/159] fix tutorial problem try4 --- doc/howToUse.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/howToUse.rst b/doc/howToUse.rst index 063266487..d3d1a687a 100644 --- a/doc/howToUse.rst +++ b/doc/howToUse.rst @@ -53,7 +53,9 @@ Examples and tutorial notebooks **eTraGo version 0.5.1:** -etrago_OpenMod_Zuerich18.ipynb + +`etrago_OpenMod_Zuerich18.ipynb ` **eTraGo version 0.9:** + Tutorial notebook will be available in the next release. From 79ff9b709e331590b91ec1fbdf04888eb7948919 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 28 Mar 2023 12:16:35 +0200 Subject: [PATCH 098/159] fix tutorial problem try5 --- doc/howToUse.rst | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/doc/howToUse.rst b/doc/howToUse.rst index d3d1a687a..2fb7b3939 100644 --- a/doc/howToUse.rst +++ b/doc/howToUse.rst @@ -53,9 +53,6 @@ Examples and tutorial notebooks **eTraGo version 0.5.1:** +`etrago_OpenMod_Zuerich18 `_. -`etrago_OpenMod_Zuerich18.ipynb ` - -**eTraGo version 0.9:** - -Tutorial notebook will be available in the next release. +**eTraGo version 0.9:** Tutorial notebook will be available in the next release. From 0ca785e2fd6630167b51cd46c40cb985c1af489c Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Mon, 3 Apr 2023 14:13:52 +0200 Subject: [PATCH 099/159] create function arrange_dc_bus0_bus1 --- etrago/cluster/spatial.py | 31 +++++++++++++++++++++++-------- 1 file changed, 23 insertions(+), 8 deletions(-) diff --git a/etrago/cluster/spatial.py b/etrago/cluster/spatial.py index 9b7197fd5..6ed5b9327 100755 --- a/etrago/cluster/spatial.py +++ b/etrago/cluster/spatial.py @@ -169,13 +169,6 @@ def group_links(network, with_time=True, carriers=None, cus_strateg=dict()): new_df : links aggregated based on bus0, bus1 and carrier new_pnl : links time series aggregated """ - if carriers is None: - carriers = network.links.carrier.unique() - - links_agg_b = network.links.carrier.isin(carriers) - links = network.links.loc[links_agg_b] - grouper = [links.bus0, links.bus1, links.carrier] - def normed_or_uniform(x): return ( x / x.sum() @@ -183,6 +176,28 @@ def normed_or_uniform(x): else pd.Series(1.0 / len(x), x.index) ) + def arrange_dc_bus0_bus1(network): + dc_links = network.links[network.links.carrier == "DC"].copy() + dc_links["n0"] = dc_links.apply(lambda x: x.bus0 if x.bus0 < x.bus1 else x.bus1, axis = 1) + dc_links["n1"] = dc_links.apply(lambda x: x.bus0 if x.bus0 > x.bus1 else x.bus1, axis = 1) + dc_links["bus0"] = dc_links["n0"] + dc_links["bus1"] = dc_links["n1"] + dc_links.drop(columns = ["n0","n1"], inplace=True) + + network.links.drop(index=dc_links.index, inplace=True) + network.links = pd.concat([network.links, dc_links]) + + return network + + network = arrange_dc_bus0_bus1(network) + + if carriers is None: + carriers = network.links.carrier.unique() + + links_agg_b = network.links.carrier.isin(carriers) + links = network.links.loc[links_agg_b] + grouper = [links.bus0, links.bus1, links.carrier] + weighting = links.p_nom.groupby(grouper, axis=0).transform( normed_or_uniform ) @@ -218,7 +233,7 @@ def normed_or_uniform(x): new_pnl[attr].columns = new_pnl[attr].columns.map(cluster_id) else: new_pnl[attr] = network.links_t[attr] - + new_pnl = pypsa.descriptors.Dict(new_pnl) return new_df, new_pnl From 5bbd5565a1a801c793b9fe76c8c2ad3e43591013 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 4 Apr 2023 11:31:19 +0200 Subject: [PATCH 100/159] Update e-Mobility carrier names in temporal disaggregation --- etrago/tools/constraints.py | 2 +- etrago/tools/execute.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index 33fde3b6f..e48decc4c 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -2559,7 +2559,7 @@ def split_dispatch_disaggregation_constraints(self, n, sns): sus = n.storage_units.index # for stores, exclude emob and dsm because of their special constraints sto = n.stores[ - (n.stores.carrier != "battery storage") & (n.stores.carrier != "dsm") + ~n.stores.carrier.isin(["battery storage", "battery_storage", "dsm"]) ].index def disaggregation_sus_soc(m, s, h): diff --git a/etrago/tools/execute.py b/etrago/tools/execute.py index ebc9ead0b..aad898425 100755 --- a/etrago/tools/execute.py +++ b/etrago/tools/execute.py @@ -435,8 +435,8 @@ def dispatch_disaggregation(self): ) # for stores, exclude emob and dsm because of their special constraints sto = self.network.stores[ - (self.network.stores.carrier != "battery storage") - & (self.network.stores.carrier != "dsm") + ~self.network.stores.carrier.isin( + ["battery_storage", "battery storage", "dsm"]) ] # save state of charge of storage units and stores at those transition snapshots From b6ca76c301cb1f5284d4b597c1e851b52ef28d75 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 4 Apr 2023 11:33:06 +0200 Subject: [PATCH 101/159] Black --- etrago/tools/execute.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/etrago/tools/execute.py b/etrago/tools/execute.py index aad898425..f7a13412a 100755 --- a/etrago/tools/execute.py +++ b/etrago/tools/execute.py @@ -360,9 +360,7 @@ def iterate_lopf( run_lopf(etrago, extra_functionality, method) if not args["lpfile"] is False: - network.model.write( - lp_path - ) + network.model.write(lp_path) return network @@ -436,7 +434,8 @@ def dispatch_disaggregation(self): # for stores, exclude emob and dsm because of their special constraints sto = self.network.stores[ ~self.network.stores.carrier.isin( - ["battery_storage", "battery storage", "dsm"]) + ["battery_storage", "battery storage", "dsm"] + ) ] # save state of charge of storage units and stores at those transition snapshots From 34a59662338a3fd64ab2cebb454f8c428f76c1e7 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 4 Apr 2023 14:22:00 +0200 Subject: [PATCH 102/159] using black and flake8 --- etrago/cluster/spatial.py | 52 ++++++++++++++++++++------------------- 1 file changed, 27 insertions(+), 25 deletions(-) diff --git a/etrago/cluster/spatial.py b/etrago/cluster/spatial.py index 6ed5b9327..f8f641052 100755 --- a/etrago/cluster/spatial.py +++ b/etrago/cluster/spatial.py @@ -160,7 +160,8 @@ def group_links(network, with_time=True, carriers=None, cus_strateg=dict()): with_time : bool says if the network object contains timedependent series. carriers : list of strings - Describe which typed of carriers should be aggregated. The default is None. + Describe which typed of carriers should be aggregated. The default + is None. strategies : dictionary custom strategies to perform the aggregation @@ -169,6 +170,7 @@ def group_links(network, with_time=True, carriers=None, cus_strateg=dict()): new_df : links aggregated based on bus0, bus1 and carrier new_pnl : links time series aggregated """ + def normed_or_uniform(x): return ( x / x.sum() @@ -178,11 +180,15 @@ def normed_or_uniform(x): def arrange_dc_bus0_bus1(network): dc_links = network.links[network.links.carrier == "DC"].copy() - dc_links["n0"] = dc_links.apply(lambda x: x.bus0 if x.bus0 < x.bus1 else x.bus1, axis = 1) - dc_links["n1"] = dc_links.apply(lambda x: x.bus0 if x.bus0 > x.bus1 else x.bus1, axis = 1) + dc_links["n0"] = dc_links.apply( + lambda x: x.bus0 if x.bus0 < x.bus1 else x.bus1, axis=1 + ) + dc_links["n1"] = dc_links.apply( + lambda x: x.bus0 if x.bus0 > x.bus1 else x.bus1, axis=1 + ) dc_links["bus0"] = dc_links["n0"] dc_links["bus1"] = dc_links["n1"] - dc_links.drop(columns = ["n0","n1"], inplace=True) + dc_links.drop(columns=["n0", "n1"], inplace=True) network.links.drop(index=dc_links.index, inplace=True) network.links = pd.concat([network.links, dc_links]) @@ -233,7 +239,7 @@ def arrange_dc_bus0_bus1(network): new_pnl[attr].columns = new_pnl[attr].columns.map(cluster_id) else: new_pnl[attr] = network.links_t[attr] - + new_pnl = pypsa.descriptors.Dict(new_pnl) return new_df, new_pnl @@ -256,7 +262,6 @@ def graph_from_edges(edges): M = nx.MultiGraph() for e in edges: - n0, n1, weight, key = e M.add_edge(n0, n1, weight=weight, key=key) @@ -288,7 +293,7 @@ def gen(nodes, n, graph): g = graph.copy() for i in range(0, len(nodes), n): - yield (nodes[i : i + n], g) + yield (nodes[i: i + n], g) def shortest_path(paths, graph): @@ -499,7 +504,6 @@ def busmap_from_psql(etrago): filter_version = "testcase" def fetch(): - query = ( etrago.session.query( egon_etrago_hv_busmap.bus0, egon_etrago_hv_busmap.bus1 @@ -576,7 +580,6 @@ def kmean_clustering(etrago, selected_network, weight, n_clusters): kmean_settings = etrago.args["network_clustering"] with threadpool_limits(limits=kmean_settings["CPU_cores"], user_api=None): - # remove stubs if kmean_settings["remove_stubs"]: network.determine_network_topology() @@ -622,8 +625,8 @@ def kmean_clustering(etrago, selected_network, weight, n_clusters): def dijkstras_algorithm(buses, connections, medoid_idx, cpu_cores): - """Function for combination of k-medoids Clustering and Dijkstra's algorithm. - Creates a busmap assigning the nodes of a original network + """Function for combination of k-medoids Clustering and Dijkstra's + algorithm. Creates a busmap assigning the nodes of a original network to the nodes of a clustered network considering the electrical distances based on Dijkstra's shortest path. Parameters @@ -665,8 +668,8 @@ def dijkstras_algorithm(buses, connections, medoid_idx, cpu_cores): else: cpu_cores = int(cpu_cores) - # calculation of shortest path between original points and k-medoids centers - # using multiprocessing + # calculation of shortest path between original points and k-medoids + # centers using multiprocessing p = mp.Pool(cpu_cores) chunksize = ceil(len(ppathss) / cpu_cores) container = p.starmap(shortest_path, gen(ppathss, chunksize, M)) @@ -701,26 +704,25 @@ def dijkstras_algorithm(buses, connections, medoid_idx, cpu_cores): def kmedoids_dijkstra_clustering( etrago, buses, connections, weight, n_clusters ): - settings = etrago.args["network_clustering"] # n_jobs was deprecated for the function fit(). scikit-learn recommends - # to use threadpool_limits: https://scikit-learn.org/stable/computing/parallelism.html + # to use threadpool_limits: + #https://scikit-learn.org/stable/computing/parallelism.html with threadpool_limits(limits=settings["CPU_cores"], user_api=None): - # remove stubs if settings["remove_stubs"]: - logger.info( - "options remove_stubs and use_reduced_coordinates not reasonable for k-medoids Dijkstra Clustering" - ) + """options remove_stubs and use_reduced_coordinates not + reasonable for k-medoids Dijkstra Clustering""" + ) bus_weightings = pd.Series(weight) buses_i = buses.index points = buses.loc[buses_i, ["x", "y"]].values.repeat( bus_weightings.reindex(buses_i).astype(int), axis=0 ) - + kmeans = KMeans( init="k-means++", n_clusters=n_clusters, @@ -730,24 +732,24 @@ def kmedoids_dijkstra_clustering( random_state=settings["random_state"], ) kmeans.fit(points) - + busmap = pd.Series( data=kmeans.predict(buses.loc[buses_i, ["x", "y"]]), index=buses_i, dtype=object, ) - + # identify medoids per cluster -> k-medoids clustering - + distances = pd.DataFrame( data=kmeans.transform(buses.loc[buses_i, ["x", "y"]].values), index=buses_i, dtype=object, ) distances = distances.apply(pd.to_numeric) - + medoid_idx = distances.idxmin() - + # dijkstra's algorithm busmap = dijkstras_algorithm( buses, From f077c57dde863623aaf88daec273f4d8ea19af87 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Wed, 5 Apr 2023 17:01:51 +0200 Subject: [PATCH 103/159] Solve merge conflict and adapt documentation of ehv clustering (load_shedding-issue) --- etrago/cluster/electrical.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index 007784625..85e29fee9 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -147,14 +147,8 @@ def adjust_no_electric_network(etrago, busmap, cluster_met): # eHV network busmap2 = {} -<<<<<<< HEAD - # Map crossborder AC buses in case that they were not part of the - # k-mean clustering - if not (etrago.args["network_clustering"]["cluster_foreign_AC"]) & ( -======= # Map crossborder AC buses in case that they were not part of the k-mean clustering if (not etrago.args["network_clustering"]["cluster_foreign_AC"]) & ( ->>>>>>> cb2d378071a6d314386a62194d10b9fe25a021bc cluster_met in ["kmeans", "kmedoids-dijkstra"] ): buses_orig = network.buses.copy() @@ -458,8 +452,7 @@ def ehv_clustering(self): Cluster the network based on Extra High Voltage (EHV) grid. If `network_clustering_ehv` argument is True, the function clusters the - network based on the EHV grid. If `network_clustering` argument is not - active, it calls the `load_shedding` function to apply load shedding. + network based on the EHV grid. Parameters ---------- From f3786865d065b071cb794455fab24646158fe739 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Wed, 5 Apr 2023 17:02:31 +0200 Subject: [PATCH 104/159] Correct some sentences and apply American English --- doc/theoretical_background.rst | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/doc/theoretical_background.rst b/doc/theoretical_background.rst index 7663224d1..03bb9eb43 100644 --- a/doc/theoretical_background.rst +++ b/doc/theoretical_background.rst @@ -13,11 +13,11 @@ eTraGo is based on the open source tool `PyPSA `_. The data includes electricity and gas grid topology as well as data on energy supply and load for the considered sectors (electricity, gas, heat and e-mobility) and data on additional electrical flexibilities such as Dynamic Line Rating or Demand Side Management. More details on the data model can be found in the documentaton of `eGon-data `_. +eTraGo fetches the input data from the `OpenEnergy Platform `_. The data includes electricity and gas grid topology as well as data on energy supply and load for the considered sectors (electricity, gas, heat and e-mobility) plus data on flexibility potential deriving from those sectors e.g. Dynamic Line Rating, Demand Side Management and flexibility potentials arising from e-mobility. More details on the data model can be found in the documentaton of `eGon-data `_. -At the moment, there are two scenarios available basing on scenario C2035 of the network expansion plan ([NEP]_), version 2021. The base one is called eGon2035. To analyse the effect of flexibility options, there is an eGon2035_lowflex scenario available which depicts a lower penetration of flexibilities. More scenarios are being developed. The eGon100RE scenario is being implemented which is characterised by a 100% renewable generation. Analog to the scenario above, a eGon100RE_lowflex scenario is available. +At the moment, there are two scenarios available basing on scenario C2035 of the network expansion plan ([NEP]_), version 2021. The base one is called eGon2035. To analyse the effect of flexibility options, there is an eGon2035_lowflex scenario available which depicts a lower penetration of flexibilities. More scenarios are being developed. The eGon100RE scenario is being implemented which is characterised by a 100% renewable generation. Analog to the scenario above, a eGon100RE_lowflex scenario will be available. -You can see the modelling concepts of the scenarios in the figure below. The components marked green have exogenous capacity and endogenous dispatch whereas the components marked in red are optimized endogenously in capacity and dispatch. +You can see the modeling concepts of the scenarios in the figure below. The components marked green have exogenous capacity and endogenous dispatch whereas the components marked in red are optimised endogenously in capacity and dispatch. .. figure:: images/modelling_concept.png :align: center @@ -28,10 +28,10 @@ Methods ======= -Optimization with PyPSA +Optimisation with PyPSA ----------------------- -Within eTraGo, the fetched data model is translated into a `PyPSA `_-network. The power flow simulations and optimizations are performed with a linear approximation assuming eTraGo to fulfill the assumptions to perfom a LOPF (as those are small voltage angle differences, branch resistances negligible to their reactances, voltage magnitudes can be kept at nominal values) since it focuses on the extra-high and high voltage levels. As objective value of the optimization, the overall system costs are considered. +Within eTraGo, the fetched data model is translated into a `PyPSA `_-network. The optimisation is performed with a linear approximation assuming eTraGo to fulfill the assumptions to perfom a LOPF (as those are small voltage angle differences, branch resistances negligible to their reactances, voltage magnitudes can be kept at nominal values) since it focuses on the extra-high and high voltage levels. As objective value of the optimisation, the overall system costs are considered. With the argument ‘pf_post_lopf’, after the LOPF a non-linear power flow simulation can be conducted. @@ -39,7 +39,7 @@ With the argument ‘pf_post_lopf’, after the LOPF a non-linear power flow sim Complexity Reduction --------------------- -The data model is characterised by a high spatial (abou 8,000 electrical and 600 gas nodes) and temporal resolution (8,760 timesteps). To reduce the complexity of the resulting optimization problem, several methods can be applied. +The data model is characterised by a high spatial (abou 8,000 electrical and 600 gas nodes) and temporal resolution (8,760 timesteps). To reduce the complexity of the resulting optimisation problem, several methods can be applied. Reduction in spatial dimension: @@ -72,9 +72,9 @@ By applying a 2-level-approach, a **temporal disaggregation** can be conducted. Grid and Storage / Store expansion ----------------------------------- -The grid expansion is realized by extending the capacities of existing lines and substations. These capacities are considered as part of the optimization problem whereby the possible extension is unlimited. With respect to the different voltage levels and lengths, MVA-specific costs are considered in the optimization. +The grid expansion is realized by extending the capacities of existing lines and substations. These capacities are considered as part of the optimisation problem whereby the possible extension is unlimited. With respect to the different voltage levels and lengths, MVA-specific costs are considered in the optimisation. -As shown in the figure above, several options to store energy are part of the modelling concept. Extendable batteries (modelled as storage units) are assigned to every node in the electrical grid. A minimum installed capacity is being considered to account for home batteries ([NEP]_). The expansion and operation is part of the optimization. Furthermore, two types of hydrogen stores (modelled as stores) are available. Overground stores are optimised in operation and dispatch without limitations whereas underground stores depicting saltcaverns are limited by geographical conditions ([BGR]_). Additionally, heat stores part of the optimisation in terms of power and energy without upper limits. +As shown in the figure above, several options to store energy are part of the modeling concept. Extendable batteries (modeled as storage units) are assigned to every node in the electrical grid. A minimum installed capacity is being considered to account for home batteries ([NEP]_). The expansion and operation is part of the optimisation. Furthermore, two types of hydrogen stores (modeled as stores) are available. Overground stores are optimised in operation and dispatch without limitations whereas underground stores depicting saltcaverns are limited by geographical conditions ([BGR]_). Additionally, heat stores part of the optimisation in terms of power and energy without upper limits. Miscellaneous Features @@ -84,15 +84,15 @@ Several features were developed to enhance the functionality of eTraGo. To customize computation settings, ‘solver_options’ and ‘generator_noise’ should be adapted. The latter adds a reproducible small random noise to the marginal costs of each generator in order to prevent an optima plateau. The specific solver options depend on the applied solver (e.g. Gurobi, CPLEX or GLPK). -In ‚extendable‘ you can adapt the type of components you want to be optimized in capacity and set upper limits for gird expansion inside Germany and of lines to foreign countries. +In ‚extendable‘ you can adapt the type of components you want to be optimised in capacity and set upper limits for gird expansion inside Germany and of lines to foreign countries. The ‚extra_functionality‘-argument allows to consider extra constraints like limits for energy imort and export or minimal renewable shares in generation. ‘branch_capacity_factor’ adds a factor to adapt all line capacities in order to consider (n-1) security. Because the average number of HV systems is much smaller than the one of eHV lines, you can choose factors for ‘HV’ and ‘eHV’ separately. -The ‘load_shedding’-argument is used for debugging complex grids in order to avoid infeasibilities. It introduces a very expensive generator at each bus to meet the demand. When optimizing storage units and grid expansion without limiting constraints, the need for load shedding should not be existent. +The ‘load_shedding’-argument is used for debugging complex grids in order to avoid infeasibilities. It introduces a very expensive generator at each bus to meet the demand. When optimising storage units and grid expansion without limiting constraints, the need for load shedding should not be existent. -With ‘foreign_lines‘ you can adapt the foreign lines to be modelled as DC-links (e.g. to avoid loop flows). +With ‘foreign_lines‘ you can adapt the foreign lines to be modeled as DC-links (e.g. to avoid loop flows). References From 12471ebf43e39205b1e57b521438abddd2817d0c Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Wed, 5 Apr 2023 17:03:06 +0200 Subject: [PATCH 105/159] Correct year typo --- etrago/appl.py | 2 +- etrago/cluster/snapshot.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index 3c61b0ed4..eda1d9cb8 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2013 Flensburg University of Applied Sciences, +# Copyright 2016-2023 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems, # DLR-Institute for Networked Energy Systems diff --git a/etrago/cluster/snapshot.py b/etrago/cluster/snapshot.py index 80ce9e809..daa0b6166 100644 --- a/etrago/cluster/snapshot.py +++ b/etrago/cluster/snapshot.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# Copyright 2016-2013 Flensburg University of Applied Sciences, +# Copyright 2016-2023 Flensburg University of Applied Sciences, # Europa-Universität Flensburg, # Centre for Sustainable Energy Systems From 24ef8b2c83079a9eb0f5dd351f57d3374f135d38 Mon Sep 17 00:00:00 2001 From: Tetens Date: Tue, 11 Apr 2023 09:51:36 +0200 Subject: [PATCH 106/159] updated python versions in installation.rst --- doc/installation.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/installation.rst b/doc/installation.rst index 33ec0c660..a62a615a3 100644 --- a/doc/installation.rst +++ b/doc/installation.rst @@ -20,7 +20,7 @@ you create a virtual environment (where you like it) and activate it: .. code-block:: bash - $ virtualenv venv --clear -p python3.7 + $ virtualenv venv --clear -p python3.8 $ source venv/bin/activate $ cd venv @@ -40,7 +40,7 @@ Windows or Mac OSX users ======================== For Windows and/or Mac OSX user we highly recommend to install and use Anaconda -for your Python3 installation. First install Conda including python 3.7 or +for your Python3 installation. First install Conda including python 3.8 or higher version from https://www.anaconda.com/download/ and open an anaconda prompt as administrator and run: From c49eaae174c815092e487890cab5f624df540c44 Mon Sep 17 00:00:00 2001 From: Tetens Date: Tue, 11 Apr 2023 09:54:22 +0200 Subject: [PATCH 107/159] changed python installation version to 3.8 --- doc/developer_notes.