1246 lines
48 KiB
Python
1246 lines
48 KiB
Python
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# coding: utf-8
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import logging
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logger = logging.getLogger(__name__)
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import pandas as pd
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idx = pd.IndexSlice
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import numpy as np
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import scipy as sp
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import xarray as xr
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import re, os
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from six import iteritems
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import geopandas as gpd
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import pypsa
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import pytz
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from vresutils.costdata import annuity
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from add_electricity import load_costs, update_transmission_costs
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#First tell PyPSA that links can have multiple outputs by
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#overriding the component_attrs. This can be done for
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#as many buses as you need with format busi for i = 2,3,4,5,....
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#See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs
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override_component_attrs = pypsa.descriptors.Dict({k : v.copy() for k,v in pypsa.components.component_attrs.items()})
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override_component_attrs["Link"].loc["bus2"] = ["string",np.nan,np.nan,"2nd bus","Input (optional)"]
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override_component_attrs["Link"].loc["bus3"] = ["string",np.nan,np.nan,"3rd bus","Input (optional)"]
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override_component_attrs["Link"].loc["efficiency2"] = ["static or series","per unit",1.,"2nd bus efficiency","Input (optional)"]
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override_component_attrs["Link"].loc["efficiency3"] = ["static or series","per unit",1.,"3rd bus efficiency","Input (optional)"]
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override_component_attrs["Link"].loc["p2"] = ["series","MW",0.,"2nd bus output","Output"]
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override_component_attrs["Link"].loc["p3"] = ["series","MW",0.,"3rd bus output","Output"]
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def add_co2_tracking(n):
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#minus sign because opposite to how fossil fuels used:
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#CH4 burning puts CH4 down, atmosphere up
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n.add("Carrier","co2",
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co2_emissions=-1.)
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#this tracks CO2 in the atmosphere
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n.add("Bus","co2 atmosphere",
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carrier="co2")
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#NB: can also be negative
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n.madd("Store",["co2 atmosphere"],
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e_nom_extendable=True,
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e_min_pu=-1,
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carrier="co2",
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bus="co2 atmosphere")
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#this tracks CO2 stored, e.g. underground
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n.add("Bus","co2 stored")
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#NB: can also be negative
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#cost of 10 euro/tCO2 for whatever stays
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#TODO move cost to data/costs.csv
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n.madd("Store",["co2 stored"],
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e_nom_extendable = True,
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marginal_cost=-1000.,
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carrier="co2 stored",
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bus="co2 stored")
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if options['dac']:
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#direct air capture consumes electricity to take CO2 from the air to the underground store
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#TODO do with cost from Breyer - later use elec and heat and capital cost
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n.madd("Link",["DAC"],
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bus0="co2 atmosphere",
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bus1="co2 stored",
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carrier="DAC",
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marginal_cost=75.,
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efficiency=1.,
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p_nom_extendable=True)
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def add_co2limit(n, Nyears=1.,limit=0.):
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cts = pop_layout.ct.value_counts().index
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co2_limit = co2_totals.loc[cts, "electricity"].sum()
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if "T" in opts:
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co2_limit += co2_totals.loc[cts, [i+ " non-elec" for i in ["rail","road"]]].sum().sum()
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if "H" in opts:
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co2_limit += co2_totals.loc[cts, [i+ " non-elec" for i in ["residential","services"]]].sum().sum()
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if "I" in opts:
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co2_limit += co2_totals.loc[cts, ["industrial non-elec","industrial processes",
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"domestic aviation","international aviation",
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"domestic navigation","international navigation"]].sum().sum()
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co2_limit *= limit*Nyears
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n.add("GlobalConstraint", "CO2Limit",
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carrier_attribute="co2_emissions", sense="<=",
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constant=co2_limit)
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def add_emission_prices(n, emission_prices=None, exclude_co2=False):
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assert False, "Needs to be fixed, adds NAN"
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if emission_prices is None:
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emission_prices = snakemake.config['costs']['emission_prices']
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if exclude_co2: emission_prices.pop('co2')
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ep = (pd.Series(emission_prices).rename(lambda x: x+'_emissions') * n.carriers).sum(axis=1)
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n.generators['marginal_cost'] += n.generators.carrier.map(ep)
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n.storage_units['marginal_cost'] += n.storage_units.carrier.map(ep)
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def set_line_s_max_pu(n):
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# set n-1 security margin to 0.5 for 37 clusters and to 0.7 from 200 clusters
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# 128 reproduces 98% of line volume in TWkm, but clustering distortions inside node
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n_clusters = len(n.buses.index[n.buses.carrier == "AC"])
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s_max_pu = np.clip(0.5 + 0.2 * (n_clusters - 37) / (200 - 37), 0.5, 0.7)
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n.lines['s_max_pu'] = s_max_pu
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dc_b = n.links.carrier == 'DC'
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n.links.loc[dc_b, 'p_max_pu'] = snakemake.config['links']['p_max_pu']
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n.links.loc[dc_b, 'p_min_pu'] = - snakemake.config['links']['p_max_pu']
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def set_line_volume_limit(n, lv):
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dc_b = n.links.carrier == 'DC'
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if lv != "opt":
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lv = float(lv)
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# Either line_volume cap or cost
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n.lines['capital_cost'] = 0.
