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Separate script for industrial production per ct from energy demand

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Tom Brown 2020-08-26 13:12:16 +02:00
parent 37f36047ca
commit 851142fe0f
4 changed files with 117 additions and 86 deletions
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18
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@ -155,15 +155,23 @@ rule build_industry_sector_ratios:
script: 'scripts/build_industry_sector_ratios.py' script: 'scripts/build_industry_sector_ratios.py'
rule build_industrial_demand_per_country: rule build_industrial_production_per_country:
input: output:
industry_sector_ratios="resources/industry_sector_ratios.csv" industrial_production_per_country="resources/industrial_production_per_country.csv"
threads: 1
resources: mem_mb=1000
script: 'scripts/build_industrial_production_per_country.py'
rule build_industrial_energy_demand_per_country:
input:
industry_sector_ratios="resources/industry_sector_ratios.csv",
industrial_production_per_country="resources/industrial_production_per_country.csv"
output: output:
industrial_demand_per_country="resources/industrial_demand_per_country.csv",
industrial_energy_demand_per_country="resources/industrial_energy_demand_per_country.csv" industrial_energy_demand_per_country="resources/industrial_energy_demand_per_country.csv"
threads: 1 threads: 1
resources: mem_mb=1000 resources: mem_mb=1000
script: 'scripts/build_industrial_demand_per_country.py' script: 'scripts/build_industrial_energy_demand_per_country.py'
rule build_industrial_demand: rule build_industrial_demand:

84
scripts/build_industrial_energy_demand_per_country.py Normal file
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@ -0,0 +1,84 @@
import pandas as pd
import numpy as np
tj_to_ktoe = 0.0238845
ktoe_to_twh = 0.01163
eb_base_dir = "data/eurostat-energy_balances-may_2018_edition"
jrc_base_dir = "data/jrc-idees-2015"
# import EU ratios df as csv
industry_sector_ratios=pd.read_csv(snakemake.input.industry_sector_ratios, sep=';', index_col=0)
#material demand per country and industry (kton/a)
countries_production = pd.read_csv(snakemake.input.industrial_production_per_country, index_col=0)
#Annual energy consumption in Switzerland by sector in 2015 (in TJ)
#From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
#http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
dic_Switzerland ={'Iron and steel': 7889.,
'Chemicals Industry': 26871.,
'Non-metallic mineral products': 15513.+3820.,
'Pulp, paper and printing': 12004.,
'Food, beverages and tobacco': 17728.,
'Non Ferrous Metals': 3037.,
'Transport Equipment': 14993.,
'Machinery Equipment': 4724.,
'Textiles and leather': 1742.,
'Wood and wood products': 0.,
'Other Industrial Sectors': 10825.,
'current electricity': 53760.}
eb_names={'NO':'Norway', 'AL':'Albania', 'BA':'Bosnia and Herzegovina',
'MK':'FYR of Macedonia', 'GE':'Georgia', 'IS':'Iceland',
'KO':'Kosovo', 'MD':'Moldova', 'ME':'Montenegro', 'RS':'Serbia',
'UA':'Ukraine', 'TR':'Turkey', }
jrc_names = {"GR" : "EL",
"GB" : "UK"}
#final energy consumption per country and industry (TWh/a)
countries_df = countries_production.dot(industry_sector_ratios.T)
countries_df*= 0.001 #GWh -> TWh (ktCO2 -> MtCO2)
non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
# save current electricity consumption
for country in countries_df.index:
if country in non_EU:
if country == 'CH':
countries_df.loc[country, 'current electricity']=dic_Switzerland['current electricity']*tj_to_ktoe*ktoe_to_twh
else:
excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,eb_names[country]),
sheet_name='2016', index_col=1,header=0, skiprows=1 ,squeeze=True)
countries_df.loc[country, 'current electricity'] = excel_balances.loc['Industry', 'Electricity']*ktoe_to_twh
else:
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(jrc_base_dir,jrc_names.get(country,country)),
sheet_name='Ind_Summary',index_col=0,header=0,squeeze=True) # the summary sheet
s_out = excel_out.iloc[27:48,-1]
countries_df.loc[country, 'current electricity'] = s_out['Electricity']*ktoe_to_twh
print(countries_df.loc[country, 'current electricity'])
rename_sectors = {'elec':'electricity',
