# Copyright (c) 2019-2023, 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.
from datetime import datetime, timedelta
import os
import numpy as np
import copy
from typing import Optional
from grid2op.Exceptions import (
ChronicsNotFoundError, HandlerError
)
from grid2op.Chronics.gridValue import GridValue
from grid2op.Chronics.handlers import BaseHandler
from grid2op.dtypes import dt_int, dt_float
[docs]class FromHandlers(GridValue):
"""This class allows to use the :class:`grid2op.Chronics.handlers.BaseHandler`
(and all the derived class, see :ref:`tshandler-module`) to
generate the "input time series" of the environment.
This class does nothing in particular beside making sure the "formalism" of the
Handlers can be adapted to generate compliant grid2op data.
.. seealso::
:ref:`tshandler-module` for more information
In order to use the handlers you need to:
- tell grid2op that you are going to generate time series from "handlers" by using `FromHandlers` class
- for each type of data ("gen_p", "gen_v", "load_p", "load_q", "maintenance", "gen_p_forecasted",
"load_p_forecasted", "load_q_forecasted" and "load_v_forecasted") you need to provide a way to
"handle" this type of data: you need a specific handler.
You need at least to provide handlers for the environment data types ("gen_p", "gen_v", "load_p", "load_q").
If you do not provide handlers for some data (*e.g* for "maintenance", "gen_p_forecasted",
"load_p_forecasted", "load_q_forecasted" and "load_v_forecasted") then it will be treated like "change nothing":
- there will be no maintenance if you do not provide a handler for maintenance
- for forecast it's a bit different... You will benefit from forecast if at least one handler generates
some (**though we do not recommend to do it**). And in that case, the "missing handlers" will be treated as
"no data available, keep as it was last time"
.. warning::
You cannot mix up all types of handler with each other. We wrote in the description of each Handlers
some conditions for them to work well.
Examples
---------
You can use the handers this way:
.. code-block:: python
import grid2op
from grid2op.Chronics import FromHandlers
from grid2op.Chronics.handlers import CSVHandler, DoNothingHandler, PerfectForecastHandler
env_name = "l2rpn_case14_sandbox"
env = grid2op.make(env_name,
data_feeding_kwargs={"gridvalueClass": FromHandlers,
"gen_p_handler": CSVHandler("prod_p"),
"load_p_handler": CSVHandler("load_p"),
"gen_v_handler": DoNothingHandler("prod_v"),
"load_q_handler": CSVHandler("load_q"),
"gen_p_for_handler": PerfectForecastHandler("prod_p_forecasted"),
"load_p_for_handler": PerfectForecastHandler("load_p_forecasted"),
"load_q_for_handler": PerfectForecastHandler("load_q_forecasted"),
}
)
obs = env.reset()
# and now you can use "env" as any grid2op environment.
More examples are given in the :ref:`tshandler-module` .
Notes
------
For the environment, data, the handler are called in the order: "load_p", "load_q", "gen_p" and finally "gen_v".
They are called once per step (per handler) at most.
Then the maintenance (and hazards) data are generated with the appropriate handler.
Finally, the forecast data are called after the environment data (and the maintenance data) once per step and per horizon.
Horizon are called "in the order" (all data "for 5 minutes", all data "for 10 minutes", all data for "15 minutes" etc.). And
for a given horizon, like the environment it is called in the order: "load_p", "load_q", "gen_p" and "gen_v".
About the seeding, the handlers are seeded in the order:
- load_p
- load_q
- gen_p
- gen_v
- maintenance
- hazards
- load_p_for
- load_q_for
- gen_p_for
- gen_v_for
Each individual handler will have its own pseudo random generator and the same seed will be used regardless of
the presence / absence of other handlers.
For example, regardless of the fact that you have a `maintenance_handler`, if you type `env.seed(0)` the
`load_p_for_handler` will behave exactly the same (it will generate the same numbers whether or not you have
maintenance or not.)
