openego /
eGon-data
| @@ 318-353 (lines=36) @@ | ||
| 315 | return dsm |
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| 316 | ||
| 317 | ||
| 318 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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| 319 | """ |
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| 320 | Import industry sites necessary to identify DSM-potential. |
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| 321 | ---------- |
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| 322 | ind_vent_share: float |
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| 323 | Share of considered application in industry demand |
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| 324 | wz: int |
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| 325 | Wirtschaftszweig to be considered within industry sites |
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| 326 | """ |
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| 327 | ||
| 328 | # import load data |
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| 329 | ||
| 330 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 331 | "ind_sites_loadcurves_individual" |
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| 332 | ] |
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| 333 | ||
| 334 | dsm = db.select_dataframe( |
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| 335 | f""" |
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| 336 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 337 | {sources['schema']}.{sources['table']} |
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| 338 | WHERE scn_name IN ('eGon2035', 'eGon100RE') |
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| 339 | AND wz = {wz} |
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| 340 | """ |
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| 341 | ) |
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| 342 | ||
| 343 | # calculate share of timeseries for ventilation |
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| 344 | ||
| 345 | timeseries = dsm["p_set"].copy() |
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| 346 | ||
| 347 | for index, liste in timeseries.items(): |
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| 348 | share = [float(item) * ind_vent_share for item in liste] |
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| 349 | timeseries.loc[index] = share |
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| 350 | ||
| 351 | dsm["p_set"] = timeseries.copy() |
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| 352 | ||
| 353 | return dsm |
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| 354 | ||
| 355 | ||
| 356 | def calc_ind_site_timeseries(scenario): |
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| @@ 280-314 (lines=35) @@ | ||
| 277 | return dsm |
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| 278 | ||
| 279 | ||
| 280 | def ind_sites_vent_data_import(ind_vent_share, wz): |
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| 281 | """ |
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| 282 | Import industry sites necessary to identify DSM-potential. |
|
| 283 | ---------- |
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| 284 | ind_vent_share: float |
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| 285 | Share of considered application in industry demand |
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| 286 | wz: int |
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| 287 | Wirtschaftszweig to be considered within industry sites |
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| 288 | """ |
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| 289 | ||
| 290 | # import load data |
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| 291 | ||
| 292 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 293 | "ind_sites_loadcurves" |
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| 294 | ] |
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| 295 | ||
| 296 | dsm = db.select_dataframe( |
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| 297 | f""" |
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| 298 | SELECT bus, scn_name, p_set FROM |
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| 299 | {sources['schema']}.{sources['table']} |
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| 300 | WHERE scn_name IN ('eGon2035', 'eGon100RE') |
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| 301 | AND wz = {wz} |
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| 302 | """ |
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| 303 | ) |
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| 304 | ||
| 305 | # calculate share of timeseries for ventilation |
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| 306 | ||
| 307 | timeseries = dsm["p_set"].copy() |
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| 308 | ||
| 309 | for index, liste in timeseries.items(): |
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| 310 | share = [float(item) * ind_vent_share for item in liste] |
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| 311 | timeseries.loc[index] = share |
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| 312 | ||
| 313 | dsm["p_set"] = timeseries.copy() |
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| 314 | ||
| 315 | return dsm |
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| 316 | ||
| 317 | ||