'biomass':'solid biomass',
'heat':'low-temperature heat'}
countries_df.rename(columns=rename_sectors,inplace=True)
countries_df.index.name = "TWh/a (MtCO2/a)"
countries_df.to_csv(snakemake.output.industrial_energy_demand_per_country,
float_format='%.2f')

100
scripts/build_industrial_demand_per_country.py → scripts/build_industrial_production_per_country.py
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@ -1,27 +1,14 @@
#%matplotlib inline
import pandas as pd import pandas as pd
import numpy as np import numpy as np
tj_to_ktoe = 0.0238845
ktoe_to_twh = 0.01163
jrc_base_dir = "data/jrc-idees-2015" jrc_base_dir = "data/jrc-idees-2015"
eb_base_dir = "data/eurostat-energy_balances-may_2018_edition" eb_base_dir = "data/eurostat-energy_balances-may_2018_edition"
tj_to_ktoe = 0.0238845
ktoe_to_twh = 0.01163
# import EU ratios df as csv
df=pd.read_csv(snakemake.input.industry_sector_ratios, sep=';', index_col=0)
sub_sheet_name_dict = { 'Iron and steel':'ISI', sub_sheet_name_dict = { 'Iron and steel':'ISI',
'Chemicals Industry':'CHI', 'Chemicals Industry':'CHI',
'Non-metallic mineral products': 'NMM', 'Non-metallic mineral products': 'NMM',
@ -38,15 +25,15 @@ index = ['elec','biomass','methane','hydrogen','heat','naphtha','process emissio
non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL'] non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
rename = {"GR" : "EL", jrc_names = {"GR" : "EL",
"GB" : "UK"} "GB" : "UK"}
eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI', 'CZ', eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI',
'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV', 'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV', 'CZ',
'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI'] + ['CY','MT'] 'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI', 'CY', 'MT']
countries = non_EU + [rename.get(eu,eu) for eu in eu28] countries = non_EU + eu28
sectors = ['Iron and steel','Chemicals Industry','Non-metallic mineral products', sectors = ['Iron and steel','Chemicals Industry','Non-metallic mineral products',
@ -68,18 +55,12 @@ sect2sub = {'Iron and steel':['Electric arc','Integrated steelworks'],
subsectors = [ss for s in sectors for ss in sect2sub[s]] subsectors = [ss for s in sectors for ss in sect2sub[s]]
#final energy consumption per country and industry (TWh/a)
countries_df = pd.DataFrame(index=countries,
columns=index,
dtype=float)
#material demand per country and industry (kton/a) #material demand per country and industry (kton/a)
countries_demand = pd.DataFrame(index=countries, countries_demand = pd.DataFrame(index=countries,
columns=subsectors, columns=subsectors,
dtype=float) dtype=float)
out_dic ={'Electric arc': 'Electric arc', out_dic ={'Electric arc': 'Electric arc',
'Integrated steelworks': 'Integrated steelworks', 'Integrated steelworks': 'Integrated steelworks',
'Basic chemicals': 'Basic chemicals (kt ethylene eq.)', 'Basic chemicals': 'Basic chemicals (kt ethylene eq.)',
@ -128,10 +109,11 @@ dic_sec_summary = {'Iron and steel': 'Iron and steel',
'Other Industrial Sectors': ' Other Industrial Sectors'} 'Other Industrial Sectors': ' Other Industrial Sectors'}
#countries=['CH'] #countries=['CH']
dic_countries={'NO':'Norway', 'AL':'Albania', 'BA':'Bosnia and Herzegovina', eb_names={'NO':'Norway', 'AL':'Albania', 'BA':'Bosnia and Herzegovina',
'MK':'FYR of Macedonia', 'GE':'Georgia', 'IS':'Iceland', 'MK':'FYR of Macedonia', 'GE':'Georgia', 'IS':'Iceland',
'KO':'Kosovo', 'MD':'Moldova', 'ME':'Montenegro', 'RS':'Serbia', 'KO':'Kosovo', 'MD':'Moldova', 'ME':'Montenegro', 'RS':'Serbia',
'UA':'Ukraine', 'TR':'Turkey', } 'UA':'Ukraine', 'TR':'Turkey', }
dic_sec ={'Iron and steel':'Iron & steel industry', dic_sec ={'Iron and steel':'Iron & steel industry',
'Chemicals Industry': 'Chemical and Petrochemical industry', 'Chemicals Industry': 'Chemical and Petrochemical industry',
'Non-metallic mineral products': 'Non-ferrous metal industry', 'Non-metallic mineral products': 'Non-ferrous metal industry',
@ -164,7 +146,6 @@ dic_Switzerland ={'Iron and steel': 7889.,
dic_sec_position={} dic_sec_position={}
for country in countries: for country in countries:
countries_df.loc[country] = 0.
