601 lines
21 KiB
Python
601 lines
21 KiB
Python
import logging
|
|
logger = logging.getLogger(__name__)
|
|
|
|
import sys
|
|
import yaml
|
|
import pypsa
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from prepare_sector_network import prepare_costs
|
|
from helper import override_component_attrs
|
|
|
|
idx = pd.IndexSlice
|
|
|
|
opt_name = {
|
|
"Store": "e",
|
|
"Line": "s",
|
|
"Transformer": "s"
|
|
}
|
|
|
|
|
|
def assign_carriers(n):
|
|
if "carrier" not in n.lines:
|
|
n.lines["carrier"] = "AC"
|
|
|
|
|
|
def assign_locations(n):
|
|
for c in n.iterate_components(n.one_port_components|n.branch_components):
|
|
ifind = pd.Series(c.df.index.str.find(" ",start=4),c.df.index)
|
|
for i in ifind.unique():
|
|
names = ifind.index[ifind == i]
|
|
if i == -1:
|
|
c.df.loc[names, 'location'] = ""
|
|
else:
|
|
c.df.loc[names, 'location'] = names.str[:i]
|
|
|
|
|
|
def calculate_nodal_cfs(n, label, nodal_cfs):
|
|
#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^{"Line","Transformer"})|n.controllable_one_port_components^{"Load","StorageUnit"}):
|
|
capacities_c = c.df.groupby(["location","carrier"])[opt_name.get(c.name,"p") + "_nom_opt"].sum()
|
|
|
|
if c.name == "Link":
|
|
p = c.pnl.p0.abs().mean()
|
|
elif c.name == "Generator":
|
|
p = c.pnl.p.abs().mean()
|
|
elif c.name == "Store":
|
|
p = c.pnl.e.abs().mean()
|
|
else:
|
|
sys.exit()
|
|
|
|
c.df["p"] = p
|
|
p_c = c.df.groupby(["location", "carrier"])["p"].sum()
|
|
|
|
cf_c = p_c/capacities_c
|
|
|
|
index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in cf_c.index.to_list()])
|
|
nodal_cfs = nodal_cfs.reindex(index.union(nodal_cfs.index))
|
|
nodal_cfs.loc[index,label] = cf_c.values
|
|
|
|
return nodal_cfs
|
|
|
|
|
|
def calculate_cfs(n, label, cfs):
|
|
|
|
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load","StorageUnit"}):
|
|
capacities_c = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby(c.df.carrier).sum()
|
|
|
|
if c.name in ["Link","Line","Transformer"]:
|
|
p = c.pnl.p0.abs().mean()
|
|
elif c.name == "Store":
|
|
p = c.pnl.e.abs().mean()
|
|
else:
|
|
p = c.pnl.p.abs().mean()
|
|
|
|
p_c = p.groupby(c.df.carrier).sum()
|
|
|
|
cf_c = p_c/capacities_c
|
|
|
|
cf_c = pd.concat([cf_c], keys=[c.list_name])
|
|
|
|
cfs = cfs.reindex(cf_c.index.union(cfs.index))
|
|
|
|
cfs.loc[cf_c.index,label] = cf_c
|
|
|
|
return cfs
|
|
|
|
|
|
def calculate_nodal_costs(n, label, nodal_costs):
|
|
#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"}):
|
|
c.df["capital_costs"] = c.df.capital_cost * c.df[opt_name.get(c.name, "p") + "_nom_opt"]
|
|
capital_costs = c.df.groupby(["location", "carrier"])["capital_costs"].sum()
|
|
index = pd.MultiIndex.from_tuples([(c.list_name, "capital") + t for t in capital_costs.index.to_list()])
|
|
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
|
|
nodal_costs.loc[index,label] = capital_costs.values
|
|
|
|
if c.name == "Link":
|
|
p = c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0).sum()
|
|
elif c.name == "Line":
|
|
continue
|
|
elif c.name == "StorageUnit":
|
|
p_all = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
|
|
p_all[p_all < 0.] = 0.
|
|
p = p_all.sum()
|
|
else:
|
|
p = c.pnl.p.multiply(n.snapshot_weightings.generators, 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.
