diff --git a/.github/workflows/typos.toml b/.github/workflows/typos.toml index eb4df77585..0254951c21 100644 --- a/.github/workflows/typos.toml +++ b/.github/workflows/typos.toml @@ -37,6 +37,9 @@ equil = "equil" astroid = "astroid" # delt == delta delt = "delt" +# FOM for fixed operating and maintenance for DISPATCHES +FOM = "FOM" +fom = "fom" # Minimal Infeasible System mis = "mis" MIS = "MIS" diff --git a/docs/reference_guides/apps/grid_integration/index.rst b/docs/reference_guides/apps/grid_integration/index.rst index 08645e3b36..99714c9434 100644 --- a/docs/reference_guides/apps/grid_integration/index.rst +++ b/docs/reference_guides/apps/grid_integration/index.rst @@ -20,6 +20,7 @@ wholesale electricity markets. For more information, please look at the introduc Bidder Tracker Coordinator + multiperiod/index Cite us ^^^^^^^ diff --git a/docs/reference_guides/apps/grid_integration/multiperiod/Price_Taker.rst b/docs/reference_guides/apps/grid_integration/multiperiod/Price_Taker.rst new file mode 100644 index 0000000000..7424bfb666 --- /dev/null +++ b/docs/reference_guides/apps/grid_integration/multiperiod/Price_Taker.rst @@ -0,0 +1,55 @@ +Price Taker +=========== + +Price takers are entities that must accept market prices since they lack the market share +to directly influence the market price. Likewise, it is assumed that a price taker's resource or energy +system is small enough such that it does not significantly impact the market. When coupled with multi-period modeling, +the price-taker model is able to synthesize grid-centric modeling with steady-state process-centric modeling, as +depicted in figure below. + +.. |pricetaker| image:: images/pricetaker.png + :width: 1200 + :alt: Alternative text + :align: middle + +|pricetaker| + +The following equations represent the multi-period price taker model, where :math:`d` are design decisions, +:math:`u` are operating decisions, :math:`δ` are power decisions, :math:`s` are scenarios (timeseries/representative days), +:math:`w` is weight/frequency, :math:`R` is revenue, :math:`π` is price data, +:math:`C` is capital and operating costs, :math:`g` is the process model, and :math:`h` is the temporal constraint. + + .. math:: + + max_{d,u, δ} = \sum_{s ∈ S} \sum_{t ∈ T} w_{s}[R(d,u_{s,t},δ_{s,t},π_{s,t}) - C(d,u_{s,t},δ_{s,t})] + + .. math:: + + g(d,u_{s,t},δ_{s,t}) = 0; ∀_{s} ∈ S, t ∈ T + + .. math:: + + h(d,u_{s,t},δ_{s,t},u_{s,t+1},δ_{s,t+1}) = 0; ∀_{s} ∈ S, t ∈ T + + +The price taker multi-period modeling workflow involves the integration of multiple software platforms into the IDAES optimization model +and can be broken down into two distinct functions, as shown in the figure below. In part 1, simulated or historical +ISO (Independent System Operator) data is used to generate locational marginal price (LMP) +signals, and production cost models (PCMs) are used to compute and optimize the time-varying dispatch schedules for each +resource based on their respective bid curves. Advanced data analytics (RAVEN) reinterpret the LMP signals and PCM +as stochastic realizations of the LMPs in the form of representative days (or simply the full-year price signals). +In part 2, PRESCIENT uses a variety of input parameters (design capacity, minimum power output, ramp rate, minimum up/down time, marginal cost, no load cost, and startup profile) +to generate data for the market surrogates. Meanwhile, IDAES uses the double loop simulation to integrate detailed +process models (b, ii) into the daily (a, c) and hourly (i, iii) grid operations workflow. + +.. |hybrid_energy_system| image:: images/hybrid_energy_system.png + :width: 1200 + :alt: Alternative text + :align: middle + +|hybrid_energy_system| + +.. module:: idaes.apps.grid_integration.multiperiod.price_taker_model + +.. autoclass:: PriceTakerModel + :members: \ No newline at end of file diff --git a/docs/reference_guides/apps/grid_integration/multiperiod/images/hybrid_energy_system.png b/docs/reference_guides/apps/grid_integration/multiperiod/images/hybrid_energy_system.png new file mode 100644 index 0000000000..119e862d94 Binary files /dev/null and b/docs/reference_guides/apps/grid_integration/multiperiod/images/hybrid_energy_system.png differ diff --git a/docs/reference_guides/apps/grid_integration/multiperiod/images/pricetaker.png b/docs/reference_guides/apps/grid_integration/multiperiod/images/pricetaker.png new file mode 100644 index 0000000000..67abeb1e9a Binary files /dev/null and b/docs/reference_guides/apps/grid_integration/multiperiod/images/pricetaker.png differ diff --git a/docs/reference_guides/apps/grid_integration/multiperiod/index.rst b/docs/reference_guides/apps/grid_integration/multiperiod/index.rst new file mode 100644 index 0000000000..2d2b972b90 --- /dev/null +++ b/docs/reference_guides/apps/grid_integration/multiperiod/index.rst @@ -0,0 +1,13 @@ +Multi-Period Modeling +===================== + +Multi-period modeling can be used to simplify dynamic optimization problems +by linking steady-state models over a time horizon with rate-limiting constraints. +Therefore, sets of constraints at one temporal index will affect decisions taken +at a different moment in time. These interactions can be used to model the relationship +between the energy systems and wholesale electricity markets. + +.. toctree:: + :maxdepth: 2 + + Price_Taker diff --git a/idaes/apps/grid_integration/__init__.py b/idaes/apps/grid_integration/__init__.py index 5d8c6f802e..232018b2f5 100644 --- a/idaes/apps/grid_integration/__init__.py +++ b/idaes/apps/grid_integration/__init__.py @@ -15,3 +15,8 @@ from .coordinator import DoubleLoopCoordinator from .forecaster import PlaceHolderForecaster from .multiperiod.multiperiod import MultiPeriodModel +from .multiperiod.price_taker_model import PriceTakerModel +from .multiperiod.design_and_operation_models import ( + DesignModel, + OperationModel, +) diff --git a/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py new file mode 100644 index 0000000000..129a478220 --- /dev/null +++ b/idaes/apps/grid_integration/multiperiod/design_and_operation_models.py @@ -0,0 +1,226 @@ +################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +################################################################################# + +import logging +from pyomo.environ import Binary, Param, Var +from pyomo.common.config import Bool, ConfigDict, ConfigValue +from idaes.core.base.process_base import declare_process_block_class +from idaes.core.base.process_base import ProcessBlockData + +_logger = logging.getLogger(__name__) + + +# pylint: disable = attribute-defined-outside-init, too-many-ancestors +# pylint: disable = invalid-name, logging-fstring-interpolation +@declare_process_block_class("DesignModel") +class DesignModelData(ProcessBlockData): + """ + Builds the 'design model' for a unit/process. + + Args: + model_func: Function that builds the design model + model_args: Dictionary containing the arguments needed for model_func + + The class defines `install_unit` binary variable that takes the value 1 + if the unit is built/installed, and 0 otherwise. + + Function model_func must declare all the necessary design variables, + relations among design variables, capital cost correlations, and fixed O&M + cost correlations. The function must also define attributes `capex` for + capital cost, and `fom` for fixed O&M cost. If not defined, these attributes + will be set to zero. + + Example Usage: + + .. code-block:: python + + def my_design_model(m, p_min, p_max, cost): + m.power = Var() + m.min_capacity = Constraint( + expr=p_min * m.install_unit <= m.power + ) + m.max_capacity = Constraint( + expr=m.power <= p_max * m.install_unit + ) + + # capex and fom must either be a constant, or Var, or Expression + m.capex = Expression(expr=cost["capex"] * m.power) + m.fom = Expression(expr=cost["fom"] * m.power) + + m = ConcreteModel() + m.unit_1 = DesignModel( + model_func=my_design_model, + model_args={ + "p_min": 150, "p_max": 600, "cost": {"capex": 10, "fom": 1}, + }, + ) + """ + + CONFIG = ConfigDict() + CONFIG.declare( + "model_func", + ConfigValue( + doc="Function that builds the design model", + ), + ) + CONFIG.declare( + "model_args", + ConfigValue( + default={}, + doc="Dictionary containing arguments needed for model_func", + ), + ) + + def build(self): + super().build() + + self.install_unit = Var( + within=Binary, + doc="Binary: 1, if the unit is installed, 0 otherwise", + ) + + if self.config.model_func is None: + # Function that builds the design model is not specified + _logger.warning( + "The function that builds the design model is not specified." + "model_func must declare all the necessary design variables," + "relations among design variables, capital cost correlations," + "and fixed operating and maintenance cost correlations." + ) + return + + # Call the function that builds the design model + self.config.model_func(self, **self.config.model_args) + + # Check if capital and fixed O&M costs are defined + if not hasattr(self, "capex"): + _logger.warning( + "'capex' attribute is not set for the design model " + f"{self.name}. Setting the capital cost of the unit to zero." + ) + self.capex = 0 + + if not hasattr(self, "fom"): + _logger.warning( + "'fom' attribute is not set for the design model " + f"{self.name}. Setting the fixed O&M cost of the unit to zero." + ) + self.fom = 0 + + +@declare_process_block_class("OperationModel") +class OperationModelData(ProcessBlockData): + """ + Builds the 'operation model' for a unit/process. + + Args: + model_func: Function that builds the operation model + model_args: Dictionary containing the arguments needed for model_func + + The class defines `op_mode`, `startup`, and `shutdown` binary variables + to track the operation, startup, and shutdown of the unit/process. + + Function model_func must declare all the necessary operation variables, + relations among operation variables, and variable O&M cost correlations. + + Example Usage: + + .. code-block:: python + + def my_operation_model(m, design_blk): + m.power = Var() + m.fuel_flow = Var() + ... + + m = ConcreteModel() + m.unit_1 = DesignModel( + model_func=my_design_model, + model_args={ + "p_min": 150, "p_max": 600, "cost": {"capex": 10, "fom": 1}, + }, + ) + m.op_unit_1 = OperationModel( + model_func=my_operation_model, model_args={"design_blk": m.unit_1}, + ) + """ + + CONFIG = ConfigDict() + CONFIG.declare( + "model_func", + ConfigValue( + doc="Function that builds the design model", + ), + ) + CONFIG.declare( + "model_args", + ConfigValue( + default={}, + doc="Dictionary containing arguments needed for model_func", + ), + ) + CONFIG.declare( + "declare_op_vars", + ConfigValue( + default=True, + domain=Bool, + doc="Should op_mode, startup, shutdown vars be defined?", + ), + ) + CONFIG.declare( + "declare_lmp_param", + ConfigValue( + default=True, + domain=Bool, + doc="Should LMP data automatically be appended to the