-
Notifications
You must be signed in to change notification settings - Fork 5
/
eia930.py
487 lines (414 loc) · 16.7 KB
/
eia930.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
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
import pandas as pd
import re
from datetime import timedelta
import os
from os.path import join
import load_data
from column_checks import get_dtypes
from filepaths import top_folder, downloads_folder, outputs_folder, manual_folder
from logging_util import get_logger
# Tell gridemissions where to find config before we load gridemissions
os.environ["GRIDEMISSIONS_CONFIG_FILE_PATH"] = top_folder("config/gridemissions.json")
from gridemissions.workflows import make_dataset
logger = get_logger(__name__)
def convert_balance_file_to_gridemissions_format(year: int, small: bool = False):
"""Converts downloaded EIA-930 Balance files to gridemissions format."""
files = [
downloads_folder() + "eia930/EIA930_{}_{}_Jul_Dec.csv",
downloads_folder() + "eia930/EIA930_{}_{}_Jan_Jun.csv",
downloads_folder() + "eia930/EIA930_{}_{}_Jul_Dec.csv",
]
years = [year - 1, year, year]
if small:
files = [downloads_folder() + "eia930/EIA930_{}_{}_Jan_Jun.csv"]
years = [year]
name_map = {
"Total Interchange (MW)": "EBA.{}-ALL.TI.H",
"Interchange (MW)": "EBA.{}-{}.ID.H",
"Demand (MW) (Adjusted)": "EBA.{}-ALL.D.H",
"Net Generation (MW) (Adjusted)": "EBA.{}-ALL.NG.H",
"Net Generation (MW) from Coal": "EBA.{}-ALL.NG.COL.H",
"Net Generation (MW) from Natural Gas": "EBA.{}-ALL.NG.NG.H",
"Net Generation (MW) from Nuclear": "EBA.{}-ALL.NG.NUC.H",
"Net Generation (MW) from All Petroleum Products": "EBA.{}-ALL.NG.OIL.H",
"Net Generation (MW) from Hydropower and Pumped Storage": "EBA.{}-ALL.NG.WAT.H",
"Net Generation (MW) from Solar": "EBA.{}-ALL.NG.SUN.H",
"Net Generation (MW) from Wind": "EBA.{}-ALL.NG.WND.H",
"Net Generation (MW) from Other Fuel Sources": "EBA.{}-ALL.NG.OTH.H",
"Net Generation (MW) from Unknown Fuel Sources": "EBA.{}-ALL.NG.UNK.H",
}
out = pd.DataFrame()
for i, file in enumerate(files):
dat_file = file.format("BALANCE", years[i])
int_file = file.format("INTERCHANGE", years[i])
# Format balance files in series format (for gridemissions)
dat = pd.read_csv(
dat_file,
usecols=[
"Balancing Authority",
"UTC Time at End of Hour",
"Total Interchange (MW)",
"Demand (MW) (Adjusted)",
"Net Generation (MW) (Adjusted)",
"Net Generation (MW) from Coal",
"Net Generation (MW) from Natural Gas",
"Net Generation (MW) from Nuclear",
"Net Generation (MW) from All Petroleum Products",
"Net Generation (MW) from Hydropower and Pumped Storage",
"Net Generation (MW) from Solar",
"Net Generation (MW) from Wind",
"Net Generation (MW) from Other Fuel Sources",
"Net Generation (MW) from Unknown Fuel Sources",
],
parse_dates=["UTC Time at End of Hour"],
thousands=",",
)
# Wide to long
dat = dat.melt(id_vars=["Balancing Authority", "UTC Time at End of Hour"])
# Find series name
dat["column"] = dat.apply(
lambda x: name_map[x.variable].format(x["Balancing Authority"]),
axis="columns",
)
# Long to wide
dat = dat[["UTC Time at End of Hour", "value", "column"]].pivot(
index="UTC Time at End of Hour", columns="column", values="value"
)
# Now for interchange
int = pd.read_csv(
int_file,
usecols=[
"Balancing Authority",
"Directly Interconnected Balancing Authority",
"Interchange (MW)",
"UTC Time at End of Hour",
],
parse_dates=["UTC Time at End of Hour"],
thousands=",",
)
int["column"] = int.apply(
lambda x: name_map["Interchange (MW)"].format(
x["Balancing Authority"],
x["Directly Interconnected Balancing Authority"],
),
axis="columns",
)
int = int[["UTC Time at End of Hour", "column", "Interchange (MW)"]].pivot(
index="UTC Time at End of Hour", columns="column", values="Interchange (MW)"
)
# Combine
dat = pd.concat([dat, int], axis="columns")
out = pd.concat([out, dat], axis="index")
out.index = out.index.tz_localize("UTC")
# Balance files are all inclusive, so hours at boundaries (July 1, Jan 1) are duplicated.
# Drop those duplicate rows
out = out[~out.index.duplicated(keep="first")]
return out
def clean_930(year: int, small: bool = False, path_prefix: str = ""):
"""
Scrape and process EIA data.
Arguments:
`year`: Year to process. Prior years, downloaded from chalendar-hosted files, are used for rolling cleaning
"""
data_folder = outputs_folder(f"{path_prefix}/eia930/")
# Format raw file
df = convert_balance_file_to_gridemissions_format(year, small=small)
raw_file = data_folder + "eia930_unadjusted_raw.csv"
df.to_csv(raw_file)
# if not small, scrape 2 months before start of year for rolling window cleaning
start = f"{year}0101T00Z" if small else f"{year-1}1001T00Z"
# Scrape 1 week if small, else 1 year (plus one day for timezone flexibility)
end = f"{year}0107T23Z" if small else f"{year+1}0101T23Z"
if small:
df = df.loc[start:end] # Don't worry about processing everything
# Adjust
logger.info("Adjusting EIA-930 time stamps")
df = manual_930_adjust(df)
df.to_csv(
join(data_folder, "eia930_raw.csv")
) # Will be read by gridemissions workflow
# Run cleaning
logger.info("Running physics-based data cleaning")
make_dataset(
start,
end,
file_name="eia930",
tmp_folder=data_folder,
folder_hist=data_folder,
scrape=False,
add_ca_fuels=False,
calc_consumed=False,
)
def reformat_chalendar(raw):
"""
reformat_chalendar
Reformat wide-format data (one row per time stamp) from Chalendar
to long (one row per data point)
Drop all columns that are not fuel-specific generation
"""
# where we have variable (NG = net generation) and fuel type
target_cols = [c for c in raw.columns if len(c.split(".")) == 5]
logger.info("Filtering")
cleaned = (
raw.loc[:, target_cols]
.melt(ignore_index=False, value_name="generation", var_name="variable")
.reset_index()
)
logger.info("Expanding cols")
cleaned[["dtype", "BA", "other BA", "var", "fuel", "interval"]] = cleaned[
"variable"
].str.split(r"[.-]", expand=True, regex=True)
logger.info("Dropping and renaming")
cleaned = cleaned.drop(columns=["dtype", "var", "interval", "other BA"])
cleaned = cleaned.rename(columns={"index": "datetime_utc"})
return cleaned
def load_chalendar(fname: str, year: int):
raw = pd.read_csv(fname, index_col=0, parse_dates=True)
raw = raw[raw.index.year == year]
return reformat_chalendar(raw)
def load_chalendar_for_pipeline(cleaned_data_filepath, year):
"""
Loads and formats cleaned hourly net generation data
for use in the data pipeline
"""
# read the data, only keeping net generation columns
data = pd.read_csv(
cleaned_data_filepath, index_col=0, parse_dates=True, dtype=get_dtypes()
).filter(like="-ALL.NG.")
# name the index
data.index = data.index.rename("datetime_utc")
# only keep data for the single year we are interested, plus or minus one
# day, to account for conversion to local time later
data = data.loc[
(data.index >= f"{year-1}-12-31") & (data.index < f"{year+1}-01-02"), :
]
# remove columns for total net generation
data = data.loc[
:,
[
col
for col in data.columns
if col not in data.filter(like="-ALL.NG.H").columns
],
]
# melt the data into long format
data = data.reset_index().melt(
id_vars="datetime_utc", value_name="net_generation_mwh_930"
)
# create columns for ba_code and fuel category
data[["ba_code", "fuel_category_eia930"]] = data["variable"].str.split(
r"[.-]", expand=True, regex=True
)[[1, 4]]
# drop BAs not located in the United States
ba_ref = pd.read_csv(manual_folder("ba_reference.csv"))
foreign_bas = list(ba_ref.loc[ba_ref["us_ba"] == "No", "ba_code"])
data = data[~data["ba_code"].isin(foreign_bas)]
data["datetime_local"] = ""
for ba in list(data["ba_code"].unique()):
data.loc[data.ba_code == ba, "datetime_local"] = (
data.loc[data.ba_code == ba, "datetime_utc"]
.dt.tz_convert(load_data.ba_timezone(ba=ba, type="local"))
.astype(str)
)
# create a report date column
data["report_date"] = data["datetime_local"].str[:7]
data["report_date"] = pd.to_datetime(data["report_date"])
# rename the fuel categories using format in
# data/manual/energy_source_groups
fuel_categories = {
"COL": "coal",
"NG": "natural_gas",
"OTH": "other",
"WAT": "hydro",
"WND": "wind",
"SUN": "solar",
"NUC": "nuclear",
"OIL": "petroleum",
"BIO": "biomass",
"GEO": "geothermal",
}
data["fuel_category_eia930"] = data["fuel_category_eia930"].map(fuel_categories)
# reorder the columns and remove the variable column
data = data[
[
"ba_code",
"fuel_category_eia930",
"datetime_utc",
"datetime_local",
"report_date",
"net_generation_mwh_930",
]
]
return data
def remove_imputed_ones(eia930_data):
filter = eia930_data["net_generation_mwh_930"].abs() < 1.5
# replace all 1.0 values with zero
logger.info(f"Replacing {sum(filter)} imputed 1 values with 0")
eia930_data.loc[filter, "net_generation_mwh_930"] = 0
return eia930_data
def remove_months_with_zero_data(eia930_data):
# remove data where the entire month is zero
zero_data = (
eia930_data.groupby(["ba_code", "fuel_category_eia930", "report_date"])
.sum(numeric_only=True)
.reset_index()
)
zero_data = zero_data[zero_data["net_generation_mwh_930"] == 0].drop(
columns="net_generation_mwh_930"
)
# filter these ba-fuel-months out of the eia930 data
eia930_data = eia930_data.merge(
zero_data,
how="outer",
on=["ba_code", "fuel_category_eia930", "report_date"],
indicator="zero_filter",
validate="m:1",
)
eia930_data = eia930_data[eia930_data["zero_filter"] == "left_only"].drop(
columns="zero_filter"
)
return eia930_data
###########################################################
# Code for adjusting 930 data in gridemissions format
#
###########################################################
def get_columns(ba: str, columns):
GEN_ID = "EBA.{}-ALL.NG.H"
GEN_TYPE_ID = "EBA.{}-ALL.NG.{}.H"
DEM_ID = "EBA.{}-ALL.D.H"
SRC = ["COL", "NG", "NUC", "OIL", "OTH", "SUN", "UNK", "WAT", "WND", "GEO", "BIO"]
cols = [
GEN_TYPE_ID.format(ba, f) for f in SRC if GEN_TYPE_ID.format(ba, f) in columns
]
cols.append(GEN_ID.format(ba))
cols.append(DEM_ID.format(ba))
return cols
def get_int_columns(ba1: str, columns, ba2: list = []):
INTER_ID = "EBA.{}-{}.ID.H"
IT_ID = "EBA.{}-ALL.TI.H"
# Looking for everyone, including ALL
if ba2 == []:
other_cols = [
c
for c in columns
if re.split(r"[-.]", c)[1] == ba1 and re.split(r"[-.]", c)[2] != "ALL"
]
ba2 = [re.split(r"[-.]", c)[2] for c in other_cols]
ba2.append("ALL")
cols = [
INTER_ID.format(ba1, ba) for ba in ba2 if (INTER_ID.format(ba1, ba) in columns)
]
if "ALL" in ba2:
if IT_ID.format(ba1) in columns: # CFE lacks "ALL" interchange
cols.append(IT_ID.format(ba1))
return cols
def manual_930_adjust(raw: pd.DataFrame):
"""
manual_930_adjust
Adjusts time stamps in 930 data. Assumes dataframe with timestamp index and
one column per series,
where column names correspond to EIA series IDs:
"EBA.%s-ALL.D.H", # Demand
"EBA.%s-ALL.NG.H", # Generation
"EBA.%s-ALL.NG.%s.H", # Generation by fuel type
"EBA.%s-ALL.TI.H", # Total Interchange
"EBA.%s-%s.ID.H", # Interchange
Adjustment Steps:
- Make all end-of-hour
- Generation
- PJM: + 1 hour
- CISO: + 1 hour
- TEPC: + 1 hour
- SC: -4 hours during daylight savings hours; -5 hours during
standard hours (this happens to = the Eastern <-> UTC offset)
Fixed in BALANCE files starting Jan 1, 2021
- Interchange
- PJM: + 4 hours
- TEPC: + 7 hours
- CFE: -11 hours
- Interchange sign
- PJM-{CPLE, CPLW, DUK, LGEE, MISO, NYIS, TVA} before
Oct 31, 2019, 4:00 UTC
- this is all interchange partners except OVEC, and excluding
total interchange
- Interchange mysterious
- AZPS - SRP flips gradually in Nov 2019, then abruptly back in June 2020.
throughout, SRP - AZPS remains constant around 3000 lb imported to AZPS from SRP
We assume SRP - AZPS is correct, and assign AZPS - SRP to be the inverse
- Make all start-of-hour
- Generation
- - 1 hour
"""
# SC offset = UTC <-> Eastern offset
sc_offsets = (
raw.index.tz_convert("US/Eastern").to_series().apply(lambda s: s.utcoffset())
)
# After Dec 31, 2020, the offset is 0
sc_offsets["2020-12-31 00:00:00+00":] = timedelta(0)
# make new data so we don't mess up other data indexing
sc_dat = raw[get_columns("SC", raw.columns)].copy()
sc_idx = pd.DatetimeIndex(sc_dat.index + sc_offsets) # make shifted dates
sc_dat.index = sc_idx # use shifted dates
sc_dat = sc_dat[~sc_dat.index.duplicated(keep="first")]
# exchange old rows with new
raw = raw.drop(columns=sc_dat.columns)
raw = pd.concat([raw, sc_dat], axis="columns")
# PJM, CISO, TEPC: shift by one hour
for ba in ["PJM", "CISO", "TEPC"]:
cols = get_columns(ba, raw.columns)
new = raw[cols].shift(1, freq="H")
raw = raw.drop(columns=cols)
raw = pd.concat([raw, new], axis="columns")
# Interchange sign. Do before we change interchange time for PJM, because
# identification of sign shift is based on raw data
cols = get_int_columns(
"PJM", raw.columns, ["CPLE", "CPLW", "DUK", "LGEE", "MISO", "NYIS", "TVA"]
)
raw.loc[raw.index < "2019-10-31 04:00:00+00", cols] = (
raw.loc[raw.index < "2019-10-31 04:00:00+00", cols] * -1
)
# Interchange AZPS - SRP is wonky before 6/1/2020 7:00 UTC. Use SRP - AZPS (inverted)
azps_srp = get_int_columns("AZPS", raw.columns, ["SRP"])
srp_azps = get_int_columns("SRP", raw.columns, ["AZPS"])
replacement = (raw.loc[:, srp_azps] * (-1)).rename(
columns={srp_azps[0]: azps_srp[0]} # rename so Pandas will do the right thing
)
raw.loc[:"2020-06-01 07:00:00+00", azps_srp] = replacement[
:"2020-06-01 07:00:00+00"
]
# Update total interchange
all_cols = [c for c in get_int_columns("AZPS", raw.columns) if "ALL" not in c]
total_col = "EBA.AZPS-ALL.TI.H"
raw.loc[:"2020-06-01 07:00:00+00", total_col] = raw.loc[
:"2020-06-01 07:00:00+00", all_cols
].sum(axis=1)
# Interchange TEPC is uniformly lagged
cols = get_int_columns("TEPC", raw.columns)
new = raw[cols].shift(-7, freq="H")
raw = raw.drop(columns=cols)
raw = pd.concat([raw, new], axis="columns")
# Interchange PJM is lagged differently across DST boundary
is_dst = raw.index.tz_convert("US/Eastern").to_series().apply(
lambda s: s.utcoffset()
) == timedelta(hours=-4)
pjm_offset = [
timedelta(hours=-3) if is_d else timedelta(hours=-4) for is_d in is_dst
]
# make new data so we don't mess up other data indexing
pjm_dat = raw[
get_int_columns(
"PJM",
raw.columns,
["CPLE", "CPLW", "DUK", "LGEE", "MISO", "NYIS", "TVA", "ALL"],
)
].copy()
# make shifted dates
pjm_idx = pd.DatetimeIndex(pjm_dat.index + pd.Series(pjm_offset))
pjm_dat.index = pjm_idx # use shifted dates
# delete duplicates
pjm_dat = pjm_dat[~pjm_dat.index.duplicated(keep="first")]
# exchange old rows with new
raw = raw.drop(columns=pjm_dat.columns)
raw = pd.concat([raw, pjm_dat], axis="columns")
# Shift all -1 hour to make start-of-hour
return raw.shift(-1, freq="H")