"""
Creates summaries of aggregated energy and costs as ``.csv`` files.
Relevant Settings
-----------------
.. code:: yaml
costs:
USD2013_to_EUR2013:
discountrate:
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 os
from six import iteritems
from itertools import product
import pandas as pd
import pypsa
from add_electricity import load_costs, update_transmission_costs
idx = pd.IndexSlice
opt_name = {"Store": "e", "Line" : "s", "Transformer" : "s"}
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()
costs = costs.reindex(costs.index|pd.MultiIndex.from_product([[c.list_name],["capital"],capital_costs_grouped.index]))
costs.loc[idx[c.list_name,"capital",list(capital_costs_grouped.index)],label] = capital_costs_grouped.values
if c.name == "Link":
p = c.pnl.p0.multiply(n.snapshot_weightings,axis=0).sum()
elif c.name == "Line":
continue
elif c.name == "StorageUnit":
p_all = c.pnl.p.multiply(n.snapshot_weightings,axis=0)
p_all[p_all < 0.] = 0.
p = p_all.sum()
else:
p = c.pnl.p.multiply(n.snapshot_weightings,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|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 n.one_port_components:
c_energies = c.pnl.p.multiply(n.snapshot_weightings,axis=0).sum().multiply(c.df.sign).groupby(c.df.carrier).sum()
else:
c_energies = (-c.pnl.p1.multiply(n.snapshot_weightings,axis=0).sum() - c.pnl.p0.multiply(n.snapshot_weightings,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):
summary = summary.reindex(summary.index | pd.MultiIndex.from_product([[p] for p in multiindexprefix] + [item.index]))
summary.loc[idx[tuple(multiindexprefix + [list(item.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)
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.loads.carrier.value_counts().index
for i in load_types:
buses = n.loads.bus[n.loads.carrier == i].values
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:
continue
s = c.pnl.p[items].max().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'carrier']).sum()
supply = supply.reindex(supply.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
supply.loc[idx[i,c.list_name,list(s.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:
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|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.loads.carrier.value_counts().index
for i in load_types:
buses = n.loads.bus[n.loads.carrier == i].values
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:
continue
s = c.pnl.p[items].sum().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'carrier']).sum()
supply_energy = supply_energy.reindex(supply_energy.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
supply_energy.loc[idx[i,c.list_name,list(s.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:
continue
s = (-1)*c.pnl["p"+end][items].sum().groupby(c.df.loc[items,'carrier']).sum()
supply_energy = supply_energy.reindex(supply_energy.index|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|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|bus_type.value_counts().index)
#WARNING: this is time-averaged, should really be load-weighted average
prices[label] = n.buses_t.marginal_price.mean().groupby(bus_type).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(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
# BROKEN don't use
#
# def calculate_market_values(n, label, market_values):
# # Warning: doesn't include storage units
# n.buses["suffix"] = n.buses.index.str[2:]
# suffix = ""
# buses = n.buses.index[n.buses.suffix == suffix]
# ## First do market value of generators ##
# generators = n.generators.index[n.buses.loc[n.generators.bus,"suffix"] == suffix]
# techs = n.generators.loc[generators,"carrier"].value_counts().index
# market_values = market_values.reindex(market_values.index | 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],"suffix"] == suffix]
# techs = n.links.loc[all_links,"carrier"].value_counts().index
# market_values = market_values.reindex(market_values.index | 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
# OLD CODE must be adapted
# def calculate_price_statistics(n, label, price_statistics):
# price_statistics = price_statistics.reindex(price_statistics.index|pd.Index(["zero_hours","mean","standard_deviation"]))
# n.buses["suffix"] = n.buses.index.str[2:]
# suffix = ""
# buses = n.buses.index[n.buses.suffix == suffix]
# 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
outputs = ["costs",
"curtailment",
"energy",
"capacity",
"supply",
"supply_energy",
"prices",
"weighted_prices",
# "price_statistics",
# "market_values",
"metrics",
]
def make_summaries(networks_dict, 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 iteritems(networks_dict):
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.sum()/8760.
costs = load_costs(Nyears, snakemake.input[0],
snakemake.config['costs'], snakemake.config['electricity'])
update_transmission_costs(n, costs, simple_hvdc_costs=False)
assign_carriers(n)
for output in outputs:
dfs[output] = globals()["calculate_" + output](n, label, dfs[output])
return dfs
def to_csv(dfs):
dir = snakemake.output[0]
os.makedirs(dir, exist_ok=True)
for key, df in iteritems(dfs):
df.to_csv(os.path.join(dir, f"{key}.csv"))
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
def expand_from_wildcard(key):
w = getattr(snakemake.wildcards, key)
return snakemake.config["scenario"][key] if w == "all" else [w]
if snakemake.wildcards.ll.endswith("all"):
ll = snakemake.config["scenario"]["ll"]
if len(snakemake.wildcards.ll) == 4:
ll = [l for l in ll if l[0] == snakemake.wildcards.ll[0]]
else:
ll = [snakemake.wildcards.ll]
networks_dict = {(simpl,clusters,l,opts) : ('results/networks/{network}_s{simpl}_{clusters}_l{ll}_{opts}.nc'
.format(network=snakemake.wildcards.network,
simpl=simpl,
clusters=clusters,
opts=opts,
ll=l))
for simpl in expand_from_wildcard("simpl")
for clusters in expand_from_wildcard("clusters")
for l in ll
for opts in expand_from_wildcard("opts")}
print(networks_dict)
dfs = make_summaries(networks_dict, country=snakemake.wildcards.country)
to_csv(dfs)