Tom Brown
GitHub
commit
2b72aeb43c
22
Snakefile
22
Snakefile
@ -278,6 +278,23 @@ rule build_industrial_demand:
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resources: mem_mb=1000
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script: 'scripts/build_industrial_demand.py'
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if config['sector']['retrofitting'].get('retro_endogen', True):
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rule build_retro_cost:
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input:
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building_stock="data/retro/data_building_stock.csv",
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u_values_PL="data/retro/u_values_poland.csv",
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tax_w="data/retro/electricity_taxes_eu.csv",
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construction_index="data/retro/comparative_level_investment.csv",
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average_surface="data/retro/average_surface_components.csv",
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floor_area_missing="data/retro/floor_area_missing.csv",
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
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cost_germany="data/retro/retro_cost_germany.csv",
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window_assumptions="data/retro/window_assumptions.csv"
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output:
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retro_cost="resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
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floor_area="resources/floor_area_{network}_s{simpl}_{clusters}.csv"
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script: "scripts/build_retro_cost.py"
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rule prepare_sector_network:
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input:
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@ -285,6 +302,7 @@ rule prepare_sector_network:
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energy_totals_name='resources/energy_totals.csv',
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co2_totals_name='resources/co2_totals.csv',
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transport_name='resources/transport_data.csv',
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traffic_data = "data/emobility/",
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biomass_potentials='resources/biomass_potentials.csv',
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timezone_mappings='data/timezone_mappings.csv',
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heat_profile="data/heat_load_profile_BDEW.csv",
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@ -314,7 +332,9 @@ rule prepare_sector_network:
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cop_air_urban="resources/cop_air_urban_{network}_s{simpl}_{clusters}.nc",
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solar_thermal_total="resources/solar_thermal_total_{network}_s{simpl}_{clusters}.nc",
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solar_thermal_urban="resources/solar_thermal_urban_{network}_s{simpl}_{clusters}.nc",
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solar_thermal_rural="resources/solar_thermal_rural_{network}_s{simpl}_{clusters}.nc"
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solar_thermal_rural="resources/solar_thermal_rural_{network}_s{simpl}_{clusters}.nc",
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retro_cost_energy = "resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
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floor_area = "resources/floor_area_{network}_s{simpl}_{clusters}.csv"
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output: config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc'
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threads: 1
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resources: mem_mb=2000
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@ -101,10 +101,16 @@ sector:
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'shipping_average_efficiency' : 0.4 #For conversion of fuel oil to propulsion in 2011
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'time_dep_hp_cop' : True
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'space_heating_fraction' : 1.0 #fraction of space heating active
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'retrofitting' : False
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'retroI-fraction' : 0.25
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'retroII-fraction' : 0.55
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'retrofitting-cost_factor' : 1.0
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'retrofitting' :
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'retro_exogen': False # space heat demand savings exogenously
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'dE': 0.4 # reduction of space heat demand (applied before losses in DH)
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'retro_endogen': True # co-optimise space heat savings
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'cost_factor' : 1.0
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'interest_rate': 0.04 # for investment in building components
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'annualise_cost': True # annualise the investment costs
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'tax_weighting': False # weight costs depending on taxes in countries
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'construction_index': True # weight costs depending on labour/material costs per ct
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'l_strength': ["0.076", "0.197"] # additional insulation thickness[m], determines number of retro steps(=generators per bus) and maximum possible savings
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'tes' : True
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'tes_tau' : 3.
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'boilers' : True
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@ -130,7 +136,6 @@ sector:
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'gas_distribution_grid_cost_factor' : 1.0 #multiplies cost in data/costs.csv
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costs:
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year: 2030
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lifetime: 25 #default lifetime
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# From a Lion Hirth paper, also reflects average of Noothout et al 2016
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discountrate: 0.07
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7
data/retro/average_surface_components.csv
Normal file
7
data/retro/average_surface_components.csv
Normal file
@ -0,0 +1,7 @@
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,Dwelling,Ceilling,Standard component surfaces (m2),component,surfaces,(m2),,
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Building type,Space(m²),Height(m),Roof,Facade,Floor,Windows,,
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Single/two family house,120,2.5,90,166,63,29,,
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Large apartment house,1457,2.5,354,1189,354,380,,
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Apartment house,5276,,598.337,2992.1,598.337,756,tabula ,http://webtool.building-typology.eu/#pdfes
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,,,,,,,,
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"Source: https://link.springer.com/article/10.1007/s12053-010-9090-6 ,p.4",,,,,,,,
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49
data/retro/comparative_level_investment.csv
Normal file
49
data/retro/comparative_level_investment.csv
Normal file
@ -0,0 +1,49 @@
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NA_ITEM,Price level indices (EU28=100),,,,,,,,,
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PPP_CAT,Actual individual consumption,,,,,,,,,
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,,,,,,,,,,
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GEO/TIME,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018
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European Union - 28 countries,100.0,100.0,100.0,100.0,100.0,100.0,100.0,100.0,100.0,100.0
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Belgium,113.6,111.9,112.4,111.5,111.0,108.9,106.3,110.3,112.3,112.5
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Bulgaria,47.1,45.7,45.5,45.0,44.2,42.6,42.2,43.2,45.1,46.3
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Czech Republic,64.5,66.6,68.9,66.9,63.3,58.3,58.4,60.5,62.4,65.0
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Denmark,141.7,140.0,139.9,140.0,139.3,138.5,135.0,140.0,138.9,138.1
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Germany,104.6,103.1,102.2,101.1,102.5,101.5,100.4,102.6,103.7,104.1
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Estonia,67.5,66.0,67.2,67.6,69.9,69.9,68.9,71.0,73.9,76.3
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Ireland,129.9,122.7,122.5,120.5,123.2,124.9,122.2,126.5,129.1,129.2
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Greece,93.6,95.4,94.9,91.9,87.8,83.8,81.0,82.3,83.0,81.8
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Spain,97.5,98.7,98.5,95.8,95.1,92.7,90.0,92.7,93.7,93.7
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France,111.2,109.9,109.6,108.7,107.0,106.0,104.0,105.8,107.1,107.4
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Croatia,70.2,70.1,68.1,65.5,64.5,62.5,60.7,61.3,63.0,64.0
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Italy,103.6,100.4,101.5,101.1,102.3,102.6,100.3,101.1,101.6,101.4
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Cyprus,92.0,94.6,95.8,96.0,95.2,92.0,88.5,89.8,91.2,90.6
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Latvia,68.1,62.3,65.5,65.9,66.0,66.0,64.2,66.9,68.3,69.5
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Lithuania,60.3,57.8,58.3,58.0,57.8,56.9,55.9,58.3,60.0,61.4
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Luxembourg,130.0,136.5,136.0,135.8,135.1,135.7,132.1,137.0,139.9,141.6
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Hungary,58.2,57.4,56.4,54.9,54.4,53.4,53.3,56.2,59.4,59.0
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Malta,75.8,76.6,78.0,78.0,80.8,80.5,79.8,81.4,81.9,83.4
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Netherlands,108.5,112.3,112.7,111.3,111.9,111.9,109.6,113.8,114.6,114.8
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Austria,109.9,109.2,110.1,108.9,109.1,109.1,107.2,110.2,112.8,113.7
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Poland,53.1,55.2,53.7,52.1,52.4,52.5,51.1,50.9,53.5,54.3
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Portugal,85.2,85.0,85.3,82.7,81.1,80.4,78.7,81.6,83.5,84.6
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Romania,49.1,46.9,47.7,45.6,47.8,47.6,47.2,46.8,48.0,48.6
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Slovenia,85.3,84.3,83.7,81.8,82.1,81.5,79.8,82.3,82.7,83.8
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Slovakia,66.6,62.5,63.4,63.4,63.4,63.3,62.3,63.6,65.4,66.1
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Finland,121.0,120.3,121.6,121.8,124.0,122.9,119.6,122.8,123.3,123.4
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Sweden,109.5,124.6,131.7,134.3,140.5,133.6,128.8,135.3,134.5,126.9
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United Kingdom,107.5,111.4,111.3,118.6,117.0,123.6,134.7,123.5,117.6,117.7
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Iceland,94.9,107.6,109.6,111.6,116.0,123.4,132.5,154.5,172.3,163.7
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Norway,142.4,158.8,165.3,172.5,166.9,157.2,152.2,155.0,157.3,155.4
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Switzerland,131.6,146.4,161.7,160.6,155.1,153.0,167.0,169.8,167.1,159.1
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Candidate and potential candidate countries except Turkey and Kosovo (under United Nations Security Council Resolution 1244/99),48.0,45.6,47.1,44.8,46.4,45.2,43.4,44.4,46.0,47.5
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Montenegro,52.3,49.5,49.3,50.1,50.5,49.3,48.0,48.7,50.5,51.1
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North Macedonia,41.4,41.3,42.7,42.1,42.5,41.9,40.9,41.7,43.2,43.3
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Albania,46.2,42.8,42.1,40.6,41.9,41.5,39.8,43.0,43.5,46.6
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Serbia,48.3,45.0,48.0,44.5,47.3,45.5,43.1,43.8,46.1,47.9
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Turkey,55.4,61.2,54.7,58.5,57.7,51.6,50.5,50.2,45.4,37.0
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Bosnia and Herzegovina,51.6,50.7,50.6,49.2,49.1,48.4,47.0,47.5,48.2,48.9
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Kosovo (under United Nations Security Council Resolution 1244/99),:,:,:,:,:,:,:,:,:,:
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United States,92.4,98,93.3,101.2,100.3,99,115.9,121.1,120.8,115.2
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Japan,115.1,126.1,127.8,133.8,101.7,94.8,96.5,113,109.4,103.9
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,,,,,,,,,,
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"Source: Eurostat Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates (2019)",,,,,,,,,,
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https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment,,,,,,,,,,
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63129
data/retro/data_building_stock.csv
Normal file
63129
data/retro/data_building_stock.csv
Normal file
File diff suppressed because it is too large
Load Diff
164
data/retro/electricity_taxes_eu.csv
Normal file
164
data/retro/electricity_taxes_eu.csv
Normal file
@ -0,0 +1,164 @@
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Electricity prices for household consumers - bi-annual data (from 2007 onwards) [nrg_pc_204],,,,
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,,,,
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Last update,30.10.19,,,
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Extracted on,14.11.19,,,
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Source of data,Eurostat,,,
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,,,,
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PRODUCT,Electrical energy,,,
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CONSOM,Band DC : 2 500 kWh < Consumption < 5 000 kWh,,,
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UNIT,Kilowatt-hour,,,
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TIME,2018S1,,,
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,,,,
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CURRENCY,Euro,Euro,Euro,
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GEO/TAX,Excluding taxes and levies,Excluding VAT and other recoverable taxes and levies,All taxes and levies included,% cost without taxes
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European Union - 28 countries,0.1285,0.1756,0.2052,0.626218323586745
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"Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19)",0.1331,0.1855,0.2188,0.608318098720293
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Belgium,0.1903,0.2279,0.2733,0.696304427369191
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Bulgaria,0.0816,0.0816,0.0979,0.833503575076609
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Czech Republic,0.1286,0.1298,0.1573,0.817546090273363
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Denmark,0.1011,0.2501,0.3126,0.32341650671785
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Germany,0.1379,0.2510,0.2987,0.461667224640107
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Estonia,0.0989,0.1123,0.1348,0.733679525222552
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Ireland,0.1846,0.2087,0.2369,0.779231743351625
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Greece,0.1132,0.1482,0.1672,0.677033492822967
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Spain,0.1873,0.1969,0.2383,0.785984053713806
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France,0.1134,0.1492,0.1748,0.648741418764302
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Croatia,0.1020,0.1160,0.1311,0.778032036613272
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Italy,0.1285,0.1873,0.2067,0.621673923560716
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Cyprus,0.1445,0.1606,0.1893,0.763338615953513
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Latvia,0.1035,0.1266,0.1531,0.676028739386022
