Merge pull request #3 from PyPSA/master

update from master upstream

README.md

@@ -26,7 +26,7 @@ the energy system and includes all greenhouse gas emitters except
 waste management, agriculture, forestry and land use.
 
 Please see the [documentation](https://pypsa-eur-sec.readthedocs.io/)
-for installation instructions and other useful information.
+for installation instructions and other useful information about the snakemake workflow.
 
 This diagram gives an overview of the sectors and the links between
 them:

config.default.yaml

@@ -15,19 +15,21 @@ scenario:
   lv: [1.0,1.5]    # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
   clusters: [45,50] # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
   opts: ['']  # only relevant for PyPSA-Eur
-  sector_opts: [Co2L0-3H-T-H-B-I-solar3-dist1]  # this is where the main scenario settings are
+  sector_opts: [Co2L0-3H-T-H-B-I-solar+p3-dist1]  # this is where the main scenario settings are
   # to really understand the options here, look in scripts/prepare_sector_network.py
   # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
   # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
   # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
   # single letters are sectors: T for land transport, H for building heating,
   # B for biomass supply, I for industry, shipping and aviation
-  # solarx or onwindx changes the available installable potential by factor x
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
   # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative 
+  # solar+p3 multiplies the available installable potential by factor 3
-  # emissions throughout the transition path in the timeframe determined by the 
+  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
+  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
+  # emissions throughout the transition path in the timeframe determined by the
   # planning_horizons), be:beta decay; ex:exponential decay
+  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
+  # decay with initial growth rate 0
   planning_horizons : [2030] # investment years for myopic and perfect; or costs year for overnight
   # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
 
@@ -69,6 +71,7 @@ pypsa_eur:
   "Link": ["DC"]
   "Generator": ["onwind", "offwind-ac", "offwind-dc", "solar", "ror"]
   "StorageUnit": ["PHS","hydro"]
+  "Store": []
 
 biomass:
   year: 2030
@@ -99,7 +102,7 @@ sector:
   'bev_dsm' : True #turns on EV battery
   'bev_availability' : 0.5  #How many cars do smart charging
   'v2g' : True #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is fossil-fuelled
+  #what is not EV or FCEV is oil-fuelled ICE
   'land_transport_fuel_cell_share': # 1 means all FCEVs
     2020: 0
     2030: 0.05
@@ -331,7 +334,7 @@ plotting:
     "Fischer-Tropsch" : "#44DD33"
     "kerosene for aviation": "#44BB11"
     "naphtha for industry" : "#44FF55"
-    "land transport fossil" : "#44DD33"
+    "land transport oil" : "#44DD33"
     "water tanks" : "#BBBBBB"
     "hot water storage" : "#BBBBBB"
     "hot water charging" : "#BBBBBB"
@@ -366,6 +369,7 @@ plotting:
     "process emissions to stored" : "#444444"
     "process emissions to atmosphere" : "#888888"
     "process emissions" : "#222222"
+    "oil emissions" : "#666666"
     "land transport fuel cell" : "#AAAAAA"
     "biogas" : "#800000"
     "solid biomass" : "#DAA520"

doc/data.csv

@@ -2,7 +2,7 @@ description,file/folder,licence,source
 JRC IDEES database,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees
 urban/rural fraction,urban_percent.csv,unknown,unknown
 JRC biomass potentials,biomass/,unknown,https://doi.org/10.2790/39014
-EEA emission statistics,eea/,unknown,https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-14
+EEA emission statistics,eea/UNFCCC_v23.csv,EEA standard re-use policy,https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
 Eurostat Energy Balances,eurostat-energy_balances-*/,Eurostat,https://ec.europa.eu/eurostat/web/energy/data/energy-balances
 Swiss energy statistics from Swiss Federal Office of Energy,switzerland-sfoe/,unknown,http://www.bfe.admin.ch/themen/00526/00541/00542/02167/index.html?dossier_id=02169
 BASt emobility statistics,emobility/,unknown,http://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Stundenwerte.html?nn=626916

doc/installation.rst

@@ -73,8 +73,8 @@ To download and extract the data bundle on the command line:
 
 .. code:: bash
 
-    projects/pypsa-eur-sec/data % wget "https://nworbmot.org/pypsa-eur-sec-data-bundle-201012.tar.gz"
+    projects/pypsa-eur-sec/data % wget "https://nworbmot.org/pypsa-eur-sec-data-bundle-210125.tar.gz"
-    projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle-201012.tar.gz
+    projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle-210125.tar.gz
 
 
 The data licences and sources are given in the following table.

