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test_cvxpy.py
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test_cvxpy.py
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# Copyright (c) 2019-2022, RTE (https://www.rte-france.com)
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
# you can obtain one at http://mozilla.org/MPL/2.0/.
# SPDX-License-Identifier: MPL-2.0
# This file is part of Chronix2Grid, A python package to generate "en-masse" chronics for loads and productions (thermal, renewable)
# this generates some chronics for a given environment, provided that all necessary files are present in its repo
import copy
import pdb
from datetime import datetime, timedelta
import json
import shutil
import time
import pandas as pd
import os
import grid2op
from grid2op.Parameters import Parameters
from grid2op.Chronics import ChangeNothing, FromNPY
from lightsim2grid import LightSimBackend
import numpy as np
from numpy.random import default_rng
from chronix2grid.generation.consumption import ConsumptionGeneratorBackend
from chronix2grid.generation.renewable import RenewableBackend
from chronix2grid.generation.dispatch.PypsaDispatchBackend import PypsaDispatcher
from chronix2grid.getting_started.example.input.generation.patterns import ref_pattern_path
from chronix2grid.generation.dispatch.EconomicDispatch import ChroniXScenario
import cvxpy as cp
# from ortools.linear_solver import pywraplp
# from ortools.init import pywrapinit
import warnings
FLOATING_POINT_PRECISION_FORMAT = '%.1f'
# TODO allow for a "debug" mode where we can save the values for the prices, the renewables generated, the renewables after dispatch
# and the renewables after the losses
# TODO add a parameter to generate data more correlated for data in the same area but less correlated within different area.
def get_last_scenario_id(env_chronics_dir):
"""This function return the last scenario id identified.
It only works with scenario id formatted like: `WHATEVER_ScenID`, for example: "2050-01-03_0" or "2050_01_03_0"
but not "2050-01-03-0"
It also supposes that the directory containing the chronics exists on the hard drive (even if it's empty)
Parameters
----------
env_chronics_dir : _type_
_description_
"""
max_ = None
list_files = os.listdir(env_chronics_dir)
for el in list_files:
this_file_path = os.path.join(env_chronics_dir, el)
if not os.path.isdir(this_file_path):
continue
try:
*date_, scen_id = el.split("_")
except ValueError:
continue
try:
scen_id = int(scen_id)
except ValueError:
continue
if max_ is None:
max_ = scen_id
if scen_id > max_:
max_ = scen_id
if max_ is None:
max_ = -1
return max_
def generate_loads(path_env, load_seed, start_date_dt, end_date_dt, dt, number_of_minutes, generic_params,
load_q_from_p_coeff=0.7,
day_lag=6):
"""
This function generates the load for each consumption on a grid
Parameters
----------
path_env : _type_
_description_
load_seed : _type_
_description_
start_date_dt : _type_
_description_
end_date_dt : _type_
_description_
dt : _type_
_description_
number_of_minutes : _type_
_description_
generic_params : _type_
_description_
Returns
-------
_type_
_description_
"""
with open(os.path.join(path_env, "params_load.json"), "r") as f:
load_params = json.load(f)
load_params["start_date"] = start_date_dt
load_params["end_date"] = end_date_dt
load_params["dt"] = int(dt)
load_params["T"] = number_of_minutes
load_params["planned_std"] = float(generic_params["planned_std"])
loads_charac = pd.read_csv(os.path.join(path_env, "loads_charac.csv"), sep=",")
load_weekly_pattern = pd.read_csv(os.path.join(ref_pattern_path, "load_weekly_pattern.csv"), sep=",")
load_generator = ConsumptionGeneratorBackend(out_path=None,
seed=load_seed,
params=load_params,
loads_charac=loads_charac,
write_results=False,
load_config_manager=None,
day_lag=day_lag)
load_p, load_p_forecasted = load_generator.run(load_weekly_pattern=load_weekly_pattern)
load_q = load_p * load_q_from_p_coeff
load_q_forecasted = load_p_forecasted * load_q_from_p_coeff
return load_p, load_q, load_p_forecasted, load_q_forecasted
def generate_renewable_energy_sources(path_env, renew_seed, start_date_dt, end_date_dt, dt, number_of_minutes, generic_params, gens_charac):
"""This function generates the amount of power produced by renewable energy sources (res).
It serves as a maximum value for the economic dispatch.
