# coding: utf-8
import logging
logger = logging.getLogger(__name__)
import pandas as pd
idx = pd.IndexSlice
import numpy as np
import scipy as sp
import xarray as xr
import re, os
from six import iteritems, string_types
import pypsa
import yaml
import pytz
from vresutils.costdata import annuity
#First tell PyPSA that links can have multiple outputs by
#overriding the component_attrs. This can be done for
#as many buses as you need with format busi for i = 2,3,4,5,....
#See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs
override_component_attrs = pypsa.descriptors.Dict({k : v.copy() for k,v in pypsa.components.component_attrs.items()})
override_component_attrs["Link"].loc["bus2"] = ["string",np.nan,np.nan,"2nd bus","Input (optional)"]
override_component_attrs["Link"].loc["bus3"] = ["string",np.nan,np.nan,"3rd bus","Input (optional)"]
override_component_attrs["Link"].loc["efficiency2"] = ["static or series","per unit",1.,"2nd bus efficiency","Input (optional)"]
override_component_attrs["Link"].loc["efficiency3"] = ["static or series","per unit",1.,"3rd bus efficiency","Input (optional)"]
override_component_attrs["Link"].loc["p2"] = ["series","MW",0.,"2nd bus output","Output"]
override_component_attrs["Link"].loc["p3"] = ["series","MW",0.,"3rd bus output","Output"]
def add_co2_tracking(n):
#minus sign because opposite to how fossil fuels used:
#CH4 burning puts CH4 down, atmosphere up
n.add("Carrier","co2",
co2_emissions=-1.)
#this tracks CO2 in the atmosphere
n.add("Bus","co2 atmosphere",
carrier="co2")
#NB: can also be negative
n.madd("Store",["co2 atmosphere"],
e_nom_extendable=True,
e_min_pu=-1,
carrier="co2",
bus="co2 atmosphere")
#this tracks CO2 stored, e.g. underground
n.add("Bus","co2 stored",
carrier="co2 stored")
#NB: can also be negative
#cost of 10 euro/tCO2 for whatever stays
#TODO move cost to data/costs.csv
n.madd("Store",["co2 stored"],
e_nom_extendable = True,
marginal_cost=-1000.,
carrier="co2 stored",
bus="co2 stored")
if options['dac']:
#direct air capture consumes electricity to take CO2 from the air to the underground store
#TODO do with cost from Breyer - later use elec and heat and capital cost
n.madd("Link",["DAC"],
bus0="co2 atmosphere",
bus1="co2 stored",
carrier="DAC",
marginal_cost=75.,
efficiency=1.,
p_nom_extendable=True)
def add_co2limit(n, Nyears=1.,limit=0.):
cts = pop_layout.ct.value_counts().index
co2_limit = co2_totals.loc[cts, "electricity"].sum()
if "T" in opts:
co2_limit += co2_totals.loc[cts, [i+ " non-elec" for i in ["rail","road"]]].sum().sum()
if "H" in opts:
co2_limit += co2_totals.loc[cts, [i+ " non-elec" for i in ["residential","services"]]].sum().sum()
if "I" in opts:
co2_limit += co2_totals.loc[cts, ["industrial non-elec","industrial processes",
"domestic aviation","international aviation",
"domestic navigation","international navigation"]].sum().sum()
co2_limit *= limit*Nyears
n.add("GlobalConstraint", "CO2Limit",
carrier_attribute="co2_emissions", sense="<=",
constant=co2_limit)
def add_emission_prices(n, emission_prices=None, exclude_co2=False):
assert False, "Needs to be fixed, adds NAN"
if emission_prices is None:
emission_prices = snakemake.config['costs']['emission_prices']
if exclude_co2: emission_prices.pop('co2')
ep = (pd.Series(emission_prices).rename(lambda x: x+'_emissions') * n.carriers).sum(axis=1)
n.generators['marginal_cost'] += n.generators.carrier.map(ep)
n.storage_units['marginal_cost'] += n.storage_units.carrier.map(ep)
def set_line_s_max_pu(n):
# set n-1 security margin to 0.5 for 37 clusters and to 0.7 from 200 clusters
# 128 reproduces 98% of line volume in TWkm, but clustering distortions inside node
n_clusters = len(n.buses.index[n.buses.carrier == "AC"])
s_max_pu = np.clip(0.5 + 0.2 * (n_clusters - 37) / (200 - 37), 0.5, 0.7)
n.lines['s_max_pu'] = s_max_pu
dc_b = n.links.carrier == 'DC'
n.links.loc[dc_b, 'p_max_pu'] = snakemake.config['links']['p_max_pu']
n.links.loc[dc_b, 'p_min_pu'] = - snakemake.config['links']['p_max_pu']
def set_line_volume_limit(n, lv):
dc_b = n.links.carrier == 'DC'
if lv != "opt":
lv = float(lv)
# Either line_volume cap or cost
n.lines['capital_cost'] = 0.
n.links.loc[dc_b,'capital_cost'] = 0.
