Separate script for industrial production per ct from energy demand
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Tom Brown
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Snakefile
18
Snakefile
@ -155,15 +155,23 @@ rule build_industry_sector_ratios:
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script: 'scripts/build_industry_sector_ratios.py'
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rule build_industrial_demand_per_country:
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input:
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industry_sector_ratios="resources/industry_sector_ratios.csv"
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rule build_industrial_production_per_country:
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output:
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industrial_production_per_country="resources/industrial_production_per_country.csv"
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threads: 1
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resources: mem_mb=1000
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script: 'scripts/build_industrial_production_per_country.py'
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rule build_industrial_energy_demand_per_country:
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input:
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industry_sector_ratios="resources/industry_sector_ratios.csv",
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industrial_production_per_country="resources/industrial_production_per_country.csv"
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output:
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industrial_demand_per_country="resources/industrial_demand_per_country.csv",
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industrial_energy_demand_per_country="resources/industrial_energy_demand_per_country.csv"
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threads: 1
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resources: mem_mb=1000
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script: 'scripts/build_industrial_demand_per_country.py'
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script: 'scripts/build_industrial_energy_demand_per_country.py'
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rule build_industrial_demand:
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84
scripts/build_industrial_energy_demand_per_country.py
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84
scripts/build_industrial_energy_demand_per_country.py
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@ -0,0 +1,84 @@
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import pandas as pd
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import numpy as np
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tj_to_ktoe = 0.0238845
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ktoe_to_twh = 0.01163
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eb_base_dir = "data/eurostat-energy_balances-may_2018_edition"
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jrc_base_dir = "data/jrc-idees-2015"
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# import EU ratios df as csv
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industry_sector_ratios=pd.read_csv(snakemake.input.industry_sector_ratios, sep=';', index_col=0)
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#material demand per country and industry (kton/a)
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countries_production = pd.read_csv(snakemake.input.industrial_production_per_country, index_col=0)
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#Annual energy consumption in Switzerland by sector in 2015 (in TJ)
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#From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
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#http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
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dic_Switzerland ={'Iron and steel': 7889.,
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'Chemicals Industry': 26871.,
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'Non-metallic mineral products': 15513.+3820.,
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'Pulp, paper and printing': 12004.,
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'Food, beverages and tobacco': 17728.,
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'Non Ferrous Metals': 3037.,
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'Transport Equipment': 14993.,
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'Machinery Equipment': 4724.,
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'Textiles and leather': 1742.,
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'Wood and wood products': 0.,
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'Other Industrial Sectors': 10825.,
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'current electricity': 53760.}
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eb_names={'NO':'Norway', 'AL':'Albania', 'BA':'Bosnia and Herzegovina',
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'MK':'FYR of Macedonia', 'GE':'Georgia', 'IS':'Iceland',
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'KO':'Kosovo', 'MD':'Moldova', 'ME':'Montenegro', 'RS':'Serbia',
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'UA':'Ukraine', 'TR':'Turkey', }
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jrc_names = {"GR" : "EL",
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"GB" : "UK"}
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#final energy consumption per country and industry (TWh/a)
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countries_df = countries_production.dot(industry_sector_ratios.T)
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countries_df*= 0.001 #GWh -> TWh (ktCO2 -> MtCO2)
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non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
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# save current electricity consumption
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for country in countries_df.index:
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if country in non_EU:
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if country == 'CH':
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countries_df.loc[country, 'current electricity']=dic_Switzerland['current electricity']*tj_to_ktoe*ktoe_to_twh
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else:
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excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,eb_names[country]),
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sheet_name='2016', index_col=1,header=0, skiprows=1 ,squeeze=True)
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countries_df.loc[country, 'current electricity'] = excel_balances.loc['Industry', 'Electricity']*ktoe_to_twh
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else:
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excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(jrc_base_dir,jrc_names.get(country,country)),
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sheet_name='Ind_Summary',index_col=0,header=0,squeeze=True) # the summary sheet
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s_out = excel_out.iloc[27:48,-1]
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countries_df.loc[country, 'current electricity'] = s_out['Electricity']*ktoe_to_twh
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print(countries_df.loc[country, 'current electricity'])
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rename_sectors = {'elec':'electricity',
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'biomass':'solid biomass',
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'heat':'low-temperature heat'}
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countries_df.rename(columns=rename_sectors,inplace=True)
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countries_df.index.name = "TWh/a (MtCO2/a)"
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countries_df.to_csv(snakemake.output.industrial_energy_demand_per_country,
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float_format='%.2f')
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@ -1,27 +1,14 @@
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#%matplotlib inline
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import pandas as pd
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import numpy as np
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tj_to_ktoe = 0.0238845
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ktoe_to_twh = 0.01163
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jrc_base_dir = "data/jrc-idees-2015"
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eb_base_dir = "data/eurostat-energy_balances-may_2018_edition"
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tj_to_ktoe = 0.0238845
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ktoe_to_twh = 0.01163
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# import EU ratios df as csv
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df=pd.read_csv(snakemake.input.industry_sector_ratios, sep=';', index_col=0)
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sub_sheet_name_dict = { 'Iron and steel':'ISI',
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'Chemicals Industry':'CHI',
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'Non-metallic mineral products': 'NMM',
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@ -38,15 +25,15 @@ index = ['elec','biomass','methane','hydrogen','heat','naphtha','process emissio
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non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
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rename = {"GR" : "EL",
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jrc_names = {"GR" : "EL",
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"GB" : "UK"}
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eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI', 'CZ',
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'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV',
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'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI'] + ['CY','MT']
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eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI',
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'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV', 'CZ',
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'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI', 'CY', 'MT']
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countries = non_EU + [rename.get(eu,eu) for eu in eu28]
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countries = non_EU + eu28
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sectors = ['Iron and steel','Chemicals Industry','Non-metallic mineral products',
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@ -68,18 +55,12 @@ sect2sub = {'Iron and steel':['Electric arc','Integrated steelworks'],
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subsectors = [ss for s in sectors for ss in sect2sub[s]]
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#final energy consumption per country and industry (TWh/a)
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countries_df = pd.DataFrame(index=countries,
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columns=index,
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dtype=float)
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#material demand per country and industry (kton/a)
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countries_demand = pd.DataFrame(index=countries,
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columns=subsectors,
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dtype=float)
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out_dic ={'Electric arc': 'Electric arc',
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'Integrated steelworks': 'Integrated steelworks',
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'Basic chemicals': 'Basic chemicals (kt ethylene eq.)',
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@ -128,10 +109,11 @@ dic_sec_summary = {'Iron and steel': 'Iron and steel',
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'Other Industrial Sectors': ' Other Industrial Sectors'}
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#countries=['CH']
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dic_countries={'NO':'Norway', 'AL':'Albania', 'BA':'Bosnia and Herzegovina',
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eb_names={'NO':'Norway', 'AL':'Albania', 'BA':'Bosnia and Herzegovina',
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'MK':'FYR of Macedonia', 'GE':'Georgia', 'IS':'Iceland',
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'KO':'Kosovo', 'MD':'Moldova', 'ME':'Montenegro', 'RS':'Serbia',
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'UA':'Ukraine', 'TR':'Turkey', }
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dic_sec ={'Iron and steel':'Iron & steel industry',
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'Chemicals Industry': 'Chemical and Petrochemical industry',
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'Non-metallic mineral products': 'Non-ferrous metal industry',
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@ -164,7 +146,6 @@ dic_Switzerland ={'Iron and steel': 7889.,
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dic_sec_position={}
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for country in countries:
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countries_df.loc[country] = 0.
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countries_demand.loc[country] = 0.
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print(country)
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for sector in sectors:
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@ -174,7 +155,7 @@ for country in countries:
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else:
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# estimate physical output
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#energy consumption in the sector and country
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excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,dic_countries[country]),
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excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,eb_names[country]),
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sheet_name='2016', index_col=2,header=0, skiprows=1 ,squeeze=True)
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e_country = excel_balances.loc[dic_sec[sector], 'Total all products']
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@ -192,62 +173,19 @@ for country in countries:
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s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],-1]
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for subsector in sect2sub[sector]:
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output = ratio_country_EU28*s_out[out_dic[subsector]]
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countries_demand.loc[country,subsector] = output
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for ind in index:
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countries_df.loc[country, ind] += float(output*df.loc[ind, subsector]) # kton * MWh = GWh (# kton * tCO2 = ktCO2)
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countries_demand.loc[country,subsector] = ratio_country_EU28*s_out[out_dic[subsector]]
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else:
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# read the input sheets
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excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(jrc_base_dir,country), sheet_name=sub_sheet_name_dict[sector],index_col=0,header=0,squeeze=True) # the summary sheet
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excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(jrc_base_dir,jrc_names.get(country,country)), sheet_name=sub_sheet_name_dict[sector],index_col=0,header=0,squeeze=True) # the summary sheet
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s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],-1]
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for subsector in sect2sub[sector]:
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output = s_out[out_dic[subsector]]
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countries_demand.loc[country,subsector] = output
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for ind in index:
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countries_df.loc[country, ind] += output*df.loc[ind, subsector] #kton * MWh = GWh (# kton * tCO2 = ktCO2)
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countries_demand.loc[country,subsector] = s_out[out_dic[subsector]]
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countries_df*= 0.001 #GWh -> TWh (ktCO2 -> MtCO2)
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countries_demand.index.name = "kton/a"
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# save current electricity consumption
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for country in countries:
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if country in non_EU:
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if country == 'CH':
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countries_df.loc[country, 'current electricity']=dic_Switzerland['current electricity']*tj_to_ktoe*ktoe_to_twh
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else:
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excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,dic_countries[country]),
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sheet_name='2016', index_col=1,header=0, skiprows=1 ,squeeze=True)
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countries_df.loc[country, 'current electricity'] = excel_balances.loc['Industry', 'Electricity']*ktoe_to_twh
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else:
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excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(jrc_base_dir,country),
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sheet_name='Ind_Summary',index_col=0,header=0,squeeze=True) # the summary sheet
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s_out = excel_out.iloc[27:48,-1]
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countries_df.loc[country, 'current electricity'] = s_out['Electricity']*ktoe_to_twh
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print(countries_df.loc[country, 'current electricity'])
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# save df as csv
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for ind in index:
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countries_df[ind]=countries_df[ind].astype('float')
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countries_df = countries_df.round(3)
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countries_df.rename(index={value : key for key,value in rename.items()},inplace=True)
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rename_sectors = {'elec':'electricity',
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'biomass':'solid biomass',
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'heat':'low-temperature heat'}
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countries_df.rename(columns=rename_sectors,inplace=True)
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countries_df.to_csv(snakemake.output.industrial_energy_demand_per_country,
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float_format='%.2f')
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countries_demand.to_csv(snakemake.output.industrial_demand_per_country,
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countries_demand.to_csv(snakemake.output.industrial_production_per_country,
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float_format='%.2f')
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@ -1372,4 +1372,5 @@ sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
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df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material
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df.index.name = "MWh/tMaterial"
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df.to_csv('resources/industry_sector_ratios.csv', sep=';')
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