"""
MULTI_CHRONICS = False
def __init__(
self,
path, # can be None !
load_p_handler,
load_q_handler,
gen_p_handler,
gen_v_handler,
maintenance_handler=None,
hazards_handler=None,
load_p_for_handler=None,
load_q_for_handler=None,
gen_p_for_handler=None,
gen_v_for_handler=None,
time_interval=timedelta(minutes=5),
sep=";", # here for compatibility with grid2op, but not used
max_iter=-1,
start_datetime=datetime(year=2019, month=1, day=1),
chunk_size=None,
h_forecast=(5,),
):
GridValue.__init__(
self,
time_interval=time_interval,
max_iter=max_iter,
start_datetime=start_datetime,
chunk_size=chunk_size,
)
self.path = path
if self.path is not None:
self._init_date_time()
# all my "handlers" (I need to perform a deepcopy otherwise data are kept between episode...)
self.gen_p_handler : BaseHandler = copy.deepcopy(gen_p_handler)
self.gen_v_handler : BaseHandler = copy.deepcopy(gen_v_handler)
self.load_p_handler : BaseHandler = copy.deepcopy(load_p_handler)
self.load_q_handler : BaseHandler = copy.deepcopy(load_q_handler)
self.maintenance_handler : Optional[BaseHandler] = copy.deepcopy(maintenance_handler)
self.hazards_handler : Optional[BaseHandler] = copy.deepcopy(hazards_handler)
self.gen_p_for_handler : Optional[BaseHandler] = copy.deepcopy(gen_p_for_handler)
self.gen_v_for_handler : Optional[BaseHandler] = copy.deepcopy(gen_v_for_handler)
self.load_p_for_handler : Optional[BaseHandler] = copy.deepcopy(load_p_for_handler)
self.load_q_for_handler : Optional[BaseHandler] = copy.deepcopy(load_q_for_handler)
# when there are no maintenance / hazards, build this only once
self._no_mh_time = None
self._no_mh_duration = None
# define the active handlers
self._active_handlers = [self.gen_p_handler, self.gen_v_handler, self.load_p_handler, self.load_q_handler]
self._forcast_handlers = []
if self.maintenance_handler is not None:
self._active_handlers.append(self.maintenance_handler)
if self.hazards_handler is not None:
self._active_handlers.append(self.hazards_handler)
if self.gen_p_for_handler is not None:
self._active_handlers.append(self.gen_p_for_handler)
self._forcast_handlers.append(self.gen_p_for_handler)
if self.gen_v_for_handler is not None:
self._active_handlers.append(self.gen_v_for_handler)
self._forcast_handlers.append(self.gen_v_for_handler)
if self.load_p_for_handler is not None:
self._active_handlers.append(self.load_p_for_handler)
self._forcast_handlers.append(self.load_p_for_handler)
if self.load_q_for_handler is not None:
self._active_handlers.append(self.load_q_for_handler)
self._forcast_handlers.append(self.load_q_for_handler)
self._check_types()
# now synch all handlers
for handl in self._forcast_handlers:
handl.set_h_forecast(h_forecast)
# set the current path of the time series
self._set_path(self.path)
if chunk_size is not None:
self.set_chunk_size(chunk_size)
if max_iter != -1:
self.set_max_iter(max_iter)
self.init_datetime()
self.current_inj = None
def _check_types(self):
for handl in self._active_handlers:
if not isinstance(handl, BaseHandler):
raise HandlerError("One of the \"handler\" used in your time series does not "
"inherit from `BaseHandler`. This is not supported.")
