"""
This module is present to handle everything related to input data that are not structural.
In the Grid2Op vocabulary a "GridValue" or "Chronics" is something that provides data to change the input parameter
of a power flow between 1 time step and the other.
It is a more generic terminology. Modification that can be performed by :class:`GridValue` object includes, but
are not limited to:
- injections such as:
- productions active production setpoint
- generators voltage setpoint
- loads active consumption
- loads reactive consumption
- structural informations such as:
- planned outage: powerline disconnection anticipated in advance
- hazards: powerline disconnection that cannot be anticipated, for example due to a windstorm.
All powergrid modification that can be performed using an :class:`grid2op.BaseAction` can be implemented as form of a
:class:`GridValue`.
The same mechanism than for :class:`grid2op.BaseAction` or :class:`grid2op.BaseObservation` is pursued here. All states
modifications made by the :class:`grid2op.Environment` must derived from the :class:`GridValue`. It is not
recommended to instanciate them directly, but rather to use the :class:`ChronicsHandler` for such a purpose.
Note that the values returned by a :class:`GridValue` are **backend dependant**. A GridValue object should always
return the data in the order expected by the :class:`grid2op.Backend`, regardless of the order in which data are given
in the files or generated by the data generator process.
This implies that changing the backend will change the output of :class:`GridValue`. More information about this
is given in the description of the :func:`GridValue.initialize` method.
Finally, compared to other Reinforcement Learning problems, is the possibility to use "forecast". This optional feature
can be accessed via the :class:`grid2op.BaseObservation` and mainly the :func:`grid2op.BaseObservation.simulate` method. The
data that are used to generate this forecasts come from the :class:`grid2op.GridValue` and are detailed in the
:func:`GridValue.forecasts` method.
"""
import numpy as np
import warnings
from datetime import datetime, timedelta
from abc import ABC, abstractmethod
import pdb
from grid2op.Exceptions import EnvError
# TODO sous echantillonner ou sur echantilloner les scenario: need to modify everything that affect the number
# TODO of time steps there, for example "Space.gen_min_time_on" or "params.NB_TIMESTEP_POWERFLOW_ALLOWED" for
# TODO example. And more generally, it would be better to have all of this attributes exported / imported in
# TODO time interval, instead of time steps.
# TODO add a class to sample "online" the data.
# TODO add a method 'skip' that can skip a given number of timestep or a until a specific date.
[docs]class GridValue(ABC):
"""
This is the base class for every kind of data for the _grid.
It allows the :class:`grid2op.Environment` to perform powergrid modification that make the "game" time dependant.
It is not recommended to directly create :class:`GridValue` object, but to use the
:attr:`grid2op.Environment.chronics_handler" for such a purpose. This is made in an attempt to make sure the
:func:`GridValue.initialize` is called. Before this initialization, it is not recommended to use any
:class:`GridValue` object.
The method :func:`GridValue.next_chronics` should be used between two epoch of the game. If there are no more
data to be generated from this object, then :func:`GridValue.load_next` should raise a :class:`StopIteration`
exception and a call to :func:`GridValue.done` should return True.
Attributes
----------
time_interval: :class:`.datetime.timedelta`
Time interval between 2 consecutive timestamps. Default 5 minutes.
start_datetime: :class:`datetime.datetime`
The datetime of the first timestamp of the scenario.
current_datetime: :class:`datetime.datetime`
The timestamp of the current scenario.
n_gen: ``int``
Number of generators in the powergrid
n_load: ``int``
Number of loads in the powergrid
n_line: ``int``
Number of powerline in the powergrid
max_iter: ``int``
Number maximum of data to generate for one episode.
curr_iter: ``int``
Duration of the current episode.
maintenance_time: ``numpy.ndarray``, dtype:``int``
Number of time steps the next maintenance will take place with the following convention:
- -1 no maintenance are planned for the forseeable future
- 0 a maintenance is taking place
- 1, 2, 3 ... a maintenance will take place in 1, 2, 3, ... time step
Some examples are given in :func:`GridValue.maintenance_time_1d`.
