Analysis of penetration of renewables

This notebook shows how to use utils function. In particular we show how to show to % of power production coming from renewables, in a some countries.



In [6]:

    
# Import Electricity Map utilities
from utils import *

# Enable inline plotting
%matplotlib inline 
import matplotlib.pyplot as plt



In [7]:

    
# Fetch data
country_codes = ['DE', 'DK', 'FR']
df_production = get_production(country_codes, '2017-01-01', '2017-01-16', 60)









    



Fetching country DE..
Fetching country DK..
Fetching country FR..



In [8]:

    
# Show the tail of the data frame
df_production.head()









    Out[8]:






  
    
      
      country
      mode
      production
      timestamp
    
  
  
    
      0
      DE
      oil
      NaN
      2017-01-01 08:00:00+00:00
    
    
      1
      DE
      nuclear
      6518.0
      2017-01-01 08:00:00+00:00
    
    
      2
      DE
      gas
      1694.0
      2017-01-01 08:00:00+00:00
    
    
      3
      DE
      biomass
      4733.0
      2017-01-01 08:00:00+00:00
    
    
      4
      DE
      coal
      17373.0
      2017-01-01 08:00:00+00:00



In [9]:

    
# Compute % of renewable production in each country
is_renewable = (df_production['mode'] == 'hydro') | (df_production['mode'] == 'wind') | (df_production['mode'] == 'solar')
df_renewable_production = df_production[is_renewable].groupby(['timestamp', 'country']).sum()
df_renewable_production['total'] = df_production.groupby(['timestamp', 'country']).sum()
df_renewable_production['ratio'] = df_renewable_production['production'] / df_renewable_production['total']
df_renewable_production.head()









    Out[9]:






  
    
      
      
      production
      total
      ratio
    
    
      timestamp
      country
      
      
      
    
  
  
    
      2017-01-01 00:00:00+00:00
      FR
      6667.0
      69369.0
      0.096109
    
    
      2017-01-01 01:00:00+00:00
      FR
      6075.0
      68585.0
      0.088576
    
    
      2017-01-01 02:00:00+00:00
      FR
      5755.0
      67242.0
      0.085586
    
    
      2017-01-01 03:00:00+00:00
      FR
      5412.0
      66127.0
      0.081843
    
    
      2017-01-01 04:00:00+00:00
      FR
      4813.0
      65851.0
      0.073089



In [10]:

    
# Plot ratio of renewable
plt.figure(figsize=(10,3));
df_renewable_production.filter(['ratio']).unstack().plot(ax=plt.gca());
plt.title('Ratio of renewable (hydro, solar and wind) compared to total production');
plt.legend(country_codes);
plt.xlabel('Time');
plt.ylim([0, 1]);



In [ ]:

	country	mode	production	timestamp
0	DE	oil	NaN	2017-01-01 08:00:00+00:00
1	DE	nuclear	6518.0	2017-01-01 08:00:00+00:00
2	DE	gas	1694.0	2017-01-01 08:00:00+00:00
3	DE	biomass	4733.0	2017-01-01 08:00:00+00:00
4	DE	coal	17373.0	2017-01-01 08:00:00+00:00

		production	total	ratio
timestamp	country
2017-01-01 00:00:00+00:00	FR	6667.0	69369.0	0.096109
2017-01-01 01:00:00+00:00	FR	6075.0	68585.0	0.088576
2017-01-01 02:00:00+00:00	FR	5755.0	67242.0	0.085586
2017-01-01 03:00:00+00:00	FR	5412.0	66127.0	0.081843
2017-01-01 04:00:00+00:00	FR	4813.0	65851.0	0.073089