[docs] def initialize(
self,
order_backend_loads,
order_backend_prods,
order_backend_lines,
order_backend_subs,
names_chronics_to_backend=None,
):
# set the current path of the time series
self._set_path(self.path)
# give the right date and times to the "handlers"
self.init_datetime()
self.n_gen = len(order_backend_prods)
self.n_load = len(order_backend_loads)
self.n_line = len(order_backend_lines)
self.curr_iter = 0
self.current_inj = None
self.gen_p_handler.initialize(order_backend_prods, names_chronics_to_backend)
self.gen_v_handler.initialize(order_backend_prods, names_chronics_to_backend)
self.load_p_handler.initialize(order_backend_loads, names_chronics_to_backend)
self.load_q_handler.initialize(order_backend_loads, names_chronics_to_backend)
self._update_max_iter() # might be used in the forecast
if self.gen_p_for_handler is not None:
self.gen_p_for_handler.initialize(order_backend_prods, names_chronics_to_backend)
if self.gen_v_for_handler is not None:
self.gen_v_for_handler.initialize(order_backend_prods, names_chronics_to_backend)
if self.load_p_for_handler is not None:
self.load_p_for_handler.initialize(order_backend_loads, names_chronics_to_backend)
if self.load_q_for_handler is not None:
self.load_q_for_handler.initialize(order_backend_loads, names_chronics_to_backend)
self._update_max_iter() # might be used in the maintenance
if self.maintenance_handler is not None:
self.maintenance_handler.initialize(order_backend_lines, names_chronics_to_backend)
if self.hazards_handler is not None:
self.hazards_handler.initialize(order_backend_lines, names_chronics_to_backend)
# when there are no maintenance / hazards, build this only once
self._no_mh_time = np.full(self.n_line, fill_value=-1, dtype=dt_int)
self._no_mh_duration = np.full(self.n_line, fill_value=0, dtype=dt_int)
self._update_max_iter()
[docs] def load_next(self):
self.current_datetime += self.time_interval
self.curr_iter += 1
res = {}
# load the injection
dict_inj, prod_v = self._load_injection()
res["injection"] = dict_inj
# load maintenance
if self.maintenance_handler is not None:
tmp_ = self.maintenance_handler.load_next(res)
if tmp_ is not None:
res["maintenance"] = tmp_
maintenance_time, maintenance_duration = self.maintenance_handler.load_next_maintenance()
else:
maintenance_time = self._no_mh_time
maintenance_duration = self._no_mh_duration
# load hazards
if self.hazard_duration is not None:
res["hazards"] = self.hazards_handler.load_next(res)
hazard_duration = self.hazards_handler.load_next_hazard()
else:
hazard_duration = self._no_mh_duration
self.current_inj = res
return (
self.current_datetime,
res,
maintenance_time,
maintenance_duration,
hazard_duration,
prod_v,
)
[docs] def max_timestep(self):
return self.max_iter
[docs] def next_chronics(self):
self.current_datetime = self.start_datetime
self.curr_iter = 0
for el in self._active_handlers:
el.next_chronics()
self._update_max_iter()
[docs] def done(self):
# I am done if the part I control is "over"
if self._max_iter > 0 and self.curr_iter > self._max_iter:
return True
# or if any of the handler is "done"
for handl in self._active_handlers:
if handl.done():
return True
return False
[docs] def check_validity(self, backend):
for el in self._active_handlers:
el.check_validity(backend)
