Merge pull request #149 from PyPSA/dh-share
Include today's district heating share for myopic optimisation
This commit is contained in:
Fabian Neumann
GitHub
commit
fb601cc051
@ -159,6 +159,7 @@ rule build_energy_totals:
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co2="data/eea/UNFCCC_v23.csv",
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swiss="data/switzerland-sfoe/switzerland-new_format.csv",
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idees="data/jrc-idees-2015",
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district_heat_share='data/district_heat_share.csv',
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eurostat=input_eurostat
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output:
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energy_name='resources/energy_totals.csv',
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@ -141,8 +141,16 @@ existing_capacities:
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sector:
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central: true
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central_fraction: 0.6
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district_heating:
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potential: 0.6 # maximum fraction of urban demand which can be supplied by district heating
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# increase of today's district heating demand to potential maximum district heating share
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# progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
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progress:
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2020: 0.0
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2030: 0.3
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2040: 0.6
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2050: 1.0
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district_heating_loss: 0.15
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bev_dsm_restriction_value: 0.75 #Set to 0 for no restriction on BEV DSM
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bev_dsm_restriction_time: 7 #Time at which SOC of BEV has to be dsm_restriction_value
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transport_heating_deadband_upper: 20.
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@ -151,7 +159,6 @@ sector:
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ICE_upper_degree_factor: 1.6
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EV_lower_degree_factor: 0.98
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EV_upper_degree_factor: 0.63
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district_heating_loss: 0.15
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bev_dsm: true #turns on EV battery
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bev_availability: 0.5 #How many cars do smart charging
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bev_energy: 0.05 #average battery size in MWh
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34
data/district_heat_share.csv
Normal file
34
data/district_heat_share.csv
Normal file
@ -0,0 +1,34 @@
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country,share to satisfy heat demand (residential) in percent,capacity[MWth]
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AT,14,11200
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BG,16,6162
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BA,8,
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HR,6.3,2221
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CZ,40,
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DK,65,
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FI,38,23390
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FR,5,
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DE,13.8,
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HU,7.92875588637399,8549
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IS,90,8079000
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IE,0.8,
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IT,3,8727
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LV,73,2254
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LT,56,
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MK,23.7745607009008,636
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NO,4,3400
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PL,42,54912
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PT,0.070754716981132,34
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RS,25,5821
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SI,8.86,1739
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ES,0.251589260787732,1273
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SE,50.4,
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UK,2,
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BY,70,
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EE,52,5406
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KO,3,207
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RO,23,9962
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SK,54,15000
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NL,4,9800
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CH,4,2792
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AL,0,
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ME,0,
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@ -25,3 +25,6 @@ Comparative level investment,comparative_level_investment.csv,Eurostat,https://e
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Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
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Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
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Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
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District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,,
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District heating missing countries,district_heat_share.csv,unkown,https://www.euroheat.org/knowledge-hub/country-profiles,,
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Can't render this file because it has a wrong number of fields in line 28.
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@ -88,6 +88,7 @@ Future release
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* Compatibility with ``xarray`` version 0.19.
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* Separate basic chemicals into HVC, chlorine, methanol and ammonia [`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
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* Add option to specify reuse, primary production, and mechanical and chemical recycling fraction of platics [`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
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* Include today's district heating shares in myopic optimisation and add option to specify exogenous path for district heating share increase under ``sector: district_heating:`` [`#149 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/149>`_].
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PyPSA-Eur-Sec 0.5.0 (21st May 2021)
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===================================
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@ -212,6 +212,12 @@ def idees_per_country(ct, year):
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assert df.index[47] == "Electricity"
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ct_totals["electricity residential"] = df[47]
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assert df.index[46] == "Derived heat"
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ct_totals["Derived heat residential"] = df[46]
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assert df.index[50] == 'Thermal uses'
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ct_totals["thermal uses residential"] = df[50]
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# services
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df = pd.read_excel(fn_services, "SER_hh_fec", index_col=0)[year]
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@ -239,6 +245,12 @@ def idees_per_country(ct, year):
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assert df.index[50] == "Electricity"
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ct_totals["electricity services"] = df[50]
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assert df.index[49] == "Derived heat"
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ct_totals["derived heat services"] = df[49]
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assert df.index[53] == 'Thermal uses'
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ct_totals["thermal uses services"] = df[53]
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# transport
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df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year]
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@ -342,6 +354,7 @@ def build_idees(countries, year):
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with mp.Pool(processes=nprocesses) as pool:
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totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
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totals = pd.concat(totals_list, axis=1)
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# convert ktoe to TWh
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@ -351,6 +364,13 @@ def build_idees(countries, year):
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# convert TWh/100km to kWh/km
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totals.loc["passenger car efficiency"] *= 10
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# district heating share
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district_heat = totals.loc[["derived heat residential",
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"derived heat services"]].sum()
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total_heat = totals.loc[["thermal uses residential",
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"thermal uses services"]].sum()
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totals.loc["district heat share"] = district_heat.div(total_heat)
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return totals.T
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@ -502,6 +522,14 @@ def build_energy_totals(countries, eurostat, swiss, idees):
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ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
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df.loc['BA', missing] = ratio * df.loc["RS", missing]
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# Missing district heating share
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dh_share = pd.read_csv(snakemake.input.district_heat_share,
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index_col=0, usecols=[0, 1])
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# make conservative assumption and take minimum from both data sets
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df["district heat share"] = (pd.concat([df["district heat share"],
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dh_share.reindex(index=df.index)/100],
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axis=1).min(axis=1))
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return df
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@ -489,8 +489,7 @@ def add_dac(n, costs):
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efficiency3 = -(costs.at['direct air capture', 'heat-input'] - costs.at['direct air capture', 'compression-heat-output'])
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n.madd("Link",
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locations,
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suffix=" DAC",
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heat_buses.str.replace(" heat", " DAC"),
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bus0="co2 atmosphere",
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bus1=spatial.co2.df.loc[locations, "nodes"].values,
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bus2=locations.values,
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@ -636,6 +635,8 @@ def prepare_data(n):
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nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
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nodal_energy_totals.index = pop_layout.index
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# district heat share not weighted by population
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district_heat_share = nodal_energy_totals["district heat share"].round(2)
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nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
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# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
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@ -758,7 +759,7 @@ def prepare_data(n):
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)
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return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data
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return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share
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# TODO checkout PyPSA-Eur script
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@ -1336,12 +1337,11 @@ def add_heat(n, costs):
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sectors = ["residential", "services"]
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nodes = create_nodes_for_heat_sector()
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nodes, dist_fraction, urban_fraction = create_nodes_for_heat_sector()
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#NB: must add costs of central heating afterwards (EUR 400 / kWpeak, 50a, 1% FOM from Fraunhofer ISE)
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urban_fraction = options['central_fraction'] * pop_layout["urban"] / pop_layout[["urban", "rural"]].sum(axis=1)
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# exogenously reduce space heat demand
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if options["reduce_space_heat_exogenously"]:
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dE = get(options["reduce_space_heat_exogenously_factor"], investment_year)
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@ -1372,15 +1372,22 @@ def add_heat(n, costs):
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## Add heat load
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for sector in sectors:
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# heat demand weighting
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if "rural" in name:
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factor = 1 - urban_fraction[nodes[name]]
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elif "urban" in name:
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factor = urban_fraction[nodes[name]]
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elif "urban central" in name:
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factor = dist_fraction[nodes[name]]
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elif "urban decentral" in name:
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factor = urban_fraction[nodes[name]] - \
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dist_fraction[nodes[name]]
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else:
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raise NotImplementedError(f" {name} not in " f"heat systems: {heat_systems}")
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if sector in name:
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heat_load = heat_demand[[sector + " water",sector + " space"]].groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor)
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if name == "urban central":
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heat_load = heat_demand.groupby(level=1,axis=1).sum()[nodes[name]].multiply(urban_fraction[nodes[name]] * (1 + options['district_heating_loss']))
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heat_load = heat_demand.groupby(level=1,axis=1).sum()[nodes[name]].multiply(factor * (1 + options['district_heating']['district_heating_loss']))
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n.madd("Load",
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nodes[name],
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@ -1661,23 +1668,39 @@ def create_nodes_for_heat_sector():
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# urban are areas with high heating density
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# urban can be split into district heating (central) and individual heating (decentral)
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ct_urban = pop_layout.urban.groupby(pop_layout.ct).sum()
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# distribution of urban population within a country
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pop_layout["urban_ct_fraction"] = pop_layout.urban / pop_layout.ct.map(ct_urban.get)
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sectors = ["residential", "services"]
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nodes = {}
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urban_fraction = pop_layout.urban / pop_layout[["rural", "urban"]].sum(axis=1)
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for sector in sectors:
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nodes[sector + " rural"] = pop_layout.index
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nodes[sector + " urban decentral"] = pop_layout.index
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if options["central"]:
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# TODO: this looks hardcoded, move to config
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urban_decentral_ct = pd.Index(["ES", "GR", "PT", "IT", "BG"])
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nodes[sector + " urban decentral"] = pop_layout.index[pop_layout.ct.isin(urban_decentral_ct)]
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else:
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nodes[sector + " urban decentral"] = pop_layout.index
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# maximum potential of urban demand covered by district heating
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central_fraction = options['district_heating']["potential"]
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# district heating share at each node
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dist_fraction_node = district_heat_share * pop_layout["urban_ct_fraction"] / pop_layout["fraction"]
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nodes["urban central"] = dist_fraction_node.index
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# if district heating share larger than urban fraction -> set urban
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# fraction to district heating share
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urban_fraction = pd.concat([urban_fraction, dist_fraction_node],
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axis=1).max(axis=1)
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# difference of max potential and today's share of district heating
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diff = (urban_fraction * central_fraction) - dist_fraction_node
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progress = get(options["district_heating"]["potential"], investment_year)
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dist_fraction_node += diff * progress
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print(
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"The current district heating share compared to the maximum",
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f"possible is increased by a progress factor of\n{progress}",
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f"resulting in a district heating share of\n{dist_fraction_node}"
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)
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# for central nodes, residential and services are aggregated
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nodes["urban central"] = pop_layout.index.symmetric_difference(nodes["residential urban decentral"])
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return nodes
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return nodes, dist_fraction_node, urban_fraction
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def add_biomass(n, costs):
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@ -1993,7 +2016,7 @@ def add_industry(n, costs):
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if options["oil_boilers"]:
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nodes_heat = create_nodes_for_heat_sector()
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nodes_heat = create_nodes_for_heat_sector()[0]
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for name in ["residential rural", "services rural", "residential urban decentral", "services urban decentral"]:
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@ -2191,18 +2214,18 @@ def limit_individual_line_extension(n, maxext):
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hvdc = n.links.index[n.links.carrier == 'DC']
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n.links.loc[hvdc, 'p_nom_max'] = n.links.loc[hvdc, 'p_nom'] + maxext
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#%%
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if __name__ == "__main__":
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if 'snakemake' not in globals():
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from helper import mock_snakemake
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snakemake = mock_snakemake(
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'prepare_sector_network',
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simpl='',
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clusters="45",
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opts="",
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clusters="37",
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lv=1.0,
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opts='',
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sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
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planning_horizons="2030",
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planning_horizons="2020",
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)
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logging.basicConfig(level=snakemake.config['logging_level'])
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@ -2254,10 +2277,10 @@ if __name__ == "__main__":
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if o == "biomasstransport":
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options["biomass_transport"] = True
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nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data = prepare_data(n)
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nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share = prepare_data(n)
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if "nodistrict" in opts:
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options["central"] = False
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options["district_heating"]["progress"] = 0.0
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if "T" in opts:
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add_land_transport(n, costs)
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