459 lines
20 KiB
Python
459 lines
20 KiB
Python
# 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, string_types
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import pypsa
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import yaml
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import pytz
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from vresutils.costdata import annuity
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from prepare_sector_network import prepare_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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override_component_attrs["Link"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"]
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override_component_attrs["Link"].loc["lifetime"] = ["float","years",np.nan,"build year","Input (optional)"]
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override_component_attrs["Generator"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"]
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override_component_attrs["Generator"].loc["lifetime"] = ["float","years",np.nan,"build year","Input (optional)"]
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override_component_attrs["Store"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"]
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override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"build year","Input (optional)"]
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def add_build_year_to_new_assets(n, baseyear):
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"""
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Parameters
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----------
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n : network
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baseyear: year in which optimized assets are built
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"""
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#Give assets with lifetimes and no build year the build year baseyear
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for c in n.iterate_components(["Link", "Generator", "Store"]):
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assets = c.df.index[~c.df.lifetime.isna() & c.df.build_year.isna()]
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c.df.loc[assets, "build_year"] = baseyear
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#add -baseyear to name
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rename = pd.Series(c.df.index, c.df.index)
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rename[assets] += "-" + str(baseyear)
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c.df.rename(index=rename, inplace=True)
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#rename time-dependent
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for attr in n.component_attrs[c.name].index[(n.component_attrs[c.name].type.str.contains("series") &
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n.component_attrs[c.name].status.str.contains("Input"))]:
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c.pnl[attr].rename(columns=rename, inplace=True)
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def add_existing_renewables(df_agg):
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"""
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Append existing renewables to the df_agg pd.DataFrame
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with the conventional power plants.
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"""
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cc = pd.read_csv('data/Country_codes.csv',
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index_col=0)
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carriers = {"solar" : "solar",
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"onwind" : "onwind",
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"offwind" : "offwind-ac"}
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for tech in ['solar', 'onwind', 'offwind']:
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carrier = carriers[tech]
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df = pd.read_csv('data/existing_infrastructure/{}_capacity_IRENA.csv'.format(tech),
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index_col=0)
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df = df.fillna(0.)
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df.columns = df.columns.astype(int)
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df.rename(index={'Czechia':'Czech Republic',
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'UK':'United Kingdom',
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'Bosnia Herzg':'Bosnia Herzegovina',
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'North Macedonia': 'Macedonia'}, inplace=True)
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df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
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# calculate yearly differences
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df.insert(loc=0, value=.0, column='1999')
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df = df.diff(axis=1).drop('1999', axis=1)
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df = df.clip(lower=0)
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#distribute capacities among nodes according to capacity factor
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#weighting with nodal_fraction
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elec_buses = n.buses.index[n.buses.carrier == "AC"].union(n.buses.index[n.buses.carrier == "DC"])
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nodal_fraction = pd.Series(0.,elec_buses)
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for country in n.buses.loc[elec_buses,"country"].unique():
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gens = n.generators.index[(n.generators.index.str[:2] == country) & (n.generators.carrier == carrier)]
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cfs = n.generators_t.p_max_pu[gens].mean()
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cfs_key = cfs/cfs.sum()
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nodal_fraction.loc[n.generators.loc[gens,"bus"]] = cfs_key.values
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nodal_df = df.loc[n.buses.loc[elec_buses,"country"]]
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nodal_df.index = elec_buses
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nodal_df = nodal_df.multiply(nodal_fraction,axis=0)
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for year in nodal_df.columns:
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for node in nodal_df.index:
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name = f"{node}-{tech}-{year}"
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capacity = nodal_df.loc[node,year]
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if capacity > 0.:
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df_agg.at[name,"Fueltype"] = tech
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df_agg.at[name,"Capacity"] = capacity
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df_agg.at[name,"YearCommissioned"] = year
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df_agg.at[name,"cluster_bus"] = node
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def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear):
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"""
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Parameters
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----------
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n : network
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grouping_years : intervals to group existing capacities
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costs : to read lifetime to estimate YearDecomissioning
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"""
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print("adding power capacities installed before baseyear")
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### add conventional capacities using 'powerplants.csv'
