Distribute heating technologies within each country by population
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Tom Brown
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a59b2bce19
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16b05a570d
@ -308,6 +308,7 @@ if config["foresight"] == "myopic":
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network=config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{co2_budget_name}_{planning_horizons}.nc',
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powerplants=pypsaeur('resources/powerplants.csv'),
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clustermaps=pypsaeur('resources/clustermaps_{network}_s{simpl}_{clusters}.h5'),
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
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costs=config['costs_dir'] + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
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cop_soil_total="resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc",
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cop_air_total="resources/cop_air_total_{network}_s{simpl}_{clusters}.nc"
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@ -44,6 +44,11 @@ override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"bui
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def add_existing_renewables(df_agg):
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"""
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Append existing renewables to the df_agg pd.DataFrame
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with the conventional power plants.
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"""
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cc = pd.read_csv('data/Country_codes.csv',
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index_col=0)
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@ -159,8 +164,6 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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df = df_agg.pivot_table(index=["grouping_year",'Fueltype'], columns='cluster_bus',
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values='Capacity', aggfunc='sum')
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print(df)
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carrier = {"OCGT" : "gas",
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"CCGT" : "gas",
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"coal" : "coal",
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@ -174,10 +177,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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capacity = capacity[~capacity.isna()]
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capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
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#print(grouping_year,generator,capacity)
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if generator in ['solar', 'onwind', 'offwind']:
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print("adding generators for",grouping_year,generator,capacity)
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if generator =='offwind':
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p_max_pu=n.generators_t.p_max_pu[capacity.index + ' offwind-ac']
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else:
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@ -196,7 +196,6 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs):
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build_year=grouping_year,
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lifetime=costs.at[generator,'lifetime'])
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else:
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print("adding links for",grouping_year,generator,capacity)
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n.madd("Link",
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capacity.index,
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suffix= " " + generator +"-" + str(grouping_year),
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@ -256,31 +255,37 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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df.fillna(0, inplace=True)
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df *= 1e3 # GW to MW
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cc = pd.read_csv('data/Country_codes.csv', sep=',', index_col=-1)
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name_to_2code = dict(zip(cc['Country'].tolist(),
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cc['2 letter code (ISO-3166-2)'].tolist()))
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df.rename(index=lambda country : name_to_2code[country], inplace=True)
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cc = pd.read_csv('data/Country_codes.csv',
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index_col=0)
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df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
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# coal and oil boilers are assimilated to oil boilers
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df['oil boiler'] =df['oil boiler'] + df['coal boiler']
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df.drop(['coal boiler'], axis=1, inplace=True)
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# rename countries with network buses names
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nodes_elec=[node for node in n.buses.index[n.buses.carrier == "AC"]]
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name_to_busname={ index : [node for node in nodes_elec if index in node][0] for index in df.index}
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df.rename(index=lambda country : name_to_busname[country], inplace=True)
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# distribute technologies to nodes by population
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,
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index_col=0)
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pop_layout["ct"] = pop_layout.index.str[:2]
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ct_total = pop_layout.total.groupby(pop_layout["ct"]).sum()
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pop_layout["ct_total"] = pop_layout["ct"].map(ct_total.get)
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pop_layout["fraction"] = pop_layout["total"]/pop_layout["ct_total"]
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nodal_df = df.loc[pop_layout.ct]
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nodal_df.index = pop_layout.index
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nodal_df = nodal_df.multiply(pop_layout.fraction,axis=0)
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# split existing capacities between residential and services
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# proportional to energy demand
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ratio_residential=pd.Series([(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] /
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(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] +
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n.loads_t.p_set.sum()['{} services rural heat'.format(node)] ))
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for node in df.index], index=df.index)
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for node in nodal_df.index], index=nodal_df.index)
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for tech in techs:
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df['residential ' + tech] = df[tech]*ratio_residential
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df['services ' + tech] = df[tech]*(1-ratio_residential)
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nodal_df['residential ' + tech] = nodal_df[tech]*ratio_residential
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nodal_df['services ' + tech] = nodal_df[tech]*(1-ratio_residential)
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nodes={}
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p_nom={}
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@ -291,14 +296,14 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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"urban central"]:
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name_type = "central" if name == "urban central" else "decentral"
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nodes[name] = pd.Index([index[0:5] for index in n.buses.index[n.buses.index.str.contains(name) & n.buses.index.str.contains('heat')]])
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nodes[name] = pd.Index([n.buses.at[index,"location"] for index in n.buses.index[n.buses.index.str.contains(name) & n.buses.index.str.contains('heat')]])
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heat_pump_type = "air" if "urban" in name else "ground"
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heat_type= "residential" if "residential" in name else "services"
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if name == "urban central":
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p_nom[name]=df['air heat pump'][nodes[name]]
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p_nom[name]=nodal_df['air heat pump'][nodes[name]]
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else:
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p_nom[name] = df['{} {} heat pump'.format(heat_type, heat_pump_type)][nodes[name]]
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p_nom[name] = nodal_df['{} {} heat pump'.format(heat_type, heat_pump_type)][nodes[name]]
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# Add heat pumps
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costs_name = "{} {}-sourced heat pump".format("decentral", heat_pump_type)
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@ -312,7 +317,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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else:
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#installation is assumed to be linear for the past 25 years (default lifetime)
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ratio = (int(grouping_year)-int(grouping_years[i-1]))/default_lifetime
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print(str(grouping_year) + ' ratio ' + str(ratio))
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n.madd("Link",
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nodes[name],
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suffix=" {} {} heat pump-{}".format(name,heat_pump_type, grouping_year),
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@ -335,7 +340,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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carrier=name + " resistive heater",
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efficiency=costs.at[name_type + ' resistive heater','efficiency'],
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capital_cost=costs.at[name_type + ' resistive heater','efficiency']*costs.at[name_type + ' resistive heater','fixed'],
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p_nom=0.5*df['{} resistive heater'.format(heat_type)][nodes[name]]*ratio,
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p_nom=0.5*nodal_df['{} resistive heater'.format(heat_type)][nodes[name]]*ratio,
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build_year=int(grouping_year),
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lifetime=costs.at[costs_name,'lifetime'])
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@ -349,7 +354,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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efficiency=costs.at[name_type + ' gas boiler','efficiency'],
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efficiency2=costs.at['gas','CO2 intensity'],
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capital_cost=costs.at[name_type + ' gas boiler','efficiency']*costs.at[name_type + ' gas boiler','fixed'],
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p_nom=0.5*df['{} gas boiler'.format(heat_type)][nodes[name]]*ratio,
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p_nom=0.5*nodal_df['{} gas boiler'.format(heat_type)][nodes[name]]*ratio,
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build_year=int(grouping_year),
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lifetime=costs.at[name_type + ' gas boiler','lifetime'])
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n.madd("Link",
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@ -362,7 +367,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
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efficiency=costs.at['decentral oil boiler','efficiency'],
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efficiency2=costs.at['oil','CO2 intensity'],
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capital_cost=costs.at['decentral oil boiler','efficiency']*costs.at['decentral oil boiler','fixed'],
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p_nom=0.5*df['{} oil boiler'.format(heat_type)][nodes[name]]*ratio,
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p_nom=0.5*nodal_df['{} oil boiler'.format(heat_type)][nodes[name]]*ratio,
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build_year=int(grouping_year),
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lifetime=costs.at[name_type + ' gas boiler','lifetime'])
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