2.4 MiB
2.4 MiB
In [1]:
import pandas as pd %matplotlib inline import numpy as np import matplotlib.pyplot as plt import seaborn as sns import os
In [2]:
df_master=pd.read_csv('master_subsector_2018_eurostat_based.csv') df_master['UTC']=pd.to_datetime(df_master['UTC']) df_master=df_master.set_index('UTC') df_master=df_master[[col for col in df_master if any(x in col for x in ['total', 'off', 'on','usolar'])]] df_master.head()
Out[2]:
| AT_total | BE_total | BG_total | CZ_total | DE_total | DK_total | EE_total | ES_total | FI_total | FR_total | ... | RO_usolar | ES_usolar | PT_usolar | NO_usolar | SE_usolar | FI_usolar | DK_usolar | IE_usolar | LU_usolar | MA_usolar | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| UTC | |||||||||||||||||||||
| 2018-01-01 00:00:00 | 30.907469 | 36.313481 | 10.400939 | 25.566075 | 207.377555 | 13.906968 | 3.620307 | 81.854783 | 31.926282 | 144.456166 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2018-01-01 01:00:00 | 30.303309 | 36.349154 | 10.302227 | 25.010636 | 203.273983 | 13.646207 | 3.632257 | 81.714458 | 32.017459 | 151.752907 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2018-01-01 02:00:00 | 30.230933 | 36.615313 | 10.251960 | 24.856255 | 202.762926 | 13.538320 | 3.478149 | 81.976657 | 32.155874 | 152.133556 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2018-01-01 03:00:00 | 31.586080 | 36.834050 | 10.387979 | 24.824960 | 203.086188 | 13.540221 | 3.557765 | 82.551277 | 32.435982 | 152.316397 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2018-01-01 04:00:00 | 32.051912 | 37.147452 | 10.753465 | 23.648323 | 207.157800 | 13.774787 | 3.669802 | 84.717832 | 32.798338 | 156.382365 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
5 rows × 91 columns
In [3]:
demand_countries=[col[:2] for col in df_master.columns if 'total' in col] off_countries = list(set([col[:2] for col in df_master.columns if 'off' in col]).intersection(set(demand_countries))) on_countries = list(set([col[:2] for col in df_master.columns if 'on' in col]).intersection(set(demand_countries))) solar_countries=list(set([col[:2] for col in df_master.columns if 'usolar' in col]).intersection(set(demand_countries))) print('demand ', len(demand_countries), demand_countries) print('offshore ', len(off_countries), off_countries) print('onshore ',len(on_countries), on_countries) print('solar ',len(solar_countries), solar_countries)
demand 26 ['AT', 'BE', 'BG', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'NL', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK', 'UK'] offshore 9 ['IT', 'GR', 'NL', 'DE', 'ES', 'FR', 'UK', 'LT', 'DK'] onshore 26 ['EE', 'IT', 'BE', 'HR', 'RO', 'DE', 'LU', 'FR', 'UK', 'LT', 'HU', 'IE', 'SE', 'DK', 'BG', 'FI', 'LV', 'SK', 'GR', 'CZ', 'ES', 'PT', 'AT', 'NL', 'SI', 'PL'] solar 26 ['EE', 'IT', 'BE', 'HR', 'RO', 'DE', 'LU', 'FR', 'UK', 'LT', 'HU', 'IE', 'SE', 'DK', 'BG', 'FI', 'LV', 'SK', 'GR', 'CZ', 'ES', 'PT', 'AT', 'NL', 'SI', 'PL']
In [4]:
annual=pd.read_excel('eurostat_annual_18.xlsx', sheet_name='Sheet 1', skiprows=9, usecols=[0,1,3,5,7], index_col=0) annual.index.name='country' annual_demand=pd.DataFrame(columns=['country', 'TWh']) for country in demand_countries: _annual_demand=annual.loc[country].sum()*11.63/1000 annual_demand=annual_demand.append({'country':country, 'TWh':_annual_demand}, ignore_index=True) annual_demand=annual_demand.set_index('country') annual_demand
Out[4]:
| TWh | |
|---|---|
| country | |
| AT | 289.611423 |
| BE | 371.321477 |
| BG | 109.006827 |
| CZ | 270.227702 |
| DE | 2263.612028 |
| DK | 151.322582 |
| EE | 31.733618 |
| ES | 873.960773 |
| FI | 277.389456 |
| FR | 1530.912724 |
| GR | 160.078809 |
| HR | 73.842359 |
| HU | 197.302950 |
| IE | 125.761005 |
| IT | 1261.879423 |
| LT | 43.753223 |
| LU | 42.863528 |
| LV | 60.846997 |
| NL | 472.015180 |
| PL | 758.512089 |
| PT | 178.831021 |
| RO | 260.394537 |
| SE | 339.569251 |
| SI | 55.992635 |
| SK | 111.206060 |
| UK | 1353.314483 |
In [5]:
df=pd.DataFrame(index=df_master.index) df.index.name='UTC' df['EU_demand']=df_master[[col for col in df_master.columns if 'total' in col]].sum(axis=1) # in GW GWh_2018=df['EU_demand'].sum() df['nuclear']=np.full((8760,1), GWh_2018*0.15/8760 ) off_base=annual_demand.loc[off_countries, 'TWh'].sum() df['offshore_CF']=0 for country in off_countries: df['offshore_CF']=df['offshore_CF']+df_master['%s_off' %country]/100*annual_demand.loc[country, 'TWh']/off_base df['onshore_CF']=0 df['solar_CF']=0 for country in on_countries: df['onshore_CF']=df['onshore_CF']+df_master['%s_on' %country]/100*annual_demand.loc[country, 'TWh']*1000/GWh_2018 df['solar_CF']=df['solar_CF']+df_master['%s_usolar' %country]/100*annual_demand.loc[country, 'TWh']*1000/GWh_2018 IC_offshore=GWh_2018/8760*0.85/3/df['offshore_CF'].mean() # offshore wind installed capacity in GW IC_onshore=GWh_2018/8760*0.85/3/df['onshore_CF'].mean() # onshore wind installed capacity in GW IC_solar=GWh_2018/8760*0.85/3/df['solar_CF'].mean() # onshore wind installed capacity in GW df['offshore']=df['offshore_CF']*IC_offshore df['onshore']=df['onshore_CF']*IC_onshore df['solar']=df['solar_CF']*IC_solar df.describe()
Out[5]:
| EU_demand | nuclear | offshore_CF | onshore_CF | solar_CF | offshore | onshore | solar | |
|---|---|---|---|---|---|---|---|---|
| count | 8760.000000 | 8.760000e+03 | 8760.000000 | 8760.000000 | 8760.000000 | 8760.000000 | 8760.000000 | 8760.000000 |
| mean | 1331.650932 | 1.997476e+02 | 0.399863 | 0.362706 | 0.203929 | 377.301097 | 377.301097 | 377.301097 |
| std | 399.254971 | 5.684666e-14 | 0.179676 | 0.140623 | 0.249004 | 169.538125 | 146.281480 | 460.697145 |
| min | 433.541746 | 1.997476e+02 | 0.037394 | 0.074668 | 0.000000 | 35.283879 | 77.673073 | 0.000000 |
| 25% | 1067.356853 | 1.997476e+02 | 0.260658 | 0.257549 | 0.000000 | 245.951011 | 267.912569 | 0.000000 |
| 50% | 1342.500462 | 1.997476e+02 | 0.380575 | 0.346048 | 0.048864 | 359.101997 | 359.972949 | 90.406982 |
| 75% | 1602.726970 | 1.997476e+02 | 0.520107 | 0.451747 | 0.420266 | 490.761038 | 469.925439 | 777.559668 |
| max | 2775.065402 | 1.997476e+02 | 0.929582 | 0.847565 | 0.812906 | 877.131947 | 881.671132 | 1504.005623 |
In [14]:
print('solar installed capacity = ' , IC_solar , 'GW') print('offshore installed capacity = ', IC_offshore, 'GW') print('onshore installed capacity = ', IC_onshore, 'GW')
solar installed capacity = 1850.1596889099628 GW offshore installed capacity = 943.5766806425966 GW onshore installed capacity = 1040.2395566214755 GW
In [17]:
df['nuclear'].mean()+df['offshore'].mean()+df['onshore'].mean()+df['solar'].mean()
Out[17]:
1331.6509315068515
In [18]:
df.to_csv('2018_EU_hourly_generation_demand.csv') df.head()
Out[18]:
| EU_demand | nuclear | offshore_CF | onshore_CF | solar_CF | offshore | onshore | solar | |
|---|---|---|---|---|---|---|---|---|
| UTC | ||||||||
| 2018-01-01 00:00:00 | 1144.947181 | 199.74764 | 0.708406 | 0.759499 | 0.0 | 668.435470 | 790.060699 | 0.0 |
| 2018-01-01 01:00:00 | 1132.042655 | 199.74764 | 0.747232 | 0.752009 | 0.0 | 705.071029 | 782.269316 | 0.0 |
| 2018-01-01 02:00:00 | 1129.987387 | 199.74764 | 0.780887 | 0.753773 | 0.0 | 736.827124 | 784.104426 | 0.0 |
| 2018-01-01 03:00:00 | 1132.673800 | 199.74764 | 0.773395 | 0.750711 | 0.0 | 729.757327 | 780.919662 | 0.0 |
| 2018-01-01 04:00:00 | 1142.915227 | 199.74764 | 0.767901 | 0.743725 | 0.0 | 724.573068 | 773.652314 | 0.0 |
In [19]:
df.describe()
Out[19]:
| EU_demand | nuclear | offshore_CF | onshore_CF | solar_CF | offshore | onshore | solar | |
|---|---|---|---|---|---|---|---|---|
| count | 8760.000000 | 8.760000e+03 | 8760.000000 | 8760.000000 | 8760.000000 | 8760.000000 | 8760.000000 | 8760.000000 |
| mean | 1331.650932 | 1.997476e+02 | 0.399863 | 0.362706 | 0.203929 | 377.301097 | 377.301097 | 377.301097 |
| std | 399.254971 | 5.684666e-14 | 0.179676 | 0.140623 | 0.249004 | 169.538125 | 146.281480 | 460.697145 |
| min | 433.541746 | 1.997476e+02 | 0.037394 | 0.074668 | 0.000000 | 35.283879 | 77.673073 | 0.000000 |
| 25% | 1067.356853 | 1.997476e+02 | 0.260658 | 0.257549 | 0.000000 | 245.951011 | 267.912569 | 0.000000 |
| 50% | 1342.500462 | 1.997476e+02 | 0.380575 | 0.346048 | 0.048864 | 359.101997 | 359.972949 | 90.406982 |
| 75% | 1602.726970 | 1.997476e+02 | 0.520107 | 0.451747 | 0.420266 | 490.761038 | 469.925439 | 777.559668 |
| max | 2775.065402 | 1.997476e+02 | 0.929582 | 0.847565 | 0.812906 | 877.131947 | 881.671132 | 1504.005623 |
In [28]:
plt.figure(figsize=(40,15)) plt.plot(df['EU_demand'], color='grey', label='demand') plt.stackplot(df.index , df[['nuclear', 'offshore', 'onshore', 'solar']].values.T, labels=['nuclear', 'offshore', 'onshore', 'solar']) plt.xlabel('date', size=28) plt.ylabel('GWh', size=28) plt.title('EU 2018 hourly generation and demand', size=36, pad=20) plt.xlim(df.index[0], df.index[-1]) plt.xticks(size=24) plt.yticks(size=24) plt.legend(prop={'size': 22}) plt.savefig('generation_demand.png') plt.show()
In [27]:
plt.figure(figsize=(40,15)) plt.stackplot(pd.date_range(start='2018-01-01', end='2018-12-31', freq='1D') , (df[['nuclear', 'offshore', 'onshore', 'solar']].groupby(df.index.date).mean()).values.T, labels=['nuclear', 'offshore', 'onshore', 'solar']) plt.plot(df['EU_demand'].groupby(df.index.date).mean(), color='grey', label='demand', linewidth=3) plt.xlabel('date', size=28) plt.ylabel('GWh/h', size=28) plt.title('EU 2018 daily average generation and demand', size=36, pad=20) plt.xlim(df.index[0], df.index[-1]) plt.xticks(size=24) plt.yticks(size=24) plt.legend(prop={'size': 22}) plt.savefig('generation_demand_daily_average.png') plt.show()
