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Update res data #22

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Oct 12, 2021
Merged

Update res data #22

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72f53ba
Include time series reindexing for target year
jokochems Oct 12, 2021
6d4afdb
Make costs files dependent on years (nuclear / coal phase outs)
jokochems Oct 12, 2021
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40 changes: 38 additions & 2 deletions pommesdata/data_prep/tools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,8 @@
- assign time-dependent NTC values,
- convert data into the structure of oemof.solph,
- load ENTSO-E data and transfer it to the format needed,
- extract capacities from the TYNDP 2018 scenario data set.
- extract capacities from the TYNDP 2018 scenario data set,
- reindex timeseries data for another year of choice.

Licensing information and Disclaimer
------------------------------------
Expand Down Expand Up @@ -430,4 +431,39 @@ def extract_tyndp_capacities(countries, no_dict, scenario="2030_DG",
'variable': 'fuel',
'value': 'capacity'})

return pp_eu_target_year
return pp_eu_target_year


def reindex_time_series(df, year):
"""Reindex a time series given for 2017 by another year

Parameters
----------
df: pd.DataFrame
original DataFrame

year: int
The year for reindexing

Returns
-------
df: pd.DataFrame
the manipulated DataFrame
"""
df.index = pd.DatetimeIndex(df.index)
df.index.freq = 'H'
date_diff = (df.index[0]
- pd.Timestamp("2017-01-01 00:00:00",
tz=df.index.tz))
ts_start = (pd.Timestamp(str(year) + "-01-01 00:00:00",
tz=df.index.tz)
+ date_diff)
# account for leap years
if ts_start.month == 12:
ts_start = ts_start + pd.Timedelta("1 days")
new_index = pd.date_range(start=ts_start,
periods=df.shape[0],
freq=df.index.freq)
df.index = new_index