Parameters
----------
path_env : _type_
_description_
renew_seed : _type_
_description_
start_date_dt : _type_
_description_
end_date_dt : _type_
_description_
dt : _type_
_description_
number_of_minutes : _type_
_description_
generic_params : _type_
_description_
gens_charac : _type_
_description_
Returns
-------
_type_
_description_
"""
with open(os.path.join(path_env, "params_res.json"), "r") as f:
renew_params = json.load(f)
renew_params["start_date"] = start_date_dt
renew_params["end_date"] = end_date_dt
renew_params["dt"] = int(dt)
renew_params["T"] = number_of_minutes
renew_params["planned_std"] = float(generic_params["planned_std"])
solar_pattern = np.load(os.path.join(ref_pattern_path, "solar_pattern.npy"))
renew_backend = RenewableBackend(out_path=None,
seed=renew_seed,
params=renew_params,
loads_charac=gens_charac,
res_config_manager=None,
write_results=False)
prod_solar, prod_solar_forecasted, prod_wind, prod_wind_forecasted = renew_backend.run(solar_pattern=solar_pattern)
return prod_solar, prod_solar_forecasted, prod_wind, prod_wind_forecasted
def generate_economic_dispatch(path_env, start_date_dt, end_date_dt, dt, number_of_minutes, generic_params,
load_p, prod_solar, prod_wind, name_gen, gen_type, scenario_id, final_gen_p, gens_charac):
"""This function emulates a perfect market where all productions need to meet the demand at the minimal cost.
It does not consider limit on powerline, nor contigencies etc. The power network does not exist here. Only the ramps and
pmin / pmax are important.
Parameters
----------
path_env : _type_
_description_
start_date_dt : _type_
_description_
end_date_dt : _type_
_description_
dt : _type_
_description_
number_of_minutes : _type_
_description_
generic_params : _type_
_description_
load_p : _type_
_description_
prod_solar : _type_
_description_
prod_wind : _type_
_description_
env : _type_
_description_
scenario_id : _type_
_description_
final_gen_p : _type_
_description_
gens_charac : _type_
_description_
Returns
-------
_type_
_description_
"""
with open(os.path.join(path_env, "params_opf.json"), "r") as f:
opf_params = json.load(f)
opf_params["start_date"] = start_date_dt
opf_params["end_date"] = end_date_dt
opf_params["dt"] = int(dt)
opf_params["T"] = number_of_minutes
opf_params["planned_std"] = float(generic_params["planned_std"])
load = pd.DataFrame(load_p.sum(axis=1))
total_solar = prod_solar.sum(axis=1)
total_wind = prod_wind.sum(axis=1)
# init the dispatcher
gens_charac_this = copy.deepcopy(gens_charac)
gens_charac_this["pmax"] = gens_charac_this["Pmax"]
gens_charac_this["pmin"] = gens_charac_this["Pmin"]
gens_charac_this["cost_per_mw"] = gens_charac_this["marginal_cost"]
economic_dispatch = PypsaDispatcher.from_dataframe(gens_charac_this)
# need to hack it to work...
n_gen = len(name_gen)
gen_p_orig = np.zeros((prod_solar.shape[0], n_gen))
economic_dispatch._chronix_scenario = ChroniXScenario(loads=1.0 * load_p,
prods=pd.DataFrame(1.0 * gen_p_orig, columns=name_gen),
scenario_name=scenario_id,
res_names={"wind": name_gen[gen_type == "wind"],
"solar": name_gen[gen_type == "solar"]
}
)
economic_dispatch.read_hydro_guide_curves(os.path.join(ref_pattern_path, 'hydro_french.csv'))
hydro_constraints = economic_dispatch.make_hydro_constraints_from_res_load_scenario()
res_dispatch = economic_dispatch.run(load * (1.0 + 0.01 * float(opf_params["losses_pct"])),
total_solar,
total_wind,
opf_params,
gen_constraints=hydro_constraints,
pyomo=False,
solver_name="cbc")
if res_dispatch is None:
error_ = RuntimeError("Pypsa failed to find a solution")
return None, None, None, error_
# now assign the results
final_gen_p = 1.0 * final_gen_p # copy the data frame to avoid modify the original one
for gen_id, gen_nm in enumerate(name_gen):
if gen_nm in res_dispatch.chronix.prods_dispatch:
final_gen_p.iloc[:, gen_id] = 1.0 * res_dispatch.chronix.prods_dispatch[gen_nm].values
#handle curtailment
mask_wind = total_wind.values > 0.001
wind_curt = (res_dispatch.chronix.prods_dispatch['agg_wind'].values[mask_wind] / total_wind.values[mask_wind]).reshape(-1,1)
final_gen_p.iloc[mask_wind, gen_type == "wind"] *= wind_curt
mask_solar = total_solar.values > 0.001 # be carefull not to divide by 0 in case of solar !
