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GridStateFromFileWithForecasts.py
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GridStateFromFileWithForecasts.py
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# Copyright (c) 2019-2020, 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 Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.
import os
import copy
import numpy as np
import pandas as pd
from datetime import timedelta
from grid2op.dtypes import dt_float, dt_bool
from grid2op.Exceptions import EnvError, IncorrectNumberOfLoads, IncorrectNumberOfLines, IncorrectNumberOfGenerators
from grid2op.Exceptions import ChronicsError
from grid2op.Chronics.GridStateFromFile import GridStateFromFile
class GridStateFromFileWithForecasts(GridStateFromFile):
"""
An extension of :class:`GridStateFromFile` that implements the "forecast" functionality.
Forecast are also read from a file. For this class, only 1 forecast per timestep is read. The "forecast"
present in the file at row $i$ is the one available at the corresponding time step, so valid for the grid state
at the next time step.
To have more advanced forecasts, this class could be overridden.
Attributes
----------
load_p_forecast: ``numpy.ndarray``, dtype: ``float``
Array used to store the forecasts of the load active values.
load_q_forecast: ``numpy.ndarray``, dtype: ``float``
Array used to store the forecasts of the load reactive values.
prod_p_forecast: ``numpy.ndarray``, dtype: ``float``
Array used to store the forecasts of the generator active production setpoint.
prod_v_forecast: ``numpy.ndarray``, dtype: ``float``
Array used to store the forecasts of the generator voltage magnitude setpoint.
maintenance_forecast: ``numpy.ndarray``, dtype: ``float``
Array used to store the forecasts of the _maintenance operations.
"""
def __init__(self, path, sep=";", time_interval=timedelta(minutes=5), max_iter=-1, chunk_size=None):
GridStateFromFile.__init__(self, path, sep=sep, time_interval=time_interval,
max_iter=max_iter, chunk_size=chunk_size)
self.load_p_forecast = None
self.load_q_forecast = None
self.prod_p_forecast = None
self.prod_v_forecast = None
self.maintenance_forecast = None
# for when you read data in chunk
self._order_load_p_forecasted = None
self._order_load_q_forecasted = None
self._order_prod_p_forecasted = None
self._order_prod_v_forecasted = None
self._order_maintenance_forecasted = None
self._data_already_in_mem = False # says if the "main" value from the base class had to be reloaded (used for chunk)
def _get_next_chunk_forecasted(self):
load_p = None
load_q = None
prod_p = None
prod_v = None
if self._data_chunk["load_p_forecasted"] is not None:
load_p = next(self._data_chunk["load_p_forecasted"])
if self._data_chunk["load_q_forecasted"] is not None:
load_q = next(self._data_chunk["load_q_forecasted"])
if self._data_chunk["prod_p_forecasted"] is not None:
prod_p = next(self._data_chunk["prod_p_forecasted"])
if self._data_chunk["prod_v_forecasted"] is not None:
prod_v = next(self._data_chunk["prod_v_forecasted"])
return load_p, load_q, prod_p, prod_v
def _data_in_memory(self):
res = super()._data_in_memory()
self._data_already_in_mem = res
return res
def initialize(self, order_backend_loads, order_backend_prods, order_backend_lines, order_backend_subs,
names_chronics_to_backend=None):
"""
The same condition as :class:`GridStateFromFile.initialize` applies also for
:attr:`GridStateFromFileWithForecasts.load_p_forecast`, :attr:`GridStateFromFileWithForecasts.load_q_forecast`,
:attr:`GridStateFromFileWithForecasts.prod_p_forecast`,
:attr:`GridStateFromFileWithForecasts.prod_v_forecast` and
:attr:`GridStateFromFileWithForecasts.maintenance_forecast`.
Parameters
----------
See help of :func:`GridValue.initialize` for a detailed help about the _parameters.
