National generation capacity: Processing notebook
This Notebook is part of the National Generation Capacity Datapackage of Open Power System Data.

1. Introductory notes

The script processes the compiled nationally aggregated generation capacity for European countries. Due to varying formats and data specifications of references for national generation capacity, the script firstly focuses on rearranging the manually compiled data. Thus, the script itself does not collect, select, download or manage data from original sources. Secondly, international data sources, such as EUROSTAT and ENTSO-E, are directly downloaded from original web sources and complement the initial data set.

2. Script setup

In [ ]:
# some functions and classes that are defined in seperate files
import functions.helper_functions as func
import functions.soaf as soaf

# core packages
import os
import pandas as pd
import numpy as np

# packages to copy files, write sqllite data bases and manipulate excel files
import shutil
import sqlite3
import openpyxl
from openpyxl.styles import PatternFill, colors, Font, Alignment
from openpyxl.utils import get_column_letter

import yaml
import json

3. Data download and processing

We compile data from different national and international sources. Firstly, national data sources are manually compiled due to varying data formats and specifications. Secondly, international sources are compiled directly and appended to the compiled data set. The international data sources comprise:

In the following section, the data sets are downloaded and uploaded to Python.

3.1 Manually compiled dataset

The manually compiled dataset is imported and rearranged to a DataFrame for further processing. The dataset comprises for each European country and specified generation technology different data entries, which are based on different sources. As these sources differ by country and year, information on the corresponding reference are directly given with the data entry.

In [ ]:
data_file = 'National_Generation_Capacities.xlsx'
filepath = os.path.join('input', data_file)

# Read data into pandas
data_raw = pd.read_excel(filepath,
                         sheet_name='Summary',
                         header=None,
                         na_values=['-'],
                         skiprows=0)




# Deal with merged cells from Excel: fill first three rows with information
data_raw.iloc[0:2] = data_raw.iloc[0:2].fillna(method='ffill', axis=1)

# Set index for rows
data_raw = data_raw.set_index([0])
data_raw.index.name = 'technology'

# Extract energylevels from raw data for later use
energylevels_raw = data_raw.iloc[:, 0:5]

energylevels_raw.head()
In [ ]:
# Delete definition of energy levels from raw data
data_raw.drop(data_raw.columns[[0, 1, 2, 3, 4, 5]], axis=1, inplace=True)

level_names = ['country', 'type', 'year', 'source',
               'source_type', 'weblink', 'capacity_definition']
# Set multiindex column names
data_raw.columns = pd.MultiIndex.from_arrays(data_raw[:7].values,
                                             names=level_names)

# Remove 3 rows which are already used as column names
data_raw = data_raw[pd.notnull(data_raw.index)]

# Extract the ordering of technologies
technology_order = data_raw.index.str.replace('- ', '').values.tolist()

data_raw.head()
In [ ]:
# Reshape dataframe to list
data_opsd = pd.DataFrame(data_raw.stack(level=level_names))

# Reset index for dataframe
data_opsd.reset_index(inplace=True)
data_opsd['technology'] = data_opsd['technology'].str.replace('- ', '')

data_opsd.rename(columns={0: 'capacity'}, inplace=True)
data_opsd['capacity'] = pd.to_numeric(data_opsd['capacity'], errors='coerce')

# For some source, permission to publish data
banlist = ['ELIA', 'BMWi', 'Mavir']
davail = 'data available, but cannot be provided'
data_opsd.loc[data_opsd['source'].isin(banlist), 'comment'] = davail

data_opsd.head()

3.2 EUROSTAT data

EUROSTAT publishes annual structural data on national electricity generation capacities for European countries. The dataset is available in the EUROSTAT database within the category 'Environment and Energy' (nrg_113a).

In [ ]:
url_eurostat = ('http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/'
                'BulkDownloadListing?sort=1&downfile=data%2Fnrg_113a.tsv.gz')


filepath_eurostat = func.downloadandcache(url_eurostat, 'Eurostat.tsv.gz', 'Eurostat')



data_eurostat = pd.read_csv(filepath_eurostat,
                               compression='gzip',
                               sep='\t|,',
                               engine='python'
                               )
data_eurostat.head()
In [ ]:
id_vars = ['unit', 'product','indic_nrg', 'geo\\time']
data_eurostat = pd.melt(data_eurostat, id_vars=id_vars,
                        var_name='year', value_name='value')

data_eurostat.head()
In [ ]:
data_definition = pd.read_csv(os.path.join('input', 'definition_EUROSTAT_indic.txt'),
                              header=None,
                              names=['indic', 'description',
                                     'energy source'],
                              sep='\t')

data_eurostat = data_eurostat.merge(data_definition,
                                    how='left',
                                    left_on='indic_nrg',
                                    right_on='indic')