solar_curt = (res_dispatch.chronix.prods_dispatch['agg_solar'].values[mask_solar] / total_solar.values[mask_solar]).reshape(-1,1)
final_gen_p.iloc[mask_solar, gen_type == "solar"] *= solar_curt
total_wind_curt = total_wind.values.sum() - res_dispatch.chronix.prods_dispatch['agg_wind'].values.sum()
total_solar_curt = total_solar.values[mask_solar].sum() - res_dispatch.chronix.prods_dispatch['agg_solar'].values[mask_solar].sum()
return final_gen_p, total_wind_curt, total_solar_curt, None
def _adjust_gens(all_loss_orig,
env_for_loss,
datetimes,
total_solar,
total_wind,
params,
env_path,
env_param,
load_without_loss,
load_p,
load_q,
gen_p,
gen_v,
economic_dispatch,
diff_,
threshold_stop=0.1, # stop when all generators move less that this
max_iter=100, # declare a failure after this number of iteration
iter_quality_decrease=50, # acept a reduction of the quality after this number of iteration
percentile_quality_decrease=99,
):
"""This function is an auxilliary function.
Like its main one (see handle_losses) it is here to make sure that if you run an AC model with the data generated,
then the generator setpoints will not change too much
(less than `threshold_stop` MW)
Parameters
----------
all_loss_orig : _type_
_description_
env_for_loss : _type_
_description_
datetimes : _type_
_description_
total_solar : _type_
_description_
total_wind : _type_
_description_
params : _type_
_description_
env_path : _type_
_description_
env_param : _type_
_description_
load_without_loss : _type_
_description_
load_p : _type_
_description_
load_q : _type_
_description_
gen_p : _type_
_description_
gen_v : _type_
_description_
economic_dispatch : _type_
_description_
diff_ : _type_
_description_
threshold_stop : float, optional
_description_, by default 0.1
Returns
-------
_type_
_description_
"""
quality_ = None
error_ = None
if np.any(~np.isfinite(gen_p)):
error_ = RuntimeError("Input data contained Nans !")
return None, error_, quality_
all_loss = all_loss_orig
res_gen_p = 1.0 * gen_p
iter_num = 0
hydro_constraints = economic_dispatch.make_hydro_constraints_from_res_load_scenario()
while True:
iter_num += 1
load = load_without_loss + all_loss
load = pd.DataFrame(load.ravel(), index=datetimes)
# "never" decrease (during iteration) some generators
min__ = diff_.min() # this is negative
gen_max_pu_t = None
gen_min_pu_t = {gen_nm: np.maximum((res_gen_p[:,gen_id] + min__) / economic_dispatch.generators.loc[gen_nm].p_nom,
env_for_loss.gen_pmin[gen_id] / economic_dispatch.generators.loc[gen_nm].p_nom
)
for gen_id, gen_nm in enumerate(env_for_loss.name_gen) if env_for_loss.gen_redispatchable[gen_id]}
### run the dispatch with the loss
dispatch_res = economic_dispatch.run(load,
total_solar=total_solar,
total_wind=total_wind,
params=params,
pyomo=False,
solver_name="cbc",
gen_constraints=copy.deepcopy(hydro_constraints),
gen_max_pu_t=gen_max_pu_t,
gen_min_pu_t=gen_min_pu_t,
)
if dispatch_res is None:
error_ = RuntimeError(f"Pypsa failed to find a solution at iteration {iter_num}")
break
# assign the generators
for gen_id, gen_nm in enumerate(env_for_loss.name_gen):
if gen_nm in dispatch_res.chronix.prods_dispatch:
res_gen_p[:, gen_id] = 1.0 * dispatch_res.chronix.prods_dispatch[gen_nm].values
#handle wind curtailment
mask_winds = total_wind.values > 0.001
res_gen_p[mask_winds, :][:,env_for_loss.gen_type == "wind"] *= (dispatch_res.chronix.prods_dispatch['agg_wind'].values[mask_winds] / total_wind.values[mask_winds]).reshape(-1,1)
