openego /
eGon-data
| @@ 311-346 (lines=36) @@ | ||
| 308 | return dsm |
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| 309 | ||
| 310 | ||
| 311 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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| 312 | """ |
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| 313 | Import industry sites necessary to identify DSM-potential. |
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| 314 | ---------- |
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| 315 | ind_vent_share: float |
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| 316 | Share of considered application in industry demand |
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| 317 | wz: int |
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| 318 | Wirtschaftszweig to be considered within industry sites |
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| 319 | """ |
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| 320 | ||
| 321 | # import load data |
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| 322 | ||
| 323 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 324 | "ind_sites_loadcurves_individual" |
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| 325 | ] |
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| 326 | ||
| 327 | dsm = db.select_dataframe( |
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| 328 | f""" |
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| 329 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 330 | {sources['schema']}.{sources['table']} |
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| 331 | WHERE wz = {wz} |
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| 332 | """ |
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| 333 | ) |
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| 334 | ||
| 335 | # calculate share of timeseries for ventilation |
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| 336 | ||
| 337 | timeseries = dsm["p_set"].copy() |
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| 338 | ||
| 339 | for index, liste in timeseries.items(): |
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| 340 | share = [float(item) * ind_vent_share for item in liste] |
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| 341 | timeseries.loc[index] = share |
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| 342 | ||
| 343 | dsm["p_set"] = timeseries.copy() |
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| 344 | ||
| 345 | return dsm |
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| 346 | ||
| 347 | ||
| 348 | def calc_ind_site_timeseries(scenario): |
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| 349 | # calculate timeseries per site |
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| @@ 274-308 (lines=35) @@ | ||
| 271 | return dsm |
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| 272 | ||
| 273 | ||
| 274 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 275 | """ |
|
| 276 | Import industry sites necessary to identify DSM-potential. |
|
| 277 | ---------- |
|
| 278 | ind_vent_share: float |
|
| 279 | Share of considered application in industry demand |
|
| 280 | wz: int |
|
| 281 | Wirtschaftszweig to be considered within industry sites |
|
| 282 | """ |
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| 283 | ||
| 284 | # import load data |
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| 285 | ||
| 286 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 287 | "ind_sites_loadcurves" |
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| 288 | ] |
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| 289 | ||
| 290 | dsm = db.select_dataframe( |
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| 291 | f""" |
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| 292 | SELECT bus, scn_name, p_set FROM |
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| 293 | {sources['schema']}.{sources['table']} |
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| 294 | WHERE wz = {wz} |
|
| 295 | """ |
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| 296 | ) |
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| 297 | ||
| 298 | # calculate share of timeseries for ventilation |
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| 299 | ||
| 300 | timeseries = dsm["p_set"].copy() |
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| 301 | ||
| 302 | for index, liste in timeseries.items(): |
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| 303 | share = [float(item) * ind_vent_share for item in liste] |
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| 304 | timeseries.loc[index] = share |
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| 305 | ||
| 306 | dsm["p_set"] = timeseries.copy() |
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| 307 | ||
| 308 | return dsm |
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| 309 | ||
| 310 | ||
| 311 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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