-
Notifications
You must be signed in to change notification settings - Fork 116
/
ReadPypowNetData.py
177 lines (156 loc) · 10.8 KB
/
ReadPypowNetData.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
# 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 warnings
from datetime import timedelta, datetime
import numpy as np
import pandas as pd
from grid2op.dtypes import dt_int
from grid2op.Chronics import GridStateFromFileWithForecasts
from grid2op.Exceptions import ChronicsError
# Names of the csv were not the same
class ReadPypowNetData(GridStateFromFileWithForecasts):
def __init__(self, path, sep=";", time_interval=timedelta(minutes=5),
max_iter=-1,
chunk_size=None):
GridStateFromFileWithForecasts.__init__(self, path, sep=sep, time_interval=time_interval,
max_iter=max_iter, chunk_size=chunk_size)
def initialize(self, order_backend_loads, order_backend_prods, order_backend_lines, order_backend_subs,
names_chronics_to_backend=None):
"""
TODO Doc
"""
self.n_gen = len(order_backend_prods)
self.n_load = len(order_backend_loads)
self.n_line = len(order_backend_lines)
self.names_chronics_to_backend = copy.deepcopy(names_chronics_to_backend)
if self.names_chronics_to_backend is None:
self.names_chronics_to_backend = {}
if not "loads" in self.names_chronics_to_backend:
self.names_chronics_to_backend["loads"] = {k: k for k in order_backend_loads}
else:
self._assert_correct(self.names_chronics_to_backend["loads"], order_backend_loads)
if not "prods" in self.names_chronics_to_backend:
self.names_chronics_to_backend["prods"] = {k: k for k in order_backend_prods}
else:
self._assert_correct(self.names_chronics_to_backend["prods"], order_backend_prods)
if not "lines" in self.names_chronics_to_backend:
self.names_chronics_to_backend["lines"] = {k: k for k in order_backend_lines}
else:
self._assert_correct(self.names_chronics_to_backend["lines"], order_backend_lines)
if not "subs" in self.names_chronics_to_backend:
self.names_chronics_to_backend["subs"] = {k: k for k in order_backend_subs}
else:
self._assert_correct(self.names_chronics_to_backend["subs"], order_backend_subs)
# print(os.listdir(self.path))
read_compressed = ".csv"
if not os.path.exists(os.path.join(self.path, "_N_loads_p.csv")):
# try to read compressed data
if os.path.exists(os.path.join(self.path, "_N_loads_p.csv.bz2")):
read_compressed = ".csv.bz2"
elif os.path.exists(os.path.join(self.path, "_N_loads_p.zip")):
read_compressed = ".zip"
elif os.path.exists(os.path.join(self.path, "_N_loads_p.csv.gzip")):
read_compressed = ".csv.gzip"
elif os.path.exists(os.path.join(self.path, "_N_loads_p.csv.xz")):
read_compressed = ".csv.xz"
else:
raise RuntimeError(
"GridStateFromFile: unable to locate the data files that should be at \"{}\"".format(self.path))
load_p = pd.read_csv(os.path.join(self.path, "_N_loads_p{}".format(read_compressed)), sep=self.sep)
load_q = pd.read_csv(os.path.join(self.path, "_N_loads_q{}".format(read_compressed)), sep=self.sep)
prod_p = pd.read_csv(os.path.join(self.path, "_N_prods_p{}".format(read_compressed)), sep=self.sep)
prod_v = pd.read_csv(os.path.join(self.path, "_N_prods_v{}".format(read_compressed)), sep=self.sep)
hazards = pd.read_csv(os.path.join(self.path, "hazards{}".format(read_compressed)), sep=self.sep)
maintenance = pd.read_csv(os.path.join(self.path, "maintenance{}".format(read_compressed)), sep=self.sep)
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 = np.array([order_backend_loads[self.names_chronics_to_backend["loads"][el]]
for el in load_p.columns]).astype(dt_int)
order_backend_load_q = np.array([order_backend_loads[self.names_chronics_to_backend["loads"][el]]
for el in load_q.columns]).astype(dt_int)
order_backend_prod_p = np.array([order_backend_prods[self.names_chronics_to_backend["prods"][el]]
for el in prod_p.columns]).astype(dt_int)
order_backend_prod_v = np.array([order_backend_prods[self.names_chronics_to_backend["prods"][el]]
for el in prod_v.columns]).astype(dt_int)
order_backend_hazards = np.array([order_backend_lines[self.names_chronics_to_backend["lines"][el]]
for el in hazards.columns]).astype(dt_int)
order_backend_maintenance = np.array([order_backend_lines[self.names_chronics_to_backend["lines"][el]]
for el in maintenance.columns]).astype(dt_int)
self.load_p = copy.deepcopy(load_p.values[:, np.argsort(order_chronics_load_p)])
self.load_q = copy.deepcopy(load_q.values[:, np.argsort(order_backend_load_q)])
self.prod_p = copy.deepcopy(prod_p.values[:, np.argsort(order_backend_prod_p)])
self.prod_v = copy.deepcopy(prod_v.values[:, np.argsort(order_backend_prod_v)])
self.hazards = copy.deepcopy(hazards.values[:, np.argsort(order_backend_hazards)])
self.maintenance = copy.deepcopy(maintenance.values[:, np.argsort(order_backend_maintenance)])
# date and time
datetimes_ = pd.read_csv(os.path.join(self.path, "_N_datetimes{}".format(read_compressed)), sep=self.sep)
self.start_datetime = datetime.strptime(datetimes_.iloc[0, 0], "%Y-%b-%d")
# there are maintenance and hazards only if the value in the file is not 0.
self.maintenance = self.maintenance != 0.
self.hazards = self.hazards != 0.
self.curr_iter = 0
if self.max_iter == -1:
# if the number of maximum time step is not set yet, we set it to be the number of
# data in the chronics (number of rows of the files) -1.
# the -1 is present because the initial grid state doesn't count as a "time step" but is read
# from these data.
self.max_iter = self.load_p.shape[0]-1
load_p = pd.read_csv(os.path.join(self.path, "_N_loads_p_planned{}".format(read_compressed)), sep=self.sep)
load_q = pd.read_csv(os.path.join(self.path, "_N_loads_q_planned{}".format(read_compressed)), sep=self.sep)
prod_p = pd.read_csv(os.path.join(self.path, "_N_prods_p_planned{}".format(read_compressed)), sep=self.sep)
prod_v = pd.read_csv(os.path.join(self.path, "_N_prods_v_planned{}".format(read_compressed)), sep=self.sep)
maintenance = pd.read_csv(os.path.join(self.path, "maintenance{}".format(read_compressed)),
sep=self.sep)
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 = np.array([order_backend_loads[self.names_chronics_to_backend["loads"][el]]
for el in load_p.columns]).astype(dt_int)
order_backend_load_q = np.array([order_backend_loads[self.names_chronics_to_backend["loads"][el]]
for el in load_q.columns]).astype(dt_int)
order_backend_prod_p = np.array([order_backend_prods[self.names_chronics_to_backend["prods"][el]]
for el in prod_p.columns]).astype(dt_int)
order_backend_prod_v = np.array([order_backend_prods[self.names_chronics_to_backend["prods"][el]]
for el in prod_v.columns]).astype(dt_int)
order_backend_maintenance = np.array([order_backend_lines[self.names_chronics_to_backend["lines"][el]]
for el in maintenance.columns]).astype(dt_int)
self.load_p_forecast = copy.deepcopy(load_p.values[:, np.argsort(order_chronics_load_p)])
self.load_q_forecast = copy.deepcopy(load_q.values[:, np.argsort(order_backend_load_q)])
self.prod_p_forecast = copy.deepcopy(prod_p.values[:, np.argsort(order_backend_prod_p)])
self.prod_v_forecast = copy.deepcopy(prod_v.values[:, np.argsort(order_backend_prod_v)])
self.maintenance_forecast = copy.deepcopy(maintenance.values[:, np.argsort(order_backend_maintenance)])
# there are maintenance and hazards only if the value in the file is not 0.
self.maintenance_time = np.zeros(shape=(self.load_p.shape[0], self.n_line), dtype=dt_int) - 1
self.maintenance_duration = np.zeros(shape=(self.load_p.shape[0], self.n_line), dtype=dt_int)
self.hazard_duration = np.zeros(shape=(self.load_p.shape[0], self.n_line), dtype=dt_int)
for line_id in range(self.n_line):
self.maintenance_time[:, line_id] = self.get_maintenance_time_1d(self.maintenance[:, line_id])
self.maintenance_duration[:, line_id] = self.get_maintenance_duration_1d(self.maintenance[:, line_id])
self.hazard_duration[:, line_id] = self.get_maintenance_duration_1d(self.hazards[:, line_id])
self.maintenance_forecast = self.maintenance != 0.
self.curr_iter = 0
if self.maintenance is not None:
n_ = self.maintenance.shape[0]
elif self.hazards is not None:
n_ = self.hazards.shape[0]
else:
n_ = None
for fn in ["prod_p", "load_p", "prod_v", "load_q"]:
ext_ = self._get_fileext(fn)
if ext_ is not None:
n_ = self._file_len(os.path.join(self.path, "{}{}".format(fn, ext_)), ext_)
break
if n_ is None:
raise ChronicsError("No files are found in directory \"{}\". If you don't want to load any chronics,"
" use \"ChangeNothing\" and not \"{}\" to load chronics."
"".format(self.path, type(self)))
self.n_ = n_ # the -1 is present because the initial grid state doesn't count as a "time step"
self.tmp_max_index = load_p.shape[0]