maintenance_duration: ``numpy.ndarray``, dtype:``int``
Duration of the next maintenance. 0 means no maintenance is happening. If a maintenance is planned for a
given powerline, this number decreases each time step, up until arriving at 0 when the maintenance is over. Note
that if a maintenance is planned (see :attr:`GridValue.maintenance_time`) this number indicates how long
the maintenance will last, and does not suppose anything on the maintenance taking place or not (= there can be
positive number here without a powerline being removed from the grid for maintenance reason). Some examples are
given in :func:`GridValue.maintenance_duration_1d`.
hazard_duration: ``numpy.ndarray``, dtype:``int``
Duration of the next hzard. 0 means no maintenance is happening. If a hazard is taking place for a
given powerline, this number decreases each time step, up until arriving at 0 when the maintenance is over. On
the contrary to :attr:`GridValue.maintenance_duration`, if a component of this vector is higher than 1, it
means that the powerline is out of service. Some examples are
given in :func:`GridValue.get_hazard_duration_1d`.
"""
def __init__(self, time_interval=timedelta(minutes=5), max_iter=-1,
start_datetime=datetime(year=2019, month=1, day=1),
chunk_size=None):
self.time_interval = time_interval
self.current_datetime = start_datetime
self.start_datetime = start_datetime
self.n_gen = None
self.n_load = None
self.n_line = None
self.max_iter = max_iter
self.curr_iter = 0
self.maintenance_time = None
self.maintenance_duration = None
self.hazard_duration = None
[docs] @abstractmethod
def initialize(self, order_backend_loads, order_backend_prods, order_backend_lines, order_backend_subs,
names_chronics_to_backend):
"""
This function is used to initialize the data generator.
It can be use to load scenarios, or to initialize noise if scenarios are generated on the fly. It must also
initialize :attr:`GridValue.maintenance_time`, :attr:`GridValue.maintenance_duration` and
:attr:`GridValue.hazard_duration`.
This function should also increment :attr:`GridValue.curr_iter` of 1 each time it is called.
The :class:`GridValue` is what makes the connection between the data (generally in a shape of files on the
hard drive) and the power grid. One of the main advantage of the Grid2Op package is its ability to change
the tool that computes the load flows. Generally, such :class:`grid2op.Backend` expects data in a specific
format that is given by the way their internal powergrid is represented, and in particular, the "same"
objects can have different name and different position. To ensure that the same chronics would
produce the same results on every backend (**ie** regardless of the order of which the Backend is expecting
the data, the outcome of the powerflow is the same) we encourage the user to provide a file that maps the name
of the object in the chronics to the name of the same object in the backend.
This is done with the "names_chronics_to_backend" dictionnary that has the following keys:
- "loads"
- "prods"
- "lines"
The value associated to each of these keys is in turn a mapping dictionnary from the chronics to the backend.
This means that each *keys* of these subdictionnary is a name of one column in the files, and each values
is the corresponding name of this same object in the dictionnary. An example is provided bellow.
Parameters
----------
order_backend_loads: ``numpy.ndarray``, dtype:str
Ordered name, in the Backend, of the loads. It is required that a :class:`grid2op.Backend` object always
output the informations in the same order. This array gives the name of the loads following this order.
See the documentation of :mod:`grid2op.Backend` for more information about this.
order_backend_prods: ``numpy.ndarray``, dtype:str
Same as order_backend_loads, but for generators.
order_backend_lines: ``numpy.ndarray``, dtype:str
Same as order_backend_loads, but for powerline.
order_backend_subs: ``numpy.ndarray``, dtype:str
Same as order_backend_loads, but for powerline.
names_chronics_to_backend: ``dict``
See in the description of the method for more information about its format.