Returns
-------
``None``
"""
super().initialize(order_backend_loads, order_backend_prods, order_backend_lines, order_backend_subs,
names_chronics_to_backend)
load_p_iter = self._get_data("load_p_forecasted")
load_q_iter = self._get_data("load_q_forecasted")
prod_p_iter = self._get_data("prod_p_forecasted")
prod_v_iter = self._get_data("prod_v_forecasted")
hazards = None # no hazards in forecast
nrows = None
if self.max_iter > 0:
nrows = self.max_iter + 1
read_compressed = self._get_fileext("maintenance_forecasted")
if read_compressed is not None:
maintenance = pd.read_csv(os.path.join(self.path, "maintenance_forecasted{}".format(read_compressed)),
sep=self.sep,
nrows=nrows)
else:
maintenance = None
if self.chunk_size is None:
load_p = load_p_iter
load_q = load_q_iter
prod_p = prod_p_iter
prod_v = prod_v_iter
else:
self._data_chunk["load_p_forecasted"] = load_p_iter
self._data_chunk["load_q_forecasted"] = load_q_iter
self._data_chunk["prod_p_forecasted"] = prod_p_iter
self._data_chunk["prod_v_forecasted"] = prod_v_iter
load_p, load_q, prod_p, prod_v = self._get_next_chunk_forecasted()
order_backend_loads = {el: i for i, el in enumerate(order_backend_loads)}
order_backend_prods = {el: i for i, el in enumerate(order_backend_prods)}
order_backend_lines = {el: i for i, el in enumerate(order_backend_lines)}
order_chronics_load_p, order_backend_load_q, \
order_backend_prod_p, order_backend_prod_v, \
order_backend_hazards, order_backend_maintenance \
= self._get_orders(load_p, load_q, prod_p, prod_v, hazards, maintenance,
order_backend_loads, order_backend_prods, order_backend_lines)
self._order_load_p_forecasted = np.argsort(order_chronics_load_p)
self._order_load_q_forecasted = np.argsort(order_backend_load_q)
self._order_prod_p_forecasted = np.argsort(order_backend_prod_p)
self._order_prod_v_forecasted = np.argsort(order_backend_prod_v)
self._order_maintenance_forecasted = np.argsort(order_backend_maintenance)
self._init_attrs_forecast(load_p, load_q, prod_p, prod_v, maintenance=maintenance)
def _init_attrs_forecast(self, load_p, load_q, prod_p, prod_v, maintenance=None):
# TODO refactor that with _init_attrs from super()
self.maintenance_forecast = None
self.load_p_forecast = None
self.load_q_forecast = None
self.prod_p_forecast = None
self.prod_v_forecast = None
if load_p is not None:
self.load_p_forecast = copy.deepcopy(load_p.values[:, self._order_load_p_forecasted].astype(dt_float))
if load_q is not None:
self.load_q_forecast = copy.deepcopy(load_q.values[:, self._order_load_q_forecasted].astype(dt_float))
if prod_p is not None:
self.prod_p_forecast = copy.deepcopy(prod_p.values[:, self._order_prod_p_forecasted].astype(dt_float))
if prod_v is not None:
self.prod_v_forecast = copy.deepcopy(prod_v.values[:, self._order_prod_v_forecasted].astype(dt_float))
if maintenance is not None:
if maintenance is not None:
self.maintenance_forecast = copy.deepcopy(maintenance.values[:, np.argsort(self._order_maintenance)])
# there are _maintenance and hazards only if the value in the file is not 0.
self.maintenance_forecast = self.maintenance != 0.
self.maintenance_forecast = self.maintenance_forecast.astype(dt_bool)
def check_validity(self, backend):
super(GridStateFromFileWithForecasts, self).check_validity(backend)
at_least_one = False
if self.load_p_forecast is not None:
if self.load_p_forecast.shape[1] != backend.n_load:
raise IncorrectNumberOfLoads("for the active part. It should be {} but is in fact {}"
"".format(backend.n_load, len(self.load_p)))
at_least_one = True
if self.load_q_forecast is not None:
if self.load_q_forecast.shape[1] != backend.n_load:
raise IncorrectNumberOfLoads("for the reactive part. It should be {} but is in fact {}"
"".format(backend.n_load, len(self.load_q)))
at_least_one = True
if self.prod_p_forecast is not None:
if self.prod_p_forecast.shape[1] != backend.n_gen:
raise IncorrectNumberOfGenerators("for the active part. It should be {} but is in fact {}"
"".format(backend.n_gen, len(self.prod_p)))
at_least_one = True
if self.prod_v_forecast is not None:
if self.prod_v_forecast.shape[1] != backend.n_gen:
raise IncorrectNumberOfGenerators("for the voltage part. It should be {} but is in fact {}"
"".format(backend.n_gen, len(self.prod_v)))
at_least_one = True
if self.maintenance_forecast is not None:
if self.maintenance_forecast.shape[1] != backend.n_line:
raise IncorrectNumberOfLines("for the _maintenance. It should be {} but is in fact {}"
"".format(backend.n_line, len(self.maintenance)))
at_least_one = True
if not at_least_one:
raise ChronicsError("You used a class that read forecasted data, yet there is no forecasted data in"
"\"{}\". Please fall back to using class \"GridStateFromFile\" instead of "
"\"{}\"".format(self.path, type(self)))
for name_arr, arr in zip(["load_q", "load_p", "prod_v", "prod_p", "maintenance"],
[self.load_q_forecast, self.load_p_forecast, self.prod_v_forecast,
self.prod_p_forecast, self.maintenance_forecast]):
if arr is not None:
if self.chunk_size is None:
if arr.shape[0] < self.n_:
raise EnvError("Array for forecast {}_forecasted as not the same number of rows of load_p. "
"The chronics cannot be loaded properly.".format(name_arr))
def _load_next_chunk_in_memory_forecast(self):
# i load the next chunk as dataframes
load_p, load_q, prod_p, prod_v = self._get_next_chunk_forecasted()
# i put these dataframes in the right order (columns)
self._init_attrs_forecast(load_p, load_q, prod_p, prod_v)
# resetting the index has been done in _load_next_chunk_in_memory, or at least it should have
def forecasts(self):
"""
This is the major difference between :class:`GridStateFromFileWithForecasts` and :class:`GridStateFromFile`.
