Compute electric mix for all countries

The main pipeline of ecodynelec presented in tutorials 1 and 2 only compute the electricity mix and impacts for one target country.

However ecodynelec can compute the electric mix for all considered countries, all at once. In facts, this information is an intermediate result in the main pipeline. The feature of extracting this intermediate result was added due to its potential usefulness. This tutorial shows how.

In technical words, a function get_inverted_matrix allow to retrieve the invert of technology matrix \((I-A)^{-1}\).

from ecodynelec.pipelines import get_inverted_matrix # Import the function
from ecodynelec.parameter import Parameter # Import the parameter handler object

# Configuration to reduce the number of time steps
my_config = Parameter().from_excel("./Spreadsheet_example.xlsx")
my_config.start="2017-02-01 12:00"
my_config.end="2017-02-01 14:00" # Only 2 hours
my_config.freq = "H" # Only in hourly time step
my_config.path.generation = "./test_data/generations/" # Generation files
my_config.path.exchanges = "./test_data/exchanges/" # Exchanges files

# Execute the function
mix = get_inverted_matrix(config=my_config)

The result is one matrix per time step, all stored in a list. Here is an overview of the first matrix, i.e. corresponding to the first time step.

display(mix[0])

	Mix_AT	Mix_CH	Mix_DE	Mix_FR	Mix_IT	Mix_Other	Biomass_AT	Fossil_Brown_coal/Lignite_AT	Fossil_Coal-derived_gas_AT	Fossil_Gas_AT	...	Hydro_Run-of-river_and_poundage_IT	Hydro_Water_Reservoir_IT	Marine_IT	Nuclear_IT	Other_fossil_IT	Other_renewable_IT	Solar_IT	Waste_IT	Wind_Offshore_IT	Wind_Onshore_IT
Mix_AT	1.000053	2.964993e-02	2.145851e-04	0.0	0.000990	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Mix_CH	0.001788	1.003252e+00	7.260827e-03	0.0	0.033487	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Mix_DE	0.224020	4.487106e-01	1.003247e+00	0.0	0.014977	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Mix_FR	0.007299	2.315305e-01	3.151219e-02	1.0	0.053158	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Mix_IT	0.004988	1.478873e-04	1.070303e-06	0.0	1.000005	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Other_renewable_IT	0.000000	0.000000e+00	0.000000e+00	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Solar_IT	0.000418	1.237887e-05	8.958943e-08	0.0	0.083705	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
Waste_IT	0.000005	1.539660e-07	1.114296e-09	0.0	0.001041	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
Wind_Offshore_IT	0.000000	0.000000e+00	0.000000e+00	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Wind_Onshore_IT	0.000417	1.236860e-05	8.951514e-08	0.0	0.083636	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0

106 rows × 106 columns

Build function to condense and visualize data

import numpy as np # need numpy functions
import pandas as pd # need pandas functions

def group_by_family(matrix):
    families = {'Fossil':['Fossil_Brown_coal/Lignite', 'Fossil_Coal-derived_gas',
                          'Fossil_Gas', 'Fossil_Hard_coal', 'Fossil_Oil', 'Fossil_Oil_shale',
                          'Fossil_Peat','Other_fossil'],
                'Nuclear':['Nuclear'],
                'Hydro':['Hydro_Pumped_Storage','Hydro_Run-of-river_and_poundage',
                         'Hydro_Water_Reservoir', 'Marine'],
                'Other Renwable':['Biomass', 'Geothermal', 'Other_renewable',
                                  'Solar', 'Waste', 'Wind_Offshore', 'Wind_Onshore'],
                'External imports':['Mix']}

    per_family = []
    for f in families:
        idx = [k for k in matrix.index if "_".join(k.split("_")[:-1]) in families[f]]
        per_family.append(pd.Series(matrix.loc[idx].sum(), name=f))
    return pd.concat(per_family, axis=1)


def group_per_country(matrix):
    """Function to group datasets per country"""
    countries = np.unique([k.split("_")[-1] for k in matrix.index])

    per_country = []
    for c in countries:
        idx = [k for k in matrix.index if k.split("_")[-1]==c]
        per_country.append(pd.Series(matrix.loc[idx].sum(), name=c))

    return pd.concat(per_country,axis=1)

Visualize the origin per production type (columns) in each country (index)

# Visualize table grouped by type of plant for 1st time step
group_by_family(mix[0].loc["Mix_Other":, :"Mix_IT"])

	Fossil	Nuclear	Hydro	Other Renwable	External imports
Mix_AT	0.485921	0.030766	0.261810	0.097588	0.123915
Mix_CH	0.303191	0.415665	0.141009	0.135522	0.004613
Mix_DE	0.593127	0.136792	0.030061	0.237861	0.002158
Mix_FR	0.126312	0.713809	0.095752	0.064128	0.000000
Mix_IT	0.666016	0.046303	0.090563	0.196964	0.000154

# Visualize table grouped by type of plant for 2st time step
group_by_family(mix[1].loc["Mix_Other":, :"Mix_IT"])

	Fossil	Nuclear	Hydro	Other Renwable	External imports
Mix_AT	0.504558	0.033226	0.234255	0.097251	0.130711
Mix_CH	0.291884	0.413615	0.163239	0.129777	0.001485
Mix_DE	0.594143	0.141349	0.028476	0.233432	0.002601
Mix_FR	0.130971	0.713597	0.089035	0.066397	0.000000
Mix_IT	0.659795	0.051923	0.104881	0.183339	0.000063

Visualize the origin per country (columns) in each country (index)

# Visualize table grouped by country for 1st time step
group_per_country(mix[0].loc["Mix_Other":, :"Mix_IT"]) # 1st time step

	AT	CH	DE	FR	IT	Other
Mix_AT	0.648394	0.000558	0.215238	0.007299	4.594943e-03	0.123915
Mix_CH	0.019224	0.313375	0.431122	0.231531	1.362325e-04	0.004613
Mix_DE	0.000139	0.002268	0.963922	0.031512	9.859542e-07	0.002158
Mix_FR	0.000000	0.000000	0.000000	1.000000	0.000000e+00	0.000000
Mix_IT	0.000642	0.010460	0.014390	0.053158	9.211959e-01	0.000154

# Visualize table grouped by country for 2nd time step
group_per_country(mix[1].loc["Mix_Other":, :"Mix_IT"]) # 2nd time step

	AT	CH	DE	FR	IT	Other
Mix_AT	0.636863	0.001394	0.218425	0.009160	3.447687e-03	0.130711
Mix_CH	0.001698	0.327280	0.419678	0.249849	9.194443e-06	0.001485
Mix_DE	0.000013	0.002588	0.957950	0.036848	7.269581e-08	0.002601
Mix_FR	0.000000	0.000000	0.000000	1.000000	0.000000e+00	0.000000
Mix_IT	0.000073	0.013993	0.017943	0.058663	9.092658e-01	0.000063