bjoern/pypsa-eur
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

Distribute heating technologies within each country by population

Browse Source
This commit is contained in:
Tom Brown 2020-08-11 11:09:39 +02:00
parent a59b2bce19
commit 16b05a570d
2 changed files with 30 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
Snakefile
View File

@ -308,6 +308,7 @@ if config["foresight"] == "myopic":
network=config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{co2_budget_name}_{planning_horizons}.nc', network=config['results_dir'] + config['run'] + '/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{co2_budget_name}_{planning_horizons}.nc',
powerplants=pypsaeur('resources/powerplants.csv'), powerplants=pypsaeur('resources/powerplants.csv'),
clustermaps=pypsaeur('resources/clustermaps_{network}_s{simpl}_{clusters}.h5'), clustermaps=pypsaeur('resources/clustermaps_{network}_s{simpl}_{clusters}.h5'),
clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
costs=config['costs_dir'] + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]), costs=config['costs_dir'] + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
cop_soil_total="resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc", cop_soil_total="resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc",
cop_air_total="resources/cop_air_total_{network}_s{simpl}_{clusters}.nc" cop_air_total="resources/cop_air_total_{network}_s{simpl}_{clusters}.nc"

53
scripts/add_existing_baseyear.py
View File

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