#!/usr/bin/env python # coding: utf-8 #

# Renewable power plants: Validation and output notebook #

#
This notebook is part of the Renewable power plants Data Package of Open Power System Data. #

# Part 1 of the script (Download and process Notebook) has downloaded and merged the original data. This Notebook subsequently checks, validates the list of renewable power plants and creates CSV/XLSX/SQLite files. It also generates a daily time series of cumulated installed capacities by energy source. # # *(Before running this script make sure you ran Part 1, so that the renewables.pickle files for each country exist in the same folder as the scripts)* # #

# # # Initialization # In[ ]: settings = { 'version': '2019-04-05', 'changes': 'Updated all countries with new data available (DE, FR, CH, DK), added data for UK and expanded renewable capacity timeseries to more countries (DK, UK, CH in addition to DE).' } # ## Script setup # In[ ]: import json import logging import os import urllib.parse import re import zipfile import pandas as pd import numpy as np import requests import sqlalchemy import yaml import hashlib import os import fiona import cartopy.io.shapereader as shpreader import shapely.geometry as sgeom from shapely.prepared import prep from shapely.ops import unary_union import fake_useragent get_ipython().run_line_magic('matplotlib', 'inline') # Option to make pandas display 40 columns max per dataframe (default is 20) pd.options.display.max_columns = 40 # Create input and output folders if they don't exist os.makedirs(os.path.join('input', 'original_data'), exist_ok=True) os.makedirs('output', exist_ok=True) os.makedirs(os.path.join('output', 'renewable_power_plants'), exist_ok=True) package_path = os.path.join('output', 'renewable_power_plants',settings['version']) os.makedirs(package_path, exist_ok=True) # ## Load data # In[ ]: countries = set(['DE', 'DK','FR','PL','CH', 'UK']) countries_non_DE = countries - set(['DE']) countries_dirty = set(['DE_outvalidated_plants', 'FR_overseas_territories']) countries_including_dirty = countries | countries_dirty # Read data from script Part 1 download_and_process dfs = {} for country in countries: dfs[country] = pd.read_pickle('intermediate/'+country+'_renewables.pickle') # ## Download coastline data # # The coastline shapefile is needed to check if the geocoordinates of the land powerplants point to a land location, and conversely, if the geocoordinates of the onshore facilities point to a location not on land. # In[ ]: coastline_url = 'https://www.ngdc.noaa.gov/mgg/shorelines/data/gshhg/latest/gshhg-shp-2.3.7.zip' user_agent = fake_useragent.UserAgent() directory_path = os.path.join('input', 'maps', 'coastline') os.makedirs(directory_path, exist_ok=True) filepath = os.path.join(directory_path, 'gshhg-shp-2.3.7.zip') # check if the file exists; if not, download it if not os.path.exists(filepath): session = requests.session() print(coastline_url) print('Downloading...') headers = {'User-Agent' : user_agent.random} r = session.get(coastline_url, headers=headers, stream=True) total_size = r.headers.get('content-length') total_size = int(total_size) chuncksize = 4096 with open(filepath, 'wb') as file: downloaded = 0 for chunck in r.iter_content(chuncksize): file.write(chunck) downloaded += chuncksize print('\rProgress: {:.2f}%'.format(100 * downloaded / float(total_size)), end='') print(' Done.') zip_ref = zipfile.ZipFile(filepath, 'r') zip_ref.extractall(directory_path) zip_ref.close() else: print('The file is already there:', filepath) filepath = '' + filepath coastline_shapefile_path = os.path.join('input', 'maps', 'coastline', 'GSHHS_shp', 'f', 'GSHHS_f_L1.shp') print("Shapefile path: ", coastline_shapefile_path) # ### Load the list of sources # In[ ]: source_df = pd.read_csv(os.path.join('input', 'sources.csv')) source_df # # Validation Markers # # This section checks the DataFrame for a set of pre-defined criteria and adds markers to the entries in an additional column. The marked data will be included in the output files, but marked, so that they can be easiliy filtered out. For creating the validation plots and the time series, suspect data is skipped. # In[ ]: validation_marker = {} mark_rows = {} # ## Germany DE # **Add marker to data according to criteria (see validation_marker above)** # In[ ]: # It seems that some DE.commissioning_date values are integers, which causes # the parts of the code dealing with dates to break. integer_dates_mask = dfs['DE'].apply(lambda row: type(row['commissioning_date']) is int, axis=1).values print("Integer dates") display(dfs['DE'][integer_dates_mask]) dfs['DE']=dfs['DE'][~integer_dates_mask] dfs['DE'].reset_index(drop=True, inplace=True) # In[ ]: # In[ ]: key = 'R_1' cutoff_date_bnetza = '2017-12-31' cutoff_date_bnetza = pd.Timestamp(2017, 12, 31) mark_rows[key] = (dfs['DE']['commissioning_date'] <= cutoff_date_bnetza) &\ (dfs['DE']['data_source'].isin(['BNetzA', 'BNetzA_PV', 'BNetzA_PV_historic'])) validation_marker[key] = { "Short explanation": "data_source = BNetzA and commissioning_date < " + str(cutoff_date_bnetza.date()), "Long explanation": "This powerplant is probably also represented by an entry from the TSO data and should therefore be filtered out." } # In[ ]: key = 'R_2' mark_rows[key] = ((dfs['DE']['notification_reason'] != 'Inbetriebnahme') & (dfs['DE']['data_source'] == 'BNetzA')) validation_marker[key] = { "Short explanation": "notification_reason other than commissioning (Inbetriebnahme)", "Long explanation": "This powerplant is probably represented by an earlier entry already (possibly also from the TSO data) and should therefore be filtered out." } key = 'R_3' mark_rows[key] = (dfs['DE']['commissioning_date'].isnull()) validation_marker[key] = { "Short explanation": "commissioning_date not specified", "Long explanation": "" } key = 'R_4' mark_rows[key] = dfs['DE'].electrical_capacity <= 0.0 validation_marker[key] = { "Short explanation": "electrical_capacity not specified", "Long explanation": "" } key = 'R_5' mark_rows[key] = dfs['DE']['grid_decommissioning_date'].isnull() == False # Just the entry which is not double should be kept, thus the other one is marked validation_marker[key] = { "Short explanation": "decommissioned from the grid", "Long explanation": "This powerplant is probably commissioned again to the grid of another grid operator and therefore this doubled entry should be filtered out." } key = 'R_6' mark_rows[key] = dfs['DE']['decommissioning_date'].isnull() == False validation_marker[key] = { "Short explanation": "decommissioned", "Long explanation": "This powerplant is completely decommissioned." } key = 'R_8' # note that we skip R7 here as R7 is used for frech oversees power plants below (we never change meanings of R markers, so R7 stays reserved for that) mark_rows[key] = (dfs['DE'].duplicated(['eeg_id'],keep='first') # note that this depends on BNetzA items to be last in list, because we want to keep the TSO items & (dfs['DE']['eeg_id'].isnull() == False)) validation_marker[key] = { "Short explanation": "duplicate_eeg_id", "Long explanation": "This power plant is twice in the data (e.g. through BNetzA and TSOs)." } # In[ ]: dfs['DE']['comment'] = '' for key, rows_to_mark in mark_rows.items(): dfs['DE'].loc[rows_to_mark, 'comment'] += key+"|" del mark_rows, key, rows_to_mark # free variables no longer needed # In[ ]: # Summarize capacity of suspect data by data_source display(dfs['DE'].groupby(['comment', 'data_source'])['electrical_capacity'].sum().to_frame()) # Summarize capacity of suspect data by energy source dfs['DE'].groupby(['comment', 'energy_source_level_2'])['electrical_capacity'].sum().to_frame() # **Create cleaned DataFrame** # # All marked entries are deleted for the cleaned version of the DataFrame that is utilized for creating time series of installation and for the validation plots. # ## France FR # In[ ]: # Create empty marker column dfs['FR']['comment'] = "" key = 'R_7' mark_rows_FR_not_in_Europe = dfs['FR'][((dfs['FR']['lat'] < 41) | (dfs['FR']['lon'] < -6) | (dfs['FR']['lon'] > 10))].index validation_marker[key] = { "Short explanation": "not connected to the European grid", "Long explanation": "This powerplant is located in regions belonging to France but not located in Europe (e.g. Guadeloupe)." } dfs['FR'].loc[mark_rows_FR_not_in_Europe, 'comment'] += key+"|" del mark_rows_FR_not_in_Europe # ## United Kingdom UK # In[ ]: # Create an empty marker column dfs['UK']['comment'] = "" # Create a function to check if an offshore powerplant is not wind geoms = fiona.open(coastline_shapefile_path) land_geom = sgeom.MultiPolygon([sgeom.shape(geom['geometry']) for geom in geoms]) land = prep(land_geom) def not_on_land_but_should_be(powerplant_data): longitude = powerplant_data['lon'] latitude = powerplant_data['lat'] if pd.isnull(longitude) or pd.isnull(latitude): return False not_on_land = not land.contains(sgeom.Point(longitude, latitude)) offshore_ok = 'Offshore' in [powerplant_data['region'], powerplant_data['municipality']] or \ (powerplant_data['energy_source_level_2'] in ['Wind', 'Marine']) return not_on_land and not offshore_ok key = 'R_9' validation_marker[key] = { "Short explanation": "Not on land, but should be.", "Long explanation": "The geocoordinates of this powerplant indicate that it is not on the UK mainland, but the facility is not an offshore wind farm." } mark_rows_UK_not_on_land = dfs['UK'].apply(lambda row: not_on_land_but_should_be(row), axis=1) dfs['UK'].loc[mark_rows_UK_not_on_land, 'comment'] += key+"|" del mark_rows_UK_not_on_land # # Harmonization # ## Harmonizing column order # In[ ]: field_lists = { 'DE': ['commissioning_date', 'decommissioning_date', 'energy_source_level_1', 'energy_source_level_2','energy_source_level_3', 'technology', 'electrical_capacity', 'voltage_level', 'tso', 'dso', 'dso_id', 'eeg_id', 'federal_state', 'postcode', 'municipality_code', 'municipality', 'address', 'lat', 'lon', 'data_source', 'comment'], 'DK': ['commissioning_date', 'energy_source_level_1', 'energy_source_level_2', 'energy_source_level_3', 'technology', 'electrical_capacity', 'dso', 'gsrn_id', 'postcode', 'municipality_code', 'municipality', 'address', 'lat', 'lon', 'hub_height', 'rotor_diameter', 'manufacturer', 'model', 'data_source'], 'FR': ['municipality_code', 'municipality', 'energy_source_level_1', 'energy_source_level_2', 'energy_source_level_3', 'technology', 'electrical_capacity', 'number_of_installations', 'lat', 'lon', 'data_source', 'as_of_year', 'comment'], 'PL': ['district', 'energy_source_level_1', 'energy_source_level_2', 'energy_source_level_3', 'technology', 'electrical_capacity', 'number_of_installations', 'data_source', 'as_of_year'], 'CH': ['commissioning_date', 'municipality', 'energy_source_level_1', 'energy_source_level_2', 'energy_source_level_3', 'technology','electrical_capacity', 'municipality_code', 'project_name', 'production', 'tariff', 'contract_period_end', 'street', 'canton', 'company', 'lat', 'lon', 'data_source'], 'UK': ['commissioning_date', 'uk_beis_id', 'site_name', 'operator', 'energy_source_level_1', 'energy_source_level_2', 'energy_source_level_3', 'technology', 'electrical_capacity', 'chp', 'capacity_individual_turbine', 'number_of_turbines', 'solar_mounting_type', 'address', 'municipality', 'region', 'country', 'postcode', 'lat', 'lon', 'data_source', 'comment'] } for country in field_lists: for field in field_lists[country]: if field not in dfs[country].columns: print(country, field) dfs[country] = dfs[country].loc[:, field_lists[country]] # ## Cleaning fields # # Five digits behind the decimal point for decimal fields. Dates should be without timestamp. # In[ ]: dfs['DE']['address'][~dfs['DE']['address'].isnull()] dfs['DK'].columns # In[ ]: cleaning_specs = { 'decimal' : { 'DE': ['electrical_capacity','lat','lon'], 'DK': ['electrical_capacity','lat','lon'], 'CH': ['electrical_capacity','lat','lon'], 'FR': ['electrical_capacity','lat','lon'], 'PL': ['electrical_capacity'], 'UK': ['electrical_capacity', 'lat', 'lon'] }, 'integer': { 'DE': ['municipality_code'], 'UK': ['uk_beis_id'] }, 'date': { 'DE': ['commissioning_date', 'decommissioning_date'], 'DK': ['commissioning_date'], 'CH': ['commissioning_date'], 'UK': ['commissioning_date'] }, 'one-line string': { 'DE' : ['federal_state', 'municipality', 'address'], 'DK' : ['municipality', 'address', 'manufacturer', 'model'], 'FR' : ['municipality'], 'PL' : ['district'], 'CH' : ['municipality', 'project_name', 'canton', 'street', 'company'], 'UK' : ['address', 'municipality', 'site_name', 'region'] } } def to_1_line(string): if pd.isnull(string) or not isinstance(string, str): return string return string.replace('\r', '').replace('\n', '') for cleaning_type, cleaning_spec in cleaning_specs.items(): for country, fields in cleaning_spec.items(): for field in fields: print('Cleaning ' + country + '.' + field +' to ' + cleaning_type + '.') if cleaning_type == 'decimal': dfs[country][field] = dfs[country][field].map(lambda x: round(x, 8)) elif cleaning_type == 'integer': dfs[country][field] = pd.to_numeric(dfs[country][field], errors='coerce') dfs[country][field] = dfs[country][field].map(lambda x: '%.0f' % x) elif cleaning_type == 'date': dfs[country][field] = dfs[country][field].apply(lambda x: x.date()) elif cleaning_type == 'one-line string': dfs[country][field] = dfs[country][field].apply(lambda x: to_1_line(x)) print('Done!') del cleaning_specs # ## Sort # In[ ]: sort_by = { 'DE': 'commissioning_date', 'DK': 'commissioning_date', 'CH': 'commissioning_date', 'FR': 'municipality_code', 'PL': 'district', 'UK': 'commissioning_date' } for country, sort_by in sort_by.items(): print('Sorting', country) try: dfs[country] = dfs[country].iloc[dfs[country][sort_by].sort_values().index] except Exception as e: print('\tException:',e) print('Done!') del sort_by # ## Leave unspecified cells blank # # This step may take some time. # In[ ]: for country in countries: print(country) dfs[country].fillna('', inplace=True) # ## Separate dirty from clean # # We separate all plants which have a validation marker in the comments column into a separate DataFrame and eventually also in a separate CSV file, so the main country files only contain "clean" plants, i.e. those without any special comment. This is useful since all our comments denote that most people would probably not like to include them in their calculations. # In[ ]: # In[ ]: dirty_keys = { 'DE' : 'DE_outvalidated_plants', 'FR' : 'FR_overseas_territories', } for country in dirty_keys.keys(): print(country) idx_dirty = dfs[country][dfs[country].comment.str.len() > 1].index dirty_key = dirty_keys[country] dfs[dirty_key] = dfs[country].loc[idx_dirty] dfs[country] = dfs[country].drop(idx_dirty) del idx_dirty, dirty_key # # Capacity time series # # This section creates a daily and yearly time series of the cumulated installed capacity by energy source for the United Kingdom, Germany, Denmark, and Switzerland. Three time series will be created for the UK: one for the whole country (GB-UKM), one for Northern Ireland (GB-NIR), and one for the Great Britain (GB-GBN). This data will be part of the output and will be compared in a plot for validation in the next section. # In[ ]: daily_timeseries = {} # In[ ]: def to_new_level(row): if(row['energy_source_level_2'] == 'Wind'): energy_type_label = (row['energy_source_level_2']+'_'+row['technology']).lower() else: energy_type_label = row['energy_source_level_2'].lower() return energy_type_label def to_daily_timeseries(df, start_date, end_date): # Combine energy levels to new standardized values df['energy_type'] = df[['energy_source_level_2', 'energy_source_level_3', 'technology']].apply(to_new_level, axis=1) # Set range of time series as index daily_timeseries = pd.DataFrame(index=pd.date_range(start=start_date, end=end_date, freq='D')) # Create cumulated time series per energy source for both yearly and daily time series for energy_type in df['energy_type'].unique(): temp = (df[['commissioning_date', 'electrical_capacity']] .loc[df['energy_type'] == energy_type]) temp_timeseries = temp.set_index('commissioning_date') temp_timeseries.index = pd.DatetimeIndex(temp_timeseries.index) # Create cumulated time series per energy_source and day daily_timeseries[energy_type] = temp_timeseries.resample('D').sum().cumsum().fillna(method='ffill') # Make sure that the columns are properly filled daily_timeseries[energy_type]= daily_timeseries[energy_type].fillna(method='ffill').fillna(value=0) # Reset the time index daily_timeseries.reset_index(inplace=True) # Set the index name daily_timeseries.rename(columns={'index': 'day'}, inplace=True) # Drop the temporary column "energy_type" df.drop('energy_type', axis=1, inplace=True) return daily_timeseries eligible_for_timeseries = [country for country in countries if 'commissioning_date' in dfs[country].columns] #eligible_for_timeseries = ['CH', 'DK', 'UK', 'DE'] possible_start_dates = [dfs[country]['commissioning_date'].min() for country in eligible_for_timeseries] possible_end_dates = [dfs[country]['commissioning_date'].max() for country in eligible_for_timeseries] #print("Possible start and end dates:") #for country in eligible_for_timeseries: # print(country, dfs[country]['commissioning_date'].min(), dfs[country]['commissioning_date'].max()) start_date = min(possible_start_dates) end_date = max(possible_end_dates) for country in eligible_for_timeseries: print("Timeseries for", country) try: daily_timeseries[country] = to_daily_timeseries(dfs[country], start_date, end_date) print('\t Done!') except Exception as e: print('\t', e) # ## Make separate series for Great Britain and Northern Ireland # In[ ]: # Create the mask for Northern Ireland ni_mask = dfs['UK']['country'] == 'Northern Ireland' # Split the UK data ni_df = dfs['UK'][ni_mask].copy() gb_df = dfs['UK'][~ni_mask].copy() # Make the timeseries for Northern Ireland daily_timeseries['GB-NIR'] = to_daily_timeseries(ni_df, start_date, end_date) # Make the timeseries for Great Briatin (England, Wales, Scotland) daily_timeseries['GB-GBN'] = to_daily_timeseries(gb_df, start_date, end_date) # Renaming the entry for UK to conform to the ISO codes daily_timeseries['GB-UKM'] = daily_timeseries.pop('UK') # ## Create total wind columns # In[ ]: # Create column "wind" as sum of onshore and offshore for country in daily_timeseries: onshore = 'wind_onshore' in daily_timeseries[country].columns offshore = 'wind_offshore' in daily_timeseries[country].columns if onshore and offshore: daily_timeseries[country]['wind'] = daily_timeseries[country]['wind_onshore'] + daily_timeseries[country]['wind_offshore'] elif onshore and not offshore: daily_timeseries[country]['wind'] = daily_timeseries[country]['wind_onshore'] elif (not onshore) and offshore: daily_timeseries[country]['wind'] = daily_timeseries[country]['wind_offshore'] # ## Create one time series file containing al countries # In[ ]: unified_daily_timeseries = pd.DataFrame(index=pd.date_range(start=start_date, end=end_date, freq='D')) # Append the country name to capacity columns' names for c in daily_timeseries: new_columns = [c + "_" + col + "_capacity" if col != 'day' else 'day' for col in daily_timeseries[c].columns] daily_timeseries[c].columns = new_columns # Unify separate series unified_daily_timeseries = pd.concat(daily_timeseries.values(), axis=1, sort=False) # Make sure the day column appears only one days = unified_daily_timeseries['day'] unified_daily_timeseries.drop('day', axis=1, inplace=True) unified_daily_timeseries['day'] = days.iloc[:, 0] # In[ ]: # sort columns alphabetically unified_daily_timeseries = unified_daily_timeseries.reindex(sorted(unified_daily_timeseries.columns), axis=1) unified_daily_timeseries = unified_daily_timeseries.set_index('day').reset_index() # move day column to first position # drop column DE_Other fossil fuels (we don't want fossil fuels in here as they don't belong into renewables) # and hydro is not all of hydro but only subsidised hydro, which could be misleading unified_daily_timeseries.drop(columns='DE_other fossil fuels_capacity', inplace=True) unified_daily_timeseries.drop(columns='DE_hydro_capacity', inplace=True) # Show some rows unified_daily_timeseries.tail(2) # # Make the normalized dataframe for all the countries # # Here, we create a dataframe containing the following data for all the countries: # # In[ ]: geographical_resolution = { 'PL' : 'district', 'FR' : 'municipality', 'CH' : 'municipality', 'DE' : 'power plant', 'DK' : 'power plant', 'UK' : 'power plant' } dfs_to_concat = [] columns = ['energy_source_level_1', 'energy_source_level_2', 'energy_source_level_3', 'electrical_capacity', 'data_source', 'municipality', 'lon', 'lat', 'commissioning_date', 'geographical_resolution', 'as_of_year' ] for country in countries: country_df = dfs[country].loc[:, columns].copy() country_df['country'] = country country_df['geographical_resolution'] = geographical_resolution[country] if country == 'PL': country_df['as_of_year'] = 2016 elif country == 'FR': country_df['as_of_year'] = 2017 dfs_to_concat.append(country_df) european_df = pd.concat(dfs_to_concat) european_df.reset_index(inplace=True, drop=True) european_df.sample(n=10) # # Output # This section finally writes the Data Package: # * CSV + XLSX + SQLite # * Meta data (JSON) # In[ ]: os.makedirs(package_path, exist_ok=True) # Make sure the daily timeseries has only the date part, not the full datetime with time information unified_daily_timeseries['day'] = unified_daily_timeseries['day'].dt.date # ## Write data files # ### Write CSV-files # # One csv-file for each country. This process will take some time depending on you hardware. # In[ ]: # Write each country's dataset as a separate csv file table_names = {} for country in countries_including_dirty: print(country) table_names[country] = 'renewable_power_plants_'+country if country not in countries_dirty else 'res_plants_separated_'+country dfs[country].to_csv(os.path.join(package_path, table_names[country]+'.csv'), sep=',', decimal='.', date_format='%Y-%m-%d', line_terminator='\n', encoding='utf-8', index=False) # In[ ]: # Write daily cumulated time series as csv unified_daily_timeseries.to_csv(os.path.join(package_path, 'renewable_capacity_timeseries.csv'), sep=',', float_format='%.3f', decimal='.', date_format='%Y-%m-%d', encoding='utf-8', index=False) print('Done!') # In[ ]: european_df.to_csv(os.path.join(package_path, 'renewable_power_plants_EU.csv'), sep=',', decimal='.', date_format='%Y-%m-%d', line_terminator='\n', encoding='utf-8', index=False) print('Done!') # In[ ]: # Write csv of Marker Explanations validation_marker_df = pd.DataFrame(validation_marker).transpose() validation_marker_df = validation_marker_df.iloc[:, ::-1] # Reverse column order validation_marker_df.index.name = 'Validation marker' validation_marker_df.reset_index(inplace=True) validation_marker_df.to_csv(os.path.join(package_path, 'validation_marker.csv'), sep=',', decimal='.', date_format='%Y-%m-%d', line_terminator='\n', encoding='utf-8', index=False) # ### Write XLSX-files # # This process will take some time depending on your hardware. # # All country power plant list will be written in one xlsx-file. Each country power plant list is written in a separate sheet. As the German power plant list has too many entries for one sheet, it will be split in two. An additional sheet includes the explanations of the marker. # In[ ]: # Write the results as xlsx file get_ipython().run_line_magic('time', "writer = pd.ExcelWriter(os.path.join(package_path, 'renewable_power_plants.xlsx'), engine='xlsxwriter', date_format='yyyy-mm-dd')") # In[ ]: print('Writing DE part 1') get_ipython().run_line_magic('time', "dfs['DE'][:1000000].to_excel(writer, index=False, sheet_name='DE part-1')") print('Writing DE part 2') get_ipython().run_line_magic('time', "dfs['DE'][1000000:].to_excel(writer, index=False, sheet_name='DE part-2')") # In[ ]: display(dfs.keys()) for country in (countries_non_DE | countries_dirty): print('Writing ' + country) get_ipython().run_line_magic('time', 'dfs[country].to_excel(writer, index=False, sheet_name=country)') print('Writing validation marker sheet') get_ipython().run_line_magic('time', "validation_marker_df.to_excel(writer, index=False, sheet_name='validation_marker')") # Save timeseries as Excel get_ipython().run_line_magic('time', "unified_daily_timeseries.to_excel(writer, index=False, sheet_name='capacity_timeseries')") print('Saving...') get_ipython().run_line_magic('time', 'writer.save()') print('...done!') # ### Write SQLite # In[ ]: # Some date columns are giving the engine some trouble, therefore cast to string: #if 'DE' in dfs: # dfs['DE'].decommissioning_date = dfs['DE'].decommissioning_date.astype(str) # dfs['DE'].commissioning_date = dfs['DE'].commissioning_date.astype(str) # dfs['DE_outvalidated_plants'].commissioning_date = dfs['DE_outvalidated_plants'].commissioning_date.astype(str) # Using chunksize parameter is for lower # memory computers. Removing it might speed things up. engine = sqlalchemy.create_engine('sqlite:///' + package_path + '/renewable_power_plants.sqlite') for country in countries_including_dirty: if country=='DE_outvalidated_plants': continue # The DE_outvalidated_plants file gives a strange error message. Therefore do not put it into SQLite. get_ipython().run_line_magic('time', 'dfs[country].to_sql(table_names[country], engine, if_exists="replace", chunksize=100000, index=False)') validation_marker_df.to_sql('validation_marker', engine, if_exists="replace", chunksize=100000, index=False) # In[ ]: # Save timeseries as sqlite get_ipython().run_line_magic('time', 'european_df.to_sql(\'renewable_power_plants_EU\', engine, if_exists="replace", chunksize=100000, index=False)') # In[ ]: # Save timeseries as sqlite get_ipython().run_line_magic('time', 'unified_daily_timeseries.to_sql(\'renewable_capacity_timeseries\', engine, if_exists="replace", chunksize=100000, index=False)') # ## Write meta data # # The Data Packages meta data are created in the specific JSON format as proposed by the Open Knowledge Foundation. Please 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 Jupyter Notebook more readable the metadata is written in the human-readable YAML format using a multi-line string and then parse the string into a Python dictionary and save it as a JSON file. # In[ ]: metadata = """ hide: yes profile: tabular-data-package _metadataVersion: 1.2 name: opsd_renewable_power_plants title: Renewable power plants description: List of renewable energy power stations longDescription: >- This Data Package contains a list of renewable energy power plants in lists of renewable energy-based power plants of Germany, Denmark, France, Switzerland, the United Kingdom and Poland. Germany: More than 1.7 million renewable power plant entries, eligible under the renewable support scheme (EEG). Denmark: Wind and phovoltaic power plants with a high level of detail. France: Aggregated capacity and number of installations per energy source per municipality (Commune). Poland: Summed capacity and number of installations per energy source per municipality (Powiat). Switzerland: Renewable power plants eligible under the Swiss feed in tariff KEV (Kostendeckende Einspeisevergütung). United Kingdom: Renewable power plants in the United Kingdom. Due to different data availability, the power plant lists are of different accurancy and partly provide different power plant parameter. Due to that, the lists are provided as seperate csv-files per country and as separate sheets in the excel file. Suspect data or entries with high probability of duplication are marked in the column 'comment'. Theses validation markers are explained in the file validation_marker.csv. Additionally, the Data Package includes daily time series of cumulated installed capacity per energy source type for Germany, Denmark, Switzerland and the United Kingdom. All data processing is conducted in Python and pandas and has been documented in the Jupyter Notebooks linked below. keywords: [master data register,power plants,renewables,germany,denmark,france,poland,switzerland,united kingdom,open power system data] spatial: location: Germany, Denmark, France, Poland, Switzerland, United Kingdom resolution: Power plants, municipalities resources: - path: renewable_power_plants_DE.csv format: csv encoding: UTF-8 missingValue: "" schema: fields: - name: commissioning_date type: date format: YYYY-MM-DD description: Date of commissioning of specific unit opsdContentfilter: "true" - name: decommissioning_date type: date format: YYYY-MM-DD description: Date of decommissioning of specific unit - name: energy_source_level_1 description: Type of energy source (e.g. Renewable energy) type: string - name: energy_source_level_2 description: Type of energy