doc/release_notes.rst

@@ -4,8 +4,13 @@ Release Notes
 
 Future release
 ===================
-*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.
+
-*Added an option to alter the capital cost of carriers by a factor via ``carrier+factor`` in the ``{opts}`` wildcard. This can be useful for exploring uncertain cost parameters. Example: ``solar+0.5`` reduces the capital cost of solar to 50\% of original values.
+* For the myopic investment option, a carbon budget and a type of decay (exponential or beta) can be selected in the ``config.yaml`` file to distribute the budget across the ``planning_horizons``. For example, ``cb40ex0`` in the ``{sector_opts}`` wildcard will distribute a carbon budget of 40 GtCO2 following an exponential decay with initial growth rate 0.
+* Added an option to alter the capital cost or maximum capacity of carriers by a factor via ``carrier+factor`` in the ``{sector_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.
+* Rename the bus for European liquid hydrocarbons from ``Fischer-Tropsch`` to ``EU oil``, since it can be supplied not just with the Fischer-Tropsch process, but also with fossil oil.
+* Bugfix: Fix reading in of ``pypsa-eur/resources/powerplants.csv`` to PyPSA-Eur Version 0.3.0 (use column attribute name ``DateIn`` instead of old ``YearDecommissioned``).
+* 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.
+
 
 PyPSA-Eur-Sec 0.4.0 (11th December 2020)
 =========================================
@@ -131,4 +136,4 @@ To make a new release of the data bundle, make an archive of the files in ``data
 
 .. code:: bash
 
-    data % tar pczf pypsa-eur-sec-data-bundle-date.tar.gz eea switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility urban_percent.csv timezone_mappings.csv heat_load_profile_DK_AdamJensen.csv WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv
+    data % tar pczf pypsa-eur-sec-data-bundle-YYMMDD.tar.gz eea/UNFCCC_v23.csv switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility urban_percent.csv timezone_mappings.csv heat_load_profile_DK_AdamJensen.csv WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv

scripts/build_biomass_potentials.py

@@ -62,6 +62,9 @@ if __name__ == "__main__":
         with open('config.yaml', encoding='utf8') as f:
             snakemake.config = yaml.safe_load(f)
     
+
+    # This is a hack, to be replaced once snakemake is unicode-conform
+
     if 'Secondary Forestry residues sawdust' in snakemake.config['biomass']['classes']['solid biomass']:
         snakemake.config['biomass']['classes']['solid biomass'].remove('Secondary Forestry residues sawdust')
         snakemake.config['biomass']['classes']['solid biomass'].append('Secondary Forestry residues – sawdust')

scripts/plot_summary.py

@@ -1,6 +1,6 @@
 
 
-
+import numpy as np
 import pandas as pd
 
 #allow plotting without Xwindows
@@ -9,7 +9,7 @@ matplotlib.use('Agg')
 
 import matplotlib.pyplot as plt
 
-
+from prepare_sector_network import co2_emissions_year
 
 #consolidate and rename
 def rename_techs(label):
@@ -237,7 +237,137 @@ def plot_balances():
 
         fig.savefig(snakemake.output.balances[:-10] + k + ".pdf",transparent=True)
 
+def historical_emissions(cts):
+    """
+    read historical emissions to add them to the carbon budget plot
+    """
+    #https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
+    #downloaded 201228 (modified by EEA last on 201221)
+    fn = "data/eea/UNFCCC_v23.csv"
+    df = pd.read_csv(fn, encoding="latin-1")
+    df.loc[df["Year"] == "1985-1987","Year"] = 1986
+    df["Year"] = df["Year"].astype(int)
+    df = df.set_index(['Year', 'Sector_name', 'Country_code', 'Pollutant_name']).sort_index()
 