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df_agg = pd.read_csv(snakemake.input.powerplants, index_col=0)
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rename_fuel = {'Hard Coal':'coal',
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'Lignite':'lignite',
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'Nuclear':'nuclear',
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'Oil':'oil',
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'OCGT':'OCGT',
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'CCGT':'CCGT',
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'Natural Gas':'gas',}
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fueltype_to_drop = ['Hydro',
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'Wind',
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'Solar',
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'Geothermal',
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'Bioenergy',
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'Waste',
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'Other',
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'CCGT, Thermal']
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technology_to_drop = ['Pv',
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'Storage Technologies']
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df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)],inplace=True)
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df_agg.drop(df_agg.index[df_agg.Technology.isin(technology_to_drop)],inplace=True)
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df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
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#assign clustered bus
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busmap_s = pd.read_hdf(snakemake.input.clustermaps,
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key="/busmap_s")
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busmap = pd.read_hdf(snakemake.input.clustermaps,
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key="/busmap")
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clustermaps = busmap_s.map(busmap)
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clustermaps.index = clustermaps.index.astype(int)
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df_agg["cluster_bus"] = df_agg.bus.map(clustermaps)
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#include renewables in df_agg
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add_existing_renewables(df_agg)
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df_agg["grouping_year"] = np.take(grouping_years,
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np.digitize(df_agg.YearCommissioned,
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grouping_years,
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right=True))
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df = df_agg.pivot_table(index=["grouping_year",'Fueltype'], columns='cluster_bus',
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values='Capacity', aggfunc='sum')
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carrier = {"OCGT" : "gas",
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"CCGT" : "gas",
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"coal" : "coal",
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"oil" : "oil",
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"lignite" : "lignite",
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"nuclear" : "uranium"}
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for grouping_year, generator in df.index:
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#capacity is the capacity in MW at each node for this
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capacity = df.loc[grouping_year, generator]
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capacity = capacity[~capacity.isna()]
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capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
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if generator in ['solar', 'onwind', 'offwind']:
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if generator =='offwind':
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p_max_pu=n.generators_t.p_max_pu[capacity.index + ' offwind-ac' + '-' + str(baseyear)]
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else:
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p_max_pu=n.generators_t.p_max_pu[capacity.index + ' ' + generator + '-' + str(baseyear)]
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n.madd("Generator",
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capacity.index,
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suffix=' ' + generator +"-"+ str(grouping_year),
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bus=capacity.index,
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carrier=generator,
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p_nom=capacity,
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marginal_cost=costs.at[generator,'VOM'],
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capital_cost=costs.at[generator,'fixed'],
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efficiency=costs.at[generator, 'efficiency'],
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p_max_pu=p_max_pu.rename(columns=n.generators.bus),
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build_year=grouping_year,
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lifetime=costs.at[generator,'lifetime'])
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else:
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n.madd("Link",
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capacity.index,
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suffix= " " + generator +"-" + str(grouping_year),
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bus0="EU " + carrier[generator],
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bus1=capacity.index,
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bus2="co2 atmosphere",
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carrier=generator,
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marginal_cost=costs.at[generator,'efficiency']*costs.at[generator,'VOM'], #NB: VOM is per MWel
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capital_cost=costs.at[generator,'efficiency']*costs.at[generator,'fixed'], #NB: fixed cost is per MWel
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p_nom=capacity/costs.at[generator,'efficiency'],
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efficiency=costs.at[generator,'efficiency'],
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efficiency2=costs.at[carrier[generator],'CO2 intensity'],
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build_year=grouping_year,
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lifetime=costs.at[generator,'lifetime'])
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def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
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"""
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Parameters
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----------
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n : network
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baseyear: last year covered in the existing capacities database
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grouping_years : intervals to group existing capacities
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linear decomissioning of heating capacities from 2020 to 2045 is
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currently assumed
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heating capacities split between residential and services proportional
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to heating load in both
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50% capacities in rural busess 50% in urban buses
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"""
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print("adding heating capacities installed before baseyear")
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# Add existing heating capacities, data comes from the study
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# "Mapping and analyses of the current and future (2020 - 2030)
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# heating/cooling fuel deployment (fossil/renewables) "
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# https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
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# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
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# retrieve existing heating capacities
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techs = ['gas boiler',
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'oil boiler',
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'resistive heater',
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'air heat pump',
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'ground heat pump']
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df = pd.read_csv('data/existing_infrastructure/existing_heating_raw.csv',
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index_col=0,
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header=0)
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# data for Albania, Montenegro and Macedonia not included in database
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df.loc['Albania']=np.nan
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df.loc['Montenegro']=np.nan
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df.loc['Macedonia']=np.nan
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df.fillna(0, inplace=True)
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df *= 1e3 # GW to MW
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cc = pd.read_csv('data/Country_codes.csv',
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index_col=0)
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df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
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# coal and oil boilers are assimilated to oil boilers
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df['oil boiler'] =df['oil boiler'] + df['coal boiler']
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df.drop(['coal boiler'], axis=1, inplace=True)
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# distribute technologies to nodes by population
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,
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index_col=0)
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pop_layout["ct"] = pop_layout.index.str[:2]
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ct_total = pop_layout.total.groupby(pop_layout["ct"]).sum()
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pop_layout["ct_total"] = pop_layout["ct"].map(ct_total.get)
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pop_layout["fraction"] = pop_layout["total"]/pop_layout["ct_total"]
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nodal_df = df.loc[pop_layout.ct]
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nodal_df.index = pop_layout.index
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nodal_df = nodal_df.multiply(pop_layout.fraction,axis=0)
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# split existing capacities between residential and services
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# proportional to energy demand
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ratio_residential=pd.Series([(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] /
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(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] +
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n.loads_t.p_set.sum()['{} services rural heat'.format(node)] ))
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for node in nodal_df.index], index=nodal_df.index)
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for tech in techs:
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nodal_df['residential ' + tech] = nodal_df[tech]*ratio_residential
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nodal_df['services ' + tech] = nodal_df[tech]*(1-ratio_residential)
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nodes={}
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p_nom={}
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for name in ["residential rural",
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"services rural",
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"residential urban decentral",
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"services urban decentral",
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"urban central"]:
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name_type = "central" if name == "urban central" else "decentral"
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nodes[name] = pd.Index([n.buses.at[index,"location"] for index in n.buses.index[n.buses.index.str.contains(name) & n.buses.index.str.contains('heat')]])
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heat_pump_type = "air" if "urban" in name else "ground"
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heat_type= "residential" if "residential" in name else "services"
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if name == "urban central":
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p_nom[name]=nodal_df['air heat pump'][nodes[name]]
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else:
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p_nom[name] = nodal_df['{} {} heat pump'.format(heat_type, heat_pump_type)][nodes[name]]
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# Add heat pumps
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costs_name = "{} {}-sourced heat pump".format("decentral", heat_pump_type)
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cop = {"air" : ashp_cop, "ground" : gshp_cop}
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efficiency = cop[heat_pump_type][nodes[name]] if time_dep_hp_cop else costs.at[costs_name,'efficiency']
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for i,grouping_year in enumerate(grouping_years):
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if int(grouping_year) + default_lifetime <= int(baseyear):
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ratio=0
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else:
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#installation is assumed to be linear for the past 25 years (default lifetime)
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ratio = (int(grouping_year)-int(grouping_years[i-1]))/default_lifetime
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n.madd("Link",
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nodes[name],
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suffix=" {} {} heat pump-{}".format(name,heat_pump_type, grouping_year),
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bus0=nodes[name],
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bus1=nodes[name] + " " + name + " heat",
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carrier="{} {} heat pump".format(name,heat_pump_type),
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efficiency=efficiency,
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capital_cost=costs.at[costs_name,'efficiency']*costs.at[costs_name,'fixed'],
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p_nom=p_nom[name]*ratio/costs.at[costs_name,'efficiency'],
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build_year=int(grouping_year),
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lifetime=costs.at[costs_name,'lifetime'])
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# add resistive heater, gas boilers and oil boilers
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# (50% capacities to rural buses, 50% to urban buses)
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n.madd("Link",
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nodes[name],
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suffix= " " + name + " resistive heater-{}".format(grouping_year),
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bus0=nodes[name],
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bus1=nodes[name] + " " + name + " heat",
