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Calculate and save cumulative costs for the myopic approach

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Creates an additional file in results/csvs including the cumulative costs with different social discount rates
This commit is contained in:
martavp 2020-12-30 15:56:34 +01:00
parent 3457d4ee38
commit 7c464f166b
1 changed files with 30 additions and 3 deletions
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33
scripts/make_summary.py
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@ -196,7 +196,25 @@ def calculate_costs(n,label,costs):
return costs
def calculate_cumulative_cost():
planning_horizons = snakemake.config['scenario']['planning_horizons']
cumulative_cost = pd.DataFrame(index = df["costs"].sum().index,
columns=pd.Series(data=np.arange(0,0.1, 0.01), name='social discount rate'))
#discount cost and express them in money value of planning_horizons[0]
for r in cumulative_cost.columns:
cumulative_cost[r]=[df["costs"].sum()[index]/((1+r)**(index[-1]-planning_horizons[0])) for index in cumulative_cost.index]
#integrate cost throughout the transition path
for r in cumulative_cost.columns:
for cluster in cumulative_cost.index.get_level_values(level=0).unique():
for lv in cumulative_cost.index.get_level_values(level=1).unique():
for sector_opts in cumulative_cost.index.get_level_values(level=2).unique():
cumulative_cost.loc[(cluster, lv, sector_opts,'cumulative cost'),r] = np.trapz(cumulative_cost.loc[idx[cluster, lv, sector_opts,planning_horizons],r].values, x=planning_horizons)
return cumulative_cost
def calculate_nodal_capacities(n,label,nodal_capacities):
#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
@ -564,16 +582,17 @@ if __name__ == "__main__":
snakemake.config = yaml.safe_load(f)
#overwrite some options
snakemake.config["run"] = "test"
snakemake.config["run"] = "version-8"
snakemake.config["scenario"]["lv"] = [1.0]
snakemake.config["scenario"]["sector_opts"] = ["Co2L0-168H-T-H-B-I-solar3-dist1"]
snakemake.config["scenario"]["sector_opts"] = ["3H-T-H-B-I-solar3-dist1"]
snakemake.config["planning_horizons"] = ['2020', '2030', '2040', '2050']
snakemake.input = Dict()
snakemake.input['heat_demand_name'] = 'data/heating/daily_heat_demand.h5'
snakemake.input['costs'] = snakemake.config['costs_dir'] + "costs_{}.csv".format(snakemake.config['scenario']['planning_horizons'][0])
snakemake.output = Dict()
for item in outputs:
snakemake.output[item] = snakemake.config['summary_dir'] + '/{name}/csvs/{item}.csv'.format(name=snakemake.config['run'],item=item)
snakemake.output['cumulative_cost'] = snakemake.config['summary_dir'] + '/{name}/csvs/cumulative_cost.csv'.format(name=snakemake.config['run'])
networks_dict = {(cluster, lv, opt+sector_opt, planning_horizon) :
snakemake.config['results_dir'] + snakemake.config['run'] + '/postnetworks/elec_s{simpl}_{cluster}_lv{lv}_{opt}_{sector_opt}_{planning_horizon}.nc'\
.format(simpl=simpl,
@ -592,6 +611,7 @@ if __name__ == "__main__":
print(networks_dict)
Nyears = 1
costs_db = prepare_costs(snakemake.input.costs,
snakemake.config['costs']['USD2013_to_EUR2013'],
snakemake.config['costs']['discountrate'],
@ -603,3 +623,10 @@ if __name__ == "__main__":
df["metrics"].loc["total costs"] = df["costs"].sum()
to_csv(df)
if snakemake.config["foresight"]=='myopic':
cumulative_cost=calculate_cumulative_cost()
cumulative_cost.to_csv(snakemake.config['summary_dir'] + '/' + snakemake.config['run'] + '/csvs/cumulative_cost.csv')