openego /
eGon-data
| @@ 314-349 (lines=36) @@ | ||
| 311 | return dsm |
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| 312 | ||
| 313 | ||
| 314 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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| 315 | """ |
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| 316 | Import industry sites necessary to identify DSM-potential. |
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| 317 | ---------- |
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| 318 | ind_vent_share: float |
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| 319 | Share of considered application in industry demand |
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| 320 | wz: int |
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| 321 | Wirtschaftszweig to be considered within industry sites |
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| 322 | """ |
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| 323 | ||
| 324 | # import load data |
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| 325 | ||
| 326 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 327 | "ind_sites_loadcurves_individual" |
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| 328 | ] |
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| 329 | ||
| 330 | dsm = db.select_dataframe( |
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| 331 | f""" |
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| 332 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 333 | {sources['schema']}.{sources['table']} |
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| 334 | WHERE scn_name IN ('eGon2035', 'eGon100RE') |
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| 335 | AND wz = {wz} |
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| 336 | """ |
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| 337 | ) |
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| 338 | ||
| 339 | # calculate share of timeseries for ventilation |
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| 340 | ||
| 341 | timeseries = dsm["p_set"].copy() |
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| 342 | ||
| 343 | for index, liste in timeseries.items(): |
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| 344 | share = [float(item) * ind_vent_share for item in liste] |
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| 345 | timeseries.loc[index] = share |
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| 346 | ||
| 347 | dsm["p_set"] = timeseries.copy() |
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| 348 | ||
| 349 | return dsm |
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| 350 | ||
| 351 | ||
| 352 | def calc_ind_site_timeseries(scenario): |
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| @@ 277-311 (lines=35) @@ | ||
| 274 | return dsm |
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| 275 | ||
| 276 | ||
| 277 | def ind_sites_vent_data_import(ind_vent_share, wz): |
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| 278 | """ |
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| 279 | Import industry sites necessary to identify DSM-potential. |
|
| 280 | ---------- |
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| 281 | ind_vent_share: float |
|
| 282 | Share of considered application in industry demand |
|
| 283 | wz: int |
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| 284 | Wirtschaftszweig to be considered within industry sites |
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| 285 | """ |
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| 286 | ||
| 287 | # import load data |
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| 288 | ||
| 289 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 290 | "ind_sites_loadcurves" |
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| 291 | ] |
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| 292 | ||
| 293 | dsm = db.select_dataframe( |
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| 294 | f""" |
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| 295 | SELECT bus, scn_name, p_set FROM |
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| 296 | {sources['schema']}.{sources['table']} |
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| 297 | WHERE wz = {wz} |
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| 298 | """ |
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| 299 | ) |
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| 300 | ||
| 301 | # calculate share of timeseries for ventilation |
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| 302 | ||
| 303 | timeseries = dsm["p_set"].copy() |
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| 304 | ||
| 305 | for index, liste in timeseries.items(): |
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| 306 | share = [float(item) * ind_vent_share for item in liste] |
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| 307 | timeseries.loc[index] = share |
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| 308 | ||
| 309 | dsm["p_set"] = timeseries.copy() |
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| 310 | ||
| 311 | return dsm |
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| 312 | ||
| 313 | ||
| 314 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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