+    e = pd.Series()
+    e["electricity"] = '1.A.1.a - Public Electricity and Heat Production'
+    e['residential non-elec'] = '1.A.4.b - Residential'
+    e['services non-elec'] = '1.A.4.a - Commercial/Institutional'
+    e['rail non-elec'] = "1.A.3.c - Railways"
+    e["road non-elec"] = '1.A.3.b - Road Transportation'
+    e["domestic navigation"] = "1.A.3.d - Domestic Navigation"
+    e['international navigation'] = '1.D.1.b - International Navigation'
+    e["domestic aviation"] = '1.A.3.a - Domestic Aviation'
+    e["international aviation"] = '1.D.1.a - International Aviation'   
+    e['total energy'] = '1 - Energy'
+    e['industrial processes'] = '2 - Industrial Processes and Product Use'
+    e['agriculture'] = '3 - Agriculture'
+    e['LULUCF'] = '4 - Land Use, Land-Use Change and Forestry'
+    e['waste management'] = '5 - Waste management'
+    e['other'] = '6 - Other Sector'
+    e['indirect'] = 'ind_CO2 - Indirect CO2'
+    e["total wL"] = "Total (with LULUCF)"
+    e["total woL"] = "Total (without LULUCF)"
+       
+    pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"] 
+    cts
+    if "GB" in cts:
+        cts.remove("GB")
+        cts.append("UK")
+         
+    year = np.arange(1990,2018).tolist()
+
+    idx = pd.IndexSlice
+    co2_totals = df.loc[idx[year,e.values,cts,pol],"emissions"].unstack("Year").rename(index=pd.Series(e.index,e.values)) 
+    
+    co2_totals = (1/1e6)*co2_totals.groupby(level=0, axis=0).sum() #Gton CO2
+
+    co2_totals.loc['industrial non-elec'] = co2_totals.loc['total energy'] - co2_totals.loc[['electricity', 'services non-elec','residential non-elec', 'road non-elec',
+                                                                              'rail non-elec', 'domestic aviation', 'international aviation', 'domestic navigation',
+                                                                              'international navigation']].sum()
+
+    emissions = co2_totals.loc["electricity"]   
+    if "T" in opts:
+        emissions += co2_totals.loc[[i+ " non-elec" for i in ["rail","road"]]].sum()
+    if "H" in opts:
+        emissions += co2_totals.loc[[i+ " non-elec" for i in ["residential","services"]]].sum()
+    if "I" in opts:
+        emissions += co2_totals.loc[["industrial non-elec","industrial processes",
+                                          "domestic aviation","international aviation",
+                                          "domestic navigation","international navigation"]].sum()           
+    return emissions
+
+
+
+def plot_carbon_budget_distribution():
+    """
+    Plot historical carbon emissions in the EU and decarbonization path
+    """ 
+    
+    import matplotlib.gridspec as gridspec
+    import seaborn as sns; sns.set()
+    sns.set_style('ticks')
+    plt.style.use('seaborn-ticks')
+    plt.rcParams['xtick.direction'] = 'in'
+    plt.rcParams['ytick.direction'] = 'in'
+    plt.rcParams['xtick.labelsize'] = 20
+    plt.rcParams['ytick.labelsize'] = 20   
+
+    plt.figure(figsize=(10, 7))
+    gs1 = gridspec.GridSpec(1, 1)
+    ax1 = plt.subplot(gs1[0,0])
+    ax1.set_ylabel('CO$_2$ emissions (Gt per year)',fontsize=22)
+    ax1.set_ylim([0,5])
+    ax1.set_xlim([1990,snakemake.config['scenario']['planning_horizons'][-1]+1])
+    
+    path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
+    countries=pd.read_csv(path_cb + 'countries.csv',  index_col=1) 
+    cts=countries.index.to_list()
+    e_1990 = co2_emissions_year(cts, opts, year=1990)     
+    CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv',  
+                        index_col=0) 
+    
+    
+    ax1.plot(e_1990*CO2_CAP[o],linewidth=3, 
+                         color='dodgerblue', label=None)
+            
+    emissions = historical_emissions(cts)
+
+    ax1.plot(emissions, color='black', linewidth=3, label=None) 
+    
+    #plot commited and uder-discussion targets  
+    #(notice that historical emissions include all countries in the
+    # network, but targets refer to EU)
+    ax1.plot([2020],[0.8*emissions[1990]],
+                     marker='*', markersize=12, markerfacecolor='black',
+                     markeredgecolor='black')    
+            
+    ax1.plot([2030],[0.45*emissions[1990]],
+                     marker='*', markersize=12, markerfacecolor='white',
+                     markeredgecolor='black')    
+            
+    ax1.plot([2030],[0.6*emissions[1990]],
+                     marker='*', markersize=12, markerfacecolor='black',
+                     markeredgecolor='black')
+            
+    ax1.plot([2050, 2050],[x*emissions[1990] for x in [0.2, 0.05]],
+                  color='gray', linewidth=2, marker='_', alpha=0.5) 
+            
+    ax1.plot([2050],[0.01*emissions[1990]],
+                     marker='*', markersize=12, markerfacecolor='white', 
+                     linewidth=0, markeredgecolor='black', 
+                     label='EU under-discussion target', zorder=10, 
+                     clip_on=False) 
+            
+    ax1.plot([2050],[0.125*emissions[1990]],'ro',
+                     marker='*', markersize=12, markerfacecolor='black',
+                     markeredgecolor='black', label='EU commited target')
+            
+    ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01), 
+                       facecolor='white', frameon=True) 
+            
+    path_cb_plot = snakemake.config['results_dir'] + snakemake.config['run'] + '/graphs/'             
+    plt.savefig(path_cb_plot+'carbon_budget_plot.pdf', dpi=300) 
 
 if __name__ == "__main__":
     # Detect running outside of snakemake and mock snakemake for testing
@@ -249,13 +379,16 @@ if __name__ == "__main__":
             snakemake.config = yaml.safe_load(f)
         snakemake.input = Dict()
         snakemake.output = Dict()
-
+        snakemake.wildcards = Dict()
+        #snakemake.wildcards['sector_opts']='3H-T-H-B-I-solar3-dist1-cb48be3'        
+        
         for item in ["costs", "energy"]:
             snakemake.input[item] = snakemake.config['summary_dir'] + '/{name}/csvs/{item}.csv'.format(name=snakemake.config['run'],item=item)
             snakemake.output[item] = snakemake.config['summary_dir'] + '/{name}/graphs/{item}.pdf'.format(name=snakemake.config['run'],item=item)
-        snakemake.input["balances"] = snakemake.config['summary_dir'] + '/test/csvs/supply_energy.csv'
+        snakemake.input["balances"] = snakemake.config['summary_dir'] + '/{name}/csvs/supply_energy.csv'.format(name=snakemake.config['run'],item=item)
-        snakemake.output["balances"] = snakemake.config['summary_dir'] + '/test/graphs/balances-energy.csv'
+        snakemake.output["balances"] = snakemake.config['summary_dir'] + '/{name}/graphs/balances-energy.csv'.format(name=snakemake.config['run'],item=item)
-
+        
+        
     n_header = 4
 
     plot_costs()
@@ -263,3 +396,9 @@ if __name__ == "__main__":
     plot_energy()
 
     plot_balances()
+    
+    for sector_opts in snakemake.config['scenario']['sector_opts']:
+        opts=sector_opts.split('-')
+        for o in opts:
+            if "cb" in o:
+                plot_carbon_budget_distribution()

scripts/prepare_sector_network.py

@@ -48,24 +48,24 @@ override_component_attrs["Store"].loc["build_year"] = ["integer","year",np.nan,"
 override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"lifetime","Input (optional)"]
 