The classification of generation capacities in the EUROSTAT dataset is specified in Regulation (EC) No 1099/2008 (Annex B, 3.3). The available EUROSTAT dataset nrg_113a covers the following indicators:

indic_nrg Description Technology in OPSD
12_1176011 Electrical capacity, main activity producers - Combustible Fuels Fossil fuels & bioenergy
12_1176012 Electrical capacity, autoproducers - Combustible Fuels Fossil fuels & bioenergy
12_1176061 Electrical capacity, main activity producers - Mixed plants
12_1176101 Electrical capacity, main activity producers - Other Sources
12_1176102 Electrical capacity, autoproducers - Other Sources
12_1176111 Electrical capacity, main activity producers - Steam
12_1176112 Electrical capacity, autoproducers - Steam
12_1176121 Electrical capacity, main activity producers - Gas Turbine
12_1176122 Electrical capacity, autoproducers - Gas Turbine
12_1176131 Electrical capacity, main activity producers - Combined Cycle
12_1176132 Electrical capacity, autoproducers - Combined Cycle
12_1176141 Electrical capacity, main activity producers - Internal Combustion
12_1176142 Electrical capacity, autoproducers - Internal Combustion
12_1176401 Electrical capacity, main activity producers - Other Type of Generation
12_1176402 Electrical capacity, autoproducers - Other Type of Generation
12_1176253 Net maximum capacity - Municipal Wastes Non-renewable waste
12_1176263 Net maximum capacity - Wood/Wood Wastes/Other Solid Wastes Other bioenergy and renewable waste
12_1176273 Net maximum capacity - Biogases Biomass and biogas
12_1176283 Net maximum capacity - Industrial Wastes (non-renewable) Non-renewable waste
12_1176343 Net maximum capacity - Liquid Biofuels Biomass and biogas
12_1176031 Electrical capacity, main activity producers - Nuclear Nuclear
12_1176032 Electrical capacity, autoproducers - Nuclear Nuclear
12_1176051 Electrical capacity, main activity producers - Hydro Hydro
12_1176052 Electrical capacity, autoproducers - Hydro Hydro
12_1176071 Net electrical capacity, main activity producers - Pure Pumped Hydro Pumped storage
12_1176072 Net electrical capacity, autoproducers - Pure Pumped Hydro Pumped storage
12_117615 Net maximum capacity - Hydro <1 MW
12_117616 Net maximum capacity - Hydro >= 1 MW and <= 10 MW
12_117617 Net maximum capacity - Hydro 10 MW and over
12_1176301 Electrical capacity, main activity producers - Tide, wave and ocean Marine
12_1176302 Electrical capacity, autoproducers - Tide, wave and ocean Marine
12_1176303 Net maximum capacity - Tide, Wave, Ocean
12_1176081 Electrical capacity, main activity producers - Geothermal Geothermal
12_1176082 Electrical capacity, autoproducers - Geothermal Geothermal
12_1176083 Net maximum capacity - Geothermal
12_1176091 Electrical capacity, main activity producers - Wind Wind
12_1176092 Electrical capacity, autoproducers - Wind Wind
12_1176233 Net maximum capacity - Solar Photovoltaic Photovoltaics
12_1176243 Net maximum capacity - Solar Thermal Electric Concentrated solar power

Bold rows indicate top level classes within the EUROSTAT classification, whereas normal and italic rows cover different kinds of subclassifications. Especially within the top level 'Combustible fuels' different kinds of subcategorizations based on fuel or technology are available. Simarily, 'Hydro' is differentiated by type (e.g. pumped-hydro storage) or capacity classes. Italic rows are not further considered within the OPSD dataset due to the mismatch with existing technology classes.

In [ ]:
data_eurostat = data_eurostat[data_eurostat['energy source'].isnull() == False]

values_as_string = data_eurostat['value'].astype(str)
string_values = values_as_string.str.split(' ', 1).str[0]
string_values.replace(':', np.nan, inplace=True)
subset_nan = string_values.isnull()

data_eurostat['value'] = string_values
data_eurostat['year'] = data_eurostat['year'].astype(int)
data_eurostat['value'] = data_eurostat['value'].astype(float)

data_eurostat.head()
In [ ]:
data_eurostat = data_eurostat.drop(['unit', 'product', 'indic_nrg',
                                    'indic', 'description'], axis=1)

data_eurostat = data_eurostat.rename(columns={'geo\\time': 'country',
                                              'energy source': 'technology',
                                              'value': 'capacity'})
In [ ]:
data_eurostat['country'].replace({'UK': 'GB', 'EL': 'GR'}, inplace=True)
drop_list = data_eurostat[data_eurostat['country'].isin(['EU28','EA19'])].index
data_eurostat.drop(drop_list, inplace=True)

by_columns = ['technology', 'year', 'country']
data_eurostat = pd.DataFrame(data_eurostat.groupby(by_columns)['capacity'].sum())
data_eurostat_isnull = data_eurostat['capacity'].isnull() == True
data_eurostat.reset_index(inplace=True)

data_eurostat.head()
In [ ]:
eurostat_pivot = data_eurostat.pivot_table(values='capacity',
                                 index=['country','year'],
                                 columns='technology')

eurostat_pivot.head()
In [ ]:
eurostat_pivot['Differently categorized solar'] = 0
eurostat_pivot['Solar'] = eurostat_pivot[['Photovoltaics', 'Concentrated solar power']].sum(axis=1)

eurostat_pivot['Differently categorized wind'] = eurostat_pivot['Wind']

bio_arr = ['Biomass and biogas', 'Other bioenergy and renewable waste']
eurostat_pivot['Bioenergy and renewable waste'] = eurostat_pivot[bio_arr].sum(axis=1)