else:
n.lines['capital_cost'] = (n.lines['length'] *
costs.at['HVAC overhead', 'fixed'])
#add HVDC inverter post factor, to maintain consistency with LV limit
n.links.loc[dc_b, 'capital_cost'] = (n.links.loc[dc_b, 'length'] *
costs.at['HVDC overhead', 'fixed'])# +
#costs.at['HVDC inverter pair', 'fixed'])
if lv != 1.0:
lines_s_nom = n.lines.s_nom.where(
n.lines.type == '',
np.sqrt(3) * n.lines.num_parallel *
n.lines.type.map(n.line_types.i_nom) *
n.lines.bus0.map(n.buses.v_nom)
)
n.lines['s_nom_min'] = lines_s_nom
n.links.loc[dc_b,'p_nom_min'] = n.links['p_nom']
n.lines['s_nom_extendable'] = True
n.links.loc[dc_b,'p_nom_extendable'] = True
if lv != "opt":
n.line_volume_limit = lv * ((lines_s_nom * n.lines['length']).sum() +
n.links.loc[dc_b].eval('p_nom * length').sum())
return n
def average_every_nhours(n, offset):
logger.info('Resampling the network to {}'.format(offset))
m = n.copy(with_time=False)
#fix copying of network attributes
#copied from pypsa/io.py, should be in pypsa/components.py#Network.copy()
allowed_types = (float,int,bool) + string_types + tuple(np.typeDict.values())
attrs = dict((attr, getattr(n, attr))
for attr in dir(n)
if (not attr.startswith("__") and
isinstance(getattr(n,attr), allowed_types)))
for k,v in iteritems(attrs):
setattr(m,k,v)
snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
m.set_snapshots(snapshot_weightings.index)
m.snapshot_weightings = snapshot_weightings
for c in n.iterate_components():
pnl = getattr(m, c.list_name+"_t")
for k, df in iteritems(c.pnl):
if not df.empty:
if c.list_name == "stores" and k == "e_max_pu":
pnl[k] = df.resample(offset).min()
elif c.list_name == "stores" and k == "e_min_pu":
pnl[k] = df.resample(offset).max()
else:
pnl[k] = df.resample(offset).mean()
return m
def generate_periodic_profiles(dt_index=pd.date_range("2011-01-01 00:00","2011-12-31 23:00",freq="H",tz="UTC"),
nodes=[],
weekly_profile=range(24*7)):
"""Give a 24*7 long list of weekly hourly profiles, generate this for
each country for the period dt_index, taking account of time
zones and Summer Time.
"""
weekly_profile = pd.Series(weekly_profile,range(24*7))
week_df = pd.DataFrame(index=dt_index,columns=nodes)
for ct in nodes:
week_df[ct] = [24*dt.weekday()+dt.hour for dt in dt_index.tz_convert(pytz.timezone(timezone_mappings[ct[:2]]))]
week_df[ct] = week_df[ct].map(weekly_profile)
return week_df
def shift_df(df,hours=1):
"""Works both on Series and DataFrame"""
df = df.copy()
df.values[:] = np.concatenate([df.values[-hours:],
df.values[:-hours]])
return df
def transport_degree_factor(temperature,deadband_lower=15,deadband_upper=20,
lower_degree_factor=0.5,
upper_degree_factor=1.6):
"""Work out how much energy demand in vehicles increases due to heating and cooling.
There is a deadband where there is no increase.
Degree factors are % increase in demand compared to no heating/cooling fuel consumption.
Returns per unit increase in demand for each place and time
"""
dd = temperature.copy()
dd[(temperature > deadband_lower) & (temperature < deadband_upper)] = 0.
dd[temperature < deadband_lower] = lower_degree_factor/100.*(deadband_lower-temperature[temperature < deadband_lower])
dd[temperature > deadband_upper] = upper_degree_factor/100.*(temperature[temperature > deadband_upper]-deadband_upper)
return dd
def prepare_data(network):
##############
#Heating
##############
#copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
heat_demand_df = xr.open_dataarray(snakemake.input.heat_demand_total).T.to_pandas().reindex(index=network.snapshots, method="ffill")
intraday_profiles = pd.read_csv(snakemake.input.heat_profile,index_col=0)
intraday_year_profiles = generate_periodic_profiles(heat_demand_df.index.tz_localize("UTC"),
nodes=heat_demand_df.columns,
weekly_profile=(list(intraday_profiles["weekday"])*5 + list(intraday_profiles["weekend"])*2)).tz_localize(None)
heat_demand_df = heat_demand_df*intraday_year_profiles
ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).T.to_pandas().reindex(index=network.snapshots)
gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).T.to_pandas().reindex(index=network.snapshots)
solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).T.to_pandas().reindex(index=network.snapshots)
#1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
solar_thermal = options['solar_cf_correction'] * solar_thermal/1e3
energy_totals = pd.read_csv(snakemake.input.energy_totals_name,index_col=0)
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
nodal_energy_totals.index = pop_layout.index
nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction,axis=0)
sectors = ["residential","services"]
nodal_energy_totals["Space Heating"] = nodal_energy_totals[["total {sector} space".format(sector=sector) for sector in sectors]].sum(axis=1)
nodal_energy_totals["Water Heating"] = nodal_energy_totals[["total {sector} water".format(sector=sector) for sector in sectors]].sum(axis=1)
space_heat_demand = (heat_demand_df/heat_demand_df.sum()).multiply(nodal_energy_totals["Space Heating"])*1e6*Nyears
water_heat_demand = (nodal_energy_totals["Water Heating"]/8760.)*1e6
heat_demand = space_heat_demand + water_heat_demand
##############
#Transport
##############
## Get overall demand curve for all vehicles
dir_name = "data/emobility/"
traffic = pd.read_csv(os.path.join(dir_name,"KFZ__count"),skiprows=2)["count"]
#Generate profiles
transport_shape = generate_periodic_profiles(dt_index=network.snapshots.tz_localize("UTC"),
nodes=pop_layout.index,
weekly_profile=traffic.values).tz_localize(None)
transport_shape = transport_shape/transport_shape.sum()
transport_data = pd.read_csv(snakemake.input.transport_name,
index_col=0)
nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.)