It returns non empty forecasts.
As explained in the :func:`GridValue.forecasts`, forecasts are made of list of tuple. Each tuple having
exactly 2 elements:
1. Is the time stamp of the forecast
2. An :class:`grid2op.BaseAction` representing the modification of the powergrid after the forecast.
For this class, only the forecast of the next time step is given, and only for the injections and maintenance.
Returns
-------
See :func:`GridValue.forecasts` for more information.
"""
if not self._data_already_in_mem:
try:
self._load_next_chunk_in_memory_forecast()
except StopIteration as e:
raise e
res = {}
dict_ = {}
if self.load_p_forecast is not None:
dict_["load_p"] = dt_float(1.0 * self.load_p_forecast[self.current_index, :])
if self.load_q_forecast is not None:
dict_["load_q"] = dt_float(1.0 * self.load_q_forecast[self.current_index, :])
if self.prod_p_forecast is not None:
dict_["prod_p"] = dt_float(1.0 * self.prod_p_forecast[self.current_index, :])
if self.prod_v_forecast is not None:
dict_["prod_v"] = dt_float(1.0 * self.prod_v_forecast[self.current_index, :])
if dict_:
res["injection"] = dict_
if self.maintenance_forecast is not None:
res["maintenance"] = self.maintenance_forecast[self.current_index, :]
forecast_datetime = self.current_datetime + self.time_interval
return [(forecast_datetime, res)]
def get_id(self) -> str:
return self.path
def _init_res_split(self, nb_rows):
res_load_p_f = None
res_load_q_f = None
res_prod_p_f = None
res_prod_v_f = None
res_maintenance_f = None
if self.prod_p_forecast is not None:
res_prod_p_f = np.zeros((nb_rows, self.n_gen), dtype=dt_float)
if self.prod_v_forecast is not None:
res_prod_v_f = np.zeros((nb_rows, self.n_gen), dtype=dt_float)
if self.load_p_forecast is not None:
res_load_p_f = np.zeros((nb_rows, self.n_load), dtype=dt_float)
if self.load_q_forecast is not None:
res_load_q_f = np.zeros((nb_rows, self.n_load), dtype=dt_float)
if self.maintenance_forecast is not None:
res_maintenance_f = np.zeros((nb_rows, self.n_line), dtype=dt_float)
res = super()._init_res_split(nb_rows)
res += tuple([res_prod_p_f, res_prod_v_f, res_load_p_f, res_load_q_f, res_maintenance_f])
return res
def _update_res_split(self, i, tmp, *arrays):
*args_super, res_prod_p_f, res_prod_v_f, res_load_p_f, res_load_q_f, res_maintenance_f = arrays
super()._update_res_split(i, tmp, *args_super)
if res_prod_p_f is not None:
res_prod_p_f[i, :] = tmp._extract_array("prod_p_forecast")
if res_prod_v_f is not None:
res_prod_v_f[i, :] = tmp._extract_array("prod_v_forecast")
if res_load_p_f is not None:
res_load_p_f[i, :] = tmp._extract_array("load_p_forecast")
if res_load_q_f is not None:
res_load_q_f[i, :] = tmp._extract_array("load_q_forecast")
if res_maintenance_f is not None:
res_maintenance_f[i, :] = tmp._extract_array("maintenance_forecast")
def _clean_arrays(self, i, *arrays):
*args_super, res_prod_p_f, res_prod_v_f, res_load_p_f, res_load_q_f, res_maintenance_f = arrays
res = super()._clean_arrays(i, *args_super)
if res_prod_p_f is not None:
res_prod_p_f = res_prod_p_f[:i, :]
if res_prod_v_f is not None:
res_prod_v_f = res_prod_v_f[:i, :]
if res_load_p_f is not None:
res_load_p_f = res_load_p_f[:i, :]
if res_load_q_f is not None:
res_load_q_f = res_load_q_f[:i, :]
if res_maintenance_f is not None:
res_maintenance_f = res_maintenance_f[:i, :]
res += tuple([res_prod_p_f, res_prod_v_f, res_load_p_f, res_load_q_f, res_maintenance_f])
return res
def _get_name_arrays_for_saving(self):
res = super()._get_name_arrays_for_saving()
res += ["prod_p_forecasted", "prod_v_forecasted", "load_p_forecasted",
"load_q_forecasted", "maintenance_forecasted"]
return res
def _get_colorder_arrays_for_saving(self):
res = super()._get_colorder_arrays_for_saving()
res += tuple([self._order_backend_prods, self._order_backend_prods,
self._order_backend_loads, self._order_backend_loads,
self._order_backend_lines])
return res