total_wind.loc[mask_winds] = 1.0 * dispatch_res.chronix.prods_dispatch["agg_wind"].values[mask_winds]
# re evaluate the losses
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
env_fixed = grid2op.make(
env_path,
test=True,
# grid_path=grid_path, # assign it the 118 grid
param=env_param,
backend=LightSimBackend(),
chronics_class=FromNPY,
# chronics_path=path_chronix2grid,
data_feeding_kwargs={"load_p": load_p,
"load_q": load_q,
"prod_p": 1.0 * res_gen_p,
"prod_v": gen_v}
)
diff_ = np.full((env_fixed.max_episode_duration(), env_fixed.n_gen), fill_value=np.NaN)
all_loss[:] = np.NaN
i = 0
obs = env_fixed.reset()
all_loss[i] = np.sum(obs.gen_p) - np.sum(obs.load_p)
diff_[i] = obs.gen_p - res_gen_p[i]
done = False
while not done:
obs, reward, done, info = env_fixed.step(env_fixed.action_space())
i += 1
if done:
break
all_loss[i] = np.sum(obs.gen_p) - np.sum(obs.load_p)
diff_[i] = obs.gen_p - res_gen_p[i]
max_diff_ = np.abs(diff_).max()
if not np.isfinite(max_diff_):
error_ = RuntimeError(f"Some nans were found in the generated data at iteration {iter_num}")
res_gen_p = None
quality_ = None
break
if max_diff_ <= threshold_stop:
quality_ = (iter_num,
float(np.mean(np.abs(diff_))),
float(np.percentile(np.abs(diff_), 95)),
float(np.percentile(np.abs(diff_), 99)),
float(max_diff_)
)
break
if iter_num >= iter_quality_decrease:
quantile = np.percentile(np.abs(diff_), percentile_quality_decrease)
if quantile <= threshold_stop:
quality_ = (iter_num,
float(np.mean(np.abs(diff_))),
float(np.percentile(np.abs(diff_), 95)),
float(np.percentile(np.abs(diff_), 99)),
float(np.max(np.abs(diff_)))
)
break
if iter_num >= max_iter:
error_ = RuntimeError("Too much iterations performed when adjusting for the losses")
res_gen_p = None
quality_ = None
break
return res_gen_p, error_, quality_
def _fix_losses_one_scenario(env_for_loss,
scenario_id,
params,
env_path,
env_param,
load_df,
threshold_stop=0.5, # decide I stop when the data move of less of 0.5 MW at maximum
max_iter=100, # maximum number of iteration
iter_quality_decrease=20, # after 20 iteration accept a degradation in the quality
percentile_quality_decrease=99, # replace the "at maximum" by "percentile 99%"
):
"""This function is an auxilliary function.
Like its main one (see handle_losses) it is here to make sure that if you run an AC model with the data generated,
then the generator setpoints will not change too much
(less than `threshold_stop` MW)
Parameters
----------
env_for_loss : _type_
_description_
scenario_id : _type_
_description_
params : _type_
_description_
env_path : _type_
_description_
env_param : _type_
_description_
load_df : _type_
_description_
threshold_stop : float, optional
_description_, by default 0.5
max_iter : int, optional
_description_, by default 100
Returns
-------
_type_
_description_
"""
gen_p_orig = np.full((env_for_loss.max_episode_duration(), env_for_loss.n_gen), fill_value=np.NaN, dtype=np.float32)
final_gen_v = np.full((env_for_loss.max_episode_duration(), env_for_loss.n_gen), fill_value=np.NaN, dtype=np.float32)
final_load_p = np.full((env_for_loss.max_episode_duration(), env_for_loss.n_load), fill_value=np.NaN, dtype=np.float32)
final_load_q = np.full((env_for_loss.max_episode_duration(), env_for_loss.n_load), fill_value=np.NaN, dtype=np.float32)
all_loss_orig = np.zeros(env_for_loss.max_episode_duration())