res_arr = ['Hydro', 'Wind', 'Solar', 'Geothermal', 'Marine', 'Bioenergy and renewable waste']
eurostat_pivot['Renewable energy sources'] = eurostat_pivot[res_arr].sum(axis=1)


eurostat_pivot['Fossil fuels'] = eurostat_pivot['Fossil fuels'] - eurostat_pivot['Bioenergy and renewable waste']
eurostat_pivot['Differently categorized fossil fuels'] = eurostat_pivot['Fossil fuels']\
                                                         - eurostat_pivot['Non-renewable waste']

total_arr = ['Fossil fuels','Nuclear','Renewable energy sources']
eurostat_pivot['Total'] = eurostat_pivot[total_arr].sum(axis=1)

eurostat_pivot.head()
In [ ]:
data_eurostat = eurostat_pivot.stack().reset_index().rename(columns={0: 'capacity'})

data_eurostat['source'] = 'EUROSTAT'
data_eurostat['source_type'] = 'Statistical Office'
data_eurostat['capacity_definition'] = 'Unknown'
data_eurostat['type'] = 'Installed capacity in MW'
data_eurostat['weblink'] = url_eurostat

data_eurostat.head()

3.3 ENTSO-E data

The ENTSO-E publishes annual data on national generation capacites in different specifications and formats. We use two relevant data sources from the ENTSOE-E, which comprises firstly statistical data within the Data Portal (up to 2015) or ENTSO-E Transparency Platform, and secondly datasets compiled within the ENTSO-E System Outlook & Adequacy Forecast (SO&AF). The ENTSO-E Transparency Platform is currently not implemented as a data source for national generation capacities.

The advantage of the ENTSO-E SO&AF is the higher granularity of the data with respect to the main fuel or technology. However, as the SO&AF provides a forecast on future system conditions in particular peak hours, the dataset also accounts for expected capacity changes throughout the years. Therefore, we only consider years which are closest to the publication year of the respective SO&AF.

3.3.1 ENTSO-E Statistical Data

In the following, we use the statistical data available in the Data Portal (up to 2015).

In [ ]:
url_entsoe = 'https://docstore.entsoe.eu/Documents/Publications/Statistics/NGC_2010-2015.xlsx'

filepath_entsoe = func.downloadandcache(url_entsoe, 'Statistics.xls',
                                     os.path.join('ENTSO-E','Data Portal 2010-2015')
                                     )

data_entsoe_raw = pd.read_excel(filepath_entsoe)

data_entsoe_raw.head()
In [ ]:
dict_energy_source = {'hydro': 'Hydro',
                      'of which storage': 'Reservoir',
                      'of which run of river': 'Run-of-river',
                      'of which pumped storage': 'Pumped storage',
                      'nuclear': 'Nuclear',
                      'of which wind': 'Wind',
                      'of which solar': 'Solar',
                      'of which biomass': 'Biomass and biogas',
                      'fossil_fuels': 'Fossil fuels',
                      'other': 'Other or unspecified energy sources',
                      "Country": "country",
                      'fossil_fueals': 'Fossil fuels'}

data_entsoe_raw.rename(columns=dict_energy_source,
                       inplace=True)

data_entsoe_raw.drop(columns='representativity', inplace=True)

data_entsoe_raw.head()
In [ ]:
data_entsoe_raw['Differently categorized solar'] = data_entsoe_raw['Solar']
data_entsoe_raw['Differently categorized wind'] = data_entsoe_raw['Wind']
data_entsoe_raw['Bioenergy and renewable waste'] = data_entsoe_raw['Biomass and biogas']
data_entsoe_raw['Differently categorized fossil fuels'] = data_entsoe_raw['Fossil fuels']


data_entsoe_raw['Differently categorized hydro'] = (
        data_entsoe_raw['Hydro']
        - data_entsoe_raw['Run-of-river']
        - data_entsoe_raw['Reservoir']
        - data_entsoe_raw['Pumped storage'])

data_entsoe_raw['Differently categorized renewable energy sources'] = (
        data_entsoe_raw['renewable']
        - data_entsoe_raw['Wind']
        - data_entsoe_raw['Solar']
        - data_entsoe_raw['Biomass and biogas'])

data_entsoe_raw.drop(columns='renewable', inplace=True)

data_entsoe_raw['Renewable energy sources'] = (
        data_entsoe_raw['Hydro']
        + data_entsoe_raw['Wind']
        + data_entsoe_raw['Solar']
        + data_entsoe_raw['Bioenergy and renewable waste']
        + data_entsoe_raw['Differently categorized renewable energy sources'])

data_entsoe_raw['Total'] = (
        data_entsoe_raw['Renewable energy sources']
        + data_entsoe_raw['Nuclear']
        + data_entsoe_raw['Fossil fuels']
        + data_entsoe_raw['Other or unspecified energy sources'])

data_entsoe = pd.melt(data_entsoe_raw,
                      id_vars=['country', 'year'],
                      var_name='technology',
                      value_name='capacity')

data_entsoe.head()
In [ ]:
data_entsoe['country'].replace('NI', 'GB', inplace=True)
# set negative capacities to zero
data_entsoe.loc[data_entsoe['capacity'] < 0, 'capacity'] = 0

data_entsoe['source'] = 'ENTSO-E Data Portal'
data_entsoe['source_type'] = 'Other association'
data_entsoe['capacity_definition'] = 'Net capacity'
data_entsoe['type'] = 'Installed capacity in MW'

data_entsoe.head()