|
|
|
|
c.df["marginal_costs"] = p*c.df.marginal_cost
|
|
marginal_costs = c.df.groupby(["location", "carrier"])["marginal_costs"].sum()
|
|
index = pd.MultiIndex.from_tuples([(c.list_name, "marginal") + t for t in marginal_costs.index.to_list()])
|
|
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
|
|
nodal_costs.loc[index, label] = marginal_costs.values
|
|
|
|
return nodal_costs
|
|
|
|
|
|
def calculate_costs(n, label, costs):
|
|
|
|
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
|
|
capital_costs = c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]
|
|
capital_costs_grouped = capital_costs.groupby(c.df.carrier).sum()
|
|
|
|
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
|
|
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name])
|
|
|
|
costs = costs.reindex(capital_costs_grouped.index.union(costs.index))
|
|
|
|
costs.loc[capital_costs_grouped.index, label] = capital_costs_grouped
|
|
|
|
if c.name == "Link":
|
|
p = c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0).sum()
|
|
elif c.name == "Line":
|
|
continue
|
|
elif c.name == "StorageUnit":
|
|
p_all = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
|
|
p_all[p_all < 0.] = 0.
|
|
p = p_all.sum()
|
|
else:
|
|
p = c.pnl.p.multiply(n.snapshot_weightings.generators, 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()
|
|
|
|
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=["marginal"])
|
|
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=[c.list_name])
|
|
|
|
costs = costs.reindex(marginal_costs_grouped.index.union(costs.index))
|
|
|
|
costs.loc[marginal_costs_grouped.index,label] = marginal_costs_grouped
|
|
|
|
# add back in all hydro
|
|
#costs.loc[("storage_units", "capital", "hydro"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="hydro", "p_nom"].sum()
|
|
#costs.loc[("storage_units", "capital", "PHS"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="PHS", "p_nom"].sum()
|
|
#costs.loc[("generators", "capital", "ror"),label] = (0.02)*3e6*n.generators.loc[n.generators.group=="ror", "p_nom"].sum()
|
|
|
|
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"}):
|
|
nodal_capacities_c = c.df.groupby(["location","carrier"])[opt_name.get(c.name,"p") + "_nom_opt"].sum()
|
|
index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in nodal_capacities_c.index.to_list()])
|
|
nodal_capacities = nodal_capacities.reindex(index.union(nodal_capacities.index))
|
|
nodal_capacities.loc[index,label] = nodal_capacities_c.values
|
|
|
|
return nodal_capacities
|
|
|
|
|
|
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_grouped = pd.concat([capacities_grouped], keys=[c.list_name])
|
|
|
|
capacities = capacities.reindex(capacities_grouped.index.union(capacities.index))
|
|
|
|
capacities.loc[capacities_grouped.index, label] = capacities_grouped
|
|
|
|
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()
|
|
used = n.generators_t.p.sum().groupby(n.generators.carrier).sum()
|
|
|
|
curtailment[label] = (((avail - used)/avail)*100).round(3)
|
|
|
|
return curtailment
|
|
|
|
|
|
def calculate_energy(n, label, energy):
|
|
|
|
for c in n.iterate_components(n.one_port_components|n.branch_components):
|
|
|
|
if c.name in n.one_port_components:
|
|
c_energies = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0).sum().multiply(c.df.sign).groupby(c.df.carrier).sum()
|
|
else:
|
|
c_energies = pd.Series(0., c.df.carrier.unique())
|
|
for port in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
|
|
totals = c.pnl["p" + port].multiply(n.snapshot_weightings.generators, axis=0).sum()
|
|
#remove values where bus is missing (bug in nomopyomo)
|
|
no_bus = c.df.index[c.df["bus" + port] == ""]
|
|
totals.loc[no_bus] = n.component_attrs[c.name].loc["p" + port, "default"]
|
|
c_energies -= totals.groupby(c.df.carrier).sum()
|
|
|
|