max_diff_orig = np.zeros(env_for_loss.max_episode_duration())
datetimes = np.zeros(env_for_loss.max_episode_duration(), dtype=datetime)
env_for_loss.set_id(scenario_id)
obs = env_for_loss.reset()
i = 0
all_loss_orig[i] = np.sum(obs.gen_p) - np.sum(obs.load_p)
final_gen_v[i] = obs.gen_v
final_load_p[i] = obs.load_p
final_load_q[i] = obs.load_q
gen_p_orig[i] = 1.0 * obs.gen_p
datetimes[i] = obs.get_time_stamp()
max_diff_orig[i] = np.max(np.abs(obs.gen_p - env_for_loss.chronics_handler.real_data._prod_p[i]))
done = False
while not done:
obs, reward, done, info = env_for_loss.step(env_for_loss.action_space())
if done:
break
i += 1
all_loss_orig[i] = np.sum(obs.gen_p) - np.sum(obs.load_p)
final_load_p[i] = 1.0 * obs.load_p
final_load_q[i] = 1.0 * obs.load_p
gen_p_orig[i] = env_for_loss.chronics_handler.real_data._prod_p[i] # 1.0 * obs.gen_p
datetimes[i] = obs.get_time_stamp()
max_diff_orig[i] = np.max(np.abs(obs.gen_p - env_for_loss.chronics_handler.real_data._prod_p[i]))
total_solar = np.sum(gen_p_orig[:, env_for_loss.gen_type == "solar"], axis=1)
total_wind = np.sum(gen_p_orig[:, env_for_loss.gen_type == "wind"], axis=1)
load_without_loss = np.sum(final_load_p, axis=1) # - total_solar - total_wind
# load the right data
df = pd.read_csv(os.path.join(env_path, "prods_charac.csv"), sep=",")
df["pmax"] = df["Pmax"]
df["pmin"] = df["Pmin"]
df["cost_per_mw"] = df["marginal_cost"]
economic_dispatch = PypsaDispatcher.from_dataframe(df)
economic_dispatch.read_hydro_guide_curves(os.path.join(ref_pattern_path, 'hydro_french.csv'))
economic_dispatch._chronix_scenario = ChroniXScenario(loads=1.0 * load_df,
prods=pd.DataFrame(1.0 * gen_p_orig, columns=env_for_loss.name_gen),
scenario_name=scenario_id,
res_names= {"wind": env_for_loss.name_gen[env_for_loss.gen_type == "wind"],
"solar": env_for_loss.name_gen[env_for_loss.gen_type == "solar"]
}
)
error_ = None
total_solar_orig = pd.Series(total_solar.ravel(), index=datetimes)
total_wind_orig = pd.Series(total_wind.ravel(), index=datetimes)
total_solar = 1.0 * total_solar_orig
total_wind = 1.0 * total_wind_orig
res_gen_p = 1.0 * gen_p_orig
diff_ = 1.0 * max_diff_orig
diff_ = diff_.reshape(-1,1)
res_gen_p, error_, quality_ = _adjust_gens(all_loss_orig,
env_for_loss,
datetimes,
total_solar,
total_wind,
params,
env_path,
env_param,
load_without_loss,
final_load_p,
final_load_q,
gen_p_orig,
final_gen_v,
economic_dispatch,
diff_,
threshold_stop=threshold_stop,
max_iter=max_iter,
iter_quality_decrease=iter_quality_decrease,
percentile_quality_decrease=percentile_quality_decrease)
if error_ is not None:
# the procedure failed
return None, error_, None
return res_gen_p, error_, quality_
def handle_losses(path_env,
n_gen,
name_gen,
gens_charac,
load_p,
load_q,
final_gen_p,
start_date_dt,
dt_dt,
scenario_id,
PmaxErrorCorrRatio=0.9,
RampErrorCorrRatio=0.95,
threshold_stop=0.5,
max_iter=100):
"""This function is here to make sure that if you run an AC model with the data generated, then the generator setpoints will not change too much
(less than `threshold_stop` MW)
Parameters
----------
path_env : _type_
_description_
n_gen : _type_
_description_
gens_charac : _type_
_description_
load_p : _type_
_description_
load_q : _type_
_description_
final_gen_p : _type_
_description_
start_date_dt : _type_
_description_
dt_dt : _type_
_description_
scenario_id : _type_
_description_
PmaxErrorCorrRatio : float, optional
_description_, by default 0.9