3.3.2 ENTSO-E SO&AF data

In [ ]:
soafs = [soaf.SoafDataRaw('https://www.entsoe.eu/fileadmin/user_upload/_library/SDC/SOAF/SO_AF_2011_-_2025_.zip',
                          'SO_AF_2011_-_2025_.zip',
                          'SO&AF 2011 - 2025 Scenario B.xls',
                          2011),

        soaf.SoafDataRaw('https://www.entsoe.eu/fileadmin/user_upload/_library/SDC/SOAF/120705_SOAF_2012_Dataset.zip',
                         '120705_SOAF_2012_Dataset.zip',
                         'SOAF 2012 Scenario B.xls',
                         2012),
                             
        soaf.SoafDataRaw('https://www.entsoe.eu/fileadmin/user_upload/_library/publications/entsoe/So_AF_2013-2030/130403_SOAF_2013-2030_dataset.zip',
                         '130403_SOAF_2013-2030_dataset.zip',
                         'ScB.xls',
                         2013),
                             
        soaf.SoafDataRaw('https://www.entsoe.eu/Documents/SDC%20documents/SOAF/140602_SOAF%202014_dataset.zip',
                         '140602_SOAF%202014_dataset.zip',
                         'ScB.xlsx',
                         2014),
                             
        soaf.SoafDataRaw('https://www.entsoe.eu/Documents/Publications/SDC/data/SO_AF_2015_dataset.zip',
                         'SO_AF_2015_dataset.zip',
                         os.path.join('SO&AF 2015 dataset', 'ScB_publication.xlsx'),
                         2016)]


data_soaf = pd.concat([s.transformed_df for s in soafs])

# Correct that in the Soaf2015 datatset the year column is 2016 instead of 2015
data_soaf['year'].replace({2016 : 2015}, inplace=True)

data_soaf.head()
In [ ]:
soaf_unstacked = func.unstackData(data_soaf)

soaf_unstacked['Differently categorized solar'] = soaf_unstacked['Solar']

soaf_unstacked['Differently categorized wind'] = soaf_unstacked['Wind']\
                                                - soaf_unstacked['Offshore']\
                                                - soaf_unstacked['Onshore']

soaf_unstacked['Differently categorized hydro'] = soaf_unstacked['Hydro']\
                                                - soaf_unstacked['Run-of-river']\
                                                - soaf_unstacked['Reservoir including pumped storage']
                                                

soaf_unstacked['Bioenergy and renewable waste'] = soaf_unstacked['Biomass and biogas']
                                                
soaf_unstacked['Differently categorized renewable energy sources'] = (
                                          soaf_unstacked['renewable']
                                        - soaf_unstacked['Wind']
                                        - soaf_unstacked['Solar']
                                        - soaf_unstacked['Biomass and biogas'])

soaf_unstacked.drop(columns='renewable', inplace=True)

subtract_fossils_arr = ['Lignite','Hard coal','Oil','Natural gas','Mixed fossil fuels']

soaf_unstacked['Differently categorized fossil fuels'] = soaf_unstacked['Fossil fuels']\
                                                        - soaf_unstacked[subtract_fossils_arr].sum(axis=1)


res_arr = ['Solar','Wind','Bioenergy and renewable waste','Hydro',
           'Differently categorized renewable energy sources']

soaf_unstacked['Renewable energy sources'] = soaf_unstacked[res_arr].sum(axis=1)

total_arr = ['Renewable energy sources','Fossil fuels','Nuclear',
             'Other or unspecified energy sources']

soaf_unstacked['Total'] = soaf_unstacked[total_arr].sum(axis=1)

soaf_unstacked.head()
In [ ]:
data_soaf = func.restackData(soaf_unstacked)

data_soaf.loc[data_soaf['capacity'] < 0, 'capacity'] = 0

data_soaf['source'] = 'ENTSO-E SOAF'
data_soaf['type'] = 'Installed capacity in MW'
data_soaf['capacity_definition'] = 'Net capacity'
data_soaf['source_type'] = 'Other association'
data_soaf['weblink'] = url_entsoe

data_soaf.head()

3.3.3 ENTSO-E Transparency Platform

In [ ]:
# file pattern for the single years
filenamepattern = '_1_InstalledGenerationCapacityAggregated.csv'
list_of_data_tables = [] # list to append

# iterate over the years from 2015 to 2020
for i in range(2015,2021):
    filepath = os.path.join('input',
                            'ENTSO-E',
                            'Transparency',
                            'InstalledGenerationCapacityAggregated',
                            str(i) + filenamepattern)
    
    list_of_data_tables.append(pd.read_csv(filepath, delimiter="\t", encoding = "UTF-16"))

# merge the datasets of the single of files into one pandas dataframe
data_transparency = pd.concat(list_of_data_tables, ignore_index=True)

data_transparency.head()
In [ ]:
# rename columns according to the opsd standards
data_transparency.rename(columns={'ProductionType': 'technology',
                                  'AggregatedInstalledCapacity': 'capacity',
                                  'MapCode': 'country',
                                  'Year': 'year'}, inplace=True)

