d4974e3241
- flexmeasures-entsoe: ENTSO-E data plugin - flexmeasures-weather: Weather data plugin - USEF Flex Trading Protocol PDF (2.4MB) - Cariflex simulator (publishes to Redis) - Dashboard Grafana updated with correct InfluxDB queries - All tools extracted in /tools/
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
import pandas as pd
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def determine_net_emission_factors(shares: pd.DataFrame) -> pd.Series:
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"""Given production shares, determine the net emission factors.
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Or given production by type, determine the net emissions.
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Use column headers that match production types listed below.
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Use any index.
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For example:
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print(shares)
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fossil_gas other fossil_hard_coal waste nuclear
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hour
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0 0.443685 0.206033 0.237596 0.050915 0.059455
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1 0.443910 0.205065 0.235022 0.052614 0.060987
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print(determine_net_emission_factors(shares))
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hour
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0 644.753221
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1 641.410093
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Name: Average emissions from Dutch electricity production (kg CO₂ eq/MWh), dtype: float64
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"""
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emission_factors = dict(
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biomass=50.4,
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fossil_brown_coal_or_lignite=None, # unknown
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fossil_coal_derived_gas=None, # unknown
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fossil_gas=464,
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fossil_hard_coal=1030,
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fossil_oil=1010,
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fossil_oil_shale=None, # unknown
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fossil_peat=None, # unknown
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geothermal=0.00664,
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hydro_pumped_storage=611,
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hydro_run_of_river_and_poundage=0.0253,
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hydro_water_reservoir=8.13,
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marine=None, # unknown
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nuclear=10.1,
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other=927, # for EU28
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other_renewable=None, # unknown
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solar=0.00591,
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waste=None, # unknown
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wind_offshore=0.133,
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wind_onshore=0.133,
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) # supplementary material from "Real-time carbon accounting method for the European electricity markets, Tranberg et al. (2019)"
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# todo: substitute placeholder for unknown emission factor of waste
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emission_factors["waste"] = emission_factors["biomass"]
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for production_type in shares.columns:
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shares[production_type] = (
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shares[production_type] * emission_factors[production_type]
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)
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return shares.sum(axis=1).rename(
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"Average emissions from Dutch electricity production (kg CO₂ eq/MWh)"
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)
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