1
|
|
|
import geopandas as gpd |
2
|
|
|
import numpy as np |
3
|
|
|
import pandas as pd |
4
|
|
|
|
5
|
|
|
from egon.data import db |
6
|
|
|
from egon.data.datasets import Dataset |
7
|
|
|
from egon.data.datasets.electricity_demand.temporal import calc_load_curve |
8
|
|
|
from egon.data.datasets.industry.temporal import identify_bus |
9
|
|
|
import egon.data.config |
10
|
|
|
|
11
|
|
|
|
12
|
|
|
class dsm_Potential(Dataset): |
13
|
|
|
def __init__(self, dependencies): |
14
|
|
|
super().__init__( |
15
|
|
|
name="DSM_potentials", |
16
|
|
|
version="0.0.4", |
17
|
|
|
dependencies=dependencies, |
18
|
|
|
tasks=(dsm_cts_ind_processing), |
19
|
|
|
) |
20
|
|
|
|
21
|
|
|
|
22
|
|
|
def dsm_cts_ind_processing(): |
23
|
|
|
def cts_data_import(con, cts_cool_vent_ac_share): |
24
|
|
|
|
25
|
|
|
""" |
26
|
|
|
Import CTS data necessary to identify DSM-potential. |
27
|
|
|
---------- |
28
|
|
|
con : |
29
|
|
|
Connection to database |
30
|
|
|
cts_share: float |
31
|
|
|
Share of cooling, ventilation and AC in CTS demand |
32
|
|
|
""" |
33
|
|
|
|
34
|
|
|
# import load data |
35
|
|
|
|
36
|
|
|
sources = egon.data.config.datasets()["DSM_CTS_industry"]["sources"][ |
37
|
|
|
"cts_loadcurves" |
38
|
|
|
] |
39
|
|
|
|
40
|
|
|
ts = db.select_dataframe( |
41
|
|
|
f"""SELECT bus_id, scn_name, p_set FROM |
42
|
|
|
{sources['schema']}.{sources['table']}""" |
43
|
|
|
) |
44
|
|
|
|
45
|
|
|
# identify relevant columns and prepare df to be returned |
46
|
|
|
|
47
|
|
|
dsm = pd.DataFrame(index=ts.index) |
48
|
|
|
|
49
|
|
|
dsm["bus"] = ts["bus_id"].copy() |
50
|
|
|
dsm["scn_name"] = ts["scn_name"].copy() |
51
|
|
|
dsm["p_set"] = ts["p_set"].copy() |
52
|
|
|
|
53
|
|
|
# calculate share of timeseries for air conditioning, cooling and ventilation out of CTS-data |
54
|
|
|
|
55
|
|
|
timeseries = dsm["p_set"].copy() |
56
|
|
|
for index, liste in timeseries.iteritems(): |
57
|
|
|
share = [] |
58
|
|
|
for item in liste: |
59
|
|
|
share.append(float(item) * cts_cool_vent_ac_share) |
60
|
|
|
timeseries.loc[index] = share |
61
|
|
|
dsm["p_set"] = timeseries.copy() |
62
|
|
|
|
63
|
|
|
return dsm |
64
|
|
|
|
65
|
|
|
def ind_osm_data_import(con, ind_vent_cool_share): |
66
|
|
|
|
67
|
|
|
""" |
68
|
|
|
Import industry data per osm-area necessary to identify DSM-potential. |
69
|
|
|
---------- |
70
|
|
|
con : |
71
|
|
|
Connection to database |
72
|
|
|
ind_share: float |
73
|
|
|
Share of considered application in industry demand |
74
|
|
|
""" |
75
|
|
|
|
76
|
|
|
# import load data |
77
|
|
|
|
78
|
|
|
sources = egon.data.config.datasets()["DSM_CTS_industry"]["sources"][ |
79
|
|
|
"ind_osm_loadcurves" |
80
|
|
|
] |
81
|
|
|
|
82
|
|
|
dsm = db.select_dataframe( |
83
|
|
|
f"""SELECT bus, scn_name, p_set FROM |
84
|
|
|
{sources['schema']}.{sources['table']}""" |
85
|
|
|
) |
86
|
|
|
|
87
|
|
|
# calculate share of timeseries for cooling and ventilation out of industry-data |
88
|
|
|
|
89
|
|
|
timeseries = dsm["p_set"].copy() |
90
|
|
|
for index, liste in timeseries.iteritems(): |
91
|
|
|
share = [] |
92
|
|
|
for item in liste: |
93
|
|
|
share.append(float(item) * ind_vent_cool_share) |
94
|
|
|
timeseries.loc[index] = share |
95
|
|
|
dsm["p_set"] = timeseries.copy() |
96
|
|
|
|
97
|
|
|
return dsm |
98
|
|
|
|
99
|
|
|
def ind_sites_vent_data_import(con, ind_vent_share, wz): |
100
|
|
|
|
101
|
|
|
""" |
102
|
|
|
Import industry sites necessary to identify DSM-potential. |
103
|
|
|
---------- |
104
|
|
|
con : |
105
|
|
|
Connection to database |
106
|
|
|
ind_vent_share: float |
107
|
|
|
Share of considered application in industry demand |
108
|
|
|
wz: int |
109
|
|
|
Wirtschaftszweig to be considered within industry sites |
110
|
|
|
""" |
111
|
|
|
|
112
|
|
|
# import load data |
113
|
|
|
|
114
|
|
|
sources = egon.data.config.datasets()["DSM_CTS_industry"]["sources"][ |
115
|
|
|
"ind_sites_loadcurves" |
116
|
|
|
] |
117
|
|
|
|
118
|
|
|
dsm = db.select_dataframe( |
119
|
|
|
f"""SELECT bus, scn_name, p_set, wz FROM |
120
|
|
|
{sources['schema']}.{sources['table']}""" |
121
|
|
|
) |
122
|
|
|
|
123
|
|
|
# select load for considered applications |
124
|
|
|
|
125
|
|
|
dsm = dsm[dsm["wz"] == wz] |
126
|
|
|
|
127
|
|
|
# calculate share of timeseries for ventilation |
128
|
|
|
|
129
|
|
|
timeseries = dsm["p_set"].copy() |
130
|
|
|
for index, liste in timeseries.iteritems(): |
131
|
|
|
share = [] |
132
|
|
|
for item in liste: |
133
|
|
|
share.append(float(item) * ind_vent_share) |
134
|
|
|
timeseries.loc[index] = share |
135
|
|
|
dsm["p_set"] = timeseries.copy() |
136
|
|
|
|
137
|
|
|
return dsm |
138
|
|
|
|
139
|
|
|
def ind_sites_data_import(con): |
140
|
|
|
|
141
|
|
|
""" |
142
|
|
|
Import industry sites data necessary to identify DSM-potential. |
143
|
|
|
---------- |
144
|
|
|
con : |
145
|
|
|
Connection to database |
146
|
|
|
""" |
147
|
|
|
|
148
|
|
|
def calc_ind_site_timeseries(scenario): |
149
|
|
|
|
150
|
|
|
# calculate timeseries per site |
151
|
|
|
# -> using code from egon.data.datasets.industry.temporal: calc_load_curves_ind_sites |
152
|
|
|
|
153
|
|
|