# drop non relevant columns
data_transparency = data_transparency.filter(items=['technology','capacity','country','year'], axis=1)
# drop countries that are not part of opsd
data_transparency = data_transparency[data_transparency['country'].isin(data_opsd.country.unique())]

data_transparency.head()
In [ ]:
# adapt energy source notation
dict_energy_source = {'Biomass': 'Biomass and biogas',
                      'Fossil Brown coal/Lignite': 'Lignite',
                      'Fossil Coal-derived gas': 'Mixed fossil fuels',
                      'Fossil Gas': 'Natural gas',
                      'Fossil Hard coal': 'Hard coal',
                      'Fossil Oil': 'Oil',
                      'Fossil Oil shale': 'Oil',
                      'Fossil Peat': 'Other fossil fuels',
                      'Hydro Pumped Storage': 'Pumped storage',
                      'Hydro Run-of-river and poundage': 'Run-of-river',
                      'Hydro Water Reservoir': 'Reservoir',
                      'Other': 'Other or unspecified energy sources',
                      'Other renewable': 'Differently categorized renewable energy sources',
                      'Waste': 'Other bioenergy and renewable waste',
                      'Wind Offshore': 'Offshore',
                      'Wind Onshore': 'Onshore',
                      ' ': np.nan}

data_transparency['technology'].replace(dict_energy_source, inplace=True)


data_transparency.head()
In [ ]:
# add missing categories
transparency_pivot = data_transparency.pivot_table(values='capacity',
                                                   index=['country','year'],
                                                   columns='technology')

# technology level
transparency_pivot['Differently categorized solar'] = transparency_pivot['Solar']
transparency_pivot['Differently categorized natural gas'] = transparency_pivot['Natural gas']
transparency_pivot['Non-renewable waste'] = 0
transparency_pivot['Differently categorized fossil fuels'] = 0

# level 3
hydro_arr = ['Pumped storage', 'Reservoir', 'Run-of-river']
transparency_pivot['Hydro'] = transparency_pivot[hydro_arr].sum(axis=1)

wind_arr = ['Onshore', 'Offshore']
transparency_pivot['Wind'] = transparency_pivot[wind_arr].sum(axis=1)

# level 2
bio_arr = ['Biomass and biogas', 'Other bioenergy and renewable waste']
transparency_pivot['Bioenergy and renewable waste'] = transparency_pivot[bio_arr].sum(axis=1)

#level 1
res_arr = ['Hydro', 'Wind', 'Solar', 'Geothermal', 'Marine',
           'Bioenergy and renewable waste', 'Differently categorized renewable energy sources']
transparency_pivot['Renewable energy sources'] = transparency_pivot[res_arr].sum(axis=1)

fossil_arr = ['Lignite', 'Hard coal', 'Oil', 'Natural gas', 'Mixed fossil fuels', 'Other fossil fuels']
transparency_pivot['Fossil fuels'] = transparency_pivot[fossil_arr].sum(axis=1)

# level 0
total_arr = ['Fossil fuels','Nuclear','Renewable energy sources', 'Other or unspecified energy sources']
transparency_pivot['Total'] = transparency_pivot[total_arr].sum(axis=1)

transparency_pivot.reset_index(inplace=True)

transparency_pivot.head()
In [ ]:
data_transparency = pd.melt(transparency_pivot,
                      id_vars=['country', 'year'],
                      var_name='technology',
                      value_name='capacity')

data_transparency = data_transparency.loc[data_transparency["year"] < 2020, :]

data_transparency['source'] = 'ENTSO-E Transparency Platform'
data_transparency['source_type'] = 'Other association'
data_transparency['capacity_definition'] = 'Net capacity'
data_transparency['type'] = 'Installed capacity in MW'
data_transparency['weblink'] = ('https://transparency.entsoe.eu/generation'
                                '/r2/installedGenerationCapacityAggregation/show')

data_transparency.head()

3.3.4 ENTSO-E Power Statistics

In [ ]:
row_of_year = {2014: 9,
               2015: 53,
               2016: 97,
               2017: 141,
               2018: 185}

dataframes = []

for year, row in row_of_year.items():
    # read the dataframe for each year
    power_statistics_raw = pd.read_excel(os.path.join('input',
                                                      'ENTSO-E',
                                                      'Power Statistics',
                                                      'NGC.xlsx'),
                                         header=[0,1], 
                                         sheet_name='NGC',
                                         skiprows=row,
                                         nrows=42)
    
    # drop non relevant columns
    power_statistics_raw.drop(columns='Coverage ratio in %', level=1, inplace=True)
    power_statistics_raw.drop(columns=['Unnamed: 1_level_1','Unnamed: 2_level_1','Unnamed: 3_level_1','Unnamed: 4_level_1'], level=1, inplace=True)

    # get rid of multi index
    df = power_statistics_raw.set_index(year).stack().reset_index().drop('level_1', axis=1)
    
    # remove leftovers of multi index in the index column
    df["technology"] = df[year].apply(lambda x: x[0])
    df.drop(columns=year, inplace=True)
    
    # stack df to the opsd standard format
    stacked_df = df.melt(id_vars='technology', var_name='country', value_name='capacity')
    