Returns
-------
``None``
Examples
--------
For example, suppose we have a :class:`grid2op.Backend` with:
- substations ids strart from 0 to N-1 (N being the number of substations in the powergrid)
- loads named "load_i" with "i" the subtations to which it is connected
- generators units named "gen_i" (i still the substation id to which it is connected)
- powerlnes are named "i_j" if it connected substations i to substation j
And on the other side, we have some files with the following conventions:
- substations are numbered from 1 to N
- loads are named "i_C" with i being the substation to which it is connected
- generators are named "i_G" with is being the id of the substations to which it is connected
- powerlines are namesd "i_j_k" where i is the origin substation, j the extremity substations and "k"
is a unique identifier of this powerline in the powergrid.
In this case, instead of renaming the powergrid (in the backend) of the data files, it is advised to build the
following elements and initialize the object gridval of type :class:`GridValue` with:
.. code-block:: python
gridval = GridValue() # Note: this code won't execute because "GridValue" is an abstract class
order_backend_loads = ['load_1', 'load_2', 'load_13', 'load_3', 'load_4', 'load_5', 'load_8', 'load_9',
'load_10', 'load_11', 'load_12']
order_backend_prods = ['gen_1', 'gen_2', 'gen_5', 'gen_7', 'gen_0']
order_backend_lines = ['0_1', '0_4', '8_9', '8_13', '9_10', '11_12', '12_13', '1_2', '1_3', '1_4', '2_3',
'3_4', '5_10', '5_11', '5_12', '3_6', '3_8', '4_5', '6_7', '6_8']
order_backend_subs = ['sub_0', 'sub_1', 'sub_10', 'sub_11', 'sub_12', 'sub_13', 'sub_2', 'sub_3', 'sub_4',
'sub_5', 'sub_6', 'sub_7', 'sub_8', 'sub_9']
names_chronics_to_backend = {"loads": {"2_C": 'load_1', "3_C": 'load_2',
"14": 'load_13', "4_C": 'load_3', "5_C": 'load_4',
"6_C": 'load_5', "9_C": 'load_8', "10_C": 'load_9',
"11_C": 'load_10', "12_C": 'load_11',
"13_C": 'load_12'},
"lines": {'1_2_1': '0_1', '1_5_2': '0_4', '9_10_16': '8_9', '9_14_17': '8_13',
'10_11_18': '9_10', '12_13_19': '11_12', '13_14_20': '12_13',
'2_3_3': '1_2', '2_4_4': '1_3', '2_5_5': '1_4', '3_4_6': '2_3',
'4_5_7': '3_4', '6_11_11': '5_10', '6_12_12': '5_11',
'6_13_13': '5_12', '4_7_8': '3_6', '4_9_9': '3_8', '5_6_10': '4_5',
'7_8_14': '6_7', '7_9_15': '6_8'},
"prods": {"1_G": 'gen_0', "3_G": "gen_2", "6_G": "gen_5",
"2_G": "gen_1", "8_G": "gen_7"},
}
gridval.initialize(order_backend_loads, order_backend_prods, order_backend_lines, names_chronics_to_backend)
"""
self.curr_iter += 1
self.current_datetime += self.time_interval
[docs] @staticmethod
def get_maintenance_time_1d(maintenance):
"""
This function allows to transform a 1d numpy aarray maintenance, where is specify:
- 0 there is no maintenance at this time step
- 1 there is a maintenance at this time step
Into the representation in terms of "next maintenance time" as specified in
:attr:`GridValue.maintenance_time` which is:
- `-1` no foreseeable maintenance operation will be performed
- `0` a maintenance operation is being performed
- `1`, `2` etc. is the number of time step the next maintenance will be performed.
Parameters
----------
maintenance: ``numpy.ndarray``
1 dimensional array representing the time series of the maintenance (0 there is no maintenance, 1 there
is a maintenance at this time step)
Returns
-------
maintenance_duration: ``numpy.ndarray``
Array representing the time series of the duration of the next maintenance forseeable.
Examples
--------
If no maintenance are planned:
.. code-block:: python
maintenance_time = GridValue.get_maintenance_time_1d(np.array([0 for _ in range(10)]))
assert np.all(maintenance_time == np.array([-1 for _ in range(10)]))
If a maintenance planned of 3 time steps starting at timestep 6 (index 5 - index starts at 0)
.. code-block:: python
maintenance = np.array([0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0])
maintenance_time = GridValue.get_maintenance_time_1d(maintenance)
assert np.all(maintenance_time == np.array([5,4,3,2,1,0,0,0,-1,-1,-1,-1,-1,-1,-1,-1]))
If a maintenance planned of 3 time steps starting at timestep 6
(index 5 - index starts at 0), and a second one for 2 time steps at time step 13
.. code-block:: python
maintenance = np.array([0,0,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0])
maintenance_time = GridValue.get_maintenance_time_1d(maintenance)
assert np.all(maintenance_time == np.array([5,4,3,2,1,0,0,0,4,3,2,1,0,0,-1,-1,-1]))
"""
res = np.full(maintenance.shape, fill_value=np.NaN, dtype=np.int)
maintenance = np.concatenate((maintenance, (0, 0)))
a = np.diff(maintenance)
# +1 is because numpy does the diff `t+1` - `t` so to get index of the initial array
# I need to "+1"
start = np.where(a == 1)[0] + 1 # start of maintenance
end = np.where(a == -1)[0] + 1 # end of maintenance
prev_ = 0
# it's efficient here as i do a loop only on the number of time there is a maintenance
# and maintenance are quite rare
for beg_, end_ in zip(start, end):
res[prev_:beg_] = list(range(beg_ - prev_, 0, -1))
res[beg_:end_] = 0
prev_ = end_
# no maintenance are planned in the forseeable future
res[prev_:] = -1
return res
[docs] @staticmethod
def get_maintenance_duration_1d(maintenance):
"""
This function allows to transform a 1d numpy aarray maintenance (or hazards), where is specify:
- 0 there is no maintenance at this time step
- 1 there is a maintenance at this time step
Into the representation in terms of "next maintenance duration" as specified in
:attr:`GridValue.maintenance_duration` which is:
- `0` no forseeable maintenance operation will be performed
- `1`, `2` etc. is the number of time step the next maintenance will last (it can be positive even in the
case that no maintenance is currently being performed.
Parameters
----------
maintenance: ``numpy.ndarray``
1 dimensional array representing the time series of the maintenance (0 there is no maintenance, 1 there
is a maintenance at this time step)
Returns
-------
maintenance_duration: ``numpy.ndarray``
Array representing the time series of the duration of the next maintenance forseeable.
Examples
--------
If no maintenance are planned:
.. code-block:: python
maintenance = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0])
maintenance_duration = GridValue.get_maintenance_duration_1d(maintenance)
assert np.all(maintenance_duration == np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]))
If a maintenance planned of 3 time steps starting at timestep 6 (index 5 - index starts at 0)
.. code-block:: python
maintenance = np.array([0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0])
maintenance_duration = GridValue.get_maintenance_duration_1d(maintenance)
assert np.all(maintenance_duration == np.array([3,3,3,3,3,3,2,1,0,0,0,0,0,0,0,0]))
If a maintenance planned of 3 time steps starting at timestep 6
(index 5 - index starts at 0), and a second one for 2 time steps at time step 13
.. code-block:: python
maintenance = np.array([0,0,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0])
maintenance_duration = GridValue.get_maintenance_duration_1d(maintenance)
assert np.all(maintenance_duration == np.array([3,3,3,3,3,3,2,1,2,2,2,2,2,1,0,0,0]))
"""
res = np.full(maintenance.shape, fill_value=np.NaN, dtype=np.int)
maintenance = np.concatenate((maintenance, (0,0)))
a = np.diff(maintenance)
# +1 is because numpy does the diff `t+1` - `t` so to get index of the initial array
# I need to "+1"
start = np.where(a == 1)[0] + 1 # start of maintenance
end = np.where(a == -1)[0] + 1 # end of maintenance
prev_ = 0
# it's efficient here as i do a loop only on the number of time there is a maintenance
# and maintenance are quite rare
for beg_, end_ in zip(start, end):
res[prev_:beg_] = end_ - beg_
res[beg_:end_] = list(range(end_ - beg_, 0, -1))
prev_ = end_
# no maintenance are planned in the foreseeable future
res[prev_:] = 0
return res
[docs] @staticmethod
def get_hazard_duration_1d(hazard):
"""
This function allows to transform a 1d numpy aarray maintenance (or hazards), where is specify:
- 0 there is no maintenance at this time step
- 1 there is a maintenance at this time step
Into the representation in terms of "hzard duration" as specified in
:attr:`GridValue.maintenance_duration` which is:
- `0` no forseeable hazard operation will be performed
- `1`, `2` etc. is the number of time step the next hzard will last (it is positive only when a hazard
affect a given powerline)
Compared to :func:`GridValue.get_maintenance_duration_1d` we only know when the hazard occurs how long it
will last.
Parameters
----------
maintenance: ``numpy.ndarray``
1 dimensional array representing the time series of the maintenance (0 there is no maintenance, 1 there
is a maintenance at this time step)
Returns
-------
maintenance_duration: ``numpy.ndarray``
Array representing the time series of the duration of the next maintenance forseeable.
Examples
--------
If no maintenance are planned:
.. code-block:: python
hazard = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0])
hazard_duration = GridValue.get_hazard_duration_1d(hazard)
assert np.all(hazard_duration == np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]))
If a maintenance planned of 3 time steps starting at timestep 6 (index 5 - index starts at 0)
.. code-block:: python
hazard = np.array([0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0])
hazard_duration = GridValue.get_hazard_duration_1d(hazard)
assert np.all(hazard_duration == np.array([0,0,0,0,0,3,2,1,0,0,0,0,0,0,0,0]))
If a maintenance planned of 3 time steps starting at timestep 6
(index 5 - index starts at 0), and a second one for 2 time steps at time step 13
.. code-block:: python
hazard = np.array([0,0,0,0,0,1,1,1,0,0,0,0,1,1,0,0,0])
hazard_duration = GridValue.get_hazard_duration_1d(hazard)
assert np.all(hazard_duration == np.array([0,0,0,0,0,3,2,1,0,0,0,0,2,1,0,0,0]))
"""
res = np.full(hazard.shape, fill_value=np.NaN, dtype=np.int)
hazard = np.concatenate((hazard, (0, 0)))
a = np.diff(hazard)
# +1 is because numpy does the diff `t+1` - `t` so to get index of the initial array
# I need to "+1"
start = np.where(a == 1)[0] + 1 # start of maintenance
end = np.where(a == -1)[0] + 1 # end of maintenance
prev_ = 0
# it's efficient here as i do a loop only on the number of time there is a maintenance
# and maintenance are quite rare
for beg_, end_ in zip(start, end):
res[prev_:beg_] = 0
res[(beg_):(end_)] = list(range(end_ - beg_, 0, -1))
prev_ = end_
# no maintenance are planned in the forseeable future
res[prev_:] = 0
return res
[docs] @abstractmethod
def load_next(self):
"""
Generate the next values, either by reading from a file, or by generating on the fly and return a dictionnary
compatible with the :class:`grid2op.BaseAction` class allowed for the :class:`Environment`.
More information about this dictionnary can be found at :func:`grid2op.BaseAction.update`.
As a (quick) reminder: this dictionnary has for keys:
- "injection" (optional): a dictionnary with keys (optional) "load_p", "load_q", "prod_p", "prod_v"
- "hazards" (optional) : the outage suffered from the _grid
- "maintenance" (optional) : the maintenance operations planned on the grid for the current time step.
Returns
-------
timestamp: ``datetime.datetime``
The current timestamp for which the modifications have been generated.
dict_: ``dict``
A dictionnary understandable by the ::func:`grid2op.BaseAction.update` method. **NB** this function should
return the dictionnary that will be converted, is should not, in any case, return an action.
maintenance_time: ``numpy.ndarray``, dtype:``int``
Information about the next planned maintenance. See :attr:`GridValue.maintenance_time` for more information.
maintenance_duration: ``numpy.ndarray``, dtype:``int``
Information about the duration of next planned maintenance. See :attr:`GridValue.maintenance_duration`
for more information.
hazard_duration: ``numpy.ndarray``, dtype:``int``
Information about the current hazard. See :attr:`GridValue.hazard_duration`
for more information.
Raises
------
StopIteration
if the chronics is over
"""
self.current_datetime += self.time_interval
return self.current_datetime, {}, self.maintenance_time, self.maintenance_duration, self.hazard_duration
[docs] @abstractmethod
def check_validity(self, backend):
"""
To make sure that the data returned by this class are of the proper dimension, a call to this method
must be performed before actually using the data generated by this class.
In the grid2op framework, this is ensure because the :class:`grid2op.Environment` calls this method
in its initialization.
Parameters
----------
backend: :class:`grid2op.Backend`
The backend used by the :class;`Environment`.
Returns
-------
"""
raise EnvError("check_validity not implemented")
[docs] def done(self):
"""
Whether the episode is over or not.
Returns
-------
done: ``bool``
``True`` means the episode has arrived to the end (no more data to generate) ``False`` means that the episode
is not over yet.
"""
if self.max_iter >= 0:
return self.curr_iter >= self.max_iter
else:
return False
[docs] def forecasts(self):
"""
This method is used to generate the forecasts that are made available to the :class:`grid2op.BaseAgent`.
This forecasts are behaving the same way than a list of tuple as the one returned by
:func:`GridValue.load_next` method.
The way they are generated depends on the GridValue class. If not forecasts are made available, then
the empty list should be returned.
Returns
-------
res: ``list``
Each element of this list having the same type as what is returned by :func:`GridValue.load_next`.
"""
return []
[docs] @abstractmethod
def next_chronics(self):
"""
Load the next batch of chronics. This function is called after an episode has finished by the
:class:`grid2op.Environment` or the :class:`grid2op.Runner`.
A call to this function should also reset :attr:`GridValue.curr_iter` to 0.
Returns
-------
``None``
"""
pass
[docs] def tell_id(self, id_num):
"""
Tell the backend to use one folder for the chronics in particular. This method is mainly use when the GridValue
object can deal with many folder. In this case, this method is used by the :class:`grid2op.Runner` to indicate
which chronics to load for the current simulated episode.
This is important to ensure reproducibility, especially in parrallel computation settings.
This should also be used in case of generation "on the fly" of the chronics to ensure the same property.
By default it does nothing.
Returns
-------
``None``
"""
warnings.warn("Class {} doesn't handle different input folder. \"tell_id\" method has no impact."
"".format(type(self).__name__))
[docs] def get_id(self) -> str:
"""
Utility to get the current name of the path of the data are looked at, if data are files.
This could also be used to return a unique identifier to the generated chronics even in the case where they are
generated on the fly, for example by return a hash of the seed.
Returns
-------
res: ``str``
A unique identifier of the chronics generated for this episode. For example, if the chronics comes from a
specific folder, this could be the path to this folder.
"""
warnings.warn("Class {} doesn't handle different input folder. \"get_id\" method will return \"\"."
"".format(type(self).__name__))
return ""
[docs] def max_timestep(self):
"""
This method returned the maximum timestep that the current episode can last.
Note that if the :class:`grid2op.BaseAgent` performs a bad action that leads to a game over, then the episode
can lasts less.
Returns
-------
res: int
-1 if possibly infinite length of a positive integer representing the maximum duration of this episode
"""
# warnings.warn("Class {} has possibly and infinite duration.".format(type(self).__name__))
return self.max_iter
[docs] def shuffle(self, shuffler):
"""
This method can be overiden if the data that are represented by this object need to be shuffle.
By default it does nothing.
Parameters
----------
shuffler: ``object``
Any function that can be used to shuffle the data.
Returns
-------
"""
pass
[docs] def set_chunk_size(self, new_chunk_size):
"""
TODO
Parameters
----------
new_chunk_size
Returns
-------
"""
pass