source (e.g. Wind, Solar) type: string opsdContentfilter: "true" - name: energy_source_level_3 description: Subtype of energy source (e.g. Biomass and biogas) type: string - name: technology description: Technology to harvest energy source (e.g. Onshore, Photovoltaics) type: string - name: electrical_capacity unit: MW description: Installed electrical capacity in MW type: number unit: MW - name: voltage_level description: Voltage level of grid connection type: string - name: tso description: Name of transmission system operator of the area the plant is located type: string - name: dso description: Name of distribution system operator of the region the plant is located in type: string - name: dso_id description: Company number of German distribution grid operator type: string - name: eeg_id description: Power plant EEG (German feed-in tariff law) remuneration number type: string - name: federal_state description: Name of German administrative level 'Bundesland' type: string - name: postcode description: German zip-code type: string - name: municipality_code description: German Gemeindenummer (municipalitiy number) type: string - name: municipality description: Name of German Gemeinde (municipality) type: string - name: address description: Street name or name of land parcel type: string - name: lat description: Latitude coordinates type: geopoint - name: lon description: Longitude coordinates type: geopoint - name: data_source description: Source of database entry type: string - name: comment description: Shortcodes for comments related to this entry, explanation can be looked up in validation_marker.csv type: string - path: renewable_power_plants_DK.csv format: csv encoding: UTF-8 missingValue: "" schema: fields: - name: commissioning_date type: date format: YYYY-MM-DD opsdContentfilter: "true" - name: energy_source_level_1 description: Type of energy source (e.g. Renewable energy) type: string - name: energy_source_level_2 description: Type of energy source (e.g. Wind, Solar) type: string opsdContentfilter: "true" - name: technology description: Technology to harvest energy source (e.g. Onshore, Photovoltaics) type: string - name: electrical_capacity unit: MW description: Installed electrical capacity in MW type: number - name: dso description: Name of distribution system operator of the region the plant is located in type: string - name: gsrn_id description: Danish wind turbine identifier number (GSRN) type: integer - name: postcode description: Danish zip-code type: string - name: municipality_code description: Danish 3-digit Kommune-Nr type: string - name: municipality description: Name of Danish Kommune type: string - name: address description: Street name or name of land parcel type: string - name: lat description: Latitude coordinates type: geopoint - name: lon description: Longitude coordinates type: geopoint - name: hub_height description: Wind turbine hub heigth in m type: number - name: rotor_diameter description: Wind turbine rotor diameter in m type: number - name: manufacturer description: Company that has built the wind turbine type: string - name: model description: Wind turbine model type type: string - name: data_source description: Source of database entry type: string - path: renewable_power_plants_FR.csv format: csv encoding: UTF-8 missingValue: "" schema: fields: - name: municipality_code description: French 5-digit INSEE code for Communes type: string - name: municipality description: Name of French Commune type: string - name: energy_source_level_1 description: Type of energy source (e.g. Renewable energy) type: string - name: energy_source_level_2 description: Type of energy source (e.g. Wind, Solar) type: string opsdContentfilter: "true" - name: energy_source_level_3 description: Subtype of energy source (e.g. Biomass and biogas) type: string - name: technology description: Technology to harvest energy source (e.g. Onshore, Photovoltaics) type: string - name: electrical_capacity unit: MW description: Installed electrical capacity in MW type: number - name: number_of_installations description: Number of installations of the energy source subtype in the municipality. Due to confidentiality reasons, the values smaller than 3 are published as ''<3'' (as in the source). type: integer bareNumber: false - name: lat description: Latitude coordinates type: geopoint - name: lon description: Longitude coordinates type: geopoint - name: data_source description: Source of database entry type: string - name: as_of_year description: Year for which the data source compiled the original dataset. type: integer - path: renewable_power_plants_PL.csv format: csv encoding: UTF-8 missingValue: "" schema: fields: - name: district description: Name of the Polish powiat type: string - name: energy_source_level_1 description: Type of energy source (e.g. Renewable energy) type: string - name: energy_source_level_2 description: Type of energy source (e.g. Wind, Solar) opsdContentfilter: "true" type: string - name: energy_source_level_3 description: Subtype of energy source (e.g. Biomass and biogas) type: string - name: technology description: Technology to harvest energy source (e.g. Onshore, Photovoltaics) type: string - name: electrical_capacity unit: MW description: Installed electrical capacity in MW type: number - name: number_of_installations description: Number of installations of the energy source subtype in the district type: integer - name: data_source description: Source of database entry type: string - name: as_of_year description: Year for which the data source compiled the original dataset. type: integer - path: renewable_power_plants_UK.csv format: csv encoding: UTF-8 missingValues: "" schema: fields: - name: commissioning_date description: Date of commissioning of specific unit type: date format: YYYY-MM-DD opsdContentfilter: "true" - name: uk_beis_id description: ID for the plant as assigned by UK BEIS. type: integer - name: site_name description: Name of site type: string - name: operator description: Name of operator type: string - name: energy_source_level_1 description: Type of energy source (e.g. Renewable energy) type: string - name: energy_source_level_2 description: Type of energy source (e.g. Wind, Solar) opsdContentfilter: "true" type: string - name: energy_source_level_3 description: Type of energy source (e.g. Biomass and biogas) type: string - name: technology description: Technology to harvest energy source (e.g. Onshore, Photovoltaics) type: string - name: electrical_capacity description: Installed electrical capacity in MW unit: MW type: number - name: chp description: Is the project capable of combined heat and power output type: string - name: capacity_individual_turbine description: For windfarms, the individual capacity of each wind turbine in megawatts (MW) type: number - name: number_of_turbines description: For windfarms, the number of wind turbines located on the site type: integer - name: solar_mounting_type description: For solar PV developments, whether the PV panels are ground or roof mounted type: string - name: address description: Address type: string - name: municipality description: Municipality type: string - name: region description: Region type: string - name: country description: The UK's constituent country in which the facility is located. type: string - name: postcode description: Postcode type: string - name: lat description: Latitude coordinates type: string - name: lon description: Longitude coordinates type: string - name: data_source description: The source of database entries type: string - name: comment description: Shortcodes for comments related to this entry, explanation can be looked up in validation_marker.csv type: string - path: renewable_power_plants_CH.csv format: csv encoding: UTF-8 missingValue: "" schema: fields: - name: commissioning_date description: Commissioning date type: date format: YYYY-MM-DD opsdContentfilter: "true" - name: municipality description: Municipality type: string - name: energy_source_level_1 description: Type of energy source (e.g. Renewable energy) type: string - name: energy_source_level_2 description: Type of energy source (e.g. Wind, Solar) type: string opsdContentfilter: "true" - name: energy_source_level_3 description: Type of energy source (e.g. Biomass and biogas) type: string - name: technology description: Technology to harvest energy source (e.g. Onshore, Photovoltaics) type: string - name: electrical_capacity unit: MW description: Installed electrical capacity in MW type: number - name: municipality_code description: Municipality code type: integer - name: project_name description: Name of the project type: string - name: production description: Yearly production in MWh type: number - name: tariff description: Tariff in CHF for 2016 type: number - name: contract_period_end description: End year of subsidy contract type: number - name: street description: Street name type: string - name: canton description: Name of the cantones/ member states of the Swiss confederation type: string - name: company description: Name of the company type: string - name: lat description: Latitude coordinates type: geopoint - name: lon description: Longitude coordinates type: geopoint - name: data_source description: Source of database entry type: string - path: res_plants_separated_DE_outvalidated_plants.csv format: csv encoding: UTF-8 missingValue: "" schema: fields: - name: commissioning_date type: date format: YYYY-MM-DD description: Date of commissioning of specific unit - name: decommissioning_date type: date format: YYYY-MM-DD description: Date of decommissioning of specific unit - name: energy_source_level_1 description: Type of energy source (e.g. Renewable energy) type: string - name: energy_source_level_2 description: Type of energy source (e.g. Wind, Solar) type: string opsdContentfilter: "true" - name: energy_source_level_3 description: Subtype of energy source (e.g. Biomass and biogas) type: string - name: technology description: Technology to harvest energy source (e.g. Onshore, Photovoltaics) type: string - name: electrical_capacity unit: MW description: Installed electrical capacity in MW type: number unit: MW - name: thermal_capacity description: Installed thermal capacity in MW type: number unit: MW - name: voltage_level description: Voltage level of grid connection type: string - name: tso description: Name of transmission system operator of the area the plant is located type: string - name: dso description: Name of distribution system operator of the region the plant is located in type: string - name: dso_id description: Company number of German distribution grid operator type: string - name: eeg_id description: Power plant EEG (German feed-in tariff law) remuneration number type: string - name: federal_state description: Name of German administrative level 'Bundesland' type: string - name: postcode description: German zip-code type: string - name: municipality_code description: German Gemeindenummer (municipalitiy number) type: string - name: municipality description: Name of German Gemeinde (municipality) type: string - name: address description: Street name or name of land parcel type: string - name: lat description: Latitude coordinates type: geopoint - name: lon description: Longitude coordinates type: geopoint - name: data_source description: Source of database entry type: string - name: comment description: Shortcodes for comments related to this entry, explanation can be looked up in validation_marker.csv type: string - path: res_plants_separated_FR_overseas_territories.csv format: csv encoding: UTF-8 missingValue: "" schema: fields: - name: municipality_code description: French 5-digit INSEE code for Communes type: string - name: municipality description: Name of French Commune type: string - name: energy_source_level_1 description: Type of energy source (e.g. Renewable energy) type: string - name: energy_source_level_2 description: Type of energy source (e.g. Wind, Solar) type: string opsdContentfilter: "true" - name: energy_source_level_3 description: Subtype of energy source (e.g. Biomass and biogas) type: string - name: technology description: Technology to harvest energy source (e.g. Onshore, Photovoltaics) type: string - name: electrical_capacity unit: MW description: Installed electrical capacity in MW type: number - name: number_of_installations description: Number of installations of the energy source subtype in the municipality type: integer - name: lat description: Latitude coordinates type: geopoint - name: lon description: Longitude coordinates type: geopoint - name: data_source description: Source of database entry type: string - path: renewable_power_plants.xlsx format: xlsx - path: validation_marker.csv format: csv encoding: UTF-8 mediatype: text/csv missingValue: "" schema: fields: - name: Validation_Marker description: Name of validation marker utilized in column comment in the renewable_power_plant_germany.csv type: string - name: Explanation description: Comment explaining meaning of validation marker type: string - path: renewable_power_plants_EU.csv format: csv encoding: UTF-8 mediatype: text/csv missingValue: "" schema: fields: - name: energy_source_level_1 description: Type of energy source (e.g. Renewable energy) type: string - name: energy_source_level_2 description: Type of energy source (e.g. Wind, Solar) type: string opsdContentfilter: "true" - name: energy_source_level_3 description: Type of energy source (e.g. Biomass and biogas) type: string - name: electrical_capacity description: Installed electrical capacity in MW unit: MW type: number - name: data_source description: Source of database entry type: string - name: municipality description: The name of the municipality in which the facility is located type: string - name: lon description: Geographical longitude type: number - name: lat description: Geographical latitude type: number - name: commissioning_date type: date format: YYYY-MM-DD description: Date of commissioning of specific unit - name: geographical_resolution description: Precision of geographical information (exact power plant location, municipality, district) type: - name: as_of_year description: Year for which the data source compiled the corresponding dataset type: integer - name: country description: The country in which the facility is located type: number - path: renewable_capacity_timeseries.csv