    # add information about the year
    stacked_df['year'] = year
    
    # append to the main list of dataframes
    dataframes.append(stacked_df)
    
power_statistics = pd.concat(dataframes)
power_statistics.head()
In [ ]:
# drop countries that are not covered in opsd
opsd_countries = data_opsd.country.unique()
drop_list_country = power_statistics.loc[~power_statistics['country'].isin(opsd_countries)].index.to_list()
power_statistics.drop(drop_list_country, inplace=True)

# technology classes to be dropped
tech_to_drop = ['Non-Renewable', 'Fossil fuels', 'Renewable','Non-renwable hydro',
                'Total Waste', 'Bio', 'Renewable Hydro', 'Comments', 'Total NGC']

drop_list_tech = power_statistics.loc[power_statistics['technology'].isin(tech_to_drop)].index.to_list()
power_statistics.drop(drop_list_tech, inplace=True)

# replace string with values that can be used in math operations 
power_statistics['capacity'].replace(to_replace='Not Expected', value=0, inplace=True)
power_statistics['capacity'].replace(to_replace='Not Available', value=np.nan, inplace=True)

power_statistics.head()
In [ ]:
# Not included because already categorized in OPSD standard:
# Nuclear, Solar, Geothermal, Wind
dict_energy_source = {'Of which hydro pure pumped storage':'Pumped storage',
                      'Of which Hydro mixed pumped storage (non renewable part)':'Pumped storage',
                      'Of which Fossil Brown coal/Lignite':'Lignite',
                      'Of which Fossil Coal-derived gas':'Differently categorized fossil fuels',
                      'Of which Fossil Gas':'Natural gas',
                      'Of which Fossil Hard coal':'Hard coal', 
                      'Of which Fossil Oil':'Oil',
                      'Of which Fossil Oil shale':'Oil', 
                      'Of which Fossil Peat':'Differently categorized fossil fuels',
                      'Of which Mixed fuels':'Mixed fossil fuels', 
                      'Of which Other fossil fuels':'Other fossil fuels',
                      'Non-renewable Waste':'Non-renewable waste', 
                      'Other non-renewable':'Differently categorized fossil fuels',                      
                      'Of which Wind offshore':'Offshore', 
                      'Of which Wind onshore':'Onshore', 
                      'Of which Solar PV':'Photovoltaics', 
                      'Of which Solar Thermal':'Differently categorized solar', 
                      'Of which Biomass':'Biomass and biogas', 
                      'Of which Biogas':'Biomass and biogas', 
                      'Renewable Waste':'Other bioenergy and renewable waste', 
                      'Of which Hydro Pure storage':'Reservoir',
                      'Of which Hydro Run-of-river and pondage':'Run-of-river',
                      'Of which Hydro mixed pumped storage (renewable part)':'Pumped storage',
                      'Of which Hydro Marine (tidal/wave)':'Marine',
                      'Other renewable (not listed)':'Differently categorized renewable energy sources',
                      'Non identified (other not listed)':'Other or unspecified energy sources', 
                      'Total Hydro':'Hydro'}

power_statistics["technology"].replace(dict_energy_source, inplace=True)
power_statistics.head()
In [ ]:
powerstats_pivot = power_statistics.pivot_table(values='capacity',
                                                   index=['country','year'],
                                                   columns='technology').reset_index()
powerstats_pivot.head()
In [ ]:
# technology level
powerstats_pivot['Differently categorized natural gas'] = powerstats_pivot['Natural gas']

# level 2
powerstats_pivot['Bioenergy and renewable waste'] = (
          powerstats_pivot['Biomass and biogas'] +
          powerstats_pivot['Other bioenergy and renewable waste'])

#level 1
fossil_techs = ['Lignite', 'Hard coal', 'Oil', 'Natural gas', 'Non-renewable waste',
               'Mixed fossil fuels', 'Other fossil fuels', 'Differently categorized fossil fuels']

powerstats_pivot['Fossil fuels'] = powerstats_pivot[fossil_techs].sum(axis=1)

res_tech = ['Hydro', 'Wind', 'Solar', 'Geothermal', 'Marine', 'Bioenergy and renewable waste',
           'Differently categorized renewable energy sources']

powerstats_pivot['Renewable energy sources'] = powerstats_pivot[res_tech].sum(axis=1)

total_arr = ['Fossil fuels','Nuclear','Renewable energy sources']
powerstats_pivot['Total'] = powerstats_pivot[total_arr].sum(axis=1)

powerstats_pivot.head()
In [ ]:
data_power_statistics = powerstats_pivot.melt(id_vars=['country', 'year'],
                                         var_name='technology',
                                         value_name='capacity')

data_power_statistics['source'] = 'ENTSO-E Power Statistics'
data_power_statistics['source_type'] = 'Other association'
data_power_statistics['capacity_definition'] = 'Net capacity'
data_power_statistics['type'] = 'Installed capacity in MW'
data_power_statistics['weblink'] = ('https://www.entsoe.eu/data/'
                                    'power-stats/net-gen-capacity/')

data_power_statistics.head()