In [24]:
df['residual_demand']=df['EU_demand']-df['nuclear']-df['offshore']-df['onshore']-df['solar']
In [25]:
plt.figure(figsize=(20,10)) plt.plot(df['residual_demand']) plt.hlines(y=0, xmax=df.index[-1], xmin=df.index[0]) plt.xlim(df.index[0], df.index[-1]) plt.xlabel('date', size=16) plt.ylabel('GWh' , size=16) plt.xticks(size=16) plt.yticks(size=16) plt.title('EU 2018 hourly residual demand', size=20, pad=10) plt.savefig('residual_demand.png') plt.show()
In [30]:
plt.figure(figsize=(40,15)) plt.stackplot([idx for idx in df.index if idx.month==2] , df.loc[df.index.month==2, ['nuclear', 'offshore', 'onshore', 'solar']].values.T, labels=['nuclear', 'offshore', 'onshore', 'solar']) plt.plot(df.loc[df.index.month==2, 'EU_demand'], color='grey', label='demand', linewidth=3) plt.xlabel('date', size=28) plt.ylabel('GWh', size=28) plt.title('EU Feb 2018 hourly generation and demand', size=36, pad=20) plt.xticks(size=24) plt.yticks(size=24) plt.legend(prop={'size': 24}) plt.xlim(pd.to_datetime('2018-02-01 00:00:00'), pd.to_datetime('2018-02-28 23:00:00')) plt.savefig('generation_demand_winter.png') plt.show()
In [32]:
plt.figure(figsize=(40,15)) plt.stackplot(pd.date_range(start='2018-02-27 00:00:00' , end='2018-03-01 23:00:00', freq='60min'), df.loc[(df.index>=pd.to_datetime('2018-02-27 00:00:00')) & (df.index<=pd.to_datetime('2018-03-01 23:00:00')), ['nuclear', 'offshore', 'onshore', 'solar']].values.T, labels=['nuclear', 'offshore', 'onshore', 'solar']) plt.plot(df.loc[(df.index>=pd.to_datetime('2018-02-27 00:00:00')) & (df.index<=pd.to_datetime('2018-03-01 23:00:00')) , 'EU_demand'], color='grey', label='demand', linewidth=3) plt.xlabel('date', size=28) plt.ylabel('GWh', size=28) plt.title('EU 2018 hourly generation and demand on peak days', size=36, pad=20) plt.xticks(size=24) plt.yticks(size=24) plt.legend(prop={'size': 24}) plt.xlim(pd.to_datetime('2018-02-27 00:00:00'), pd.to_datetime('2018-03-01 23:00:00')) plt.savefig('generation_demand_peak.png') plt.show()
In [34]:
plt.figure(figsize=(40,15)) plt.stackplot(pd.date_range(start='2018-06-15 00:00:00' , end='2018-07-15 23:00:00', freq='60min'), df.loc[(df.index>=pd.to_datetime('2018-06-15 00:00:00')) & (df.index<=pd.to_datetime('2018-07-15 23:00:00')), ['nuclear', 'offshore', 'onshore', 'solar']].values.T, labels=['nuclear', 'offshore', 'onshore', 'solar']) plt.plot(df.loc[(df.index>=pd.to_datetime('2018-06-15 00:00:00')) & (df.index<=pd.to_datetime('2018-07-15 23:00:00')) , 'EU_demand'], color='grey', label='demand', linewidth=3) plt.xlabel('date', size=28) plt.ylabel('GWh', size=28) plt.title('EU summer 2018 hourly generation and demand', size=36, pad=20) plt.xticks(size=24) plt.yticks(size=24) plt.legend(prop={'size': 24}) plt.xlim(pd.to_datetime('2018-06-15 00:00:00'), pd.to_datetime('2018-07-15 23:00:00')) plt.savefig('generation_demand_summer.png') plt.show()
In [ ]: