Merge pull request #97 from PyPSA/retro-updated

Retro updated

.gitignore

@@ -26,6 +26,7 @@ gurobi.log
 /data/switzerland*
 /data/.nfs*
 /data/Industrial_Database.csv
+/data/retro/tabula-calculator-calcsetbuilding.csv
 
 *.org
 

Snakefile

@@ -281,14 +281,15 @@ rule build_industrial_demand:
 rule build_retro_cost:
     input:
         building_stock="data/retro/data_building_stock.csv",
+        data_tabula="data/retro/tabula-calculator-calcsetbuilding.csv",
+        air_temperature = "resources/temp_air_total_{network}_s{simpl}_{clusters}.nc",
         u_values_PL="data/retro/u_values_poland.csv",
         tax_w="data/retro/electricity_taxes_eu.csv",
         construction_index="data/retro/comparative_level_investment.csv",
-        average_surface="data/retro/average_surface_components.csv",
         floor_area_missing="data/retro/floor_area_missing.csv",
         clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
         cost_germany="data/retro/retro_cost_germany.csv",
-        window_assumptions="data/retro/window_assumptions.csv"
+        window_assumptions="data/retro/window_assumptions.csv",
     output:
         retro_cost="resources/retro_cost_elec_s{simpl}_{clusters}.csv",
         floor_area="resources/floor_area_elec_s{simpl}_{clusters}.csv"

config.default.yaml

@@ -119,20 +119,25 @@ sector:
   'time_dep_hp_cop' : True #time dependent heat pump coefficient of performance
   'heat_pump_sink_T' : 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
    # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  'retrofitting' :
-    'retro_exogen': True # space heat demand savings exogenously
-    'dE': # reduction of space heat demand (applied before losses in DH)
-      2020 : 0.
-      2030 : 0.15
-      2040 : 0.3
-      2050 : 0.4
+  'reduce_space_heat_exogenously': True  # reduces space heat demand by a given factor (applied before losses in DH)
+  # this can represent e.g. building renovation, building demolition, or if
+  # the factor is negative: increasing floor area, increased thermal comfort, population growth
+  'reduce_space_heat_exogenously_factor': # per unit reduction in space heat demand
+  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
+          2020:    0.10  # this results in a space heat demand reduction of 10%
+          2025:    0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
+          2030:    0.09
+          2035:    0.11
+          2040:    0.16
+          2045:    0.21
+          2050:    0.29
+  'retrofitting' :  # co-optimises building renovation to reduce space heat demand
     'retro_endogen': False  # co-optimise space heat savings
-    'cost_factor' : 1.0
+    'cost_factor' : 1.0   # weight costs for building renovation
     'interest_rate': 0.04  # for investment in building components
     'annualise_cost': True  # annualise the investment costs
     'tax_weighting': False   # weight costs depending on taxes in countries
-    'construction_index': True   # weight costs depending on labour/material costs per ct
-    'l_strength': ["0.076", "0.197"] # additional insulation thickness[m], determines number of retro steps(=generators per bus) and maximum possible savings
+    'construction_index': True   # weight costs depending on labour/material costs per country
   'tes' : True
   'tes_tau' : 3.
   'boilers' : True

data/retro/average_surface_components.csv

@@ -1,7 +0,0 @@
-,Dwelling,Ceilling,Standard component surfaces (m2),component,surfaces,(m2),,
-Building type,Space(m²),Height(m),Roof,Facade,Floor,Windows,,
-Single/two family house,120,2.5,90,166,63,29,,
-Large apartment house,1457,2.5,354,1189,354,380,,
-Apartment house,5276,,598.337,2992.1,598.337,756,tabula ,http://webtool.building-typology.eu/#pdfes
-,,,,,,,,
-"Source: https://link.springer.com/article/10.1007/s12053-010-9090-6  ,p.4",,,,,,,,

doc/data.csv

@@ -22,5 +22,5 @@ U-values Poland,u_values_poland.csv,unknown,https://data.europa.eu/euodp/de/data
 Floor area missing in hotmaps building stock data,floor_area_missing.csv,unknown,https://data.europa.eu/euodp/de/data/dataset/building-stock-observatory
 Comparative level investment,comparative_level_investment.csv,Eurostat,https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment
 Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
-Average surface components,average_surface_components.csv,unknown,http://webtool.building-typology.eu/#bm
+Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
 Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/