3.4 Merge data sources

In [ ]:
dataframes = [data_opsd, data_eurostat, data_soaf, data_entsoe, data_transparency, data_power_statistics]
data = pd.concat(dataframes, sort=False)
data['comment'] = data['comment'].fillna('').astype(str)

col_order = ['technology', 'source', 'source_type', 'weblink', 'year', 'type',
             'country', 'capacity_definition', 'capacity', 'comment']

data = data[col_order]


energy_source_mapping = pd.read_csv(os.path.join('input','energy_source_mapping.csv'),
                                    index_col ='name')
energy_source_mapping.replace({0: False, 1: True}, inplace=True)

data = data.merge(energy_source_mapping,
                  left_on='technology',
                  right_index=True,
                  how='left')

new_level_names = {"Level 0": "energy_source_level_0",
                   "Level 1": "energy_source_level_1",
                   "Level 2": "energy_source_level_2",
                   "Level 3": "energy_source_level_3",
                   "Technology level": "technology_level"}

data.rename(columns=new_level_names, inplace=True)

data.head()

4. Convert stacked data to crosstable format

In [ ]:
cols = ['technology', 'source', 'source_type', 'weblink','year',
        'type', 'country', 'capacity_definition', 'capacity']

data_crosstable = pd.pivot_table(data[cols],
                                 index=['technology'],
                                 columns=['country', 'type', 'year',
                                          'source', 'source_type',
                                          'weblink', 'capacity_definition'],
                                 values='capacity')

# Apply initial ordering of technologies
data_crosstable = data_crosstable.reindex(technology_order)

# Delete index naming
data_crosstable.index.name = None
data_crosstable.columns.names = ('Country (ISO code)',
                                 'Type of data', 'Year',
                                 'Source', 'Type of source',
                                 'Weblink',
                                 'Capacity definition (net, gross, unknown)')

data_crosstable.head()
In [ ]:
energylevels_table = energylevels_raw[6:]
energylevels_table.columns = pd.MultiIndex.from_arrays(energylevels_raw[:6].values,
                                                       names=['country', 'type', 'year',
                                                              'source', 'source_type',
                                                              'capacity_definition'
                                                              ])

energylevels_table = energylevels_table.reset_index()
energylevels_table['technology'] = energylevels_table['technology'].str.replace('- ', '')
energylevels_table = energylevels_table.set_index('technology')

# Delete index naming
energylevels_table.index.name = None
energylevels_table.columns.names = ('Country (ISO code)',
                                    'Description', None,
                                    None, None,
                                    'Level')

energylevels_table.head()

5. Output

Delete downloaded zip files

In [ ]:
for root, dirs, files in os.walk("download"):
    for file in files:
        item = os.path.join(root, file)
        if item.endswith(".zip"):
            os.remove(item)
            print("Deleted: " + item)

Copy input files

In [ ]:
orig_data_path = os.path.join('output', 'original_data')
shutil.rmtree(orig_data_path, ignore_errors=True)
func.copydir(os.path.join('input'), orig_data_path)
func.copydir(os.path.join('download'), orig_data_path)

5.1 Write results to file

Write stacked data to formats: csv, xls and sql.

In [ ]:
# Write the result to file
data.to_csv(os.path.join('output', 'national_generation_capacity_stacked.csv'),
            encoding='utf-8', index_label='ID')

# Write the results to excel file
data.to_excel(os.path.join('output', 'national_generation_capacity_stacked.xlsx'),
              sheet_name='output', index_label='ID')

# Write the results to sql database
data.to_sql('national_generation_capacity_stacked',
            sqlite3.connect(os.path.join('output',
                                         'national_generation_capacity.sqlite')),
            if_exists="replace", index_label='ID')

Write data in human readable form to excel.

In [ ]:
# Write crosstable data to excel file
writer = pd.ExcelWriter(os.path.join('output', 'national_generation_capacity.xlsx'))
data_crosstable.to_excel(writer, sheet_name='output')
energylevels_table.to_excel(writer, sheet_name='technology levels')
writer.save()

5.2 Formatting of Excel tables

In [ ]:
outputxls = openpyxl.load_workbook(os.path.join('output',
                                          'national_generation_capacity.xlsx'))

ws1 = outputxls['output']
ws2 = outputxls['technology levels']
ws1_rows, ws1_cols = data_crosstable.shape
amount_cols = ws1_cols + 1 # correct 0 index

ws1.column_dimensions['A'].width = 50
ws2.column_dimensions['A'].width = 50
In [ ]:
blackfont = Font(color=colors.BLACK, italic=False, bold=False)
blackfontitalic = Font(color=colors.BLACK, italic=True, bold=False)
blackfontbold = Font(color=colors.BLACK, italic=False, bold=True)

align0 = Alignment(horizontal='left', indent=0)
align1 = Alignment(horizontal='left', indent=1)
align2 = Alignment(horizontal='left', indent=2)

# darkest grey
colour = "{0:02X}{1:02X}{2:02X}".format(166, 166, 166)
grey166 = PatternFill(fgColor=colour, bgColor=colour, patternType="solid")

# darker grey
colour = "{0:02X}{1:02X}{2:02X}".format(191, 191, 191)
grey191 = PatternFill(fgColor=colour, bgColor=colour, patternType="solid")

# lighter grey
colour = "{0:02X}{1:02X}{2:02X}".format(217, 217, 217)
grey217 = PatternFill(fgColor=colour, bgColor=colour, patternType="solid")