format: csv encoding: UTF-8 mediatype: text/csv missingValue: "" schema: fields: - name: day type: date description: The day of the timeseries entry opsdContentfilter: "true" - name: CH_bioenergy_capacity description: Cumulative bioenergy electrical capacity for Switzerland in MW unit: MW opsdProperties: Region: Switzerland Variable: Bioenergy type: number source: name: Own calculation based on plant-level data from Swiss Federal Office of Energy - name: CH_hydro_capacity description: Cumulative hydro electrical capacity for Switzerland in MW unit: MW opsdProperties: Region: Switzerland Variable: Hydro type: number source: name: Own calculation based on plant-level data from Swiss Federal Office of Energy - name: CH_solar_capacity description: Cumulative solar electrical capacity for Switzerland in MW unit: MW opsdProperties: Region: Switzerland Variable: Solar type: number source: name: Own calculation based on plant-level data from Swiss Federal Office of Energy - name: CH_wind_capacity ription: Cumulative total wind electrical capacity for Switzerland in MW unit: MW opsdProperties: Region: Switzerland Variable: Wind type: number source: name: Own calculation based on plant-level data from Swiss Federal Office of Energy - name: CH_wind_onshore_capacity description: Cumulative onshore wind electrical capacity for Switzerland in MW unit: MW opsdProperties: Region: Switzerland Variable: Wind onshore type: number source: name: Own calculation based on plant-level data from Swiss Federal Office of Energy - name: DE_bioenergy_capacity description: Cumulative bioenergy electrical capacity for Germany in MW unit: MW opsdProperties: Region: Germany Variable: Bioenergy type: number source: name: Own calculation based on plant-level data from BNetzA and Netztransparenz.de - name: DE_geothermal_capacity description: Cumulative geothermal electrical capacity for Germany in MW unit: MW opsdProperties: Region: Germany Variable: Geothermal type: number source: name: Own calculation based on plant-level data from BNetzA and Netztransparenz.de - name: DE_solar_capacity description: Cumulative solar electrical capacity for Germany in MW unit: MW opsdProperties: Region: Germany Variable: Solar type: number source: name: Own calculation based on plant-level data from BNetzA and Netztransparenz.de - name: DE_storage_capacity description: Cumulative storage electrical capacity for Germany in MW unit: MW opsdProperties: Region: Germany Variable: Storage type: number source: name: Own calculation based on plant-level data from BNetzA and Netztransparenz.de - name: DE_wind_offshore_capacity description: Cumulative offshore wind electrical capacity for Germany in MW unit: MW opsdProperties: Region: Germany Variable: Wind offshore type: number source: name: Own calculation based on plant-level data from BNetzA and Netztransparenz.de - name: DE_wind_capacity ription: Cumulative total wind electrical capacity for Germany in MW unit: MW opsdProperties: Region: Germany Variable: Wind type: number source: name: Own calculation based on plant-level data from BNetzA and Netztransparenz.de - name: DE_wind_onshore_capacity description: Cumulative onshore wind electrical capacity for Germany in MW unit: MW opsdProperties: Region: Germany Variable: Wind onshore type: number source: name: Own calculation based on plant-level data from BNetzA and Netztransparenz.de - name: DK_solar_capacity description: Cumulative solar electrical capacity for Denmark in MW unit: MW opsdProperties: Region: Denmark Variable: Solar type: number source: name: Own calculation based on plant-level data from Energinet.dk - name: DK_wind_offshore_capacity description: Cumulative offshore wind electrical capacity for Denmark in MW unit: MW opsdProperties: Region: Denmark Variable: Wind offshore type: number source: name: Own calculation based on plant-level data from Danish Energy Agency - name: DK_wind_capacity ription: Cumulative total wind electrical capacity for Denmark in MW unit: MW opsdProperties: Region: Denmark Variable: Wind type: number source: name: Own calculation based on plant-level data from Danish Energy Agency - name: DK_wind_onshore_capacity description: Cumulative onshore wind electrical capacity for Denmark in MW unit: MW opsdProperties: Region: Denmark Variable: Wind onshore type: number source: name: Own calculation based on plant-level data from Danish Energy Agency - name: GB-GBN_bioenergy_capacity description: Cumulative bioenergy electrical capacity for Great Britain (England, Scotland, Wales) in MW unit: MW opsdProperties: Region: Great Britain (England, Scotland, Wales) Variable: Bioenergy type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-GBN_hydro_capacity description: Cumulative hydro electrical capacity for Great Britain (England, Scotland, Wales) in MW unit: MW opsdProperties: Region: Great Britain (England, Scotland, Wales) Variable: Hydro type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-GBN_marine_capacity description: Cumulative marine electrical capacity for Great Britain (England, Scotland, Wales) in MW unit: MW opsdProperties: Region: Great Britain (England, Scotland, Wales) Variable: Marine type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-GBN_solar_capacity description: Cumulative solar electrical capacity for Great Britain (England, Scotland, Wales) in MW unit: MW opsdProperties: Region: Great Britain (England, Scotland, Wales) Variable: Solar type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-GBN_wind_offshore_capacity description: Cumulative offshore wind electrical capacity for Great Britain (England, Scotland, Wales) in MW unit: MW opsdProperties: Region: Great Britain (England, Scotland, Wales) Variable: Wind offshore type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-GBN_wind_capacity description: Cumulative total wind electrical capacity for Great Britain (England, Scotland, Wales) in MW unit: MW opsdProperties: Region: Great Britain (England, Scotland, Wales) Variable: Wind type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-GBN_wind_onshore_capacity description: Cumulative onshore wind electrical capacity for Great Britain (England, Scotland, Wales) in MW unit: MW opsdProperties: Region: Great Britain (England, Scotland, Wales) Variable: Wind onshore type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-NIR_bioenergy_capacity description: Cumulative bioenergy electrical capacity for Northern Ireland in MW unit: MW opsdProperties: Region: Northern Ireland Variable: Bioenergy type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-NIR_marine_capacity description: Cumulative marine electrical capacity for Northern Ireland in MW unit: MW opsdProperties: Region: Northern Ireland Variable: Marine type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-NIR_solar_capacity description: Cumulative solar electrical capacity for Northern Ireland in MW unit: MW opsdProperties: Region: Northern Ireland Variable: Solar type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-NIR_wind_capacity description: Cumulative total wind electrical capacity for Northern Ireland in MW unit: MW opsdProperties: Region: Northern Ireland Variable: Wind