# TODO other things here maybe ???
return True
def _aux_forecasts(self, h_id, dict_, key,
for_handler, base_handler, handlers):
if for_handler is not None:
tmp_ = for_handler.forecast(h_id, self.current_inj, dict_, base_handler, handlers)
if tmp_ is not None:
dict_[key] = dt_float(1.0) * tmp_
[docs] def forecasts(self):
res = []
if not self._forcast_handlers:
# nothing to handle forecast in this class
return res
handlers = (self.load_p_handler, self.load_q_handler, self.gen_p_handler, self.gen_v_handler)
for h_id, h in enumerate(self._forcast_handlers[0].get_available_horizons()):
dict_ = {}
self._aux_forecasts(h_id, dict_, "load_p", self.load_p_for_handler, self.load_p_handler, handlers)
self._aux_forecasts(h_id, dict_, "load_q", self.load_q_for_handler, self.load_q_handler, handlers)
self._aux_forecasts(h_id, dict_, "prod_p", self.gen_p_for_handler, self.gen_p_handler, handlers)
self._aux_forecasts(h_id, dict_, "prod_v", self.gen_v_for_handler, self.gen_v_handler, handlers)
res_d = {}
if dict_:
res_d["injection"] = dict_
forecast_datetime = self.current_datetime + timedelta(minutes=h)
res.append((forecast_datetime, res_d))
return res
[docs] def get_kwargs(self, dict_):
dict_["gen_p_handler"] = copy.deepcopy(self.gen_p_handler)._clear() if self.gen_p_handler is not None else None
dict_["gen_v_handler"] = copy.deepcopy(self.gen_v_handler)._clear() if self.gen_v_handler is not None else None
dict_["load_p_handler"] = copy.deepcopy(self.load_p_handler)._clear() if self.load_p_handler is not None else None
dict_["load_q_handler"] = copy.deepcopy(self.load_q_handler)._clear() if self.load_q_handler is not None else None
dict_["maintenance_handler"] = copy.deepcopy(self.maintenance_handler)._clear() if self.maintenance_handler is not None else None
dict_["hazards_handler"] = copy.deepcopy(self.hazards_handler)._clear() if self.hazards_handler is not None else None
dict_["gen_p_for_handler"] = copy.deepcopy(self.gen_p_for_handler)._clear() if self.gen_p_for_handler is not None else None
dict_["gen_v_for_handler"] = copy.deepcopy(self.gen_v_for_handler)._clear() if self.gen_v_for_handler is not None else None
dict_["load_p_for_handler"] = copy.deepcopy(self.load_p_for_handler)._clear() if self.load_p_for_handler is not None else None
dict_["load_q_for_handler"] = copy.deepcopy(self.load_q_for_handler)._clear() if self.load_q_for_handler is not None else None
return dict_
[docs] def get_id(self) -> str:
if self.path is not None:
return self.path
else:
# TODO
raise NotImplementedError()
[docs] def shuffle(self, shuffler=None):
# TODO
pass
[docs] def sample_next_chronics(self, probabilities=None):
# TODO
pass
[docs] def set_chunk_size(self, new_chunk_size):
# TODO
for el in self._active_handlers:
el.set_chunk_size(new_chunk_size)
def set_max_iter(self, max_iter):
self.max_iter = int(max_iter)
for el in self._active_handlers:
el.set_max_iter(max_iter)
def init_datetime(self):
for handl in self._active_handlers:
handl.set_times(self.start_datetime, self.time_interval)
[docs] def seed(self, seed):
super().seed(seed)
max_seed = np.iinfo(dt_int).max
seeds = self.space_prng.randint(max_seed, size=10)
# this way of doing ensure the same seed given by the environment is
# used even if some "handlers" are missing
# (if env.seed(0) is called, then regardless of maintenance_handler or not,
# gen_p_for_handler will always be seeded with the same number)
lp_seed = self.load_p_handler.seed(seeds[0])
lq_seed = self.load_q_handler.seed(seeds[1])
gp_seed = self.gen_p_handler.seed(seeds[2])
gv_seed = self.gen_v_handler.seed(seeds[3])
maint_seed = None
if self.maintenance_handler is not None:
maint_seed = self.maintenance_handler.seed(seeds[4])
haz_seed = None
if self.hazards_handler is not None:
haz_seed = self.hazards_handler.seed(seeds[5])
lpf_seed = None
if self.load_p_for_handler is not None:
lpf_seed = self.load_p_for_handler.seed(seeds[6])
lqf_seed = None
if self.load_q_for_handler is not None:
lqf_seed = self.load_q_for_handler.seed(seeds[7])
gpf_seed = None
if self.gen_p_for_handler is not None:
gpf_seed = self.gen_p_for_handler.seed(seeds[8])
gvf_seed = None
if self.gen_v_for_handler is not None:
gvf_seed = self.gen_v_for_handler.seed(seeds[9])
return (seed, gp_seed, gv_seed, lp_seed, lq_seed,
maint_seed, haz_seed, gpf_seed, gvf_seed,
lpf_seed, lqf_seed)
def _set_path(self, path):
"""tell the handler where this chronics is located"""
if path is None:
return
for el in self._active_handlers:
el.set_path(path)
def set_max_episode_duration(self, max_ep_dur):
for handl in self._active_handlers:
handl.set_max_episode_duration(max_ep_dur)
def _update_max_iter(self):
# get the max iter from the handlers
max_iters = [el.get_max_iter() for el in self._active_handlers]
max_iters = [el for el in max_iters if el != -1]
# get the max iter from myself
if self._max_iter != -1:
max_iters.append(self.max_iter)
# prevent empty list
if not max_iters:
max_iters.append(self.max_iter)
# take the minimum
self.max_iter = np.min(max_iters)
# update everyone with the "new" max iter
max_ep_dur = [el.max_episode_duration for el in self._active_handlers]
max_ep_dur = [el for el in max_ep_dur if el is not None]
if max_ep_dur:
if self.max_iter == -1:
self.max_iter = np.min(max_ep_dur)
else:
self.max_iter = min(self.max_iter, np.min(max_ep_dur))
if self.max_iter != -1:
self.set_max_episode_duration(self.max_iter)
def _load_injection(self):
dict_ = {}
prod_v = None
if self.load_p_handler is not None:
tmp_ = self.load_p_handler.load_next(dict_)
if tmp_ is not None:
dict_["load_p"] = dt_float(1.0) * tmp_
if self.load_q_handler is not None:
tmp_ = self.load_q_handler.load_next(dict_)
if tmp_ is not None:
dict_["load_q"] = dt_float(1.0) * tmp_
if self.gen_p_handler is not None:
tmp_ = self.gen_p_handler.load_next(dict_)
if tmp_ is not None:
dict_["prod_p"] = dt_float(1.0) * tmp_
if self.gen_v_handler is not None:
tmp_ = self.gen_v_handler.load_next(dict_)
if tmp_ is not None:
prod_v = dt_float(1.0) * tmp_
return dict_, prod_v
def _init_date_time(self): # in csv handler
if os.path.exists(os.path.join(self.path, "start_datetime.info")):
with open(os.path.join(self.path, "start_datetime.info"), "r") as f:
a = f.read().rstrip().lstrip()
try:
tmp = datetime.strptime(a, "%Y-%m-%d %H:%M")
except ValueError:
tmp = datetime.strptime(a, "%Y-%m-%d")
except Exception:
raise ChronicsNotFoundError(
'Impossible to understand the content of "start_datetime.info". Make sure '
'it\'s composed of only one line with a datetime in the "%Y-%m-%d %H:%M"'
"format."
)
self.start_datetime = tmp
self.current_datetime = tmp
if os.path.exists(os.path.join(self.path, "time_interval.info")):
with open(os.path.join(self.path, "time_interval.info"), "r") as f:
a = f.read().rstrip().lstrip()
try:
tmp = datetime.strptime(a, "%H:%M")
except ValueError:
tmp = datetime.strptime(a, "%M")
except Exception:
raise ChronicsNotFoundError(
'Impossible to understand the content of "time_interval.info". Make sure '
'it\'s composed of only one line with a datetime in the "%H:%M"'
"format."
)
self.time_interval = timedelta(hours=tmp.hour, minutes=tmp.minute)
[docs] def fast_forward(self, nb_timestep):
for _ in range(nb_timestep):
self.load_next()
# for this class I suppose the real data AND the forecast are read each step
self.forecasts()