c_energies = pd.concat([c_energies], keys=[c.list_name])
|
|
|
|
energy = energy.reindex(c_energies.index.union(energy.index))
|
|
|
|
energy.loc[c_energies.index, label] = c_energies
|
|
|
|
return energy
|
|
|
|
|
|
def calculate_supply(n, label, supply):
|
|
"""calculate the max dispatch of each component at the buses aggregated by carrier"""
|
|
|
|
bus_carriers = n.buses.carrier.unique()
|
|
|
|
for i in bus_carriers:
|
|
bus_map = (n.buses.carrier == i)
|
|
bus_map.at[""] = False
|
|
|
|
for c in n.iterate_components(n.one_port_components):
|
|
|
|
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
|
|
|
|
if len(items) == 0:
|
|
continue
|
|
|
|
s = c.pnl.p[items].max().multiply(c.df.loc[items, 'sign']).groupby(c.df.loc[items, 'carrier']).sum()
|
|
s = pd.concat([s], keys=[c.list_name])
|
|
s = pd.concat([s], keys=[i])
|
|
|
|
supply = supply.reindex(s.index.union(supply.index))
|
|
supply.loc[s.index,label] = s
|
|
|
|
|
|
for c in n.iterate_components(n.branch_components):
|
|
|
|
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
|
|
|
|
items = c.df.index[c.df["bus" + end].map(bus_map).fillna(False)]
|
|
|
|
if len(items) == 0:
|
|
continue
|
|
|
|
#lots of sign compensation for direction and to do maximums
|
|
s = (-1)**(1-int(end))*((-1)**int(end)*c.pnl["p"+end][items]).max().groupby(c.df.loc[items, 'carrier']).sum()
|
|
s.index = s.index + end
|
|
s = pd.concat([s], keys=[c.list_name])
|
|
s = pd.concat([s], keys=[i])
|
|
|
|
supply = supply.reindex(s.index.union(supply.index))
|
|
supply.loc[s.index, label] = s
|
|
|
|
return supply
|
|
|
|
def calculate_supply_energy(n, label, supply_energy):
|
|
"""calculate the total energy supply/consuption of each component at the buses aggregated by carrier"""
|
|
|
|
|
|
bus_carriers = n.buses.carrier.unique()
|
|
|
|
for i in bus_carriers:
|
|
bus_map = (n.buses.carrier == i)
|
|
bus_map.at[""] = False
|
|
|
|
for c in n.iterate_components(n.one_port_components):
|
|
|
|
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
|
|
|
|
if len(items) == 0:
|
|
continue
|
|
|
|
s = c.pnl.p[items].multiply(n.snapshot_weightings.generators,axis=0).sum().multiply(c.df.loc[items, 'sign']).groupby(c.df.loc[items, 'carrier']).sum()
|
|
s = pd.concat([s], keys=[c.list_name])
|
|
s = pd.concat([s], keys=[i])
|
|
|
|
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
|
|
supply_energy.loc[s.index, label] = s
|
|
|
|
|
|
for c in n.iterate_components(n.branch_components):
|
|
|
|
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
|
|
|
|
items = c.df.index[c.df["bus" + str(end)].map(bus_map).fillna(False)]
|
|
|
|
if len(items) == 0:
|
|
continue
|
|
|
|
s = (-1)*c.pnl["p"+end][items].multiply(n.snapshot_weightings.generators,axis=0).sum().groupby(c.df.loc[items, 'carrier']).sum()
|
|
s.index = s.index + end
|
|
s = pd.concat([s], keys=[c.list_name])
|
|
s = pd.concat([s], keys=[i])
|
|
|
|
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
|
|
|
|
supply_energy.loc[s.index, label] = s
|
|
|
|
return supply_energy
|
|
|
|
|
|
def calculate_metrics(n, label, metrics):
|
|
|
|
metrics_list = [
|
|
"line_volume",
|
|
"line_volume_limit",
|
|
"line_volume_AC",
|
|
"line_volume_DC",
|
|
"line_volume_shadow",
|
|
"co2_shadow"
|
|
]
|
|
|
|
metrics = metrics.reindex(pd.Index(metrics_list).union(metrics.index))
|
|
|
|
metrics.at["line_volume_DC",label] = (n.links.length * n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
|
|
metrics.at["line_volume_AC",label] = (n.lines.length * n.lines.s_nom_opt).sum()
|
|
metrics.at["line_volume",label] = metrics.loc[["line_volume_AC", "line_volume_DC"], label].sum()
|
|
|
|
if hasattr(n, "line_volume_limit"):
|
|
metrics.at["line_volume_limit", label] = n.line_volume_limit
|
|
metrics.at["line_volume_shadow", label] = n.line_volume_limit_dual
|
|
|
|
if "CO2Limit" in n.global_constraints.index:
|
|
metrics.at["co2_shadow", label] = n.global_constraints.at["CO2Limit", "mu"]
|
|
|
|
return metrics
|
|
|
|
|
|
def calculate_prices(n, label, prices):
|
|
|
|
prices = prices.reindex(prices.index.union(n.buses.carrier.unique()))
|
|
|
|
#WARNING: this is time-averaged, see weighted_prices for load-weighted average
|
|
prices[label] = n.buses_t.marginal_price.mean().groupby(n.buses.carrier).mean()
|
|
|
|
return prices
|
|
|
|
|
|
def calculate_weighted_prices(n, label, weighted_prices):
|
|
# Warning: doesn't include storage units as loads
|
|
|
|
weighted_prices = weighted_prices.reindex(pd.Index([
|
|
"electricity",
|
|
"heat",
|
|
"space heat",
|
|
"urban heat",
|
|
"space urban heat",
|
|
"gas",
|
|
"H2"
|
|
]))
|
|
|
|
link_loads = {"electricity": ["heat pump", "resistive heater", "battery charger", "H2 Electrolysis"],
|
|
"heat": ["water tanks charger"],
|
|
"urban heat": ["water tanks charger"],
|
|
"space heat": [],
|
|
"space urban heat": [],
|
|
"gas": ["OCGT", "gas boiler", "CHP electric", "CHP heat"],
|
|
"H2": ["Sabatier", "H2 Fuel Cell"]}
|
|
|
|
for carrier in link_loads:
|
|
|
|
if carrier == "electricity":
|
|
suffix = ""
|
|
elif carrier[:5] == "space":
|
|
suffix = carrier[5:]
|
|
else:
|
|
suffix = " " + carrier
|
|
|
|
buses = n.buses.index[n.buses.index.str[2:] == suffix]
|
|
|
|
if buses.empty:
|
|
continue
|
|
|
|
if carrier in ["H2", "gas"]:
|
|
load = pd.DataFrame(index=n.snapshots, columns=buses, data=0.)
|
|
elif carrier[:5] == "space":
|
|
load = heat_demand_df[buses.str[:2]].rename(columns=lambda i: str(i)+suffix)
|
|
else:
|
|
load = n.loads_t.p_set[buses]
|
|
|
|
for tech in link_loads[carrier]:
|
|
|
|
names = n.links.index[n.links.index.to_series().str[-len(tech):] == tech]
|
|
|
|
if names.empty:
|
|
continue
|
|
|
|
load += n.links_t.p0[names].groupby(n.links.loc[names, "bus0"],axis=1).sum()
|
|
|
|
# Add H2 Store when charging
|
|
#if carrier == "H2":
|
|
# stores = n.stores_t.p[buses+ " Store"].groupby(n.stores.loc[buses+ " Store", "bus"],axis=1).sum(axis=1)
|
|
# stores[stores > 0.] = 0.
|
|
# load += -stores
|
|
|
|
weighted_prices.loc[carrier,label] = (load * n.buses_t.marginal_price[buses]).sum().sum() / load.sum().sum()
|
|
|
|
# still have no idea what this is for, only for debug reasons.
|
|
if carrier[:5] == "space":
|
|
logger.debug(load * n.buses_t.marginal_price[buses])
|
|
|
|
return weighted_prices
|
|
|
|
|
|
def calculate_market_values(n, label, market_values):
|
|
# Warning: doesn't include storage units
|
|
|
|
carrier = "AC"
|
|
|
|
buses = n.buses.index[n.buses.carrier == carrier]
|
|
|
|
## First do market value of generators ##
|
|
|
|
generators = n.generators.index[n.buses.loc[n.generators.bus, "carrier"] == carrier]
|
|
|
|
techs = n.generators.loc[generators, "carrier"].value_counts().index
|
|
|
|
market_values = market_values.reindex(market_values.index.union(techs))
|
|
|
|
|
|
for tech in techs:
|
|
gens = generators[n.generators.loc[generators, "carrier"] == tech]
|
|
|
|
dispatch = n.generators_t.p[gens].groupby(n.generators.loc[gens, "bus"], axis=1).sum().reindex(columns=buses, fill_value=0.)
|
|
|
|
revenue = dispatch * n.buses_t.marginal_price[buses]
|
|
|
|
market_values.at[tech,label] = revenue.sum().sum() / dispatch.sum().sum()
|
|
|
|
|
|
## Now do market value of links ##
|
|
|
|
for i in ["0", "1"]:
|
|
all_links = n.links.index[n.buses.loc[n.links["bus"+i], "carrier"] == carrier]
|
|
|
|
techs = n.links.loc[all_links, "carrier"].value_counts().index
|
|
|
|
market_values = market_values.reindex(market_values.index.union(techs))
|
|
|
|
for tech in techs:
|
|
links = all_links[n.links.loc[all_links, "carrier"] == tech]
|
|
|
|
dispatch = n.links_t["p"+i][links].groupby(n.links.loc[links, "bus"+i], axis=1).sum().reindex(columns=buses, fill_value=0.)
|
|
|
|
revenue = dispatch * n.buses_t.marginal_price[buses]
|
|
|
|
market_values.at[tech,label] = revenue.sum().sum() / dispatch.sum().sum()
|
|
|
|
return market_values
|
|
|
|
|
|
def calculate_price_statistics(n, label, price_statistics):
|
|
|
|
|
|
price_statistics = price_statistics.reindex(price_statistics.index.union(pd.Index(["zero_hours", "mean", "standard_deviation"])))
|
|
|
|
buses = n.buses.index[n.buses.carrier == "AC"]
|
|
|
|
threshold = 0.1 # higher than phoney marginal_cost of wind/solar
|
|
|
|
df = pd.DataFrame(data=0., columns=buses, index=n.snapshots)
|
|
|
|
df[n.buses_t.marginal_price[buses] < threshold] = 1.
|
|
|
|
price_statistics.at["zero_hours", label] = df.sum().sum() / (df.shape[0] * df.shape[1])
|
|
|
|
price_statistics.at["mean", label] = n.buses_t.marginal_price[buses].unstack().mean()
|
|
|
|
price_statistics.at["standard_deviation", label] = n.buses_t.marginal_price[buses].unstack().std()
|
|
|
|
return price_statistics
|
|
|
|
|
|
def make_summaries(networks_dict):
|
|
|
|
outputs = [
|
|
"nodal_costs",
|
|
"nodal_capacities",
|
|
"nodal_cfs",
|
|
"cfs",
|
|
"costs",
|
|
"capacities",
|
|
"curtailment",
|
|
"energy",
|
|
"supply",
|
|
"supply_energy",
|
|
"prices",
|
|
"weighted_prices",
|
|
"price_statistics",
|
|
"market_values",
|
|
"metrics",
|
|
]
|
|
|
|
columns = pd.MultiIndex.from_tuples(
|
|
networks_dict.keys(),
|
|
names=["cluster", "lv", "opt", "planning_horizon"]
|
|
)
|
|
|
|
df = {}
|
|
|
|
for output in outputs:
|
|
df[output] = pd.DataFrame(columns=columns, dtype=float)
|
|
|
|
for label, filename in networks_dict.items():
|
|
logger.info(f"Make summary for scenario {label}, using {filename}")
|
|
|
|
overrides = override_component_attrs(snakemake.input.overrides)
|
|
n = pypsa.Network(filename, override_component_attrs=overrides)
|
|
|
|
assign_carriers(n)
|
|
assign_locations(n)
|
|
|
|
for output in outputs:
|
|
df[output] = globals()["calculate_" + output](n, label, df[output])
|
|
|
|
return df
|
|
|
|
|
|
def to_csv(df):
|
|
for key in df:
|
|
df[key].to_csv(snakemake.output[key])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if 'snakemake' not in globals():
|
|
from helper import mock_snakemake
|
|
snakemake = mock_snakemake('make_summary')
|
|
|
|
logging.basicConfig(level=snakemake.config['logging_level'])
|
|
|
|
networks_dict = {
|
|
(cluster, lv, opt+sector_opt, planning_horizon) :
|
|
snakemake.config['results_dir'] + snakemake.config['run'] + f'/postnetworks/elec_s{simpl}_{cluster}_lv{lv}_{opt}_{sector_opt}_{planning_horizon}.nc' \
|
|
for simpl in snakemake.config['scenario']['simpl'] \
|
|
for cluster in snakemake.config['scenario']['clusters'] \
|
|
for opt in snakemake.config['scenario']['opts'] \
|
|
for sector_opt in snakemake.config['scenario']['sector_opts'] \
|
|
for lv in snakemake.config['scenario']['lv'] \
|
|
for planning_horizon in snakemake.config['scenario']['planning_horizons']
|
|
}
|
|
|
|
Nyears = 1
|
|
|
|
costs_db = prepare_costs(
|
|
snakemake.input.costs,
|
|
snakemake.config['costs']['USD2013_to_EUR2013'],
|
|
snakemake.config['costs']['discountrate'],
|
|
Nyears,
|
|
snakemake.config['costs']['lifetime']
|
|
)
|
|
|
|
df = make_summaries(networks_dict)
|
|
|
|
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')
|
|
|
|
|