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-210125.tar.gz"
-    projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle-210125.tar.gz
+    projects/pypsa-eur-sec/data % wget "https://nworbmot.org/pypsa-eur-sec-data-bundle-210418.tar.gz"
+    projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle-210418.tar.gz
 
 
 The data licences and sources are given in the following table.

doc/release_notes.rst

@@ -6,6 +6,7 @@ Future release
 ===================
 
 * 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.
+* The cost database for retrofitting of the thermal envelope of buildings has been updated. Now, for calculating the space heat savings of a building, losses by thermal bridges and ventilation are included as well as heat gains (internal and by solar radiation). See the section :ref:`retro` for more details on the retrofitting module.
 * 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: The new separation of land transport by carrier in Version 0.4.0 failed to account for the carbon dioxide emissions from internal combustion engines. This is now treated as a negative load on the atmospheric carbon dioxide bus, just like aviation emissions.
@@ -137,4 +138,4 @@ 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-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
+    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 retro/tabula-calculator-calcsetbuilding.csv

doc/supply_demand.rst

@@ -108,6 +108,43 @@ Small for decentral applications.
 
 Big water pit storage for district heating.
 
+.. _retro:
+
+Retrofitting of the thermal envelope of buildings
+===================================================
+Co-optimising building renovation is only enabled if in the ``config.yaml`` the
+option :mod:`retro_endogen: True`. To reduce the computational burden
+default setting is
+
+.. literalinclude:: ../config.default.yaml
+    :language: yaml
+    :lines: 134-135
+
+Renovation of the thermal envelope reduces the space heating demand and is
+optimised at each node for every heat bus. Renovation measures through additional
+insulation material and replacement of energy inefficient windows are considered.
+
+In a first step, costs per energy savings are estimated in :mod:`build_retro_cost.py`.
+They depend on the insulation condition of the building stock and costs for
+renovation of the building elements.
+In a second step, for those cost per energy savings two possible renovation
+strengths are determined: a moderate renovation with lower costs and lower
+maximum possible space heat savings, and an ambitious renovation with associated
+higher costs and higher efficiency gains. They are added by step-wise
+linearisation in form of two additional generations in
+:mod:`prepare_sector_network.py`.
+
+Settings in the config.yaml concerning the endogenously optimisation of building
+renovation
+
+.. literalinclude:: ../config.default.yaml
+    :language: yaml
+    :lines: 136-140
+
+Further information are given in the publication
+
+`Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021) <https://arxiv.org/abs/2012.01831>`_.
+
 
 Hydrogen demand
 ==================

scripts/make_summary.py

@@ -196,25 +196,25 @@ def calculate_costs(n,label,costs):
 
     return costs
 
-def calculate_cumulative_cost():        
+def calculate_cumulative_cost():
     planning_horizons = snakemake.config['scenario']['planning_horizons']
 
     cumulative_cost = pd.DataFrame(index = df["costs"].sum().index,
                                   columns=pd.Series(data=np.arange(0,0.1, 0.01), name='social discount rate'))
-    
+
     #discount cost and express them in money value of planning_horizons[0]
     for r in cumulative_cost.columns:
         cumulative_cost[r]=[df["costs"].sum()[index]/((1+r)**(index[-1]-planning_horizons[0])) for index in cumulative_cost.index]
-    
+
     #integrate cost throughout the transition path
-    for r in cumulative_cost.columns:       
+    for r in cumulative_cost.columns:
         for cluster in cumulative_cost.index.get_level_values(level=0).unique():
             for lv in cumulative_cost.index.get_level_values(level=1).unique():
                 for sector_opts in cumulative_cost.index.get_level_values(level=2).unique():
                     cumulative_cost.loc[(cluster, lv, sector_opts,'cumulative cost'),r] = np.trapz(cumulative_cost.loc[idx[cluster, lv, sector_opts,planning_horizons],r].values, x=planning_horizons)
 