 
-def co2_emissions_year(year):
+
+def co2_emissions_year(cts, opts, year):
+
     """
     calculate co2 emissions in one specific year (e.g. 1990 or 2018).
     """
     eea_co2 = build_eea_co2(year)
-    
+
-    #TODO: read Eurostat data from year>2014, this only affects the estimation of 
+    #TODO: read Eurostat data from year>2014, this only affects the estimation of
+
     # CO2 emissions for "BA","RS","AL","ME","MK"
     if year > 2014:
         eurostat_co2 = build_eurostat_co2(year=2014)
     else:
         eurostat_co2 = build_eurostat_co2(year)
-        
+
     co2_totals=build_co2_totals(eea_co2, eurostat_co2, year)
-    
+
-    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
-    pop_layout["ct"] = pop_layout.index.str[:2]
-    cts = pop_layout.ct.value_counts().index
 
     co2_emissions = co2_totals.loc[cts, "electricity"].sum()
 
@@ -80,173 +80,57 @@ def co2_emissions_year(year):
     co2_emissions *=0.001 #MtCO2 to GtCO2
     return co2_emissions
 
-def historical_emissions():
-    """
-    read historical emissions to add them to the carbon budget plot
-    """
-    #https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
-    #downloaded 201228 (modified by EEA last on 201221)
-    fn = "data/eea/UNFCCC_v23.csv"
-    df = pd.read_csv(fn, encoding="latin-1")
-    df.loc[df["Year"] == "1985-1987","Year"] = 1986
-    df["Year"] = df["Year"].astype(int)
-    df = df.set_index(['Year', 'Sector_name', 'Country_code', 'Pollutant_name']).sort_index()
-
-    e = pd.Series()
-    e["electricity"] = '1.A.1.a - Public Electricity and Heat Production'
-    e['residential non-elec'] = '1.A.4.b - Residential'
-    e['services non-elec'] = '1.A.4.a - Commercial/Institutional'
-    e['rail non-elec'] = "1.A.3.c - Railways"
-    e["road non-elec"] = '1.A.3.b - Road Transportation'
-    e["domestic navigation"] = "1.A.3.d - Domestic Navigation"
-    e['international navigation'] = '1.D.1.b - International Navigation'
-    e["domestic aviation"] = '1.A.3.a - Domestic Aviation'
-    e["international aviation"] = '1.D.1.a - International Aviation'   
-    e['total energy'] = '1 - Energy'
-    e['industrial processes'] = '2 - Industrial Processes and Product Use'
-    e['agriculture'] = '3 - Agriculture'
-    e['LULUCF'] = '4 - Land Use, Land-Use Change and Forestry'
-    e['waste management'] = '5 - Waste management'
-    e['other'] = '6 - Other Sector'
-    e['indirect'] = 'ind_CO2 - Indirect CO2'
-    e["total wL"] = "Total (with LULUCF)"
-    e["total woL"] = "Total (without LULUCF)"
-       
-    pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"] 
-
-    
-    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
-    pop_layout["ct"] = pop_layout.index.str[:2]
-    cts = pop_layout.ct.value_counts().index.to_list()
-    if "GB" in cts:
-        cts.remove("GB")
-        cts.append("UK")
-         
-    year = np.arange(1990,2018).tolist()
-
-    idx = pd.IndexSlice
-    co2_totals = df.loc[idx[year,e.values,cts,pol],"emissions"].unstack("Year").rename(index=pd.Series(e.index,e.values)) 
-    
-    co2_totals = (1/1e6)*co2_totals.groupby(level=0, axis=0).sum() #Gton CO2
-
-    co2_totals.loc['industrial non-elec'] = co2_totals.loc['total energy'] - co2_totals.loc[['electricity', 'services non-elec','residential non-elec', 'road non-elec',
-                                                                              'rail non-elec', 'domestic aviation', 'international aviation', 'domestic navigation',
-                                                                              'international navigation']].sum()
-
-    emissions = co2_totals.loc["electricity"]   
-    if "T" in opts:
-        emissions += co2_totals.loc[[i+ " non-elec" for i in ["rail","road"]]].sum()
-    if "H" in opts:
-        emissions += co2_totals.loc[[i+ " non-elec" for i in ["residential","services"]]].sum()
-    if "I" in opts:
-        emissions += co2_totals.loc[["industrial non-elec","industrial processes",
-                                          "domestic aviation","international aviation",
-                                          "domestic navigation","international navigation"]].sum()           
-    return emissions
 
 
 def build_carbon_budget(o):
     #distribute carbon budget following beta or exponential transition path
-    if "be" in o:    
+    if "be" in o:
         #beta decay
         carbon_budget = float(o[o.find("cb")+2:o.find("be")])
-        be=float(o[o.find("be")+2:])        
+        be=float(o[o.find("be")+2:])
-    if "ex" in o:   
+    if "ex" in o:
         #exponential decay
         carbon_budget = float(o[o.find("cb")+2:o.find("ex")])
         r=float(o[o.find("ex")+2:])
-            
+
-    e_1990 = co2_emissions_year(year=1990) 
+
-            
+    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
+    pop_layout["ct"] = pop_layout.index.str[:2]
+    cts = pop_layout.ct.value_counts().index
+
+    e_1990 = co2_emissions_year(cts, opts, year=1990)
+
     #emissions at the beginning of the path (last year available 2018)
-    e_0 = co2_emissions_year(year=2018) 
+    e_0 = co2_emissions_year(cts, opts, year=2018)
     #emissions in 2019 and 2020 assumed equal to 2018 and substracted
     carbon_budget -= 2*e_0
     planning_horizons = snakemake.config['scenario']['planning_horizons']
-    CO2_CAP = pd.DataFrame(index = pd.Series(data=planning_horizons, 
+    CO2_CAP = pd.DataFrame(index = pd.Series(data=planning_horizons,
                                              name='planning_horizon'),
                                              columns=pd.Series(data=[],
-                                                              name='paths', 
+                                                              name='paths',
                                                               dtype='float'))
     t_0 = planning_horizons[0]
-    if "be" in o: 
+    if "be" in o:
-        #beta decay                
+        #beta decay
-        t_f = t_0 + (2*carbon_budget/e_0).round(0) # final year in the path 
+        t_f = t_0 + (2*carbon_budget/e_0).round(0) # final year in the path
         #emissions (relative to 1990)
         CO2_CAP[o] = [(e_0/e_1990)*(1-beta.cdf((t-t_0)/(t_f-t_0), be, be)) for t in planning_horizons]
-            
+
-    if "ex" in o:   
+    if "ex" in o:
         #exponential decay without delay
         T=carbon_budget/e_0
         m=(1+np.sqrt(1+r*T))/T
         CO2_CAP[o] = [(e_0/e_1990)*(1+(m+r)*(t-t_0))*np.exp(-m*(t-t_0)) for t in planning_horizons]
-                
-    CO2_CAP.to_csv(path_cb + 'carbon_budget_distribution.csv', sep=',', 
-                   line_terminator='\n',  float_format='%.3f') 
- 
-    """
-    Plot historical carbon emissions in the EU and decarbonization path
-    """ 
-    import matplotlib.pyplot as plt
-    import matplotlib.gridspec as gridspec
-    import seaborn as sns; sns.set()
-    sns.set_style('ticks')
-    plt.style.use('seaborn-ticks')
-    plt.rcParams['xtick.direction'] = 'in'
-    plt.rcParams['ytick.direction'] = 'in'
-    plt.rcParams['xtick.labelsize'] = 20
-    plt.rcParams['ytick.labelsize'] = 20   
 
-    plt.figure(figsize=(10, 7))
-    gs1 = gridspec.GridSpec(1, 1)
-    ax1 = plt.subplot(gs1[0,0])
-    ax1.set_ylabel('CO$_2$ emissions (Gt per year)',fontsize=22)
-    ax1.set_ylim([0,5])
-    ax1.set_xlim([1990,planning_horizons[-1]+1])
-    ax1.plot(e_1990*CO2_CAP[o],linewidth=3, 
-                         color='dodgerblue', label=None)
-            
-    emissions = historical_emissions()
 
-    ax1.plot(emissions, color='black', linewidth=3, label=None) 
+    CO2_CAP.to_csv(path_cb + 'carbon_budget_distribution.csv', sep=',',
-    
+                   line_terminator='\n',  float_format='%.3f')
-    #plot commited and uder-discussion targets  
+    countries=pd.Series(data=cts)
-    #(notice that historical emissions include all countries in the
+    countries.to_csv(path_cb + 'countries.csv', sep=',',
-    # network, but targets refer to EU)
+               line_terminator='\n',  float_format='%.3f')
-    ax1.plot([2020],[0.8*emissions[1990]],
+
-                     marker='*', markersize=12, markerfacecolor='black',
+
-                     markeredgecolor='black')    
-            
-    ax1.plot([2030],[0.45*emissions[1990]],
-                     marker='*', markersize=12, markerfacecolor='white',
-                     markeredgecolor='black')    
-            
-    ax1.plot([2030],[0.6*emissions[1990]],
-                     marker='*', markersize=12, markerfacecolor='black',
-                     markeredgecolor='black')
-            
-    ax1.plot([2050, 2050],[x*emissions[1990] for x in [0.2, 0.05]],
-                  color='gray', linewidth=2, marker='_', alpha=0.5) 
-            
-    ax1.plot([2050],[0.01*emissions[1990]],
-                     marker='*', markersize=12, markerfacecolor='white', 
-                     linewidth=0, markeredgecolor='black', 
-                     label='EU under-discussion target', zorder=10, 
-                     clip_on=False) 
-            
-    ax1.plot([2050],[0.125*emissions[1990]],'ro',
-                     marker='*', markersize=12, markerfacecolor='black',
-                     markeredgecolor='black', label='EU commited target')
-            
-    ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01), 
-                       facecolor='white', frameon=True) 
-            
-    path_cb_plot = snakemake.config['results_dir'] + snakemake.config['run'] + '/graphs/'             
-    if not os.path.exists(path_cb_plot):
-        os.makedirs(path_cb_plot)
-    print('carbon budget distribution saved to ' + path_cb_plot + 'carbon_budget_plot.pdf')
-    plt.savefig(path_cb_plot+'carbon_budget_plot.pdf', dpi=300) 
-            
 def add_lifetime_wind_solar(n):
     """
     Add lifetime for solar and wind generators
@@ -799,7 +683,7 @@ def add_generation(network):
                      capital_cost=0.) #could correct to e.g. 0.2 EUR/kWh * annuity and O&M
 
         network.add("Generator",
-                    "EU fossil " + carrier,
+                    "EU " + carrier,
                     bus="EU " + carrier,
                     p_nom_extendable=True,
                     carrier=carrier,
@@ -1179,14 +1063,14 @@ def add_land_transport(network):
 
     fuel_cell_share = get_parameter(options["land_transport_fuel_cell_share"])
     electric_share = get_parameter(options["land_transport_electric_share"])
-    fossil_share = 1 - fuel_cell_share - electric_share
+    ice_share = 1 - fuel_cell_share - electric_share
 
     print("shares of FCEV, EV and ICEV are",
           fuel_cell_share,
           electric_share,
-          fossil_share)
+          ice_share)
 
-    if fossil_share < 0:
+    if ice_share < 0:
         print("Error, more FCEV and EV share than 1.")
         sys.exit()
 
@@ -1264,14 +1148,14 @@ def add_land_transport(network):
                      p_set=fuel_cell_share/options['transport_fuel_cell_efficiency']*transport[nodes])
 
 
-    if fossil_share > 0:
+    if ice_share > 0:
 
         network.madd("Load",
                      nodes,
-                     suffix=" land transport fossil",
+                     suffix=" land transport oil",
-                     bus="Fischer-Tropsch",
+                     bus="EU oil",
-                     carrier="land transport fossil",
+                     carrier="land transport oil",
-                     p_set=fossil_share/options['transport_internal_combustion_efficiency']*transport[nodes])
+                     p_set=ice_share/options['transport_internal_combustion_efficiency']*transport[nodes])
 
 
 
@@ -1463,7 +1347,7 @@ def add_heat(network):
                              capital_cost=costs.at['central gas CHP','fixed']*costs.at['central gas CHP','efficiency'] + costs.at['biomass CHP capture','fixed']*costs.at['gas','CO2 intensity'],
                              marginal_cost=costs.at['central gas CHP','VOM'],
                              efficiency=costs.at['central gas CHP','efficiency'] - costs.at['gas','CO2 intensity']*(costs.at['biomass CHP capture','electricity-input'] + costs.at['biomass CHP capture','compression-electricity-input']),
-                             efficiency2=costs.at['central gas CHP','efficiency']/costs.at['central gas CHP','c_b'] + costs.at['gas','CO2 intensity']*(costs.at['biomass CHP capture','heat-output'] + costs.at['biomass CHP capture','compression-heat-output'] - costs.at['biomass CHP capture','heat-output']),
+                             efficiency2=costs.at['central gas CHP','efficiency']/costs.at['central gas CHP','c_b'] + costs.at['gas','CO2 intensity']*(costs.at['biomass CHP capture','heat-output'] + costs.at['biomass CHP capture','compression-heat-output'] - costs.at['biomass CHP capture','heat-input']),
                              efficiency3=costs.at['gas','CO2 intensity']*(1-costs.at['biomass CHP capture','capture_rate']),
                              efficiency4=costs.at['gas','CO2 intensity']*costs.at['biomass CHP capture','capture_rate'],
                              lifetime=costs.at['central gas CHP','lifetime'])
@@ -1682,7 +1566,7 @@ def add_biomass(network):
                      capital_cost=costs.at['central solid biomass CHP','fixed']*costs.at['central solid biomass CHP','efficiency'] + costs.at['biomass CHP capture','fixed']*costs.at['solid biomass','CO2 intensity'],
                      marginal_cost=costs.at['central solid biomass CHP','VOM'],
                      efficiency=costs.at['central solid biomass CHP','efficiency'] - costs.at['solid biomass','CO2 intensity']*(costs.at['biomass CHP capture','electricity-input'] + costs.at['biomass CHP capture','compression-electricity-input']),
-                     efficiency2=costs.at['central solid biomass CHP','efficiency-heat'] + costs.at['solid biomass','CO2 intensity']*(costs.at['biomass CHP capture','heat-output'] + costs.at['biomass CHP capture','compression-heat-output'] - costs.at['biomass CHP capture','heat-output']),
+                     efficiency2=costs.at['central solid biomass CHP','efficiency-heat'] + costs.at['solid biomass','CO2 intensity']*(costs.at['biomass CHP capture','heat-output'] + costs.at['biomass CHP capture','compression-heat-output'] - costs.at['biomass CHP capture','heat-input']),
                      efficiency3=-costs.at['solid biomass','CO2 intensity']*costs.at['biomass CHP capture','capture_rate'],
                      efficiency4=costs.at['solid biomass','CO2 intensity']*costs.at['biomass CHP capture','capture_rate'],
                      lifetime=costs.at['central solid biomass CHP','lifetime'])
@@ -1787,27 +1671,30 @@ def add_industry(network):
                  carrier="H2 for shipping",
                  p_set = nodal_energy_totals.loc[nodes,["total international navigation","total domestic navigation"]].sum(axis=1)*1e6*options['shipping_average_efficiency']/costs.at["fuel cell","efficiency"]/8760.)
 
