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Merge branch 'master' into brownfield-chp

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lisazeyen 2024-04-12 09:32:44 +02:00 committed by GitHub
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4 changed files with 37 additions and 10 deletions
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4
config/config.default.yaml
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@ -360,8 +360,8 @@ solar_thermal:
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#existing-capacities # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#existing-capacities
existing_capacities: existing_capacities:
grouping_years_power: [1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030] grouping_years_power: [1895, 1920, 1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019] # these should not extend 2020 grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020] # heat grouping years >= baseyear will be ignored
threshold_capacity: 10 threshold_capacity: 10
default_heating_lifetime: 20 default_heating_lifetime: 20
conventional_carriers: conventional_carriers:

3
doc/release_notes.rst
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@ -9,6 +9,9 @@ Release Notes
Upcoming Release Upcoming Release
================ ================
* Group existing capacities to the earlier grouping_year for consistency with optimized capacities.
* bugfix: installed heating capacities were 5% lower than existing heating capacities
* Include gas and oil fields and saline aquifers in estimation of CO2 sequestration potential. * Include gas and oil fields and saline aquifers in estimation of CO2 sequestration potential.

4
scripts/add_brownfield.py
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@ -40,8 +40,8 @@ def add_brownfield(n, n_p, year):
# CO2 or global EU values since these are already in n # CO2 or global EU values since these are already in n
n_p.mremove(c.name, c.df.index[c.df.lifetime == np.inf]) n_p.mremove(c.name, c.df.index[c.df.lifetime == np.inf])
# remove assets whose build_year + lifetime < year # remove assets whose build_year + lifetime <= year
n_p.mremove(c.name, c.df.index[c.df.build_year + c.df.lifetime < year]) n_p.mremove(c.name, c.df.index[c.df.build_year + c.df.lifetime <= year])
# remove assets if their optimized nominal capacity is lower than a threshold # remove assets if their optimized nominal capacity is lower than a threshold
# since CHP heat Link is proportional to CHP electric Link, make sure threshold is compatible # since CHP heat Link is proportional to CHP electric Link, make sure threshold is compatible

36
scripts/add_existing_baseyear.py
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@ -189,8 +189,19 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
phased_out = df_agg[df_agg["DateOut"] < baseyear].index phased_out = df_agg[df_agg["DateOut"] < baseyear].index
df_agg.drop(phased_out, inplace=True) df_agg.drop(phased_out, inplace=True)
older_assets = (df_agg.DateIn < min(grouping_years)).sum()
if older_assets:
logger.warning(
f"There are {older_assets} assets with build year "
f"before first power grouping year {min(grouping_years)}. "
"These assets are dropped and not considered."
"Consider to redefine the grouping years to keep them."
)
to_drop = df_agg[df_agg.DateIn < min(grouping_years)].index
df_agg.drop(to_drop, inplace=True)
df_agg["grouping_year"] = np.take( df_agg["grouping_year"] = np.take(
grouping_years, np.digitize(df_agg.DateIn, grouping_years, right=True) grouping_years[::-1], np.digitize(df_agg.DateIn, grouping_years[::-1])
) )
# calculate (adjusted) remaining lifetime before phase-out (+1 because assuming # calculate (adjusted) remaining lifetime before phase-out (+1 because assuming
@ -451,12 +462,25 @@ def add_heating_capacities_installed_before_baseyear(
else: else:
efficiency = costs.at[costs_name, "efficiency"] efficiency = costs.at[costs_name, "efficiency"]
for i, grouping_year in enumerate(grouping_years): valid_grouping_years = pd.Series(
if int(grouping_year) + default_lifetime <= int(baseyear): [
continue int(grouping_year)
for grouping_year in grouping_years
if int(grouping_year) + default_lifetime > int(baseyear)
and int(grouping_year) < int(baseyear)
]
)
# installation is assumed to be linear for the past default_lifetime years # get number of years of each interval
ratio = (int(grouping_year) - int(grouping_years[i - 1])) / default_lifetime _years = (
valid_grouping_years.diff()
.shift(-1)
.fillna(baseyear - valid_grouping_years.iloc[-1])
)
# Installation is assumed to be linear for the past
ratios = _years / _years.sum()
for ratio, grouping_year in zip(ratios, valid_grouping_years):
n.madd( n.madd(
"Link", "Link",