countries_demand.loc[country] = 0. countries_demand.loc[country] = 0.
print(country) print(country)
for sector in sectors: for sector in sectors:
@ -174,7 +155,7 @@ for country in countries:
else: else:
# estimate physical output # estimate physical output
#energy consumption in the sector and country #energy consumption in the sector and country
excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,dic_countries[country]), excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,eb_names[country]),
sheet_name='2016', index_col=2,header=0, skiprows=1 ,squeeze=True) sheet_name='2016', index_col=2,header=0, skiprows=1 ,squeeze=True)
e_country = excel_balances.loc[dic_sec[sector], 'Total all products'] e_country = excel_balances.loc[dic_sec[sector], 'Total all products']
@ -192,62 +173,19 @@ for country in countries:
s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],-1] s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],-1]
for subsector in sect2sub[sector]: for subsector in sect2sub[sector]:
output = ratio_country_EU28*s_out[out_dic[subsector]] countries_demand.loc[country,subsector] = ratio_country_EU28*s_out[out_dic[subsector]]
countries_demand.loc[country,subsector] = output
for ind in index:
countries_df.loc[country, ind] += float(output*df.loc[ind, subsector]) # kton * MWh = GWh (# kton * tCO2 = ktCO2)
else: else:
# read the input sheets # read the input sheets
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 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
s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],-1] s_out = excel_out.iloc[loc_dic[sector][0]:loc_dic[sector][1],-1]
for subsector in sect2sub[sector]: for subsector in sect2sub[sector]:
output = s_out[out_dic[subsector]] countries_demand.loc[country,subsector] = s_out[out_dic[subsector]]
countries_demand.loc[country,subsector] = output
for ind in index:
countries_df.loc[country, ind] += output*df.loc[ind, subsector] #kton * MWh = GWh (# kton * tCO2 = ktCO2)
countries_df*= 0.001 #GWh -> TWh (ktCO2 -> MtCO2) countries_demand.index.name = "kton/a"
# save current electricity consumption countries_demand.to_csv(snakemake.output.industrial_production_per_country,
for country in countries:
if country in non_EU:
if country == 'CH':
countries_df.loc[country, 'current electricity']=dic_Switzerland['current electricity']*tj_to_ktoe*ktoe_to_twh
else:
excel_balances = pd.read_excel('{}/{}.XLSX'.format(eb_base_dir,dic_countries[country]),
sheet_name='2016', index_col=1,header=0, skiprows=1 ,squeeze=True)
countries_df.loc[country, 'current electricity'] = excel_balances.loc['Industry', 'Electricity']*ktoe_to_twh
else:
excel_out = pd.read_excel('{}/JRC-IDEES-2015_Industry_{}.xlsx'.format(jrc_base_dir,country),
sheet_name='Ind_Summary',index_col=0,header=0,squeeze=True) # the summary sheet
s_out = excel_out.iloc[27:48,-1]
countries_df.loc[country, 'current electricity'] = s_out['Electricity']*ktoe_to_twh
print(countries_df.loc[country, 'current electricity'])
# save df as csv
for ind in index:
countries_df[ind]=countries_df[ind].astype('float')
countries_df = countries_df.round(3)
countries_df.rename(index={value : key for key,value in rename.items()},inplace=True)
rename_sectors = {'elec':'electricity',
'biomass':'solid biomass',
'heat':'low-temperature heat'}
countries_df.rename(columns=rename_sectors,inplace=True)
countries_df.to_csv(snakemake.output.industrial_energy_demand_per_country,
float_format='%.2f')
countries_demand.to_csv(snakemake.output.industrial_demand_per_country,
float_format='%.2f') float_format='%.2f')

1
scripts/build_industry_sector_ratios.py
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@ -1372,4 +1372,5 @@ sources=['elec','biomass', 'methane', 'hydrogen', 'heat','naphtha']
df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material df.loc[sources,sector] = df.loc[sources,sector]*conv_factor/s_out['Physical output (index)'] # unit MWh/t material
df.index.name = "MWh/tMaterial"
df.to_csv('resources/industry_sector_ratios.csv', sep=';') df.to_csv('resources/industry_sector_ratios.csv', sep=';')