# lightest grey
colour = "{0:02X}{1:02X}{2:02X}".format(242, 242, 242)
grey242 = PatternFill(fgColor=colour, bgColor=colour, patternType="solid")
In [ ]:
for col in range(2, amount_cols+1):
    colname = openpyxl.utils.cell.get_column_letter(col)
    ws1.column_dimensions[colname].width = 16


for col in range(1, amount_cols+1):
    # format column name block
    for row in range(2,8):
        
        ws1.cell(row=row, column=col).font = blackfont
        

    # format cells that contain the values
    for row in range(9, 48):
        ws1.cell(row=row, column=col).fill = grey242
        ws1.cell(row=row, column=1).font = blackfontitalic
        ws1.cell(row=row, column=1).alignment = align2
    
    
    # format row 'Total' with dark grey
    ws1.cell(row=47, column=col).fill = grey166
    ws1.cell(row=47, column=col).font = blackfontbold
    
    
    # format level 1
    for row in [9, 22, 23, 46]:
        ws1.cell(row=row, column=col).fill = grey191
        ws1.cell(row=row, column=col).font = blackfontbold
        ws1.cell(row=row, column=1).alignment = align0
        
    
    # format level 2
    for row in [10, 11, 12, 13, 18, 19, 20, 21, 24, 31, 35, 39, 40, 41, 45]:
        ws1.cell(row=row, column=col).fill = grey217
        ws1.cell(row=row, column=1).alignment = align1

    
ws1.cell(row=47, column=1).alignment = align0
ws1.freeze_panes = ws1['B8'] #freeze first column and header rows
In [ ]:
# do the same for the second worksheet 'technology levels' 
for col in range(1, 7):
    colname = get_column_letter(col + 1)
    ws2.column_dimensions[colname].width = 25
    
    for row in range(2, 8):
        ws2.cell(row=row, column=col).font = blackfont
        
        
    for row in range(9, 48):     
        ws2.cell(row=row, column=col).fill = grey242
        ws2.cell(row=row, column=1).font = blackfontitalic
        ws2.cell(row=row, column=1).alignment = align2
        
        
    # format row 'Total' with dark grey
    ws2.cell(row=46, column=col).fill = grey166
    
    
    # format level 1
    for row in [8, 21, 22, 45]:
        ws2.cell(row=row, column=col).fill = grey191
        ws2.cell(row=row, column=col).font = blackfontbold
        ws2.cell(row=row, column=1).font = blackfontbold
        ws2.cell(row=row, column=1).alignment = align0
        
    
    # format level 2
    for row in [9, 10, 11, 12, 17, 18, 19, 20, 23, 30, 34, 38, 39, 40, 44]:
        ws2.cell(row=row, column=col).fill = grey217
        ws2.cell(row=row, column=1).alignment = align1

    
ws2.cell(row=46, column=1).alignment = align0
In [ ]:
additional_notes = openpyxl.load_workbook(os.path.join('input',
                                          'National_Generation_Capacities.xlsx'))['Additional notes']
# copy additional notes to output file
for col in range(1, 3):
    for row in range(1, 10):
        add_notes_value = additional_notes.cell(row=row, column=col).value
        ws1.cell(row=row + 50, column=col).value = add_notes_value
        ws1.cell(row=51, column=1).font = blackfontbold
        ws1.cell(row=row + 51, column=1).font = blackfontitalic
In [ ]:
outputxls.save(os.path.join('output', 'national_generation_capacity.xlsx'))

5.3 Write checksums

In [ ]:
files = ['national_generation_capacity.xlsx',
        'national_generation_capacity_stacked.csv',
        'national_generation_capacity_stacked.xlsx',
        'national_generation_capacity.sqlite']

hash_dict = {}
filesize_dict = {}

with open('checksums.txt', 'w') as f:
    for file_name in files:
        path = os.path.join('output', file_name)
        file_hash = func.get_sha_hash(path)
        hash_dict[file_name] = file_hash
        filesize_dict[file_name] = os.path.getsize(path)
        f.write('{},{}\n'.format(file_name, file_hash))

6. Documentation of the data package

We document the data packages meta data in the specific format JSON as proposed by the Open Knowledge Foundation. See the Frictionless Data project by OKFN (http://data.okfn.org/) and the Data Package specifications (http://dataprotocols.org/data-packages/) for more details.

In order to keep the notebook more readable, we first formulate the metadata in the human-readable YAML format using a multi-line string. We then parse the string into a Python dictionary and save that to disk as a JSON file.

In [ ]:
with open(os.path.join('input', 'metadata.yml'), 'r') as f:
    metadata = yaml.load(f.read(), Loader=yaml.BaseLoader)
    
metadata['resources'][0]['hash'] = hash_dict['national_generation_capacity.xlsx']
metadata['resources'][1]['hash'] = hash_dict['national_generation_capacity_stacked.csv']
metadata['resources'][0]['bytes'] = filesize_dict['national_generation_capacity.xlsx']
metadata['resources'][1]['bytes'] = filesize_dict['national_generation_capacity_stacked.csv']
In [ ]:
datapackage_json = json.dumps(metadata, indent=4, separators=(',', ': '))
    
# Write the information of the metadata
with open(os.path.join('output', 'datapackage.json'), 'w') as f:
    f.write(datapackage_json)

End of script.