return df
36 changes: 22 additions & 14 deletions pommesdata/data_preparation.ipynb
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -15,9 +15,9 @@
"* renewable generators (Sources / Transformers)\n",
"* power demand (Sinks)\n",
"\n",
"Two separate data sets are created:\n",
"* one containing the power plant status as of 2017 with an update for 2019 and\n",
"* one containing an estimated exogeneously determined power plant status for 2030.\n",
"A data sets for any year between 2017 and 2030 can be created.\n",
"* For future years, power plant commissionings and decommissionings are taken into account and RES are expanded by a given pathway.\n",
"* Note that so far, we have included **time series information only for 2017**. For any other year, 2017 time series are used and simply reindex. To have a comparable simulation year with 8760 hours, for leap years, the last day is simply neglected.\n",
"\n",
"> **Outline:** The correspondend elements, named components, used in the framework [oemof.solph](https://github.com/oemof/oemof-solph) determine the outline of the given notebook.\n",
"> _NOTE: It is recommended to run the notebook sequentially since variables declared earlier may be needed later on, even in later sections._\n",
Expand Down Expand Up @@ -196,7 +196,7 @@
"metadata": {},
"outputs": [],
"source": [
"year = 2030 # between 2017 and 2030 \n",
"year = 2017 # between 2017 and 2030 \n",
"shutdown_assumption = 2022 # between 2017 and 2030\n",
"eeg_clusters_per_technology = 20\n",
"res_capacity_projection = \"Prognos\" # \"EEG_2021\"\n",
Expand Down Expand Up @@ -1982,6 +1982,7 @@
"min_load_ts.index= pd.date_range(start='2017-01-01 00:00:00', end='2017-12-31 23:00:00', freq='H')\n",
"\n",
"min_load_ts = min_load_ts.round(4)\n",
"min_load_ts = tools.reindex_time_series(min_load_ts, year)\n",
"min_load_ts.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"transformers_minload_ts\"] + \"_\" + str(year) + \".csv\"\n",
")\n",
Expand Down Expand Up @@ -2533,6 +2534,8 @@
"\n",
" NTC_actual[connector] = actual_capacity\n",
" \n",
"NTC_actual = tools.reindex_time_series(NTC_actual, year)\n",
"\n",
"NTC_actual.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"linking_transformers_ts\"] + \"_\" + str(year) + \".csv\"\n",
") \n",
Expand Down Expand Up @@ -3152,9 +3155,9 @@
"GHG_df = GHG_df.mul(1e6)\n",
"\n",
"GHG_df.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"emission_limits\"] + \"_\" + str(year) + \".csv\"\n",
" main_path[\"outputs\"] + output_file[\"emission_limits\"] + \".csv\"\n",
")\n",
"GHG_df.to_excel(writer, sheet_name='emission_limits' + \"_\" + str(year))"
"GHG_df.to_excel(writer, sheet_name='emission_limits')"
]
},
{
Expand Down Expand Up @@ -3534,6 +3537,7 @@
" year=2017\n",
")\n",
"\n",
"market_values_ts = tools.reindex_time_series(market_values_ts, year)\n",
"market_values_ts.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"costs_market_values\"] + \"_\" + str(year) + \".csv\"\n",
")"
Expand Down Expand Up @@ -3960,6 +3964,9 @@
"sources_RES.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"sources_renewables\"] + \"_\" + str(year) + \".csv\"\n",
")\n",
"\n",
"sources_ts_res = tools.reindex_time_series(sources_ts_res, year)\n",
"\n",
"sources_ts_res.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"sources_renewables_ts\"] + \"_\" + str(year) + \".csv\"\n",
")\n",
Expand Down Expand Up @@ -4842,7 +4849,7 @@
"\n",
"costs_carbon = pd.DataFrame(index=sources_commodity.index, columns=range(2015,2050), data=5.8)\n",
"costs_carbon.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"costs_carbon\"] + \".csv\"\n",
" main_path[\"outputs\"] + output_file[\"costs_carbon\"] + \"_\" + str(year) + \".csv\"\n",
")"
]
},
Expand Down Expand Up @@ -4911,6 +4918,7 @@
"\n",
"# interpolate NaN values (Czech and France)\n",
"sinks_demand_el_ts = sinks_demand_el_ts.interpolate(method='linear')\n",
"sinks_demand_el_ts = tools.reindex_time_series(sinks_demand_el_ts, year)\n",
"\n",
"sinks_demand_el.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"sinks_demand_el\"] + \"_\" + str(year) + \".csv\"\n",
Expand Down Expand Up @@ -5063,7 +5071,7 @@
"costs_opex = costs_opex.round(2).astype(np.float32)\n",
"\n",
"costs_opex.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"costs_operation\"] + \".csv\"\n",
" main_path[\"outputs\"] + output_file[\"costs_operation\"] + \"_\" + str(year) + \".csv\"\n",
")"
]
},
Expand All @@ -5087,7 +5095,7 @@
" pd.DataFrame(index=ee_agg['from'].unique(), columns=range(2015,2051), data=0)])\n",
"\n",
"costs_ramping.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"costs_ramping\"] + \".csv\"\n",
" main_path[\"outputs\"] + output_file[\"costs_ramping\"] + \"_\" + str(year) + \".csv\"\n",
")"
]
},
Expand All @@ -5108,7 +5116,7 @@
"\n",
"costs_fuel = costs_fuel.round(2).astype(np.float32)\n",
"costs_fuel.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"costs_fuel_middle\"] + \".csv\"\n",
" main_path[\"outputs\"] + output_file[\"costs_fuel_middle\"] + \"_\" + str(year) + \".csv\"\n",
")"
]
},
Expand All @@ -5120,7 +5128,7 @@
"source": [
"opex_storages = pd.DataFrame(index=storages_el.index, columns=range(2015,2051), data=1)\n",
"opex_storages.to_csv(\n",
" main_path[\"outputs\"] + output_file[\"costs_operation_storages\"] + \".csv\"\n",
" main_path[\"outputs\"] + output_file[\"costs_operation_storages\"] + \"_\" + str(year) + \".csv\"\n",
")"
]
},
Expand Down Expand Up @@ -5177,7 +5185,7 @@
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
"width": "396.533px"
"width": "677.533px"
},
"toc_section_display": true,
"toc_window_display": true
Expand Down Expand Up @@ -5208,9 +5216,9 @@
"oldHeight": 923.85,
"position": {
"height": "945.85px",
"left": "1897px",
"left": "1887px",
"right": "20px",
"top": "149px",
"top": "136px",
"width": "583px"
},
"types_to_exclude": [
Expand Down