GridStateFromFileWithForecasts.py

# 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
import random

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):
        # TODO merge this class with GridStateFromFile
        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

        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)
        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