RampErrorCorrRatio : float, optional
_description_, by default 0.95
threshold_stop : float, optional
_description_, by default 0.5
max_iter : int, optional
_description_, by default 100
Returns
-------
_type_
_description_
"""
with open(os.path.join(path_env, "params_opf.json"), "r") as f:
loss_param = json.load(f)
loss_param["loss_pct"] = 0. # losses are handled better in this function
loss_param["PmaxErrorCorrRatio"] = PmaxErrorCorrRatio
loss_param["RampErrorCorrRatio"] = RampErrorCorrRatio
# do not treat the slack differently
loss_param["slack_ramp_limit_ratio"] = loss_param["RampErrorCorrRatio"]
if "slack_pmin" in loss_param:
del loss_param["slack_pmin"]
if "slack_pmax" in loss_param:
del loss_param["slack_pmax"]
env_param = Parameters()
env_param.NO_OVERFLOW_DISCONNECTION = True
gen_v = np.tile(np.array([float(gens_charac.loc[gens_charac["name"] == nm_gen].V) for nm_gen in name_gen ]),
load_p.shape[0]).reshape(-1, n_gen)
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
env_for_loss = grid2op.make(
path_env,
test=True,
# grid_path=grid_path, # assign it the 118 grid
param=env_param,
backend=LightSimBackend(),
chronics_class=FromNPY,
# chronics_path=path_chronix2grid,
data_feeding_kwargs={"load_p": load_p.values, # np.concatenate([load_p.values[0].reshape(1,-1), load_p.values]),
"load_q": load_q.values, # np.concatenate([load_q.values[0].reshape(1,-1), load_q.values]),
"prod_p": 1.0 * final_gen_p.values, # 1.0 * np.concatenate([final_gen_p.values[0].reshape(1,-1), final_gen_p.values]),
"prod_v": gen_v, # np.concatenate([gen_v[0].reshape(1,-1), gen_v])}
"start_datetime": start_date_dt,
"time_interval": dt_dt,
}
)
res_gen_p, error_, quality_ = _fix_losses_one_scenario(env_for_loss,
scenario_id,
loss_param,
path_env,
env_for_loss.parameters,
load_df=load_p,
threshold_stop=threshold_stop,
max_iter=max_iter
)
if error_ is not None:
return None, error_, None
# reformat the generators
res_gen_p_df = pd.DataFrame(res_gen_p, index=final_gen_p.index, columns=env_for_loss.name_gen)
return res_gen_p_df, error_, quality_
def save_generated_data(this_scen_path, load_p, load_p_forecasted, load_q, load_q_forecasted, prod_p, prod_p_forecasted,
sep=';',
float_prec=FLOATING_POINT_PRECISION_FORMAT):
"""This function saves the data that have been generated by this script.
Parameters
----------
this_scen_path : _type_
_description_
load_p : _type_
_description_
load_p_forecasted : _type_
_description_
load_q : _type_
_description_
load_q_forecasted : _type_
_description_
prod_p : _type_
_description_
prod_p_forecasted : _type_
_description_
sep : str, optional
_description_, by default ';'
float_prec : _type_, optional
_description_, by default FLOATING_POINT_PRECISION_FORMAT
"""
for df, nm in zip([load_p, load_p_forecasted, load_q, load_q_forecasted, prod_p, prod_p_forecasted],
["load_p", "load_p_forecasted", "load_q", "load_q_forecasted", "prod_p", "prod_p_forecasted"]):
df.to_csv(os.path.join(this_scen_path, f'{nm}.csv.bz2'),
sep=sep,
float_format=float_prec,
header=True,
index=False)
def save_meta_data(this_scen_path,
path_env,
start_date_dt,
dt_dt : timedelta,
load_seed,
renew_seed,
gen_p_forecast_seed,
quality,
total_load,
total_gen,
losses_mwh,
losses_avg,
wind_curtailed_opf,
wind_curtailed_losses,
solar_curtailed_opf,
generation_time,
saving_time,
files_to_copy=("maintenance_meta.json",)):
"""This function saves the "meta data" required for a succesful grid2op run !
Parameters
----------
this_scen_path : _type_
_description_
path_env : _type_
_description_
start_date_dt : _type_
_description_
dt_dt : timedelta
_description_
load_seed : _type_
_description_
renew_seed : _type_
_description_
gen_p_forecast_seed : _type_
_description_
quality: tuple
"""
with open(os.path.join(this_scen_path, "time_interval.info"), "w", encoding="utf-8") as f:
# f.write(datetime.strftime(dt_dt, "%H:%M")) # what I want to do but cannot (TypeError: descriptor 'strftime' for 'datetime.date' objects doesn't apply to a 'datetime.timedelta' object)
total_s = dt_dt.total_seconds()
hours = total_s // 3600
mins = (total_s - 3600 * hours) // 60
f.write(f"{int(hours):02d}:{int(mins):02d}")
with open(os.path.join(this_scen_path, "start_datetime.info"), "w", encoding="utf-8") as f:
f.write(datetime.strftime(start_date_dt, "%Y-%m-%d %H:%M"))
with open(os.path.join(this_scen_path, "_seeds_info.json"), "w", encoding="utf-8") as f:
json.dump(obj={"load_seed": int(load_seed), "renew_seed": int(renew_seed), "gen_p_forecast_seed": int(gen_p_forecast_seed)},
fp=f)
with open(os.path.join(this_scen_path, "generation_quality.json"), "w", encoding="utf-8") as f:
iter_num, mean_, percent_95, percent_99, max_ = quality
json.dump(obj={"iter_num": int(iter_num),
"avg": float(mean_),
"percent_95": float(percent_95),
"percent_99": float(percent_99),
"max": float(max_),
"info": ("avg, percent_95, percent_99, max: this 'quality' is the difference between the DC solver and the AC solver. This is the number of "
"MW that will differ from the grid2op observation compared to the generated data by chronix2grid.",
"total_load, total_gen: total amount of energy consumed / produced for the generated scenario.",
"losses_mwh: total amount of losses for the scenario (in energy)",
"losses_avg: average (per step) of the loss (avg[loss_this_step / total_generation_this_step])",
"wind_curtailed_opf: total (in energy) wind power curtailed by the OPF",
"wind_curtailed_losses: total (in energy) wind power curtailed by the loss",
"solar_curtailed_opf: total (in energy) solar power curtailed by the OPF",
"iter_num: number of iteration of the loss algorithm",
"generation_time: total time spent to generate these data (in seconds)",
"saving_time: total time spent to save the generated data (in seconds), this excludes the metadata saving time",
),
"total_load": float(total_load),
"total_gen": float(total_gen),
"losses_mwh": float(losses_mwh),
"losses_avg": float(losses_avg),
"wind_curtailed_opf": float(wind_curtailed_opf),
"wind_curtailed_losses": float(wind_curtailed_losses),
"solar_curtailed_opf": float(solar_curtailed_opf),
"generation_time": float(generation_time),
"saving_time": float(saving_time),
},
fp=f,
sort_keys=True,
indent=4)
for fn_ in files_to_copy:
src_path = os.path.join(path_env, fn_)
if os.path.exists(src_path):
shutil.copy(src=src_path,
dst=os.path.join(this_scen_path, fn_))
def generate_a_scenario(path_env,
name_gen,
gen_type,
output_dir,
start_date,
dt,
scen_id,
load_seed,
renew_seed,
gen_p_forecast_seed,
handle_loss=True,
nb_steps=None):
"""This function generates and save the data for a scenario.
Generation includes:
- load active value
- load reactive value
- renewable generation
- controlable generation
Put "outputdir=None" if you don't want to save the data.
Parameters
----------
env_name : _type_
_description_
name_gen : _type_
_description_
gen_type: _type_
_description_
output_dir : _type_
_description_
start_date : _type_
_description_
dt : _type_
_description_
scen_id : _type_
_description_
load_seed : _type_
_description_
renew_seed : _type_
_description_
gen_p_forecast_seed : _type_
_description_
Returns
-------
_type_
_description_
"""
beg_ = time.perf_counter()
scenario_id = f"{start_date}_{scen_id}"
dt_dt = timedelta(minutes=int(dt))
start_date_dt = datetime.strptime(start_date, "%Y-%m-%d") - dt_dt
if nb_steps is None:
end_date_dt = start_date_dt + timedelta(days=7) + 2 * dt_dt
else:
end_date_dt = start_date_dt + (int(nb_steps) + 2) * dt_dt
end_date = datetime.strftime(end_date_dt, "%Y-%m-%d %H:%M:%S")
with open(os.path.join(path_env, "params.json"), "r") as f:
generic_params = json.load(f)
number_of_minutes = int((end_date_dt - start_date_dt).total_seconds() // 60)
gens_charac = pd.read_csv(os.path.join(path_env, "prods_charac.csv"), sep=",")
# conso generation
load_p, load_q, load_p_forecasted, load_q_forecasted = generate_loads(path_env,
load_seed,
start_date_dt,
end_date_dt,
dt,
number_of_minutes,
generic_params,
day_lag=6 # TODO 6 because it's 2050
)
# renewable energy sources generation
res_renew = generate_renewable_energy_sources(path_env,renew_seed, start_date_dt, end_date_dt, dt, number_of_minutes, generic_params, gens_charac)
prod_solar, prod_solar_forecasted, prod_wind, prod_wind_forecasted = res_renew
if prod_solar.isna().any().any():
error_ = RuntimeError("Nan generated in solar data")
return error_, None, None, None, None, None, None, None
if prod_wind.isna().any().any():
error_ = RuntimeError("Nan generated in wind data")
return error_, None, None, None, None, None, None, None
# create the result data frame for the generators
final_gen_p = pd.merge(prod_solar, prod_wind, left_index=True, right_index=True)
for el in name_gen:
if el in final_gen_p:
continue
final_gen_p[str(el)] = np.NaN
final_gen_p = final_gen_p[name_gen]
# generate economic dispatch
res_disp = generate_economic_dispatch(path_env, start_date_dt, end_date_dt, dt, number_of_minutes, generic_params,
load_p, prod_solar, prod_wind, name_gen, gen_type, scenario_id, final_gen_p, gens_charac)
gen_p_after_dispatch, total_wind_curt_opf, total_solar_curt_opf, error_ = res_disp
if error_ is not None:
# TODO log that !
return error_, None, None, None, None, None, None, None
# now try to move the generators so that when I run an AC powerflow, the setpoint of generators does not change "too much"
if handle_loss:
n_gen = len(name_gen)
res_gen_p_df, error_, quality_ = handle_losses(path_env,
n_gen,
name_gen,
gens_charac,
load_p,
load_q,
gen_p_after_dispatch,
start_date_dt,
dt_dt,
scenario_id,
PmaxErrorCorrRatio=0.9,
RampErrorCorrRatio=0.95,
threshold_stop=0.5,
max_iter=100)
if error_ is not None:
# TODO log that !
return error_, None, None, None, None, None, None, None
else:
res_gen_p_df = 1.0 * gen_p_after_dispatch
quality_ = (-1, float("Nan"), float("Nan"), float("Nan"), float("Nan"))
prng = default_rng(gen_p_forecast_seed)
res_gen_p_forecasted_df = res_gen_p_df * prng.lognormal(mean=0.0, sigma=float(generic_params["planned_std"]), size=res_gen_p_df.shape)
res_gen_p_forecasted_df = res_gen_p_forecasted_df.shift(-1)
res_gen_p_forecasted_df.iloc[-1] = 1.0 * res_gen_p_forecasted_df.iloc[-2]
end_ = time.perf_counter()
if output_dir is not None:
beg_save = time.perf_counter()
this_scen_path = os.path.join(output_dir, scenario_id)
if not os.path.exists(this_scen_path):
os.mkdir(this_scen_path)
save_generated_data(this_scen_path, load_p, load_p_forecasted, load_q, load_q_forecasted, res_gen_p_df, res_gen_p_forecasted_df)
total_load = float(load_p.sum().sum())
total_gen = float(res_gen_p_df.sum().sum())
gen_p_per_step = res_gen_p_df.sum(axis=1)
proper_MWh_unit = float(dt_dt.total_seconds() / 3600.)
wind_curtailed_losses = (gen_p_after_dispatch.iloc[:, gen_type=="wind"].sum().sum() - res_gen_p_df.iloc[:, gen_type=="wind"].sum().sum())
end_save = time.perf_counter()
save_meta_data(this_scen_path,
path_env,
start_date_dt,
dt_dt,
load_seed,
renew_seed,
gen_p_forecast_seed,
quality_,
total_load=total_load * proper_MWh_unit,
total_gen=total_gen * proper_MWh_unit,
losses_mwh=(total_gen - total_load) * proper_MWh_unit,
losses_avg=np.mean((gen_p_per_step - load_p.sum(axis=1)) / gen_p_per_step),
wind_curtailed_opf=float(total_wind_curt_opf * proper_MWh_unit),
wind_curtailed_losses=float(wind_curtailed_losses * proper_MWh_unit),
solar_curtailed_opf=float(total_solar_curt_opf * proper_MWh_unit),
generation_time=end_ - beg_,
saving_time=end_save - beg_save
)
return error_, quality_, load_p, load_p_forecasted, load_q, load_q_forecasted, res_gen_p_df, res_gen_p_forecasted_df
if __name__ == "__main__":
scen_id = "0"
start_date = "2050-01-03"
nb_steps = None