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Record total capacities and total costs

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This commit is contained in:
Tom Brown 2019-05-03 17:11:13 +02:00
parent c47ffb8d03
commit 5357b7b92f
3 changed files with 23 additions and 2 deletions
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1
Snakefile
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@ -219,6 +219,7 @@ rule make_summary:
#heat_demand_name='data/heating/daily_heat_demand.h5'
output:
costs=config['summary_dir'] + '/' + config['run'] + '/csvs/costs.csv',
capacities=config['summary_dir'] + '/' + config['run'] + '/csvs/capacities.csv',
curtailment=config['summary_dir'] + '/' + config['run'] + '/csvs/curtailment.csv',
energy=config['summary_dir'] + '/' + config['run'] + '/csvs/energy.csv',
supply=config['summary_dir'] + '/' + config['run'] + '/csvs/supply.csv',

4
config.yaml
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@ -2,13 +2,13 @@ logging_level: INFO
results_dir: 'results/'
summary_dir: results
run: '190501-256'
run: '190502-181'
scenario:
sectors: [E] # ,E+EV,E+BEV,E+BEV+V2G] # [ E+EV, E+BEV, E+BEV+V2G ]
simpl: ['']
lv: [1.0,1.25]#[1.0, 1.125, 1.25, 1.5, 2.0, opt]# or opt
clusters: [256] #[90, 128, 181] #[45, 64, 90, 128, 181, 256] #, 362] # (2**np.r_[5.5:9:.5]).astype(int) minimum is 37
clusters: [181] #[90, 128, 181] #[45, 64, 90, 128, 181, 256] #, 362] # (2**np.r_[5.5:9:.5]).astype(int) minimum is 37
opts: [''] #for pypsa-eur
sector_opts: [Co2L0-3H-T-H-B-I-solar3,Co2L0-3H-T-H-B-I-onwind0p25-solar3]#,Co2L0p05-3H-T-H-B-I,Co2L0p10-3H-T-H-B-I,Co2L0p20-3H-T-H-B-I,Co2L0p30-3H-T-H-B-I,Co2L0p50-3H-T-H-B-I]#[Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0-3H-T-H,Co2L0p20-3H-T-H] #Co2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p20-3H-T-HCo2L-3H-T-H,Co2L0p10-3H-T-H,Co2L0p30-3H-T-H,Co2L0p50-3H-T-H] #Co2L-3H,Co2L-3H-T,, LC-FL, LC-T, Ep-T, Co2L-T]
# Co2L will give default (5%); Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions

20
scripts/make_summary.py
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@ -75,6 +75,11 @@ def calculate_costs(n,label,costs):
else:
p = c.pnl.p.multiply(n.snapshot_weightings,axis=0).sum()
#correct sequestration cost
if c.name == "Store":
items = c.df.index[(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.)]
c.df.loc[items,"marginal_cost"] = -20.
marginal_costs = p*c.df.marginal_cost
marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
@ -100,6 +105,18 @@ def calculate_costs(n,label,costs):
def calculate_capacities(n,label,capacities):
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
capacities_grouped = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby(c.df.carrier).sum()
capacities = capacities.reindex(capacities.index|pd.MultiIndex.from_product([[c.list_name],capacities_grouped.index]))
capacities.loc[idx[c.list_name,list(capacities_grouped.index)],label] = capacities_grouped.values
return capacities
def calculate_curtailment(n,label,curtailment):
avail = n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt).sum().groupby(n.generators.carrier).sum()
@ -373,6 +390,7 @@ def calculate_price_statistics(n, label, price_statistics):
outputs = ["costs",
"capacities",
"curtailment",
"energy",
"supply",
@ -449,4 +467,6 @@ if __name__ == "__main__":
df = make_summaries(networks_dict)
df["metrics"].loc["total costs"] = df["costs"].sum()
to_csv(df)