model?", + ), + ) + + # noinspection PyAttributeOutsideInit + def build(self): + super().build() + + # Build the model + if self.config.declare_op_vars: + self.op_mode = Var( + within=Binary, + doc="Binary: 1 if the unit is operating, 0 otherwise", + ) + + self.startup = Var( + within=Binary, + doc="Binary: 1 if the startup is initiated, 0 otherwise", + ) + + self.shutdown = Var( + within=Binary, + doc="Binary: 1 if the shutdown is initiated, 0 otherwise", + ) + + if self.config.declare_lmp_param: + self.LMP = Param( + initialize=1, + mutable=True, + doc="Time-varying locational marginal prices (LMPs) [in $/MWh]", + ) + + if self.config.model_func is None: + _logger.warning( + "The function that builds the operation model is not specified." + "model_func must declare all the necessary operation variables," + "relations among operation variables, and variable" + "operating and maintenance cost correlations." + ) + return + + # Call the function that builds the operation model + self.config.model_func(self, **self.config.model_args) diff --git a/idaes/apps/grid_integration/multiperiod/price_taker_model.py b/idaes/apps/grid_integration/multiperiod/price_taker_model.py new file mode 100644 index 0000000000..e24f5b560e --- /dev/null +++ b/idaes/apps/grid_integration/multiperiod/price_taker_model.py @@ -0,0 +1,1073 @@ +################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +################################################################################# +""" + Immediate #TODO list: + - Write methods to handle the representative day case + - automate cash flow expressions (likely use a + weighted sum for representative days?) + - automate startup/shutdown constraints to + appropriately address the differences when + using representative days + - Add documentation to the IDAES readthedocs + - Add a warning when costs/revenues are given, + but are never used during cashflow construction +""" +import pandas as pd +import numpy as np + +from scipy.cluster.vq import kmeans, kmeans2, vq, whiten + +import matplotlib.pyplot as plt + +import os.path + +from pyomo.environ import ( + ConcreteModel, + Block, + Var, + RangeSet, + Objective, + Constraint, + NonNegativeReals, + Expression, + maximize, +) + +from idaes.apps.grid_integration import MultiPeriodModel +from idaes.apps.grid_integration.multiperiod.design_and_operation_models import ( + DesignModelData, + OperationModelData, +) + + +import idaes.logger as idaeslog +from idaes.core.util.exceptions import ConfigurationError + +_logger = idaeslog.getLogger(__name__) + + +def _compute_sse(data, centroids, idx): + """ + PRIVATE FUNCTION + + Function used to compute the inertia (sum of square errors) for k clusters. + + Args: + data: Columnar data for a given LMP signal + centroids: Array of k centroids + idx: Index for data + + Returns: + inertia: Sum of square errors for k clusters + """ + inertia = 0 + for i, centroid in enumerate(centroids): + cluster_points = data[idx == i] + inertia += np.sum((cluster_points - centroid) ** 2) + return inertia + + +class PriceTakerModel(ConcreteModel): + def __init__(self, seed=20, horizon_length=24): + super().__init__() + self.seed = seed + self.horizon_length = horizon_length + + @property + def seed(self): + return self._seed + + @seed.setter + def seed(self, value): + if not isinstance(value, int): + raise TypeError(f"seed must be an integer, but {value} is not an integer") + self._seed = value + + @property + def horizon_length(self): + return self._horizon_length + + @horizon_length.setter + def horizon_length(self, value): + if value <= 0: + raise ValueError(f"horizon_length must be > 0, but {value} is provided.") + if not isinstance(value, int): + raise TypeError( + f"horizon_length must be an integer, but {value} is not an integer" + ) + self._horizon_length = value + + def generate_daily_data(self, raw_data): + """ + Function used to generate the daily data in a usable format + from the raw data provided. + + Args: + raw_data: Columnar data for a given LMP signal + + Returns: + daily_data: Correctly formatted daily LMP data for later use + """ + day_list = list(range(1, (len(raw_data) // self._horizon_length) + 1)) + + daily_data = pd.DataFrame(columns=day_list) + + # Extracting data to populate empty dataframe + i = 0 + j = self._horizon_length + day = 1 + + if j > len(raw_data): + raise ValueError( + f"tried to generate daily data, but horizon length of {self._horizon_length} exceeds raw_data length of {len(raw_data)}" + ) + + while j <= len(raw_data): + daily_data[day] = raw_data[i:j].reset_index(drop=True) + i = j + j = j + self._horizon_length + day = day + 1 + + return daily_data + + def get_optimal_n_clusters( + self, + daily_data, + kmin=None, + kmax=None, + ): + """ + Determines the appropriate number of clusters needed for a + given price signal. + + Args: + daily_data: LMP signal grouped by days (output of generate_daily_data function) + kmin: minimum number of clusters + kmax: maximum number of clusters + + Returns: + n_clusters: the optimal number of clusters for the given data + inertia_values: within-cluster sum-of-squares + """ + # Check kmin and kmax for validity + if kmin is None: + kmin = 4 + if kmax is None: + kmax = 30 + _logger.warning("kmax was not set - using a default value of 30.") + + if not isinstance(kmin, int): + raise ValueError(f"kmin must be an integer, but {kmin} is not an integer") + if not isinstance(kmax, int): + raise ValueError(f"kmax must be an integer, but {kmax} is not an integer") + if kmin < 1: + raise ValueError(f"kmin must be > 0, but {kmin} is provided.") + if kmax < 1: + raise ValueError(f"kmax must be > 0, but {kmax} is provided.") + if kmin >= kmax: + raise ValueError(f"kmin must be less than kmax, but {kmin} >= {kmax}") + + k_values = range(kmin, kmax + 1) + inertia_values = [] + + np.random.seed(self._seed) + + whitened_daily_data = whiten(daily_data.transpose()) + + for k in k_values: + centroids, _ = kmeans(whitened_daily_data, k) + idx, _ = vq(whitened_daily_data, centroids) + + # Compute the inertia (SSE) for k clusters + inertia = _compute_sse(whitened_daily_data, centroids, idx) + inertia_values.append(inertia) + + # Calculate the second derivative + first_deriv = np.diff(inertia_values) + second_deriv = np.diff(first_deriv) + + # Determine the optimal number of clusters + # The +2 accounts for the dimension being reduced twice by derivatives + n_clusters = np.argmin(second_deriv) + 2 + + if n_clusters is None: + raise ValueError( + "Could not find elbow point for given kmin, kmax. Consider increasing the range of kmin, kmax." + ) + + _logger.info(f"Optimal # of clusters is: {n_clusters}") + + if int(n_clusters) + 2 >= kmax: + _logger.warning( + f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." + ) + + return int(n_clusters), inertia_values + + def generate_elbow_plot( + self, + daily_data, + kmin=None, + kmax=None, + ): + + if kmin is None: + kmin = 4 + if kmax is None: + kmax = 30 + _logger.warning("kmax was not set - using a default value of 30.") + + if not isinstance(kmin, int): + raise ValueError(f"kmin must be an integer, but {kmin} is not an integer") + if not isinstance(kmax, int): + raise ValueError(f"kmax must be an integer, but {kmax} is not an integer") + if kmin < 1: + raise ValueError(f"kmin must be > 0, but {kmin} is provided.") + if kmax < 1: + raise ValueError(f"kmax must be > 0, but {kmax} is provided.") + if kmin >= kmax: + raise ValueError(f"kmin must be less than kmax, but {kmin} >= {kmax}") + + k_values = range(kmin, kmax + 1) + + n_clusters, inertia_values = self.get_optimal_n_clusters( + daily_data=daily_data, kmin=kmin, kmax=kmax + ) + + plt.plot(k_values, inertia_values) + plt.axvline(x=n_clusters, color="red", linestyle="--", label="Elbow") + plt.xlabel("Number of clusters") + plt.ylabel("Inertia") + plt.title("Elbow Method") + plt.xlim(kmin, kmax) + plt.grid() + + def cluster_lmp_data(self, raw_data, n_clusters): + """ + Clusters the given price signal in n_clusters. This method supports k-means, + k-medoids, etc., techniques for clustering. + + Args: + raw_data: Columnar data for a given LMP signal. + n_clusters: Number of clusters desired for the data (representative days). + + Returns: + lmp_data: A dictionary of representative day LMP data, indices are indexed + by integers starting at 1. Example: :: + + {1: {1: 4, 2: 3, 3: 5}, + 2: {1: 1, 2: 7, 3: 3}} + + weights: A dictionary of weights for each representative day, indexed the + same way as lmp_data. Example: :: + + {1: 45, 2: 56} + """ + # testing if n_integers is valid + if n_clusters is not None: + if not isinstance(n_clusters, int): + raise ValueError( + f"n_clusters must be an integer, but {n_clusters} is not an integer" + ) + if n_clusters < 1: + raise ValueError( + f"n_clusters must be > 0, but {n_clusters} is provided." + ) + + # reconfiguring raw data + daily_data = self.generate_daily_data(raw_data) + whitened_daily_data = whiten(daily_data.transpose()) + + # KMeans clustering with the optimal number of clusters + centroids, labels = kmeans2(whitened_daily_data, n_clusters) + + # Set any centroid values that are < 1e-4 to 0 to avoid noise + centroids = centroids * (abs(centroids) >= 1e-4) + + # Compute weight for each cluster by counting its occurrences in the dataset + unique_labels, weights_counter = np.unique(labels, return_counts=True) + + # Create dicts for lmp data and the weight of each cluster + rep_days_data = {} + weights_data = {} + + rep_days_data = pd.DataFrame( + centroids.transpose(), columns=range(1, n_clusters + 1) + ) + lmp_data = rep_days_data.to_dict() + weights_data = pd.DataFrame(weights_counter) + weights_data.index = np.arange(1, len(weights_data) + 1) + weights = weights_data.to_dict() + + return lmp_data, weights + + def append_lmp_data( + self, + file_path, + sheet=None, + column_name=None, + n_clusters=None, + horizon_length=None, + ): + """ + This function appends LMP data to the PriceTakerModel using single + or multiple year signals, as well as using full price data or + clustering to use representative days. + + Args: + file_path: path to the file containing LMP data + sheet: if the file is an excel file, the sheet name + where the LMP data is located - (default: None) + column_name: string name of the column to be used for the + LMP signal + n_clusters: number of clusters for the data (representative days) + None if representative days not used - (default: None) + horizon_length: if a value is given, this will be used to set the + horizon_length attribute of the PriceTakerModel. + (default: None --> use existing horizon_length (default: 24)) + + Returns: + + """ + if column_name is None: + raise ValueError( + "Data was provided but no column name was provided. Please supply a value for column_name." + ) + + if horizon_length is not None: + self.horizon_length = horizon_length + + if n_clusters is not None: + if not isinstance(n_clusters, int): + raise ValueError( + f"n_clusters