config.yaml: Remove battery and H2 Stores from PyPSA-Eur
I.e. what's taken over from PyPSA-Eur in config["pypsa_eur"] from "Store" is []. PyPSA-Eur-Sec adds its own batteries and H2 Stores.
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Tom Brown
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@ -25,10 +25,10 @@ scenario:
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# solar+c0.5 reduces the capital cost of solar to 50\% of reference value
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# solar+p3 multiplies the available installable potential by factor 3
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# dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
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# for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
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# emissions throughout the transition path in the timeframe determined by the
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# for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
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# emissions throughout the transition path in the timeframe determined by the
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# planning_horizons), be:beta decay; ex:exponential decay
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# cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
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# cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
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# decay with initial growth rate 0
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planning_horizons : [2030] # investment years for myopic and perfect; or costs year for overnight
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# for example, set to [2020, 2030, 2040, 2050] for myopic foresight
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@ -71,6 +71,7 @@ pypsa_eur:
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"Link": ["DC"]
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"Generator": ["onwind", "offwind-ac", "offwind-dc", "solar", "ror"]
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"StorageUnit": ["PHS","hydro"]
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"Store": []
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biomass:
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year: 2030
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@ -4,8 +4,11 @@ Release Notes
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Future release
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===================
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*For the myopic option, a carbon budget and a type of decay (exponential or beta) can be selected in the config file to distribute the budget across the planning_horizons.
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*Added an option to alter the capital cost or maximum capacity of carriers by a factor via ``carrier+factor`` in the ``{opts}`` wildcard. This can be useful for exploring uncertain cost parameters. Example: ``solar+c0.5`` reduces the capital cost of solar to 50\% of original values. Similarly ``solar+p3`` multiplies the p_nom_max by 3.
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* For the myopic option, a carbon budget and a type of decay (exponential or beta) can be selected in the config file to distribute the budget across the planning_horizons.
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* Added an option to alter the capital cost or maximum capacity of carriers by a factor via ``carrier+factor`` in the ``{opts}`` wildcard. This can be useful for exploring uncertain cost parameters. Example: ``solar+c0.5`` reduces the capital cost of solar to 50\% of original values. Similarly ``solar+p3`` multiplies the p_nom_max by 3.
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* Bugfix: Fix reading in of ``pypsa-eur/resources/powerplants.csv`` to PyPSA-Eur Version 0.3.0 (use ``DateIn`` instead of old ``YearDecommissioned``).
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* Bugfix: Make sure that ``Store`` components (battery and H2) are also removed from PyPSA-Eur, so they can be added later by PyPSA-Eur-Sec.
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PyPSA-Eur-Sec 0.4.0 (11th December 2020)
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=========================================
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@ -53,14 +53,14 @@ def co2_emissions_year(cts, opts, year):
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calculate co2 emissions in one specific year (e.g. 1990 or 2018).
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"""
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eea_co2 = build_eea_co2(year)
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#TODO: read Eurostat data from year>2014, this only affects the estimation of
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#TODO: read Eurostat data from year>2014, this only affects the estimation of
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# CO2 emissions for "BA","RS","AL","ME","MK"
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if year > 2014:
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eurostat_co2 = build_eurostat_co2(year=2014)
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else:
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eurostat_co2 = build_eurostat_co2(year)
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co2_totals=build_co2_totals(eea_co2, eurostat_co2, year)
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co2_emissions = co2_totals.loc[cts, "electricity"].sum()
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@ -79,51 +79,51 @@ def co2_emissions_year(cts, opts, year):
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def build_carbon_budget(o):
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#distribute carbon budget following beta or exponential transition path
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if "be" in o:
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if "be" in o:
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#beta decay
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carbon_budget = float(o[o.find("cb")+2:o.find("be")])
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be=float(o[o.find("be")+2:])
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if "ex" in o:
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be=float(o[o.find("be")+2:])
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if "ex" in o:
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#exponential decay
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carbon_budget = float(o[o.find("cb")+2:o.find("ex")])
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r=float(o[o.find("ex")+2:])
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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pop_layout["ct"] = pop_layout.index.str[:2]
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cts = pop_layout.ct.value_counts().index
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e_1990 = co2_emissions_year(cts, opts, year=1990)
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cts = pop_layout.ct.value_counts().index
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e_1990 = co2_emissions_year(cts, opts, year=1990)
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#emissions at the beginning of the path (last year available 2018)
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e_0 = co2_emissions_year(cts, opts, year=2018)
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e_0 = co2_emissions_year(cts, opts, year=2018)
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#emissions in 2019 and 2020 assumed equal to 2018 and substracted
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carbon_budget -= 2*e_0
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planning_horizons = snakemake.config['scenario']['planning_horizons']
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CO2_CAP = pd.DataFrame(index = pd.Series(data=planning_horizons,
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CO2_CAP = pd.DataFrame(index = pd.Series(data=planning_horizons,
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name='planning_horizon'),
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columns=pd.Series(data=[],
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name='paths',
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name='paths',
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dtype='float'))
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t_0 = planning_horizons[0]
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if "be" in o:
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#beta decay
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t_f = t_0 + (2*carbon_budget/e_0).round(0) # final year in the path
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if "be" in o:
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#beta decay
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t_f = t_0 + (2*carbon_budget/e_0).round(0) # final year in the path
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#emissions (relative to 1990)
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CO2_CAP[o] = [(e_0/e_1990)*(1-beta.cdf((t-t_0)/(t_f-t_0), be, be)) for t in planning_horizons]
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if "ex" in o:
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if "ex" in o:
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#exponential decay without delay
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T=carbon_budget/e_0
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m=(1+np.sqrt(1+r*T))/T
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CO2_CAP[o] = [(e_0/e_1990)*(1+(m+r)*(t-t_0))*np.exp(-m*(t-t_0)) for t in planning_horizons]
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CO2_CAP.to_csv(path_cb + 'carbon_budget_distribution.csv', sep=',',
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line_terminator='\n', float_format='%.3f')
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CO2_CAP.to_csv(path_cb + 'carbon_budget_distribution.csv', sep=',',
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line_terminator='\n', float_format='%.3f')
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countries=pd.Series(data=cts)
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countries.to_csv(path_cb + 'countries.csv', sep=',',
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countries.to_csv(path_cb + 'countries.csv', sep=',',
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line_terminator='\n', float_format='%.3f')
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def add_lifetime_wind_solar(n):
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"""
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Add lifetime for solar and wind generators
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@ -1854,7 +1854,7 @@ if __name__ == "__main__":
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from vresutils.snakemake import MockSnakemake
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snakemake = MockSnakemake(
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wildcards=dict(network='elec', simpl='', clusters='37', lv='1.0',
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opts='', planning_horizons='2020',
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opts='', planning_horizons='2020',
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sector_opts='120H-T-H-B-I-onwind+p3-dist1-cb48be3'),
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input=dict( network='../pypsa-eur/networks/{network}_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc',
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energy_totals_name='resources/energy_totals.csv',
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@ -1999,19 +1999,19 @@ if __name__ == "__main__":
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print("CO2 limit set to",limit)
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for o in opts:
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if "cb" in o:
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if "cb" in o:
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path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
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if not os.path.exists(path_cb):
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os.makedirs(path_cb)
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try:
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CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv', index_col=0)
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except:
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except:
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build_carbon_budget(o)
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CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv', index_col=0)
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limit=CO2_CAP.loc[investment_year]
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limit=CO2_CAP.loc[investment_year]
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print("overriding CO2 limit with scenario limit",limit)
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for o in opts:
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if "Co2L" in o:
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limit = o[o.find("Co2L")+4:]
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@ -2032,13 +2032,13 @@ if __name__ == "__main__":
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if snakemake.config["sector"]['electricity_distribution_grid']:
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insert_electricity_distribution_grid(n)
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for o in opts:
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if "+" in o:
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oo = o.split("+")
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carrier_list=np.hstack((n.generators.carrier.unique(), n.links.carrier.unique(),
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n.stores.carrier.unique(), n.storage_units.carrier.unique()))
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suptechs = map(lambda c: c.split("-", 2)[0], carrier_list)
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carrier_list=np.hstack((n.generators.carrier.unique(), n.links.carrier.unique(),
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n.stores.carrier.unique(), n.storage_units.carrier.unique()))
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suptechs = map(lambda c: c.split("-", 2)[0], carrier_list)
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if oo[0].startswith(tuple(suptechs)):
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carrier = oo[0]
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attr_lookup = {"p": "p_nom_max", "c": "capital_cost"}
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@ -2051,12 +2051,12 @@ if __name__ == "__main__":
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comps = {"Generator", "Link", "StorageUnit"} if attr=='p_nom_max' else {"Generator", "Link", "StorageUnit", "Store"}
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for c in n.iterate_components(comps):
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if carrier=='solar':
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sel = c.df.carrier.str.contains(carrier) & ~c.df.carrier.str.contains("solar rooftop")
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sel = c.df.carrier.str.contains(carrier) & ~c.df.carrier.str.contains("solar rooftop")
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else:
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sel = c.df.carrier.str.contains(carrier)
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c.df.loc[sel,attr] *= factor
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print("changing", attr ,"for",carrier,"by factor",factor)
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if snakemake.config["sector"]['gas_distribution_grid']:
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insert_gas_distribution_costs(n)
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if snakemake.config["sector"]['electricity_grid_connection']:
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