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Lithuania,0.0771,0.0906,0.1097,0.702825888787603
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Luxembourg,0.1283,0.1547,0.1671,0.767803710353082
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Hungary,0.0885,0.0885,0.1123,0.78806767586821
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Malta,0.1209,0.1224,0.1285,0.940856031128405
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Netherlands,0.1187,0.1410,0.1706,0.6957796014068
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Austria,0.1232,0.1638,0.1966,0.626653102746694
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Poland,0.0906,0.1146,0.1410,0.642553191489362
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Portugal,0.1007,0.1826,0.2246,0.448352626892253
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Romania,0.0990,0.1120,0.1333,0.742685671417854
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Slovenia,0.1108,0.1322,0.1613,0.686918784872908
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Slovakia,0.0942,0.1305,0.1566,0.601532567049808
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Finland,0.1074,0.1300,0.1612,0.666253101736973
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Sweden,0.1202,0.1513,0.1891,0.635642517186674
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United Kingdom,0.1347,0.1797,0.1887,0.713831478537361
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Iceland,0.1222,0.1246,0.1545,0.790938511326861
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Liechtenstein,:,:,:,#VALUE!
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Norway,0.1254,0.1434,0.1751,0.716162193032553
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Montenegro,0.0828,0.0844,0.1024,0.80859375
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North Macedonia,0.0662,0.0662,0.0781,0.847631241997439
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Albania,:,:,:,#VALUE!
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Serbia,0.0539,0.0587,0.0705,0.764539007092199
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Turkey,0.0727,0.0766,0.0904,0.804203539823009
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Bosnia and Herzegovina,0.0722,0.0738,0.0864,0.835648148148148
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Kosovo (under United Nations Security Council Resolution 1244/99),0.0569,0.0586,0.0633,0.898894154818325
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Moldova,0.1020,0.1020,0.1020,1
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Ukraine,0.0342,0.0342,0.0410,0.834146341463415
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,,,0.157271052631579,
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Special value:,,,,
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:,not available,,,
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,,,,
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PRODUCT,Electrical energy,,,
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CONSOM,Band DC : 2 500 kWh < Consumption < 5 000 kWh,,,
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UNIT,Kilowatt-hour,,,
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TIME,2018S2,,,
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,,,,
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CURRENCY,Euro,Euro,Euro,
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GEO/TAX,Excluding taxes and levies,Excluding VAT and other recoverable taxes and levies,All taxes and levies included,
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European Union - 28 countries,0.1329,0.1810,0.2113,
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"Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19)",0.1376,0.1902,0.2242,
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Belgium,0.1998,0.2429,0.2937,
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Bulgaria,0.0838,0.0838,0.1005,
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Czechia,0.1299,0.1311,0.1586,
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Denmark,0.1116,0.2499,0.3123,
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Germany (until 1990 former territory of the FRG),0.1378,0.2521,0.3000,
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Estonia,0.1048,0.1182,0.1418,
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Ireland,0.2006,0.2237,0.2539,
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Greece,0.1125,0.1458,0.1646,
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Spain,0.1947,0.2047,0.2477,
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France,0.1168,0.1537,0.1799,
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Croatia,0.1028,0.1169,0.1321,
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Italy,0.1416,0.1964,0.2161,
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Cyprus,0.1745,0.1850,0.2183,
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Latvia,0.1041,0.1249,0.1511,
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Lithuania,0.0771,0.0906,0.1097,
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Luxembourg,0.1302,0.1566,0.1691,
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Hungary,0.0880,0.0880,0.1118,
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Malta,0.1229,0.1244,0.1306,
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Netherlands,0.1212,0.1420,0.1707,
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Austria,0.1265,0.1676,0.2012,
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Poland,0.0889,0.1135,0.1396,
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Portugal,0.1028,0.1864,0.2293,
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Romania,0.0964,0.1107,0.1317,
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Slovenia,0.1125,0.1342,0.1638,
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Slovakia,0.0849,0.1218,0.1462,
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Finland,0.1144,0.1369,0.1698,
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Sweden,0.1287,0.1592,0.1990,
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United Kingdom,0.1401,0.1927,0.2024,
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Iceland,0.1152,0.1175,0.1457,
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Liechtenstein,:,:,:,
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Norway,0.1382,0.1562,0.1907,
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Montenegro,0.0829,0.0848,0.1030,
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North Macedonia,0.0667,0.0667,0.0787,
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Albania,0.0759,0.0759,0.0910,
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Serbia,0.0542,0.0591,0.0709,
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Turkey,0.0688,0.0726,0.0857,
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Bosnia and Herzegovina,0.0729,0.0744,0.0871,
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Kosovo (under United Nations Security Council Resolution 1244/99),0.0579,0.0591,0.0638,
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Moldova,0.0960,0.0960,0.1029,
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Ukraine,0.0342,0.0342,0.0410,
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,,,,
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Special value:,,,,
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:,not available,,,
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,,,,
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PRODUCT,Electrical energy,,,
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CONSOM,Band DC : 2 500 kWh < Consumption < 5 000 kWh,,,
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UNIT,Kilowatt-hour,,,
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TIME,2019S1,,,
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,,,,
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CURRENCY,Euro,Euro,Euro,
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GEO/TAX,Excluding taxes and levies,Excluding VAT and other recoverable taxes and levies,All taxes and levies included,
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European Union - 28 countries,0.1351,0.1841,0.2147,
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"Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19)",0.1396,0.1928,0.2270,
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Belgium,0.1965,0.2355,0.2839,
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Bulgaria,0.0831,0.0831,0.0997,
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Czechia,0.1433,0.1444,0.1748,
|
||||
Denmark,0.1084,0.2387,0.2984,
|
||||
Germany (until 1990 former territory of the FRG),0.1473,0.2595,0.3088,
|
||||
Estonia,0.0982,0.1131,0.1357,
|
||||
Ireland,0.2027,0.2134,0.2423,
|
||||
Greece,0.1139,0.1482,0.1650,
|
||||
Spain,0.1889,0.1986,0.2403,
|
||||
France,0.1138,0.1508,0.1765,
|
||||
Croatia,0.1028,0.1169,0.1321,
|
||||
Italy,0.1432,0.2090,0.2301,
|
||||
Cyprus,0.1762,0.1867,0.2203,
|
||||
Latvia,0.1136,0.1347,0.1629,
|
||||
Lithuania,0.0947,0.1037,0.1255,
|
||||
Luxembourg,0.1326,0.1666,0.1798,
|
||||
Hungary,0.0882,0.0882,0.1120,
|
||||
Malta,0.1228,0.1243,0.1305,
|
||||
Netherlands,0.1357,0.1708,0.2052,
|
||||
Austria,0.1316,0.1695,0.2034,
|
||||
Poland,0.0884,0.1092,0.1343,
|
||||
Portugal,0.1103,0.1751,0.2154,
|
||||
Romania,0.0983,0.1141,0.1358,
|
||||
Slovenia,0.1125,0.1339,0.1634,
|
||||
Slovakia,0.0962,0.1314,0.1577,
|
||||
Finland,0.1173,0.1398,0.1734,
|
||||
Sweden,0.1297,0.1612,0.2015,
|
||||
United Kingdom,0.1450,0.2021,0.2122,
|
||||
Iceland,0.1112,0.1134,0.1406,
|
||||
Liechtenstein,:,:,:,
|
||||
Norway,0.1360,0.1529,0.1867,
|
||||
Montenegro,0.0834,0.0850,0.1032,
|
||||
North Macedonia,:,:,:,
|
||||
Albania,:,:,:,
|
||||
Serbia,0.0541,0.0589,0.0706,
|
||||
Turkey,0.0684,0.0718,0.0847,
|
||||
Bosnia and Herzegovina,0.0729,0.0746,0.0873,
|
||||
Kosovo (under United Nations Security Council Resolution 1244/99),0.0537,0.0556,0.0600,
|
||||
Moldova,0.0936,0.0936,0.0936,
|
||||
Ukraine,0.0369,0.0369,0.0442,
|
||||
,,,,
|
||||
Special value:,,,,
|
||||
:,not available,,,
|
||||
|
17
data/retro/floor_area_missing.csv
Normal file
17
data/retro/floor_area_missing.csv
Normal file
@ -0,0 +1,17 @@
|
||||
country,sector,estimated,value,source,,comments,population [in Million],
|
||||
AL,residential,0,64,p.13 1.6 million m² = 2.5% of total floor area,https://www.buildup.eu/sites/default/files/content/sled_albania_residential_building_eng.pdf,,,
|
||||
AL,services,0,,,,,,
|
||||
BA,residential,0,125.89,Tabula,https://episcope.eu/building-typology/country/ba/,strong differences ? other source claims more than 300 Million m²,,https://www.buildup.eu/sites/default/files/content/sled_serbia_building_eng.pdf
|
||||
BA,services,0,,,,,,
|
||||
RS,residential,0,72.3,Odyssee(2011),https://odyssee.enerdata.net/database/,,,
|
||||
RS,services,0,,,,,,
|
||||
MK,residential,0,,"Worldbank p.7 Skopje 75% residential, 25% commercial",http://documents.albankaldawli.org/curated/ar/838951574180734318/pdf/Project-Information-Document-North-Macedonia-Public-Sector-Energy-Efficiency-Project-P149990.pdf,15 % live in illegal constructed buildings ? not part of the statistics,2.1,
|
||||
MK,services,0,,,,,,
|
||||
ME,residential,0,19.625,p.13 0.314 million m² = 1.6% of total floor area,buildup.eu/sites/default/files/content/sled_montenegro_building_eng.pdf,Only 50 % of the floor area is heated p.12,,buildup.eu/sites/default/files/content/sled_montenegro_building_eng.pdf
|
||||
ME,services,0,,,,,,
|
||||
CH,residential,0,99.45,Odyssee(2015),,,,
|
||||
CH,services,1,78.1392857142857,p.8 44%floor area is services,https://bta.climate-kic.org/wp-content/uploads/2018/04/171123-CK-BTA-DEF-BMB_SWITZERLAND_.pdf,,,
|
||||
NO,residential,0,121.55,Odyssee(2015),,,,
|
||||
NO,services,0,115.21,Odyssee(2015),,,,
|
||||
PL,residential,0,1028.41,EU Building Database,,,,
|
||||
PL,services,0,498.84,EU Building Database,,,,
|
||||
|
7
data/retro/retro_cost_germany.csv
Normal file
7
data/retro/retro_cost_germany.csv
Normal file
@ -0,0 +1,7 @@
|
||||
component,cost_fix,cost_var,life_time,comment,additional source
|
||||
wall,70.34,2.36,40,Agora Energiewende p.110,
|
||||
floor,39.39,1.3,40,Agora Energiewende p.110,
|
||||
roof,75.61,1.3,40,Agora Energiewende p.110,https://www.baulinks.de/webplugin/2018/1524.php4
|
||||
window,nan,nan,35,,
|
||||
source: p.37 https://www.umweltbundesamt.de/sites/default/files/medien/1410/publikationen/2019-10-29_texte_132-2019_energieaufwand-gebaeudekonzepte.pdf,,,https://www.agora-energiewende.de/en/publications/building-sector-efficiency-a-crucial-component-of-the-energy-transition/,,
|
||||
,,,p.115,,
|
||||
|
9
data/retro/u_values_poland.csv
Normal file
9
data/retro/u_values_poland.csv
Normal file
@ -0,0 +1,9 @@
|
||||
component,Before 1945,1945 - 1969,1970 - 1979,1980 - 1989,1990 - 1999,2000 - 2010,Post 2010,sector
|
||||
Walls,1.7,1.4,0.9,0.9,0.6,0.4,1.7,residential
|
||||
Windows,4.6,3.6,2.6,2.6,2.1,2.1,2.1,residential
|
||||
Roof,0.8,0.7,0.6,0.6,0.6,0.4,0.33,residential
|
||||
Floor,1.9,1.4,1.2,1.1,0.9,0.6,0.45,residential
|
||||
Walls,1.3,1.3,1.3,0.8,0.6,0.6,0.6,services
|
||||
Windows,4.7,3.7,2.6,2.6,2.3,2.1,2.1,services
|
||||
Roof,1,0.9,0.7,0.5,0.3,0.3,0.3,services
|
||||
Floor,1.6,1.2,1.2,1.1,1,0.7,0.7,services
|
||||
|
8
data/retro/window_assumptions.csv
Normal file
8
data/retro/window_assumptions.csv
Normal file
@ -0,0 +1,8 @@
|
||||
strength,u_value,cost,u_limit,comment
|
||||
[m],[W/m^2K],EUR/m^2,[W/m^2K],
|
||||
0.076,1.34,180.08,3.5,Double-glazing
|
||||
0.197,0.8,225,1.3,Triple-glazing
|
||||
,,,,
|
||||
"source: https://www.agora-energiewende.de/en/publications/building-sector-efficiency-a-crucial-component-of-the-energy-transition/
|
||||
p.115
|
||||
",,,,
|
||||
|
@ -442,7 +442,8 @@ if __name__ == "__main__":
|
||||
costs = prepare_costs(snakemake.input.costs,
|
||||
snakemake.config['costs']['USD2013_to_EUR2013'],
|
||||
snakemake.config['costs']['discountrate'],
|
||||
Nyears)
|
||||
Nyears,
|
||||
snakemake.config['costs']['lifetime'])
|
||||
|
||||
grouping_years=snakemake.config['existing_capacities']['grouping_years']
|
||||
add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear)
|
||||
|
||||
477
scripts/build_retro_cost.py
Normal file
477
scripts/build_retro_cost.py
Normal file
@ -0,0 +1,477 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Mon Jan 20 14:57:21 2020
|
||||
|
||||
@author: bw0928
|
||||
|
||||
*****************************************************************************
|
||||
This script calculates cost-energy_saving-curves for retrofitting
|
||||
for the EU-37 countries, based on the building stock data from hotmaps and
|
||||
the EU building stock database
|
||||
*****************************************************************************
|
||||
|
||||
Structure:
|
||||
|
||||
(1) set assumptions and parameters
|
||||
(2) read and prepare data
|
||||
(3) calculate (€-dE-curves)
|
||||
(4) save in csv
|
||||
|
||||
*****************************************************************************
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
pd.options.mode.chained_assignment = None
|
||||
|
||||
#%% ************ FUCNTIONS ***************************************************
|
||||
|
||||
# windows ---------------------------------------------------------------
|
||||
def window_limit(l, window_assumptions):
|
||||
"""
|
||||
define limit u value from which on window is retrofitted
|
||||
"""
|
||||
m = (window_assumptions.diff()["u_limit"] /
|
||||
window_assumptions.diff()["strength"]).dropna().iloc[0]
|
||||
a = window_assumptions["u_limit"][0] - m * window_assumptions["strength"][0]
|
||||
return m*l + a
|
||||
|
||||
def u_retro_window(l, window_assumptions):
|
||||
"""
|
||||
define retrofitting value depending on renovation strength
|
||||
"""
|
||||
m = (window_assumptions.diff()["u_value"] /
|
||||
window_assumptions.diff()["strength"]).dropna().iloc[0]
|
||||
a = window_assumptions["u_value"][0] - m * window_assumptions["strength"][0]
|
||||
return max(m*l + a, 0.8)
|
||||
|
||||
def window_cost(u, cost_retro, window_assumptions):
|
||||
"""
|
||||
get costs for new windows depending on u value
|
||||
|
||||
"""
|
||||
m = (window_assumptions.diff()["cost"] /
|
||||
window_assumptions.diff()["u_value"]).dropna().iloc[0]
|
||||
a = window_assumptions["cost"][0] - m * window_assumptions["u_value"][0]
|
||||
window_cost = m*u + a
|
||||
if annualise_cost:
|
||||
window_cost = window_cost * interest_rate / (1 - (1 + interest_rate)
|
||||
** -cost_retro.loc["Windows", "life_time"])
|
||||
return window_cost
|
||||
|
||||
# functions for intermediate steps (~l, ~area) -----------------------------
|
||||
def calculate_new_u(u_values, l, l_weight, k=0.035):
|
||||
"""
|
||||
calculate U-values after building retrofitting, depending on the old
|
||||
U-values (u_values).
|
||||
They depend for the components Roof, Wall, Floor on the additional
|
||||
insulation thickness (l), and the weighting for the corresponding
|
||||
component (l_weight).
|
||||
Windows are renovated to new ones with U-value (function: u_retro_window(l))
|
||||
only if the are worse insulated than a certain limit value
|
||||
(function: window_limit).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
u_values: pd.DataFrame
|
||||
l: string
|
||||
l_weight: pd.DataFrame (component, weight)
|
||||
k: thermal conductivity
|
||||
|
||||
"""
|
||||
return u_values.apply(lambda x:
|
||||
k / ((k / x.value) +
|
||||
(float(l) * l_weight.loc[x.type][0]))
|
||||
if x.type!="Windows"
|
||||
else (min(x.value, u_retro_window(float(l), window_assumptions))
|
||||
if x.value>window_limit(float(l), window_assumptions) else x.value),
|
||||
axis=1)
|
||||
|
||||
def calculate_dE(u_values, l, average_surface_w):
|
||||
"""
|
||||
returns energy demand after retrofit (per unit of unrefurbished energy
|
||||
demand) depending on current and retrofitted U-values, this energy demand
|
||||
is weighted depending on the average surface of each component for the
|
||||
building type of the assumend subsector
|
||||
"""
|
||||
return u_values.apply(lambda x: x[l] / x.value *
|
||||
average_surface_w.loc[x.assumed_subsector,
|
||||
x.type],
|
||||
axis=1)
|
||||
|
||||
|
||||
def calculate_costs(u_values, l, cost_retro, average_surface):
|
||||
"""
|
||||
returns costs for a given retrofitting strength weighted by the average
|
||||
surface/volume ratio of the component for each building type
|
||||
"""
|
||||
return u_values.apply(lambda x: (cost_retro.loc[x.type, "cost_var"] *
|
||||
100 * float(l) * l_weight.loc[x.type][0]
|
||||
+ cost_retro.loc[x.type, "cost_fix"]) *
|
||||
average_surface.loc[x.assumed_subsector, x.type] /
|
||||
average_surface.loc[x.assumed_subsector, "surface"]
|
||||
if x.type!="Windows"
|
||||
else (window_cost(x[l], cost_retro, window_assumptions) *
|
||||
average_surface.loc[x.assumed_subsector, x.type] /
|
||||
average_surface.loc[x.assumed_subsector, "surface"]
|
||||
if x.value>window_limit(float(l), window_assumptions) else 0),
|
||||
axis=1)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
def prepare_building_stock_data():
|
||||
"""
|
||||
reads building stock data and cleans up the format, returns
|
||||
--------
|
||||
u_values: pd.DataFrame current U-values
|
||||
average_surface: pd.DataFrame (index= building type,
|
||||
columns = [surface [m],height [m],
|
||||
components area [m^2]])
|
||||
average_surface_w: pd.DataFrame weighted share of the components per
|
||||
building type
|
||||
area_tot: heated floor area per country and sector [Mm²]
|
||||
area: heated floor area [Mm²] for country, sector, building
|
||||
type and period
|
||||
|
||||
"""
|
||||
|
||||
building_data = pd.read_csv(snakemake.input.building_stock,
|
||||
usecols=list(range(13)))
|
||||
|
||||
# standardize data
|
||||
building_data["type"].replace(
|
||||
{'Covered area: heated [Mm²]': 'Heated area [Mm²]',
|
||||
'Windows ': 'Windows',
|
||||
'Walls ': 'Walls',
|
||||
'Roof ': 'Roof',
|
||||
'Floor ': 'Floor'}, inplace=True)
|
||||
|
||||
building_data.country_code = building_data.country_code.str.upper()
|
||||
building_data["subsector"].replace({'Hotels and Restaurants':
|
||||
'Hotels and restaurants'}, inplace=True)
|
||||
building_data["sector"].replace({'Residential sector': 'residential',
|
||||
'Service sector': 'services'},
|
||||
inplace=True)
|
||||
# extract u-values
|
||||
u_values = building_data[(building_data.feature.str.contains("U-values"))
|
||||
& (building_data.subsector != "Total")]
|
||||
|
||||
components = list(u_values.type.unique())
|
||||
|
||||
country_iso_dic = building_data.set_index("country")["country_code"].to_dict()
|
||||
|
||||
# add missing /rename countries
|
||||
country_iso_dic.update({'Norway': 'NO',
|
||||
'Iceland': 'IS',
|
||||
'Montenegro': 'ME',
|
||||
'Serbia': 'RS',
|
||||
'Albania': 'AL',
|
||||
'United Kingdom': 'GB',
|
||||
'Bosnia and Herzegovina': 'BA',
|
||||
'Switzerland': 'CH'})
|
||||
|
||||
# heated floor area ----------------------------------------------------------
|
||||
area = building_data[(building_data.type == 'Heated area [Mm²]') &
|
||||
(building_data.subsector != "Total")]
|
||||
area_tot = area.groupby(["country", "sector"]).sum()
|
||||
area["weight"] = area.apply(lambda x: x.value /
|
||||
area_tot.value.loc[(x.country, x.sector)],
|
||||
axis=1)
|
||||
area = area.groupby(['country', 'sector', 'subsector', 'bage']).sum()
|
||||
area_tot.rename(index=country_iso_dic, inplace=True)
|
||||
|
||||
# add for some missing countries floor area from other data sources
|
||||
area_missing = pd.read_csv(snakemake.input.floor_area_missing,
|
||||
index_col=[0, 1], usecols=[0, 1, 2, 3])
|
||||
area_tot = area_tot.append(area_missing.unstack(level=-1).dropna().stack())
|
||||
area_tot = area_tot.loc[~area_tot.index.duplicated(keep='last')]
|
||||
|
||||
# for still missing countries calculate floor area by population size
|
||||
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
|
||||
pop_layout["ct"] = pop_layout.index.str[:2]
|
||||
ct_total = pop_layout.total.groupby(pop_layout["ct"]).sum()
|
||||
|
||||
area_per_pop = area_tot.unstack().apply(lambda x: x / ct_total[x.index])
|
||||
missing_area_ct = ct_total.index.difference(area_tot.index.levels[0])
|
||||
for ct in missing_area_ct:
|
||||
averaged_data = pd.DataFrame(
|
||||
area_per_pop.value.reindex(map_for_missings[ct]).mean()
|
||||
* ct_total[ct],
|
||||
columns=["value"])
|
||||
index = pd.MultiIndex.from_product([[ct], averaged_data.index.to_list()])
|
||||
averaged_data.index = index
|
||||
averaged_data["estimated"] = 1
|
||||
if ct not in area_tot.index.levels[0]:
|
||||
area_tot = area_tot.append(averaged_data, sort=True)
|
||||
else:
|
||||
area_tot.loc[averaged_data.index] = averaged_data
|
||||
|
||||
# u_values for Poland are missing -> take them from eurostat -----------
|
||||
u_values_PL = pd.read_csv(snakemake.input.u_values_PL)
|
||||
area_PL = area.loc["Poland"].reset_index()
|
||||
data_PL = pd.DataFrame(columns=u_values.columns, index=area_PL.index)
|
||||
data_PL["country"] = "Poland"
|
||||
data_PL["country_code"] = "PL"
|
||||
# data from area
|
||||
for col in ["sector", "subsector", "bage"]:
|
||||
data_PL[col] = area_PL[col]
|
||||
data_PL["btype"] = area_PL["subsector"]
|
||||
|
||||
data_PL_final = pd.DataFrame()
|
||||
for component in components:
|
||||
data_PL["type"] = component
|
||||
data_PL["value"] = data_PL.apply(lambda x: u_values_PL[(u_values_PL.component==component)
|
||||
& (u_values_PL.sector==x["sector"])]
|
||||
[x["bage"]].iloc[0], axis=1)
|
||||
data_PL_final = data_PL_final.append(data_PL)
|
||||
|
||||
u_values = pd.concat([u_values,
|
||||
data_PL_final]).reset_index(drop=True)
|
||||
|
||||
# clean data ---------------------------------------------------------------
|
||||
# smallest possible today u values for windows 0.8 (passive house standard)
|
||||
# maybe the u values for the glass and not the whole window including frame
|
||||
# for those types assumed in the dataset
|
||||
u_values[(u_values.type=="Windows") & (u_values.value<0.8)]["value"] = 0.8
|
||||
# drop unnecessary columns
|
||||
u_values.drop(['topic', 'feature','detail', 'estimated','unit'],
|
||||
axis=1, inplace=True, errors="ignore")
|
||||
# only take in config.yaml specified countries into account
|
||||
countries = ct_total.index
|
||||
area_tot = area_tot.loc[countries]
|
||||
|
||||
# average component surface --------------------------------------------------
|
||||
average_surface = (pd.read_csv(snakemake.input.average_surface,
|
||||
nrows=3,
|
||||
header=1,
|
||||
index_col=0).rename(
|
||||
{'Single/two family house': 'Single family- Terraced houses',
|
||||
'Large apartment house': 'Multifamily houses',
|
||||
'Apartment house': 'Appartment blocks'},
|
||||
axis="index")).iloc[:, :6]
|
||||
average_surface.columns = ["surface", "height", "Roof",
|
||||
"Walls", "Floor", "Windows"]
|
||||
# get area share of component
|
||||
average_surface_w = average_surface[components].apply(lambda x: x / x.sum(),
|
||||
axis=1)
|
||||
|
||||
return (u_values, average_surface,
|
||||
average_surface_w, area_tot, area, country_iso_dic, countries)
|
||||
|
||||
|
||||
def prepare_cost_retro():
|
||||
"""
|
||||
read and prepare retro costs, annualises them if annualise_cost=True
|
||||
"""
|
||||
cost_retro = pd.read_csv(snakemake.input.cost_germany,
|
||||
nrows=4, index_col=0, usecols=[0, 1, 2, 3])
|
||||
cost_retro.index = cost_retro.index.str.capitalize()
|
||||
cost_retro.rename(index={"Window": "Windows", "Wall": "Walls"}, inplace=True)
|
||||
|
||||
window_assumptions = pd.read_csv(snakemake.input.window_assumptions,
|
||||
skiprows=[1], usecols=[0,1,2,3], nrows=2)
|
||||
|
||||
if annualise_cost:
|
||||
cost_retro[["cost_fix", "cost_var"]] = (cost_retro[["cost_fix", "cost_var"]]
|
||||
.apply(lambda x: x * interest_rate /
|
||||
(1 - (1 + interest_rate)
|
||||
** -cost_retro.loc[x.index,
|
||||
"life_time"])))
|
||||
|
||||
return cost_retro, window_assumptions
|
||||
|
||||
|
||||
def calculate_cost_energy_curve(u_values, l_strength, l_weight, average_surface_w,
|
||||
average_surface, area, country_iso_dic,
|
||||
countries):
|
||||
"""
|
||||
returns energy demand per unit of unrefurbished (dE) and cost for given
|
||||
renovation strength (l_strength), data for missing countries is
|
||||
approximated by countries with similar building stock (dict:map_for_missings)
|
||||
|
||||
parameter
|
||||
-------- input -----------
|
||||
u_values: pd.DataFrame current U-values
|
||||
l_strength: list of strings (strength of retrofitting)
|
||||
l_weight: pd.DataFrame (component, weight)
|
||||
average_surface: pd.DataFrame (index= building type,
|
||||
columns = [surface [m],height [m],
|
||||
components area [m^2]])
|
||||
average_surface_w: pd.DataFrame weighted share of the components per
|
||||
building type
|
||||
area: heated floor area [Mm²] for country, sector, building
|
||||
type and period
|
||||
country_iso_dic: dict (maps country name to 2-letter-iso-code)
|
||||
countries: pd.Index (specified countries in config.yaml)
|
||||
-------- output ----------
|
||||
res: pd.DataFrame(index=pd.MultiIndex([country, sector]),
|
||||
columns=pd.MultiIndex([(dE/cost), l_strength]))
|
||||
"""
|
||||
|
||||
energy_saved = u_values[['country', 'sector', 'subsector', 'bage', 'type']]
|
||||
costs = u_values[['country', 'sector', 'subsector', 'bage', 'type']]
|
||||
|
||||
for l in l_strength:
|
||||
u_values[l] = calculate_new_u(u_values, l, l_weight)
|
||||
energy_saved[l] = calculate_dE(u_values, l, average_surface_w)
|
||||
costs[l] = calculate_costs(u_values, l, cost_retro, average_surface)
|
||||
|
||||
# energy and costs per country, sector, subsector and year
|
||||
e_tot = energy_saved.groupby(['country', 'sector', 'subsector', 'bage']).sum()
|
||||
cost_tot = costs.groupby(['country', 'sector', 'subsector', 'bage']).sum()
|
||||
|
||||
# weighting by area -> energy and costs per country and sector
|
||||
# in case of missing data first concat
|
||||
energy_saved = pd.concat([e_tot, area.weight], axis=1)
|
||||
cost_res = pd.concat([cost_tot, area.weight], axis=1)
|
||||
energy_saved = (energy_saved.apply(lambda x: x * x.weight, axis=1)
|
||||
.groupby(level=[0, 1]).sum())
|
||||
cost_res = (cost_res.apply(lambda x: x * x.weight, axis=1)
|
||||
.groupby(level=[0, 1]).sum())
|
||||
|
||||
res = pd.concat([energy_saved[l_strength], cost_res[l_strength]],
|
||||
axis=1, keys=["dE", "cost"])
|
||||
res.rename(index=country_iso_dic, inplace=True)
|
||||
|
||||
res = res.loc[countries]
|
||||
|
||||
# map missing countries
|
||||
for ct in map_for_missings.keys():
|
||||
averaged_data = pd.DataFrame(res.loc[map_for_missings[ct], :].mean(level=1))
|
||||
index = pd.MultiIndex.from_product([[ct], averaged_data.index.to_list()])
|
||||
averaged_data.index = index
|
||||
if ct not in res.index.levels[0]:
|
||||
res = res.append(averaged_data)
|
||||
else:
|
||||
res.loc[averaged_data.index] = averaged_data
|
||||
|
||||
return res
|
||||
|
||||
|
||||
# %% **************** MAIN ************************************************
|
||||
if __name__ == "__main__":
|
||||
# for testing
|
||||
if 'snakemake' not in globals():
|
||||
import yaml
|
||||
import os
|
||||
from vresutils.snakemake import MockSnakemake
|
||||
snakemake = MockSnakemake(
|
||||
wildcards=dict(
|
||||
network='elec',
|
||||
simpl='',
|
||||
clusters='37',
|
||||
lv='1',
|
||||
opts='Co2L-3H',
|
||||
sector_opts="[Co2L0p0-168H-T-H-B-I]"),
|
||||
input=dict(
|
||||
building_stock="data/retro/data_building_stock.csv",
|
||||
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_{network}_s{simpl}_{clusters}.csv",
|
||||
cost_germany="data/retro/retro_cost_germany.csv",
|
||||
window_assumptions="data/retro/window_assumptions.csv"),
|
||||
output=dict(
|
||||
retro_cost="resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
|
||||
floor_area="resources/floor_area_{network}_s{simpl}_{clusters}.csv")
|
||||
)
|
||||
with open('config.yaml', encoding='utf8') as f:
|
||||
snakemake.config = yaml.safe_load(f)
|
||||
|
||||
# ******** (1) ASSUMPTIONS - PARAMETERS **********************************
|
||||
retro_opts = snakemake.config["sector"]["retrofitting"]
|
||||
interest_rate = retro_opts["interest_rate"]
|
||||
annualise_cost = retro_opts["annualise_cost"] # annualise the investment costs
|
||||
tax_weighting = retro_opts["tax_weighting"] # weight costs depending on taxes in countries
|
||||
construction_index = retro_opts["construction_index"] # weight costs depending on labour/material costs per ct
|
||||
# additional insulation thickness, determines maximum possible savings
|
||||
l_strength = retro_opts["l_strength"]
|
||||
|
||||
k = 0.035 # thermal conductivity standard value
|
||||
# strenght of relative retrofitting depending on the component
|
||||
# determined by historical data of insulation thickness for retrofitting
|
||||
l_weight = pd.DataFrame({"weight": [1.95, 1.48, 1.]},
|
||||
index=["Roof", "Walls", "Floor"])
|
||||
|
||||
|
||||
# mapping missing countries by neighbours
|
||||
map_for_missings = {
|
||||
"AL": ["BG", "RO", "GR"],
|
||||
"BA": ["HR"],
|
||||
"RS": ["BG", "RO", "HR", "HU"],
|
||||
"MK": ["BG", "GR"],
|
||||
"ME": ["BA", "AL", "RS", "HR"],
|
||||
"CH": ["SE", "DE"],
|
||||
"NO": ["SE"],
|
||||
}
|
||||
|
||||
# %% ************ (2) DATA ***************************************************
|
||||
|
||||
# building stock data -----------------------------------------------------
|
||||
(u_values, average_surface, average_surface_w,
|
||||
area_tot, area, country_iso_dic, countries) = prepare_building_stock_data()
|
||||
|
||||
# costs for retrofitting -------------------------------------------------
|
||||
cost_retro, window_assumptions = prepare_cost_retro()
|
||||
|
||||
# weightings of costs
|
||||
if construction_index:
|
||||
cost_w = pd.read_csv(snakemake.input.construction_index,
|
||||
skiprows=3, nrows=32, index_col=0)
|
||||
# since German retrofitting costs are assumed
|
||||
cost_w = ((cost_w["2018"] / cost_w.loc["Germany", "2018"])
|
||||
.rename(index=country_iso_dic))
|
||||
|
||||
if tax_weighting:
|
||||
tax_w = pd.read_csv(snakemake.input.tax_w,
|
||||
header=12, nrows=39, index_col=0, usecols=[0, 4])
|
||||
tax_w.rename(index=country_iso_dic, inplace=True)
|
||||
tax_w = tax_w.apply(pd.to_numeric, errors='coerce').iloc[:, 0]
|
||||
tax_w.dropna(inplace=True)
|
||||
|
||||
# %% ********** (3) CALCULATE COST-ENERGY-CURVES ****************************
|
||||
|
||||
# for missing weighting of surfaces of building types assume MultiFamily houses
|
||||
u_values["assumed_subsector"] = u_values.subsector
|
||||
u_values.assumed_subsector[
|
||||
~u_values.subsector.isin(average_surface.index)] = 'Multifamily houses'
|
||||
|
||||
dE_and_cost = calculate_cost_energy_curve(u_values, l_strength, l_weight,
|
||||
average_surface_w, average_surface, area,
|
||||
country_iso_dic, countries)
|
||||
|
||||
# weights costs after construction index
|
||||
if construction_index:
|
||||
for ct in list(map_for_missings.keys() - cost_w.index):
|
||||
cost_w.loc[ct] = cost_w.reindex(index=map_for_missings[ct]).mean()
|
||||
dE_and_cost.cost = dE_and_cost.cost.apply(lambda x: x * cost_w[x.index.levels[0]])
|
||||
|
||||
# weights cost depending on country taxes
|
||||
if tax_weighting:
|
||||
for ct in list(map_for_missings.keys() - tax_w.index):
|
||||
tax_w[ct] = tax_w.reindex(index=map_for_missings[ct]).mean()
|
||||
dE_and_cost.cost = dE_and_cost.cost.apply(lambda x: x * tax_w[x.index.levels[0]])
|
||||
|
||||
# get share of residential and sevice floor area
|
||||
sec_w = (area_tot / area_tot.groupby(["country"]).sum())["value"]
|
||||
# get the total cost-energy-savings weight by sector area
|
||||
tot = dE_and_cost.apply(lambda col: col * sec_w, axis=0).groupby(level=0).sum()
|
||||
tot.set_index(pd.MultiIndex.from_product([list(tot.index), ["tot"]]),
|
||||
inplace=True)
|
||||
dE_and_cost = dE_and_cost.append(tot).unstack().stack()
|
||||
|
||||
summed_area = pd.DataFrame(area_tot.groupby("country").sum())
|
||||
summed_area.set_index(pd.MultiIndex.from_product(
|
||||
[list(summed_area.index), ["tot"]]), inplace=True)
|
||||
area_tot = area_tot.append(summed_area).unstack().stack()
|
||||
|
||||
# %% ******* (4) SAVE ************************************************
|
||||
|
||||
dE_and_cost.to_csv(snakemake.output.retro_cost)
|
||||
area_tot.to_csv(snakemake.output.floor_area)
|
||||
|
||||
|
||||
|
||||
@ -592,7 +592,8 @@ if __name__ == "__main__":
|
||||
costs_db = prepare_costs(snakemake.input.costs,
|
||||
snakemake.config['costs']['USD2013_to_EUR2013'],
|
||||
snakemake.config['costs']['discountrate'],
|
||||
Nyears)
|
||||
Nyears,
|
||||
snakemake.config['costs']['lifetime'])
|
||||
|
||||
df = make_summaries(networks_dict)
|
||||
|
||||
|
||||
@ -114,6 +114,13 @@ def update_wind_solar_costs(n,costs):
|
||||
n.generators.loc[n.generators.carrier==tech,'capital_cost'] = capital_cost.rename(index=lambda node: node + ' ' + tech)
|
||||
|
||||
|
||||
def retro_exogen(demand, dE):
|
||||
"""
|
||||
reduces space heat demand exogenously
|
||||
demand: current space heat demand
|
||||
dE: energy savings
|
||||
"""
|
||||
return demand * (1-dE)
|
||||
def add_carrier_buses(n, carriers):
|
||||
"""
|
||||
Add buses to connect e.g. coal, nuclear and oil plants
|
||||
@ -451,8 +458,8 @@ def prepare_data(network):
|
||||
|
||||
## Get overall demand curve for all vehicles
|
||||
|
||||
dir_name = "data/emobility/"
|
||||
traffic = pd.read_csv(os.path.join(dir_name,"KFZ__count"),skiprows=2)["count"]
|
||||
traffic = pd.read_csv(snakemake.input.traffic_data + "KFZ__count",
|
||||
skiprows=2)["count"]
|
||||
|
||||
#Generate profiles
|
||||
transport_shape = generate_periodic_profiles(dt_index=network.snapshots.tz_localize("UTC"),
|
||||
@ -506,7 +513,8 @@ def prepare_data(network):
|
||||
|
||||
## derive plugged-in availability for PKW's (cars)
|
||||
|
||||
traffic = pd.read_csv(os.path.join(dir_name,"Pkw__count"),skiprows=2)["count"]
|
||||
traffic = pd.read_csv(snakemake.input.traffic_data + "Pkw__count",
|
||||
skiprows=2)["count"]
|
||||
|
||||
avail_max = 0.95
|
||||
|
||||
@ -540,7 +548,7 @@ def prepare_data(network):
|
||||
|
||||
|
||||
|
||||
def prepare_costs(cost_file, USD_to_EUR, discount_rate, Nyears):
|
||||
def prepare_costs(cost_file, USD_to_EUR, discount_rate, Nyears, lifetime):
|
||||
|
||||
#set all asset costs and other parameters
|
||||
costs = pd.read_csv(cost_file,index_col=list(range(2))).sort_index()
|
||||
@ -558,7 +566,7 @@ def prepare_costs(cost_file, USD_to_EUR, discount_rate, Nyears):
|
||||
"efficiency" : 1,
|
||||
"fuel" : 0,
|
||||
"investment" : 0,
|
||||
"lifetime" : 25
|
||||
"lifetime" : lifetime
|
||||
})
|
||||
|
||||
costs["fixed"] = [(annuity(v["lifetime"],v["discount rate"])+v["FOM"]/100.)*v["investment"]*Nyears for i,v in costs.iterrows()]
|
||||
@ -1077,7 +1085,21 @@ def add_heat(network):
|
||||
|
||||
urban_fraction = options['central_fraction']*pop_layout["urban"]/(pop_layout[["urban","rural"]].sum(axis=1))
|
||||
|
||||
for name in ["residential rural","services rural","residential urban decentral","services urban decentral","urban central"]:
|
||||
# building retrofitting, exogenously reduce space heat demand
|
||||
if options["retrofitting"]["retro_exogen"]:
|
||||
dE = options["retrofitting"]["dE"]
|
||||
if snakemake.config["foresight"]=='myopic':
|
||||
year = int(snakemake.wildcards.planning_horizons[-4:])
|
||||
dE = dE[snakemake.config["scenario"]["planning_horizons"].index(year)]
|
||||
print("retrofitting exogenously, assumed space heat reduction of ",
|
||||
dE)
|
||||
for sector in sectors:
|
||||
heat_demand[sector + " space"] = heat_demand[sector + " space"].apply(lambda x: retro_exogen(x, dE))
|
||||
|
||||
heat_systems = ["residential rural", "services rural",
|
||||
"residential urban decentral","services urban decentral",
|
||||
"urban central"]
|
||||
for name in heat_systems:
|
||||
|
||||
name_type = "central" if name == "urban central" else "decentral"
|
||||
|
||||
@ -1100,6 +1122,7 @@ def add_heat(network):
|
||||
if sector in name:
|
||||
heat_load = heat_demand[[sector + " water",sector + " space"]].groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor)
|
||||
|
||||
|
||||
if name == "urban central":
|
||||
heat_load = heat_demand.groupby(level=1,axis=1).sum()[nodes[name]].multiply(urban_fraction[nodes[name]]*(1+options['district_heating_loss']))
|
||||
|
||||
@ -1291,64 +1314,100 @@ def add_heat(network):
|
||||
lifetime=costs.at['micro CHP','lifetime'])
|
||||
|
||||
|
||||
#NB: this currently doesn't work for pypsa-eur model
|
||||
if options['retrofitting']:
|
||||
if options['retrofitting']['retro_endogen']:
|
||||
|
||||
retro_nodes = pd.Index(["DE"])
|
||||
print("adding retrofitting endogenously")
|
||||
|
||||
space_heat_demand = space_heat_demand[retro_nodes]
|
||||
# resample heat demand temporal 'heat_demand_r' depending on in config
|
||||
# specified temporal resolution, to not overestimate retrofitting
|
||||
hours = list(filter(re.compile(r'^\d+h$', re.IGNORECASE).search, opts))
|
||||
if len(hours)==0:
|
||||
hours = [n.snapshots[1] - n.snapshots[0]]
|
||||
heat_demand_r = heat_demand.resample(hours[0]).mean()
|
||||
|
||||
square_metres = population[retro_nodes]/population['DE']*5.7e9 #HPI 3.4e9m^2 for DE res, 2.3e9m^2 for tert https://doi.org/10.1016/j.rser.2013.09.012
|
||||
|
||||
space_peak = space_heat_demand.max()
|
||||
|
||||
space_pu = space_heat_demand.divide(space_peak)
|
||||
# retrofitting data 'retro_data' with 'costs' [EUR/m^2] and heat
|
||||
# demand 'dE' [per unit of original heat demand] for each country and
|
||||
# different retrofitting strengths [additional insulation thickness in m]
|
||||
retro_data = pd.read_csv(snakemake.input.retro_cost_energy,
|
||||
index_col=[0, 1], skipinitialspace=True,
|
||||
header=[0, 1])
|
||||
# heated floor area [10^6 * m^2] per country
|
||||
floor_area = pd.read_csv(snakemake.input.floor_area, index_col=[0, 1])
|
||||
|
||||
network.add("Carrier", "retrofitting")
|
||||
|
||||
network.madd('Generator',
|
||||
retro_nodes,
|
||||
suffix=' retrofitting I',
|
||||
bus=retro_nodes+' heat',
|
||||
carrier="retrofitting",
|
||||
p_nom_extendable=True,
|
||||
p_nom_max=options['retroI-fraction']*space_peak*(1-urban_fraction),
|
||||
p_max_pu=space_pu,
|
||||
p_min_pu=space_pu,
|
||||
capital_cost=options['retrofitting-cost_factor']*costs.at['retrofitting I','fixed']*square_metres/(options['retroI-fraction']*space_peak))
|
||||
# share of space heat demand 'w_space' of total heat demand
|
||||
w_space = {}
|
||||
for sector in sectors:
|
||||
w_space[sector] = heat_demand_r[sector + " space"] / \
|
||||
(heat_demand_r[sector + " space"] + heat_demand_r[sector + " water"])
|
||||
w_space["tot"] = ((heat_demand_r["services space"] +
|
||||
heat_demand_r["residential space"]) /
|
||||
heat_demand_r.groupby(level=[1], axis=1).sum())
|
||||
|
||||
network.madd('Generator',
|
||||
retro_nodes,
|
||||
suffix=' retrofitting II',
|
||||
bus=retro_nodes+' heat',
|
||||
carrier="retrofitting",
|
||||
p_nom_extendable=True,
|
||||
p_nom_max=options['retroII-fraction']*space_peak*(1-urban_fraction),
|
||||
p_max_pu=space_pu,
|
||||
p_min_pu=space_pu,
|
||||
capital_cost=options['retrofitting-cost_factor']*costs.at['retrofitting II','fixed']*square_metres/(options['retroII-fraction']*space_peak))
|
||||
|
||||
network.madd('Generator',
|
||||
retro_nodes,
|
||||
suffix=' urban retrofitting I',
|
||||
bus=retro_nodes+' urban heat',
|
||||
carrier="retrofitting",
|
||||
p_nom_extendable=True,
|
||||
p_nom_max=options['retroI-fraction']*space_peak*urban_fraction,
|
||||
p_max_pu=space_pu,
|
||||
p_min_pu=space_pu,
|
||||
capital_cost=options['retrofitting-cost_factor']*costs.at['retrofitting I','fixed']*square_metres/(options['retroI-fraction']*space_peak))
|
||||
for name in network.loads[network.loads.carrier.isin([x + " heat" for x in heat_systems])].index:
|
||||
|
||||
node = network.buses.loc[name, "location"]
|
||||
ct = pop_layout.loc[node, "ct"]
|
||||
|
||||
# weighting 'f' depending on the size of the population at the node
|
||||
f = urban_fraction[node] if "urban" in name else (1-urban_fraction[node])
|
||||
if f == 0:
|
||||
continue
|
||||
# get sector name ("residential"/"services"/or both "tot" for urban central)
|
||||
sec = [x if x in name else "tot" for x in sectors][0]
|
||||
|
||||
# get floor aread at node and region (urban/rural) in m^2
|
||||
floor_area_node = ((pop_layout.loc[node].fraction
|
||||
* floor_area.loc[ct, "value"] * 10**6).loc[sec] * f)
|
||||
# total heat demand at node [MWh]
|
||||
demand = (network.loads_t.p_set[name].resample(hours[0])
|
||||
.mean())
|
||||
|
||||
# space heat demand at node [MWh]
|
||||
space_heat_demand = demand * w_space[sec][node]
|
||||
# normed time profile of space heat demand 'space_pu' (values between 0-1),
|
||||
# p_max_pu/p_min_pu of retrofitting generators
|
||||
space_pu = (space_heat_demand / space_heat_demand.max()).to_frame(name=node)
|
||||
|
||||
# minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1)
|
||||
dE = retro_data.loc[(ct, sec), ("dE")]
|
||||
# get addtional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
|
||||
dE_diff = abs(dE.diff()).fillna(1-dE.iloc[0])
|
||||
# convert costs Euro/m^2 -> Euro/MWh
|
||||
capital_cost = retro_data.loc[(ct, sec), ("cost")] * floor_area_node / \
|
||||
((1 - dE) * space_heat_demand.max())
|
||||
# number of possible retrofitting measures 'strengths' (set in list at config.yaml 'l_strength')
|
||||
# given in additional insulation thickness [m]
|
||||
# for each measure, a retrofitting generator is added at the node
|
||||
strengths = retro_data.columns.levels[1]
|
||||
|
||||
# check that ambitious retrofitting has higher costs per MWh than moderate retrofitting
|
||||
if (capital_cost.diff() < 0).sum():
|
||||
print(
|
||||
"warning, costs are not linear for ", ct, " ", sec)
|
||||
s = capital_cost[(capital_cost.diff() < 0)].index
|
||||
strengths = strengths.drop(s)
|
||||
|
||||
# reindex normed time profile of space heat demand back to hourly resolution
|
||||
space_pu = (space_pu.reindex(index=heat_demand.index)
|
||||
.fillna(method="ffill"))
|
||||
|
||||
# add for each retrofitting strength a generator with heat generation profile following the profile of the heat demand
|
||||
for strength in strengths:
|
||||
network.madd('Generator',
|
||||
[node],
|
||||
suffix=' retrofitting ' + strength + " " + name[6::],
|
||||
bus=name,
|
||||
carrier="retrofitting",
|
||||
p_nom_extendable=True,
|
||||
p_nom_max=dE_diff[strength] * space_heat_demand.max(), # maximum energy savings for this renovation strength
|
||||
p_max_pu=space_pu,
|
||||
p_min_pu=space_pu,
|
||||
country=ct,
|
||||
capital_cost=capital_cost[strength] * options['retrofitting']['cost_factor'])
|
||||
|
||||
network.madd('Generator',
|
||||
retro_nodes,
|
||||
suffix=' urban retrofitting II',
|
||||
bus=retro_nodes+' urban heat',
|
||||
carrier="retrofitting",
|
||||
p_nom_extendable=True,
|
||||
p_nom_max=options['retroII-fraction']*space_peak*urban_fraction,
|
||||
p_max_pu=space_pu,
|
||||
p_min_pu=space_pu,
|
||||
capital_cost=options['retrofitting-cost_factor']*costs.at['retrofitting II','fixed']*square_metres/(options['retroII-fraction']*space_peak))
|
||||
|
||||
|
||||
def create_nodes_for_heat_sector():
|
||||
@ -1782,25 +1841,44 @@ if __name__ == "__main__":
|
||||
wildcards=dict(network='elec', simpl='', clusters='37', lv='1.0',
|
||||
opts='', planning_horizons='2020',
|
||||
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1'),
|
||||
input=dict(network='pypsa-eur/networks/{network}_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc',
|
||||
timezone_mappings='pypsa-eur-sec/data/timezone_mappings.csv',
|
||||
clustered_pop_layout='pypsa-eur-sec/resources/pop_layout_{network}_s{simpl}_{clusters}.csv',
|
||||
costs='technology-data/outputs/costs_{planning_horizons}.csv',
|
||||
profile_offwind_ac='pypsa-eur/resources/profile_offwind-ac.nc',
|
||||
profile_offwind_dc='pypsa-eur/resources/profile_offwind-dc.nc',
|
||||
busmap_s='pypsa-eur/resources/busmap_{network}_s{simpl}.csv',
|
||||
busmap='pypsa-eur/resources/busmap_{network}_s{simpl}_{clusters}.csv',
|
||||
cop_air_total='pypsa-eur-sec/resources/cop_air_total_{network}_s{simpl}_{clusters}.nc',
|
||||
cop_soil_total='pypsa-eur-sec/resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc',
|
||||
solar_thermal_total='pypsa-eur-sec/resources/solar_thermal_total_{network}_s{simpl}_{clusters}.nc',
|
||||
energy_totals_name='pypsa-eur-sec/data/energy_totals.csv',
|
||||
heat_demand_total='pypsa-eur-sec/resources/heat_demand_total_{network}_s{simpl}_{clusters}.nc',
|
||||
heat_profile='pypsa-eur-sec/data/heat_load_profile_BDEW.csv',
|
||||
transport_name='pypsa-eur-sec/data/transport_data.csv',
|
||||
temp_air_total='pypsa-eur-sec/resources/temp_air_total_{network}_s{simpl}_{clusters}.nc',
|
||||
co2_totals_name='pypsa-eur-sec/data/co2_totals.csv',
|
||||
biomass_potentials='pypsa-eur-sec/data/biomass_potentials.csv',
|
||||
industrial_demand='pypsa-eur-sec/resources/industrial_demand_{network}_s{simpl}_{clusters}.csv',),
|
||||
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',
|
||||
transport_name='resources/transport_data.csv',
|
||||
biomass_potentials='resources/biomass_potentials.csv',
|
||||
biomass_transport='data/biomass/biomass_transport_costs.csv',
|
||||
timezone_mappings='data/timezone_mappings.csv',
|
||||
heat_profile="data/heat_load_profile_BDEW.csv",
|
||||
costs="../technology-data/outputs/costs_{planning_horizons}.csv",
|
||||
h2_cavern = "data/hydrogen_salt_cavern_potentials.csv",
|
||||
profile_offwind_ac="../pypsa-eur/resources/profile_offwind-ac.nc",
|
||||
profile_offwind_dc="../pypsa-eur/resources/profile_offwind-dc.nc",
|
||||
clustermaps='../pypsa-eur/resources/clustermaps_{network}_s{simpl}_{clusters}.h5',
|
||||
clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
|
||||
simplified_pop_layout="resources/pop_layout_{network}_s{simpl}.csv",
|
||||
industrial_demand="resources/industrial_energy_demand_{network}_s{simpl}_{clusters}.csv",
|
||||
heat_demand_urban="resources/heat_demand_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
heat_demand_rural="resources/heat_demand_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
heat_demand_total="resources/heat_demand_total_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_soil_total="resources/temp_soil_total_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_soil_rural="resources/temp_soil_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_soil_urban="resources/temp_soil_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_air_total="resources/temp_air_total_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_air_rural="resources/temp_air_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
temp_air_urban="resources/temp_air_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_total="resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_rural="resources/cop_soil_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_soil_urban="resources/cop_soil_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_total="resources/cop_air_total_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_rural="resources/cop_air_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
cop_air_urban="resources/cop_air_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_total="resources/solar_thermal_total_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_urban="resources/solar_thermal_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
traffic_data = "data/emobility/",
|
||||
solar_thermal_rural="resources/solar_thermal_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
retro_cost_energy = "resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
|
||||
floor_area = "resources/floor_area_{network}_s{simpl}_{clusters}.csv"
|
||||
),
|
||||
output=['pypsa-eur-sec/results/test/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc']
|
||||
)
|
||||
import yaml
|
||||
@ -1834,7 +1912,8 @@ if __name__ == "__main__":
|
||||
costs = prepare_costs(snakemake.input.costs,
|
||||
snakemake.config['costs']['USD2013_to_EUR2013'],
|
||||
snakemake.config['costs']['discountrate'],
|
||||
Nyears)
|
||||
Nyears,
|
||||
snakemake.config['costs']['lifetime'])
|
||||
|
||||
remove_elec_base_techs(n)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user