# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Creates summaries of aggregated energy and costs as ``.csv`` files.
Relevant Settings
-----------------
.. code:: yaml
costs:
year:
version:
fill_values:
marginal_cost:
capital_cost:
electricity:
max_hours:
.. seealso::
Documentation of the configuration file ``config.yaml`` at
:ref:`costs_cf`, :ref:`electricity_cf`
Inputs
------
Outputs
-------
Description
-----------
The following rule can be used to summarize the results in seperate .csv files:
.. code::
snakemake results/summaries/elec_s_all_lall_Co2L-3H_all
clusters
line volume or cost cap
- options
- all countries
the line volume/cost cap field can be set to one of the following:
* ``lv1.25`` for a particular line volume extension by 25%
* ``lc1.25`` for a line cost extension by 25 %
* ``lall`` for all evalutated caps
* ``lvall`` for all line volume caps
* ``lcall`` for all line cost caps
Replacing '/summaries/' with '/plots/' creates nice colored maps of the results.
"""
import logging
from _helpers import configure_logging
import os
import pypsa
import pandas as pd
from add_electricity import load_costs, update_transmission_costs
idx = pd.IndexSlice
logger = logging.getLogger(__name__)
opt_name = {"Store": "e", "Line" : "s", "Transformer" : "s"}
def _add_indexed_rows(df, raw_index):
new_index = df.index.union(pd.MultiIndex.from_product(raw_index))
if isinstance(new_index, pd.Index):
new_index = pd.MultiIndex.from_tuples(new_index)
return df.reindex(new_index)
def assign_carriers(n):
if "carrier" not in n.loads:
n.loads["carrier"] = "electricity"
for carrier in ["transport","heat","urban heat"]:
n.loads.loc[n.loads.index.str.contains(carrier),"carrier"] = carrier
n.storage_units['carrier'].replace({'hydro': 'hydro+PHS', 'PHS': 'hydro+PHS'}, inplace=True)
if "carrier" not in n.lines:
n.lines["carrier"] = "AC"
n.lines["carrier"].replace({"AC": "lines"}, inplace=True)
if n.links.empty: n.links["carrier"] = pd.Series(dtype=str)
n.links["carrier"].replace({"DC": "lines"}, inplace=True)
if "EU gas store" in n.stores.index and n.stores.loc["EU gas Store","carrier"] == "":
n.stores.loc["EU gas Store","carrier"] = "gas Store"
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()
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([[c.list_name],["capital"],list(capital_costs_grouped.index)])
costs = _add_indexed_rows(costs, raw_index)
costs.loc[idx[raw_index],label] = capital_costs_grouped.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()
marginal_costs = p*c.df.marginal_cost
marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
costs = costs.reindex(costs.index.union(pd.MultiIndex.from_product([[c.list_name],["marginal"],marginal_costs_grouped.index])))
costs.loc[idx[c.list_name,"marginal",list(marginal_costs_grouped.index)],label] = marginal_costs_grouped.values
return costs
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 {'Generator', 'Load', 'ShuntImpedance'}:
c_energies = c.pnl.p.multiply(n.snapshot_weightings.generators,axis=0).sum().multiply(c.df.sign).groupby(c.df.carrier).sum()
elif c.name in {'StorageUnit', 'Store'}:
c_energies = c.pnl.p.multiply(n.snapshot_weightings.stores,axis=0).sum().multiply(c.df.sign).groupby(c.df.carrier).sum()
else:
c_energies = (-c.pnl.p1.multiply(n.snapshot_weightings.generators,axis=0).sum() - c.pnl.p0.multiply(n.snapshot_weightings.generators,axis=0).sum()).groupby(c.df.carrier).sum()
energy = include_in_summary(energy, [c.list_name], label, c_energies)
return energy
def include_in_summary(summary, multiindexprefix, label, item):
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([multiindexprefix,list(item.index)])
summary = _add_indexed_rows(summary, raw_index)
summary.loc[idx[raw_index], label] = item.values
return summary
def calculate_capacity(n,label,capacity):
for c in n.iterate_components(n.one_port_components):
if 'p_nom_opt' in c.df.columns:
c_capacities = abs(c.df.p_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum()
capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
elif 'e_nom_opt' in c.df.columns:
c_capacities = abs(c.df.e_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum()
capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
for c in n.iterate_components(n.passive_branch_components):
c_capacities = c.df['s_nom_opt'].groupby(c.df.carrier).sum()
capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
for c in n.iterate_components(n.controllable_branch_components):
c_capacities = c.df.p_nom_opt.groupby(c.df.carrier).sum()
capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
return capacity
def calculate_supply(n, label, supply):
"""calculate the max dispatch of each component at the buses where the loads are attached"""
load_types = n.buses.carrier.unique()
for i in load_types:
buses = n.buses.query("carrier == @i").index
bus_map = pd.Series(False,index=n.buses.index)
bus_map.loc[buses] = True
for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map)]
if len(items) == 0 or c.pnl.p.empty:
continue
s = c.pnl.p[items].max().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'carrier']).sum()
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([[i],[c.list_name],list(s.index)])
supply = _add_indexed_rows(supply, raw_index)
supply.loc[idx[raw_index],label] = s.values
for c in n.iterate_components(n.branch_components):
for end in ["0","1"]:
items = c.df.index[c.df["bus" + end].map(bus_map)]
if len(items) == 0 or c.pnl["p"+end].empty:
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()
supply = supply.reindex(supply.index.union(pd.MultiIndex.from_product([[i],[c.list_name],s.index])))
supply.loc[idx[i,c.list_name,list(s.index)],label] = s.values
return supply
def calculate_supply_energy(n, label, supply_energy):
"""calculate the total dispatch of each component at the buses where the loads are attached"""
load_types = n.buses.carrier.unique()
for i in load_types:
buses = n.buses.query("carrier == @i").index
bus_map = pd.Series(False,index=n.buses.index)
bus_map.loc[buses] = True
for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map)]
if len(items) == 0 or c.pnl.p.empty:
continue
s = c.pnl.p[items].sum().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'carrier']).sum()
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([[i],[c.list_name],list(s.index)])
supply_energy = _add_indexed_rows(supply_energy, raw_index)
supply_energy.loc[idx[raw_index],label] = s.values
for c in n.iterate_components(n.branch_components):
for end in ["0","1"]:
items = c.df.index[c.df["bus" + end].map(bus_map)]
if len(items) == 0 or c.pnl['p' + end].empty:
continue
s = (-1)*c.pnl["p"+end][items].sum().groupby(c.df.loc[items,'carrier']).sum()
supply_energy = supply_energy.reindex(supply_energy.index.union(pd.MultiIndex.from_product([[i],[c.list_name],s.index])))
supply_energy.loc[idx[i,c.list_name,list(s.index)],label] = s.values
return supply_energy
def calculate_metrics(n,label,metrics):
metrics = metrics.reindex(metrics.index.union(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"])))
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
if hasattr(n,"line_volume_limit_dual"):
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):
bus_type = pd.Series(n.buses.index.str[3:],n.buses.index).replace("","electricity")
prices = prices.reindex(prices.index.union(bus_type.value_counts().index))
logger.warning("Prices are time-averaged, not load-weighted")
prices[label] = n.buses_t.marginal_price.mean().groupby(bus_type).mean()
return prices
def calculate_weighted_prices(n,label,weighted_prices):
logger.warning("Weighted prices don'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(axis=1)
# 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()
if carrier[:5] == "space":
print(load*n.buses_t.marginal_price[buses])
return weighted_prices
outputs = ["costs",
"curtailment",
"energy",
"capacity",
"supply",
"supply_energy",
"prices",
"weighted_prices",
"metrics",
]
def make_summaries(networks_dict, paths, config, country='all'):
columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["simpl","clusters","ll","opts"])
dfs = {}
for output in outputs:
dfs[output] = pd.DataFrame(columns=columns,dtype=float)
for label, filename in networks_dict.items():
print(label, filename)
if not os.path.exists(filename):
print("does not exist!!")
continue
try:
n = pypsa.Network(filename)
except OSError:
logger.warning("Skipping {filename}".format(filename=filename))
continue
if country != 'all':
n = n[n.buses.country == country]
Nyears = n.snapshot_weightings.objective.sum() / 8760.
costs = load_costs(paths[0], config['costs'], config['electricity'], Nyears)
update_transmission_costs(n, costs)
assign_carriers(n)
for output in outputs:
dfs[output] = globals()["calculate_" + output](n, label, dfs[output])
return dfs
def to_csv(dfs, dir):
os.makedirs(dir, exist_ok=True)
for key, df in dfs.items():
df.to_csv(os.path.join(dir, f"{key}.csv"))
if __name__ == "__main__":
if 'snakemake' not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('make_summary', network='elec', simpl='',
clusters='5', ll='copt', opts='Co2L-24H', country='all')
network_dir = os.path.join('..', 'results', 'networks')
else:
network_dir = os.path.join('results', 'networks')
configure_logging(snakemake)
config = snakemake.config
wildcards = snakemake.wildcards
def expand_from_wildcard(key, config):
w = getattr(wildcards, key)
return config["scenario"][key] if w == "all" else [w]
if wildcards.ll.endswith("all"):
ll = config["scenario"]["ll"]
if len(wildcards.ll) == 4:
ll = [l for l in ll if l[0] == wildcards.ll[0]]
else:
ll = [wildcards.ll]
networks_dict = {(simpl,clusters,l,opts) :
os.path.join(network_dir, f'elec_s{simpl}_'
f'{clusters}_ec_l{l}_{opts}.nc')
for simpl in expand_from_wildcard("simpl", config)
for clusters in expand_from_wildcard("clusters", config)
for l in ll
for opts in expand_from_wildcard("opts", config)}
dfs = make_summaries(networks_dict, snakemake.input, config, country=wildcards.country)
to_csv(dfs, snakemake.output[0])