openego /
eGon-data
| @@ 310-345 (lines=36) @@ | ||
| 307 | return dsm |
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| 308 | ||
| 309 | ||
| 310 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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| 311 | """ |
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| 312 | Import industry sites necessary to identify DSM-potential. |
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| 313 | ---------- |
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| 314 | ind_vent_share: float |
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| 315 | Share of considered application in industry demand |
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| 316 | wz: int |
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| 317 | Wirtschaftszweig to be considered within industry sites |
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| 318 | """ |
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| 319 | ||
| 320 | # import load data |
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| 321 | ||
| 322 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 323 | "ind_sites_loadcurves_individual" |
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| 324 | ] |
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| 325 | ||
| 326 | dsm = db.select_dataframe( |
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| 327 | f""" |
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| 328 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 329 | {sources['schema']}.{sources['table']} |
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| 330 | WHERE scn_name IN ('eGon2035', 'eGon100RE') |
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| 331 | AND 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.iteritems(): |
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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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| @@ 273-307 (lines=35) @@ | ||
| 270 | return dsm |
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| 271 | ||
| 272 | ||
| 273 | def ind_sites_vent_data_import(ind_vent_share, wz): |
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| 274 | """ |
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| 275 | Import industry sites necessary to identify DSM-potential. |
|
| 276 | ---------- |
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| 277 | ind_vent_share: float |
|
| 278 | Share of considered application in industry demand |
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| 279 | wz: int |
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| 280 | Wirtschaftszweig to be considered within industry sites |
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| 281 | """ |
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| 282 | ||
| 283 | # import load data |
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| 284 | ||
| 285 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 286 | "ind_sites_loadcurves" |
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| 287 | ] |
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| 288 | ||
| 289 | dsm = db.select_dataframe( |
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| 290 | f""" |
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| 291 | SELECT bus, scn_name, p_set FROM |
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| 292 | {sources['schema']}.{sources['table']} |
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| 293 | WHERE wz = '{wz}' |
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| 294 | """ |
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| 295 | ) |
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| 296 | ||
| 297 | # calculate share of timeseries for ventilation |
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| 298 | ||
| 299 | timeseries = dsm["p_set"].copy() |
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| 300 | ||
| 301 | for index, liste in timeseries.iteritems(): |
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| 302 | share = [float(item) * ind_vent_share for item in liste] |
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| 303 | timeseries.loc[index] = share |
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| 304 | ||
| 305 | dsm["p_set"] = timeseries.copy() |
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| 306 | ||
| 307 | return dsm |
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| 308 | ||
| 309 | ||
| 310 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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