must be an integer, but {n_clusters} is not an integer" + ) + if n_clusters < 1: + raise ValueError( + f"n_clusters must be > 0, but {n_clusters} is provided." + ) + + if os.path.exists(file_path): + path_to_file = file_path + else: + raise ValueError( + f"The file path {file_path} does not exist. Please check your file path." + ) + + if ".xls" in path_to_file.suffix: + if sheet is None: + _logger.warning( + "Excel file was provided but no sheet was specified. Using the first sheet of the excel file." + ) + sheet = 0 + full_data = pd.read_excel(path_to_file, sheet_name=[sheet])[sheet] + elif ".csv" in path_to_file.suffix: + full_data = pd.read_csv( + path_to_file, + ) + + if n_clusters is not None: + # Single price signal, use representative days + self.set_years = None + self.set_days = RangeSet(1, n_clusters) + self._n_time_points = ( + self.horizon_length if self.horizon_length is not None else 24 + ) + self.set_time = RangeSet(self._n_time_points) + + self.LMP = {} + self.WEIGHTS = {} + + price_data = full_data[column_name] + lmp_data, weights = self.cluster_lmp_data(price_data, n_clusters) + + for d in self.set_days: + for t in self.set_time: + self.LMP[t, d] = lmp_data[d][t - 1] + self.WEIGHTS[d] = weights[0][d] + + return + + else: + # Single price signal, use full year's price signal + self.set_years = None + self.set_days = None + self._n_time_points = len(full_data) + self.set_time = RangeSet(self._n_time_points) + + price_data = full_data[column_name].to_list() + self.LMP = {t: price_data[t - 1] for t in self.set_time} + + return + + def build_multiperiod_model(self, **kwargs): + """ + Builds the multiperiod model using other price taker class functions + to populate the important sets (self.set_days, self.set_years, etc.) + + Args: + **kwargs: keyword argument dictionary to be passed to the + MultiPeriodModel class to build the time-series + based price taker model. + """ + if not hasattr(self, "_n_time_points"): + raise ConfigurationError( + "MultiPeriodModel requires n_time_points as an argument. Before invoking the build_multiperiod_model method, call the append_lmp_data method on PriceTakerModel class first, which will assign the number of time points, n_time_points, to be used in the MultiPeriodModel." + ) + # For now, strictly requiring the user to call append_lmp_data before build_multiperiod_model. + if (not hasattr(self, "set_days")) or (not hasattr(self, "set_years")): + raise ConfigurationError( + "Before invoking the build_multiperiod_model method, call the append_lmp_data method on PriceTakerModel class first." + ) + + self.mp_model = MultiPeriodModel( + n_time_points=self._n_time_points, + set_days=self.set_days, + set_years=self.set_years, + use_stochastic_build=True, + **kwargs, + ) + + # If declare_lmp_param config option on OperationModel is True, need to append the LMP data to the OperationModel. + # Check if LMP has already been defined on the PriceTakerModel with the append_lmp_data + # function above + LMP_exists = hasattr(self, "LMP") + + # Iterate through model to append LMP data if it's been defined + # and the model says it should be (default) + period = self.mp_model.period + for p in period: + for blk in period[p].component_data_objects(Block): + if isinstance(blk, OperationModelData): + if blk.config.declare_lmp_param: + if not LMP_exists: + raise ConfigurationError( + "OperationModelData has been defined to automatically " + "populate LMP data. However, self.LMP does not exist, where self is an instance of PriceTakerModel. " + "Please run the append_lmp_data method from the PriceTakerModel class first or set the " + "declare_lmp_param configuration option to False when configuring " + "your OperationModelData object." + ) + blk.LMP = self.LMP[p] + + def add_capacity_limits( + self, + op_blk, + design_blk, + commodity_var, + capacity_min, + capacity_max, + constraint_type, + linearization=False, + ): + """ + Adds capacity limit constraints of the form: + capacity_min * y(t) <= commodity_var(t) <= capacity_max * y(t) + ex: P_min * y(t) <= P(t) <= P_max * y(t) [where P(t) is power at time t] + + + Args: + op_blk: String of the name of the operation model block, ex: ("fs.op_name") + design_blk: String of the name of the design model block, ex: ("m.design_name") + commodity_var: String of the name of the entity on the model the capacity constraints + will be applied to, ex: ("total_power") + capacity_min: String of the name of the minimum capacity, ex: ("min_power_capacity") + capacity_max: String of the name of the maximum capacity, ex: ("max_power_capacity") + constraint_type: String to choose between linear and nonlinear constraints. Valid + inputs are in: ["linear", "nonlinear"] + linearization: Boolean indicating whether linearization is used when constraint_type is + "nonlinear", True to use linearization, False otherwise + + Assumptions/relationship: + capacity min <= capacity max + capacity min >= 0 + capacity max >= 0 + """ + op_mode = { + t: self.mp_model.period[t].find_component(op_blk + ".op_mode") + for t in self.mp_model.period + } + var_commodity = { + t: self.mp_model.period[t].find_component(op_blk + "." + commodity_var) + for t in self.mp_model.period + } + max_capacity = self.find_component(design_blk + "." + capacity_max) + min_capacity = self.find_component(design_blk + "." + capacity_min) + + # Importing in the necessary variables + if not hasattr(self, "range_time_steps"): + self.range_time_steps = RangeSet(len(self.mp_model.set_period)) + + blk_name = op_blk.split(".")[-1] + "_capacity_limits" + setattr(self.mp_model, blk_name, Block()) + blk = getattr(self.mp_model, blk_name) + + # Constraint rules for avoiding overlap for multiple-commodity naming + def capacity_low_limit_rule(b, t): + return ( + min_capacity * op_mode[self.mp_model.set_period.at(t)] + <= var_commodity[self.mp_model.set_period.at(t)] + ) + + def capacity_high_limit_rule(b, t): + return ( + max_capacity * op_mode[self.mp_model.set_period.at(t)] + >= var_commodity[self.mp_model.set_period.at(t)] + ) + + if constraint_type == "linear" or ( + constraint_type == "nonlinear" and not linearization + ): + # Set constraints that have same form + setattr( + blk, + "capacity_limit_low_" + commodity_var, + Constraint(self.range_time_steps, rule=capacity_low_limit_rule), + ) + setattr( + blk, + "capacity_limit_high_" + commodity_var, + Constraint(self.range_time_steps, rule=capacity_high_limit_rule), + ) + + elif constraint_type == "nonlinear" and linearization: + raise NotImplementedError( + "You tried use nonlinear capacity with linearization. This is not yet supported." + ) + else: + raise ValueError( + f"constraint_type must be either linear, or nonliner, but {constraint_type} is not." + ) + + # Logger info for where constraint is located on the model + _logger.info( + f"Created capacity limit constraints for variable ({commodity_var}) at (" + + str(blk) + + ")" + ) + + def add_ramping_constraints( + self, + op_blk, + design_blk, + capacity_var, + ramping_var, + constraint_type, + linearization=True, + op_range_lb=0.6, + startup_rate=0.7, + shutdown_rate=0.7, + ramp_up_rate=0.7, + ramp_down_rate=0.7, + ): + """ + Adds ramping constraints of the form: + -ramp_down_limit <= var(t) - var(t-1) <= ramp_up_limit on var + + + Args: + op_blk: String of the name of the operation model block, ex: ("fs.op_name") + design_blk: String of the name of the design model block, ex: ("m.design_name") + capacity_var: String of the name of the entity on the model the ramping constraints + will be applied to, ex: ("total_power") + ramping_var: String of the name of the variable that the ramping constraints will + be applied to + constraint_type: String to choose between linear and nonlinear constraints. Valid + inputs are in: ["linear", "nonlinear"] + linearization: Boolean indicating whether linearization is used when constraint_type is + "nonlinear", True to use linearization, False otherwise + op_range_lb: The fraction of the maximum capacity that represents the lower operating + bound (between 0 and 1) + startup_rate: The fraction of the maximum capacity that variable ramping_var can + increase during startup (between 0 and 1) + shutdown_rate: The fraction of the maximum capacity that variable ramping_var can + decrease during shutdown (between 0 and 1) + ramp_up_rate: The fraction of the maximum capacity that variable ramping_var can + increase during operation (between 0 and 1) + ramp_down_rate: The fraction of the maximum capacity that variable ramping_var can + decrease during operation (between 0 and 1) + + + Assumptions/relationship: + total_power_upper_bound >= ramp_up_limit >= startup_limit >= total_power_lower_bound > 0 + total_power_upper_bound >= ramp_down_limit >= shutdown_limit >= total_power_lower_bound > 0 + """ + # Checking that all ramping rates are between 0 and 1 and that the + # lower bound for operation is less than the startup/shutdown ramps + if startup_rate <= 0 or startup_rate > 1: + raise ValueError( + f"startup_rate fraction must be between 0 and 1, but {startup_rate} is not." + ) + if shutdown_rate <= 0 or shutdown_rate > 1: + raise ValueError( + f"shutdown_rate fraction must be between 0 and 1, but {shutdown_rate} is not." + ) + if ramp_up_rate <= 0 or ramp_up_rate > 1: + raise ValueError( + f"ramp_up_rate fraction must be between 0 and 1, but {ramp_up_rate} is not." + ) + if ramp_down_rate <= 0 or ramp_down_rate > 1: + raise ValueError( + f"ramp_down_rate fraction must be between 0 and 1, but {ramp_down_rate} is not." + ) + if op_range_lb < 0 or op_range_lb > 1: + raise ValueError( + f"op_range_lb fraction must be between 0 and 1, but {op_range_lb} is not." + ) + if op_range_lb > shutdown_rate: + raise ValueError( + "op_range_lb fraction must be <= shut_down_rate, otherwise the system cannot reach the off state." + ) + + start_up = { + t: self.mp_model.period[t].find_component(op_blk + ".startup") + for t in self.mp_model.period + } + op_mode = { + t: self.mp_model.period[t].find_component(op_blk + ".op_mode") + for t in self.mp_model.period + } + shut_down = { + t: self.mp_model.period[t].find_component(op_blk + ".shutdown") + for t in self.mp_model.period + } + var_ramping = { + t: self.mp_model.period[t].find_component(op_blk + "." + ramping_var) + for t in self.mp_model.period + } + + if constraint_type == "linear": + var_capacity = self.find_component(design_blk + "." + capacity_var) + act_startup_rate = { + t: var_capacity * start_up[t] for t in self.mp_model.period + } + act_shutdown_rate = { + t: var_capacity * shut_down[t] for t in self.mp_model.period + } + act_op_mode_rate = { + t: var_capacity * op_mode[t] for t in self.mp_model.period + } + + elif constraint_type == "nonlinear": + if linearization == True: + raise NotImplementedError( + "You tried use nonlinear capacity with linearization. This is not yet supported." + ) + elif linearization == False: + var_capacity = self.find_component(design_blk + "." + capacity_var) + act_startup_rate = { + t: var_capacity * start_up[t] for t in self.mp_model.period + } + act_shutdown_rate = { + t: var_capacity * shut_down[t] for t in self.mp_model.period + } + act_op_mode_rate = { + t: var_capacity * op_mode[t] for t in self.mp_model.period + } + else: + raise ValueError( + f"constraint_type must be either linear, or nonliner, but {constraint_type} is not." + ) + + # Importing in the necessary variables + if not hasattr(self, "range_time_steps"): + self.range_time_steps = RangeSet(len(self.mp_model.set_period)) + + # Creating the pyomo block + blk_name = op_blk.split(".")