-    network.madd("Bus",
+    if "EU oil" not in network.buses.index:
-                 ["Fischer-Tropsch"],
+        network.madd("Bus",
-                 location="EU",
+                     ["EU oil"],
-                 carrier="Fischer-Tropsch")
+                     location="EU",
+                     carrier="oil")
 
     #use madd to get carrier inserted
-    network.madd("Store",
+    if "EU oil Store" not in network.stores.index:
-                 ["Fischer-Tropsch Store"],
+        network.madd("Store",
-                 bus="Fischer-Tropsch",
+                     ["EU oil Store"],
-                 e_nom_extendable=True,
+                     bus="EU oil",
-                 e_cyclic=True,
+                     e_nom_extendable=True,
-                 carrier="Fischer-Tropsch",
+                     e_cyclic=True,
-                 capital_cost=0.) #could correct to e.g. 0.001 EUR/kWh * annuity and O&M
+                     carrier="oil",
+                     capital_cost=0.) #could correct to e.g. 0.001 EUR/kWh * annuity and O&M
 
-    network.add("Generator",
+    if "EU oil" not in network.generators.index:
-                "fossil oil",
+        network.add("Generator",
-                bus="Fischer-Tropsch",
+                    "EU oil",
-                p_nom_extendable=True,
+                    bus="EU oil",
-                carrier="oil",
+                    p_nom_extendable=True,
-                capital_cost=0.,
+                    carrier="oil",
-                marginal_cost=costs.at["oil",'fuel'])
+                    capital_cost=0.,
+                    marginal_cost=costs.at["oil",'fuel'])
 
     if options["oil_boilers"]:
 
@@ -1817,7 +1704,7 @@ def add_industry(network):
             network.madd("Link",
                          nodes_heat[name] + " " + name + " oil boiler",
                          p_nom_extendable=True,
-                         bus0=["Fischer-Tropsch"] * len(nodes_heat[name]),
+                         bus0="EU oil",
                          bus1=nodes_heat[name] + " " + name + " heat",
                          bus2="co2 atmosphere",
                          carrier=name + " oil boiler",
@@ -1830,7 +1717,7 @@ def add_industry(network):
     network.madd("Link",
                  nodes + " Fischer-Tropsch",
                  bus0=nodes + " H2",
-                 bus1="Fischer-Tropsch",
+                 bus1="EU oil",
                  bus2="co2 stored",
                  carrier="Fischer-Tropsch",
                  efficiency=costs.at["Fischer-Tropsch",'efficiency'],
@@ -1841,13 +1728,13 @@ def add_industry(network):
 
     network.madd("Load",
                  ["naphtha for industry"],
-                 bus="Fischer-Tropsch",
+                 bus="EU oil",
                  carrier="naphtha for industry",
                  p_set = industrial_demand.loc[nodes,"naphtha"].sum()/8760.)
 
     network.madd("Load",
                  ["kerosene for aviation"],
-                 bus="Fischer-Tropsch",
+                 bus="EU oil",
                  carrier="kerosene for aviation",
                  p_set = nodal_energy_totals.loc[nodes,["total international aviation","total domestic aviation"]].sum(axis=1).sum()*1e6/8760.)
 
@@ -1857,9 +1744,9 @@ def add_industry(network):
     co2 = network.loads.loc[["naphtha for industry","kerosene for aviation"],"p_set"].sum()*costs.at["oil",'CO2 intensity'] - industrial_demand.loc[nodes,"process emission from feedstock"].sum()/8760.
 
     network.madd("Load",
-                 ["Fischer-Tropsch emissions"],
+                 ["oil emissions"],
                  bus="co2 atmosphere",
-                 carrier="Fischer-Tropsch emissions",
+                 carrier="oil emissions",
                  p_set=-co2)
 
     network.madd("Load",
@@ -1935,13 +1822,6 @@ def add_waste_heat(network):
             network.links.loc[urban_central + " H2 Fuel Cell","bus2"] = urban_central + " urban central heat"
             network.links.loc[urban_central + " H2 Fuel Cell","efficiency2"] = 0.95 - network.links.loc[urban_central + " H2 Fuel Cell","efficiency"]
 
-
-def restrict_technology_potential(n,tech,limit):
-    print("restricting potentials (p_nom_max) for {} to {} of technical potential".format(tech,limit))
-    gens = n.generators.index[n.generators.carrier.str.contains(tech)]
-    #beware if limit is 0 and p_nom_max is np.inf, 0*np.inf is nan
-    n.generators.loc[gens,"p_nom_max"] *=limit
-
 def decentral(n):
     n.lines.drop(n.lines.index,inplace=True)
     n.links.drop(n.links.index[n.links.carrier.isin(["DC","B2B"])],inplace=True)
@@ -1984,8 +1864,9 @@ if __name__ == "__main__":
         from vresutils.snakemake import MockSnakemake
         snakemake = MockSnakemake(
             wildcards=dict(network='elec', simpl='', clusters='37', lv='1.0',
-                           opts='', planning_horizons='2050', 
+                           opts='', planning_horizons='2020',
-                           sector_opts='Co2L0-180H-T-H-B-I-solar3-dist1-solar+c0.5-Sabatier+c0.5'),
+                           sector_opts='120H-T-H-B-I-onwind+p3-dist1-cb48be3'),
+
             input=dict( network='../pypsa-eur/networks/{network}_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc',
                         energy_totals_name='resources/energy_totals.csv',
                         co2_totals_name='resources/co2_totals.csv',
@@ -2024,7 +1905,8 @@ if __name__ == "__main__":
                 	    retro_cost_energy = "resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
                         floor_area = "resources/floor_area_{network}_s{simpl}_{clusters}.csv"
             ),
-            output=['results/version-0/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc']
+            output=['results/version-cb48be3/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc']
+    
         )
         import yaml
         with open('config.yaml', encoding='utf8') as f:
@@ -2129,19 +2011,21 @@ if __name__ == "__main__":
     print("CO2 limit set to",limit)
 
     for o in opts:
-        if "cb" in o:    
+
+        if "cb" in o:
             path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
             if not os.path.exists(path_cb):
                 os.makedirs(path_cb)
             try:
                 CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv', index_col=0)
-            except:    
+            except:
                 build_carbon_budget(o)
                 CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv', index_col=0)
-            
+
-            limit=CO2_CAP.loc[investment_year]            
+            limit=CO2_CAP.loc[investment_year]
             print("overriding CO2 limit with scenario limit",limit)
-            
+
+
     for o in opts:
         if "Co2L" in o:
             limit = o[o.find("Co2L")+4:]
@@ -2150,14 +2034,8 @@ if __name__ == "__main__":
 
     print("adding CO2 budget limit as per unit of 1990 levels of",limit)
     add_co2limit(n, Nyears, limit)
-           
+
     for o in opts:
-        for tech in ["solar","onwind","offwind"]:
-            if tech in o and "+" not in o:
-                limit = o[o.find(tech)+len(tech):]
-                limit = float(limit.replace("p",".").replace("m","-"))
-                print("changing potential for",tech,"by factor",limit)
-                restrict_technology_potential(n,tech,limit)
 
         if o[:10] == 'linemaxext':
             maxext = float(o[10:])*1e3
@@ -2169,28 +2047,31 @@ if __name__ == "__main__":
 
     if snakemake.config["sector"]['electricity_distribution_grid']:
         insert_electricity_distribution_grid(n)
-        
     for o in opts:
         if "+" in o:
             oo = o.split("+")
-            carrier_list=np.hstack((n.generators.carrier.unique(), n.links.carrier.unique(), 
+            carrier_list=np.hstack((n.generators.carrier.unique(), n.links.carrier.unique(),
-                                    n.stores.carrier.unique(), n.storage_units.carrier.unique()))            
+                                    n.stores.carrier.unique(), n.storage_units.carrier.unique()))
-            suptechs = map(lambda c: c.split("-", 2)[0], carrier_list) 
+            suptechs = map(lambda c: c.split("-", 2)[0], carrier_list)
             if oo[0].startswith(tuple(suptechs)):
                 carrier = oo[0]
-                # handles only p_nom_max as stores and lines have no potentials
                 attr_lookup = {"p": "p_nom_max", "c": "capital_cost"}
                 attr = attr_lookup[oo[1][0]]
                 factor = float(oo[1][1:])
+                #beware if factor is 0 and p_nom_max is np.inf, 0*np.inf is nan
                 if carrier == "AC":  # lines do not have carrier
                     n.lines[attr] *= factor
                 else:
-                    comps = {"Generator", "Link", "StorageUnit", "Store"}
+                    comps = {"Generator", "Link", "StorageUnit"} if attr=='p_nom_max' else {"Generator", "Link", "StorageUnit", "Store"}
                     for c in n.iterate_components(comps):
-                        sel = c.df.carrier.str.contains(carrier)
+                        if carrier=='solar':
+                            sel = c.df.carrier.str.contains(carrier) & ~c.df.carrier.str.contains("solar rooftop")
+                        else:
+                            sel = c.df.carrier.str.contains(carrier)
                         c.df.loc[sel,attr] *= factor
                 print("changing", attr ,"for",carrier,"by factor",factor)
-            
+
+
     if snakemake.config["sector"]['gas_distribution_grid']:
         insert_gas_distribution_costs(n)
     if snakemake.config["sector"]['electricity_grid_connection']: