# coding: utf-8 import logging logger = logging.getLogger(__name__) import pandas as pd idx = pd.IndexSlice import numpy as np import scipy as sp import xarray as xr import re, os from six import iteritems, string_types import pypsa import yaml import pytz from vresutils.costdata import annuity from prepare_sector_network import prepare_costs #First tell PyPSA that links can have multiple outputs by #overriding the component_attrs. This can be done for #as many buses as you need with format busi for i = 2,3,4,5,.... #See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs override_component_attrs = pypsa.descriptors.Dict({k : v.copy() for k,v in pypsa.components.component_attrs.items()}) override_component_attrs["Link"].loc["bus2"] = ["string",np.nan,np.nan,"2nd bus","Input (optional)"] override_component_attrs["Link"].loc["bus3"] = ["string",np.nan,np.nan,"3rd bus","Input (optional)"] override_component_attrs["Link"].loc["efficiency2"] = ["static or series","per unit",1.,"2nd bus efficiency","Input (optional)"] override_component_attrs["Link"].loc["efficiency3"] = ["static or series","per unit",1.,"3rd bus efficiency","Input (optional)"] override_component_attrs["Link"].loc["p2"] = ["series","MW",0.,"2nd bus output","Output"] override_component_attrs["Link"].loc["p3"] = ["series","MW",0.,"3rd bus output","Output"] override_component_attrs["Link"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"] override_component_attrs["Link"].loc["lifetime"] = ["float","years",np.nan,"build year","Input (optional)"] override_component_attrs["Generator"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"] override_component_attrs["Generator"].loc["lifetime"] = ["float","years",np.nan,"build year","Input (optional)"] override_component_attrs["Store"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"] override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"build year","Input (optional)"] def add_power_capacities_installed_before_baseyear(n, grouping_years, costs): """ Parameters ---------- n : network grouping_years : intervals to group existing capacities costs : to read lifetime to estimate YearDecomissioning """ print("adding power capacities installed before baseyear") ### add conventional capacities using 'powerplants.csv' df_agg = pd.read_csv('../pypsa-eur/resources/powerplants.csv', index_col=0) rename_fuel = {'Hard Coal':'coal', 'Lignite':'lignite', 'Nuclear':'nuclear', 'Oil':'oil', 'OCGT':'OCGT', 'CCGT':'CCGT', 'Natural Gas':'gas',} fueltype_to_drop = ['Hydro', 'Wind', 'Solar', 'Geothermal', 'Bioenergy', 'Waste', 'Other', 'CCGT, Thermal'] technology_to_drop = ['Pv', 'Storage Technologies'] df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)],inplace=True) df_agg.drop(df_agg.index[df_agg.Technology.isin(technology_to_drop)],inplace=True) df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel) # add existing solar and wind capacities # source: https://www.irena.org/Statistics/Download-Data cc = pd.read_csv('data/Country_codes.csv', sep=',', index_col=-1) name_to_2code = dict(zip(cc['Country'].tolist(), cc['2 letter code (ISO-3166-2)'].tolist())) for tech in ['solar', 'onwind', 'offwind']: df = pd.read_csv('data/existing_infrastructure/{}_capacity_IRENA.csv'.format(tech), sep=',', index_col=0) df.rename(index={'Czechia':'Czech Republic', 'UK':'United Kingdom', 'Bosnia Herzg':'Bosnia Herzegovina', 'North Macedonia': 'Macedonia'}, inplace=True) df.rename(index=lambda country : name_to_2code[country], inplace=True) # calculate yearly differences df.insert(loc=0, value=.0, column='1999') df = df.diff(axis=1).drop('1999', axis=1) df = df.clip(lower=0) df.replace(to_replace=0.0, value=np.nan, inplace=True) for year in df.columns: for country in df.index: if df.notnull().loc[country,year]: df_agg = df_agg.append({'Fueltype':tech, 'Country':country, 'Capacity':df.loc[country,year], 'YearCommissioned':int(year), 'YearDecommissioning':int(float(year)+costs.at[tech, 'lifetime'])}, ignore_index=True) nodes=set([node[0:2] for node in n.buses.index[n.buses.carrier == "AC"]]) #TODO: Check if we want to change YearCommisioned into YearRetrofited for i,grouping_year in enumerate(grouping_years): if i==0: index = df_agg.YearCommissioned < int(grouping_year) else: index = (int(grouping_years[i-1]) < df_agg.YearCommissioned) & (df_agg.YearCommissioned < int(grouping_year)) df = df_agg[index].pivot_table(index='Country', columns='Fueltype', values='Capacity', aggfunc='sum') for node in nodes: #if a country has more than one node, selects the first one bus_selected=[bus for bus in n.buses.index[n.buses.carrier == "AC"] if bus[0:2]==node][0] for generator,carrier in [("OCGT","gas"), ("CCGT", "gas"), ("coal", "coal"), ("oil","oil"), ("nuclear","uranium")]: try: if node in df.index and not np.isnan(df.loc[node, generator]): #use madd so that we can insert the carrier attribute n.madd("Link", [bus_selected + " " + generator +"-" + grouping_year], bus0="EU " + carrier, bus1=bus_selected, bus2="co2 atmosphere", carrier=generator, 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=df.loc[node, generator], efficiency=costs.at[generator,'efficiency'], efficiency2=costs.at[carrier,'CO2 intensity'], build_year=int(grouping_year), lifetime=costs.at[generator,'lifetime']) except: print("No capacity installed around " + grouping_year + " of " + generator + " in node " + node) for generator in ['solar', 'onwind', 'offwind']: try: if not np.isnan(df.loc[node, generator]): if generator =='offwind': p_max_pu=n.generators_t.p_max_pu[bus_selected + ' offwind-ac'] else: p_max_pu=n.generators_t.p_max_pu[bus_selected + ' ' + generator] n.add("Generator", bus_selected + ' ' + generator +"-"+ grouping_year, bus=bus_selected, carrier=generator, p_nom=df.loc[node, generator], marginal_cost=costs.at[generator,'VOM'], capital_cost=costs.at[generator,'fixed'], efficiency=costs.at[generator, 'efficiency'], p_max_pu=p_max_pu, build_year=int(grouping_year), lifetime=costs.at[generator,'lifetime']) except: print("No capacity installed around " + grouping_year + " of " + generator + " in node " + node) # delete generators if their lifetime is over and p_nom=0 n.mremove("Generator", [index for index in n.generators.index.to_list() if grouping_year in index and n.generators.p_nom[index] < snakemake.config['existing_capacities']['threshold_capacity']]) n.mremove("Link", [index for index in n.links.index.to_list() if grouping_year in index and n.links.p_nom[index] < snakemake.config['existing_capacities']['threshold_capacity']]) def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime): """ Parameters ---------- n : network baseyear: last year covered in the existing capacities database grouping_years : intervals to group existing capacities linear decomissioning of heating capacities from 2020 to 2045 is currently assumed heating capacities split between residential and services proportional to heating load in both 50% capacities in rural busess 50% in urban buses """ print("adding heating capacities installed before baseyear") # Add existing heating capacities, data comes from the study # "Mapping and analyses of the current and future (2020 - 2030) # heating/cooling fuel deployment (fossil/renewables) " # https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1 # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv". # retrieve existing heating capacities techs = ['gas boiler', 'oil boiler', 'resistive heater', 'air heat pump', 'ground heat pump'] df = pd.read_csv('data/existing_infrastructure/existing_heating_raw.csv', index_col=0, header=0) # data for Albania, Montenegro and Macedonia not included in database df.loc['Albania']=np.nan df.loc['Montenegro']=np.nan df.loc['Macedonia']=np.nan df.fillna(0, inplace=True) df *= 1e3 # GW to MW cc = pd.read_csv('data/Country_codes.csv', sep=',', index_col=-1) name_to_2code = dict(zip(cc['Country'].tolist(), cc['2 letter code (ISO-3166-2)'].tolist())) df.rename(index=lambda country : name_to_2code[country], inplace=True) # coal and oil boilers are assimilated to oil boilers df['oil boiler'] =df['oil boiler'] + df['coal boiler'] df.drop(['coal boiler'], axis=1, inplace=True) # rename countries with network buses names nodes_elec=[node for node in n.buses.index[n.buses.carrier == "AC"]] name_to_busname={ index : [node for node in nodes_elec if index in node][0] for index in df.index} df.rename(index=lambda country : name_to_busname[country], inplace=True) # split existing capacities between residential and services # proportional to energy demand ratio_residential=pd.Series([(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] / (n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] + n.loads_t.p_set.sum()['{} services rural heat'.format(node)] )) for node in df.index], index=df.index) for tech in techs: df['residential ' + tech] = df[tech]*ratio_residential df['services ' + tech] = df[tech]*(1-ratio_residential) nodes={} p_nom={} for name in ["residential rural", "services rural", "residential urban decentral", "services urban decentral", "urban central"]: name_type = "central" if name == "urban central" else "decentral" nodes[name] = pd.Index([index[0:5] for index in n.buses.index[n.buses.index.str.contains(name) & n.buses.index.str.contains('heat')]]) heat_pump_type = "air" if "urban" in name else "ground" heat_type= "residential" if "residential" in name else "services" if name == "urban central": p_nom[name]=df['air heat pump'][nodes[name]] else: p_nom[name] = df['{} {} heat pump'.format(heat_type, heat_pump_type)][nodes[name]] # Add heat pumps costs_name = "{} {}-sourced heat pump".format("decentral", heat_pump_type) cop = {"air" : ashp_cop, "ground" : gshp_cop} efficiency = cop[heat_pump_type][nodes[name]] if time_dep_hp_cop else costs.at[costs_name,'efficiency'] for i,grouping_year in enumerate(grouping_years): if int(grouping_year) + default_lifetime <= int(baseyear): ratio=0 else: #installation is assumed to be linear for the past 25 years (default lifetime) ratio = (int(grouping_year)-int(grouping_years[i-1]))/default_lifetime print(grouping_year + ' ratio ' + str(ratio)) n.madd("Link", nodes[name], suffix=" {} {} heat pump-{}".format(name,heat_pump_type, grouping_year), bus0=nodes[name], bus1=nodes[name] + " " + name + " heat", carrier="{} {} heat pump".format(name,heat_pump_type), efficiency=efficiency, capital_cost=costs.at[costs_name,'efficiency']*costs.at[costs_name,'fixed'], p_nom=p_nom[name]*ratio, build_year=int(grouping_year), lifetime=costs.at[costs_name,'lifetime']) # add resistive heater, gas boilers and oil boilers # (50% capacities to rural buses, 50% to urban buses) n.madd("Link", nodes[name], suffix= " " + name + " resistive heater-{}".format(grouping_year), bus0=nodes[name], bus1=nodes[name] + " " + name + " heat", carrier=name + " resistive heater", efficiency=costs.at[name_type + ' resistive heater','efficiency'], capital_cost=costs.at[name_type + ' resistive heater','efficiency']*costs.at[name_type + ' resistive heater','fixed'], p_nom=0.5*df['{} resistive heater'.format(heat_type)][nodes[name]]*ratio, build_year=int(grouping_year), lifetime=costs.at[costs_name,'lifetime']) n.madd("Link", nodes[name], suffix= " " + name + " gas boiler-{}".format(grouping_year), bus0=["EU gas"]*len(nodes[name]), bus1=nodes[name] + " " + name + " heat", bus2="co2 atmosphere", carrier=name + " gas boiler", efficiency=costs.at[name_type + ' gas boiler','efficiency'], efficiency2=costs.at['gas','CO2 intensity'], capital_cost=costs.at[name_type + ' gas boiler','efficiency']*costs.at[name_type + ' gas boiler','fixed'], p_nom=0.5*df['{} gas boiler'.format(heat_type)][nodes[name]]*ratio, build_year=int(grouping_year), lifetime=costs.at[name_type + ' gas boiler','lifetime']) n.madd("Link", nodes[name], suffix=" " + name + " oil boiler-{}".format(grouping_year), bus0=["EU oil"]*len(nodes[name]), bus1=nodes[name] + " " + name + " heat", bus2="co2 atmosphere", carrier=name + " oil boiler", efficiency=costs.at['decentral oil boiler','efficiency'], efficiency2=costs.at['oil','CO2 intensity'], capital_cost=costs.at['decentral oil boiler','efficiency']*costs.at['decentral oil boiler','fixed'], p_nom=0.5*df['{} oil boiler'.format(heat_type)][nodes[name]]*ratio, build_year=int(grouping_year), lifetime=costs.at[name_type + ' gas boiler','lifetime']) # delete links with p_nom=nan corresponding to extra nodes in country n.mremove("Link", [index for index in n.links.index.to_list() if grouping_year in index and np.isnan(n.links.p_nom[index])]) # delete links if their lifetime is over and p_nom=0 n.mremove("Link", [index for index in n.links.index.to_list() if grouping_year in index and n.links.p_nom[index]