type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-NIR_wind_onshore_capacity description: Cumulative onshore wind electrical capacity for Northern Ireland in MW unit: MW opsdProperties: Region: Northern Ireland Variable: Wind onshore type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-UKM_bioenergy_capacity description: Cumulative bioenergy electrical capacity for the United Kingdom of Great Britain and Northern Ireland in MW unit: MW opsdProperties: Region: United Kingdom of Great Britain and Northern Ireland Variable: Bioenergy type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-UKM_hydro_capacity description: Cumulative hydro electrical capacity for the United Kingdom of Great Britain and Northern Ireland in MW unit: MW opsdProperties: Region: United Kingdom of Great Britain and Northern Ireland Variable: Hydro type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-UKM_marine_capacity description: Cumulative marine electrical capacity for the United Kingdom of Great Britain and Northern Ireland in MW unit: MW opsdProperties: Region: United Kingdom of Great Britain and Northern Ireland Variable: Marine type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-UKM_solar_capacity description: Cumulative solar electrical capacity for the United Kingdom of Great Britain and Northern Ireland in MW unit: MW opsdProperties: Region: United Kingdom of Great Britain and Northern Ireland Variable: Solar type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-UKM_wind_offshore_capacity description: Cumulative offshore wind electrical capacity for the United Kingdom of Great Britain and Northern Ireland in MW unit: MW opsdProperties: Region: United Kingdom of Great Britain and Northern Ireland Variable: Wind offshore type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-UKM_wind_capacity description: Cumulative total wind electrical capacity for the United Kingdom of Great Britain and Northern Ireland in MW unit: MW opsdProperties: Region: United Kingdom of Great Britain and Northern Ireland Variable: Wind type: number source: name: Own calculation based on plant-level data from BEIS - name: GB-UKM_wind_onshore_capacity description: Cumulative onshore wind electrical capacity for the United Kingdom of Great Britain and Northern Ireland in MW unit: MW opsdProperties: Region: United Kingdom of Great Britain and Northern Ireland Variable: Wind onshore type: number source: name: Own calculation based on plant-level data from BEIS sources: - title: BNetzA path: https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/ErneuerbareEnergien/ZahlenDatenInformationen/VOeFF_Registerdaten/2018_12_Veroeff_RegDaten.xlsx?__blob=publicationFile&v=2 description: Bundesnetzagentur register of renewable power plants (excl. PV) - title: BNetzA_PV path: https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/ErneuerbareEnergien/ZahlenDatenInformationen/PV_Datenmeldungen/Meldungen_Juli17-Dez18.xlsx?__blob=publicationFile&v=2 description: Bundesnetzagentur register of PV power plants - title: BNetzA_PV_historic path: https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/ErneuerbareEnergien/ZahlenDatenInformationen/PV_Datenmeldungen/Archiv_PV/Meldungen_Aug-Juni2017.xlsx?__blob=publicationFile&v=2 description: Bundesnetzagentur register of PV power plants - title: TransnetBW, TenneT, Amprion, 50Hertz, Netztransparenz.de path: https://www.netztransparenz.de/de/Anlagenstammdaten.htm description: Netztransparenz.de - information platform of German TSOs (register of renewable power plants in their control area) - title: Postleitzahlen Deutschland path: http://www.suche-postleitzahl.org/downloads description: Zip codes of Germany linked to geo-information - title: Energinet.dk path: http://www.energinet.dk/SiteCollectionDocuments/Danske%20dokumenter/El/SolcelleGraf.xlsx description: register of Danish wind power plants - title: Energistyrelsen path: https://ens.dk/sites/ens.dk/files/Statistik/anlaegprodtilnettet.xls description: ens.dk - register of Danish Wind power plants - title: GeoNames path: http://download.geonames.org/export/zip/ description: geonames.org - title: Ministry for the Ecological and Inclusive Transition path: https://www.statistiques.developpement-durable.gouv.fr/donnees-locales-relatives-aux-installations-de-production-delectricite-renouvelable-beneficiant-0?rubrique=23&dossier=189 - title: OpenDataSoft path: http://public.opendatasoft.com/explore/dataset/correspondance-code-insee-code-postal/download/'\ '?format=csv&refine.statut=Commune%20simple&timezone=Europe/Berlin&use_labels_for_header=true description: Code Postal - Code INSEE - title: Urzad Regulacji Energetyki (URE) path: http://www.ure.gov.pl/uremapoze/mapa.html description: Energy Regulatory Office of Poland - title: Bundesamt für Energie (BFE) path: https://www.bfe.admin.ch/bfe/de/home/foerderung/erneuerbare-energien/einspeiseverguetung/_jcr_content/par/tabs/items/tab/tabpar/externalcontent.external.exturl.xlsx/aHR0cHM6Ly9wdWJkYi5iZmUuYWRtaW4uY2gvZGUvcHVibGljYX/Rpb24vZG93bmxvYWQvOTMxMC54bHN4.xlsx description: Swiss Federal Office of Energy - title: UK Government Department of Business, Energy & Industrial Strategy (BEIS) path: https://www.gov.uk/government/publications/renewable-energy-planning-database-monthly-extract description: Renewable Energy Planning Database quarterly extract contributors: - title: Ingmar Schlecht role: Maintainer, developer organization: Neon GmbH email: schlecht@neon-energie.de - title: Milos Simic role: Developer email: milos.simic.ms@gmail.com """ metadata = yaml.load(metadata) metadata['homepage'] = 'https://data.open-power-system-data.org/renewable_power_plants/'+settings['version'] metadata['id'] = 'https://doi.org/10.25832/renewable_power_plants/'+settings['version'] metadata['last_changes'] = settings['changes'] metadata['version'] = settings['version'] lastYear = int(settings['version'][0:4])-1 metadata['temporal'] = { 'referenceDate': str(lastYear)+'-12-31' } metadata['documentation'] = 'https://github.com/Open-Power-System-Data/renewable_power_plants/blob/'+settings['version']+'/main.ipynb' datapackage_json = json.dumps(metadata, indent=4, separators=(',', ': '), ensure_ascii=False) # Write the information of the metadata with open(os.path.join(package_path, 'datapackage.json'), 'w', encoding='utf-8') as f: f.write(datapackage_json) f.close() # ## Generate checksums # # Generates checksums.txt # In[ ]: def get_sha_hash(path, blocksize=65536): sha_hasher = hashlib.sha256() with open(path, 'rb') as f: buffer = f.read(blocksize) while len(buffer) > 0: sha_hasher.update(buffer) buffer = f.read(blocksize) return sha_hasher.hexdigest() files = [ 'validation_marker.csv', 'renewable_power_plants.sqlite', 'renewable_power_plants.xlsx', ] for country in countries_including_dirty: files.append(table_names[country]+'.csv') files.append('renewable_capacity_timeseries.csv') files.append('renewable_power_plants_EU.csv') with open('checksums.txt', 'w') as f: for file_name in sorted(files): print(file_name) file_hash = get_sha_hash(os.path.join(package_path, file_name)) f.write('{},{}\n'.format(file_name, file_hash)) print('Done!')

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