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n.links.loc[dc_b,'capital_cost'] = 0.
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else:
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n.lines['capital_cost'] = (n.lines['length'] *
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costs.at['HVAC overhead', 'fixed'])
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#add HVDC inverter post factor, to maintain consistency with LV limit
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n.links.loc[dc_b, 'capital_cost'] = (n.links.loc[dc_b, 'length'] *
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costs.at['HVDC overhead', 'fixed'])# +
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#costs.at['HVDC inverter pair', 'fixed'])
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if lv != 1.0:
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lines_s_nom = n.lines.s_nom.where(
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n.lines.type == '',
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np.sqrt(3) * n.lines.num_parallel *
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n.lines.type.map(n.line_types.i_nom) *
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n.lines.bus0.map(n.buses.v_nom)
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)
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n.lines['s_nom_min'] = lines_s_nom
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n.links.loc[dc_b,'p_nom_min'] = n.links['p_nom']
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n.lines['s_nom_extendable'] = True
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n.links.loc[dc_b,'p_nom_extendable'] = True
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if lv != "opt":
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n.line_volume_limit = lv * ((lines_s_nom * n.lines['length']).sum() +
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n.links.loc[dc_b].eval('p_nom * length').sum())
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return n
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def average_every_nhours(n, offset):
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logger.info('Resampling the network to {}'.format(offset))
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m = n.copy(with_time=False)
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snapshot_weightings = n.snapshot_weightings.resample(offset).sum()
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m.set_snapshots(snapshot_weightings.index)
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m.snapshot_weightings = snapshot_weightings
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for c in n.iterate_components():
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pnl = getattr(m, c.list_name+"_t")
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for k, df in iteritems(c.pnl):
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if not df.empty:
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if c.list_name == "stores" and k == "e_max_pu":
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pnl[k] = df.resample(offset).min()
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elif c.list_name == "stores" and k == "e_min_pu":
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pnl[k] = df.resample(offset).max()
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else:
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pnl[k] = df.resample(offset).mean()
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return m
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timezone_mappings = pd.read_csv("data/timezone_mappings.csv",index_col=0,squeeze=True,header=None)
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def generate_periodic_profiles(dt_index=pd.date_range("2011-01-01 00:00","2011-12-31 23:00",freq="H",tz="UTC"),
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nodes=[],
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weekly_profile=range(24*7)):
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"""Give a 24*7 long list of weekly hourly profiles, generate this for
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each country for the period dt_index, taking account of time
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zones and Summer Time.
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"""
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weekly_profile = pd.Series(weekly_profile,range(24*7))
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week_df = pd.DataFrame(index=dt_index,columns=nodes)
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for ct in nodes:
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week_df[ct] = [24*dt.weekday()+dt.hour for dt in dt_index.tz_convert(pytz.timezone(timezone_mappings[ct[:2]]))]
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week_df[ct] = week_df[ct].map(weekly_profile)
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return week_df
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def shift_df(df,hours=1):
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"""Works both on Series and DataFrame"""
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df = df.copy()
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df.values[:] = np.concatenate([df.values[-hours:],
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df.values[:-hours]])
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return df
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def transport_degree_factor(temperature,deadband_lower=15,deadband_upper=20,
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lower_degree_factor=0.5,
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upper_degree_factor=1.6):
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"""Work out how much energy demand in vehicles increases due to heating and cooling.
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There is a deadband where there is no increase.
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Degree factors are % increase in demand compared to no heating/cooling fuel consumption.
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Returns per unit increase in demand for each place and time
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"""
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dd = temperature.copy()
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dd[(temperature > deadband_lower) & (temperature < deadband_upper)] = 0.
