monthly prices: adjust manual cost assumptions
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@ -62,7 +62,7 @@ renewable:
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flatten_dispatch: 0.01
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conventional:
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unit_commitment: false
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unit_commitment: true
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dynamic_fuel_price: true
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nuclear:
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p_max_pu: "data/nuclear_p_max_pu.csv"
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@ -34,11 +34,9 @@ and translates them from different input sources to pypsa syntax
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Data sources:
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[1] Fuel price index. Destatis
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https://www.destatis.de/EN/Home/_node.html
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[2] average annual import price (coal, gas, oil) Agora, slide 24
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https://static.agora-energiewende.de/fileadmin/Projekte/2019/Jahresauswertung_2019/A-EW_German-Power-Market-2019_Summary_EN.pdf
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[3] average annual fuel price lignite, ENTSO-E
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[2] average annual fuel price lignite, ENTSO-E
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https://2020.entsos-tyndp-scenarios.eu/fuel-commodities-and-carbon-prices/
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[4] CO2 Prices, Emission spot primary auction, EEX
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[3] CO2 Prices, Emission spot primary auction, EEX
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https://www.eex.com/en/market-data/environmental-markets/eua-primary-auction-spot-download
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@ -71,14 +69,14 @@ sheet_name_map = {
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# import fuel price 2015 in Eur/MWh
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# source for coal, oil, gas, Agora, slide 24 [2]
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# source lignite, price for 2020, scaled by price index, ENTSO-E [3]
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price_2020 = (
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pd.Series({"coal": 3.0, "oil": 10.6, "gas": 5.6, "lignite": 1.1}) * 3.6
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) # Eur/MWh
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# manual correction for better fit:
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price_2020["coal"] = 2.2
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# manual adjustment of coal price
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price_2020["coal"] = 2.4 * 3.6
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price_2020["lignite"] = 1.6 * 3.6
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def get_fuel_price():
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