# select demands per industrial site including the subsector information |
154
|
|
|
source1 = egon.data.config.datasets()["DSM_CTS_industry"][ |
155
|
|
|
"sources" |
156
|
|
|
]["demandregio_ind_sites"] |
157
|
|
|
|
158
|
|
|
demands_ind_sites = db.select_dataframe( |
159
|
|
|
f"""SELECT industrial_sites_id, wz, demand |
160
|
|
|
FROM {source1['schema']}.{source1['table']} |
161
|
|
|
WHERE scenario = '{scenario}' |
162
|
|
|
AND demand > 0 |
163
|
|
|
""" |
164
|
|
|
).set_index(["industrial_sites_id"]) |
165
|
|
|
|
166
|
|
|
# select industrial sites as demand_areas from database |
167
|
|
|
source2 = egon.data.config.datasets()["DSM_CTS_industry"][ |
168
|
|
|
"sources" |
169
|
|
|
]["ind_sites"] |
170
|
|
|
|
171
|
|
|
demand_area = db.select_geodataframe( |
172
|
|
|
f"""SELECT id, geom, subsector FROM |
173
|
|
|
{source2['schema']}.{source2['table']}""", |
174
|
|
|
index_col="id", |
175
|
|
|
geom_col="geom", |
176
|
|
|
epsg=3035, |
177
|
|
|
) |
178
|
|
|
|
179
|
|
|
# replace entries to bring it in line with demandregio's subsector definitions |
180
|
|
|
demands_ind_sites.replace(1718, 17, inplace=True) |
181
|
|
|
share_wz_sites = demands_ind_sites.copy() |
182
|
|
|
|
183
|
|
|
# create additional df on wz_share per industrial site, which is always set to one |
184
|
|
|
# as the industrial demand per site is subsector specific |
185
|
|
|
share_wz_sites.demand = 1 |
186
|
|
|
share_wz_sites.reset_index(inplace=True) |
187
|
|
|
|
188
|
|
|
share_transpose = pd.DataFrame( |
189
|
|
|
index=share_wz_sites.industrial_sites_id.unique(), |
190
|
|
|
columns=share_wz_sites.wz.unique(), |
191
|
|
|
) |
192
|
|
|
share_transpose.index.rename("industrial_sites_id", inplace=True) |
193
|
|
|
for wz in share_transpose.columns: |
194
|
|
|
share_transpose[wz] = ( |
195
|
|
|
share_wz_sites[share_wz_sites.wz == wz] |
196
|
|
|
.set_index("industrial_sites_id") |
197
|
|
|
.demand |
198
|
|
|
) |
199
|
|
|
|
200
|
|
|
# calculate load curves |
201
|
|
|
load_curves = calc_load_curve( |
202
|
|
|
share_transpose, demands_ind_sites["demand"] |
203
|
|
|
) |
204
|
|
|
|
205
|
|
|
# identify bus per industrial site |
206
|
|
|
curves_bus = identify_bus(load_curves, demand_area) |
207
|
|
|
curves_bus.index = curves_bus["id"].astype(int) |
208
|
|
|
|
209
|
|
|
# initialize dataframe to be returned |
210
|
|
|
|
211
|
|
|
ts = pd.DataFrame( |
212
|
|
|
data=curves_bus["bus_id"], index=curves_bus["id"].astype(int) |
213
|
|
|
) |
214
|
|
|
ts["scenario_name"] = scenario |
215
|
|
|
curves_bus.drop({"id", "bus_id", "geom"}, axis=1, inplace=True) |
216
|
|
|
ts["p_set"] = curves_bus.values.tolist() |
217
|
|
|
|
218
|
|
|
# add subsector to relate to Schmidt's tables afterwards |
219
|
|
|
ts["application"] = demand_area["subsector"] |
220
|
|
|
|
221
|
|
|
return ts |
222
|
|
|
|
223
|
|
|
def relate_to_Schmidt_sites(dsm): |
224
|
|
|
|
225
|
|
|
# import industrial sites by Schmidt |
226
|
|
|
|
227
|
|
|
source = egon.data.config.datasets()["DSM_CTS_industry"][ |
228
|
|
|
"sources" |
229
|
|
|
]["ind_sites_schmidt"] |
230
|
|
|
|
231
|
|
|
schmidt = db.select_dataframe( |
232
|
|
|
f"""SELECT application, geom FROM |
233
|
|
|
{source['schema']}.{source['table']}""" |
234
|
|
|
) |
235
|
|
|
|
236
|
|
|
# relate calculated timeseries (dsm) to Schmidt's industrial sites |
237
|
|
|
|
238
|
|
|
applications = np.unique(schmidt["application"]) |
239
|
|
|
dsm = pd.DataFrame(dsm[dsm["application"].isin(applications)]) |
240
|
|
|
|
241
|
|
|
# initialize dataframe to be returned |
242
|
|
|
|
243
|
|
|
dsm.rename( |
244
|
|
|
columns={"scenario_name": "scn_name", "bus_id": "bus"}, |
245
|
|
|
inplace=True, |
246
|
|
|
) |
247
|
|
|
|
248
|
|
|
return dsm |
249
|
|
|
|
250
|
|
|
# calculate timeseries per site |
251
|
|
|
|
252
|
|
|
# scenario eGon2035 |
253
|
|
|
dsm_2035 = calc_ind_site_timeseries("eGon2035") |
254
|
|
|
dsm_2035.reset_index(inplace=True) |
255
|
|
|
# scenario eGon100RE |
256
|
|
|
dsm_100 = calc_ind_site_timeseries("eGon100RE") |
257
|
|
|
dsm_100.reset_index(inplace=True) |
258
|
|
|
# bring df for both scenarios together |
259
|
|
|
dsm_100.index = range(len(dsm_2035), (len(dsm_2035) + len((dsm_100)))) |
260
|
|
|
dsm = dsm_2035.append(dsm_100) |
261
|
|
|
|
262
|
|
|
# relate calculated timeseries to Schmidt's industrial sites |
263
|
|
|
|
264
|
|
|
dsm = relate_to_Schmidt_sites(dsm) |
265
|
|
|
|
266
|
|
|
return dsm |
267
|
|
|
|
268
|
|
|
def calculate_potentials(s_flex, s_util, s_inc, s_dec, delta_t, dsm): |
269
|
|
|
|
270
|
|
|
""" |
271
|
|
|
Calculate DSM-potential per bus using the methods by Heitkoetter et. al.: |
272
|
|
|
https://doi.org/10.1016/j.adapen.2020.100001 |
273
|
|
|
Parameters |
274
|
|
|
---------- |
275
|
|
|
s_flex: float |
276
|
|
|
Feasability factor to account for socio-technical restrictions |
277
|
|
|
s_util: float |
278
|
|
|
Average annual utilisation rate |
279
|
|
|
s_inc: float |
280
|
|
|
Shiftable share of installed capacity up to which load can be increased considering technical limitations |
281
|
|
|
s_dec: float |
282
|
|
|
Shiftable share of installed capacity up to which load can be decreased considering technical limitations |
