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"""Create a basic scenario from the internal data structure. |
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SPDX-FileCopyrightText: 2016-2019 Uwe Krien <[email protected]> |
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SPDX-License-Identifier: MIT |
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""" |
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import os |
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from types import SimpleNamespace |
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import pandas as pd |
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from reegis.tools import download_file |
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from scenario_builder import config as cfg |
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TRANSLATION_FUEL = { |
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"Abfall": "waste", |
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"Kernenergie": "nuclear", |
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"Braunkohle": "lignite", |
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"Steinkohle": "hard coal", |
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"Erdgas": "natural gas", |
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"GuD": "natural gas", |
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"Gasturbine": "natural gas", |
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"Öl": "oil", |
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"Sonstige": "other", |
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"Emissionszertifikatspreis": "co2_price", |
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} |
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def get_ewi_data(): |
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""" |
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Returns |
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------- |
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namedtuple |
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Examples |
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-------- |
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>>> my_ewi_data = get_ewi_data() |
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>>> round(my_ewi_data.fuel_costs.loc["hard coal", "value"], 2) |
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11.28 |
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""" |
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# Download file |
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url = ( |
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"https://www.ewi.uni-koeln.de/cms/wp-content/uploads/2019/12" |
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"/EWI_Merit_Order_Tool_2019_1_4.xlsm" |
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) |
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fn = os.path.join(cfg.get("paths", "general"), "ewi.xlsm") |
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download_file(fn, url) |
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# Create named tuple with all sub tables |
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ewi_tables = { |
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"fuel_costs": {"skiprows": 7, "usecols": "C:F", "nrows": 7}, |
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"transport_costs": {"skiprows": 21, "usecols": "C:F", "nrows": 7}, |
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"variable_costs": {"skiprows": 31, "usecols": "C:F", "nrows": 8}, |
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"downtime_factor": { |
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"skiprows": 31, |
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"usecols": "H:K", |
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"nrows": 8, |
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"scale": 0.01, |
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}, |
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"emission": {"skiprows": 31, "usecols": "M:P", "nrows": 7}, |
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"co2_price": {"skiprows": 17, "usecols": "C:F", "nrows": 1}, |
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} |
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ewi_data = {} |
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cols = ["fuel", "value", "unit", "source"] |
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xls = pd.ExcelFile(fn) |
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for table in ewi_tables.keys(): |
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tmp = xls.parse("Start", header=[0], **ewi_tables[table]).replace( |
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TRANSLATION_FUEL |
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) |
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tmp.drop_duplicates(tmp.columns[0], keep="first", inplace=True) |
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tmp.columns = cols |
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ewi_data[table] = tmp.set_index("fuel") |
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if "scale" in ewi_tables[table]: |
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ewi_data[table]["value"] *= ewi_tables[table]["scale"] |
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return SimpleNamespace(**ewi_data) |
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reegis /
scenario_builder