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/developer_notes.rst b/doc/developer_notes.rst index 37a3bc4ff..a4a3d036a 100644 --- a/doc/developer_notes.rst +++ b/doc/developer_notes.rst @@ -15,7 +15,7 @@ Installation for Developers .. code-block:: bash - $ virtualenv --clear -p python3.7 etrago`` + $ virtualenv --clear -p python3.8 etrago`` $ cd etrago/ $ source bin/activate From 1296a35523759e9abd7dd947949308075a7e2687 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 11 Apr 2023 09:59:01 +0200 Subject: [PATCH 108/159] do not generate geometries for lines --- etrago/tools/utilities.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 3202b89c9..51f787cc7 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -1150,18 +1150,6 @@ def count(bus): l_new = agg_series_lines(lines_group, network) l_new["bus0"] = new_lines.at[l, "bus0"] l_new["bus1"] = new_lines.at[l, "bus1"] - l_new["geom"] = LineString( - [ - ( - network.buses.at[l_new["bus0"], "x"], - network.buses.at[l_new["bus0"], "y"], - ), - ( - network.buses.at[l_new["bus1"], "x"], - network.buses.at[l_new["bus1"], "y"], - ), - ] - ) new_lines_df["s_nom_extendable"] = new_lines_df[ "s_nom_extendable" ].astype(bool) From 6b23f76c31b07c822dfb89cece85bae040739247 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 11 Apr 2023 10:13:24 +0200 Subject: [PATCH 109/159] using Black and Flake8 --- etrago/tools/utilities.py | 100 +++++++++++++++++++------------------- 1 file changed, 50 insertions(+), 50 deletions(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 51f787cc7..7ce5dc302 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -31,7 +31,7 @@ from egoio.tools import db from pyomo.environ import Constraint, PositiveReals, Var -from shapely.geometry import LineString, Point +from shapely.geometry import Point import geopandas as gpd import numpy as np import pandas as pd @@ -778,10 +778,10 @@ def loading_minimization(network, snapshots): network.model.passive_branch_p_index, within=PositiveReals ) - def cRule(model, c, l, t): + def cRule(model, c, l0, t): return ( - model.number1[c, l, t] - model.number2[c, l, t] - == model.passive_branch_p[c, l, t] + model.number1[c, l0, t] - model.number2[c, l0, t] + == model.passive_branch_p[c, l0, t] ) network.model.cRule = Constraint( @@ -814,7 +814,7 @@ def _normed(s): return s / tot -def agg_series_lines(l, network): +def agg_series_lines(l0, network): attrs = network.components["Line"]["attrs"] columns = set( attrs.index[attrs.static & attrs.status.str.startswith("Input")] @@ -841,27 +841,27 @@ def agg_series_lines(l, network): ) } - Line = l["Line"].iloc[0] + Line = l0["Line"].iloc[0] data = dict( - r=l["r"].sum(), - x=l["x"].sum(), - g=1.0 / (1.0 / l["g"]).sum(), - b=1.0 / (1.0 / l["b"]).sum(), - terrain_factor=l["terrain_factor"].mean(), - s_max_pu=(l["s_max_pu"] * _normed(l["s_nom"])).sum(), - s_nom=l["s_nom"].iloc[0], - s_nom_min=l["s_nom_min"].max(), - s_nom_max=l["s_nom_max"].min(), - s_nom_extendable=l["s_nom_extendable"].any(), - num_parallel=l["num_parallel"].max(), - capital_cost=(_normed(l["s_nom"]) * l["capital_cost"]).sum(), - length=l["length"].sum(), - v_ang_min=l["v_ang_min"].max(), - v_ang_max=l["v_ang_max"].min(), + r=l0["r"].sum(), + x=l0["x"].sum(), + g=1.0 / (1.0 / l0["g"]).sum(), + b=1.0 / (1.0 / l0["b"]).sum(), + terrain_factor=l0["terrain_factor"].mean(), + s_max_pu=(l0["s_max_pu"] * _normed(l0["s_nom"])).sum(), + s_nom=l0["s_nom"].iloc[0], + s_nom_min=l0["s_nom_min"].max(), + s_nom_max=l0["s_nom_max"].min(), + s_nom_extendable=l0["s_nom_extendable"].any(), + num_parallel=l0["num_parallel"].max(), + capital_cost=(_normed(l0["s_nom"]) * l0["capital_cost"]).sum(), + length=l0["length"].sum(), + v_ang_min=l0["v_ang_min"].max(), + v_ang_max=l0["v_ang_max"].min(), ) - data.update((f, consense[f](l[f])) for f in columns.difference(data)) + data.update((f, consense[f](l0[f])) for f in columns.difference(data)) return pd.Series( - data, index=[f for f in l.columns if f in columns], name=Line + data, index=[f for f in l0.columns if f in columns], name=Line ) @@ -881,7 +881,7 @@ def group_parallel_lines(network): """ - def agg_parallel_lines(l): + def agg_parallel_lines(l0): attrs = network.components["Line"]["attrs"] columns = set( attrs.index[attrs.static & attrs.status.str.startswith("Input")] @@ -913,27 +913,27 @@ def agg_parallel_lines(l): } data = dict( - Line=l["Line"].iloc[0], - r=1.0 / (1.0 / l["r"]).sum(), - x=1.0 / (1.0 / l["x"]).sum(), - g=l["g"].sum(), - b=l["b"].sum(), - terrain_factor=l["terrain_factor"].mean(), - s_max_pu=(l["s_max_pu"] * _normed(l["s_nom"])).sum(), - s_nom=l["s_nom"].sum(), - s_nom_min=l["s_nom_min"].sum(), - s_nom_max=l["s_nom_max"].sum(), - s_nom_extendable=l["s_nom_extendable"].any(), - num_parallel=l["num_parallel"].sum(), - capital_cost=(_normed(l["s_nom"]) * l["capital_cost"]).sum(), - length=l["length"].mean(), - sub_network=consense["sub_network"](l["sub_network"]), - v_ang_min=l["v_ang_min"].max(), - v_ang_max=l["v_ang_max"].min(), - geom=l["geom"].iloc[0], + Line=l0["Line"].iloc[0], + r=1.0 / (1.0 / l0["r"]).sum(), + x=1.0 / (1.0 / l0["x"]).sum(), + g=l0["g"].sum(), + b=l0["b"].sum(), + terrain_factor=l0["terrain_factor"].mean(), + s_max_pu=(l0["s_max_pu"] * _normed(l0["s_nom"])).sum(), + s_nom=l0["s_nom"].sum(), + s_nom_min=l0["s_nom_min"].sum(), + s_nom_max=l0["s_nom_max"].sum(), + s_nom_extendable=l0["s_nom_extendable"].any(), + num_parallel=l0["num_parallel"].sum(), + capital_cost=(_normed(l0["s_nom"]) * l0["capital_cost"]).sum(), + length=l0["length"].mean(), + sub_network=consense["sub_network"](l0["sub_network"]), + v_ang_min=l0["v_ang_min"].max(), + v_ang_max=l0["v_ang_max"].min(), + geom=l0["geom"].iloc[0], ) - data.update((f, consense[f](l[f])) for f in columns.difference(data)) - return pd.Series(data, index=[f for f in l.columns if f in columns]) + data.update((f, consense[f](l0[f])) for f in columns.difference(data)) + return pd.Series(data, index=[f for f in l0.columns if f in columns]) # Make bus0 always the greattest to identify repeated lines lines_2 = network.lines.copy() @@ -1068,10 +1068,10 @@ def count(bus): delete_bus = [] for bus in ac_buses[ac_buses["n_lines"] == 2].index: - l = lines_cap[(lines_cap.bus0 == bus) | (lines_cap.bus1 == bus)][ + l0 = lines_cap[(lines_cap.bus0 == bus) | (lines_cap.bus1 == bus)][ "s_nom" ].unique() - if len(l) != 1: + if len(l0) != 1: delete_bus.append(bus) ac_buses.drop(delete_bus, inplace=True) @@ -1141,15 +1141,15 @@ def count(bus): new_lines_df = pd.DataFrame(columns=lines.columns).rename_axis("Lines") - for l in new_lines.index: + for l0 in new_lines.index: lines_group = ( - lines[lines.index.isin(new_lines.at[l, "lines"])] + lines[lines.index.isin(new_lines.at[l0, "lines"])] .copy() .reset_index() ) l_new = agg_series_lines(lines_group, network) - l_new["bus0"] = new_lines.at[l, "bus0"] - l_new["bus1"] = new_lines.at[l, "bus1"] + l_new["bus0"] = new_lines.at[l0, "bus0"] + l_new["bus1"] = new_lines.at[l0, "bus1"] new_lines_df["s_nom_extendable"] = new_lines_df[ "s_nom_extendable" ].astype(bool) From 359120cd4a612418397396d6aad50471889904a6 Mon Sep 17 00:00:00 2001 From: Tetens Date: Mon, 17 Apr 2023 11:18:58 +0200 Subject: [PATCH 110/159] fixed problem with missing nodes when not considering foreign buses in gas clustering --- etrago/cluster/gas.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 05534ab3f..bda93bc54 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -56,6 +56,11 @@ def preprocessing(etrago): ch4_filter & (network_ch4.buses["country"] != "DE") ).sum() + network_ch4.links = network_ch4.links.loc[ + network_ch4.links["bus0"].isin(network_ch4.buses.loc[ch4_filter].index) + & network_ch4.links["bus1"].isin(network_ch4.buses.loc[ch4_filter].index) + ] + # select buses dependent on whether they should be clustered in (only DE or DE+foreign) if not settings["cluster_foreign_gas"]: network_ch4.buses = network_ch4.buses.loc[ @@ -75,10 +80,7 @@ def preprocessing(etrago): else: network_ch4.buses = network_ch4.buses.loc[ch4_filter] n_clusters = settings["n_clusters_gas"] - network_ch4.links = network_ch4.links.loc[ - network_ch4.links["bus0"].isin(network_ch4.buses.index) - & network_ch4.links["bus1"].isin(network_ch4.buses.index) - ] + def weighting_for_scenario(ch4_buses, save=None): """ From 073e069208cb00e97d607f4c98d914332d0de677 Mon Sep 17 00:00:00 2001 From: Tetens Date: Mon, 17 Apr 2023 11:20:51 +0200 Subject: [PATCH 111/159] black and isort --- etrago/cluster/gas.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index bda93bc54..d6f7771a6 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -58,7 +58,9 @@ def preprocessing(etrago): network_ch4.links = network_ch4.links.loc[ network_ch4.links["bus0"].isin(network_ch4.buses.loc[ch4_filter].index) - & network_ch4.links["bus1"].isin(network_ch4.buses.loc[ch4_filter].index) + & network_ch4.links["bus1"].isin( + network_ch4.buses.loc[ch4_filter].index + ) ] # select buses dependent on whether they should be clustered in (only DE or DE+foreign) @@ -81,7 +83,6 @@ def preprocessing(etrago): network_ch4.buses = network_ch4.buses.loc[ch4_filter] n_clusters = settings["n_clusters_gas"] - def weighting_for_scenario(ch4_buses, save=None): """ Calculate CH4-bus weightings dependant on the connected From 5ccee552db6935394b29eb940fee539d87e18eb3 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 17 Apr 2023 16:29:42 +0200 Subject: [PATCH 112/159] Rename figure to 'Tool Overview' --- doc/about.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/about.rst b/doc/about.rst index 59b9652a2..a7b91cf1e 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -41,8 +41,8 @@ this platform. -Model overview -============== +Tool overview +============= From ff39064193d0e1f782e8a21041ce0a051f1e41bd Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 17 Apr 2023 16:33:43 +0200 Subject: [PATCH 113/159] Add WhatsNew section for release 0.9.0 --- doc/whatsnew.rst | 1 + doc/whatsnew/v0_9_0.rst | 29 +++++++++++++++++++++++++++++ 2 files changed, 30 insertions(+) create mode 100644 doc/whatsnew/v0_9_0.rst diff --git a/doc/whatsnew.rst b/doc/whatsnew.rst index c7894943c..87ed81be6 100644 --- a/doc/whatsnew.rst +++ b/doc/whatsnew.rst @@ -8,6 +8,7 @@ These are new features and improvements of note in each release. :local: :backlinks: top +.. include:: whatsnew/v0_9_0.rst .. include:: whatsnew/v0_8_0.rst .. include:: whatsnew/v0_7_2.rst .. include:: whatsnew/v0_7_1.rst diff --git a/doc/whatsnew/v0_9_0.rst b/doc/whatsnew/v0_9_0.rst new file mode 100644 index 000000000..83d1cc53b --- /dev/null +++ b/doc/whatsnew/v0_9_0.rst @@ -0,0 +1,29 @@ +Release 0.9.0 (XXXXX, 2023) +++++++++++++++++++++++++++++ + +Added features +-------------- + +* eTraGo is now compatible with Python 3.8 +* eTraGo can now import and optimize networks that include other energy sectors such as gas, heating and mobility +* Various flexibility options from different energy sectors can be considered in the optimization: +** Weather dependent capacity of transmission lines +** Demand side management +** Flexible charging of electric vehicles +** Heat and hydrogen stores +** Power2Hydrogen, Hydrogen2Power +** Methanation and Steam-Methane-Reduction +* eTraGo arguments can now be partially provided and updated. +* eTraGo can now import datamodels from databases without using the ego.io +* Existing clustering methods were adapted to be able to reduce the complexity of not electrical sectors. +* Improvement of the ehv clustering (much faster now) +* A new clustering method "kmedoids-dijkstra" was implemented. This method considers the electrical distance between the buses in the network. It is also available for the methane grid. +* It is possible to select if the foreign buses are considered or not during the clustering process. +* The number of CPUs used to perform the clustering can be provided by the user. +* A function to analyse the clustering results is included. +* Some more options are available to conduct a reduction in temporal dimension: +** segmentation: clustering of adjacent hours to segments of variable length +** clustering to typical periods extended to cluster on weeks and months +* A temporal disaggregation is available through a 2-level-approach including a dispatch optimization on the temporally fullcomplex model +* New plotting functions to visualize the optimization results from all the included energy sectors were implemented +* Functions to analyze results were updated to consider new sectors From f54e6d949b9dfb64c99c2d7441d2a0c1cb5b6dc5 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 17 Apr 2023 17:26:15 +0200 Subject: [PATCH 114/159] Fix nested list --- doc/whatsnew/v0_9_0.rst | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/doc/whatsnew/v0_9_0.rst b/doc/whatsnew/v0_9_0.rst index 83d1cc53b..b952a3215 100644 --- a/doc/whatsnew/v0_9_0.rst +++ b/doc/whatsnew/v0_9_0.rst @@ -7,12 +7,12 @@ Added features * eTraGo is now compatible with Python 3.8 * eTraGo can now import and optimize networks that include other energy sectors such as gas, heating and mobility * Various flexibility options from different energy sectors can be considered in the optimization: -** Weather dependent capacity of transmission lines -** Demand side management -** Flexible charging of electric vehicles -** Heat and hydrogen stores -** Power2Hydrogen, Hydrogen2Power -** Methanation and Steam-Methane-Reduction +- Weather dependent capacity of transmission lines +- Demand side management +- Flexible charging of electric vehicles +- Heat and hydrogen stores +- Power2Hydrogen, Hydrogen2Power +- Methanation and Steam-Methane-Reduction * eTraGo arguments can now be partially provided and updated. * eTraGo can now import datamodels from databases without using the ego.io * Existing clustering methods were adapted to be able to reduce the complexity of not electrical sectors. @@ -20,10 +20,9 @@ Added features * A new clustering method "kmedoids-dijkstra" was implemented. This method considers the electrical distance between the buses in the network. It is also available for the methane grid. * It is possible to select if the foreign buses are considered or not during the clustering process. * The number of CPUs used to perform the clustering can be provided by the user. -* A function to analyse the clustering results is included. * Some more options are available to conduct a reduction in temporal dimension: -** segmentation: clustering of adjacent hours to segments of variable length -** clustering to typical periods extended to cluster on weeks and months +- segmentation: clustering of adjacent hours to segments of variable length +- clustering to typical periods extended to cluster on weeks and months * A temporal disaggregation is available through a 2-level-approach including a dispatch optimization on the temporally fullcomplex model * New plotting functions to visualize the optimization results from all the included energy sectors were implemented * Functions to analyze results were updated to consider new sectors From 2f25f500065a6bce519b2781e3a2b99025f4cbdc Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Tue, 18 Apr 2023 08:20:58 +0200 Subject: [PATCH 115/159] Adapt explanation of temporal disaggregation and correct som typos --- doc/whatsnew/v0_9_0.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/doc/whatsnew/v0_9_0.rst b/doc/whatsnew/v0_9_0.rst index b952a3215..5fb2fdf5b 100644 --- a/doc/whatsnew/v0_9_0.rst +++ b/doc/whatsnew/v0_9_0.rst @@ -7,22 +7,22 @@ Added features * eTraGo is now compatible with Python 3.8 * eTraGo can now import and optimize networks that include other energy sectors such as gas, heating and mobility * Various flexibility options from different energy sectors can be considered in the optimization: -- Weather dependent capacity of transmission lines -- Demand side management +- Weather dependent capacity of transmission lines (Dynamic Line Rating) +- Demand Side Management - Flexible charging of electric vehicles - Heat and hydrogen stores - Power2Hydrogen, Hydrogen2Power -- Methanation and Steam-Methane-Reduction -* eTraGo arguments can now be partially provided and updated. +- Methanation and Steam Methane Reforming +* eTraGo arguments can now be partially provided and updated * eTraGo can now import datamodels from databases without using the ego.io -* Existing clustering methods were adapted to be able to reduce the complexity of not electrical sectors. +* Existing clustering methods were adapted to be able to reduce the complexity of not electrical sectors * Improvement of the ehv clustering (much faster now) -* A new clustering method "kmedoids-dijkstra" was implemented. This method considers the electrical distance between the buses in the network. It is also available for the methane grid. -* It is possible to select if the foreign buses are considered or not during the clustering process. +* A new clustering method named "k-medoids Dijkstra Clustering" (can be called by "kmedoids-dijkstra") was implemented. This method considers the electrical distance between the buses in the network. It is also available for the methane grid. +* It is possible to select if foreign buses are considered or not during the clustering process. * The number of CPUs used to perform the clustering can be provided by the user. * Some more options are available to conduct a reduction in temporal dimension: - segmentation: clustering of adjacent hours to segments of variable length - clustering to typical periods extended to cluster on weeks and months -* A temporal disaggregation is available through a 2-level-approach including a dispatch optimization on the temporally fullcomplex model +* A temporal disaggregation is available through a 2-level-approach including a dispatch optimization on the temporally fullcomplex model. To limit the RAM usage, you can optionally divide the optimisation problem into a chosen number of slices. * New plotting functions to visualize the optimization results from all the included energy sectors were implemented * Functions to analyze results were updated to consider new sectors From 77927a4d4e0056998f03ee02204b353217ce627e Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Tue, 18 Apr 2023 09:00:11 +0200 Subject: [PATCH 116/159] Apply black --- etrago/cluster/spatial.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/etrago/cluster/spatial.py b/etrago/cluster/spatial.py index 71b20de86..bbf176b36 100755 --- a/etrago/cluster/spatial.py +++ b/etrago/cluster/spatial.py @@ -312,7 +312,7 @@ def gen(nodes, n, graph): g = graph.copy() for i in range(0, len(nodes), n): - yield (nodes[i: i + n], g) + yield (nodes[i : i + n], g) def shortest_path(paths, graph): @@ -588,7 +588,6 @@ def kmean_clustering(etrago, selected_network, weight, n_clusters): kmean_settings = etrago.args["network_clustering"] with threadpool_limits(limits=kmean_settings["CPU_cores"], user_api=None): - # remove stubs if kmean_settings["remove_stubs"]: network.determine_network_topology() @@ -746,9 +745,8 @@ def kmedoids_dijkstra_clustering( # n_jobs was deprecated for the function fit(). scikit-learn recommends # to use threadpool_limits: - #https://scikit-learn.org/stable/computing/parallelism.html + # https://scikit-learn.org/stable/computing/parallelism.html with threadpool_limits(limits=settings["CPU_cores"], user_api=None): - # remove stubs if settings["remove_stubs"]: logger.info( From 95c22f016fba3dfb22074fa7b5c0c7832a089482 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Thu, 20 Apr 2023 17:09:42 +0200 Subject: [PATCH 117/159] Replace `.busmap` with `.buses.loc[:, "cluster"]` This partially reverts commit 73b9e3b3. The `.busmap` attribute isn't really necessary, as `.buses.loc[:, "cluster"]` returns the same `Series`, i.e. the same indices, values and mapping of index to value. The only difference is in the order of the entries, which shouldn't matter. --- etrago/cluster/disaggregation.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index c727fc479..296977aa2 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -26,11 +26,10 @@ def __init__( self.original_network = original_network self.clustered_network = clustered_network self.clustering = clustering - self.busmap = pd.Series(clustering.busmap) self.buses = pd.merge( original_network.buses, - self.busmap.to_frame(name="cluster"), + self.clustering.busmap.to_frame(name="cluster"), left_index=True, right_index=True, ) @@ -122,7 +121,7 @@ def is_bus_in_cluster(conn, busflags=busflags): ) def from_busmap(x): - return self.idx_prefix + self.busmap.loc[x] + return self.idx_prefix + self.buses.loc[x, "cluster"] if not left_external_connectors.empty: ca_option = pd.get_option("mode.chained_assignment") @@ -271,7 +270,7 @@ def solve(self, scenario, solver): :param scenario: :param solver: Solver that may be used to optimize partial networks """ - clusters = set(self.busmap.values) + clusters = set(self.buses.loc[:, "cluster"].values) n = len(clusters) self.stats = { "clusters": pd.DataFrame( @@ -425,7 +424,7 @@ def _validate_disaggregation_generators(self, cluster, f): def extra_functionality(network, snapshots): f(network, snapshots) generators = self.original_network.generators.assign( - bus=lambda df: df.bus.map(self.busmap) + bus=lambda df: df.bus.map(self.buses.loc[:, "cluster"]) ) def construct_constraint(model, snapshot, carrier): @@ -477,7 +476,9 @@ def extra_functionality(network, snapshots): ]: generators = getattr( self.original_network, bustype_pypsa - ).assign(bus=lambda df: df.bus.map(self.busmap)) + ).assign( + bus=lambda df: df.bus.map(self.buses.loc[:, "cluster"]) + ) for suffix in suffixes: def construct_constraint(model, snapshot): From 0a9c5156358c267728798f2f4c8c5734a396993f Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 18 Apr 2023 17:47:26 +0200 Subject: [PATCH 118/159] Filter links by `bus0` instead of `bus1` This fixes a bug in the original `p0`, `p1` disaggregation implementation for `links` in commit 988d19cb9. For components having a `bus0` and a `bus1` instead of just a `bus`, `bus0` is taken as corresponding to `bus`. So filtering out these components should be done by looking at `bus0`, not `bus1`. --- etrago/cluster/disaggregation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 296977aa2..67b77522a 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -610,7 +610,7 @@ def solve_partial_network( i for i, row in enumerate(pn_buses.itertuples()) for bus in [ - row.bus if hasattr(row, "bus") else row.bus1 + row.bus if hasattr(row, "bus") else row.bus0 ] if not bus.startswith(self.idx_prefix) ] From 7a5a100eac7b1464a07aa555893f23dd67330ceb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 21 Apr 2023 06:18:14 +0200 Subject: [PATCH 119/159] Move `bus == cluster` filtering out of the loop Nothing in the filter uses the loop variable, so the filtering can be moved out of the loop. Also, since `cl_buses` is only used to determine the value of `clb` for the duration of the loop and nowhere else, filtering can be done directly when assigning `cl_buses` and `clb` can be determined directly by querying the filtered DataFrame. Filtering before determining the groups massively speeds up the disaggregation. --- etrago/cluster/disaggregation.