[-1] + "_rampup_rampdown" + setattr(self.mp_model, blk_name, Block()) + blk = getattr(self.mp_model, blk_name) + + # The linearized ramping constraints + @blk.Constraint(self.range_time_steps) + def ramp_up_con(b, t): + if t == 1: + return Constraint.Skip + else: + return ( + var_ramping[self.mp_model.set_period.at(t)] + - var_ramping[self.mp_model.set_period.at(t - 1)] + <= (startup_rate - ramp_up_rate) + * act_startup_rate[self.mp_model.set_period.at(t)] + + ramp_up_rate * act_op_mode_rate[self.mp_model.set_period.at(t)] + ) + + @blk.Constraint(self.range_time_steps) + def ramp_down_con(b, t): + if t == 1: + return Constraint.Skip + else: + return ( + var_ramping[self.mp_model.set_period.at(t - 1)] + - var_ramping[self.mp_model.set_period.at(t)] + <= shutdown_rate * act_shutdown_rate[self.mp_model.set_period.at(t)] + + ramp_down_rate * act_op_mode_rate[self.mp_model.set_period.at(t)] + ) + + # Logger info for where constraint is located on the model + _logger.info( + f"Created ramping constraints for variable ({capacity_var}) at (" + + str(blk) + + ")" + ) + + def add_startup_shutdown( + self, + op_blk, + design_blk, + build_binary_var, + up_time=1, + down_time=1, + ): + """ + Adds startup/shutdown and minimum uptime/downtime constraints on + a given unit/process + + + Args: + op_blk: String of the name of the operation model block, ex: ("fs.op_name") + design_blk: String of the name of the design model block, ex: ("m.design_name") + build_binary_var: String of the name of the binary variable which indicates if we + should build (1) or not build (0) the design corresponding to the + 'design_blk' referenced above + up_time: Time period required for the system to start up fully + ex: 4 time periods + down_time: Time period required for the system to shutdown fully + ex: 4 time periods + + Returns: + + Assumption: + up_time >= 1 & down_time >= 1 + """ + # Check up_time and down_time for validity + if not isinstance(up_time, int): + raise ValueError( + f"up_time must be an integer, but {up_time} is not an integer" + ) + if up_time < 1: + raise ValueError(f"up_time must be >= 1, but {up_time} is not") + if not isinstance(down_time, int): + raise ValueError( + f"down_time must be an integer, but {down_time} is not an integer" + ) + if down_time < 1: + raise ValueError(f"down_time must be >= 1, but {down_time} is not") + + start_up = { + t: self.mp_model.period[t].find_component(op_blk + ".startup") + for t in self.mp_model.period + } + op_mode = { + t: self.mp_model.period[t].find_component(op_blk + ".op_mode") + for t in self.mp_model.period + } + shut_down = { + t: self.mp_model.period[t].find_component(op_blk + ".shutdown") + for t in self.mp_model.period + } + + if design_blk is not None: + build = self.find_component(design_blk + "." + build_binary_var) + + if not hasattr(self, "range_time_steps"): + self.range_time_steps = RangeSet(len(self.mp_model.set_period)) + number_time_steps = len(self.mp_model.set_period) + + blk_name = op_blk.split(".")[-1] + "_startup_shutdown" + setattr(self.mp_model, blk_name, Block()) + blk = getattr(self.mp_model, blk_name) + + if design_blk is not None: + + @blk.Constraint(self.range_time_steps) + def design_op_relationship_con(b, t): + return op_mode[self.mp_model.set_period.at(t)] <= build + + @blk.Constraint(self.range_time_steps) + def binary_relationship_con(b, t): + if t == 1 or t > number_time_steps: + return Constraint.Skip + return ( + op_mode[self.mp_model.set_period.at(t)] + - op_mode[self.mp_model.set_period.at(t - 1)] + == start_up[self.mp_model.set_period.at(t)] + - shut_down[self.mp_model.set_period.at(t)] + ) + + # Check to see if there is a representative day structure + if self.set_days is not None: + raise NotImplementedError( + "You tried to use representative days with minimum up or minimum downtime constraints. This is not yet supported." + ) + else: + if up_time > 1: + + @blk.Constraint(self.range_time_steps) + def minimum_up_time_con(b, t): + if t == 1 or t < up_time or t > number_time_steps: + return Constraint.Skip + else: + return ( + sum( + start_up[self.mp_model.set_period.at(i)] + for i in range(t - up_time + 1, t + 1) + ) + <= op_mode[self.mp_model.set_period.at(t)] + ) + + if down_time > 1: + + @blk.Constraint(self.range_time_steps) + def minimum_down_time_con(b, t): + if t < down_time or t == 1 or t > number_time_steps: + return Constraint.Skip + return ( + sum( + shut_down[self.mp_model.set_period.at(i)] + for i in range(t - down_time + 1, t + 1) + ) + <= 1 - op_mode[self.mp_model.set_period.at(t)] + ) + + # Logger info for where constraint is located on the model + _logger.info( + f"Created startup/shutdown constraints for operating block ({op_blk}) at (" + + str(blk) + + ")" + ) + + def build_hourly_cashflows(self, revenue_streams=None, costs=None): + """ + Adds an expression for the net cash inflow for each operational + block. This is the new cash inflow for each time period of the + PriceTakerModel. Default costs for each model should include + 'non_fuel_vom' (non-fuel variable operating costs), 'fuel_cost' + (cost of fuel), and 'carbon_price' (cost associated with producing + carbon; i.e., a carbon tax). The net cash inflow is calculated as: + + Sum(revenue streams) - Sum(costs) + + for every time period in the PriceTakerModel's MultiPeriodModel + + Args: + revenue_streams: List of strings representing the names of the + revenue streams coming from the model. + default: None + Coproduction Example: :: + + ['elec_revenue', 'H2_revenue', ] + + costs: List of strings representing the names of the + costs associated with operating at a time period. + default: None + Example: :: + + ['hourly_fixed_cost', 'electricity_cost',] + + Returns: + + """ + period = self.mp_model.period + + count_op_blks = 0 + for p in period: + # Set net profit contribution expressions to 0 + total_cost_expr = 0 + total_revenue_expr = 0 + if costs is None: + _logger.warning( + "No costs were provided while building the hourly cashflow. Costs will be set to 0." + ) + costs = [] + + if revenue_streams is None: + _logger.warning( + "No revenues were provided while building the hourly cashflow. Revenues will be set to 0." + ) + revenue_streams = [] + + for blk in period[p].component_data_objects(Block): + if isinstance(blk, OperationModelData): + count_op_blks += 1 + + # Add costs for each block. If more than one block, may have + # costs that exist on one block and not on another. (i.e., coproduction) + for ind, cost in enumerate(costs): + curr_cost = 0 + try: + curr_cost += blk.find_component(costs[ind]) + except: + pass + total_cost_expr += curr_cost + + # Add revenue streams for each block. If more than one block, may have + # revenues that exist on one block and not on another. (i.e., coproduction) + for ind, rev in enumerate(revenue_streams): + curr_rev = 0 + try: + curr_rev += blk.find_component(revenue_streams[ind]) + except: + pass + total_revenue_expr += curr_rev + + for cost in costs: + curr_cost = 0 + try: + curr_cost += period[p].find_component(cost) + except: + pass + + total_cost_expr += curr_cost + + for rev in revenue_streams: + curr_rev = 0 + try: + curr_rev += period[p].find_component(rev) + except: + pass + + total_revenue_expr += curr_rev + + # Set total cost expression + self.mp_model.period[p].total_cost = Expression(expr=total_cost_expr) + + # Set total revenue expression + self.mp_model.period[p].total_revenue = Expression(expr=total_revenue_expr) + + # Individual cost expression can be found on the operation model itself. No need to add these here. + + period[p].net_cash_inflow = Expression( + expr=period[p].total_revenue - period[p].total_cost + ) + + if count_op_blks < 1: + _logger.warning( + "build_hourly_cashflows was called but no operation blocks were found so hourly cashflow of the model was set to 0. If you have hourly costs, please manually assign them." + ) + + # Logger info for where constraint is located on the model + _logger.info( + "Created hourly cashflow expressions at (mp_model.period[i].net_cash_inflow)" + ) + + def build_cashflows( + self, + lifetime=30, + discount_rate=0.08, + corp_tax=0.2, + other_costs=0, + other_revenue=0, + objective="NPV", + ): + """ + Builds overall cashflow expressions and appends the objective function + in terms of cashflows to the PriceTakerModel + + Args: + lifetime: Number of years (lifetime) to evaluate the equipment + discount_rate: Fractional rate of discount used in NPV calculations. + Must be between 0 and 1. + corp_tax: Fractional value of corporate tax used in NPV calculations. + other_costs: Pyomo expression for other costs + other_revenue: Pyomo expression for other sources of revenue + objective: String to choose which objective form is used in the model. + Options: ["NPV", "Annualized NPV", "Net Profit"] + + Returns: + + """ + + capex_expr = 0 + fom_expr = 0 + + count_des_blks = 0 + for blk in self.component_data_objects(Block): + if isinstance(blk, DesignModelData): + count_des_blks += 1 + + capex_expr += blk.capex + fom_expr += blk.fom + + self.CAPEX = Var(within=NonNegativeReals, doc="Total CAPEX") + self.capex_calculation = Constraint(expr=self.CAPEX == capex_expr) + + self.FOM = Var(within=NonNegativeReals, doc="Yearly Fixed O&M") + self.fom_calculation = Constraint(expr=self.FOM == fom_expr) + + self.DEPRECIATION = Var(within=NonNegativeReals, doc="Yearly depreciation") + self.dep_calculation = Constraint( + expr=self.DEPRECIATION == self.CAPEX / lifetime + ) + + self.NET_CASH_INFLOW = Var(doc="Net cash inflow") + self.net_cash_inflow_calculation = Constraint( + expr=self.NET_CASH_INFLOW + == sum( + self.mp_model.period[p].net_cash_inflow for p in self.mp_model.period + ) + ) + + self.CORP_TAX = Var(within=NonNegativeReals, doc="Corporate tax") + self.corp_tax_calculation = Constraint( + expr=self.CORP_TAX + >= corp_tax + * ( + self.NET_CASH_INFLOW + + other_revenue + - other_costs + - self.FOM + - self.DEPRECIATION + ) + ) + + self.NET_PROFIT = Var(doc="Net profit after taxes") + self.net_profit_calculation = Constraint( + expr=self.NET_PROFIT + == self.NET_CASH_INFLOW + + other_revenue + - other_costs + - self.FOM + - self.CORP_TAX + ) + + constant_cf_factor = (1 - (1 + discount_rate) ** (-lifetime)) / discount_rate + self.NPV = Expression(expr=constant_cf_factor * self.NET_PROFIT - self.CAPEX) + self.Annualized_NPV = Expression( + expr=self.NET_PROFIT - (1 / constant_cf_factor) * self.CAPEX, + ) + + obj_set = False + + if objective == "NPV": + self.obj = Objective(expr=self.NPV, sense=maximize) + obj_set = True + + elif objective == "Annualized NPV": + self.obj = Objective(expr=self.Annualized_NPV, sense=maximize) + obj_set = True + + elif objective == "Net Profit": + self.obj = Objective(expr=self.NET_PROFIT, sense=maximize) + obj_set = True + + if not obj_set: + _logger.warning( + f"build_cashflows was called, but the objective type provided, {objective}, is invalid. The objective has been set to 0. Please manually add your cost objective if you require one." + ) + self.obj = Objective(expr=0, sense=maximize) + + if count_des_blks < 1: + _logger.warning( + "build_cashflows was called, but no design blocks were found so capex and FOM are 0. Please manually add your cost objective if you require one." + ) diff --git a/idaes/apps/grid_integration/multiperiod/tests/FLECCS_princeton.csv b/idaes/apps/grid_integration/multiperiod/tests/FLECCS_princeton.csv new file mode 100644 index 0000000000..6c037b23d4 --- /dev/null +++ b/idaes/apps/grid_integration/multiperiod/tests/FLECCS_princeton.csv @@ -0,0 +1,8761 @@ +Date Time,TimeStep,BaseCaseTax,HighWindTax,HighSolarTax,WinterNYTax +1/1/2030 0:00,1,9.19,0.00,46.68,54.20 +1/1/2030 1:00,2,46.68,0.00,46.68,55.98 +1/1/2030 2:00,3,46.68,0.00,62.25,139.62 +1/1/2030 3:00,4,54.20,9.19,62.25,59.15 +1/1/2030 4:00,5,54.20,9.19,62.25,59.15 +1/1/2030 5:00,6,54.20,9.19,62.25,55.98 +1/1/2030 6:00,7,54.20,43.32,62.25,54.20 +1/1/2030 7:00,8,54.20,46.68,62.25,55.98 +1/1/2030 8:00,9,54.20,46.68,62.25,55.98 +1/1/2030 9:00,10,69.24,46.68,98.08,55.98 +1/1/2030 10:00,11,46.68,46.68,46.68,54.20 +1/1/2030 11:00,12,46.68,46.68,46.68,9.19 +1/1/2030 12:00,13,46.68,46.68,46.68,9.19 +1/1/2030 13:00,14,46.68,46.68,9.19,9.19 +1/1/2030 14:00,15,46.68,46.68,9.19,9.19 +1/1/2030 15:00,16,46.68,46.68,1.18,32.64 +1/1/2030 16:00,17,46.68,46.68,0.00,54.20 +1/1/2030 17:00,18,46.68,46.68,18.39,54.20 +1/1/2030 18:00,19,46.68,46.68,46.68,54.20 +1/1/2030 19:00,20,54.20,46.68,54.20,61.84 +1/1/2030 20:00,21,54.20,46.68,54.20,83.25 +1/1/2030 21:00,22,54.20,46.68,54.20,83.25 +1/1/2030 22:00,23,54.20,46.68,54.20,221.45 +1/1/2030 23:00,24,54.20,46.68,54.20,83.25 +1/2/2030 0:00,25,54.20,46.68,54.20,70.13 +1/2/2030 0:59,26,54.20,46.68,54.20,71.85 +1/2/2030 1:59,27,57.76,46.68,62.25,73.57 +1/2/2030 2:59,28,62.25,46.68,67.04,70.13 +1/2/2030 3:59,29,71.20,54.20,67.04,58.02 +1/2/2030 4:59,30,62.25,54.20,67.04,55.98 +1/2/2030 5:59,31,62.25,54.20,67.04,55.98 +1/2/2030 6:59,32,62.25,54.20,62.25,55.98 +1/2/2030 7:59,33,62.25,54.20,66.52,55.98 +1/2/2030 8:59,34,62.64,54.20,88.26,55.98 +1/2/2030 9:59,35,54.20,54.20,46.68,54.20 +1/2/2030 10:59,36,46.68,57.76,46.68,52.59 +1/2/2030 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+################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +################################################################################# + +import pytest +from pyomo.environ import ConcreteModel, Constraint, Expression, Var +from idaes.apps.grid_integration import ( + DesignModel, + OperationModel, +) + + +@pytest.mark.unit +def test_design_model_class(caplog): + """Tests the DesignModel class""" + + def _my_design_model(m, p_min, p_max, cost): + m.power = Var() + m.min_capacity = Constraint(expr=p_min * m.install_unit <= m.power) + m.max_capacity = Constraint(expr=m.power <= p_max * m.install_unit) + + # capex and fom must either be a constant, or Var, or Expression + m.capex = Expression(expr=cost["capex"] * m.power) + m.fom = Expression(expr=cost["fom"] * m.power) + + blk = ConcreteModel() + blk.unit_1 = DesignModel( + model_func=_my_design_model, + model_args={ + "p_min": 150, + "p_max": 600, + "cost": {"capex": 10, "fom": 1}, + }, + ) + + # Begin checks + for attr in ["install_unit", "power", "min_capacity", "max_capacity"]: + assert hasattr(blk.unit_1, attr) + + assert isinstance(blk.unit_1.capex, Expression) + assert isinstance(blk.unit_1.fom, Expression) + assert "Setting the capital cost of the unit to zero." not in caplog.text + assert "Setting the fixed O&M cost of the unit to zero." not in caplog.text + + # Test empty design model + blk.unit_2 = DesignModel() + assert hasattr(blk.unit_2, "install_unit") + for attr in ["power", "min_capacity", "max_capacity", "capex", "fom"]: + assert not hasattr(blk.unit_2, attr) + + assert "Setting the capital cost of the unit to zero." not in caplog.text + assert "Setting the fixed O&M cost of the unit to zero." not in caplog.text + + # Test warning messages + def dummy_func(_): + pass + + blk.unit_3 = DesignModel(model_func=dummy_func) + assert hasattr(blk.unit_3, "install_unit") + assert ( + "'capex' attribute is not set for the design model " + "unit_3. Setting the capital cost of the unit to zero." + ) in caplog.text + assert ( + "'fom' attribute is not set for the design model " + "unit_3. Setting the fixed O&M cost of the unit to zero." + ) in caplog.text + assert blk.unit_3.capex == 0 + assert blk.unit_3.fom == 0 + + +@pytest.mark.unit +def test_design_model_class_logger_message1(caplog): + caplog.clear() + + blk = ConcreteModel() + blk.unit_1 = DesignModel( + model_args={ + "p_min": 150, + "p_max": 600, + "cost": {"capex": 10, "fom": 1}, + }, + ) + + assert ( + "The function that builds the design model is not specified." + "model_func must declare all the necessary design variables," + "relations among design variables, capital cost correlations," + "and fixed operating and maintenance cost correlations." in caplog.text + ) + + +@pytest.mark.unit +def test_operation_model_class(): + """Tests the OperationModel class""" + + def des_model(m): + m.power = Var() + m.capex = 0 + m.fom = 0 + + def op_model(m, des_blk): + m.power = Var() + m.scaled_power = Expression( + expr=m.power / des_blk.power, + doc="Instantaneous power scaled with the design power", + ) + + blk = ConcreteModel() + blk.unit_1_design = DesignModel(model_func=des_model) + blk.unit_1_op = OperationModel( + model_func=op_model, model_args={"des_blk": blk.unit_1_design} + ) + + # Begin checks + for attr in ["op_mode", "startup", "shutdown", "power", "LMP"]: + assert hasattr(blk.unit_1_op, attr) + + assert isinstance(blk.unit_1_op.scaled_power, Expression) + + # Empty operation model + blk.unit_2_op = OperationModel(declare_op_vars=False, declare_lmp_param=False) + for attr in ["op_mode", "startup", "shutdown", "power", "LMP"]: + assert not hasattr(blk.unit_2_op, attr) + + +@pytest.mark.unit +def test_operation_model_class_logger_message1(caplog): + caplog.clear() + + def des_model(m): + m.power = Var() + m.capex = 0 + m.fom = 0 + + blk = ConcreteModel() + blk.unit_1_design = DesignModel(model_func=des_model) + blk.unit_1_op = OperationModel(model_args={"des_blk": blk.unit_1_design}) + + assert ( + "The function that builds the operation model is not specified." + "model_func must declare all the necessary operation variables," + "relations among operation variables, and variable" + "operating and maintenance cost correlations." in caplog.text + ) diff --git a/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py new file mode 100644 index 0000000000..bf45cf9c9a --- /dev/null +++ b/idaes/apps/grid_integration/multiperiod/tests/test_price_taker.py @@ -0,0 +1,1566 @@ +################################################################################# +# The Institute for the Design of Advanced Energy Systems Integrated Platform +# Framework (IDAES IP) was produced under the DOE Institute for the +# Design of Advanced Energy Systems (IDAES). +# +# Copyright (c) 2018-2023 by the software owners: The Regents of the +# University of California, through Lawrence Berkeley National Laboratory, +# National Technology & Engineering Solutions of Sandia, LLC, Carnegie Mellon +# University, West Virginia University Research Corporation, et al. +# All rights reserved. Please see the files COPYRIGHT.md and LICENSE.md +# for full copyright and license information. +################################################################################# + +from pathlib import Path +import pytest +import matplotlib.pyplot as plt +import pandas as pd + +from pyomo.environ import ( + Var, + Binary, + NonNegativeReals, +) + +import pyomo.environ as aml +from pyomo.common.config import ConfigValue + +from idaes.core import FlowsheetBlock + +from idaes.core.base.process_base import declare_process_block_class +from idaes.models.unit_models import SkeletonUnitModelData + +from idaes.apps.grid_integration import DesignModel, OperationModel + +import idaes.logger as idaeslog + +from idaes.apps.grid_integration.multiperiod.price_taker_model import PriceTakerModel +from idaes.core.util.exceptions import ConfigurationError + + +@pytest.fixture +def excel_data(): + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton.csv" + data = pd.read_csv(file_path) + return data + + +@pytest.mark.unit +def test_seed(): + m = PriceTakerModel() + assert isinstance(m._seed, int) + + +@pytest.mark.unit +def test_seed_value(): + value = "fifty" + with pytest.raises( + TypeError, + match=("seed must be an integer, but fifty is not an integer"), + ): + + m = PriceTakerModel() + m.seed = value + + +@pytest.mark.unit +def test_daily_data_size(excel_data): + m = PriceTakerModel() + + # Generate price data for each hour of every day in the data + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + + # Check that there is a row for each horizon length in a representative day + assert len(daily_data) == m.horizon_length + + +@pytest.mark.unit +def test_determine_optimal_num_clusters(excel_data): + m = PriceTakerModel() + + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data, kmax=30) + + assert n_clusters == 11 + + +@pytest.mark.unit +def test_generate_elbow_plot(excel_data): + m = PriceTakerModel() + + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + m.generate_elbow_plot(daily_data) + + # Test that a figure was created + assert plt.gcf() is not None + # Test that axes were created + assert plt.gca() is not None + # Test that the plot has data + assert plt.gca().has_data() + + plt.close("all") + + +@pytest.mark.unit +def test_cluster_lmp_data(excel_data): + # This function gets within both if statement expression in the + # cluster_lmp_data function currently. + m = PriceTakerModel() + + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + n_clusters, inertia_values = m.get_optimal_n_clusters(daily_data) + lmp_data, weights = m.cluster_lmp_data( + excel_data["BaseCaseTax"], n_clusters=n_clusters + ) + + sum_of_weights = 0 + for i in range(1, n_clusters + 1): + sum_of_weights = sum_of_weights + weights[0][i] + assert sum_of_weights == 365 + + assert len(lmp_data) == n_clusters + + +@pytest.mark.unit +def test_init_logger_messages_clusters(excel_data, caplog): + with caplog.at_level(idaeslog.WARNING): + m = PriceTakerModel() + + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + m.get_optimal_n_clusters(daily_data) + + assert "kmax was not set - using a default value of 30." in caplog.text + + +@pytest.mark.unit +def test_init_logger_message1(excel_data, caplog): + # Testing horizon_length input value errors + value = 0 + with pytest.raises( + ValueError, + match=("horizon_length must be > 0, but 0 is provided."), + ): + m = PriceTakerModel() + m.horizon_length = value + + +@pytest.mark.unit +def test_init_logger_message2(excel_data, caplog): + value = 12.34 + m = PriceTakerModel() + with pytest.raises( + TypeError, + match=("horizon_length must be an integer, but 12.34 is not an integer"), + ): + m.horizon_length = value + + +@pytest.mark.unit +def test_init_logger_message3(excel_data, caplog): + # Testing seed errors + value = 12.34 + m = PriceTakerModel() + with pytest.raises( + TypeError, + match=("seed must be an integer, but 12.34 is not an integer"), + ): + m.seed = value + + +@pytest.mark.unit +def test_min_up_time_logger_message1(excel_data): + des = DesignModel() + oper = OperationModel() + build_bin_var = "build" + up_time = [10, -5, 2.2] + down_time = [10, -5, 2.2] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("up_time must be an integer, but 2.2 is not an integer"), + ): + m.add_startup_shutdown(des, oper, build_bin_var, up_time[2], down_time[0]) + + +@pytest.mark.unit +def test_min_up_time_logger_message2(excel_data): + des = DesignModel() + oper = OperationModel() + build_bin_var = "build" + up_time = [10, -5, 2.2] + down_time = [10, -5, 2.2] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("up_time must be >= 1, but -5 is not"), + ): + m.add_startup_shutdown(des, oper, build_bin_var, up_time[1], down_time[0]) + + +@pytest.mark.unit +def test_min_down_time_logger_message1(excel_data): + des = DesignModel() + oper = OperationModel() + build_bin_var = "build" + up_time = [10, -5, 2.2] + down_time = [10, -5, 2.2] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("down_time must be an integer, but 2.2 is not an integer"), + ): + m.add_startup_shutdown(des, oper, build_bin_var, up_time[0], down_time[2]) + + +@pytest.mark.unit +def test_min_down_time_logger_message2(excel_data): + des = DesignModel() + oper = OperationModel() + build_bin_var = "build" + up_time = [10, -5, 2.2] + down_time = [10, -5, 2.2] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("down_time must be >= 1, but -5 is not"), + ): + m.add_startup_shutdown(des, oper, build_bin_var, up_time[0], down_time[1]) + + +@pytest.mark.unit +def test_init_logger_messages_clusters_min_up_down_time(excel_data, caplog): + # Test Not Implemented Error (Rep. Days used for su/sd code) + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + n_clusters=2, + ) + + m.sofc_design = DesignModel( + model_func=SOFC_design_model, + model_args={"min_power": 200, "max_power": 650}, + ) + + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + with pytest.raises( + NotImplementedError, + match=( + "You tried to use representative days with minimum up or minimum downtime constraints. This is not yet supported." + ), + ): + # add capacity limit constraints + m.add_startup_shutdown( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + build_binary_var="build_unit", + up_time=4, + down_time=4, + ) + + +@pytest.mark.unit +def test_optimal_clusters_kmin_logger_message1(excel_data): + kmin = [-5, 10.2, 9] + kmax = [-5, 10.2, 8] + m = PriceTakerModel() + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + with pytest.raises( + ValueError, + match=("kmin must be an integer, but 10.2 is not an integer"), + ): + n_clusters, inertia_values = m.get_optimal_n_clusters( + daily_data, kmin=kmin[1], kmax=kmax[2] + ) + + +@pytest.mark.unit +def test_optimal_clusters_kmin_logger_message2(excel_data): + kmin = [-5, 10.2, 9] + kmax = [-5, 10.2, 8] + m = PriceTakerModel() + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + with pytest.raises( + ValueError, + match=("kmin must be > 0, but -5 is provided."), + ): + n_clusters, inertia_values = m.get_optimal_n_clusters( + daily_data, kmin=kmin[0], kmax=kmax[2] + ) + + +@pytest.mark.unit +def test_optimal_clusters_kmin_logger_message3(excel_data): + kmin = [-5, 10.2, 9] + kmax = [-5, 10.2, 8] + m = PriceTakerModel() + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + with pytest.raises( + ValueError, + match=("kmin must be less than kmax, but 9 >= 8"), + ): + n_clusters, inertia_values = m.get_optimal_n_clusters( + daily_data, kmin=kmin[2], kmax=kmax[2] + ) + + +@pytest.mark.unit +def test_optimal_clusters_kmax_logger_message1(excel_data): + kmin = [-5, 10.2, 9] + kmax = [-5, 10.2, 8] + m = PriceTakerModel() + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + with pytest.raises( + ValueError, + match=("kmax must be an integer, but 10.2 is not an integer"), + ): + n_clusters, inertia_values = m.get_optimal_n_clusters( + daily_data, kmin=kmin[2], kmax=kmax[1] + ) + + +@pytest.mark.unit +def test_optimal_clusters_kmax_logger_message2(excel_data): + kmin = [-5, 10.2, 9] + kmax = [-5, 10.2, 8] + m = PriceTakerModel() + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + with pytest.raises( + ValueError, + match=("kmax must be > 0, but -5 is provided."), + ): + n_clusters, inertia_values = m.get_optimal_n_clusters( + daily_data, kmin=kmin[2], kmax=kmax[0] + ) + m.cluster_lmp_data(excel_data, n_clusters) + + +@pytest.mark.unit +def test_optimal_clusters_close_to_kmax(excel_data, caplog): + caplog.clear() + with caplog.at_level(idaeslog.WARNING): + m = PriceTakerModel() + kmin = 1 + kmax = 4 + + daily_data = m.generate_daily_data(excel_data["BaseCaseTax"]) + m.get_optimal_n_clusters(daily_data, kmin=kmin, kmax=kmax) + + assert ( + f"Optimal number of clusters is close to kmax: {kmax}. Consider increasing kmax." + in caplog.text + ) + + +@pytest.mark.unit +def test_ramping_constraint_logger_message1(excel_data): + des = DesignModel() + oper = OperationModel() + ramping_var = "power" + capac_var = "power_max" + constraint_type = "linear" + linearization = False + op_range_lb = [-0.1, 0.5, 1.0] + su_rate = [-0.1, 0.5] + sd_rate = [-0.1, 0.5] + op_ru_rate = [-0.1, 0.5] + op_rd_rate = [-0.1, 0.5] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("startup_rate fraction must be between 0 and 1, but -0.1 is not."), + ): + m.add_ramping_constraints( + des, + oper, + capac_var, + ramping_var, + constraint_type, + linearization, + op_range_lb[1], + su_rate[0], + sd_rate[1], + op_ru_rate[1], + op_rd_rate[1], + ) + + +@pytest.mark.unit +def test_ramping_constraint_logger_message2(excel_data): + des = DesignModel() + oper = OperationModel() + ramping_var = "power" + capac_var = "power_max" + constraint_type = "linear" + linearization = False + op_range_lb = [-0.1, 0.5, 1.0] + su_rate = [-0.1, 0.5] + sd_rate = [-0.1, 0.5] + op_ru_rate = [-0.1, 0.5] + op_rd_rate = [-0.1, 0.5] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("shutdown_rate fraction must be between 0 and 1, but -0.1 is not."), + ): + m.add_ramping_constraints( + des, + oper, + capac_var, + ramping_var, + constraint_type, + linearization, + op_range_lb[1], + su_rate[1], + sd_rate[0], + op_ru_rate[1], + op_rd_rate[1], + ) + + +@pytest.mark.unit +def test_ramping_constraint_logger_message3(excel_data): + des = DesignModel() + oper = OperationModel() + ramping_var = "power" + capac_var = "power_max" + constraint_type = "linear" + linearization = False + op_range_lb = [-0.1, 0.5, 1.0] + su_rate = [-0.1, 0.5] + sd_rate = [-0.1, 0.5] + op_ru_rate = [-0.1, 0.5] + op_rd_rate = [-0.1, 0.5] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("ramp_up_rate fraction must be between 0 and 1, but -0.1 is not."), + ): + m.add_ramping_constraints( + des, + oper, + capac_var, + ramping_var, + constraint_type, + linearization, + op_range_lb[1], + su_rate[1], + sd_rate[1], + op_ru_rate[0], + op_rd_rate[1], + ) + + +@pytest.mark.unit +def test_ramping_constraint_logger_message4(excel_data): + des = DesignModel() + oper = OperationModel() + ramping_var = "power" + capac_var = "power_max" + constraint_type = "linear" + linearization = False + op_range_lb = [-0.1, 0.5, 1.0] + su_rate = [-0.1, 0.5] + sd_rate = [-0.1, 0.5] + op_ru_rate = [-0.1, 0.5] + op_rd_rate = [-0.1, 0.5] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("ramp_down_rate fraction must be between 0 and 1, but -0.1 is not."), + ): + m.add_ramping_constraints( + des, + oper, + capac_var, + ramping_var, + constraint_type, + linearization, + op_range_lb[1], + su_rate[1], + sd_rate[1], + op_ru_rate[1], + op_rd_rate[0], + ) + + +@pytest.mark.unit +def test_ramping_constraint_logger_message5(excel_data): + des = DesignModel() + oper = OperationModel() + ramping_var = "power" + capac_var = "power_max" + constraint_type = "linear" + linearization = False + op_range_lb = [-0.1, 0.5, 1.0] + su_rate = [-0.1, 0.5] + sd_rate = [-0.1, 0.5] + op_ru_rate = [-0.1, 0.5] + op_rd_rate = [-0.1, 0.5] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("op_range_lb fraction must be between 0 and 1, but -0.1 is not."), + ): + m.add_ramping_constraints( + des, + oper, + capac_var, + ramping_var, + constraint_type, + linearization, + op_range_lb[0], + su_rate[1], + sd_rate[1], + op_ru_rate[1], + op_rd_rate[1], + ) + + +@pytest.mark.unit +def test_ramping_constraint_logger_message6(excel_data): + des = DesignModel() + oper = OperationModel() + ramping_var = "power" + capac_var = "power_max" + constraint_type = "linear" + linearization = False + op_range_lb = [-0.1, 0.5, 1.0] + su_rate = [-0.1, 0.5] + sd_rate = [-0.1, 0.5] + op_ru_rate = [-0.1, 0.5] + op_rd_rate = [-0.1, 0.5] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=( + "op_range_lb fraction must be <= shut_down_rate, otherwise the system cannot reach the off state." + ), + ): + m.add_ramping_constraints( + des, + oper, + capac_var, + ramping_var, + constraint_type, + linearization, + op_range_lb[2], + su_rate[1], + sd_rate[1], + op_ru_rate[1], + op_rd_rate[1], + ) + + +@pytest.mark.unit +def test_ramping_constraint_logger_message1(excel_data): + des = DesignModel() + oper = OperationModel() + ramping_var = "power" + capac_var = "power_max" + constraint_type = "linear" + linearization = False + op_range_lb = [-0.1, 0.5, 1.0] + su_rate = [-0.1, 0.5] + sd_rate = [-0.1, 0.5] + op_ru_rate = [-0.1, 0.5] + op_rd_rate = [-0.1, 0.5] + + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + + # m.sofc_design = DesignModel(model_func=SOFC_design_model, model_args={"min_power": 200, "max_power": 650}) + m.sofc_design = aml.Block() + m.sofc_design.PMAX = 650 + m.sofc_design.PMIN = 200 + m.sofc_design.build_unit = 1 + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # Test NotImplementedError (linearization = True) + with pytest.raises( + NotImplementedError, + match=( + "You tried use nonlinear capacity with linearization. This is not yet supported." + ), + ): + + m.add_ramping_constraints( + "fs.sofc_operation", + "sofc_design", + capac_var, + ramping_var, + "nonlinear", + True, + op_range_lb[1], + su_rate[1], + sd_rate[1], + op_ru_rate[1], + op_rd_rate[1], + ) + + +@pytest.mark.unit +def test_ramping_constraint_logger_message1(excel_data): + des = DesignModel() + oper = OperationModel() + ramping_var = "power" + capac_var = "power_max" + constraint_type = "linear" + linearization = False + op_range_lb = [-0.1, 0.5, 1.0] + su_rate = [-0.1, 0.5] + sd_rate = [-0.1, 0.5] + op_ru_rate = [-0.1, 0.5] + op_rd_rate = [-0.1, 0.5] + + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + # m.sofc_design = DesignModel(model_func=SOFC_design_model, model_args={"min_power": 200, "max_power": 650}) + m.sofc_design = aml.Block() + m.sofc_design.PMAX = 650 + m.sofc_design.PMIN = 200 + m.sofc_design.build_unit = 1 + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # Test Value Error (wrong constraint type) + with pytest.raises( + ValueError, + match=( + "constraint_type must be either linear, or nonliner, but garbage is not." + ), + ): + + m.add_ramping_constraints( + "fs.sofc_operation", + "sofc_design", + capac_var, + ramping_var, + "garbage", + True, + op_range_lb[1], + su_rate[1], + sd_rate[1], + op_ru_rate[1], + op_rd_rate[1], + ) + + +@pytest.mark.unit +def test_add_capacity_limits_logger_message1(excel_data, caplog): + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + + m.sofc_design = DesignModel( + model_func=SOFC_design_model, + model_args={"min_power": 200, "max_power": 650}, + ) + + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # Test Value Error (wrong constraint type) + with pytest.raises( + ValueError, + match=( + "constraint_type must be either linear, or nonliner, but garbage is not." + ), + ): + # add capacity limit constraints + m.add_capacity_limits( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + commodity_var="power", + capacity_max="PMAX", + capacity_min="PMIN", + constraint_type="garbage", + linearization=False, + ) + + +@pytest.mark.unit +def test_add_capacity_limits_logger_message2(excel_data, caplog): + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + + m.sofc_design = DesignModel( + model_func=SOFC_design_model, + model_args={"min_power": 200, "max_power": 650}, + ) + + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # Test Not Implemented Error (linearization is True when nonlinear is chosen) + with pytest.raises( + NotImplementedError, + match=( + "You tried use nonlinear capacity with linearization. This is not yet supported." + ), + ): + # add capacity limit constraints + m.add_capacity_limits( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + commodity_var="power", + capacity_max="PMAX", + capacity_min="PMIN", + constraint_type="nonlinear", + linearization=True, + ) + + +@pytest.mark.unit +def test_append_lmp_data_logger_message1(excel_data, caplog): + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton.csv" + n_clusters = [-5, 1.7, 10] + with pytest.raises( + ValueError, + match=("n_clusters must be an integer, but 1.7 is not an integer"), + ): + m = PriceTakerModel() + m.append_lmp_data( + file_path, + column_name="BaseCaseTax", + n_clusters=n_clusters[1], + horizon_length=24, + ) + + +@pytest.mark.unit +def test_append_lmp_data_logger_message1(excel_data, caplog): + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton.csv" + n_clusters = [-5, 1.7, 10] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("n_clusters must be > 0, but -5 is provided."), + ): + m.append_lmp_data( + file_path, + column_name="BaseCaseTax", + n_clusters=n_clusters[0], + horizon_length=24, + ) + + +@pytest.mark.unit +def test_append_lmp_data_logger_message1(excel_data, caplog): + file_path = "trash" + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("The file path trash does not exist. Please check your file path."), + ): + m.append_lmp_data( + file_path, + ) + + +@pytest.mark.unit +def test_append_lmp_data_logger_message1(excel_data, caplog): + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton.csv" + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=( + "Data was provided but no column name was provided. Please supply a value for column_name." + ), + ): + m.append_lmp_data( + file_path, + ) + + +@pytest.mark.unit +def test_cluster_lmp_data_logger_message1(excel_data): + n_clusters = [-5, 1.7, 10] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("n_clusters must be an integer, but 1.7 is not an integer"), + ): + _, _ = m.cluster_lmp_data(excel_data, n_clusters[1]) + + +@pytest.mark.unit +def test_cluster_lmp_data_logger_message2(excel_data): + n_clusters = [-5, 1.7, 10] + m = PriceTakerModel() + with pytest.raises( + ValueError, + match=("n_clusters must be > 0, but -5 is provided."), + ): + _, _ = m.cluster_lmp_data(excel_data, n_clusters[0]) + + +@pytest.mark.unit +def test_generate_daily_data_logger_messages(): + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton.csv" + raw_data = pd.read_csv(file_path) + m = PriceTakerModel(horizon_length=9000) + with pytest.raises( + ValueError, + match=( + f"tried to generate daily data, but horizon length of {9000} exceeds raw_data length of {len(raw_data['BaseCaseTax'])}" + ), + ): + m.append_lmp_data( + file_path, + column_name="BaseCaseTax", + n_clusters=10, + ) + + +@pytest.mark.unit +def test_build_hourly_cashflow_logger_message_no_op_blks(excel_data, caplog): + # Tests building the model with startup/shutdown then ramping rate with LMP as a single year with all time points + caplog.clear() + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + + m.sofc_design = DesignModel( + model_func=SOFC_design_model, + model_args={"min_power": 200, "max_power": 650}, + ) + + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet_no_op_blk, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # ramping and startup constraints + m.add_startup_shutdown( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + build_binary_var="build_unit", + up_time=4, + down_time=4, + ) + + m.add_ramping_constraints( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + capacity_var="PMAX", + ramping_var="power", + constraint_type="linear", + linearization=True, + op_range_lb=200 / 650, + startup_rate=1.0, + shutdown_rate=1.0, + ramp_up_rate=1.0, + ramp_down_rate=1.0, + ) + + with caplog.at_level(idaeslog.WARNING): + m.build_hourly_cashflows( + revenue_streams=None, + costs=["fuel_cost", "hourly_fixed_costs"], + ) + + m.build_cashflows( + objective="NPV", + ) + + assert ( + "build_hourly_cashflows was called but no operation blocks were found so hourly cashflow of the model was set to 0. If you have hourly costs, please manually assign them." + in caplog.text + ) + + +@pytest.mark.unit +def test_build_multiperiod_model_no_LMP_logger_message(): + # Checks the exception raised if issue arises with attaching LMP data to OperationModel + # Create an instance of the PriceTakerModel class + m = PriceTakerModel() + + m._n_time_points = 240 + m.set_days = None + m.set_years = None + + with pytest.raises( + ConfigurationError, + match=( + "OperationModelData has been defined to automatically " + + "populate LMP data. However, self.LMP does not exist, where self is an instance of PriceTakerModel. " + + "Please run the append_lmp_data method from the PriceTakerModel class first or set the " + + "declare_lmp_param configuration option to False when configuring " + + "your OperationModelData object." + ), + ): + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet_no_LMP, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + +@pytest.mark.unit +def test_build_multiperiod_model_before_append_lmp_data_logger_messages(): + m = PriceTakerModel() + + # First exception arises if _n_time_points is not an attribute of PriceTakerMode + with pytest.raises( + ConfigurationError, + match=( + "MultiPeriodModel requires n_time_points as an argument. Before invoking the build_multiperiod_model method, call the append_lmp_data method on PriceTakerModel class first, which will assign the number of time points, n_time_points, to be used in the MultiPeriodModel." + ), + ): + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet_no_LMP, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # Next exception that would arise relates to either set_years or set_days attributes not existing, indicating append_lmp_data method was not run first. + m1 = PriceTakerModel() + m1._n_time_points = 240 + m1.set_days = None + + with pytest.raises( + ConfigurationError, + match=( + "Before invoking the build_multiperiod_model method, call the append_lmp_data method on PriceTakerModel class first, which will assign the number of time points, n_time_points, to be used in the MultiPeriodModel." + ), + ): + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet_no_LMP, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + m2 = PriceTakerModel() + m2._n_time_points = 240 + m2.set_years = None + + with pytest.raises( + ConfigurationError, + match=( + "Before invoking the build_multiperiod_model method, call the append_lmp_data method on PriceTakerModel class first, which will assign the number of time points, n_time_points, to be used in the MultiPeriodModel." + ), + ): + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet_no_LMP, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + +@pytest.mark.unit +def test_build_hourly_cashflow_logger_message_no_des_blks(excel_data, caplog): + # Tests building the model with startup/shutdown then ramping rate with LMP as a single year with all time points + caplog.clear() + # Create an instance of the Pricetrackermodel class + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + + # m.sofc_design = DesignModel(model_func=SOFC_design_model, model_args={"min_power": 200, "max_power": 650}) + m.sofc_design = aml.Block() + m.sofc_design.PMAX = 650 + m.sofc_design.PMIN = 200 + m.sofc_design.build_unit = 1 + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # ramping and startup constraints + m.add_startup_shutdown( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + build_binary_var="build_unit", + up_time=4, + down_time=4, + ) + + m.add_ramping_constraints( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + capacity_var="PMAX", + ramping_var="power", + constraint_type="linear", + linearization=True, + op_range_lb=200 / 650, + startup_rate=1.0, + shutdown_rate=1.0, + ramp_up_rate=1.0, + ramp_down_rate=1.0, + ) + + with caplog.at_level(idaeslog.WARNING): + m.build_hourly_cashflows( + revenue_streams=None, + costs=["fuel_cost", "hourly_fixed_costs"], + ) + + m.build_cashflows( + objective="NPV", + ) + + assert ( + "build_cashflows was called, but no design blocks were found so capex and FOM are 0. Please manually add your cost objective if you require one." + in caplog.text + ) + + +@pytest.mark.unit +def test_build_hourly_cashflow_logger_messages_and_build_1(excel_data, caplog): + # Tests building the model with startup/shutdown then ramping rate with LMP as a single year with all time points + caplog.clear() + # Create an instance of the Pricetrackermodel class + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + + m.sofc_design = DesignModel( + model_func=SOFC_design_model, + model_args={"min_power": 200, "max_power": 650}, + ) + + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # ramping and startup constraints + m.add_startup_shutdown( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + build_binary_var="build_unit", + up_time=4, + down_time=4, + ) + + m.add_ramping_constraints( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + capacity_var="PMAX", + ramping_var="power", + constraint_type="linear", + linearization=True, + op_range_lb=200 / 650, + startup_rate=1.0, + shutdown_rate=1.0, + ramp_up_rate=1.0, + ramp_down_rate=1.0, + ) + + # add capacity limit constraints + m.add_capacity_limits( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + commodity_var="power", + capacity_max="PMAX", + capacity_min="PMIN", + constraint_type="nonlinear", + linearization=False, + ) + + with caplog.at_level(idaeslog.WARNING): + m.build_hourly_cashflows( + revenue_streams=None, + costs=["fuel_cost", "hourly_fixed_costs"], + ) + + m.build_cashflows( + objective="NPV", + ) + + assert ( + "No revenues were provided while building the hourly cashflow. Revenues will be set to 0." + in caplog.text + ) + + +@pytest.mark.unit +def test_build_hourly_cashflow_logger_messages_and_build_2(excel_data, caplog): + # Tests building the model with ramping rate then startup/shutdown with LMP as a single year with representative days + caplog.clear() + # Create an instance of the Pricetrackermodel class + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + + m.sofc_design = DesignModel( + model_func=SOFC_design_model, + model_args={"min_power": 200, "max_power": 650}, + ) + + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # ramping and startup constraints + m.add_ramping_constraints( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + capacity_var="PMAX", + ramping_var="power", + constraint_type="linear", + linearization=True, + op_range_lb=200 / 650, + startup_rate=1.0, + shutdown_rate=1.0, + ramp_up_rate=1.0, + ramp_down_rate=1.0, + ) + + m.add_startup_shutdown( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + build_binary_var="build_unit", + up_time=4, + down_time=4, + ) + + # add capacity limit constraints + m.add_capacity_limits( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + commodity_var="power", + capacity_max="PMAX", + capacity_min="PMIN", + constraint_type="nonlinear", + linearization=False, + ) + + with caplog.at_level(idaeslog.WARNING): + m.build_hourly_cashflows( + revenue_streams=["elec_revenue", "H2_revenue"], + costs=None, + ) + + m.build_cashflows( + objective="Annualized NPV", + ) + + assert ( + "No costs were provided while building the hourly cashflow. Costs will be set to 0." + in caplog.text + ) + + +@pytest.mark.unit +def test_build_hourly_cashflow_logger_messages_and_build_3(excel_data, caplog): + # Tests building the model with ramping rate then startup/shutdown with LMP as a single year with representative days + caplog.clear() + # Create an instance of the Pricetrackermodel class + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + + m.sofc_design = DesignModel( + model_func=SOFC_design_model, + model_args={"min_power": 200, "max_power": 650}, + ) + + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # add capacity limit constraints + m.add_capacity_limits( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + commodity_var="power", + capacity_max="PMAX", + capacity_min="PMIN", + constraint_type="nonlinear", + linearization=False, + ) + + # ramping and startup constraints + m.add_ramping_constraints( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + capacity_var="PMAX", + ramping_var="power", + constraint_type="linear", + linearization=True, + op_range_lb=200 / 650, + startup_rate=1.0, + shutdown_rate=1.0, + ramp_up_rate=1.0, + ramp_down_rate=1.0, + ) + + m.add_startup_shutdown( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + build_binary_var="build_unit", + up_time=4, + down_time=4, + ) + + with caplog.at_level(idaeslog.WARNING): + m.build_hourly_cashflows( + revenue_streams=["elec_revenue", "H2_revenue"], + costs=["fuel_cost", "hourly_fixed_costs"], + ) + + m.build_cashflows( + objective="Net Profit", + ) + + +@pytest.mark.unit +def test_build_hourly_cashflow_logger_messages_and_build_4(excel_data, caplog): + # Tests building the model with ramping rate then startup/shutdown with LMP as a single year with representative days + caplog.clear() + # Create an instance of the Pricetrackermodel class + m = PriceTakerModel() + + # Appending the data to the model + DATA_DIR = Path(__file__).parent + file_path = DATA_DIR / "FLECCS_princeton_shortened.csv" + m.append_lmp_data( + file_path=file_path, + column_name="BaseCaseTax", + ) + + m.sofc_design = DesignModel( + model_func=SOFC_design_model, + model_args={"min_power": 200, "max_power": 650}, + ) + + # Build the multiperiod model + m.build_multiperiod_model( + process_model_func=build_sofc_flowsheet, + linking_variable_func=None, + flowsheet_options={"sofc_design": None}, + ) + + # add capacity limit constraints + m.add_capacity_limits( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + commodity_var="power", + capacity_max="PMAX", + capacity_min="PMIN", + constraint_type="nonlinear", + linearization=False, + ) + + # ramping and startup constraints + m.add_ramping_constraints( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + capacity_var="PMAX", + ramping_var="power", + constraint_type="linear", + linearization=True, + op_range_lb=200 / 650, + startup_rate=1.0, + shutdown_rate=1.0, + ramp_up_rate=1.0, + ramp_down_rate=1.0, + ) + + m.add_startup_shutdown( + op_blk="fs.sofc_operation", + design_blk="sofc_design", + build_binary_var="build_unit", + up_time=4, + down_time=4, + ) + + with caplog.at_level(idaeslog.WARNING): + m.build_hourly_cashflows( + revenue_streams=["elec_revenue", "H2_revenue"], + costs=["fuel_cost", "hourly_fixed_costs"], + ) + + bad_obj = "Garbage" + m.build_cashflows( + objective=bad_obj, + ) + + assert ( + "build_cashflows was called, but the objective type provided, Garbage, is invalid. The objective has been set to 0. Please manually add your cost objective if you require one." + in caplog.text + ) + + +# Model for testing builds with Linear capacity constraints +############################################################# +def SOFC_design_model( + m, + max_power=None, + min_power=None, +): + # Capacity parameters + m.PMAX = aml.Param(initialize=max_power, mutable=False) + m.PMIN = aml.Param(initialize=min_power, mutable=False) + + m.build_unit = aml.Param(initialize=1, mutable=False) + + # Capital investment cost ($) + m.capex = 616441.20 + # Fixed operating and investment ($ / year) + m.fom = 433882.80 + + +def SOFC_operation_model( + m, + sofc_design_blk=None, +): + # LMP value dummy + # m.LMP = aml.Param(initialize=1, mutable=True) + + # Operation Variables + m.power = aml.Var(domain=aml.NonNegativeReals) + + # cost expressions + # Linear version of surrogate cost constraint (combined fuel_cost and non_fuel_vom) + m.fuel_cost = aml.Expression(expr=(23.2934 * m.power + 49.21504)) + # Nonlinear version of surrogate cost constraint (combined fuel_cost and non_fuel_vom) + # m.fuel_cost = aml.Expression(expr=(23.2934 * m.power + 0.287571e-3 * m.power ** 2 + 0.1155903e-5 * m.power ** 3 + 49.21504)) + m.elec_revenue = aml.Expression(expr=(m.power * m.LMP)) + m.H2_revenue = aml.Expression(expr=(0)) + m.non_fuel_vom = aml.Expression(expr=(0)) + m.carbon_price = aml.Expression(expr=(0)) + + fixed_cap_hourly = 70.37 * 1e6 / 365 / 24 + fixed_op_hourly = 49.53 * 1e6 / 365 / 24 + + m.hourly_fixed_cost = aml.Expression(expr=(fixed_cap_hourly + fixed_op_hourly)) + + +def build_sofc_flowsheet( + m, + sofc_design, +): + + m.fs = FlowsheetBlock(dynamic=False) + m.fs.sofc_operation = OperationModel( + model_func=SOFC_operation_model, + model_args={ + "sofc_design_blk": sofc_design, + }, + ) + + # Flowsheet level aml.Variables (none for this case) + + # Flowsheet level constraints (none for this case) + + +############################################################# +# End model for testing builds with Linear + + +# Extra functions for different edge-case warning messages +############################################################# +@declare_process_block_class("SOFC_op") +class SOFC_op_blk(SkeletonUnitModelData): + CONFIG = SkeletonUnitModelData.CONFIG() + CONFIG.declare( + "model_func", + ConfigValue( + doc="Function that builds the design model", + ), + ) + CONFIG.declare( + "model_args", + ConfigValue( + default={}, + doc="Dictionary containing arguments needed for model_func", + ), + ) + + def build(self): + super().build() + + # Declare variables + self.power = Var( + within=NonNegativeReals, + doc="Total power generated [in MW]", + ) + self.op_mode = Var( + within=Binary, + doc="1: In Operation, 0: Shutdown", + ) + self.startup = Var( + within=Binary, + doc="1: Plant is startup at this hour, 0: Otherwise", + ) + self.shutdown = Var( + within=Binary, + doc="1: Plant is shutdown at this hour, 0: Otherwise", + ) + + +def build_sofc_flowsheet_no_op_blk( + m, + sofc_design, +): + + m.fs = FlowsheetBlock(dynamic=False) + m.fs.sofc_operation = SOFC_op( + model_func=SOFC_operation_model, + model_args={ + "sofc_design_blk": sofc_design, + }, + ) + + # Flowsheet level aml.Variables (none for this case) + + # Flowsheet level constraints (none for this case) + + +def SOFC_operation_model_no_LMP( + m, + sofc_design_blk=None, +): + # LMP value dummy + # m.LMP = aml.Param(initialize=1, mutable=True) + + # Operation Variables + m.power = aml.Var(domain=aml.NonNegativeReals) + + # cost expressions + # Linear version of surrogate cost constraint (combined fuel_cost and non_fuel_vom) + m.fuel_cost = aml.Expression(expr=(23.2934 * m.power + 49.21504)) + # Nonlinear version of surrogate cost constraint (combined fuel_cost and non_fuel_vom) + # m.fuel_cost = aml.Expression(expr=(23.2934 * m.power + 0.287571e-3 * m.power ** 2 + 0.1155903e-5 * m.power ** 3 + 49.21504)) + m.elec_revenue = aml.Expression(expr=(m.power * 1.0)) + m.H2_revenue = aml.Expression(expr=(0)) + m.non_fuel_vom = aml.Expression(expr=(0)) + m.carbon_price = aml.Expression(expr=(0)) + + fixed_cap_hourly = 70.37 * 1e6 / 365 / 24 + fixed_op_hourly = 49.53 * 1e6 / 365 / 24 + + m.hourly_fixed_cost = aml.Expression(expr=(fixed_cap_hourly + fixed_op_hourly)) + + +def build_sofc_flowsheet_no_LMP( + m, + sofc_design, +): + + m.fs = FlowsheetBlock(dynamic=False) + m.fs.sofc_operation = OperationModel( + model_func=SOFC_operation_model_no_LMP, + model_args={ + "sofc_design_blk": sofc_design, + }, + ) + + # Flowsheet level aml.Variables (none for this case) + + # Flowsheet level constraints (none for this case) + + +# Test cases to be done (4 total): +# (LMP1, RR1, MUD1, OBJ1) x +# (LMP2, RR2, MUD2, OBJ2) x +# (LMP3, RR3, MUD2, OBJ3) +# (LMP4, MUD1, RR1, OBJ1)