nodal_transport_data.index = pop_layout.index
nodal_transport_data["number cars"] = pop_layout["fraction"]*nodal_transport_data["number cars"]
nodal_transport_data.loc[nodal_transport_data["average fuel efficiency"] == 0.,"average fuel efficiency"] = transport_data["average fuel efficiency"].mean()
#electric motors are more efficient, so alter transport demand
#kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
plug_to_wheels_eta = 0.20
battery_to_wheels_eta = plug_to_wheels_eta*0.9
efficiency_gain = nodal_transport_data["average fuel efficiency"]/battery_to_wheels_eta
#get heating demand for correction to demand time series
temperature = xr.open_dataarray(snakemake.input.temp_air_total).T.to_pandas()
#correction factors for vehicle heating
dd_ICE = transport_degree_factor(temperature,
options['transport_heating_deadband_lower'],
options['transport_heating_deadband_upper'],
options['ICE_lower_degree_factor'],
options['ICE_upper_degree_factor'])
dd_EV = transport_degree_factor(temperature,
options['transport_heating_deadband_lower'],
options['transport_heating_deadband_upper'],
options['EV_lower_degree_factor'],
options['EV_upper_degree_factor'])
#divide out the heating/cooling demand from ICE totals
ICE_correction = (transport_shape*(1+dd_ICE)).sum()/transport_shape.sum()
transport = (transport_shape.multiply(nodal_energy_totals["total road"] + nodal_energy_totals["total rail"]
- nodal_energy_totals["electricity rail"])*1e6*Nyears).divide(efficiency_gain*ICE_correction)
#multiply back in the heating/cooling demand for EVs
transport = transport.multiply(1+dd_EV)
## derive plugged-in availability for PKW's (cars)
traffic = pd.read_csv(os.path.join(dir_name,"Pkw__count"),skiprows=2)["count"]
avail_max = 0.95
avail_mean = 0.8
avail = avail_max - (avail_max - avail_mean)*(traffic - traffic.min())/(traffic.mean() - traffic.min())
avail_profile = generate_periodic_profiles(dt_index=network.snapshots.tz_localize("UTC"),
nodes=pop_layout.index,
weekly_profile=avail.values).tz_localize(None)
dsm_week = np.zeros((24*7,))
dsm_week[(np.arange(0,7,1)*24+options['dsm_restriction_time'])] = options['dsm_restriction_value']
dsm_profile = generate_periodic_profiles(dt_index=network.snapshots.tz_localize("UTC"),
nodes=pop_layout.index,
weekly_profile=dsm_week).tz_localize(None)
###############
#CO2
###############
#1e6 to convert Mt to tCO2
co2_totals = 1e6*pd.read_csv(snakemake.input.co2_totals_name,index_col=0)
return nodal_energy_totals, heat_demand, space_heat_demand, water_heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, co2_totals, nodal_transport_data
def prepare_costs():
#set all asset costs and other parameters
costs = pd.read_csv(snakemake.input.costs,index_col=list(range(3))).sort_index()
#correct units to MW and EUR
costs.loc[costs.unit.str.contains("/kW"),"value"]*=1e3
costs.loc[costs.unit.str.contains("USD"),"value"]*=snakemake.config['costs']['USD2013_to_EUR2013']
cost_year = snakemake.config['costs']['year']
costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)
costs = costs.fillna({"CO2 intensity" : 0,
"FOM" : 0,
"VOM" : 0,
"discount rate" : snakemake.config['costs']['discountrate'],
"efficiency" : 1,
"fuel" : 0,
"investment" : 0,
"lifetime" : 25
})
costs["fixed"] = [(annuity(v["lifetime"],v["discount rate"])+v["FOM"]/100.)*v["investment"]*Nyears for i,v in costs.iterrows()]
return costs
def add_generation(network):
print("adding electricity generation")
nodes = pop_layout.index
conventionals = [("OCGT","gas")]
for generator,carrier in [("OCGT","gas")]:
network.add("Carrier",
carrier)
network.add("Bus",
"EU " + carrier,
carrier=carrier)
#use madd to get carrier inserted
network.madd("Store",
["EU " + carrier + " Store"],
bus=["EU " + carrier],
e_nom_extendable=True,
#force fossil to be empty at end of period; can start higher to represent fossil input
e_max_pu=pd.DataFrame({ "EU " + carrier + " Store" : pd.Series([1.]*(len(network.snapshots)-1)+[0.],index=network.snapshots)}),
carrier=carrier,
marginal_cost=costs.at[carrier,'fuel'])
network.madd("Link",
nodes + " " + generator,
bus0=["EU " + carrier]*len(nodes),
bus1=nodes,
bus2="co2 atmosphere",
marginal_cost=costs.at[generator,'efficiency']*costs.at[generator,'VOM'], #NB: VOM is per MWel
capital_cost=costs.at[generator,'efficiency']*costs.at[generator,'fixed'], #NB: fixed cost is per MWel
p_nom_extendable=True,
carrier=generator,
efficiency=costs.at[generator,'efficiency'],
efficiency2=costs.at[carrier,'CO2 intensity'])
def add_storage(network):
print("adding electricity storage")
nodes = pop_layout.index
network.add("Carrier","H2")
network.madd("Bus",
nodes+ " H2",
carrier="H2")
network.madd("Link",
nodes + " H2 Electrolysis",
bus1=nodes + " H2",
bus0=nodes,
p_nom_extendable=True,
carrier="H2 Electrolysis",
efficiency=costs.at["electrolysis","efficiency"],
capital_cost=costs.at["electrolysis","fixed"])
network.madd("Link",
nodes + " H2 Fuel Cell",
bus0=nodes + " H2",
bus1=nodes,
p_nom_extendable=True,
carrier ="H2 Fuel Cell",
efficiency=costs.at["fuel cell","efficiency"],
capital_cost=costs.at["fuel cell","fixed"]*costs.at["fuel cell","efficiency"]) #NB: fixed cost is per MWel
network.madd("Store",
nodes + " H2 Store",
bus=nodes + " H2",
e_nom_extendable=True,
e_cyclic=True,
carrier="H2 Store",
capital_cost=costs.at["hydrogen storage","fixed"])
network.add("Carrier","battery")
network.madd("Bus",
nodes + " battery",
carrier="battery")
network.madd("Store",
nodes + " battery",
bus=nodes + " battery",
e_cyclic=True,
e_nom_extendable=True,
carrier="battery",
capital_cost=costs.at['battery storage','fixed'])
network.madd("Link",
nodes + " battery charger",
bus0=nodes,
bus1=nodes + " battery",
carrier="battery charger",
efficiency=costs.at['battery inverter','efficiency']**0.5,
capital_cost=costs.at['battery inverter','fixed'],
p_nom_extendable=True)
network.madd("Link",
nodes + " battery discharger",
bus0=nodes + " battery",
bus1=nodes,
carrier="battery discharger",
efficiency=costs.at['battery inverter','efficiency']**0.5,
marginal_cost=options['marginal_cost_storage'],
p_nom_extendable=True)
if options['methanation']:
network.madd("Link",
nodes + " Sabatier",
bus0=nodes+" H2",
bus1=["EU gas"]*len(nodes),
bus2="co2 stored",
p_nom_extendable=True,
carrier="Sabatier",
efficiency=costs.at["methanation","efficiency"],
efficiency2=-costs.at["methanation","efficiency"]*costs.at['gas','CO2 intensity'],
capital_cost=costs.at["methanation","fixed"])
if options['helmeth']:
network.madd("Link",
nodes + " helmeth",
bus0=nodes,
bus1=["EU gas"]*len(nodes),
bus2="co2 stored",
carrier="helmeth",
p_nom_extendable=True,
efficiency=costs.at["helmeth","efficiency"],
efficiency2=-costs.at["helmeth","efficiency"]*costs.at['gas','CO2 intensity'],
capital_cost=costs.at["helmeth","fixed"])
def add_transport(network):
print("adding transport")
nodes = pop_layout.index
network.add("Carrier","Li ion")
network.madd("Bus",
nodes,
suffix=" EV battery",
carrier="Li ion")
network.madd("Load",
nodes,
suffix=" transport",
bus=nodes + " EV battery",
p_set=(1-options['transport_fuel_cell_share'])*(transport[nodes]+shift_df(transport[nodes],1)+shift_df(transport[nodes],2))/3.)
p_nom = nodal_transport_data["number cars"]*0.011*(1-options['transport_fuel_cell_share']) #3-phase charger with 11 kW * x% of time grid-connected
network.madd("Link",
nodes,
suffix= " BEV charger",
bus0=nodes,
bus1=nodes + " EV battery",
p_nom=p_nom,
carrier="BEV charger",
p_max_pu=avail_profile[nodes],
efficiency=0.9, #[B]
#These were set non-zero to find LU infeasibility when availability = 0.25
#p_nom_extendable=True,
#p_nom_min=p_nom,
#capital_cost=1e6, #i.e. so high it only gets built where necessary
)
if options["v2g"]:
network.madd("Link",
nodes,
suffix=" V2G",
bus1=nodes,
bus0=nodes + " EV battery",
p_nom=p_nom,
carrier="V2G",
p_max_pu=avail_profile[nodes],
efficiency=0.9) #[B]
if options["bev"]:
network.madd("Store",
nodes,
suffix=" battery storage",
bus=nodes + " EV battery",
carrier="battery storage",
e_cyclic=True,
e_nom=nodal_transport_data["number cars"]*0.05*options["bev_availability"]*(1-options['transport_fuel_cell_share']), #50 kWh battery http://www.zeit.de/mobilitaet/2014-10/auto-fahrzeug-bestand
e_max_pu=1,
e_min_pu=dsm_profile[nodes])
if options['transport_fuel_cell_share'] != 0:
network.madd("Load",
nodes,
suffix=" transport fuel cell",
bus=nodes + " H2",
p_set=options['transport_fuel_cell_share']/0.58*transport[nodes])
def add_heat(network):
print("adding heat")
nodes = pop_layout.index
network.add("Carrier","heat")
network.add("Carrier","water tanks")
#urban are high density locations
if options["central"]:
urban_ct = pd.Index(["ES","GR","PT","IT","BG"])
urban = pop_layout.index[pop_layout.ct.isin(urban_ct)]
else:
urban = nodes
#NB: must add costs of central heating afterwards (EUR 400 / kWpeak, 50a, 1% FOM from Fraunhofer ISE)
#central are urban nodes with district heating
central = nodes ^ urban
urban_fraction = options['central_fraction']*pop_layout["urban"]/(pop_layout[["urban","rural"]].sum(axis=1))
network.madd("Bus",
nodes + " heat",
carrier="heat")
network.madd("Bus",
nodes + " urban heat",
carrier="heat")
network.madd("Load",
nodes,
suffix=" heat",
bus=nodes + " heat",
p_set= heat_demand[nodes].multiply((1-urban_fraction)))
network.madd("Load",
nodes,
suffix=" urban heat",
bus=nodes + " urban heat",
p_set= heat_demand[nodes].multiply(urban_fraction).divide((1-options['district_heating_loss'])))
network.madd("Link",
urban,
suffix=" urban heat pump",
bus0=urban,
bus1=urban + " urban heat",
carrier="urban heat pump",
efficiency=ashp_cop[urban] if options["time_dep_hp_cop"] else costs.at['decentral air-sourced heat pump','efficiency'],
capital_cost=costs.at['decentral air-sourced heat pump','efficiency']*costs.at['decentral air-sourced heat pump','fixed'],
p_nom_extendable=True)
network.madd("Link",
central,
suffix=" central heat pump",
bus0=central,
bus1=central + " urban heat",
carrier="central heat pump",
efficiency=ashp_cop[central] if options["time_dep_hp_cop"] else costs.at['central air-sourced heat pump','efficiency'],
capital_cost=costs.at['central air-sourced heat pump','efficiency']*costs.at['central air-sourced heat pump','fixed'],
p_nom_extendable=True)
network.madd("Link",
nodes,
suffix=" ground heat pump",
bus0=nodes,
bus1=nodes + " heat",
carrier="ground heat pump",
efficiency=gshp_cop[nodes] if options["time_dep_hp_cop"] else costs.at['decentral ground-sourced heat pump','efficiency'],
capital_cost=costs.at['decentral ground-sourced heat pump','efficiency']*costs.at['decentral ground-sourced heat pump','fixed'],
p_nom_extendable=True)
if options['retrofitting']:
retro_nodes = pd.Index(["DE"])
space_heat_demand = space_heat_demand[retro_nodes]
square_metres = population[retro_nodes]/population['DE']*5.7e9 #HPI 3.4e9m^2 for DE res, 2.3e9m^2 for tert https://doi.org/10.1016/j.rser.2013.09.012
space_peak = space_heat_demand.max()
space_pu = space_heat_demand.divide(space_peak)
network.add("Carrier", "retrofitting")
network.madd('Generator',
retro_nodes,
suffix=' retrofitting I',
bus=retro_nodes+' heat',
carrier="retrofitting",
p_nom_extendable=True,
p_nom_max=options['retroI-fraction']*space_peak*(1-urban_fraction),
p_max_pu=space_pu,
p_min_pu=space_pu,
capital_cost=options['retrofitting-cost_factor']*costs.at['retrofitting I','fixed']*square_metres/(options['retroI-fraction']*space_peak))
network.madd('Generator',
retro_nodes,
suffix=' retrofitting II',
bus=retro_nodes+' heat',
carrier="retrofitting",
p_nom_extendable=True,
p_nom_max=options['retroII-fraction']*space_peak*(1-urban_fraction),
p_max_pu=space_pu,
p_min_pu=space_pu,
capital_cost=options['retrofitting-cost_factor']*costs.at['retrofitting II','fixed']*square_metres/(options['retroII-fraction']*space_peak))
network.madd('Generator',
retro_nodes,
suffix=' urban retrofitting I',
bus=retro_nodes+' urban heat',
carrier="retrofitting",
p_nom_extendable=True,
p_nom_max=options['retroI-fraction']*space_peak*urban_fraction,
p_max_pu=space_pu,
p_min_pu=space_pu,
capital_cost=options['retrofitting-cost_factor']*costs.at['retrofitting I','fixed']*square_metres/(options['retroI-fraction']*space_peak))
network.madd('Generator',
retro_nodes,
suffix=' urban retrofitting II',
bus=retro_nodes+' urban heat',
carrier="retrofitting",
p_nom_extendable=True,
p_nom_max=options['retroII-fraction']*space_peak*urban_fraction,
p_max_pu=space_pu,
p_min_pu=space_pu,
capital_cost=options['retrofitting-cost_factor']*costs.at['retrofitting II','fixed']*square_metres/(options['retroII-fraction']*space_peak))
if options["tes"]:
network.madd("Bus",
nodes + " water tanks",
carrier="water tanks")
network.madd("Link",
nodes + " water tanks charger",
bus0=nodes + " heat",
bus1=nodes + " water tanks",
efficiency=costs.at['water tank charger','efficiency'],
carrier="water tanks charger",
p_nom_extendable=True)
network.madd("Link",
nodes + " water tanks discharger",
bus0=nodes + " water tanks",
bus1=nodes + " heat",
carrier="water tanks discharger",
efficiency=costs.at['water tank discharger','efficiency'],
p_nom_extendable=True)
network.madd("Store",
nodes + " water tank",
bus=nodes + " water tanks",
e_cyclic=True,
e_nom_extendable=True,
carrier="water tank",
standing_loss=1-np.exp(-1/(24.*options["tes_tau"])), # [HP] 180 day time constant for centralised, 3 day for decentralised
capital_cost=costs.at['decentral water tank storage','fixed']/(1.17e-3*40)) #conversion from EUR/m^3 to EUR/MWh for 40 K diff and 1.17 kWh/m^3/K
network.madd("Bus",
urban + " urban water tanks",
carrier="water tanks")
network.madd("Link",
urban + " urban water tanks charger",
bus0=urban + " urban heat",
bus1=urban + " urban water tanks",
carrier="urban water tanks charger",
efficiency=costs.at['water tank charger','efficiency'],
p_nom_extendable=True)
network.madd("Link",
urban + " urban water tanks discharger",
bus0=urban + " urban water tanks",
bus1=urban + " urban heat",
carrier="urban water tanks discharger",
efficiency=costs.at['water tank discharger','efficiency'],
p_nom_extendable=True)
network.madd("Store",
urban + " urban water tank",
bus=urban + " urban water tanks",
e_cyclic=True,
e_nom_extendable=True,
carrier="urban water tank",
standing_loss=1-np.exp(-1/(24.*options["tes_tau"])), # [HP] 180 day time constant for centralised, 3 day for decentralised
capital_cost=costs.at['decentral water tank storage','fixed']/(1.17e-3*40)) #conversion from EUR/m^3 to EUR/MWh for 40 K diff and 1.17 kWh/m^3/K
network.madd("Bus",
central + " central water tanks",
carrier="water tanks")
network.madd("Link",
central + " central water tanks charger",
bus0=central + " urban heat",
bus1=central + " central water tanks",
p_nom_extendable=True,
carrier="central water tanks charger",
efficiency=costs.at['water tank charger','efficiency'])
network.madd("Link",
central + " central water tanks discharger",
bus0=central + " central water tanks",
bus1=central + " urban heat",
carrier="central water tanks discharger",
p_nom_extendable=True,
efficiency=costs.at['water tank discharger','efficiency'])
network.madd("Store",
central,
suffix=" central water tank",
bus=central + " central water tanks",
e_cyclic=True,
carrier="central water tank",
e_nom_extendable=True,
standing_loss=1-np.exp(-1/(24.*180.)), # [HP] 180 day time constant for centralised, 3 day for decentralised
capital_cost=costs.at['central water tank storage','fixed']/(1.17e-3*40)) #convert EUR/m^3 to EUR/MWh for 40 K diff and 1.17 kWh/m^3/K
if options["boilers"]:
network.madd("Link",
nodes + " resistive heater",
bus0=nodes,
bus1=nodes + " heat",
carrier="resistive heater",
efficiency=costs.at['decentral resistive heater','efficiency'],
capital_cost=costs.at['decentral resistive heater','efficiency']*costs.at['decentral resistive heater','fixed'],
p_nom_extendable=True)
network.madd("Link",
urban + " urban resistive heater",
bus0=urban,
bus1=urban + " urban heat",
carrier="urban resistive heater",
efficiency=costs.at['decentral resistive heater','efficiency'],
capital_cost=costs.at['decentral resistive heater','efficiency']*costs.at['decentral resistive heater','fixed'],
p_nom_extendable=True)
network.madd("Link",
central + " central resistive heater",
bus0=central,
bus1=central + " urban heat",
p_nom_extendable=True,
carrier="central resistive heater",
capital_cost=costs.at['central resistive heater','efficiency']*costs.at['central resistive heater','fixed'],
efficiency=costs.at['central resistive heater','efficiency'])
network.madd("Link",
nodes + " gas boiler",
p_nom_extendable=True,
bus0=["EU gas"]*len(nodes),
bus1=nodes + " heat",
bus2="co2 atmosphere",
carrier="gas boiler",
efficiency=costs.at['decentral gas boiler','efficiency'],
efficiency2=costs.at['gas','CO2 intensity'],
capital_cost=costs.at['decentral gas boiler','efficiency']*costs.at['decentral gas boiler','fixed'])
network.madd("Link",
urban + " urban gas boiler",
p_nom_extendable=True,
bus0=["EU gas"]*len(urban),
bus1=urban + " urban heat",
bus2="co2 atmosphere",
carrier="urban gas boiler",
efficiency=costs.at['decentral gas boiler','efficiency'],
efficiency2=costs.at['gas','CO2 intensity'],
capital_cost=costs.at['decentral gas boiler','efficiency']*costs.at['decentral gas boiler','fixed'])
network.madd("Link",
central + " central gas boiler",
bus0=["EU gas"]*len(central),
bus1=central + " urban heat",
bus2="co2 atmosphere",
carrier="central gas boiler",
p_nom_extendable=True,
capital_cost=costs.at['central gas boiler','efficiency']*costs.at['central gas boiler','fixed'],
efficiency2=costs.at['gas','CO2 intensity'],
efficiency=costs.at['central gas boiler','efficiency'])
if options["chp"]:
#additional bus, to which we can also connect biomass
network.madd("Bus",
central + " central CHP",
carrier="chp")
network.madd("Link",
central + " gas to central CHP",
bus0="EU gas",
bus1=central + " central CHP",
bus2="co2 atmosphere",
bus3="co2 stored",
efficiency2=costs.at['gas','CO2 intensity']*(1-options["ccs_fraction"]),
efficiency3=costs.at['gas','CO2 intensity']*options["ccs_fraction"],
carrier="gas to central CHP",
p_nom_extendable=True)
network.madd("Link",
central + " central CHP electric",
bus0=central + " central CHP",
bus1=central,
carrier="central CHP electric",
p_nom_extendable=True,
capital_cost=costs.at['central CHP','fixed']*options['chp_parameters']['eta_elec'],
efficiency=options['chp_parameters']['eta_elec'])
network.madd("Link",
central + " central CHP heat",
bus0=central + " central CHP",
bus1=central + " urban heat",
carrier="central CHP heat",
p_nom_extendable=True,
efficiency=options['chp_parameters']['eta_elec']/options['chp_parameters']['c_v'])
if options["solar_thermal"]:
network.add("Carrier","solar thermal")
network.madd("Generator",
nodes,
suffix=" solar thermal collector",
bus=nodes + " heat",
carrier="solar thermal",
p_nom_extendable=True,
capital_cost=costs.at['decentral solar thermal','fixed'],
p_max_pu=solar_thermal[nodes])
network.madd("Generator",
urban,
suffix=" urban solar thermal collector",
bus=urban + " urban heat",
carrier="solar thermal",
p_nom_extendable=True,
capital_cost=costs.at['decentral solar thermal','fixed'],
p_max_pu=solar_thermal[urban])
network.madd("Generator",
central,
suffix=" central solar thermal collector",
bus=central + " urban heat",
carrier="solar thermal",
p_nom_extendable=True,
capital_cost=costs.at['central solar thermal','fixed'],
p_max_pu=solar_thermal[central])
def add_biomass(network):
print("adding biomass")
nodes = pop_layout.index
#biomass distributed at country level - i.e. transport within country allowed
cts = pop_layout.ct.value_counts().index
#urban are high density locations
if options["central"]:
urban_cts = pd.Index(["ES","GR","PT","IT","BG"])
urban = pop_layout.index[pop_layout.ct.isin(urban_cts)]
else:
urban_cts = cts
urban = nodes
#central are urban nodes with district heating
central = nodes ^ urban
#central_cts are urban countries with district heating
central_cts = cts ^ urban_cts
biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials,
index_col=0)
network.add("Carrier","biogas")
network.add("Carrier","solid biomass")
network.madd("Bus",
cts + " biogas",
carrier="biogas")
network.madd("Bus",
cts + " solid biomass",
carrier="solid biomass")
network.madd("Store",
cts,
suffix=" biogas",
bus=cts + " biogas",
carrier="biogas",
e_nom=biomass_potentials.loc[cts,"biogas"],
marginal_cost=costs.at['biogas','fuel'],
e_initial=biomass_potentials.loc[cts,"biogas"])
network.madd("Store",
cts,
suffix=" solid biomass",
bus=cts + " solid biomass",
carrier="solid biomass",
e_nom=biomass_potentials.loc[cts,"solid biomass"],
marginal_cost=costs.at['solid biomass','fuel'],
e_initial=biomass_potentials.loc[cts,"solid biomass"])
network.madd("Link",
cts + " biogas to gas",
bus0=cts + " biogas",
bus1="EU gas",
bus2="co2 atmosphere",
carrier="biogas",
efficiency2=-costs.at['gas','CO2 intensity'],
p_nom_extendable=True)
#with BECCS
network.madd("Link",
central + " solid biomass to CHP",
bus0=central.str[:2] + " solid biomass",
bus1=central + " central CHP",
bus2="co2 atmosphere",
bus3="co2 stored",
efficiency2=-costs.at['solid biomass','CO2 intensity']*options["ccs_fraction"],
efficiency3=costs.at['solid biomass','CO2 intensity']*options["ccs_fraction"],
carrier="solid biomass",
p_nom_extendable=True)
def add_industry(network):
print("adding industrial demand")
nodes = pop_layout.index
industrial_demand = pd.read_csv(snakemake.input.industrial_demand,
index_col=0)
network.madd("Bus",
nodes + " process heat",
carrier="process heat")
network.madd("Load",
nodes,
suffix=" process heat",
bus=nodes + " process heat",
p_set = industrial_demand.loc[nodes,"industry process heat"]/8760.)
#with BECCS
network.madd("Link",
nodes + " solid biomass to process heat",
bus0=nodes.str[:2] + " solid biomass",
bus1=nodes + " process heat",
bus2="co2 atmosphere",
bus3="co2 stored",
efficiency2=-costs.at['solid biomass','CO2 intensity']*options["ccs_fraction"],
efficiency3=costs.at['solid biomass','CO2 intensity']*options["ccs_fraction"],
carrier="solid biomass to process heat",
p_nom_extendable=True)
network.madd("Link",
nodes + " gas to process heat",
bus0="EU gas",
bus1=nodes + " process heat",
bus2="co2 atmosphere",
bus3="co2 stored",
efficiency2=costs.at['gas','CO2 intensity']*(1-options["ccs_fraction"]),
efficiency3=costs.at['gas','CO2 intensity']*options["ccs_fraction"],
carrier="gas to process heat",
p_nom_extendable=True)
network.madd("Link",
nodes + " H2 to process heat",
bus0=nodes + " H2",
bus1=nodes + " process heat",
carrier="H2 to process heat",
p_nom_extendable=True)
network.madd("Load",
nodes,
suffix=" shipping",
bus=nodes + " H2",
p_set = industrial_demand.loc[nodes,"shipping H2"]/8760.)
network.add("Bus",
"Fischer-Tropsch",
carrier="Fischer-Tropsch")
#TODO: Add capital cost
#NB: CO2 gets released again to atmosphere when plastics decay or kerosene is burned
network.madd("Link",
nodes + " Fischer-Tropsch",
bus0=nodes + " H2",
bus1="Fischer-Tropsch",
bus2="co2 stored",
bus3="co2 atmosphere",
carrier="Fischer-Tropsch",
efficiency=0.8,
efficiency2=-0.26*0.8,
efficiency3=0.26*0.8,
p_nom_extendable=True)
network.add("Load",
"Fischer-Tropsch",
bus="Fischer-Tropsch",
p_set = industrial_demand.loc[nodes,["aviation kerosene","naptha feedstock"]].sum().sum()/8760.)
network.madd("Load",
nodes,
suffix=" industry new electricity",
bus=nodes,
p_set = industrial_demand.loc[nodes,"industry new electricity"]/8760.)
network.add("Load",
"process emissions to atmosphere",
bus="co2 atmosphere",
p_set = -industrial_demand.loc[nodes,"process emissions"].sum()*(1-options["ccs_fraction"])/8760.)
network.add("Load",
"process emissions to stored",
bus="co2 stored",
p_set = -industrial_demand.loc[nodes,"process emissions"].sum()*options["ccs_fraction"]/8760.)
def restrict_technology_potential(n,tech,limit):
print("restricting potentials (p_nom_max) for {} to {} of technical potential".format(tech,limit))
gens = n.generators.index[n.generators.carrier == tech]
n.generators.loc[gens,"p_nom_max"] *=limit
if __name__ == "__main__":
# Detect running outside of snakemake and mock snakemake for testing
if 'snakemake' not in globals():
from vresutils.snakemake import MockSnakemake
snakemake = MockSnakemake(
wildcards=dict(network='elec', simpl='', clusters='37', lv='2', opts='Co2L-3H'),
input=dict(network='../pypsa-eur/networks/{network}_s{simpl}_{clusters}.nc', timezone_mappings='data/timezone_mappings.csv'),
output=['networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc']
)
with open('config.yaml') as f:
snakemake.config = yaml.load(f)
logging.basicConfig(level=snakemake.config['logging_level'])
timezone_mappings = pd.read_csv(snakemake.input.timezone_mappings,index_col=0,squeeze=True,header=None)
options = snakemake.config["sector"]
opts = snakemake.wildcards.sector_opts.split('-')
n = pypsa.Network(snakemake.input.network,
override_component_attrs=override_component_attrs)
Nyears = n.snapshot_weightings.sum()/8760.
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,index_col=0)
pop_layout["ct"] = pop_layout.index.str[:2]
ct_total = pop_layout.total.groupby(pop_layout["ct"]).sum()
pop_layout["ct_total"] = pop_layout["ct"].map(ct_total.get)
pop_layout["fraction"] = pop_layout["total"]/pop_layout["ct_total"]
costs = prepare_costs()
add_co2_tracking(n)
add_generation(n)
add_storage(n)
nodal_energy_totals, heat_demand, space_heat_demand, water_heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, co2_totals, nodal_transport_data = prepare_data(n)
if "T" in opts:
add_transport(n)
if "H" in opts:
add_heat(n)
if "B" in opts:
add_biomass(n)
if "I" in opts:
add_industry(n)
for o in opts:
m = re.match(r'^\d+h$', o, re.IGNORECASE)
if m is not None:
n = average_every_nhours(n, m.group(0))
break
else:
logger.info("No resampling")
for o in opts:
if "Co2L" in o:
limit = o[o.find("Co2L")+4:]
print(o,limit)
if limit == "":
limit = snakemake.config['co2_reduction']
else:
limit = float(limit.replace("p",".").replace("m","-"))
add_co2limit(n, Nyears, limit)
# add_emission_prices(n, exclude_co2=True)
# if 'Ep' in opts:
# add_emission_prices(n)
if "onwind" in o:
limit = o[o.find("onwind")+6:]
limit = float(limit.replace("p",".").replace("m","-"))
restrict_technology_potential(n,"onwind",limit)
n.export_to_netcdf(snakemake.output[0])