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carrier=name + " resistive heater",
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efficiency=costs.at[name_type + ' resistive heater','efficiency'],
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capital_cost=costs.at[name_type + ' resistive heater','efficiency']*costs.at[name_type + ' resistive heater','fixed'],
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p_nom=0.5*nodal_df['{} resistive heater'.format(heat_type)][nodes[name]]*ratio/costs.at[name_type + ' resistive heater','efficiency'],
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build_year=int(grouping_year),
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lifetime=costs.at[costs_name,'lifetime'])
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n.madd("Link",
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nodes[name],
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suffix= " " + name + " gas boiler-{}".format(grouping_year),
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bus0=["EU gas"]*len(nodes[name]),
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bus1=nodes[name] + " " + name + " heat",
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bus2="co2 atmosphere",
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carrier=name + " gas boiler",
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efficiency=costs.at[name_type + ' gas boiler','efficiency'],
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efficiency2=costs.at['gas','CO2 intensity'],
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capital_cost=costs.at[name_type + ' gas boiler','efficiency']*costs.at[name_type + ' gas boiler','fixed'],
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p_nom=0.5*nodal_df['{} gas boiler'.format(heat_type)][nodes[name]]*ratio/costs.at[name_type + ' gas boiler','efficiency'],
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build_year=int(grouping_year),
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lifetime=costs.at[name_type + ' gas boiler','lifetime'])
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n.madd("Link",
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nodes[name],
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suffix=" " + name + " oil boiler-{}".format(grouping_year),
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bus0=["EU oil"]*len(nodes[name]),
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bus1=nodes[name] + " " + name + " heat",
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bus2="co2 atmosphere",
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carrier=name + " oil boiler",
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efficiency=costs.at['decentral oil boiler','efficiency'],
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efficiency2=costs.at['oil','CO2 intensity'],
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capital_cost=costs.at['decentral oil boiler','efficiency']*costs.at['decentral oil boiler','fixed'],
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p_nom=0.5*nodal_df['{} oil boiler'.format(heat_type)][nodes[name]]*ratio/costs.at['decentral oil boiler','efficiency'],
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build_year=int(grouping_year),
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lifetime=costs.at[name_type + ' gas boiler','lifetime'])
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# delete links with p_nom=nan corresponding to extra nodes in country
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n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and np.isnan(n.links.p_nom[index])])
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# delete links if their lifetime is over and p_nom=0
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n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and n.links.p_nom[index]<snakemake.config['existing_capacities']['threshold_capacity']])
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if __name__ == "__main__":
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# Detect running outside of snakemake and mock snakemake for testing
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if 'snakemake' not in globals():
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from vresutils.snakemake import MockSnakemake
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snakemake = MockSnakemake(
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wildcards=dict(network='elec', simpl='', clusters='39', lv='1.0',
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sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
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co2_budget_name='b30b3',
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planning_horizons='2020'),
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input=dict(network='pypsa-eur-sec/results/test/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc',
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powerplants='pypsa-eur/resources/powerplants.csv',
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clustermaps='pypsa-eur/resources/clustermaps_{network}_s{simpl}_{clusters}.h5',
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costs='pypsa-eur-sec/data/costs/costs_{planning_horizons}.csv',
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cop_air_total="pypsa-eur-sec/resources/cop_air_total_{network}_s{simpl}_{clusters}.nc",
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cop_soil_total="pypsa-eur-sec/resources/cop_soil_total_{network}_s{simpl}_{clusters}.nc"),
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output=['pypsa-eur-sec/results/test/prenetworks_brownfield/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc'],
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)
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import yaml
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with open('config.yaml', encoding='utf8') as f:
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snakemake.config = yaml.safe_load(f)
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logging.basicConfig(level=snakemake.config['logging_level'])
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options = snakemake.config["sector"]
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opts = snakemake.wildcards.sector_opts.split('-')
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baseyear= snakemake.config['scenario']["planning_horizons"][0]
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n = pypsa.Network(snakemake.input.network,
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override_component_attrs=override_component_attrs)
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add_build_year_to_new_assets(n, baseyear)
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Nyears = n.snapshot_weightings.sum()/8760.
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costs = prepare_costs(snakemake.input.costs,
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snakemake.config['costs']['USD2013_to_EUR2013'],
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snakemake.config['costs']['discountrate'],
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Nyears)
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grouping_years=snakemake.config['existing_capacities']['grouping_years']
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add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear)
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if "H" in opts:
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time_dep_hp_cop = options["time_dep_hp_cop"]
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ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).T.to_pandas().reindex(index=n.snapshots)
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gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).T.to_pandas().reindex(index=n.snapshots)
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default_lifetime = snakemake.config['costs']['lifetime']
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add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
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n.export_to_netcdf(snakemake.output[0])
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