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dd[temperature < deadband_lower] = lower_degree_factor/100.*(deadband_lower-temperature[temperature < deadband_lower])
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dd[temperature > deadband_upper] = upper_degree_factor/100.*(temperature[temperature > deadband_upper]-deadband_upper)
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return dd
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def prepare_data(network):
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##############
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#Heating
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##############
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#copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
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heat_demand_df = xr.open_dataarray(snakemake.input.heat_demand_total).T.to_pandas().reindex(index=network.snapshots, method="ffill")
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intraday_profiles = pd.read_csv("data/heating/heat_load_profile_DK_AdamJensen.csv",index_col=0)
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intraday_year_profiles = generate_periodic_profiles(heat_demand_df.index.tz_localize("UTC"),
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nodes=heat_demand_df.columns,
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weekly_profile=(list(intraday_profiles["weekday"])*5 + list(intraday_profiles["weekend"])*2)).tz_localize(None)
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heat_demand_df = heat_demand_df*intraday_year_profiles
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ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).T.to_pandas().reindex(index=network.snapshots)
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gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).T.to_pandas().reindex(index=network.snapshots)
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solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).T.to_pandas().reindex(index=network.snapshots)
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#1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
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solar_thermal = options['solar_cf_correction'] * solar_thermal/1e3
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energy_totals = pd.read_csv(snakemake.input.energy_totals_name,index_col=0)
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nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
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nodal_energy_totals.index = pop_layout.index
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nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction,axis=0)
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sectors = ["residential","services"]
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nodal_energy_totals["Space Heating"] = nodal_energy_totals[["total {sector} space".format(sector=sector) for sector in sectors]].sum(axis=1)
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nodal_energy_totals["Water Heating"] = nodal_energy_totals[["total {sector} water".format(sector=sector) for sector in sectors]].sum(axis=1)
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space_heat_demand = (heat_demand_df/heat_demand_df.sum()).multiply(nodal_energy_totals["Space Heating"])*1e6*Nyears
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water_heat_demand = (nodal_energy_totals["Water Heating"]/8760.)*1e6
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heat_demand = space_heat_demand + water_heat_demand
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##############
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#Transport
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##############
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## Get overall demand curve for all vehicles
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dir_name = "data/emobility/"
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traffic = pd.read_csv(os.path.join(dir_name,"KFZ__count"),skiprows=2)["count"]
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#Generate profiles
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transport_shape = generate_periodic_profiles(dt_index=network.snapshots.tz_localize("UTC"),
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nodes=pop_layout.index,
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weekly_profile=traffic.values).tz_localize(None)
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transport_shape = transport_shape/transport_shape.sum()
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transport_data = pd.read_csv(snakemake.input.transport_name,
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index_col=0)
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nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.)
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nodal_transport_data.index = pop_layout.index
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nodal_transport_data["number cars"] = pop_layout["fraction"]*nodal_transport_data["number cars"]
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nodal_transport_data.loc[nodal_transport_data["average fuel efficiency"] == 0.,"average fuel efficiency"] = transport_data["average fuel efficiency"].mean()
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#electric motors are more efficient, so alter transport demand
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#kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
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plug_to_wheels_eta = 0.20
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battery_to_wheels_eta = plug_to_wheels_eta*0.9
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efficiency_gain = nodal_transport_data["average fuel efficiency"]/battery_to_wheels_eta
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#get heating demand for correction to demand time series
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temperature = xr.open_dataarray(snakemake.input.temp_air_total).T.to_pandas()
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#correction factors for vehicle heating
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dd_ICE = transport_degree_factor(temperature,
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options['transport_heating_deadband_lower'],
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options['transport_heating_deadband_upper'],
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options['ICE_lower_degree_factor'],
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options['ICE_upper_degree_factor'])
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dd_EV = transport_degree_factor(temperature,
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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("data/costs.csv",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=['networks/{network}_s{simpl}_{clusters}.nc'],
|
||
|
|
output=['networks/{network}_s{simpl}_{clusters}_lv{lv}_{opts}.nc']
|
||
|
|
)
|
||
|
|
|
||
|
|
logging.basicConfig(level=snakemake.config['logging_level'])
|
||
|
|
|
||
|
|
options = snakemake.config["sector"]
|
||
|
|
|
||
|
|
opts = snakemake.wildcards.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)
|
||
|
|
|
||
|
|
set_line_s_max_pu(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)
|
||
|
|
|
||
|
|
|
||
|
|
set_line_volume_limit(n, snakemake.wildcards.lv)
|
||
|
|
|
||
|
|
n.export_to_netcdf(snakemake.output[0])
|