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 67b77522a..06d044318 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -577,8 +577,10 @@ def solve_partial_network( pn_t = getattr(partial_network, bustype + "_t") cl_t = getattr(self.clustered_network, bustype + "_t") pn_buses = getattr(partial_network, bustype) - cl_buses = getattr(self.clustered_network, bustype) - + cl_buses = getattr(self.clustered_network, bustype)[ + lambda df: df.loc[:, "bus" if "bus" in df.columns else "bus0"] + == cluster + ] groups = product( *[ [ @@ -589,15 +591,10 @@ def solve_partial_network( ] ) for group in groups: - clb = ( - cl_buses[cl_buses.bus == cluster] - if "bus" in cl_buses.columns - else cl_buses[cl_buses.bus0 == cluster] - ) query = " & ".join( ["({key} == {value!r})".format(**axis) for axis in group] ) - clb = clb.query(query) + clb = cl_buses.query(query) if len(clb) == 0: continue assert len(clb) == 1, ( From a17f05dface0fc9eeb27f99c98179818b1b9bbac Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 18 Apr 2023 17:45:17 +0200 Subject: [PATCH 120/159] Adjust `query` for the special case of `"links"` For links, the `query` contains references to `bus1`, which has a different meaning depending on whether we are dealing with a cluster or the partial network the cluster represents. For clusters, the query is correct, because the group was generated by looking up the `bus1` values of links attached to the cluster. But to find the corresponding links in the partial network, one has to look for those links whose `bus1` value matches the index of one of the buses whose `"cluster"` entry is equal to the group's `bus1` value. Or in other words, since the group's `bus1` value identifies a cluster, one has to - first find all buses corresponding to that cluster, - then find all links whose `bus1` value points to one of these buses. --- etrago/cluster/disaggregation.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 06d044318..45e089093 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -612,6 +612,14 @@ def solve_partial_network( if not bus.startswith(self.idx_prefix) ] ] + if bustype == "links": + index = self.buses[ + self.buses.loc[:, "cluster"] == group[1]["value"] + ].index.tolist() + query = ( + f"(carrier == {group[0]['value']!r})" + f" & (bus1 in {index})" + ) pnb = pnb.query(query) assert not pnb.empty, ( "Cluster has a bus for:" From e579d20dec8004fea643328a9fd7417aa17b5659 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 21 Apr 2023 06:49:59 +0200 Subject: [PATCH 121/159] Use values in cluster columns to define groups This is the correct way anyway and fixes a bug, since the values stored in each group (should) uniquely identify a cluster. The bug just never surfaced, as these values are usually also found in the partial network. This isn't the case for the values of `bus`, `bus0` and `bus1` though, because these point to other cluster or partial network buses, so they differ depending on whether the attribute is on a cluster or a partial network component, so the bug was finally caught. --- etrago/cluster/disaggregation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 45e089093..abb6f3665 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -585,7 +585,7 @@ def solve_partial_network( *[ [ {"key": key, "value": value} - for value in set(pn_buses.loc[:, key]) + for value in set(cl_buses.loc[:, key]) ] for key in bustypes[bustype]["group_by"] ] From 12a1933d996e33a713fd12d7d4d0a218eeadfb7f Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Tue, 18 Apr 2023 17:46:48 +0200 Subject: [PATCH 122/159] Allow `pnb` to be empty in certain cases --- etrago/cluster/disaggregation.py | 20 +++++++++++++++++++- 1 file changed, 19 insertions(+), 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index abb6f3665..a4144ae86 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -621,7 +621,23 @@ def solve_partial_network( f" & (bus1 in {index})" ) pnb = pnb.query(query) - assert not pnb.empty, ( + assert not pnb.empty or ( + # In some cases, a district heating grid is connected to a + # substation only via a resistive_heater but not e.g. by a + # heat_pump or one of the other listed `carrier`s. + # In the clustered network, there are both. + # In these cases, the `pnb` can actually be empty. + group[0]["value"] + in [ + "central_gas_boiler", + "central_heat_pump", + "central_gas_CHP_heat", + "central_gas_CHP", + "CH4", + "DC", + "OCGT", + ] + ), ( "Cluster has a bus for:" + "\n ".join( ["{key}: {value!r}".format(**axis) for axis in group] @@ -629,6 +645,8 @@ def solve_partial_network( + "\nbut no matching buses in its corresponding " + "partial network." ) + if pnb.empty: + continue if not ( pnb.loc[:, extendable_flag].all() From 774ee1a7f4ec192fd7eeb55a9ea071b8b0bef956 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Wed, 19 Apr 2023 09:30:02 +0200 Subject: [PATCH 123/159] Assert that time series sum matches cluster value --- etrago/cluster/disaggregation.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index a4144ae86..407e03169 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -741,6 +741,12 @@ def solve_partial_network( for bus_id in filtered.index } ) + delta = abs((new_columns.sum(axis=1) - clt).sum()) + epsilon = 1e-5 + assert delta < epsilon, ( + "Sum of disaggregated time series does not match" + f" aggregated timeseries: {delta=} > {epsilon=}." + ) pn_t[s].loc[:, new_columns.columns] = new_columns def transfer_results(self, *args, **kwargs): From b45c1d36e51f66879429b4ed06e18b0913f4cabb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Fri, 21 Apr 2023 11:40:40 +0200 Subject: [PATCH 124/159] Make string containing "{expression}" an f-string It was obviously meant that way, so this is just a bugfix. Add punctuation and a unit for better readability, too. --- etrago/cluster/disaggregation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 407e03169..dfd635ac6 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -347,7 +347,7 @@ def solve(self, scenario, solver): ) profile.disable() self.stats["check"] = time.time() - t - log.info("Checks computed in {self.stats['check']}") + log.info(f"Checks computed in {self.stats['check']}s.") profile.print_stats(sort="cumtime") From 71e2f8c81f03758f4ce1f6fd4d6f4f0af9d84c49 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stephan=20G=C3=BCnther?= Date: Sun, 23 Apr 2023 22:54:35 +0200 Subject: [PATCH 125/159] Add links to "clustered - disaggregated" checks This code prints the differences of the sum of all values of a certain attribute or timeseries in the clustered network to the sum of the corresponding disaggregated values in the original network. Usually these should be close to zero. For `links`'s "p0" and "p1" timeseries these aren't close to zero, but that's OK as these include values from the CH4 links, which aren't disaggregated. But it's probably still a good idea to have those printed in order to be able to check whether these differences are in the correct ballpark. Maybe the check can later be extended to be filtered so that they are close to zero again. --- etrago/cluster/disaggregation.py | 1 + 1 file changed, 1 insertion(+) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index dfd635ac6..054525fb5 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -320,6 +320,7 @@ def solve(self, scenario, solver): for bt, ts in ( ("generators", {"p": fs, "q": fs}), ("storage_units", {"p": fs, "state_of_charge": fs, "q": fs}), + ("links", {"p0": fs, "p1": fs}), ): log.info(f"Attribute sums, {bt}, clustered - disaggregated:") cnb = getattr(self.clustered_network, bt) From de6c716497ecebd4ff607245645194b829a34258 Mon Sep 17 00:00:00 2001 From: Carlos Epia <62600091+CarlosEpia@users.noreply.github.com> Date: Tue, 25 Apr 2023 11:56:20 +0200 Subject: [PATCH 126/159] Change comment understanding of args --- doc/howToUse.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/howToUse.rst b/doc/howToUse.rst index 2fb7b3939..918fc3c85 100644 --- a/doc/howToUse.rst +++ b/doc/howToUse.rst @@ -12,8 +12,8 @@ the ‘appl.py’ is used as a simple user interface which can be edited with the preferred python-editor. Here parameters, calculation methods and scenario settings are set in a python dictionary called 'args'. -To run the desired calculation, it is crucial to understand these parameters, -since for particular cases some of them contradict the usage of others. +To run the desired calculation, it is crucial to understand these parameters. +In addition, some of them contradict the usage of others. You find the documentation of all defined parameters from the 'args' here: :func:`etrago.appl.run_etrago`. From 3fed0145aa8ac7e37e7f9050a7b8ec9b955d9519 Mon Sep 17 00:00:00 2001 From: Katharina Esterl Date: Mon, 8 May 2023 10:16:06 +0200 Subject: [PATCH 127/159] Fix export of information when using segmentation --- etrago/cluster/snapshot.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/etrago/cluster/snapshot.py b/etrago/cluster/snapshot.py index daa0b6166..a5c48936e 100644 --- a/etrago/cluster/snapshot.py +++ b/etrago/cluster/snapshot.py @@ -653,7 +653,10 @@ def run( ) if segmentation != False: - timeseries.to_csv( + pd.DataFrame( + timeseries.reset_index(), + columns=["dates", "SegmentNo", "SegmentDuration"], + ).set_index("SegmentNo").to_csv( "timeseries_segmentation=" + str(segment_no) + ".csv" ) else: @@ -698,7 +701,7 @@ def prepare_pypsa_timeseries(network): Timeseries to be considered when clustering. """ - + loads = network.loads_t.p_set.copy() loads.columns = "L" + loads.columns @@ -795,7 +798,7 @@ def skip_snapshots(self): None. """ - + # save second network for optional dispatch disaggregation if ( self.args["temporal_disaggregation"]["active"] == True @@ -806,7 +809,6 @@ def skip_snapshots(self): n_skip = self.args["skip_snapshots"] if n_skip: - last_weight = ( int( ( @@ -834,4 +836,3 @@ def skip_snapshots(self): self.network.snapshot_weightings.loc[ self.network.snapshot_weightings.index[-1] ]["generators"] = last_weight - From 012d51cfea8c377617b927329e1b353bff96da98 Mon Sep 17 00:00:00 2001 From: ulfmueller Date: Wed, 17 May 2023 11:28:16 +0200 Subject: [PATCH 128/159] changed end_snapshot --- etrago/appl.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/appl.py b/etrago/appl.py index 0e6724009..76b84f4bb 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -63,7 +63,7 @@ "q_allocation": "p_nom", # allocate reactive power via 'p_nom' or 'p' }, "start_snapshot": 1, - "end_snapshot": 2, + "end_snapshot": 10, "solver": "gurobi", # glpk, cplex or gurobi "solver_options": { "BarConvTol": 1.0e-5, From 3c3b19634db909aa2d384942fc951a003e7f6d15 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 28 Jun 2023 14:38:24 +0200 Subject: [PATCH 129/159] include status2019 as a valid scn name --- etrago/tools/utilities.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 575f07761..66b37ecd6 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -2336,7 +2336,9 @@ def check_args(etrago): """ - names = ["eGon2035", "eGon100RE", "eGon2035_lowflex", "eGon100RE_lowflex"] + names = ["eGon2035", "eGon100RE", "eGon2035_lowflex", "eGon100RE_lowflex", + "status2019"] + assert ( etrago.args["scn_name"] in names ), f"'scn_name' has to be in {names} but is {etrago.args['scn_name']}." From c9193f559fdabefcdf6942065171b709de627eb6 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Wed, 28 Jun 2023 14:40:30 +0200 Subject: [PATCH 130/159] make H2 buses optional --- etrago/tools/network.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/etrago/tools/network.py b/etrago/tools/network.py index 97ffbfe77..a5900238f 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -324,7 +324,8 @@ def build_network_from_db(self): self.decommissioning() - self.add_ch4_h2_correspondence() + if "H2" in self.network.buses.carrier: + self.add_ch4_h2_correspondence() logger.info("Imported network from db") From c8d7427aa12f375212d9972c48be5e07b899294b Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Mon, 10 Jul 2023 13:47:20 +0200 Subject: [PATCH 131/159] REVERT-just for testing purposes --- etrago/appl.py | 54 ++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 48 insertions(+), 6 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index ab64b5e17..6a6158f58 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -50,7 +50,7 @@ args = { # Setup and Configuration: - "db": "egon-data", # database session + "db": "powerd-data-server", # database session "gridversion": None, # None for model_draft or Version number "method": { # Choose method and settings for optimization "type": "lopf", # type of optimization, currently only 'lopf' @@ -63,7 +63,7 @@ "q_allocation": "p_nom", # allocate reactive power via 'p_nom' or 'p' }, "start_snapshot": 1, - "end_snapshot": 10, + "end_snapshot": 8760, "solver": "gurobi", # glpk, cplex or gurobi "solver_options": { "BarConvTol": 1.0e-5, @@ -74,7 +74,7 @@ "threads": 4, }, "model_formulation": "kirchhoff", # angles or kirchhoff - "scn_name": "eGon2035", # scenario: eGon2035 or eGon100RE + "scn_name": "status2019", # scenario: eGon2035 or eGon100RE # Scenario variations: "scn_extension": None, # None or array of extension scenarios "scn_decommissioning": None, # None or decommissioning scenario @@ -155,7 +155,7 @@ }, # Simplifications: "branch_capacity_factor": {"HV": 0.5, "eHV": 0.7}, # p.u. branch derating - "load_shedding": True, # meet the demand at value of loss load cost + "load_shedding": False, # meet the demand at value of loss load cost "foreign_lines": { "carrier": "AC", # 'DC' for modeling foreign lines as links "capacity": "osmTGmod", # 'osmTGmod', 'tyndp2020', 'ntc_acer' or 'thermal_acer' @@ -455,10 +455,52 @@ def run_etrago(args, json_path): # import network from database etrago.build_network_from_db() - + # adjust network regarding eTraGo setting etrago.adjust_network() - + breakpoint() + + # Generators + gen = etrago.network.generators + gen["country"] = gen.bus.map(etrago.network.buses.country) + import pandas as pd + ins_cap = pd.DataFrame() + for (country, carrier), df in gen.groupby(["country", "carrier"]): + ins_cap.at[country, carrier] = df.p_nom.sum() + #ins_cap.drop(columns=["load shedding"], inplace=True) + + # Loads + load = etrago.network.loads + load_t = etrago.network.loads_t.p_set + load["country"] = load.bus.map(etrago.network.buses.country) + load_country = pd.DataFrame() + for (country, carrier), df in load.groupby(["country", "carrier"]): + loads_id = df.index + load_country.at[country, carrier] = load_t[loads_id].sum().sum() + + # storage_units + sto = etrago.network.storage_units + sto["country"] = sto.bus.map(etrago.network.buses.country) + sto_country = pd.DataFrame() + for (country, carrier), df in sto.groupby(["country", "carrier"]): + sto_country.at[country, carrier] = df.p_nom.sum() + + # links + link = etrago.network.links + link_t = etrago.network.links_t + link_with_t = link.loc[link.index.isin(link_t["p_max_pu"].columns)] + + # buses + bus = etrago.network.buses + bus_country = pd.DataFrame() + for (country, carrier), df in bus.groupby(["country", "carrier"]): + bus_country.at[country, carrier] = len(df) + + # subnetworks + etrago.network.determine_network_topology() + sub_network = etrago.network.buses.loc[etrago.network.buses.carrier=="AC"] + sub_network = sub_network.loc[sub_network.country=="DE"] + sub_network.sub_network.value_counts() # ehv network clustering etrago.ehv_clustering() From f4faddd7c4c83a275558cd25fda1f2a7b3ae7096 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Mon, 17 Jul 2023 08:41:07 +0200 Subject: [PATCH 132/159] more checks for status2019 --- etrago/appl.py | 80 +++++++++++++++++++++---------------------- etrago/cluster/gas.py | 2 +- 2 files changed, 41 insertions(+), 41 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index 6a6158f58..a539752fb 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -63,7 +63,7 @@ "q_allocation": "p_nom", # allocate reactive power via 'p_nom' or 'p' }, "start_snapshot": 1, - "end_snapshot": 8760, + "end_snapshot": 10, "solver": "gurobi", # glpk, cplex or gurobi "solver_options": { "BarConvTol": 1.0e-5, @@ -458,51 +458,51 @@ def run_etrago(args, json_path): # adjust network regarding eTraGo setting etrago.adjust_network() - breakpoint() + #breakpoint() - # Generators - gen = etrago.network.generators - gen["country"] = gen.bus.map(etrago.network.buses.country) - import pandas as pd - ins_cap = pd.DataFrame() - for (country, carrier), df in gen.groupby(["country", "carrier"]): - ins_cap.at[country, carrier] = df.p_nom.sum() - #ins_cap.drop(columns=["load shedding"], inplace=True) + # # Generators + # gen = etrago.network.generators + # gen["country"] = gen.bus.map(etrago.network.buses.country) + # import pandas as pd + # ins_cap = pd.DataFrame() + # for (country, carrier), df in gen.groupby(["country", "carrier"]): + # ins_cap.at[country, carrier] = df.p_nom.sum() + # #ins_cap.drop(columns=["load shedding"], inplace=True) - # Loads - load = etrago.network.loads - load_t = etrago.network.loads_t.p_set - load["country"] = load.bus.map(etrago.network.buses.country) - load_country = pd.DataFrame() - for (country, carrier), df in load.groupby(["country", "carrier"]): - loads_id = df.index - load_country.at[country, carrier] = load_t[loads_id].sum().sum() + # # Loads + # load = etrago.network.loads + # load_t = etrago.network.loads_t.p_set + # load["country"] = load.bus.map(etrago.network.buses.country) + # load_country = pd.DataFrame() + # for (country, carrier), df in load.groupby(["country", "carrier"]): + # loads_id = df.index + # load_country.at[country, carrier] = load_t[loads_id].sum().sum() - # storage_units - sto = etrago.network.storage_units - sto["country"] = sto.bus.map(etrago.network.buses.country) - sto_country = pd.DataFrame() - for (country, carrier), df in sto.groupby(["country", "carrier"]): - sto_country.at[country, carrier] = df.p_nom.sum() + # # storage_units + # sto = etrago.network.storage_units + # sto["country"] = sto.bus.map(etrago.network.buses.country) + # sto_country = pd.DataFrame() + # for (country, carrier), df in sto.groupby(["country", "carrier"]): + # sto_country.at[country, carrier] = df.p_nom.sum() - # links - link = etrago.network.links - link_t = etrago.network.links_t - link_with_t = link.loc[link.index.isin(link_t["p_max_pu"].columns)] + # # links + # link = etrago.network.links + # link_t = etrago.network.links_t + # link_with_t = link.loc[link.index.isin(link_t["p_max_pu"].columns)] - # buses - bus = etrago.network.buses - bus_country = pd.DataFrame() - for (country, carrier), df in bus.groupby(["country", "carrier"]): - bus_country.at[country, carrier] = len(df) + # # buses + # bus = etrago.network.buses + # bus_country = pd.DataFrame() + # for (country, carrier), df in bus.groupby(["country", "carrier"]): + # bus_country.at[country, carrier] = len(df) - # subnetworks - etrago.network.determine_network_topology() - sub_network = etrago.network.buses.loc[etrago.network.buses.carrier=="AC"] - sub_network = sub_network.loc[sub_network.country=="DE"] - sub_network.sub_network.value_counts() - # ehv network clustering - etrago.ehv_clustering() + # # subnetworks + # etrago.network.determine_network_topology() + # sub_network = etrago.network.buses.loc[etrago.network.buses.carrier=="AC"] + # sub_network = sub_network.loc[sub_network.country=="DE"] + # sub_network.sub_network.value_counts() + # # ehv network clustering + # etrago.ehv_clustering() # spatial clustering etrago.spatial_clustering() diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 733b9905a..713905d7d 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -345,7 +345,7 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None): + str(settings["n_clusters_gas"]) + "_result.csv" ) - + breakpoint() busmap = get_h2_clusters(etrago, busmap) # Add all other buses to busmap From 0067605fbf8250afc7d907816d3292c3b85755c2 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 18 Jul 2023 15:13:19 +0200 Subject: [PATCH 133/159] omit H2 clustering when no H2 --- etrago/cluster/gas.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 713905d7d..4385b55a2 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -345,8 +345,9 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None): + str(settings["n_clusters_gas"]) + "_result.csv" ) - breakpoint() - busmap = get_h2_clusters(etrago, busmap) + + if 'H2' in etrago.network.buses.carrier.unique(): + busmap = get_h2_clusters(etrago, busmap) # Add all other buses to busmap missing_idx = list( From f8f4c49fcdf8d9e58698c73358e9dce67793785a Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 18 Jul 2023 15:50:36 +0200 Subject: [PATCH 134/159] include status2019 in gas constraints --- etrago/tools/constraints.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index b2d9a0446..a93f8d148 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -1285,6 +1285,12 @@ def read_max_gas_generation(self): """ scn_name = self.args["scn_name"] arg_def = { + ##################### UPDATE VALUE FOR 2019 ########################### + "status2019": { + "CH4": 36000000, + "biogas": 10000000, + }, # [MWh] Netzentwicklungsplan Gas 2020–2030 + ##################### UPDATE VALUE FOR 2019 ########################### "eGon2035": { "CH4": 36000000, "biogas": 10000000, @@ -1297,7 +1303,6 @@ def read_max_gas_generation(self): "biogas": 14450103 }, # [MWh] Value from reference p-e-s run used in eGon-data } - engine = db.connection(section=self.args["db"]) try: sql = f""" @@ -1341,6 +1346,7 @@ def add_ch4_constraints(self, network, snapshots): gas_carrier = arg.keys() carrier_names = { + "status2019": {"CH4": "CH4_NG", "biogas": "CH4_biogas"}, "eGon2035": {"CH4": "CH4_NG", "biogas": "CH4_biogas"}, "eGon2035_lowflex": {"CH4": "CH4_NG", "biogas": "CH4_biogas"}, "eGon100RE": {"biogas": "CH4"}, From 8248da5a1e1a260232ee95f872ea18febe0fe0e1 Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 18 Jul 2023 16:05:14 +0200 Subject: [PATCH 135/159] revert commits done for checking process --- etrago/appl.py | 47 +---------------------------------------------- 1 file changed, 1 insertion(+), 46 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index a539752fb..95d9242ba 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -50,7 +50,7 @@ args = { # Setup and Configuration: - "db": "powerd-data-server", # database session + "db": "powerd-data", # database session "gridversion": None, # None for model_draft or Version number "method": { # Choose method and settings for optimization "type": "lopf", # type of optimization, currently only 'lopf' @@ -458,51 +458,6 @@ def run_etrago(args, json_path): # adjust network regarding eTraGo setting etrago.adjust_network() - #breakpoint() - - # # Generators - # gen = etrago.network.generators - # gen["country"] = gen.bus.map(etrago.network.buses.country) - # import pandas as pd - # ins_cap = pd.DataFrame() - # for (country, carrier), df in gen.groupby(["country", "carrier"]): - # ins_cap.at[country, carrier] = df.p_nom.sum() - # #ins_cap.drop(columns=["load shedding"], inplace=True) - - # # Loads - # load = etrago.network.loads - # load_t = etrago.network.loads_t.p_set - # load["country"] = load.bus.map(etrago.network.buses.country) - # load_country = pd.DataFrame() - # for (country, carrier), df in load.groupby(["country", "carrier"]): - # loads_id = df.index - # load_country.at[country, carrier] = load_t[loads_id].sum().sum() - - # # storage_units - # sto = etrago.network.storage_units - # sto["country"] = sto.bus.map(etrago.network.buses.country) - # sto_country = pd.DataFrame() - # for (country, carrier), df in sto.groupby(["country", "carrier"]): - # sto_country.at[country, carrier] = df.p_nom.sum() - - # # links - # link = etrago.network.links - # link_t = etrago.network.links_t - # link_with_t = link.loc[link.index.isin(link_t["p_max_pu"].columns)] - - # # buses - # bus = etrago.network.buses - # bus_country = pd.DataFrame() - # for (country, carrier), df in bus.groupby(["country", "carrier"]): - # bus_country.at[country, carrier] = len(df) - - # # subnetworks - # etrago.network.determine_network_topology() - # sub_network = etrago.network.buses.loc[etrago.network.buses.carrier=="AC"] - # sub_network = sub_network.loc[sub_network.country=="DE"] - # sub_network.sub_network.value_counts() - # # ehv network clustering - # etrago.ehv_clustering() # spatial clustering etrago.spatial_clustering() From e919893d42670894854db862b7e452cdd31d649a Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Tue, 25 Jul 2023 09:19:00 +0200 Subject: [PATCH 136/159] omit gas constraints for status2019 --- etrago/tools/constraints.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index a93f8d148..82a5037b9 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -1285,12 +1285,6 @@ def read_max_gas_generation(self): """ scn_name = self.args["scn_name"] arg_def = { - ##################### UPDATE VALUE FOR 2019 ########################### - "status2019": { - "CH4": 36000000, - "biogas": 10000000, - }, # [MWh] Netzentwicklungsplan Gas 2020–2030 - ##################### UPDATE VALUE FOR 2019 ########################### "eGon2035": { "CH4": 36000000, "biogas": 10000000, @@ -1346,7 +1340,6 @@ def add_ch4_constraints(self, network, snapshots): gas_carrier = arg.keys() carrier_names = { - "status2019": {"CH4": "CH4_NG", "biogas": "CH4_biogas"}, "eGon2035": {"CH4": "CH4_NG", "biogas": "CH4_biogas"}, "eGon2035_lowflex": {"CH4": "CH4_NG", "biogas": "CH4_biogas"}, "eGon100RE": {"biogas": "CH4"}, @@ -2744,10 +2737,12 @@ def functionality(self, network, snapshots): if "CH4" in network.buses.carrier.values: if self.args["method"]["pyomo"]: add_chp_constraints(network, snapshots) - add_ch4_constraints(self, network, snapshots) + if self.args["scn_name"] != "status2019": + add_ch4_constraints(self, network, snapshots) else: add_chp_constraints_nmp(network) - add_ch4_constraints_nmp(self, network, snapshots) + if self.args["scn_name"] != "status2019": + add_ch4_constraints_nmp(self, network, snapshots) for constraint in self.args["extra_functionality"].keys(): try: From bfc4d0ae2d40dfee804836ffccd4fb0a5fb58cfd Mon Sep 17 00:00:00 2001 From: CarlosEpia Date: Thu, 10 Aug 2023 14:09:39 +0200 Subject: [PATCH 137/159] restoring app.py and apply black --- etrago/appl.py | 11 +++++++---- etrago/tools/utilities.py | 9 +++++++-- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index 95d9242ba..03e1f0319 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -50,7 +50,7 @@ args = { # Setup and Configuration: - "db": "powerd-data", # database session + "db": "egon-data", # database session "gridversion": None, # None for model_draft or Version number "method": { # Choose method and settings for optimization "type": "lopf", # type of optimization, currently only 'lopf' @@ -74,7 +74,7 @@ "threads": 4, }, "model_formulation": "kirchhoff", # angles or kirchhoff - "scn_name": "status2019", # scenario: eGon2035 or eGon100RE + "scn_name": "eGon2035", # scenario: eGon2035, eGon100RE or status2019 # Scenario variations: "scn_extension": None, # None or array of extension scenarios "scn_decommissioning": None, # None or decommissioning scenario @@ -155,7 +155,7 @@ }, # Simplifications: "branch_capacity_factor": {"HV": 0.5, "eHV": 0.7}, # p.u. branch derating - "load_shedding": False, # meet the demand at value of loss load cost + "load_shedding": True, # meet the demand at value of loss load cost "foreign_lines": { "carrier": "AC", # 'DC' for modeling foreign lines as links "capacity": "osmTGmod", # 'osmTGmod', 'tyndp2020', 'ntc_acer' or 'thermal_acer' @@ -455,10 +455,13 @@ def run_etrago(args, json_path): # import network from database etrago.build_network_from_db() - + # adjust network regarding eTraGo setting etrago.adjust_network() + # ehv network clustering + etrago.ehv_clustering() + # spatial clustering etrago.spatial_clustering() etrago.spatial_clustering_gas() diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 66b37ecd6..6a37c3cfa 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -2336,8 +2336,13 @@ def check_args(etrago): """ - names = ["eGon2035", "eGon100RE", "eGon2035_lowflex", "eGon100RE_lowflex", - "status2019"] + names = [ + "eGon2035", + "eGon100RE", + "eGon2035_lowflex", + "eGon100RE_lowflex", + "status2019", + ] assert ( etrago.args["scn_name"] in names From 4f7598fc31e24327e34767df35a21168aff3c393 Mon Sep 17 00:00:00 2001 From: birgits Date: Tue, 5 Sep 2023 15:06:12 +0200 Subject: [PATCH 138/159] Add missing packages --- doc/api/etrago.rst | 30 ------------------------------ doc/api/modules.rst | 7 ------- setup.py | 7 ++++++- 3 files changed, 6 insertions(+), 38 deletions(-) delete mode 100644 doc/api/etrago.rst delete mode 100644 doc/api/modules.rst diff --git a/doc/api/etrago.rst b/doc/api/etrago.rst deleted file mode 100644 index a09ffc783..000000000 --- a/doc/api/etrago.rst +++ /dev/null @@ -1,30 +0,0 @@ -etrago package -============== - -Subpackages ------------ - -.. toctree:: - - etrago.cluster - etrago.tools - -Submodules ----------- - -etrago\.appl module -------------------- - -.. automodule:: etrago.appl - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: etrago - :members: - :undoc-members: - :show-inheritance: diff --git a/doc/api/modules.rst b/doc/api/modules.rst deleted file mode 100644 index fda8d67db..000000000 --- a/doc/api/modules.rst +++ /dev/null @@ -1,7 +0,0 @@ -etrago -====== - -.. toctree:: - :maxdepth: 4 - - etrago diff --git a/setup.py b/setup.py index 5984f5b0e..e84d234d5 100755 --- a/setup.py +++ b/setup.py @@ -67,7 +67,12 @@ def read(*names, **kwargs): "tsam", ], extras_require={ - "docs": ["sphinx >= 1.4", "sphinx_rtd_theme"], + "docs": [ + "nbsphinx", + "numpydoc", + "sphinx >= 1.4", + "sphinx_rtd_theme", + ], "gurobipy": ["gurobipy"], "cartopy": ["cartopy", "requests"], }, From b8161e3a08c2b6736f611419c24710fdd84303a5 Mon Sep 17 00:00:00 2001 From: birgits Date: Tue, 5 Sep 2023 15:07:42 +0200 Subject: [PATCH 139/159] Change structure of API doc and update modules --- doc/api.rst | 7 +++-- doc/api/appl.rst | 7 +++++ doc/api/etrago.cluster.rst | 32 +++++++++++++++------- doc/api/etrago.tools.rst | 54 ++++++++++++++++++++++---------------- 4 files changed, 66 insertions(+), 34 deletions(-) create mode 100644 doc/api/appl.rst diff --git a/doc/api.rst b/doc/api.rst index 83b7a11a8..d65337975 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -1,8 +1,11 @@ -.. make doc-string generated documentation appear here +API +==== .. toctree:: :maxdepth: 7 :glob: :titlesonly: - API + api/etrago.cluster + api/etrago.tools + api/appl.rst diff --git a/doc/api/appl.rst b/doc/api/appl.rst new file mode 100644 index 000000000..fe45967b0 --- /dev/null +++ b/doc/api/appl.rst @@ -0,0 +1,7 @@ +etrago.appl module +------------------- + +.. automodule:: etrago.appl + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/doc/api/etrago.cluster.rst b/doc/api/etrago.cluster.rst index 1e2fc858b..40fc8db3c 100644 --- a/doc/api/etrago.cluster.rst +++ b/doc/api/etrago.cluster.rst @@ -1,18 +1,31 @@ -etrago\.cluster package +etrago.cluster package ======================= -Submodules ----------- +etrago.cluster.disaggregation module +----------------------------------------- -etrago\.cluster\.networkclustering module +.. automodule:: etrago.cluster.disaggregation + :members: + :undoc-members: + :show-inheritance: + +etrago.cluster.electrical module ----------------------------------------- -.. automodule:: etrago.cluster.networkclustering +.. automodule:: etrago.cluster.electrical :members: :undoc-members: :show-inheritance: -etrago\.cluster\.snapshot module +etrago.cluster.gas module +----------------------------------------- + +.. automodule:: etrago.cluster.gas + :members: + :undoc-members: + :show-inheritance: + +etrago.cluster.snapshot module -------------------------------- .. automodule:: etrago.cluster.snapshot @@ -20,11 +33,10 @@ etrago\.cluster\.snapshot module :undoc-members: :show-inheritance: -Module contents ---------------- +etrago.cluster.spatial module +-------------------------------- -.. automodule:: etrago.cluster +.. automodule:: etrago.cluster.spatial :members: :undoc-members: :show-inheritance: - diff --git a/doc/api/etrago.tools.rst b/doc/api/etrago.tools.rst index 33586cbf9..2b5495d38 100644 --- a/doc/api/etrago.tools.rst +++ b/doc/api/etrago.tools.rst @@ -1,56 +1,66 @@ -etrago\.tools package +etrago.tools package ===================== -Submodules ----------- +etrago.tools.calc\_results module +---------------------------------- -etrago\.tools\.io module ------------------------- +.. automodule:: etrago.tools.calc_results + :members: + :undoc-members: + :show-inheritance: -.. automodule:: etrago.tools.io +etrago.tools.constraints module +---------------------------------- + +.. automodule:: etrago.tools.constraints :members: :undoc-members: :show-inheritance: +etrago.tools.execute module +---------------------------------- -etrago\.tools\.extendable module ------------------------- +.. automodule:: etrago.tools.execute + :members: + :undoc-members: + :show-inheritance: + +etrago.tools.extendable module +---------------------------------- .. automodule:: etrago.tools.extendable :members: :undoc-members: :show-inheritance: +etrago.tools.io module +------------------------ -etrago\.tools\.plot module --------------------------- - -.. automodule:: etrago.tools.plot +.. automodule:: etrago.tools.io :members: :undoc-members: :show-inheritance: -etrago\.tools\.snapshot\_clustering module ------------------------------------------- +etrago.tools.network module +----------------------------- -.. automodule:: etrago.tools.snapshot_clustering +.. automodule:: etrago.tools.network :members: :undoc-members: :show-inheritance: -etrago\.tools\.utilities module -------------------------------- +etrago.tools.plot module +--------------------------- -.. automodule:: etrago.tools.utilities +.. automodule:: etrago.tools.plot :members: :undoc-members: :show-inheritance: +etrago.tools.utilities module +------------------------------- -Module contents ---------------- - -.. automodule:: etrago.tools +.. automodule:: etrago.tools.utilities :members: :undoc-members: :show-inheritance: From 85de9f99ab77cedc700b0b5c24ab06df3c5c7dc0 Mon Sep 17 00:00:00 2001 From: birgits Date: Tue, 5 Sep 2023 15:08:24 +0200 Subject: [PATCH 140/159] Add rtd link to readme --- README.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.rst b/README.rst index 098db7417..34e1fe540 100644 --- a/README.rst +++ b/README.rst @@ -27,6 +27,8 @@ The focus of optimization are flexibility options with a special focus on energy storages. Grid expansion measures are not part of this tool and will be instead part of 'eGo' https://github.com/openego/eGo +eTraGo is documented on `readthedocs `_. + .. warning:: From now on eTraGo depends on a sector coupled data-model. This is not published on the oedb yet, the data has to be created using From fd46b9f7c178ba1f220783750748304eea0350c1 Mon Sep 17 00:00:00 2001 From: birgits Date: Tue, 5 Sep 2023 15:08:38 +0200 Subject: [PATCH 141/159] Set language to English --- doc/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/conf.py b/doc/conf.py index 313e191cb..5fc0bd671 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -122,7 +122,7 @@ # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = None +language = "English" # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: From 2a99b40daa9010b13f4ed2fb86a3ba53062cb369 Mon Sep 17 00:00:00 2001 From: birgits Date: Tue, 5 Sep 2023 15:09:47 +0200 Subject: [PATCH 142/159] Solve errors in API doc --- README.rst | 3 +- doc/theoretical_background.rst | 2 +- doc/whatsnew/v0_5_1.rst | 2 +- doc/whatsnew/v0_6_1.rst | 2 +- doc/whatsnew/v0_7_0.rst | 4 +- doc/whatsnew/v0_7_1.rst | 2 +- doc/whatsnew/v0_7_2.rst | 2 +- doc/whatsnew/v0_8_0.rst | 2 +- etrago/appl.py | 218 +++++++-------- etrago/cluster/disaggregation.py | 26 +- etrago/cluster/electrical.py | 2 +- etrago/cluster/gas.py | 16 +- etrago/cluster/snapshot.py | 9 +- etrago/cluster/spatial.py | 12 +- etrago/tools/calc_results.py | 447 ++++++++++++++++--------------- etrago/tools/constraints.py | 38 +-- etrago/tools/io.py | 87 +++--- etrago/tools/plot.py | 107 ++++---- etrago/tools/utilities.py | 85 +++--- 19 files changed, 542 insertions(+), 524 deletions(-) diff --git a/README.rst b/README.rst index 34e1fe540..3a5f9bb4b 100644 --- a/README.rst +++ b/README.rst @@ -19,7 +19,7 @@ eTraGo Optimization of flexibility options for transmission grids based on PyPSA A speciality in this context is that transmission grids are described by the -380, 220 and 110 kV in Germany. Conventionally the 110kV grid is part of the +380, 220 and 110 kV in Germany. Conventionally, the 110kV grid is part of the distribution grid. The integration of the transmission and 'upper' distribution grid is part of eTraGo. @@ -37,7 +37,6 @@ eTraGo is documented on `readthedocs `_. When you want to use eTraGo for optimizations, please use the latest release 0.8.0. - Installation ============ eTraGo is designed as a Python package therefore it is mandatory to have diff --git a/doc/theoretical_background.rst b/doc/theoretical_background.rst index 03bb9eb43..6df61b821 100644 --- a/doc/theoretical_background.rst +++ b/doc/theoretical_background.rst @@ -78,7 +78,7 @@ As shown in the figure above, several options to store energy are part of the mo Miscellaneous Features --------- +---------------------- Several features were developed to enhance the functionality of eTraGo. diff --git a/doc/whatsnew/v0_5_1.rst b/doc/whatsnew/v0_5_1.rst index ac1f83a9f..ad37004f1 100644 --- a/doc/whatsnew/v0_5_1.rst +++ b/doc/whatsnew/v0_5_1.rst @@ -1,5 +1,5 @@ Release 0.5.1 (February 01, 2018) -++++++++++++++++++++++++++++++++ +++++++++++++++++++++++++++++++++++ eTraGo works with ego.io 0.3.0 diff --git a/doc/whatsnew/v0_6_1.rst b/doc/whatsnew/v0_6_1.rst index 5b9ec69d5..630828a3b 100644 --- a/doc/whatsnew/v0_6_1.rst +++ b/doc/whatsnew/v0_6_1.rst @@ -1,5 +1,5 @@ Release 0.6.1 (Juli 18, 2018) -++++++++++++++++++++++++++++ +++++++++++++++++++++++++++++++ eTraGo works with pypi and is suitable for eGo 0.2.0 Added features diff --git a/doc/whatsnew/v0_7_0.rst b/doc/whatsnew/v0_7_0.rst index e690bd5e2..abedaaf9d 100644 --- a/doc/whatsnew/v0_7_0.rst +++ b/doc/whatsnew/v0_7_0.rst @@ -1,9 +1,9 @@ Release 0.7.0 (September 6, 2018) -++++++++++++++++++++++++++++ +++++++++++++++++++++++++++++++++++ eTraGo is able to produce feasible non-linear power flows based on optimization results and allows the disaggregation of clustered results to original spatial complexities. Added features --------------- +--------------- * The pf_post_lopf function was improved. Due to changes in the data set now the non-linear power flow (pf) creates feasible solutions. If network optimization is turned on, a second lopf which regards the updated reactances and optimizes only dispatch is performed before the pf is executed. * The disaggregation method was included. When using a network clustering method to reduce the spatial complexity of the given network, a disaggregation method can be used afterwards to distribute the nodal results (generation and storage timeseries) to the original complexity. The method 'disaggregation': 'uniform' can be used as an interface functionality for distribution grid planning tools like eDisGo. diff --git a/doc/whatsnew/v0_7_1.rst b/doc/whatsnew/v0_7_1.rst index 53eebb24c..d7e692677 100644 --- a/doc/whatsnew/v0_7_1.rst +++ b/doc/whatsnew/v0_7_1.rst @@ -1,5 +1,5 @@ Release 0.7.1 (October 25, 2018) -++++++++++++++++++++++++++++ +++++++++++++++++++++++++++++++++ A minor release adding new options for additional constraints, modelling assumptions and plotting. Added features diff --git a/doc/whatsnew/v0_7_2.rst b/doc/whatsnew/v0_7_2.rst index 58ee28ca6..c972f76bc 100644 --- a/doc/whatsnew/v0_7_2.rst +++ b/doc/whatsnew/v0_7_2.rst @@ -1,5 +1,5 @@ Release 0.7.2 (Juni 15, 2020) -++++++++++++++++++++++++++++ +++++++++++++++++++++++++++++++ A minor release adding the following features. Added features diff --git a/doc/whatsnew/v0_8_0.rst b/doc/whatsnew/v0_8_0.rst index 420ce9e08..b4f28df3e 100644 --- a/doc/whatsnew/v0_8_0.rst +++ b/doc/whatsnew/v0_8_0.rst @@ -1,5 +1,5 @@ Release 0.8.0 (April 8, 2021) -++++++++++++++++++++++++++++ +++++++++++++++++++++++++++++++ eTraGo has now a more object-oriented programming design. Added features diff --git a/etrago/appl.py b/etrago/appl.py index 0e6724009..b0d953c70 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -166,92 +166,79 @@ def run_etrago(args, json_path): """Function to conduct optimization considering the following arguments. - Parameters - ---------- - - db : str - ``'oedb'``, - Name of Database session setting stored in *config.ini* of *.egoio* - - gridversion : NoneType or str - ``'v0.4.6'``, - Name of the data version number of oedb: state ``'None'`` for - model_draft (sand-box) or an explicit version number - (e.g. 'v0.4.6') for the grid schema. - - method : dict - {'type': 'lopf', 'n_iter': 4, 'pyomo': True}, - Choose 'lopf' for 'type'. In case of extendable lines, several lopfs - have to be performed. Choose either 'n_init' and a fixed number of - iterations or 'thershold' and a threashold of the objective function as - abort criteria. - Set 'pyomo' to False for big optimization problems, currently only - possible when solver is 'gurobi'. - - pf_post_lopf : dict - {'active': True, 'add_foreign_lopf': True, 'q_allocation': 'p_nom'}, - Option to run a non-linear power flow (pf) directly after the - linear optimal power flow (and thus the dispatch) has finished. - If foreign lines are modeled as DC-links (see foreign_lines), results - of the lopf can be added by setting 'add_foreign_lopf'. - Reactive power can be distributed either by 'p_nom' or 'p'. - - start_snapshot : int - 1, - Start hour of the scenario year to be calculated. - - end_snapshot : int - 2, - End hour of the scenario year to be calculated. - If snapshot clusterung is used, the selected snapshots should cover - the number of periods / segments. - - solver : str - 'glpk', - Choose your preferred solver. Current options: 'glpk' (open-source), - 'cplex' or 'gurobi'. - - solver_options : dict - Choose settings of solver to improve simulation time and result. - Options are described in documentation of chosen solver. - - model_formulation : str - 'kirchoff', - Choose formulation of pyomo-model. - Current options: angles, cycles, kirchhoff, ptdf - + Parameters + ---------- + db : str + Name of Database session setting stored in *config.ini* of *.egoio*, e.g. + ``'oedb'``. + gridversion : NoneType or str + Name of the data version number of oedb: state ``'None'`` for + model_draft (sand-box) or an explicit version number + (e.g. 'v0.4.6') for the grid schema. + method : dict + {'type': 'lopf', 'n_iter': 4, 'pyomo': True}, + Choose 'lopf' for 'type'. In case of extendable lines, several lopfs + have to be performed. Choose either 'n_init' and a fixed number of + iterations or 'thershold' and a threashold of the objective function as + abort criteria. + Set 'pyomo' to False for big optimization problems, currently only + possible when solver is 'gurobi'. + pf_post_lopf : dict + {'active': True, 'add_foreign_lopf': True, 'q_allocation': 'p_nom'}, + Option to run a non-linear power flow (pf) directly after the + linear optimal power flow (and thus the dispatch) has finished. + If foreign lines are modeled as DC-links (see foreign_lines), results + of the lopf can be added by setting 'add_foreign_lopf'. + Reactive power can be distributed either by 'p_nom' or 'p'. + start_snapshot : int + 1, + Start hour of the scenario year to be calculated. + end_snapshot : int + 2, + End hour of the scenario year to be calculated. + If snapshot clusterung is used, the selected snapshots should cover + the number of periods / segments. + solver : str + 'glpk', + Choose your preferred solver. Current options: 'glpk' (open-source), + 'cplex' or 'gurobi'. + solver_options : dict + Choose settings of solver to improve simulation time and result. + Options are described in documentation of chosen solver. + model_formulation : str + 'kirchoff', + Choose formulation of pyomo-model. + Current options: angles, cycles, kirchhoff, ptdf scn_name : str 'eGon2035', Choose your scenario. Currently, there are two different scenarios: 'eGon2035', 'eGon100RE'. - scn_extension : NoneType or str - None, Choose extension-scenarios which will be added to the existing network container. Data of the extension scenarios are located in extension-tables (e.g. model_draft.ego_grid_pf_hv_extension_bus) - with the prefix 'extension_'. - Currently there are three overlay networks: - 'nep2035_confirmed' includes all planed new lines confirmed by the - Bundesnetzagentur - 'nep2035_b2' includes all new lines planned by the - Netzentwicklungsplan 2025 in scenario 2035 B2 - 'BE_NO_NEP 2035' includes planned lines to Belgium and Norway and - adds BE and NO as electrical neighbours - - scn_decommissioning : NoneType or str - None, + with the prefix 'extension\_'. + Currently, there are three overlay networks: + * 'nep2035_confirmed' includes all planed new lines confirmed by the + Bundesnetzagentur + * 'nep2035_b2' includes all new lines planned by the + Netzentwicklungsplan 2025 in scenario 2035 B2 + * 'BE_NO_NEP 2035' includes planned lines to Belgium and Norway and + adds BE and NO as electrical neighbours + + scn_decommissioning : NoneType or str Choose an extra scenario which includes lines you want to decommise from the existing network. Data of the decommissioning scenarios are located in extension-tables (e.g. model_draft.ego_grid_pf_hv_extension_bus) with the prefix - 'decommissioning_'. + 'decommissioning\_'. Currently, there are two decommissioning_scenarios which are linked to extension-scenarios: - 'nep2035_confirmed' includes all lines that will be replaced in - confirmed projects - 'nep2035_b2' includes all lines that will be replaced in - NEP-scenario 2035 B2 + + * 'nep2035_confirmed' includes all lines that will be replaced in + confirmed projects + * 'nep2035_b2' includes all lines that will be replaced in + NEP-scenario 2035 B2 lpfile : bool or str False, @@ -279,18 +266,21 @@ def run_etrago(args, json_path): The list 'extendable_components' defines a set of components to optimize. Settings can be added in /tools/extendable.py. The most important possibilities: - 'as_in_db': leaves everything as it is defined in the data coming - from the database - 'network': set all lines, links and transformers in electrical - grid extendable - 'german_network': set lines and transformers in German electrical - grid extendable - 'foreign_network': set foreign lines and transformers in electrical - grid extendable - 'transformers': set all transformers extendable - 'storages' / 'stores': allow to install extendable storages - (unlimited in size) at each grid node in order to meet - the flexibility demand. + + * 'as_in_db' + leaves everything as it is defined in the data coming from the database + * 'network' + set all lines, links and transformers in electrical grid extendable + * 'german_network' + set lines and transformers in German electrical grid extendable + * 'foreign_network' + set foreign lines and transformers in electrical grid extendable + * 'transformers' + set all transformers extendable + * 'storages' / 'stores' + allow to install extendable storages (unlimited in size) at each grid node + in order to meet the flexibility demand. + Upper bounds for electrical grid expansion can be defined for lines in Germany relative to the existing capacity using 'grid_max_D'. Alternatively, absolute maximum capacities between two electrical buses @@ -303,35 +293,35 @@ def run_etrago(args, json_path): of each generator in order to prevent an optima plateau. To reproduce a noise, choose the same integer (seed number). - extra_functionality : dict - None, + extra_functionality : dict or None Choose extra functionalities and their parameters. Settings can be added in /tools/constraints.py. Current options are: - 'max_line_ext': float - Maximal share of network extension in p.u. - 'min_renewable_share': float - Minimal share of renewable generation in p.u. - 'cross_border_flow': array of two floats - Limit AC cross-border-flows between Germany and its neigbouring - countries, set values in MWh for all snapshots, e.g. [-x, y] - (with x Import, y Export, positiv: export from Germany) - 'cross_border_flows_per_country': dict of cntr and array of floats - Limit AC cross-border-flows between Germany and its neigbouring - countries, set values in in MWh for each country, e.g. [-x, y] - (with x Import, y Export, positiv: export from Germany) - 'capacity_factor': dict of arrays - Limit overall energy production for each carrier, - set upper/lower limit in p.u. - 'capacity_factor_per_gen': dict of arrays - Limit overall energy production for each generator by carrier, - set upper/lower limit in p.u. - 'capacity_factor_per_cntr': dict of dict of arrays - Limit overall energy production country-wise for each carrier, - set upper/lower limit in p.u. - 'capacity_factor_per_gen_cntr': dict of dict of arrays - Limit overall energy production country-wise for each generator - by carrier, set upper/lower limit in p.u. + + * 'max_line_ext': float + Maximal share of network extension in p.u. + * 'min_renewable_share': float + Minimal share of renewable generation in p.u. + * 'cross_border_flow': array of two floats + Limit AC cross-border-flows between Germany and its neigbouring + countries, set values in MWh for all snapshots, e.g. [-x, y] + (with x Import, y Export, positiv: export from Germany) + * 'cross_border_flows_per_country': dict of cntr and array of floats + Limit AC cross-border-flows between Germany and its neigbouring + countries, set values in in MWh for each country, e.g. [-x, y] + (with x Import, y Export, positiv: export from Germany) + * 'capacity_factor': dict of arrays + Limit overall energy production for each carrier, + set upper/lower limit in p.u. + * 'capacity_factor_per_gen': dict of arrays + Limit overall energy production for each generator by carrier, + set upper/lower limit in p.u. + * 'capacity_factor_per_cntr': dict of dict of arrays + Limit overall energy production country-wise for each carrier, + set upper/lower limit in p.u. + * 'capacity_factor_per_gen_cntr': dict of dict of arrays + Limit overall energy production country-wise for each generator + by carrier, set upper/lower limit in p.u. network_clustering_ehv : bool False, @@ -371,7 +361,7 @@ def run_etrago(args, json_path): from busmap; both only within the k-means Clustering. The other options affect the clustering algorithm in both methods and should only be changed carefully, documentation and possible settings are described - in sklearn-package (sklearn/cluster/k_means_.py). + in sklearn-package (sklearn/cluster/kmeans.py). sector_coupled_clustering : dict {'active': True, 'carrier_data': { @@ -413,7 +403,7 @@ def run_etrago(args, json_path): to reduce temporal complexity. temporal_disaggregation : dict - {'active': False, 'no_slices': 4}, + {'active': False, 'no_slices': 4}, State if you to apply a second lopf considering dispatch only to disaggregate the dispatch to the whole temporal complexity. Be aware that a load shedding will be applied in this optimization. @@ -437,8 +427,8 @@ def run_etrago(args, json_path): foreign_lines : dict {'carrier':'AC', 'capacity': 'osmTGmod}' Choose transmission technology and capacity of foreign lines: - 'carrier': 'AC' or 'DC' - 'capacity': 'osmTGmod', 'ntc_acer' or 'thermal_acer' + * 'carrier': 'AC' or 'DC' + * 'capacity': 'osmTGmod', 'ntc_acer' or 'thermal_acer' comments : str None diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 0b4378836..5f6718d39 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -65,9 +65,9 @@ def construct_partial_network(self, cluster, scenario): :param cluster: Index of the cluster to disaggregate :return: Tuple of (partial_network, external_buses) where - `partial_network` is the result of the partial decomposition - and `external_buses` represent clusters adjacent to `cluster` that may - be influenced by calculations done on the partial network. + `partial_network` is the result of the partial decomposition + and `external_buses` represent clusters adjacent to `cluster` that may + be influenced by calculations done on the partial network. """ # Create an empty network @@ -228,16 +228,15 @@ def from_busmap(x): # series accordingly, but there must be bug somewhere because # using it, the time series in the clusters and sums of the # time series after disaggregation don't match up. - """ - series = getattr(self.original_network, bustype + '_t') - partial_series = type(series)() - for s in series: - partial_series[s] = series[s].loc[ - :, - getattr(partial_network, bustype) - .index.intersection(series[s].columns)] - setattr(partial_network, bustype + '_t', partial_series) - """ + + # series = getattr(self.original_network, bustype + '_t') + # partial_series = type(series)() + # for s in series: + # partial_series[s] = series[s].loc[ + # :, + # getattr(partial_network, bustype) + # .index.intersection(series[s].columns)] + # setattr(partial_network, bustype + '_t', partial_series) # Just a simple sanity check # TODO: Remove when sure that disaggregation will not go insane anymore @@ -262,6 +261,7 @@ def solve(self, scenario, solver): """ Decompose each cluster into separate units and try to optimize them separately + :param scenario: :param solver: Solver that may be used to optimize partial networks """ diff --git a/etrago/cluster/electrical.py b/etrago/cluster/electrical.py index 8a7bf7549..e46d6b0ff 100755 --- a/etrago/cluster/electrical.py +++ b/etrago/cluster/electrical.py @@ -112,9 +112,9 @@ def adjust_no_electric_network(etrago, busmap, cluster_met): ------- network : pypsa.Network Container for all network components of the clustered network. - busmap : dict Maps old bus_ids to new bus_ids including all sectors. + """ network = etrago.network # network2 is supposed to contain all the not electrical or gas buses diff --git a/etrago/cluster/gas.py b/etrago/cluster/gas.py index 733b9905a..b4e90c3a8 100644 --- a/etrago/cluster/gas.py +++ b/etrago/cluster/gas.py @@ -311,11 +311,10 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None): Returns ------- - Tuple containing: - network_gasgrid_c : pypsa.Network - A pypsa.Network containing the clustered network. - busmap : pd.Series - A Pandas Series mapping each bus to its corresponding cluster ID. + network_gasgrid_c : pypsa.Network + A pypsa.Network containing the clustered network. + busmap : pd.Series + A Pandas Series mapping each bus to its corresponding cluster ID. """ settings = etrago.args["network_clustering"] @@ -932,11 +931,12 @@ def run_spatial_clustering_gas(self): buses and links, and then performs postprocessing to finalize the changes. Returns - None + -------- + None Raises - ValueError: If the selected method is not "kmeans" or - "kmedoids-dijkstra". + ------- + ValueError: If the selected method is not "kmeans" or "kmedoids-dijkstra". """ if "CH4" in self.network.buses.carrier.values: diff --git a/etrago/cluster/snapshot.py b/etrago/cluster/snapshot.py index daa0b6166..5f4e0d1c9 100644 --- a/etrago/cluster/snapshot.py +++ b/etrago/cluster/snapshot.py @@ -107,8 +107,7 @@ def tsam_cluster( Number of clusters for typical_periods. The default is 10. how : {'daily', 'weekly', 'monthly'}, optional Definition of period for typical_periods. The default is 'daily'. - extremePeriodMethod : {'None','append','new_cluster_center', - 'replace_cluster_center'}, optional + extremePeriodMethod : {'None','append','new_cluster_center', 'replace_cluster_center'}, optional Method to consider extreme snapshots in reduced timeseries. The default is 'None'. segmentation : boolean, optional Argument to activate segmenation method. The default is False. @@ -324,8 +323,7 @@ def segmentation_extreme_periods( Dataframe wit timeseries to cluster. timeseries : pd.DataFrame Information on segments after segmentation. - extremePeriodMethod : {'None','append','new_cluster_center', - 'replace_cluster_center'}, optional + extremePeriodMethod : {'None','append','new_cluster_center', 'replace_cluster_center'}, optional Method to consider extreme snapshots in reduced timeseries. The default is 'None'. Raises @@ -612,8 +610,7 @@ def run( Definition of period for typical_periods. The default is 'daily'. segmented_to : int, optional Number of segments for segmentation. The default is False. - extremePeriodMethod : {'None','append','new_cluster_center', - 'replace_cluster_center'}, optional + extremePeriodMethod : {'None','append','new_cluster_center', 'replace_cluster_center'}, optional Method to consider extreme snapshots in reduced timeseries. The default is 'None'. Returns diff --git a/etrago/cluster/spatial.py b/etrago/cluster/spatial.py index bbf176b36..43af10b9b 100755 --- a/etrago/cluster/spatial.py +++ b/etrago/cluster/spatial.py @@ -186,8 +186,10 @@ def group_links(network, with_time=True, carriers=None, cus_strateg=dict()): Returns ------- - new_df : links aggregated based on bus0, bus1 and carrier - new_pnl : links time series aggregated + new_df : + links aggregated based on bus0, bus1 and carrier + new_pnl : + links time series aggregated """ def normed_or_uniform(x): @@ -276,7 +278,7 @@ def graph_from_edges(edges): Returns ------- - M : :class:`networkx.classes.multigraph.MultiGraph + M : :class:`networkx.classes.multigraph.MultiGraph` """ M = nx.MultiGraph() @@ -301,7 +303,7 @@ def gen(nodes, n, graph): List of nodes in the system. n : int Number of desired multiprocessing units. - graph : :class:`networkx.classes.multigraph.MultiGraph + graph : :class:`networkx.classes.multigraph.MultiGraph` Graph representation of an electrical grid. Returns @@ -323,7 +325,7 @@ def shortest_path(paths, graph): ---------- paths : list List of pairs containing a source and a target node - graph : :class:`networkx.classes.multigraph.MultiGraph + graph : :class:`networkx.classes.multigraph.MultiGraph` Graph representation of an electrical grid. Returns diff --git a/etrago/tools/calc_results.py b/etrago/tools/calc_results.py index b12dd79bf..fca593a97 100755 --- a/etrago/tools/calc_results.py +++ b/etrago/tools/calc_results.py @@ -40,272 +40,277 @@ def _calc_storage_expansion(self): - """ Function that calulates storage expansion in MW + """ Function that calulates storage expansion in MW + Returns + ------- + float + storage expansion in MW - Returns - ------- - float - storage expansion in MW + """ + return (self.network.storage_units.p_nom_opt - + self.network.storage_units.p_nom_min + )[self.network.storage_units.p_nom_extendable]\ + .groupby(self.network.storage_units.carrier).sum() - """ - return (self.network.storage_units.p_nom_opt - - self.network.storage_units.p_nom_min - )[self.network.storage_units.p_nom_extendable]\ - .groupby(self.network.storage_units.carrier).sum() def _calc_store_expansion(self): - """ Function that calulates store expansion in MW + """ Function that calulates store expansion in MW - Returns - ------- - float - store expansion in MW + Returns + ------- + float + store expansion in MW + + """ + return (self.network.stores.e_nom_opt - + self.network.stores.e_nom_min + )[self.network.stores.e_nom_extendable] - """ - return (self.network.stores.e_nom_opt - - self.network.stores.e_nom_min - )[self.network.stores.e_nom_extendable] def _calc_sectorcoupling_link_expansion(self): - """ Function that calulates expansion of sectorcoupling links in MW + """ Function that calulates expansion of sectorcoupling links in MW + + Returns + ------- + float + link expansion in MW (differentiating between technologies) - Returns - ------- - float - link expansion in MW (differentiating between technologies) + """ + ext_links = self.network.links[self.network.links.p_nom_extendable] - """ - ext_links = self.network.links[self.network.links.p_nom_extendable] + links = [0, 0, 0, 0] - links = [0, 0, 0, 0] + l1 = ext_links[ext_links.carrier=='H2_to_power'] + l2 = ext_links[ext_links.carrier=='power_to_H2'] + l3 = ext_links[ext_links.carrier=='H2_to_CH4'] + l4 = ext_links[ext_links.carrier=='CH4_to_H2'] - l1 = ext_links[ext_links.carrier=='H2_to_power'] - l2 = ext_links[ext_links.carrier=='power_to_H2'] - l3 = ext_links[ext_links.carrier=='H2_to_CH4'] - l4 = ext_links[ext_links.carrier=='CH4_to_H2'] + links[0] = (l1.p_nom_opt-l1.p_nom_min).sum() + links[1] = (l2.p_nom_opt-l2.p_nom_min).sum() + links[2] = (l3.p_nom_opt-l3.p_nom_min).sum() + links[3] = (l4.p_nom_opt-l4.p_nom_min).sum() - links[0] = (l1.p_nom_opt-l1.p_nom_min).sum() - links[1] = (l2.p_nom_opt-l2.p_nom_min).sum() - links[2] = (l3.p_nom_opt-l3.p_nom_min).sum() - links[3] = (l4.p_nom_opt-l4.p_nom_min).sum() + return links - return links def _calc_network_expansion(self): - """ Function that calulates electrical network expansion in MW + """ Function that calulates electrical network expansion in MW - Returns - ------- - float - network expansion (AC lines and DC links) in MW + Returns + ------- + float + network expansion (AC lines and DC links) in MW - """ + """ - network = self.network + network = self.network - lines = (network.lines.s_nom_opt - - network.lines.s_nom_min - )[network.lines.s_nom_extendable] + lines = (network.lines.s_nom_opt - + network.lines.s_nom_min + )[network.lines.s_nom_extendable] - ext_links = network.links[network.links.p_nom_extendable] - ext_dc_lines = ext_links[ext_links.carrier=='DC'] + ext_links = network.links[network.links.p_nom_extendable] + ext_dc_lines = ext_links[ext_links.carrier=='DC'] - dc_links = (ext_dc_lines.p_nom_opt - - ext_dc_lines.p_nom_min) + dc_links = (ext_dc_lines.p_nom_opt - + ext_dc_lines.p_nom_min) + + return lines, dc_links - return lines, dc_links def calc_investment_cost(self): - """ Function that calulates overall annualized investment costs. + """ Function that calulates overall annualized investment costs. - Returns - ------- - network_costs : float - Investments in line expansion (AC+DC) - link_costs : float - Investments in sectorcoupling link expansion - stor_costs : float - Investments in storage and store expansion + Returns + ------- + network_costs : float + Investments in line expansion (AC+DC) + link_costs : float + Investments in sectorcoupling link expansion + stor_costs : float + Investments in storage and store expansion - """ - network = self.network + """ + network = self.network - # electrical grid: AC lines, DC lines + # electrical grid: AC lines, DC lines - network_costs = [0, 0] + network_costs = [0, 0] - ext_lines = network.lines[network.lines.s_nom_extendable] - ext_trafos = network.transformers[network.transformers.s_nom_extendable] - ext_links = network.links[network.links.p_nom_extendable] - ext_dc_lines = ext_links[ext_links.carrier=='DC'] + ext_lines = network.lines[network.lines.s_nom_extendable] + ext_trafos = network.transformers[network.transformers.s_nom_extendable] + ext_links = network.links[network.links.p_nom_extendable] + ext_dc_lines = ext_links[ext_links.carrier=='DC'] - if not ext_lines.empty: - network_costs[0] = ((ext_lines.s_nom_opt-ext_lines.s_nom_min - )*ext_lines.capital_cost).sum() + if not ext_lines.empty: + network_costs[0] = ((ext_lines.s_nom_opt-ext_lines.s_nom_min + )*ext_lines.capital_cost).sum() - if not ext_trafos.empty: - network_costs[0] = network_costs[0]+(( - ext_trafos.s_nom_opt-ext_trafos.s_nom - )*ext_trafos.capital_cost).sum() + if not ext_trafos.empty: + network_costs[0] = network_costs[0]+(( + ext_trafos.s_nom_opt-ext_trafos.s_nom + )*ext_trafos.capital_cost).sum() - if not ext_dc_lines.empty: - network_costs[1] = ((ext_dc_lines.p_nom_opt-ext_dc_lines.p_nom_min - )*ext_dc_lines.capital_cost).sum() + if not ext_dc_lines.empty: + network_costs[1] = ((ext_dc_lines.p_nom_opt-ext_dc_lines.p_nom_min + )*ext_dc_lines.capital_cost).sum() - # links in other sectors / coupling different sectors + # links in other sectors / coupling different sectors - link_costs = 0 + link_costs = 0 - ext_links = ext_links[ext_links.carrier!='DC'] + ext_links = ext_links[ext_links.carrier!='DC'] - if not ext_links.empty: - link_costs = ((ext_links.p_nom_opt-ext_links.p_nom_min - )*ext_links.capital_cost).sum() + if not ext_links.empty: + link_costs = ((ext_links.p_nom_opt-ext_links.p_nom_min + )*ext_links.capital_cost).sum() - # storage and store costs + # storage and store costs - sto_costs = [0, 0] + sto_costs = [0, 0] - ext_storage = network.storage_units[network.storage_units.p_nom_extendable] - ext_store = network.stores[network.stores.e_nom_extendable] + ext_storage = network.storage_units[network.storage_units.p_nom_extendable] + ext_store = network.stores[network.stores.e_nom_extendable] - if not ext_storage.empty: - sto_costs[0] = (ext_storage.p_nom_opt* - ext_storage.capital_cost).sum() + if not ext_storage.empty: + sto_costs[0] = (ext_storage.p_nom_opt* + ext_storage.capital_cost).sum() - if not ext_store.empty: - sto_costs[1] = (ext_store.e_nom_opt* - ext_store.capital_cost).sum() + if not ext_store.empty: + sto_costs[1] = (ext_store.e_nom_opt* + ext_store.capital_cost).sum() + + return network_costs, link_costs, sto_costs - return network_costs, link_costs, sto_costs def calc_marginal_cost(self): - """ - Function that caluclates and returns marginal costs, considering - generation and link and storage dispatch costs - - Returns - ------- - marginal_cost : float - Annual marginal cost in EUR - - """ - network = self.network - gen = network.generators_t.p.mul( - network.snapshot_weightings.objective, axis=0).sum(axis=0).mul( - network.generators.marginal_cost).sum() - link = abs(network.links_t.p0).mul( - network.snapshot_weightings.objective, axis=0).sum(axis=0).mul( - network.links.marginal_cost).sum() - stor = network.storage_units_t.p.mul( - network.snapshot_weightings.objective, axis=0).sum(axis=0).mul( - network.storage_units.marginal_cost).sum() - marginal_cost = gen + link + stor - return marginal_cost + """ + Function that caluclates and returns marginal costs, considering + generation and link and storage dispatch costs + + Returns + ------- + marginal_cost : float + Annual marginal cost in EUR + + """ + network = self.network + gen = network.generators_t.p.mul( + network.snapshot_weightings.objective, axis=0).sum(axis=0).mul( + network.generators.marginal_cost).sum() + link = abs(network.links_t.p0).mul( + network.snapshot_weightings.objective, axis=0).sum(axis=0).mul( + network.links.marginal_cost).sum() + stor = network.storage_units_t.p.mul( + network.snapshot_weightings.objective, axis=0).sum(axis=0).mul( + network.storage_units.marginal_cost).sum() + marginal_cost = gen + link + stor + return marginal_cost + def calc_etrago_results(self): - """ Function that calculates main results of grid optimization - and adds them to Etrago object. - - Returns - ------- - None. - - """ - self.results = pd.DataFrame(columns=['unit', 'value'], - index=['annual system costs', - 'annual investment costs', - 'annual marginal costs', - 'annual electrical grid investment costs', - 'annual ac grid investment costs', - 'annual dc grid investment costs', - 'annual links investment costs', - 'annual storage+store investment costs', - 'annual electrical storage investment costs', - 'annual store investment costs', - 'battery storage expansion', - 'store expansion', - 'H2 store expansion', - 'CH4 store expansion', - 'heat store expansion', - 'storage+store expansion', - 'fuel cell links expansion', - 'electrolyzer links expansion', - 'methanisation links expansion', - 'Steam Methane Reformation links expansion', - 'abs. electrical grid expansion', - 'abs. electrical ac grid expansion', - 'abs. electrical dc grid expansion', - 'rel. electrical ac grid expansion', - 'rel. electrical dc grid expansion']) - - self.results.unit[self.results.index.str.contains('cost')] = 'EUR/a' - self.results.unit[self.results.index.str.contains('expansion')] = 'MW' - self.results.unit[self.results.index.str.contains('rel.')] = 'p.u.' - - # system costs - - self.results.value['annual ac grid investment costs'] = calc_investment_cost(self)[0][0] - self.results.value['annual dc grid investment costs'] = calc_investment_cost(self)[0][1] - self.results.value['annual electrical grid investment costs'] = sum(calc_investment_cost(self)[0]) - - self.results.value['annual links investment costs'] = calc_investment_cost(self)[1] - - self.results.value['annual electrical storage investment costs'] = calc_investment_cost(self)[2][0] - self.results.value['annual store investment costs'] = calc_investment_cost(self)[2][1] - self.results.value['annual storage+store investment costs'] = sum(calc_investment_cost(self)[2]) - - - self.results.value['annual investment costs'] = \ - sum(calc_investment_cost(self)[0]) + calc_investment_cost(self)[1] + sum(calc_investment_cost(self)[2]) - self.results.value['annual marginal costs'] = calc_marginal_cost(self) - - self.results.value['annual system costs'] = \ - self.results.value['annual investment costs'] + self.results.value['annual marginal costs'] - - # storage and store expansion - - network = self.network - - if not network.storage_units[network.storage_units.p_nom_extendable].empty: - - self.results.value['battery storage expansion'] = \ - _calc_storage_expansion(self).sum() - - store = _calc_store_expansion(self) - self.results.value['store expansion'] = store.sum() - self.results.value['H2 store expansion'] = \ - store[store.index.str.contains('H2')].sum() - self.results.value['CH4 store expansion'] = \ - store[store.index.str.contains('CH4')].sum() - self.results.value['heat store expansion'] = \ - store[store.index.str.contains('heat')].sum() - - self.results.value['storage+store expansion'] = \ - self.results.value['battery storage expansion'] + self.results.value['store expansion'] - - # links expansion - - if not network.links[network.links.p_nom_extendable].empty: - - links = _calc_sectorcoupling_link_expansion(self) - self.results.value['fuel cell links expansion'] = links[0] - self.results.value['electrolyzer links expansion'] = links[1] - self.results.value['methanisation links expansion'] = links[2] - self.results.value['Steam Methane Reformation links expansion'] = links[3] - - # grid expansion - - if not network.lines[network.lines.s_nom_extendable].empty: - - self.results.value['abs. electrical ac grid expansion'] = _calc_network_expansion(self)[0].sum() - self.results.value['abs. electrical dc grid expansion'] = _calc_network_expansion(self)[1].sum() - self.results.value['abs. electrical grid expansion'] = self.results.value['abs. electrical ac grid expansion'] + self.results.value['abs. electrical dc grid expansion'] - - ext_lines = network.lines[network.lines.s_nom_extendable] - ext_links = network.links[network.links.p_nom_extendable] - ext_dc_lines = ext_links[ext_links.carrier=='DC'] - - self.results.value['rel. electrical ac grid expansion'] = (_calc_network_expansion(self)[0].sum() / ext_lines.s_nom.sum()) - self.results.value['rel. electrical dc grid expansion'] = (_calc_network_expansion(self)[1].sum() / ext_dc_lines.p_nom.sum()) \ No newline at end of file + """ Function that calculates main results of grid optimization + and adds them to Etrago object. + + Returns + ------- + None. + + """ + self.results = pd.DataFrame(columns=['unit', 'value'], + index=['annual system costs', + 'annual investment costs', + 'annual marginal costs', + 'annual electrical grid investment costs', + 'annual ac grid investment costs', + 'annual dc grid investment costs', + 'annual links investment costs', + 'annual storage+store investment costs', + 'annual electrical storage investment costs', + 'annual store investment costs', + 'battery storage expansion', + 'store expansion', + 'H2 store expansion', + 'CH4 store expansion', + 'heat store expansion', + 'storage+store expansion', + 'fuel cell links expansion', + 'electrolyzer links expansion', + 'methanisation links expansion', + 'Steam Methane Reformation links expansion', + 'abs. electrical grid expansion', + 'abs. electrical ac grid expansion', + 'abs. electrical dc grid expansion', + 'rel. electrical ac grid expansion', + 'rel. electrical dc grid expansion']) + + self.results.unit[self.results.index.str.contains('cost')] = 'EUR/a' + self.results.unit[self.results.index.str.contains('expansion')] = 'MW' + self.results.unit[self.results.index.str.contains('rel.')] = 'p.u.' + + # system costs + + self.results.value['annual ac grid investment costs'] = calc_investment_cost(self)[0][0] + self.results.value['annual dc grid investment costs'] = calc_investment_cost(self)[0][1] + self.results.value['annual electrical grid investment costs'] = sum(calc_investment_cost(self)[0]) + + self.results.value['annual links investment costs'] = calc_investment_cost(self)[1] + + self.results.value['annual electrical storage investment costs'] = calc_investment_cost(self)[2][0] + self.results.value['annual store investment costs'] = calc_investment_cost(self)[2][1] + self.results.value['annual storage+store investment costs'] = sum(calc_investment_cost(self)[2]) + + + self.results.value['annual investment costs'] = \ + sum(calc_investment_cost(self)[0]) + calc_investment_cost(self)[1] + sum(calc_investment_cost(self)[2]) + self.results.value['annual marginal costs'] = calc_marginal_cost(self) + + self.results.value['annual system costs'] = \ + self.results.value['annual investment costs'] + self.results.value['annual marginal costs'] + + # storage and store expansion + + network = self.network + + if not network.storage_units[network.storage_units.p_nom_extendable].empty: + + self.results.value['battery storage expansion'] = \ + _calc_storage_expansion(self).sum() + + store = _calc_store_expansion(self) + self.results.value['store expansion'] = store.sum() + self.results.value['H2 store expansion'] = \ + store[store.index.str.contains('H2')].sum() + self.results.value['CH4 store expansion'] = \ + store[store.index.str.contains('CH4')].sum() + self.results.value['heat store expansion'] = \ + store[store.index.str.contains('heat')].sum() + + self.results.value['storage+store expansion'] = \ + self.results.value['battery storage expansion'] + self.results.value['store expansion'] + + # links expansion + + if not network.links[network.links.p_nom_extendable].empty: + + links = _calc_sectorcoupling_link_expansion(self) + self.results.value['fuel cell links expansion'] = links[0] + self.results.value['electrolyzer links expansion'] = links[1] + self.results.value['methanisation links expansion'] = links[2] + self.results.value['Steam Methane Reformation links expansion'] = links[3] + + # grid expansion + + if not network.lines[network.lines.s_nom_extendable].empty: + + self.results.value['abs. electrical ac grid expansion'] = _calc_network_expansion(self)[0].sum() + self.results.value['abs. electrical dc grid expansion'] = _calc_network_expansion(self)[1].sum() + self.results.value['abs. electrical grid expansion'] = self.results.value['abs. electrical ac grid expansion'] + self.results.value['abs. electrical dc grid expansion'] + + ext_lines = network.lines[network.lines.s_nom_extendable] + ext_links = network.links[network.links.p_nom_extendable] + ext_dc_lines = ext_links[ext_links.carrier=='DC'] + + self.results.value['rel. electrical ac grid expansion'] = (_calc_network_expansion(self)[0].sum() / ext_lines.s_nom.sum()) + self.results.value['rel. electrical dc grid expansion'] = (_calc_network_expansion(self)[1].sum() / ext_dc_lines.p_nom.sum()) \ No newline at end of file diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index b2d9a0446..1a2b5a710 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -311,11 +311,14 @@ def _cross_border_flow(self, network, snapshots): Extra_functionality that limits overall AC crossborder flows from/to Germany. Add key 'cross_border_flow' and array with minimal and maximal import/export Example: {'cross_border_flow': [-x, y]} (with x Import, y Export) + + Parameters ---------- network : :class:`pypsa.Network Overall container of PyPSA snapshots : pandas.DatetimeIndex List of timesteps considered in the optimization + Returns ------- None. @@ -398,11 +401,14 @@ def _cross_border_flow_nmp(self, network, snapshots): Extra_functionality that limits overall crossborder flows from/to Germany. Add key 'cross_border_flow' and array with minimal and maximal import/export Example: {'cross_border_flow': [-x, y]} (with x Import, y Export) + + Parameters ---------- network : :class:`pypsa.Network Overall container of PyPSA snapshots : pandas.DatetimeIndex List of timesteps considered in the optimization + Returns ------- None. @@ -464,11 +470,14 @@ def _cross_border_flow_per_country_nmp(self, network, snapshots): Add key 'cross_border_flow_per_country' to args.extra_functionality and define dictionary of country keys and desired limitations of im/exports in MWh Example: {'cross_border_flow_per_country': {'DK':[-X, Y], 'FR':[0,0]}} + + Parameters ---------- network : :class:`pypsa.Network Overall container of PyPSA snapshots : pandas.DatetimeIndex List of timesteps considered in the optimization + Returns ------- None. @@ -550,11 +559,14 @@ def _cross_border_flow_per_country(self, network, snapshots): Add key 'cross_border_flow_per_country' to args.extra_functionality and define dictionary of country keys and desired limitations of im/exports in MWh Example: {'cross_border_flow_per_country': {'DK':[-X, Y], 'FR':[0,0]}} + + Parameters ---------- network : :class:`pypsa.Network Overall container of PyPSA snapshots : pandas.DatetimeIndex List of timesteps considered in the optimization + Returns ------- None. @@ -712,7 +724,6 @@ def _capacity_factor(self, network, snapshots): a dictonary as a fraction of generation potential. Example: 'capacity_factor': {'run_of_river': [0, 0.5], 'solar': [0.1, 1]} - Parameters ---------- network : :class:`pypsa.Network @@ -765,7 +776,6 @@ def _capacity_factor_nmp(self, network, snapshots): a dictonary as a fraction of generation potential. Example: 'capacity_factor': {'run_of_river': [0, 0.5], 'solar': [0.1, 1]} - Parameters ---------- network : :class:`pypsa.Network @@ -954,7 +964,6 @@ def _capacity_factor_per_gen(self, network, snapshots): Example: 'capacity_factor_per_gen': {'run_of_river': [0, 0.5], 'solar': [0.1, 1]} - Parameters ---------- network : :class:`pypsa.Network @@ -1023,7 +1032,6 @@ def _capacity_factor_per_gen_nmp(self, network, snapshots): Example: 'capacity_factor_per_gen': {'run_of_river': [0, 0.5], 'solar': [0.1, 1]} - Parameters ---------- network : :class:`pypsa.Network @@ -1324,7 +1332,7 @@ def add_ch4_constraints(self, network, snapshots): Parameters ---------- - network : :class:`pypsa.Network + network : :class:`pypsa.Network` Overall container of PyPSA snapshots : pandas.DatetimeIndex List of timesteps considered in the optimization @@ -1413,7 +1421,7 @@ def add_ch4_constraints_nmp(self, network, snapshots): Parameters ---------- - network : :class:`pypsa.Network + network : :class:`pypsa.Network` Overall container of PyPSA snapshots : pandas.DatetimeIndex List of timesteps considered in the optimization @@ -1497,7 +1505,7 @@ def snapshot_clustering_daily_bounds(self, network, snapshots): Parameters ---------- - network : :class:`pypsa.Network + network : :class:`pypsa.Network` Overall container of PyPSA snapshots : pandas.DatetimeIndex List of timesteps that will be constrained @@ -1536,7 +1544,7 @@ def snapshot_clustering_daily_bounds_nmp(self, network, snapshots): Parameters ---------- - network : :class:`pypsa.Network + network : :class:`pypsa.Network` Overall container of PyPSA snapshots : pandas.DatetimeIndex List of timesteps that will be constrained @@ -1610,7 +1618,7 @@ def snapshot_clustering_seasonal_storage( Parameters ---------- - network : :class:`pypsa.Network + network : :class:`pypsa.Network` Overall container of PyPSA snapshots : list A list of datetime objects representing the timestamps of the snapshots @@ -2430,7 +2438,7 @@ def snapshot_clustering_seasonal_storage_hourly(self, network, snapshots): Parameters ---------- - network : :class:`pypsa.Network + network : :class:`pypsa.Network` Overall container of PyPSA snapshots : list A list of datetime objects representing the timestamps of the snapshots @@ -2535,7 +2543,7 @@ def snapshot_clustering_seasonal_storage_nmp(self, n, sns, simplified=False): Parameters ---------- - n : :class:`pypsa.Network + n : :class:`pypsa.Network` Overall container of PyPSA sns : list A list of datetime objects representing the timestamps of the snapshots @@ -2637,7 +2645,7 @@ def snapshot_clustering_seasonal_storage_hourly_nmp(self, n, sns): Parameters ---------- - n : :class:`pypsa.Network + n : :class:`pypsa.Network` Overall container of PyPSA sns : list A list of datetime objects representing the timestamps of the snapshots @@ -2665,7 +2673,7 @@ def split_dispatch_disaggregation_constraints(self, n, sns): Parameters ---------- - network : :class:`pypsa.Network + network : :class:`pypsa.Network` Overall container of PyPSA snapshots : pandas.DatetimeIndex List of timesteps considered in the optimization @@ -2729,10 +2737,10 @@ def functionality(self, network, snapshots): Parameters ---------- - network : :class:`pypsa.Network + network : :class:`pypsa.Network` Overall container of PyPSA snapshots : pandas.DatetimeIndex - List of timesteps considered in the optimization + List of timesteps considered in the optimization """ if "CH4" in network.buses.carrier.values: diff --git a/etrago/tools/io.py b/etrago/tools/io.py index d1edbbd88..5bb2b019e 100644 --- a/etrago/tools/io.py +++ b/etrago/tools/io.py @@ -24,10 +24,8 @@ Input/output operations between powerflow schema in the oedb and PyPSA. Additionally oedb wrapper classes to instantiate PyPSA network objects. - Attributes ----------- - +----------- packagename: str Package containing orm class definitions temp_ormclass: str @@ -36,7 +34,7 @@ Orm class name of table with carrier id to carrier name datasets Notes ------ +------- A configuration file connecting the chosen optimization method with components to be queried is needed for NetworkScenario class. """ @@ -74,7 +72,6 @@ class ScenarioBase: Parameters ---------- - session : sqla.orm.session.Session Handles conversations with the database. version : str @@ -175,8 +172,6 @@ def configure_timeindex(self): ) self.timeindex = timeindex[self.start_snapshot - 1 : self.end_snapshot] - """ pandas.tseries.index.DateTimeIndex : - Index of snapshots or timesteps. """ def id_to_source(self): ormclass = self._mapped["Source"] @@ -768,8 +763,8 @@ def extension(self, **kwargs): To connect it to the existing network, transformers are needed. All components and its timeseries of the additional scenario need to be - inserted in the fitting 'model_draft.ego_grid_pf_hv_extension_' table. - The scn_name in the tables have to be labled with 'extension_' + scn_name + inserted in the fitting 'model_draft.ego_grid_pf_hv_extension\_' table. + The scn_name in the tables have to be labled with 'extension\_' + scn_name (e.g. 'extension_nep2035'). Until now, the tables include three additional scenarios: @@ -785,18 +780,21 @@ def extension(self, **kwargs): Generation, loads and its timeseries in Belgium and Norway for scenario 'NEP 2035' - - Parameters - ----- - network : The existing network container (e.g. scenario 'NEP 2035') - session : session-data - overlay_scn_name : Name of the additional scenario - (WITHOUT 'extension_') - start_snapshot, end_snapshot: Simulation time + Parameters + ----------- + network : + The existing network container (e.g. scenario 'NEP 2035') + session : + session-data + overlay_scn_name : + Name of the additional scenario (WITHOUT 'extension\_') + start_snapshot : + end_snapshot: + Simulation time Returns - ------ - network : Network container including existing and additional network + ------- + Network container including existing and additional network """ if self.args["scn_extension"] is not None: @@ -833,21 +831,22 @@ def decommissioning(self, **kwargs): Currently, only lines can be decommissioned. All components of the decommissioning scenario need to be inserted in - the fitting 'model_draft.ego_grid_pf_hv_extension_' table. - The scn_name in the tables have to be labled with 'decommissioning_' + the fitting 'model_draft.ego_grid_pf_hv_extension\_' table. + The scn_name in the tables have to be labled with 'decommissioning\_' + scn_name (e.g. 'decommissioning_nep2035'). - Parameters - ----- - network : The existing network container (e.g. scenario 'NEP 2035') - session : session-data - overlay_scn_name : Name of the decommissioning scenario - + ----------- + network : + The existing network container (e.g. scenario 'NEP 2035') + session : + session-data + overlay_scn_name : + Name of the decommissioning scenario Returns ------ - network : Network container including decommissioning + Network container including decommissioning """ if self.args["scn_decommissioning"] is not None: @@ -895,18 +894,20 @@ def distance(x0, x1, y0, y1): """ Function that calculates the square of the distance between two points. - Parameters - ----- - x0: x - coordinate of point 0 - x1: x - coordinate of point 1 - y0: y - coordinate of point 0 - y1: y - coordinate of point 1 - + --------- + x0 : + x - coordinate of point 0 + x1 : + x - coordinate of point 1 + y0 : + y - coordinate of point 0 + y1 : + y - coordinate of point 1 Returns - ------ - distance : float + -------- + distance : float square of distance """ @@ -922,18 +923,16 @@ def calc_nearest_point(bus1, network): Function that finds the geographical nearest point in a network from a given bus. - Parameters - ----- - bus1: float + ----------- + bus1 : float id of bus - network: Pypsa network container + network : Pypsa network container network including the comparable buses - Returns - ------ - bus0 : float + ------- + bus0 : float bus_id of nearest point """ diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index 89573506d..4326f323f 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -220,9 +220,11 @@ def plot_line_loading_diff(networkA, networkB, timestep=0, osm=False): osm : bool or dict, e.g. {'x': [1,20], 'y': [47, 56], 'zoom' : 6} If not False, osm is set as background with the following settings as dict: - 'x': array of two floats, x axis boundaries (lat) - 'y': array of two floats, y axis boundaries (long) - 'zoom' : resolution of osm + + * 'x': array of two floats, x axis boundaries (lat) + * 'y': array of two floats, y axis boundaries (long) + * 'zoom' : resolution of osm + """ if osm != False: if set_epsg_network.counter == 0: @@ -239,21 +241,26 @@ def shiftedColorMap( data with a negative min and positive max and you want the middle of the colormap's dynamic range to be at zero - Input - ----- - cmap : The matplotlib colormap to be altered - start : Offset from lowest point in the colormap's range. - Defaults to 0.0 (no lower ofset). Should be between - 0.0 and `midpoint`. - midpoint : The new center of the colormap. Defaults to - 0.5 (no shift). Should be between 0.0 and 1.0. In - general, this should be 1 - vmax/(vmax + abs(vmin)) - For example if your data range from -15.0 to +5.0 and - you want the center of the colormap at 0.0, `midpoint` - should be set to 1 - 5/(5 + 15)) or 0.75 - stop : Offset from highets point in the colormap's range. - Defaults to 1.0 (no upper ofset). Should be between - `midpoint` and 1.0. + Parameters + ----------- + cmap : + The matplotlib colormap to be altered + start : + Offset from lowest point in the colormap's range. + Defaults to 0.0 (no lower ofset). Should be between + 0.0 and `midpoint`. + midpoint : + The new center of the colormap. Defaults to + 0.5 (no shift). Should be between 0.0 and 1.0. In + general, this should be 1 - vmax/(vmax + abs(vmin)) + For example if your data range from -15.0 to +5.0 and + you want the center of the colormap at 0.0, `midpoint` + should be set to 1 - 5/(5 + 15)) or 0.75 + stop : + Offset from highets point in the colormap's range. + Defaults to 1.0 (no upper ofset). Should be between + `midpoint` and 1.0. + """ cdict = {"red": [], "green": [], "blue": [], "alpha": []} @@ -350,9 +357,10 @@ def network_expansion_diff( osm : bool or dict, e.g. {'x': [1,20], 'y': [47, 56], 'zoom' : 6} If not False, osm is set as background with the following settings as dict: - 'x': array of two floats, x axis boundaries (lat) - 'y': array of two floats, y axis boundaries (long) - 'zoom' : resolution of osm + + * 'x': array of two floats, x axis boundaries (lat) + * 'y': array of two floats, y axis boundaries (long) + * 'zoom' : resolution of osm """ if osm != False: @@ -579,13 +587,12 @@ def plot_gen_diff( """ Plot difference in generation between two networks grouped by carrier type - Parameters ---------- networkA : PyPSA network container with switches networkB : PyPSA network container without switches - leave_out_carriers : list of carriers to leave out (default to all small - carriers) + leave_out_carriers : + list of carriers to leave out (default to all small carriers) Returns ------- @@ -644,7 +651,6 @@ def plot_voltage(network, boundaries=[], osm=False): """ Plot voltage at buses as hexbin - Parameters ---------- network : PyPSA network container @@ -652,9 +658,10 @@ def plot_voltage(network, boundaries=[], osm=False): osm : bool or dict, e.g. {'x': [1,20], 'y': [47, 56], 'zoom' : 6} If not False, osm is set as background with the following settings as dict: - 'x': array of two floats, x axis boundaries (lat) - 'y': array of two floats, y axis boundaries (long) - 'zoom' : resolution of osm + + * 'x': array of two floats, x axis boundaries (lat) + * 'y': array of two floats, y axis boundaries (long) + * 'zoom' : resolution of osm Returns ------- @@ -694,7 +701,6 @@ def curtailment(network, carrier="solar", filename=None): """ Plot curtailment of selected carrier - Parameters ---------- network : PyPSA network container @@ -704,7 +710,6 @@ def curtailment(network, carrier="solar", filename=None): filename: str or None Save figure in this direction - Returns ------- Plot @@ -1045,9 +1050,10 @@ def nodal_gen_dispatch( osm : bool or dict, e.g. {'x': [1,20], 'y': [47, 56], 'zoom' : 6} If not False, osm is set as background with the following settings as dict: - 'x': array of two floats, x axis boundaries (lat) - 'y': array of two floats, y axis boundaries (long) - 'zoom' : resolution of osm + + * 'x': array of two floats, x axis boundaries (lat) + * 'y': array of two floats, y axis boundaries (long) + * 'zoom' : resolution of osm Returns ------- @@ -1351,7 +1357,6 @@ def storage_soc_sorted(network, filename=None): ---------- network : PyPSA network container Holds topology of grid including results from powerflow analysis - filename : path to folder Returns @@ -1498,7 +1503,6 @@ def calc_ac_loading(network, timesteps): def calc_dc_loading(network, timesteps): """Calculates loading of DC-lines - Parameters ---------- network : :class:`pypsa.Network @@ -2227,35 +2231,39 @@ def plot_grid( ): """Function that plots etrago.network and results for lines and buses - - Parameters ---------- line_colors : str Set static line color or attribute to plot e.g. 'expansion_abs' Current options: - 'line_loading': mean line loading in p.u. in selected timesteps - 'v_nom': nominal voltage of lines - 'expansion_abs': absolute network expansion in MVA - 'expansion_rel': network expansion in p.u. of existing capacity - 'q_flow_max': maximal reactive flows + + * 'line_loading': mean line loading in p.u. in selected timesteps + * 'v_nom': nominal voltage of lines + * 'expansion_abs': absolute network expansion in MVA + * 'expansion_rel': network expansion in p.u. of existing capacity + * 'q_flow_max': maximal reactive flows + bus_sizes : float, optional Size of buses. The default is 0.001. bus_colors : str, optional Set static bus color or attribute to plot. The default is 'grey'. Current options: - 'nodal_production_balance': net producer/consumer in selected timeteps - 'storage_expansion': storage expansion per bus and technology - 'storage_distribution': installed storage units per bus - 'gen_dist': dispatch per carrier in selected timesteps + + * 'nodal_production_balance': net producer/consumer in selected timeteps + * 'storage_expansion': storage expansion per bus and technology + * 'storage_distribution': installed storage units per bus + * 'gen_dist': dispatch per carrier in selected timesteps + timesteps : array, optional Timesteps consideredd in time depended plots. The default is range(2). osm : bool or dict, e.g. {'x': [1,20], 'y': [47, 56], 'zoom' : 6} If not False, osm is set as background with the following settings as dict: - 'x': array of two floats, x axis boundaries (lat) - 'y': array of two floats, y axis boundaries (long) - 'zoom' : resolution of osm. The default is False. + + * 'x': array of two floats, x axis boundaries (lat) + * 'y': array of two floats, y axis boundaries (long) + * 'zoom' : resolution of osm. The default is False. + boundaries: array Set fixed boundaries of heatmap axis. The default is None. filename: str or None @@ -2587,6 +2595,7 @@ def plot_clusters( gas_pipelines : bool, optional The default is False. Define if the original gas pipelines are plotted or not. + Returns ------- None. diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index b77b69816..47ac8916e 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -449,15 +449,15 @@ def set_q_national_loads(self, cos_phi): Parameters ---------- - network : :class:`pypsa.Network - Overall container of PyPSA + network : :class:`pypsa.Network` + Overall container of PyPSA cos_phi : float - Choose ration of active and reactive power of foreign loads + Choose ration of active and reactive power of foreign loads Returns ------- - network : :class:`pypsa.Network - Overall container of PyPSA + network : :class:`pypsa.Network` + Overall container of PyPSA """ network = self.network @@ -592,6 +592,7 @@ def load_shedding(self, temporal_disaggregation=False, **kwargs): Marginal costs for load shedding p_nom : int Installed capacity of load shedding generator + Returns ------- @@ -744,6 +745,7 @@ def export_to_csv(self, path): path: str or False or None Choose path for csv-files. Specify `""`, `False` or `None` to not do anything. + Returns ------- None @@ -852,7 +854,6 @@ def _make_consense(component, attr): attr : str specify the name of the attribute of the commponent being considered. - Returns ------- function @@ -1293,20 +1294,24 @@ def set_line_costs(self, cost110=230, cost220=290, cost380=85, costDC=375): ---------- network : :class:`pypsa.Network Overall container of PyPSA - args: dict containing settings from appl.py - cost110 : capital costs per km for 110kV lines and cables - default: 230€/MVA/km, source: costs for extra circuit in - dena Verteilnetzstudie, p. 146) - cost220 : capital costs per km for 220kV lines and cables - default: 280€/MVA/km, source: costs for extra circuit in - NEP 2025, capactity from most used 220 kV lines in model - cost380 : capital costs per km for 380kV lines and cables - default: 85€/MVA/km, source: costs for extra circuit in - NEP 2025, capactity from most used 380 kV lines in NEP - costDC : capital costs per km for DC-lines - default: 375€/MVA/km, source: costs for DC transmission line - in NEP 2035 - ------- + args: dict + containing settings from appl.py + cost110 : + capital costs per km for 110kV lines and cables + default: 230€/MVA/km, source: costs for extra circuit in + dena Verteilnetzstudie, p. 146) + cost220 : + capital costs per km for 220kV lines and cables + default: 280€/MVA/km, source: costs for extra circuit in + NEP 2025, capactity from most used 220 kV lines in model + cost380 : + capital costs per km for 380kV lines and cables + default: 85€/MVA/km, source: costs for extra circuit in + NEP 2025, capactity from most used 380 kV lines in NEP + costDC : + capital costs per km for DC-lines + default: 375€/MVA/km, source: costs for DC transmission line + in NEP 2035 """ @@ -1345,13 +1350,16 @@ def set_trafo_costs( ---------- network : :class:`pypsa.Network Overall container of PyPSA - cost110_220 : capital costs for 110/220kV transformer - default: 7500€/MVA, source: costs for extra trafo in - dena Verteilnetzstudie, p. 146; S of trafo used in osmTGmod - cost110_380 : capital costs for 110/380kV transformer - default: 17333€/MVA, source: NEP 2025 - cost220_380 : capital costs for 220/380kV transformer - default: 14166€/MVA, source: NEP 2025 + cost110_220 : + capital costs for 110/220kV transformer + default: 7500€/MVA, source: costs for extra trafo in + dena Verteilnetzstudie, p. 146; S of trafo used in osmTGmod + cost110_380 : + capital costs for 110/380kV transformer + default: 17333€/MVA, source: NEP 2025 + cost220_380 : + capital costs for 220/380kV transformer + default: 14166€/MVA, source: NEP 2025 """ @@ -1385,10 +1393,6 @@ def set_trafo_costs( def add_missing_components(self): - # Munich - # TODO: Manually adds lines between hard-coded buses. Has to be - # changed for the next dataversion and should be moved to data - # processing """ Add a missing transformer at Heizkraftwerk Nord in Munich and a missing transformer in Stuttgart. @@ -1405,6 +1409,11 @@ def add_missing_components(self): """ + # Munich + # TODO: Manually adds lines between hard-coded buses. Has to be + # changed for the next dataversion and should be moved to data + # processing + """ "https://www.swm.de/privatkunden/unternehmen/energieerzeugung" + "/heizkraftwerke.html?utm_medium=301" @@ -1634,7 +1643,6 @@ def convert_capital_costs(self): ---------- etrago : :class:`etrago.Etrago Transmission grid object - ------- """ @@ -1847,9 +1855,11 @@ def get_clustering_data(self, path): ---------- path : str Name of folder from which to import CSVs of network data. + Returns - None ------- + None + """ if (self.args["network_clustering_ehv"]) | ( @@ -1901,10 +1911,8 @@ def set_random_noise(self, sigma=0.01): ---------- etrago : :class:`etrago.Etrago Transmission grid object - seed: int seed number, needed to reproduce results - sigma: float Default: 0.01 standard deviation, small values reduce impact on dispatch @@ -1961,7 +1969,6 @@ def set_line_country_tags(network): network : :class:`pypsa.Network Overall container of PyPSA - """ transborder_lines_0 = network.lines[ @@ -2482,8 +2489,8 @@ def drop_sectors(self, drop_carriers): drop_carriers : array List of sectors that will be dropped. e.g. ['dsm', 'CH4', 'H2_saltcavern', 'H2_grid', - 'central_heat', 'rural_heat', 'central_heat_store', - 'rural_heat_store', 'Li ion'] means everything but AC + 'central_heat', 'rural_heat', 'central_heat_store', + 'rural_heat_store', 'Li ion'] means everything but AC Returns ------- @@ -2566,10 +2573,12 @@ def drop_sectors(self, drop_carriers): def update_busmap(self, new_busmap): """ Update busmap after any clustering process + Parameters ---------- new_busmap : dictionary busmap used to clusted the network. + Returns ------- None. From 47b0404a185c5d712f0f94ddc099ac02f6b004ce Mon Sep 17 00:00:00 2001 From: birgits Date: Wed, 6 Sep 2023 18:01:13 +0200 Subject: [PATCH 143/159] Move import of egoio to if statement to avoid import on rtd --- etrago/tools/constraints.py | 5 ++++- etrago/tools/network.py | 5 ++++- etrago/tools/utilities.py | 4 +++- 3 files changed, 11 insertions(+), 3 deletions(-) diff --git a/etrago/tools/constraints.py b/etrago/tools/constraints.py index 1a2b5a710..17a7716cb 100755 --- a/etrago/tools/constraints.py +++ b/etrago/tools/constraints.py @@ -21,9 +21,9 @@ """ Constraints.py includes additional constraints for eTraGo-optimizations """ +import os import logging -from egoio.tools import db from pyomo.environ import Constraint from pypsa.descriptors import expand_series from pypsa.linopt import define_constraints, define_variables, get_var, linexpr @@ -32,6 +32,9 @@ import pandas as pd import pyomo.environ as po +if "READTHEDOCS" not in os.environ: + from egoio.tools import db + logger = logging.getLogger(__name__) __copyright__ = ( diff --git a/etrago/tools/network.py b/etrago/tools/network.py index 97ffbfe77..3a1e2c89e 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -22,13 +22,16 @@ Define class Etrago """ +import os import logging -from egoio.tools import db from pypsa.components import Network from sqlalchemy.orm import sessionmaker import pandas as pd +if "READTHEDOCS" not in os.environ: + from egoio.tools import db + from etrago import __version__ from etrago.cluster.disaggregation import run_disaggregation from etrago.cluster.electrical import ehv_clustering, run_spatial_clustering diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 47ac8916e..cf52b576a 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -29,7 +29,6 @@ import math import os -from egoio.tools import db from pyomo.environ import Constraint, PositiveReals, Var from shapely.geometry import Point import geopandas as gpd @@ -38,6 +37,9 @@ import pypsa import sqlalchemy.exc +if "READTHEDOCS" not in os.environ: + from egoio.tools import db + logger = logging.getLogger(__name__) From 2b718c27459103f0bff471dd90c80a20d200505b Mon Sep 17 00:00:00 2001 From: birgits Date: Wed, 6 Sep 2023 18:10:51 +0200 Subject: [PATCH 144/159] Move import of geopandas and shapely to if statement to avoid import on rtd --- etrago/tools/plot.py | 4 ++-- etrago/tools/utilities.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index 4326f323f..191e542ca 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -42,13 +42,13 @@ except ImportError: cartopy_present = False from pypsa.plot import draw_map_cartopy -from shapely.geometry import LineString, MultiPoint, Point, Polygon -import geopandas as gpd logger = logging.getLogger(__name__) if "READTHEDOCS" not in os.environ: from geoalchemy2.shape import to_shape + import geopandas as gpd + from shapely.geometry import LineString, MultiPoint, Point, Polygon __copyright__ = ( "Flensburg University of Applied Sciences, " diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index cf52b576a..b1a244b57 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -30,8 +30,6 @@ import os from pyomo.environ import Constraint, PositiveReals, Var -from shapely.geometry import Point -import geopandas as gpd import numpy as np import pandas as pd import pypsa @@ -39,6 +37,8 @@ if "READTHEDOCS" not in os.environ: from egoio.tools import db + import geopandas as gpd + from shapely.geometry import Point logger = logging.getLogger(__name__) From f40632ce120e41f2be8d56e21533767d0c970225 Mon Sep 17 00:00:00 2001 From: birgits Date: Wed, 6 Sep 2023 18:11:03 +0200 Subject: [PATCH 145/159] Add saio and pyomo to rtd requirements --- requirements-doc.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/requirements-doc.txt b/requirements-doc.txt index abb50f991..fdbd50d64 100644 --- a/requirements-doc.txt +++ b/requirements-doc.txt @@ -10,3 +10,5 @@ sqlalchemy geoalchemy2 matplotlib nbsphinx +saio +pyomo != 6.4.3 \ No newline at end of file From 19812087bf9991b243d0187f3db43a53e194899e Mon Sep 17 00:00:00 2001 From: birgits Date: Wed, 6 Sep 2023 18:15:29 +0200 Subject: [PATCH 146/159] Move import of pyproj to if statement to avoid import on rtd --- etrago/tools/plot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index 191e542ca..d1218f80e 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -26,7 +26,6 @@ import os from matplotlib import pyplot as plt -from pyproj import Proj, transform import matplotlib import matplotlib.patches as mpatches import numpy as np @@ -48,6 +47,7 @@ if "READTHEDOCS" not in os.environ: from geoalchemy2.shape import to_shape import geopandas as gpd + from pyproj import Proj, transform from shapely.geometry import LineString, MultiPoint, Point, Polygon __copyright__ = ( From a8feae0ea2afba7ce46877ee26c0fe9783b4bf9b Mon Sep 17 00:00:00 2001 From: birgits Date: Wed, 6 Sep 2023 18:19:25 +0200 Subject: [PATCH 147/159] Move import of tilemapbase to if statement to avoid import on rtd --- etrago/tools/plot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/tools/plot.py b/etrago/tools/plot.py index d1218f80e..d3ae2068a 100644 --- a/etrago/tools/plot.py +++ b/etrago/tools/plot.py @@ -30,7 +30,6 @@ import matplotlib.patches as mpatches import numpy as np import pandas as pd -import tilemapbase cartopy_present = True try: @@ -49,6 +48,7 @@ import geopandas as gpd from pyproj import Proj, transform from shapely.geometry import LineString, MultiPoint, Point, Polygon + import tilemapbase __copyright__ = ( "Flensburg University of Applied Sciences, " From e6657a039f37f982d5bc60fc848bc130428b3923 Mon Sep 17 00:00:00 2001 From: birgits Date: Wed, 6 Sep 2023 18:24:28 +0200 Subject: [PATCH 148/159] Adapt rtd requirements to setup.py --- requirements-doc.txt | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/requirements-doc.txt b/requirements-doc.txt index fdbd50d64..383cf8367 100644 --- a/requirements-doc.txt +++ b/requirements-doc.txt @@ -2,10 +2,8 @@ # Using single requirments for docs, see: # https://github.com/rtfd/readthedocs.org/issues/2070 sphinx_rtd_theme -pandas >= 0.25, <= 0.25 -pypsa >= 0.11.0, <= 0.11.0 -numpy == 1.16.2 -numpydoc == 0.7.0 +pypsa == 0.20.1 +numpydoc sqlalchemy geoalchemy2 matplotlib From 13f61c102f2c500b4d347cfc3cbd9989204c2fe5 Mon Sep 17 00:00:00 2001 From: birgits Date: Thu, 7 Sep 2023 15:23:01 +0200 Subject: [PATCH 149/159] Make run_etrago docstring prettier --- etrago/appl.py | 36 +++++++++++++++++++++++++----------- 1 file changed, 25 insertions(+), 11 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index b0d953c70..12a34c317 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -384,19 +384,33 @@ def run_etrago(args, json_path): the same clusters of CH4 and AC. snapshot_clustering : dict - {'active': False, 'method':'typical_periods', 'extreme_periods': None, 'how': 'daily', - 'storage_constraints': 'soc_constraints', 'n_clusters': 5, 'n_segments': 5}, State if you want to apply a temporal clustering and run the optimization only on a subset of snapshot periods. - You can choose between a method clustering to typical periods, e.g. days or weeks - or a method clustering to segments of adjacent hours. - With ``'extreme_periods'`` you define the consideration of timesteps with - extreme residual load while temporal aggregation. - With ``'how'``, ``'storage_constraints'`` and ``'n_clusters'`` you choose - the length of the periods, constraints considering the storages and the number - of clusters for the usage of the method typical_periods. - With ``'n_segments'`` you choose the number of segments for the usage of - the method segmentation. + The provided dictionary can have the following entries: + + * "active" : bool + Choose, if clustering is activated or not. If True, it is activated. + Default: None. + * "method" : str + Method to apply. Possible options are "typical_periods" and "segmentation". + Default: "segmentation". + * "extreme_periods" : None or str + Method used to consider extreme snapshots (time steps with extreme residual + load) in reduced timeseries. + Possible options are None, "append", "new_cluster_center", and + "replace_cluster_center". The default is None, in which case extreme periods + are not considered. + * "how" : str + Definition of period in case `method` is set to "typical_periods". + Possible options are "daily", "weekly", and "monthly". Default: "daily". + * "storage_constraints" : str + Defines additional constraints for storage units in case `method` is set to + "typical_periods". Possible options are "daily_bounds", "soc_constraints" + and "soc_constraints_simplified". Default: "soc_constraints". + * "n_clusters" : int + Number of clusters in case `method` is set to "typical_periods". Default: 5. + * "n_segments" : int + Number of segments in case `method` is set to "segmentation". Default: 5. skip_snapshots : bool or int State if you only want to consider every n-th timestep From f31d60706113270879d0e3cf5534e680ebe7ea28 Mon Sep 17 00:00:00 2001 From: birgits Date: Thu, 7 Sep 2023 18:20:14 +0200 Subject: [PATCH 150/159] Fix KeyError retrieving marginal costs Fixes the following KeyError: KeyError Traceback (most recent call last) Cell In[6], line 1 ----> 1 etrago.adjust_network() File ~/virtualenvs/hyBit/git_repos/eTraGo/etrago/tools/network.py:351, in Etrago.adjust_network(self) 347 self.geolocation_buses() 349 self.load_shedding() --> 351 self.adjust_CH4_gen_carriers() 353 self.set_random_noise(0.01) 355 self.set_q_national_loads(cos_phi=0.9) File ~/virtualenvs/hyBit/git_repos/eTraGo/etrago/tools/utilities.py:2635, in adjust_CH4_gen_carriers(self) 2629 df = pd.read_sql(sql, engine) 2630 # TODO: There might be a bug in here raising a KeyError. 2631 # If you encounter it, that means you have live data 2632 # to test against. Please do a git blame on these 2633 # lines and follow the hints in the commit message to 2634 # fix the bug. -> 2635 marginal_cost = df["marginal_cost"] 2636 except sqlalchemy.exc.ProgrammingError: 2637 marginal_cost = marginal_cost_def File ~/virtualenvs/hyBit/lib/python3.8/site-packages/pandas/core/series.py:981, in Series.getitem(self, key) 978 return self._values[key] 980 elif key_is_scalar: --> 981 return self._get_value(key) 983 if is_hashable(key): 984 # Otherwise index.get_value will raise InvalidIndexError 985 try: 986 # For labels that don't resolve as scalars like tuples and frozensets File ~/virtualenvs/hyBit/lib/python3.8/site-packages/pandas/core/series.py:1089, in Series._get_value(self, label, takeable) 1086 return self._values[label] 1088 # Similar to Index.get_value, but we do not fall back to positional -> 1089 loc = self.index.get_loc(label) 1090 return self.index._get_values_for_loc(self, loc, label) File ~/virtualenvs/hyBit/lib/python3.8/site-packages/pandas/core/indexes/range.py:395, in RangeIndex.get_loc(self, key, method, tolerance) 393 raise KeyError(key) from err 394 self._check_indexing_error(key) --> 395 raise KeyError(key) 396 return super().get_loc(key, method=method, tolerance=tolerance) KeyError: 'marginal_cost' --- etrago/tools/utilities.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 6a37c3cfa..b141b76a9 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -2624,12 +2624,7 @@ def adjust_CH4_gen_carriers(self): FROM scenario.egon_scenario_parameters WHERE name = '{self.args["scn_name"]}';""" df = pd.read_sql(sql, engine) - # TODO: There might be a bug in here raising a `KeyError`. - # If you encounter it, that means you have live data - # to test against. Please do a `git blame` on these - # lines and follow the hints in the commit message to - # fix the bug. - marginal_cost = df["marginal_cost"] + marginal_cost = df["gas_parameters"][0]["marginal_cost"] except sqlalchemy.exc.ProgrammingError: marginal_cost = marginal_cost_def From 8d417058ba090f28bdde9536a85a2d647b4c055e Mon Sep 17 00:00:00 2001 From: birgits Date: Wed, 13 Sep 2023 17:09:33 +0200 Subject: [PATCH 151/159] Minor doc fix --- etrago/tools/utilities.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index b1a244b57..82abc6656 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -740,7 +740,7 @@ def export_to_csv(self, path): Parameters ---------- - network : :class:`pypsa.Network + network : :class:`pypsa.Network` Overall container of PyPSA args: dict Contains calculation settings of appl.py From 52a220bb2095fe073ebe637753515747559af0ed Mon Sep 17 00:00:00 2001 From: birgits Date: Wed, 13 Sep 2023 17:25:28 +0200 Subject: [PATCH 152/159] Merge infos from docstring and args dict and make it pretty --- etrago/appl.py | 518 ++++++++++++++++++++++++++++++++----------------- 1 file changed, 343 insertions(+), 175 deletions(-) diff --git a/etrago/appl.py b/etrago/appl.py index 12a34c317..b5d7874e0 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -136,7 +136,7 @@ }, }, }, - "disaggregation": None, # None, 'mini' or 'uniform' + "disaggregation": None, # None or 'uniform' # Temporal Complexity: "snapshot_clustering": { "active": False, # choose if clustering is activated @@ -171,63 +171,102 @@ def run_etrago(args, json_path): db : str Name of Database session setting stored in *config.ini* of *.egoio*, e.g. ``'oedb'``. - gridversion : NoneType or str + gridversion : None or str Name of the data version number of oedb: state ``'None'`` for model_draft (sand-box) or an explicit version number (e.g. 'v0.4.6') for the grid schema. method : dict - {'type': 'lopf', 'n_iter': 4, 'pyomo': True}, - Choose 'lopf' for 'type'. In case of extendable lines, several lopfs - have to be performed. Choose either 'n_init' and a fixed number of - iterations or 'thershold' and a threashold of the objective function as - abort criteria. - Set 'pyomo' to False for big optimization problems, currently only - possible when solver is 'gurobi'. + Choose method and settings for optimization. + The provided dictionary can have the following entries: + + * "lopf" : str + Type of optimization, currently only "lopf". Default: "lopf". + * "n_iter" : int + In case of extendable lines, several LOPFs have to be performed. + You can either set "n_iter" and specify a fixed number of iterations + or set "threshold" and specify a threshold of the objective function as + abort criteria of the iterative optimization. + Default: 4. + * "threshold" : int + In case of extendable lines, several LOPFs have to be performed. + You can either set "n_iter" and specify a fixed number of iterations + or set "threshold" and specify a threshold of the objective function as + abort criteria of the iterative optimization. Per default, "n_iter" of 4 + is set. + * "pyomo" : bool + Set to True, if pyomo is used for model building. + Set to False for big optimization problems - currently only + possible when solver is "gurobi". + pf_post_lopf : dict - {'active': True, 'add_foreign_lopf': True, 'q_allocation': 'p_nom'}, - Option to run a non-linear power flow (pf) directly after the - linear optimal power flow (and thus the dispatch) has finished. - If foreign lines are modeled as DC-links (see foreign_lines), results - of the lopf can be added by setting 'add_foreign_lopf'. - Reactive power can be distributed either by 'p_nom' or 'p'. + Settings for option to run a non-linear power flow (PF) directly after the + linear optimal power flow (LOPF), and thus the dispatch optimisation, has + finished. + The provided dictionary can have the following entries: + + * "active" : bool + If True, a PF is performed after the LOPF. Default: True. + * "add_foreign_lopf" : bool + If foreign lines are modeled as DC-links (see parameter `foreign_lines`), + results of the LOPF can be added by setting "add_foreign_lopf" to True. + Default: True. + * "q_allocation" : bool + Allocate reactive power to all generators at the same bus either by "p_nom" + or "p". + Default: "p_nom". + start_snapshot : int - 1, - Start hour of the scenario year to be calculated. + Start hour of the scenario year to be calculated. Default: 1. end_snapshot : int - 2, - End hour of the scenario year to be calculated. - If snapshot clusterung is used, the selected snapshots should cover - the number of periods / segments. + End hour of the scenario year to be calculated. If snapshot clustering is used + (see parameter `snapshot_clustering`), the selected snapshots should cover + the number of periods / segments. Default: 2. solver : str - 'glpk', - Choose your preferred solver. Current options: 'glpk' (open-source), - 'cplex' or 'gurobi'. + Choose your preferred solver. Current options: "glpk" (open-source), + "cplex" or "gurobi". Default: "gurobi". solver_options : dict Choose settings of solver to improve simulation time and result. - Options are described in documentation of chosen solver. + Options are described in documentation of chosen solver. Per default, the + following dictionary is set: + + { + "BarConvTol": 1.0e-5, + "FeasibilityTol": 1.0e-5, + "method": 2, + "crossover": 0, + "logFile": "solver_etrago.log", + "threads": 4, + } model_formulation : str - 'kirchoff', Choose formulation of pyomo-model. - Current options: angles, cycles, kirchhoff, ptdf + Current options are: "angles", "cycles", "kirchhoff", "ptdf". + "angels" works best for small networks, while "kirchhoff" works best for larger + networks. + Default: "kirchhoff". scn_name : str - 'eGon2035', Choose your scenario. Currently, there are two different - scenarios: 'eGon2035', 'eGon100RE'. - scn_extension : NoneType or str + scenarios: "eGon2035", "eGon100RE". Default: "eGon2035". + scn_extension : None or str + This option does currently not work! + Choose extension-scenarios which will be added to the existing network container. Data of the extension scenarios are located in extension-tables (e.g. model_draft.ego_grid_pf_hv_extension_bus) with the prefix 'extension\_'. - Currently, there are three overlay networks: - * 'nep2035_confirmed' includes all planed new lines confirmed by the - Bundesnetzagentur - * 'nep2035_b2' includes all new lines planned by the - Netzentwicklungsplan 2025 in scenario 2035 B2 - * 'BE_NO_NEP 2035' includes planned lines to Belgium and Norway and - adds BE and NO as electrical neighbours + There are three overlay networks: + * 'nep2035_confirmed' includes all planed new lines confirmed by the + Bundesnetzagentur + * 'nep2035_b2' includes all new lines planned by the + Netzentwicklungsplan 2025 in scenario 2035 B2 + * 'BE_NO_NEP 2035' includes planned lines to Belgium and Norway and + adds BE and NO as electrical neighbours + + Default: None. scn_decommissioning : NoneType or str - Choose an extra scenario which includes lines you want to decommise + This option does currently not work! + + Choose an extra scenario which includes lines you want to decommission from the existing network. Data of the decommissioning scenarios are located in extension-tables (e.g. model_draft.ego_grid_pf_hv_extension_bus) with the prefix @@ -240,157 +279,278 @@ def run_etrago(args, json_path): * 'nep2035_b2' includes all lines that will be replaced in NEP-scenario 2035 B2 + Default: None. lpfile : bool or str - False, State if and where you want to save pyomo's lp file. Options: - False or '/path/tofile.lp' - + False or '/path/tofile.lp'. Default: False. csv_export : bool or str - False, - State if and where you want to save results as csv files.Options: - False or '/path/tofolder'. + State if and where you want to save results as csv files. Options: + False or '/path/tofolder'. Default: False. extendable : dict - {'extendable_components': ['as_in_db'], - 'upper_bounds_grid': { - 'grid_max_D': None, - 'grid_max_abs_D': { - '380':{'i':1020, 'wires':4, 'circuits':4}, - '220':{'i':1020, 'wires':4, 'circuits':4}, - '110':{'i':1020, 'wires':4, 'circuits':2}, - 'dc':0}, - 'grid_max_foreign': 4, - 'grid_max_abs_foreign': None}}, - ['network', 'storages'], Choose components you want to optimize and set upper bounds for grid expansion. - The list 'extendable_components' defines a set of components to optimize. - Settings can be added in /tools/extendable.py. - The most important possibilities: - - * 'as_in_db' - leaves everything as it is defined in the data coming from the database - * 'network' - set all lines, links and transformers in electrical grid extendable - * 'german_network' - set lines and transformers in German electrical grid extendable - * 'foreign_network' - set foreign lines and transformers in electrical grid extendable - * 'transformers' - set all transformers extendable - * 'storages' / 'stores' - allow to install extendable storages (unlimited in size) at each grid node - in order to meet the flexibility demand. - - Upper bounds for electrical grid expansion can be defined for lines in - Germany relative to the existing capacity using 'grid_max_D'. - Alternatively, absolute maximum capacities between two electrical buses - can be defined per voltage level using 'grid_max_abs_D'. - Upper bounds for bordercrossing electrical lines can be defined accrodingly - using 'grid_max_foreign' or 'grid_max_abs_foreign'. + The provided dictionary can have the following entries: + + * "extendable_components" : list(str) + The list defines a set of components to optimize. + Settings can be added in /tools/extendable.py. + The most important possibilities: + + * 'as_in_db' + leaves everything as it is defined in the data coming from the database + * 'network' + set all lines, links and transformers in electrical grid extendable + * 'german_network' + set lines and transformers in German electrical grid extendable + * 'foreign_network' + set foreign lines and transformers in electrical grid extendable + * 'transformers' + set all transformers extendable + * 'storages' / 'stores' + allow to install extendable storages (unlimited in size) at each grid + node in order to meet the flexibility demand + + Default: "as_in_db". + + * "upper_bounds_grid" : dict + Dictionary can have the following entries: + + * 'grid_max_D' + Upper bounds for electrical grid expansion can be defined for lines in + Germany relative to the existing capacity. Alternatively, + 'grid_max_abs_D' can be used. Per default, this is set to None and + 'grid_max_abs_D' is set. + + * 'grid_max_abs_D' + Upper bounds for electrical grid expansion can be defined for lines in + Germany as absolute maximum capacities between two electrical buses + per voltage level. Per default the following dictionary is set: + + { + "380": {"i": 1020, "wires": 4, "circuits": 4}, + "220": {"i": 1020, "wires": 4, "circuits": 4}, + "110": {"i": 1020, "wires": 4, "circuits": 2}, + "dc": 0, + } + * 'grid_max_foreign' + Upper bounds for border-crossing electrical lines can be defined + relative to the existing capacity. Alternatively, 'grid_max_abs_foreign' + can be set. + Default: 4. + * 'grid_max_abs_foreign' + Upper bounds for border-crossing electrical lines can be defined equally + to 'grid_max_abs_D' as absolute capacity per voltage level. + Default: None. generator_noise : bool or int State if you want to apply a small random noise to the marginal costs of each generator in order to prevent an optima plateau. To reproduce - a noise, choose the same integer (seed number). - + a noise, choose the same integer (seed number). Default: 789456. extra_functionality : dict or None Choose extra functionalities and their parameters. Settings can be added in /tools/constraints.py. Current options are: - * 'max_line_ext': float + * 'max_line_ext' : float Maximal share of network extension in p.u. - * 'min_renewable_share': float + * 'min_renewable_share' : float Minimal share of renewable generation in p.u. - * 'cross_border_flow': array of two floats - Limit AC cross-border-flows between Germany and its neigbouring - countries, set values in MWh for all snapshots, e.g. [-x, y] - (with x Import, y Export, positiv: export from Germany) - * 'cross_border_flows_per_country': dict of cntr and array of floats - Limit AC cross-border-flows between Germany and its neigbouring - countries, set values in in MWh for each country, e.g. [-x, y] - (with x Import, y Export, positiv: export from Germany) - * 'capacity_factor': dict of arrays - Limit overall energy production for each carrier, - set upper/lower limit in p.u. - * 'capacity_factor_per_gen': dict of arrays - Limit overall energy production for each generator by carrier, - set upper/lower limit in p.u. + * 'cross_border_flow' : array of two floats + Limit AC cross-border-flows between Germany and its neighbouring + countries. Set values in MWh for all snapshots, e.g. [-x, y] + (with x Import, y Export, positive: export from Germany). + * 'cross_border_flows_per_country' : dict of cntr and array of floats + Limit AC cross-border-flows between Germany and its neighbouring + countries. Set values in MWh for each country, e.g. [-x, y] + (with x Import, y Export, positive: export from Germany). + * 'capacity_factor' : dict of arrays + Limit overall energy production for each carrier. + Set upper/lower limit in p.u. + * 'capacity_factor_per_gen' : dict of arrays + Limit overall energy production for each generator by carrier. + Set upper/lower limit in p.u. * 'capacity_factor_per_cntr': dict of dict of arrays - Limit overall energy production country-wise for each carrier, - set upper/lower limit in p.u. + Limit overall energy production country-wise for each carrier. + Set upper/lower limit in p.u. * 'capacity_factor_per_gen_cntr': dict of dict of arrays Limit overall energy production country-wise for each generator - by carrier, set upper/lower limit in p.u. + by carrier. Set upper/lower limit in p.u. network_clustering_ehv : bool - False, Choose if you want to cluster the full HV/EHV dataset down to only the EHV buses. In that case, all HV buses are assigned to their closest EHV substation, taking into account the shortest distance on power lines. - + Default: False. network_clustering : dict - {'active': True, method: 'kmedoids-dijkstra', 'n_clusters_AC': 30, - 'cluster_foreign_AC': False, method_gas: 'kmeans', 'n_clusters_gas': 30, - 'cluster_foreign_gas': False, 'k_elec_busmap': False, 'k_gas_busmap': False, - 'bus_weight_tocsv': None, 'bus_weight_fromcsv': None, - 'gas_weight_tocsv': None, 'gas_weight_fromcsv': None, ' - line_length_factor': 1, 'remove_stubs': False, 'use_reduced_coordinates': False, - 'random_state': 42, 'n_init': 10, 'max_iter': 100, 'tol': 1e-6}, - State if you want to apply a clustering of all network buses. - When ``'active'`` is set to True, the AC buses are clustered down to ``'n_clusters_AC'`` - and the gas buses are clustered down to``'n_clusters_gas'``. - If ``'cluster_foreign_AC'`` is set to False, the AC buses outside Germany are not - clustered and the buses inside Germany are clustered to complete ``'n_clusters'``. - ``'cluster_foreign_gas'`` controls whether gas buses of neighboring countries - are considered for clustering. - With ``'method'`` you can choose between two clustering methods: - k-means Clustering considering geopraphical locations of buses or - k-medoids Dijkstra Clustering considering electrical distances between buses. - With ``'k_elec_busmap'`` or ``'k_ch4_busmap'``you can load cluster coordinates - from a previous run for the respecting carrier. It should be considered that - once this option is set to True, the provided number of clusters will be ignored. - In genral, the weighting takes place considering generation and load at each node. - CH4 nodes also take non-transport capacities into account. - With the ``'bus_weight_tocsv'`` you can load an own weighting for the buses - or store the weighting with ``'bus_weight_fromcsv'``. - ``'line_length_factor'`` defines the factor to multiply the crow-flies distance - between new buses in order to get new line lengths. - Option ``'remove_stubs'`` reduces the overestimating of line meshes and - ``'use_reduced_coordinates'`` does not average cluster coordinates, but take - from busmap; both only within the k-means Clustering. - The other options affect the clustering algorithm in both methods and should only be - changed carefully, documentation and possible settings are described - in sklearn-package (sklearn/cluster/kmeans.py). + Choose if you want to apply a clustering of all network buses and specify + settings. + The provided dictionary can have the following entries: + + * "active" : bool + If True, the AC buses are clustered down to ``'n_clusters_AC'`` + and the gas buses are clustered down to``'n_clusters_gas'``. + Default: True. + * "method" : str + Method used for AC clustering. You can choose between two clustering + methods: + * "kmeans": considers geographical locations of buses + * "kmedoids-dijkstra": considers electrical distances between buses + + Default: "kmedoids-dijkstra". + * "n_clusters_AC" : int + Defines total number of resulting AC nodes including DE and foreign nodes + if `cluster_foreign_AC` is set to True, otherwise only DE nodes. + Default: 30. + * "cluster_foreign_AC" : bool + If set to False, the AC buses outside Germany are not clustered + and the buses inside Germany are clustered to complete ``'n_clusters_AC'``. + If set to True, foreign AC buses are clustered as well and included + in number of clusters specified through ``'n_clusters_AC'``. + Default: False. + * "method_gas" : str + Method used for gas clustering. You can choose between two clustering + methods: + * "kmeans": considers geographical locations of buses + * "kmedoids-dijkstra": considers 'electrical' distances between buses + + Default: "kmedoids-dijkstra". + * "n_clusters_gas" : int + Defines total number of resulting CH4 nodes including DE and foreign nodes + if `cluster_foreign_gas` is set to True, otherwise only DE nodes. + Default: 17. + * "cluster_foreign_gas" : bool + If set to False, the gas buses outside Germany are not clustered + and the buses inside Germany are clustered to complete ``'n_clusters_gas'``. + If set to True, foreign gas buses are clustered as well and included + in number of clusters specified through ``'n_clusters_gas'``. + Default: False. + * "k_elec_busmap" : bool or str + With this option you can load cluster coordinates from a previous AC + clustering run. Options are False, in which case no previous busmap is + loaded, and path/to/busmap.csv in which case the busmap is loaded from the + specified file. Please note, that when a path is provided, the set number + of clusters will be ignored. + Default: False. + * "k_gas_busmap" : bool or str + With this option you can load cluster coordinates from a previous gas + clustering run. Options are False, in which case no previous busmap is + loaded, and path/to/busmap.csv in which case the busmap is loaded from the + specified file. Please note, that when a path is provided, the set number + of clusters will be ignored. + Default: False. + * "bus_weight_fromcsv" : None or str + In general, the weighting of AC buses takes place considering generation and + load at each node. With this option, you can load an own weighting for + the AC buses by providing a path to a csv file. If None, weighting is + conducted as described above. + Default: None. + * "bus_weight_tocsv" : None or str + Specifies whether to store the weighting of AC buses to csv or not. If + None, it is not stored. Otherwise, it is stored to the provided + path/to/bus_weight.csv. + Default: None. + * "gas_weight_fromcsv" : None or str + In general, the weighting of CH4 nodes takes place considering generation + and load at each node, as well as non-transport capacities at each node. + With this option, you can load an own weighting for the CH4 buses by + providing a path to a csv file. If None, weighting is conducted as + described above. + Default: None. + * "gas_weight_tocsv" : None or str + Specifies whether to store the weighting of gas buses to csv or not. If + None, it is not stored. Otherwise, it is stored to the provided + path/to/gas_bus_weight.csv. + Default: None. + * "line_length_factor" : float + Defines the factor to multiply the crow-flies distance + between new buses by, in order to get new line lengths. + Default: 1. + * "remove_stubs" : bool + If True, remove stubs before k-means clustering, which reduces the + overestimating of line meshes. + This option is only used within the k-means clustering. + Default: False. + * "use_reduced_coordinates" : bool + If True, do not average cluster coordinates, but take from busmap. + This option is only used within the k-means clustering. + Default: False. + * "random_state" : int + Random state for replicability of clustering results. Default: 42. + * "n_init" : int + Affects clustering algorithm, only change when necessary! Documentation + and possible settings are described in sklearn-package + (sklearn/cluster/kmeans.py). + Default: 10. + * "max_iter" : int + Affects clustering algorithm, only change when necessary! Documentation + and possible settings are described in sklearn-package + (sklearn/cluster/kmeans.py). + Default: 100. + * "tol" : float + Affects clustering algorithm, only change when necessary! Documentation + and possible settings are described in sklearn-package + (sklearn/cluster/kmeans.py). + Default: 1e-6. + * "CPU_cores" : int or str + Number of cores used in clustering. Specify a concrete number or "max" to + use all cores available. + Default: 4. sector_coupled_clustering : dict - {'active': True, 'carrier_data': { - 'central_heat': {'base': ['CH4', 'AC'], 'strategy': "simultaneous"}, - } - State if you want to apply clustering of sector coupled carriers, such - as central_heat. The approach builds on already clustered - buses (AC and CH4) building clusters around the topology of those buses - with carrier ``'base'`` for all buses of a specific carrier, e.g. - ``'central_heat'``. With ``'strategy'`` it is possible to apply either - ``'consecutive'`` or ``'simultaneous'`` clustering. The consecutive - strategy clusters around the buses of the first carrier in the list. - The links to other buses are preserved. All buses, that have no - connection to the first carrier will then be clustered around the buses - of the second carrier in the list. The simultanous strategy looks for - links connecting the buses of the carriers in the list and aggregates - buses in case they have the same set of links connected. For example, - a heat bus connected to CH4 via gas boiler and to AC via heat pump will - only form a cluster with other buses, if these have the same links to - the same clusters of CH4 and AC. + Choose if you want to apply a clustering of sector coupled carriers, such + as central_heat, and specify settings. + The provided dictionary can have the following entries: + * "active" : bool + State if you want to apply clustering of sector coupled carriers, such + as central_heat. + Default: True. + * "carrier_data" : dict[str, dict] + Keys of the dictionary specify carriers affected by sector coupling, e.g. + "central_heat". The corresponding dictionaries specify, how the carrier + should be clustered. This dictionary must contain the following entries: + + * "base" : list(str) + The approach bases on already clustered buses (AC and CH4) and builds + clusters around the topology of those buses. With this option, you can + specify the carriers to use as base. See `strategy` for more + information. + * "strategy" : str + Strategy to use in the clustering. Possible options are: + + * "consecutive" + This strategy clusters around the buses of the first carrier in the + `'base'`` list. The links to other buses are preserved. All buses, + that have no connection to the first carrier will then be clustered + around the buses of the second carrier in the list. + * "simultaneous" + This strategy looks for links connecting the buses of the carriers + in the ``'base'`` list and aggregates buses in case they have the + same set of links connected. For example, a heat bus connected to + CH4 via gas boiler and to AC via heat pump will only form a cluster + with other buses, if these have the same links to + the same clusters of CH4 and AC. + + Per default, the following dictionary is set: + { + "central_heat": { + "base": ["CH4", "AC"], + "strategy": "simultaneous", + }, + } + + disaggregation : None or str + Specify None, in order to not perform a spatial disaggregation, or the + method you want to use for the spatial disaggregation. Only possible + option is currently "uniform". snapshot_clustering : dict State if you want to apply a temporal clustering and run the optimization - only on a subset of snapshot periods. + only on a subset of snapshot periods, and specify settings. The provided dictionary can have the following entries: * "active" : bool Choose, if clustering is activated or not. If True, it is activated. - Default: None. + Default: False. * "method" : str Method to apply. Possible options are "typical_periods" and "segmentation". Default: "segmentation". @@ -413,39 +573,47 @@ def run_etrago(args, json_path): Number of segments in case `method` is set to "segmentation". Default: 5. skip_snapshots : bool or int - State if you only want to consider every n-th timestep - to reduce temporal complexity. - + State None, if you want to use all time steps, or provide a number, + if you only want to consider every n-th timestep to reduce + temporal complexity. Default: 5. temporal_disaggregation : dict - {'active': False, 'no_slices': 4}, - State if you to apply a second lopf considering dispatch only - to disaggregate the dispatch to the whole temporal complexity. + State if you want to apply a second LOPF considering dispatch only (no capacity + optimization) to disaggregate the dispatch to the whole temporal complexity. Be aware that a load shedding will be applied in this optimization. - With "no_slices" the optimization problem will be calculated as a given - number of subproblems while using some information on the state of charge - of storage units and stores from the former optimization (at the moment - only possible with skip_snapshots; extra_functionalities disregarded) + The provided dictionary must have the following entries: - branch_capacity_factor : dict - {'HV': 0.5, 'eHV' : 0.7}, + * "active" : bool + Choose, if temporal disaggregation is activated or not. If True, it is + activated. + Default: False. + * "no_slices" : int + With "no_slices" the optimization problem will be calculated as a given + number of sub-problems while using some information on the state of charge + of storage units and stores from the former optimization (at the moment + only possible with skip_snapshots and extra_functionalities are + disregarded). + Default: 8. + + branch_capacity_factor : dict[str, float] Add a factor here if you want to globally change line capacities - (e.g. to "consider" an (n-1) criterion or for debugging purposes). - + (e.g. to "consider" an (n-1) criterion or for debugging purposes). The factor + specifies the p.u. branch rating, e.g. 0.5 to allow half the line capacity. + Per default, it is set to {'HV': 0.5, 'eHV' : 0.7}. load_shedding : bool - False, State here if you want to make use of the load shedding function which is helpful when debugging: a very expensive generator is set to each - bus and meets the demand when regular - generators cannot do so. - + bus and meets the demand when regular generators cannot do so. + Default: False. foreign_lines : dict - {'carrier':'AC', 'capacity': 'osmTGmod}' Choose transmission technology and capacity of foreign lines: + * 'carrier': 'AC' or 'DC' - * 'capacity': 'osmTGmod', 'ntc_acer' or 'thermal_acer' + * 'capacity': 'osmTGmod', 'tyndp2020', 'ntc_acer' or 'thermal_acer' + + Per default, it is set to {'carrier':'AC', 'capacity': 'osmTGmod'}. comments : str - None + Can be any comment you wish to make. Returns ------- From 8edef52b88feed3d61e35be9a9f88f460efc87c4 Mon Sep 17 00:00:00 2001 From: birgits Date: Wed, 13 Sep 2023 17:36:08 +0200 Subject: [PATCH 153/159] Add warning in docstring --- etrago/appl.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/etrago/appl.py b/etrago/appl.py index b5d7874e0..4dd577d8a 100644 --- a/etrago/appl.py +++ b/etrago/appl.py @@ -237,6 +237,9 @@ def run_etrago(args, json_path): "logFile": "solver_etrago.log", "threads": 4, } + + Make sure to reset or adapt these settings when using another solver! Otherwise, + you may run into errors. model_formulation : str Choose formulation of pyomo-model. Current options are: "angles", "cycles", "kirchhoff", "ptdf". From 355cf3c11bd3dd51f2180a71b44cacdacd13c507 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Thu, 14 Sep 2023 10:05:53 +0200 Subject: [PATCH 154/159] Set upper version limits for pandas and pyomo to avoid conflicts with pypsa --- setup.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/setup.py b/setup.py index 8b6dde20d..89c9b4584 100755 --- a/setup.py +++ b/setup.py @@ -54,9 +54,10 @@ def read(*names, **kwargs): "loguru", "matplotlib >= 3.0.3", "oedialect", - # PyPSA uses a deprecated import that errors with Pyomo 6.4.3. - # Upstream has a fix but it's not yet released. - "pyomo != 6.4.3", + # Fix upper version limits for pyomo and pandas + # Related to problems with old pypsa version + "pandas < 2", + "pyomo == 6.4.1", "pypsa == 0.20.1", "rtree", "saio", From 589123b591a47dd658a68b6ee0b3289c78037439 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Thu, 14 Sep 2023 10:09:19 +0200 Subject: [PATCH 155/159] Fix flake8 warning about too long line --- etrago/cluster/disaggregation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/etrago/cluster/disaggregation.py b/etrago/cluster/disaggregation.py index 5f6718d39..a10b19b79 100644 --- a/etrago/cluster/disaggregation.py +++ b/etrago/cluster/disaggregation.py @@ -66,8 +66,8 @@ def construct_partial_network(self, cluster, scenario): :param cluster: Index of the cluster to disaggregate :return: Tuple of (partial_network, external_buses) where `partial_network` is the result of the partial decomposition - and `external_buses` represent clusters adjacent to `cluster` that may - be influenced by calculations done on the partial network. + and `external_buses` represent clusters adjacent to `cluster` that + may be influenced by calculations done on the partial network. """ # Create an empty network From 4ec5fec390ecfe360f37f43d67a766b86052292d Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Thu, 14 Sep 2023 10:11:10 +0200 Subject: [PATCH 156/159] Apply isort --- etrago/tools/utilities.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/tools/utilities.py b/etrago/tools/utilities.py index 82abc6656..77ac45d30 100755 --- a/etrago/tools/utilities.py +++ b/etrago/tools/utilities.py @@ -37,8 +37,8 @@ if "READTHEDOCS" not in os.environ: from egoio.tools import db - import geopandas as gpd from shapely.geometry import Point + import geopandas as gpd logger = logging.getLogger(__name__) From 37d4d2c85408e667fbe3e44fde09bd8af39569af Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Thu, 14 Sep 2023 10:42:04 +0200 Subject: [PATCH 157/159] Apply isort --- etrago/tools/network.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/etrago/tools/network.py b/etrago/tools/network.py index 3a1e2c89e..7f427627c 100644 --- a/etrago/tools/network.py +++ b/etrago/tools/network.py @@ -22,8 +22,8 @@ Define class Etrago """ -import os import logging +import os from pypsa.components import Network from sqlalchemy.orm import sessionmaker From cb29280c20b0c1d4807de37665fa54e9316b0793 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 18 Sep 2023 11:48:21 +0200 Subject: [PATCH 158/159] Allow pyomo version 6.5.0 --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 89c9b4584..0853fbbf1 100755 --- a/setup.py +++ b/setup.py @@ -57,7 +57,7 @@ def read(*names, **kwargs): # Fix upper version limits for pyomo and pandas # Related to problems with old pypsa version "pandas < 2", - "pyomo == 6.4.1", + "pyomo>6.4, <6.6", "pypsa == 0.20.1", "rtree", "saio", From b21a59a2e15960374b747edf90284198a647b799 Mon Sep 17 00:00:00 2001 From: ClaraBuettner Date: Mon, 18 Sep 2023 13:39:30 +0200 Subject: [PATCH 159/159] Exclude not-working pyomo-version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 0853fbbf1..10846360f 100755 --- a/setup.py +++ b/setup.py @@ -57,7 +57,7 @@ def read(*names, **kwargs): # Fix upper version limits for pyomo and pandas # Related to problems with old pypsa version "pandas < 2", - "pyomo>6.4, <6.6", + "pyomo>6.4, <6.6, !=6.4.3", "pypsa == 0.20.1", "rtree", "saio",

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