-    return cumulative_cost 
-    
+    return cumulative_cost
+
 def calculate_nodal_capacities(n,label,nodal_capacities):
     #Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
     for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
@@ -285,7 +285,7 @@ def calculate_supply(n,label,supply):
 
         for c in n.iterate_components(n.one_port_components):
 
-            items = c.df.index[c.df.bus.map(bus_map)]
+            items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
 
             if len(items) == 0:
                 continue
@@ -330,7 +330,7 @@ def calculate_supply_energy(n,label,supply_energy):
 
         for c in n.iterate_components(n.one_port_components):
 
-            items = c.df.index[c.df.bus.map(bus_map)]
+            items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
 
             if len(items) == 0:
                 continue
@@ -611,7 +611,7 @@ if __name__ == "__main__":
     print(networks_dict)
 
     Nyears = 1
-    
+
     costs_db = prepare_costs(snakemake.input.costs,
                              snakemake.config['costs']['USD2013_to_EUR2013'],
                              snakemake.config['costs']['discountrate'],
@@ -623,10 +623,9 @@ if __name__ == "__main__":
     df["metrics"].loc["total costs"] =  df["costs"].sum()
 
     to_csv(df)
-    
+
     if snakemake.config["foresight"]=='myopic':
         cumulative_cost=calculate_cumulative_cost()
         cumulative_cost.to_csv(snakemake.config['summary_dir'] + '/' + snakemake.config['run'] + '/csvs/cumulative_cost.csv')
 
 
-                                                     

scripts/prepare_sector_network.py

@@ -248,6 +248,14 @@ def remove_elec_base_techs(n):
         n.carriers.drop(to_remove, inplace=True, errors="ignore")
 
 
+def remove_non_electric_buses(n):
+    """
+    remove buses from pypsa-eur with carriers which are not AC buses
+    """
+    print("drop buses from PyPSA-Eur with carrier: ", n.buses[~n.buses.carrier.isin(["AC", "DC"])].carrier.unique())
+    n.buses = n.buses[n.buses.carrier.isin(["AC", "DC"])]
+
+
 def add_co2_tracking(n):
 
 
@@ -1174,11 +1182,10 @@ def add_heat(network):
 
     urban_fraction = options['central_fraction']*pop_layout["urban"]/(pop_layout[["urban","rural"]].sum(axis=1))
 
-    # building retrofitting, exogenously reduce space heat demand
-    if options["retrofitting"]["retro_exogen"]:
-        dE = get_parameter(options["retrofitting"]["dE"])
-        print("retrofitting exogenously, assumed space heat reduction of ",
-              dE)
+    # exogenously reduce space heat demand
+    if options["reduce_space_heat_exogenously"]:
+        dE = get_parameter(options["reduce_space_heat_exogenously_factor"])
+        print("assumed space heat reduction of {} %".format(dE*100))
         for sector in sectors:
             heat_demand[sector + " space"] = (1-dE)*heat_demand[sector + " space"]
 
@@ -1908,8 +1915,7 @@ if __name__ == "__main__":
                 	    retro_cost_energy = "resources/retro_cost_elec_s{simpl}_{clusters}.csv",
                         floor_area = "resources/floor_area_elec_s{simpl}_{clusters}.csv"
             ),
-            output=['results/version-cb48be3/prenetworks/elec_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:
@@ -1949,6 +1955,8 @@ if __name__ == "__main__":
 
     n.loads["carrier"] = "electricity"
 
+    remove_non_electric_buses(n)
+
     n.buses["location"] = n.buses.index
 
     update_wind_solar_costs(n, costs)