283
|
|
|
delta_t: int |
284
|
|
|
Maximum shift duration in hours |
285
|
|
|
dsm: DataFrame |
286
|
|
|
List of existing buses with DSM-potential including timeseries of loads |
287
|
|
|
""" |
288
|
|
|
|
289
|
|
|
# copy relevant timeseries |
290
|
|
|
timeseries = dsm["p_set"].copy() |
291
|
|
|
|
292
|
|
|
# calculate scheduled load L(t) |
293
|
|
|
|
294
|
|
|
scheduled_load = timeseries.copy() |
295
|
|
|
|
296
|
|
|
for index, liste in scheduled_load.iteritems(): |
297
|
|
|
share = [] |
298
|
|
|
for item in liste: |
299
|
|
|
share.append(item * s_flex) |
300
|
|
|
scheduled_load.loc[index] = share |
301
|
|
|
|
302
|
|
|
# calculate maximum capacity Lambda |
303
|
|
|
|
304
|
|
|
# calculate energy annual requirement |
305
|
|
|
energy_annual = pd.Series(index=timeseries.index, dtype=float) |
306
|
|
|
for index, liste in timeseries.iteritems(): |
307
|
|
|
energy_annual.loc[index] = sum(liste) |
308
|
|
|
|
309
|
|
|
# calculate Lambda |
310
|
|
|
lam = (energy_annual * s_flex) / (8760 * s_util) |
311
|
|
|
|
312
|
|
|
# calculation of P_max and P_min |
313
|
|
|
|
314
|
|
|
# P_max |
315
|
|
|
p_max = scheduled_load.copy() |
316
|
|
|
for index, liste in scheduled_load.iteritems(): |
317
|
|
|
lamb = lam.loc[index] |
318
|
|
|
p = [] |
319
|
|
|
for item in liste: |
320
|
|
|
value = lamb * s_inc - item |
321
|
|
|
if value < 0: |
322
|
|
|
value = 0 |
323
|
|
|
p.append(value) |
324
|
|
|
p_max.loc[index] = p |
325
|
|
|
|
326
|
|
|
# P_min |
327
|
|
|
p_min = scheduled_load.copy() |
328
|
|
|
for index, liste in scheduled_load.iteritems(): |
329
|
|
|
lamb = lam.loc[index] |
330
|
|
|
p = [] |
331
|
|
|
for item in liste: |
332
|
|
|
value = -(item - lamb * s_dec) |
333
|
|
|
if value > 0: |
334
|
|
|
value = 0 |
335
|
|
|
p.append(value) |
336
|
|
|
p_min.loc[index] = p |
337
|
|
|
|
338
|
|
|
# calculation of E_max and E_min |
339
|
|
|
|
340
|
|
|
e_max = scheduled_load.copy() |
341
|
|
|
e_min = scheduled_load.copy() |
342
|
|
|
|
343
|
|
|
for index, liste in scheduled_load.iteritems(): |
344
|
|
|
emin = [] |
345
|
|
|
emax = [] |
346
|
|
|
for i in range(0, len(liste)): |
347
|
|
|
if i + delta_t > len(liste): |
348
|
|
|
emax.append( |
349
|
|
|
sum(liste[i : len(liste)]) |
350
|
|
|
+ sum(liste[0 : delta_t - (len(liste) - i)]) |
351
|
|
|
) |
352
|
|
|
else: |
353
|
|
|
emax.append(sum(liste[i : i + delta_t])) |
354
|
|
|
if i - delta_t < 0: |
355
|
|
|
emin.append( |
356
|
|
|
-1 |
357
|
|
|
* ( |
358
|
|
|
sum(liste[0:i]) |
359
|
|
|
+ sum(liste[len(liste) - delta_t + i : len(liste)]) |
360
|
|
|
) |
361
|
|
|
) |
362
|
|
|
else: |
363
|
|
|
emin.append(-1 * sum(liste[i - delta_t : i])) |
364
|
|
|
e_max.loc[index] = emax |
365
|
|
|
e_min.loc[index] = emin |
366
|
|
|
|
367
|
|
|
return p_max, p_min, e_max, e_min |
368
|
|
|
|
369
|
|
|
def create_dsm_components( |
370
|
|
|
con, p_max, p_min, e_max, e_min, dsm, export_aggregated=True |
371
|
|
|
): |
372
|
|
|
|
373
|
|
|
""" |
374
|
|
|
Create components representing DSM. |
375
|
|
|
Parameters |
376
|
|
|
---------- |
377
|
|
|
con : |
378
|
|
|
Connection to database |
379
|
|
|
p_max: DataFrame |
380
|
|
|
Timeseries identifying maximum load increase |
381
|
|
|
p_min: DataFrame |
382
|
|
|
Timeseries identifying maximum load decrease |
383
|
|
|
e_max: DataFrame |
384
|
|
|
Timeseries identifying maximum energy amount to be preponed |
385
|
|
|
e_min: DataFrame |
386
|
|
|
Timeseries identifying maximum energy amount to be postponed |
387
|
|
|
dsm: DataFrame |
388
|
|
|
List of existing buses with DSM-potential including timeseries of loads |
389
|
|
|
""" |
390
|
|
|
|
391
|
|
|
# if components should be exported seperately |
392
|
|
|
# and not as aggregated DSM-components: |
393
|
|
|
|
394
|
|
|
if not export_aggregated: |
395
|
|
|
|
396
|
|
|
# calculate P_nom and P per unit |
397
|
|
|
p_nom = pd.Series(index=p_max.index, dtype=float) |
398
|
|
|
for index, row in p_max.iteritems(): |
399
|
|
|
nom = max(max(row), abs(min(p_min.loc[index]))) |
400
|
|
|
p_nom.loc[index] = nom |
401
|
|
|
new = [element / nom for element in row] |
402
|
|
|
p_max.loc[index] = new |
403
|
|
|
new = [element / nom for element in p_min.loc[index]] |
404
|
|
|
p_min.loc[index] = new |
405
|
|
|
|
406
|
|
|
# calculate E_nom and E per unit |
407
|
|
|
e_nom = pd.Series(index=p_min.index, dtype=float) |
408
|
|
|
for index, row in e_max.iteritems(): |
409
|
|
|
nom = max(max(row), abs(min(e_min.loc[index]))) |
410
|
|
|
e_nom.loc[index] = nom |
411
|
|
|
new = [element / nom for element in row] |
412
|
|
|
e_max.loc[index] = new |
413
|
|
|
new = [element / nom for element in e_min.loc[index]] |
414
|
|
|
e_min.loc[index] = new |
415
|
|
|
|
416
|
|
|
# add DSM-buses to "original" buses |
417
|
|
|
dsm_buses = gpd.GeoDataFrame(index=dsm.index) |
418
|
|
|
dsm_buses["original_bus"] = dsm["bus"].copy() |
419
|
|
|
dsm_buses["scn_name"] = dsm["scn_name"].copy() |
420
|
|
|
|
421
|
|
|
# get original buses and add copy of relevant information |
422
|
|
|
target1 = egon.data.config.datasets()["DSM_CTS_industry"]["targets"][ |
423
|
|
|
"bus" |
424
|
|
|
] |
425
|
|
|
original_buses = db.select_geodataframe( |
426
|
|
|
f"""SELECT bus_id, v_nom, scn_name, x, y, geom FROM |
427
|
|
|
{target1['schema']}.{target1['table']}""", |
428
|
|
|
geom_col="geom", |
429
|
|
|
epsg=4326, |
430
|
|
|
) |
431
|
|
|
|
432
|
|
|
# copy relevant information from original buses to DSM-buses |
433
|
|
|
dsm_buses["index"] = dsm_buses.index |
434
|
|
|
originals = original_buses[ |
435
|
|
|
original_buses["bus_id"].isin(np.unique(dsm_buses["original_bus"])) |
436
|
|
|
] |
437
|
|
|
dsm_buses = originals.merge( |
438
|
|
|
dsm_buses, |
439
|
|
|
left_on=["bus_id", "scn_name"], |
440
|
|
|
right_on=["original_bus", "scn_name"], |
441
|
|
|
) |
442
|
|
|
dsm_buses.index = dsm_buses["index"] |
443
|
|
|
dsm_buses.drop(["bus_id", "index"], axis=1, inplace=True) |
444
|
|
|
|
445
|
|
|
# new bus_ids for DSM-buses |
446
|
|
|
max_id = original_buses["bus_id"].max() |
447
|
|
|
if np.isnan(max_id): |
448
|
|
|
max_id = 0 |
449
|
|
|
dsm_id = max_id + 1 |
450
|
|
|
bus_id = pd.Series(index=dsm_buses.index, dtype=int) |
451
|
|
|
|
452
|
|
|
# Get number of DSM buses for both scenarios |
453
|
|
|
rows_per_scenario = ( |
454
|
|
|
dsm_buses.groupby("scn_name").count().original_bus.to_dict() |
455
|
|
|
) |
456
|
|
|
|
457
|
|
|
# Assignment of DSM ids |
458
|
|
|
bus_id.iloc[0 : rows_per_scenario.get("eGon2035", 0)] = range( |
459
|
|
|
dsm_id, dsm_id + rows_per_scenario.get("eGon2035", 0) |
460
|
|
|
) |
461
|
|
|
bus_id.iloc[ |
462
|
|
|
rows_per_scenario.get("eGon2035", 0) : rows_per_scenario.get( |
463
|
|
|
"eGon2035", 0 |
464
|
|
|
) |
465
|
|
|
+ rows_per_scenario.get("eGon100RE", 0) |
466
|
|
|
] = range(dsm_id, dsm_id + rows_per_scenario.get("eGon100RE", 0)) |
467
|
|
|
|
468
|
|
|
dsm_buses["bus_id"] = bus_id |
469
|
|
|
|
470
|
|
|
# add links from "orignal" buses to DSM-buses |
471
|
|
|
|
472
|
|
|
dsm_links = pd.DataFrame(index=dsm_buses.index) |
473
|
|
|
dsm_links["original_bus"] = dsm_buses["original_bus"].copy() |
474
|
|
|
dsm_links["dsm_bus"] = dsm_buses["bus_id"].copy() |
475
|
|
|
dsm_links["scn_name"] = dsm_buses["scn_name"].copy() |
476
|
|
|
|
477
|
|
|
# set link_id |
478
|
|
|
target2 = egon.data.config.datasets()["DSM_CTS_industry"]["targets"][ |
479
|
|
|
"link" |
480
|
|
|
] |
481
|
|
|
sql = f"""SELECT link_id FROM {target2['schema']}.{target2['table']}""" |
482
|
|
|
max_id = pd.read_sql_query(sql, con) |
483
|
|
|
max_id = max_id["link_id"].max() |
484
|
|
|
if np.isnan(max_id): |
485
|
|
|
max_id = 0 |
486
|
|
|
dsm_id = max_id + 1 |
487
|
|
|
link_id = pd.Series(index=dsm_buses.index, dtype=int) |
488
|
|
|
|
489
|
|
|
# Assignment of link ids |
490
|
|
|
link_id.iloc[0 : rows_per_scenario.get("eGon2035", 0)] = range( |
491
|
|
|
dsm_id, dsm_id + rows_per_scenario.get("eGon2035", 0) |
492
|
|
|
) |
493
|
|
|
link_id.iloc[ |
494
|
|
|
rows_per_scenario.get("eGon2035", 0) : rows_per_scenario.get( |
495
|
|
|
"eGon2035", 0 |
496
|
|
|
) |
497
|
|
|
+ rows_per_scenario.get("eGon100RE", 0) |
498
|
|
|
] = range(dsm_id, dsm_id + rows_per_scenario.get("eGon100RE", 0)) |
499
|
|
|
|
500
|
|
|
dsm_links["link_id"] = link_id |
501
|
|
|
|
502
|
|
|
# add calculated timeseries to df to be returned |
503
|
|
|
if not export_aggregated: |
504
|
|
|
dsm_links["p_nom"] = p_nom |
|
|
|
|
505
|
|
|
dsm_links["p_min"] = p_min |
506
|
|
|
dsm_links["p_max"] = p_max |
507
|
|
|
|
508
|
|
|
# add DSM-stores |
509
|
|
|
|
510
|
|
|
dsm_stores = pd.DataFrame(index=dsm_buses.index) |
511
|
|
|
dsm_stores["bus"] = dsm_buses["bus_id"].copy() |
512
|
|
|
dsm_stores["scn_name"] = dsm_buses["scn_name"].copy() |
513
|
|
|
dsm_stores["original_bus"] = dsm_buses["original_bus"].copy() |
514
|
|
|
|
515
|
|
|
# set store_id |
516
|
|
|
target3 = egon.data.config.datasets()["DSM_CTS_industry"]["targets"][ |
517
|
|
|
"store" |
518
|
|
|
] |
519
|
|
|
sql = ( |
520
|
|
|
f"""SELECT store_id FROM {target3['schema']}.{target3['table']}""" |
521
|
|
|
) |
522
|
|
|
max_id = pd.read_sql_query(sql, con) |
523
|
|
|
max_id = max_id["store_id"].max() |
524
|
|
|
if np.isnan(max_id): |
525
|
|
|
max_id = 0 |
526
|
|
|
dsm_id = max_id + 1 |
527
|
|
|
store_id = pd.Series(index=dsm_buses.index, dtype=int) |
528
|
|
|
|
529
|
|
|
# Assignment of store ids |
530
|
|
|
store_id.iloc[0 : rows_per_scenario.get("eGon2035", 0)] = range( |
531
|
|
|
dsm_id, dsm_id + rows_per_scenario.get("eGon2035", 0) |
532
|
|
|
) |
533
|
|
|
store_id.iloc[ |
534
|
|
|
rows_per_scenario.get("eGon2035", 0) : rows_per_scenario.get( |
535
|
|
|
"eGon2035", 0 |
536
|
|
|
) |
537
|
|
|
+ rows_per_scenario.get("eGon100RE", 0) |
538
|
|
|
] = range(dsm_id, dsm_id + rows_per_scenario.get("eGon100RE", 0)) |
539
|
|
|
|
540
|
|
|
dsm_stores["store_id"] = store_id |
541
|
|
|
|
542
|
|
|
# add calculated timeseries to df to be returned |
543
|
|
|
if not export_aggregated: |
544
|
|
|
dsm_stores["e_nom"] = e_nom |
|
|
|
|
545
|
|
|
dsm_stores["e_min"] = e_min |
546
|
|
|
dsm_stores["e_max"] = e_max |
547
|
|
|
|
548
|
|
|
return dsm_buses, dsm_links, dsm_stores |
549
|
|
|
|
550
|
|
|
def aggregate_components(con, df_dsm_buses, df_dsm_links, df_dsm_stores): |
551
|
|
|
|
552
|
|
|
# aggregate buses |
553
|
|
|
|
554
|
|
|
grouper = [df_dsm_buses.original_bus, df_dsm_buses.scn_name] |
555
|
|
|
df_dsm_buses = df_dsm_buses.groupby(grouper).first() |
556
|
|
|
|
557
|
|
|
df_dsm_buses.reset_index(inplace=True) |
558
|
|
|
df_dsm_buses.sort_values("scn_name", inplace=True) |
559
|
|
|
|
560
|
|
|
# aggregate links |
561
|
|
|
|
562
|
|
|
df_dsm_links["p_max"] = df_dsm_links["p_max"].apply( |
563
|
|
|
lambda x: np.array(x) |
564
|
|
|
) |
565
|
|
|
df_dsm_links["p_min"] = df_dsm_links["p_min"].apply( |
566
|
|
|
lambda x: np.array(x) |
567
|
|
|
) |
568
|
|
|
|
569
|
|
|
grouper = [df_dsm_links.original_bus, df_dsm_links.scn_name] |
570
|
|
|
|
571
|
|
|
p_max = df_dsm_links.groupby(grouper)["p_max"].apply(np.sum) |
572
|
|
|
p_min = df_dsm_links.groupby(grouper)["p_min"].apply(np.sum) |
573
|
|
|
|
574
|
|
|
df_dsm_links = df_dsm_links.groupby(grouper).first() |
575
|
|
|
df_dsm_links.p_max = p_max |
576
|
|
|
df_dsm_links.p_min = p_min |
577
|
|
|
|
578
|
|
|
df_dsm_links.reset_index(inplace=True) |
579
|
|
|
df_dsm_links.sort_values("scn_name", inplace=True) |
580
|
|
|
|
581
|
|
|
# calculate P_nom and P per unit |
582
|
|
|
for index, row in df_dsm_links.iterrows(): |
583
|
|
|
nom = max(max(row.p_max), abs(min(row.p_min))) |
584
|
|
|
df_dsm_links.at[index, "p_nom"] = nom |
585
|
|
|
df_dsm_links["p_max"] = df_dsm_links["p_max"] / df_dsm_links["p_nom"] |
586
|
|
|
df_dsm_links["p_min"] = df_dsm_links["p_min"] / df_dsm_links["p_nom"] |
587
|
|
|
|
588
|
|
|
df_dsm_links["p_max"] = df_dsm_links["p_max"].apply(lambda x: list(x)) |
589
|
|
|
df_dsm_links["p_min"] = df_dsm_links["p_min"].apply(lambda x: list(x)) |
590
|
|
|
|
591
|
|
|
# aggregate stores |
592
|
|
|
|
593
|
|
|
df_dsm_stores["e_max"] = df_dsm_stores["e_max"].apply( |
594
|
|
|
lambda x: np.array(x) |
595
|
|
|
) |
596
|
|
|
df_dsm_stores["e_min"] = df_dsm_stores["e_min"].apply( |
597
|
|
|
lambda x: np.array(x) |
598
|
|
|
) |
599
|
|
|
|
600
|
|
|
grouper = [df_dsm_stores.original_bus, df_dsm_stores.scn_name] |
601
|
|
|
|
602
|
|
|
e_max = df_dsm_stores.groupby(grouper)["e_max"].apply(np.sum) |
603
|
|
|
e_min = df_dsm_stores.groupby(grouper)["e_min"].apply(np.sum) |
604
|
|
|
|
605
|
|
|
df_dsm_stores = df_dsm_stores.groupby(grouper).first() |
606
|
|
|
df_dsm_stores.e_max = e_max |
607
|
|
|
df_dsm_stores.e_min = e_min |
608
|
|
|
|
609
|
|
|
df_dsm_stores.reset_index(inplace=True) |
610
|
|
|
df_dsm_stores.sort_values("scn_name", inplace=True) |
611
|
|
|
|
612
|
|
|
# calculate E_nom and E per unit |
613
|
|
|
for index, row in df_dsm_stores.iterrows(): |
614
|
|
|
nom = max(max(row.e_max), abs(min(row.e_min))) |
615
|
|
|
df_dsm_stores.at[index, "e_nom"] = nom |
616
|
|
|
|
617
|
|
|
df_dsm_stores["e_max"] = ( |
618
|
|
|
df_dsm_stores["e_max"] / df_dsm_stores["e_nom"] |
619
|
|
|
) |
620
|
|
|
df_dsm_stores["e_min"] = ( |
621
|
|
|
df_dsm_stores["e_min"] / df_dsm_stores["e_nom"] |
622
|
|
|
) |
623
|
|
|
|
624
|
|
|
df_dsm_stores["e_max"] = df_dsm_stores["e_max"].apply( |
625
|
|
|
lambda x: list(x) |
626
|
|
|
) |
627
|
|
|
df_dsm_stores["e_min"] = df_dsm_stores["e_min"].apply( |
628
|
|
|
lambda x: list(x) |
629
|
|
|
) |
630
|
|
|
|
631
|
|
|
# select new bus_ids for aggregated buses and add to links and stores |
632
|
|
|
bus_id = db.next_etrago_id("Bus") + df_dsm_buses.index |
633
|
|
|
|
634
|
|
|
df_dsm_buses["bus_id"] = bus_id |
635
|
|
|
df_dsm_links["dsm_bus"] = bus_id |
636
|
|
|
df_dsm_stores["bus"] = bus_id |
637
|
|
|
|
638
|
|
|
# select new link_ids for aggregated links |
639
|
|
|
link_id = db.next_etrago_id("Link") + df_dsm_links.index |
640
|
|
|
|
641
|
|
|
df_dsm_links["link_id"] = link_id |
642
|
|
|
|
643
|
|
|
# select new store_ids to aggregated stores |
644
|
|
|
|
645
|
|
|
store_id = db.next_etrago_id("Store") + df_dsm_stores.index |
646
|
|
|
|
647
|
|
|
df_dsm_stores["store_id"] = store_id |
648
|
|
|
|
649
|
|
|
return df_dsm_buses, df_dsm_links, df_dsm_stores |
650
|
|
|
|
651
|
|
|
def data_export(con, dsm_buses, dsm_links, dsm_stores, carrier): |
652
|
|
|
|
653
|
|
|
""" |
654
|
|
|
Export new components to database. |
655
|
|
|
Parameters |
656
|
|
|
---------- |
657
|
|
|
con : |
658
|
|
|
Connection to database |
659
|
|
|
dsm_buses: DataFrame |
660
|
|
|
Buses representing locations of DSM-potential |
661
|
|
|
dsm_links: DataFrame |
662
|
|
|
Links connecting DSM-buses and DSM-stores |
663
|
|
|
dsm_stores: DataFrame |
664
|
|
|
Stores representing DSM-potential |
665
|
|
|
carrier: String |
666
|
|
|
Remark to be filled in column 'carrier' identifying DSM-potential |
667
|
|
|
""" |
668
|
|
|
|
669
|
|
|
targets = egon.data.config.datasets()["DSM_CTS_industry"]["targets"] |
670
|
|
|
|
671
|
|
|
# dsm_buses |
672
|
|
|
|
673
|
|
|
insert_buses = gpd.GeoDataFrame( |
674
|
|
|
index=dsm_buses.index, |
675
|
|
|
data=dsm_buses["geom"], |
676
|
|
|
geometry="geom", |
677
|
|
|
crs=dsm_buses.crs, |
678
|
|
|
) |
679
|
|
|
insert_buses["scn_name"] = dsm_buses["scn_name"] |
680
|
|
|
insert_buses["bus_id"] = dsm_buses["bus_id"] |
681
|
|
|
insert_buses["v_nom"] = dsm_buses["v_nom"] |
682
|
|
|
insert_buses["carrier"] = carrier |
683
|
|
|
insert_buses["x"] = dsm_buses["x"] |
684
|
|
|
insert_buses["y"] = dsm_buses["y"] |
685
|
|
|
|
686
|
|
|
# insert into database |
687
|
|
|
insert_buses.to_postgis( |
688
|
|
|
targets["bus"]["table"], |
689
|
|
|
con=db.engine(), |
690
|
|
|
schema=targets["bus"]["schema"], |
691
|
|
|
if_exists="append", |
692
|
|
|
index=False, |
693
|
|
|
dtype={"geom": "geometry"}, |
694
|
|
|
) |
695
|
|
|
|
696
|
|
|
# dsm_links |
697
|
|
|
|
698
|
|
|
insert_links = pd.DataFrame(index=dsm_links.index) |
699
|
|
|
insert_links["scn_name"] = dsm_links["scn_name"] |
700
|
|
|
insert_links["link_id"] = dsm_links["link_id"] |
701
|
|
|
insert_links["bus0"] = dsm_links["original_bus"] |
702
|
|
|
insert_links["bus1"] = dsm_links["dsm_bus"] |
703
|
|
|
insert_links["carrier"] = carrier |
704
|
|
|
insert_links["p_nom"] = dsm_links["p_nom"] |
705
|
|
|
|
706
|
|
|
# insert into database |
707
|
|
|
insert_links.to_sql( |
708
|
|
|
targets["link"]["table"], |
709
|
|
|
con=db.engine(), |
710
|
|
|
schema=targets["link"]["schema"], |
711
|
|
|
if_exists="append", |
712
|
|
|
index=False, |
713
|
|
|
) |
714
|
|
|
|
715
|
|
|
insert_links_timeseries = pd.DataFrame(index=dsm_links.index) |
716
|
|
|
insert_links_timeseries["scn_name"] = dsm_links["scn_name"] |
717
|
|
|
insert_links_timeseries["link_id"] = dsm_links["link_id"] |
718
|
|
|
insert_links_timeseries["p_min_pu"] = dsm_links["p_min"] |
719
|
|
|
insert_links_timeseries["p_max_pu"] = dsm_links["p_max"] |
720
|
|
|
insert_links_timeseries["temp_id"] = 1 |
721
|
|
|
|
722
|
|
|
# insert into database |
723
|
|
|
insert_links_timeseries.to_sql( |
724
|
|
|
targets["link_timeseries"]["table"], |
725
|
|
|
con=db.engine(), |
726
|
|
|
schema=targets["link_timeseries"]["schema"], |
727
|
|
|
if_exists="append", |
728
|
|
|
index=False, |
729
|
|
|
) |
730
|
|
|
|
731
|
|
|
# dsm_stores |
732
|
|
|
|
733
|
|
|
insert_stores = pd.DataFrame(index=dsm_stores.index) |
734
|
|
|
insert_stores["scn_name"] = dsm_stores["scn_name"] |
735
|
|
|
insert_stores["store_id"] = dsm_stores["store_id"] |
736
|
|
|
insert_stores["bus"] = dsm_stores["bus"] |
737
|
|
|
insert_stores["carrier"] = carrier |
738
|
|
|
insert_stores["e_nom"] = dsm_stores["e_nom"] |
739
|
|
|
|
740
|
|
|
# insert into database |
741
|
|
|
insert_stores.to_sql( |
742
|
|
|
targets["store"]["table"], |
743
|
|
|
con=db.engine(), |
744
|
|
|
schema=targets["store"]["schema"], |
745
|
|
|
if_exists="append", |
746
|
|
|
index=False, |
747
|
|
|
) |
748
|
|
|
|
749
|
|
|
insert_stores_timeseries = pd.DataFrame(index=dsm_stores.index) |
750
|
|
|
insert_stores_timeseries["scn_name"] = dsm_stores["scn_name"] |
751
|
|
|
insert_stores_timeseries["store_id"] = dsm_stores["store_id"] |
752
|
|
|
insert_stores_timeseries["e_min_pu"] = dsm_stores["e_min"] |
753
|
|
|
insert_stores_timeseries["e_max_pu"] = dsm_stores["e_max"] |
754
|
|
|
insert_stores_timeseries["temp_id"] = 1 |
755
|
|
|
|
756
|
|
|
# insert into database |
757
|
|
|
insert_stores_timeseries.to_sql( |
758
|
|
|
targets["store_timeseries"]["table"], |
759
|
|
|
con=db.engine(), |
760
|
|
|
schema=targets["store_timeseries"]["schema"], |
761
|
|
|
if_exists="append", |
762
|
|
|
index=False, |
763
|
|
|
) |
764
|
|
|
|
765
|
|
|
def delete_dsm_entries(carrier): |
766
|
|
|
|
767
|
|
|
""" |
768
|
|
|
Deletes DSM-components from database if they already exist before creating new ones. |
769
|
|
|
Parameters |
770
|
|
|
---------- |
771
|
|
|
carrier: String |
772
|
|
|
Remark in column 'carrier' identifying DSM-potential |
773
|
|
|
""" |
774
|
|
|
|
775
|
|
|
targets = egon.data.config.datasets()["DSM_CTS_industry"]["targets"] |
776
|
|
|
|
777
|
|
|
# buses |
778
|
|
|
|
779
|
|
|
sql = f"""DELETE FROM {targets["bus"]["schema"]}.{targets["bus"]["table"]} b |
780
|
|
|
WHERE (b.carrier LIKE '{carrier}');""" |
781
|
|
|
db.execute_sql(sql) |
782
|
|
|
|
783
|
|
|
# links |
784
|
|
|
|
785
|
|
|
sql = f"""DELETE FROM {targets["link_timeseries"]["schema"]}.{targets["link_timeseries"]["table"]} t |
786
|
|
|
WHERE t.link_id IN |
787
|
|
|
(SELECT l.link_id FROM {targets["link"]["schema"]}.{targets["link"]["table"]} l |
788
|
|
|
WHERE l.carrier LIKE '{carrier}');""" |
789
|
|
|
db.execute_sql(sql) |
790
|
|
|
sql = f"""DELETE FROM {targets["link"]["schema"]}.{targets["link"]["table"]} l |
791
|
|
|
WHERE (l.carrier LIKE '{carrier}');""" |
792
|
|
|
db.execute_sql(sql) |
793
|
|
|
|
794
|
|
|
# stores |
795
|
|
|
|
796
|
|
|
sql = f"""DELETE FROM {targets["store_timeseries"]["schema"]}.{targets["store_timeseries"]["table"]} t |
797
|
|
|
WHERE t.store_id IN |
798
|
|
|
(SELECT s.store_id FROM {targets["store"]["schema"]}.{targets["store"]["table"]} s |
799
|
|
|
WHERE s.carrier LIKE '{carrier}');""" |
800
|
|
|
db.execute_sql(sql) |
801
|
|
|
sql = f"""DELETE FROM {targets["store"]["schema"]}.{targets["store"]["table"]} s |
802
|
|
|
WHERE (s.carrier LIKE '{carrier}');""" |
803
|
|
|
db.execute_sql(sql) |
804
|
|
|
|
805
|
|
|
def dsm_cts_ind( |
806
|
|
|
con=db.engine(), |
807
|
|
|
cts_cool_vent_ac_share=0.22, |
808
|
|
|
ind_cool_vent_share=0.039, |
809
|
|
|
ind_vent_share=0.017, |
810
|
|
|
): |
811
|
|
|
|
812
|
|
|
""" |
813
|
|
|
Execute methodology to create and implement components for DSM considering |
814
|
|
|
a) CTS per osm-area: combined potentials of cooling, ventilation and air conditioning |
815
|
|
|
b) Industry per osm-are: combined potentials of cooling and ventilation |
816
|
|
|
c) Industrial Sites: potentials of ventilation in sites of "Wirtschaftszweig" (WZ) 23 |
817
|
|
|
d) Industrial Sites: potentials of sites specified by subsectors identified by Schmidt (https://zenodo.org/record/3613767#.YTsGwVtCRhG): |
818
|
|
|
Paper, Recycled Paper, Pulp, Cement |
819
|
|
|
Modelled using the methods by Heitkoetter et. al.: https://doi.org/10.1016/j.adapen.2020.100001 |
820
|
|
|
Parameters |
821
|
|
|
---------- |
822
|
|
|
con : |
823
|
|
|
Connection to database |
824
|
|
|
cts_cool_vent_ac_share: float |
825
|
|
|
Share of cooling, ventilation and AC in CTS demand |
826
|
|
|
ind_cool_vent_share: float |
827
|
|
|
Share of cooling and ventilation in industry demand |
828
|
|
|
ind_vent_share: float |
829
|
|
|
Share of ventilation in industry demand in sites of WZ 23 |
830
|
|
|
|
831
|
|
|
""" |
832
|
|
|
|
833
|
|
|
# CTS per osm-area: cooling, ventilation and air conditioning |
834
|
|
|
|
835
|
|
|
print(" ") |
836
|
|
|
print("CTS per osm-area: cooling, ventilation and air conditioning") |
837
|
|
|
print(" ") |
838
|
|
|
|
839
|
|
|
dsm = cts_data_import(con, cts_cool_vent_ac_share) |
840
|
|
|
|
841
|
|
|
# calculate combined potentials of cooling, ventilation and air conditioning in CTS |
842
|
|
|
# using combined parameters by Heitkoetter et. al. |
843
|
|
|
p_max, p_min, e_max, e_min = calculate_potentials( |
844
|
|
|
s_flex=0.5, s_util=0.67, s_inc=1, s_dec=0, delta_t=1, dsm=dsm |
845
|
|
|
) |
846
|
|
|
|
847
|
|
|
dsm_buses, dsm_links, dsm_stores = create_dsm_components( |
848
|
|
|
con, p_max, p_min, e_max, e_min, dsm |
849
|
|
|
) |
850
|
|
|
|
851
|
|
|
df_dsm_buses = dsm_buses.copy() |
852
|
|
|
df_dsm_links = dsm_links.copy() |
853
|
|
|
df_dsm_stores = dsm_stores.copy() |
854
|
|
|
|
855
|
|
|
# industry per osm-area: cooling and ventilation |
856
|
|
|
|
857
|
|
|
print(" ") |
858
|
|
|
print("industry per osm-area: cooling and ventilation") |
859
|
|
|
print(" ") |
860
|
|
|
|
861
|
|
|
dsm = ind_osm_data_import(con, ind_cool_vent_share) |
862
|
|
|
|
863
|
|
|
# calculate combined potentials of cooling and ventilation in industrial sector |
864
|
|
|
# using combined parameters by Heitkoetter et. al. |
865
|
|
|
p_max, p_min, e_max, e_min = calculate_potentials( |
866
|
|
|
s_flex=0.5, s_util=0.73, s_inc=0.9, s_dec=0.5, delta_t=1, dsm=dsm |
867
|
|
|
) |
868
|
|
|
|
869
|
|
|
dsm_buses, dsm_links, dsm_stores = create_dsm_components( |
870
|
|
|
con, p_max, p_min, e_max, e_min, dsm |
871
|
|
|
) |
872
|
|
|
|
873
|
|
|
df_dsm_buses = gpd.GeoDataFrame( |
874
|
|
|
pd.concat([df_dsm_buses, dsm_buses], ignore_index=True), |
875
|
|
|
crs="EPSG:4326", |
876
|
|
|
) |
877
|
|
|
df_dsm_links = pd.DataFrame( |
878
|
|
|
pd.concat([df_dsm_links, dsm_links], ignore_index=True) |
879
|
|
|
) |
880
|
|
|
df_dsm_stores = pd.DataFrame( |
881
|
|
|
pd.concat([df_dsm_stores, dsm_stores], ignore_index=True) |
882
|
|
|
) |
883
|
|
|
|
884
|
|
|
# industry sites |
885
|
|
|
|
886
|
|
|
# industry sites: different applications |
887
|
|
|
|
888
|
|
|
dsm = ind_sites_data_import(con) |
889
|
|
|
|
890
|
|
|
print(" ") |
891
|
|
|
print("industry sites: paper") |
892
|
|
|
print(" ") |
893
|
|
|
|
894
|
|
|
dsm_paper = gpd.GeoDataFrame( |
895
|
|
|
dsm[ |
896
|
|
|
dsm["application"].isin( |
897
|
|
|
[ |
898
|
|
|
"Graphic Paper", |
899
|
|
|
"Packing Paper and Board", |
900
|
|
|
"Hygiene Paper", |
901
|
|
|
"Technical/Special Paper and Board", |
902
|
|
|
] |
903
|
|
|
) |
904
|
|
|
] |
905
|
|
|
) |
906
|
|
|
|
907
|
|
|
# calculate potentials of industrial sites with paper-applications |
908
|
|
|
# using parameters by Heitkoetter et. al. |
909
|
|
|
p_max, p_min, e_max, e_min = calculate_potentials( |
910
|
|
|
s_flex=0.15, |
911
|
|
|
s_util=0.86, |
912
|
|
|
s_inc=0.95, |
913
|
|
|
s_dec=0, |
914
|
|
|
delta_t=3, |
915
|
|
|
dsm=dsm_paper, |
916
|
|
|
) |
917
|
|
|
|
918
|
|
|
dsm_buses, dsm_links, dsm_stores = create_dsm_components( |
919
|
|
|
con, p_max, p_min, e_max, e_min, dsm_paper |
920
|
|
|
) |
921
|
|
|
|
922
|
|
|
df_dsm_buses = gpd.GeoDataFrame( |
923
|
|
|
pd.concat([df_dsm_buses, dsm_buses], ignore_index=True), |
924
|
|
|
crs="EPSG:4326", |
925
|
|
|
) |
926
|
|
|
df_dsm_links = pd.DataFrame( |
927
|
|
|
pd.concat([df_dsm_links, dsm_links], ignore_index=True) |
928
|
|
|
) |
929
|
|
|
df_dsm_stores = pd.DataFrame( |
930
|
|
|
pd.concat([df_dsm_stores, dsm_stores], ignore_index=True) |
931
|
|
|
) |
932
|
|
|
|
933
|
|
|
print(" ") |
934
|
|
|
print("industry sites: recycled paper") |
935
|
|
|
print(" ") |
936
|
|
|
|
937
|
|
|
# calculate potentials of industrial sites with recycled paper-applications |
938
|
|
|
# using parameters by Heitkoetter et. al. |
939
|
|
|
dsm_recycled_paper = gpd.GeoDataFrame( |
940
|
|
|
dsm[dsm["application"] == "Recycled Paper"] |
941
|
|
|
) |
942
|
|
|
|
943
|
|
|
p_max, p_min, e_max, e_min = calculate_potentials( |
944
|
|
|
s_flex=0.7, |
945
|
|
|
s_util=0.85, |
946
|
|
|
s_inc=0.95, |
947
|
|
|
s_dec=0, |
948
|
|
|
delta_t=3, |
949
|
|
|
dsm=dsm_recycled_paper, |
950
|
|
|
) |
951
|
|
|
|
952
|
|
|
dsm_buses, dsm_links, dsm_stores = create_dsm_components( |
953
|
|
|
con, p_max, p_min, e_max, e_min, dsm_recycled_paper |
954
|
|
|
) |
955
|
|
|
|
956
|
|
|
df_dsm_buses = gpd.GeoDataFrame( |
957
|
|
|
pd.concat([df_dsm_buses, dsm_buses], ignore_index=True), |
958
|
|
|
crs="EPSG:4326", |
959
|
|
|
) |
960
|
|
|
df_dsm_links = pd.DataFrame( |
961
|
|
|
pd.concat([df_dsm_links, dsm_links], ignore_index=True) |
962
|
|
|
) |
963
|
|
|
df_dsm_stores = pd.DataFrame( |
964
|
|
|
pd.concat([df_dsm_stores, dsm_stores], ignore_index=True) |
965
|
|
|
) |
966
|
|
|
|
967
|
|
|
print(" ") |
968
|
|
|
print("industry sites: pulp") |
969
|
|
|
print(" ") |
970
|
|
|
|
971
|
|
|
dsm_pulp = gpd.GeoDataFrame( |
972
|
|
|
dsm[dsm["application"] == "Mechanical Pulp"] |
973
|
|
|
) |
974
|
|
|
|
975
|
|
|
# calculate potentials of industrial sites with pulp-applications |
976
|
|
|
# using parameters by Heitkoetter et. al. |
977
|
|
|
p_max, p_min, e_max, e_min = calculate_potentials( |
978
|
|
|
s_flex=0.7, |
979
|
|
|
s_util=0.83, |
980
|
|
|
s_inc=0.95, |
981
|
|
|
s_dec=0, |
982
|
|
|
delta_t=2, |
983
|
|
|
dsm=dsm_pulp, |
984
|
|
|
) |
985
|
|
|
|
986
|
|
|
dsm_buses, dsm_links, dsm_stores = create_dsm_components( |
987
|
|
|
con, p_max, p_min, e_max, e_min, dsm_pulp |
988
|
|
|
) |
989
|
|
|
|
990
|
|
|
df_dsm_buses = gpd.GeoDataFrame( |
991
|
|
|
pd.concat([df_dsm_buses, dsm_buses], ignore_index=True), |
992
|
|
|
crs="EPSG:4326", |
993
|
|
|
) |
994
|
|
|
df_dsm_links = pd.DataFrame( |
995
|
|
|
pd.concat([df_dsm_links, dsm_links], ignore_index=True) |
996
|
|
|
) |
997
|
|
|
df_dsm_stores = pd.DataFrame( |
998
|
|
|
pd.concat([df_dsm_stores, dsm_stores], ignore_index=True) |
999
|
|
|
) |
1000
|
|
|
|
1001
|
|
|
# industry sites: cement |
1002
|
|
|
|
1003
|
|
|
print(" ") |
1004
|
|
|
print("industry sites: cement") |
1005
|
|
|
print(" ") |
1006
|
|
|
|
1007
|
|
|
dsm_cement = gpd.GeoDataFrame(dsm[dsm["application"] == "Cement Mill"]) |
1008
|
|
|
|
1009
|
|
|
# calculate potentials of industrial sites with cement-applications |
1010
|
|
|
# using parameters by Heitkoetter et. al. |
1011
|
|
|
p_max, p_min, e_max, e_min = calculate_potentials( |
1012
|
|
|
s_flex=0.61, |
1013
|
|
|
s_util=0.65, |
1014
|
|
|
s_inc=0.95, |
1015
|
|
|
s_dec=0, |
1016
|
|
|
delta_t=4, |
1017
|
|
|
dsm=dsm_cement, |
1018
|
|
|
) |
1019
|
|
|
|
1020
|
|
|
dsm_buses, dsm_links, dsm_stores = create_dsm_components( |
1021
|
|
|
con, p_max, p_min, e_max, e_min, dsm_cement |
1022
|
|
|
) |
1023
|
|
|
|
1024
|
|
|
df_dsm_buses = gpd.GeoDataFrame( |
1025
|
|
|
pd.concat([df_dsm_buses, dsm_buses], ignore_index=True), |
1026
|
|
|
crs="EPSG:4326", |
1027
|
|
|
) |
1028
|
|
|
df_dsm_links = pd.DataFrame( |
1029
|
|
|
pd.concat([df_dsm_links, dsm_links], ignore_index=True) |
1030
|
|
|
) |
1031
|
|
|
df_dsm_stores = pd.DataFrame( |
1032
|
|
|
pd.concat([df_dsm_stores, dsm_stores], ignore_index=True) |
1033
|
|
|
) |
1034
|
|
|
|
1035
|
|
|
# industry sites: ventilation in WZ23 |
1036
|
|
|
|
1037
|
|
|
print(" ") |
1038
|
|
|
print("industry sites: ventilation in WZ23") |
1039
|
|
|
print(" ") |
1040
|
|
|
|
1041
|
|
|
dsm = ind_sites_vent_data_import(con, ind_vent_share, wz=23) |
1042
|
|
|
|
1043
|
|
|
# drop entries of Cement Mills whose DSM-potentials have already been modelled |
1044
|
|
|
cement = np.unique(dsm_cement["bus"].values) |
1045
|
|
|
index_names = np.array(dsm[dsm["bus"].isin(cement)].index) |
1046
|
|
|
dsm.drop(index_names, inplace=True) |
1047
|
|
|
|
1048
|
|
|
# calculate potentials of ventialtion in industrial sites of WZ 23 |
1049
|
|
|
# using parameters by Heitkoetter et. al. |
1050
|
|
|
p_max, p_min, e_max, e_min = calculate_potentials( |
1051
|
|
|
s_flex=0.5, s_util=0.8, s_inc=1, s_dec=0.5, delta_t=1, dsm=dsm |
1052
|
|
|
) |
1053
|
|
|
|
1054
|
|
|
dsm_buses, dsm_links, dsm_stores = create_dsm_components( |
1055
|
|
|
con, p_max, p_min, e_max, e_min, dsm |
1056
|
|
|
) |
1057
|
|
|
|
1058
|
|
|
df_dsm_buses = gpd.GeoDataFrame( |
1059
|
|
|
pd.concat([df_dsm_buses, dsm_buses], ignore_index=True), |
1060
|
|
|
crs="EPSG:4326", |
1061
|
|
|
) |
1062
|
|
|
df_dsm_links = pd.DataFrame( |
1063
|
|
|
pd.concat([df_dsm_links, dsm_links], ignore_index=True) |
1064
|
|
|
) |
1065
|
|
|
df_dsm_stores = pd.DataFrame( |
1066
|
|
|
pd.concat([df_dsm_stores, dsm_stores], ignore_index=True) |
1067
|
|
|
) |
1068
|
|
|
|
1069
|
|
|
# aggregate DSM components per substation |
1070
|
|
|
|
1071
|
|
|
dsm_buses, dsm_links, dsm_stores = aggregate_components( |
1072
|
|
|
con, df_dsm_buses, df_dsm_links, df_dsm_stores |
1073
|
|
|
) |
1074
|
|
|
|
1075
|
|
|
# export aggregated DSM components to database |
1076
|
|
|
|
1077
|
|
|
delete_dsm_entries("dsm-cts") |
1078
|
|
|
delete_dsm_entries("dsm-ind-osm") |
1079
|
|
|
delete_dsm_entries("dsm-ind-sites") |
1080
|
|
|
delete_dsm_entries("dsm") |
1081
|
|
|
|
1082
|
|
|
data_export(con, dsm_buses, dsm_links, dsm_stores, carrier="dsm") |
1083
|
|
|
|
1084
|
|
|
dsm_cts_ind() |
1085
|
|
|
|