Using with a spreadsheet-based configuration
ecodynelec offers the possibility to configure the execution via a
spreadsheet, for more user-friendly handling. This tutorial shows the
different steps and possibilities in this regard. As ecodynelec is
firstly designed for research purposes, there also is the possibility to
entirely rely on a python-based configuration , as developed in the
“Using fully with
Python”
tutorial.
Initialization
To download and install ecodynelec to being used as a python
package, the user is referred to either the getting started
tutorial.
Configuration
The configuration of ecodynelec is to be handled in a spreadsheet.
Figure 1-3 show the three sections of the spreadsheet used in this
example. The spreadsheet can be downloaded from the github
repository.
The configuration is composed of 3 parts. The detail about the meaning of each is developed in the input data section.
the first tab (Figure 1) contains the elements to configure the execution itself.
the second tab (Figure 2) deals with all paths to information files,
directory containing information, or where to write and save information
before, during and after the computation. Note that, for the
generation, exchanges and savedir directories fields, the
specified directory will be created if it does not already exist. For
every other file path element, a default file is used if the field is
left blank, and an error is returned if the information passed does not
correspond to any existing file on your local machine.
the third tab (Figure 3) deals with information related to the ENTSO-E
server, as electricity data from the ENTSO-E server is at the center of
ecodynelec. More on this topic is covered on the next paragraph and
on the dedicated downloading
tutorial.
Note that per default, the field use server is set to FALSE,
i.e. no download will occur. Also note that the username field is
supposed to be an email address.
Downloading Entso-E data
The downloading
tutorial
covers the specificities about how to download the ENTSO-E data or
include the download as part of the ecodynelec pipeline execution.
This feature is not triggered per default and ecodynelec is
expecting to find already downloaded ENTSO-E files.
Execution
ecodynelec is build out of a myriad of modules that can be used
relatively independently, under the condition that inputs data is shaped
the correct way. Fortunately, the entire pipeline starting from a set of
parameters and computing down to the calculation of impact metrics.
The usage of this entire pipeline is demonstrated below. This pipeline
allows to save results into files (c.f. paragraph on
configuration).
However results are also always returned for further in-script use.
These results are stored in the impacts variable for later
paragraphs in this tutorial.
from ecodynelec.pipelines import execute
impacts = execute(config="./Spreadsheet_example.xlsx", is_verbose=True)
Load auxiliary datasets...
Load generation data...
Generation data.
Data loading: 0.02 sec..
Memory usage table: 0.18 MB
Autocomplete... 5/5)...
Completed.
Extraction raw generation: 0.13 sec.
Extraction time: 0.16 sec.
4/4 - Resample exchanges to H steps...
Get and reduce importation data...
Cross-border flow data.
Data loading: 0.01 sec..
Memory usage table: 0.04 MB
Autocomplete... ...
Completed.
Extraction raw import: 0.09 sec.
Extraction time: 0.11 sec.
Resample exchanges to H steps...
Gather generation and importation...
Import of data: 0.3 sec
Importing information...
Tracking origin of electricity...
compute for day 1/1
Electricity tracking: 0.3 sec.
Compute the electricity impacts...
Global...
Carbon intensity...
Human carcinogenic toxicity...
Fine particulate matter formation...
Land use...
Impact computation: 0.0 sec.
Adapt timezone: UTC >> UTC
done.
Outcome and Visualization
The outcome is stored in files and returned for further in-script use.
In the previous section, results were stored in the impacts
variable. The current section highlights the content returned and shows
some basic possibilities for data visualization.
import numpy as np
import pandas as pd
Description of the outcome
The impacts variable contains a collection of tables. This
collection is a dict with one Global key, and one other key per
impact category:
print(impacts.keys())
dict_keys(['Global', 'Carbon intensity', 'Human carcinogenic toxicity', 'Fine particulate matter formation', 'Land use'])
The Global table is the sum across all technologies for each
index, as it is shown for the first few time steps:
display(impacts['Global'].head())
| Carbon intensity | Human carcinogenic toxicity | Fine particulate matter formation | Land use | |
|---|---|---|---|---|
| 2017-02-01 00:00:00 | 0.459054 | 0.030574 | 0.000351 | 0.007278 |
| 2017-02-01 01:00:00 | 0.459154 | 0.030907 | 0.000351 | 0.007191 |
| 2017-02-01 02:00:00 | 0.447345 | 0.030145 | 0.000344 | 0.007016 |
| 2017-02-01 03:00:00 | 0.447053 | 0.030208 | 0.000347 | 0.006967 |
| 2017-02-01 04:00:00 | 0.454442 | 0.030573 | 0.000358 | 0.006873 |
The other tables are, for each impact category, the breakdown into all possible sources:
for i in impacts: # Iterate for all impact categories
if i=='Global': continue; # Skip the Global, already visualized above.
print(f"#############\nimpacts for {i}:")
display( impacts[i].head(3).T ) # Transpose table for readability
#############
impacts for Carbon intensity:
| 2017-02-01 00:00:00 | 2017-02-01 01:00:00 | 2017-02-01 02:00:00 | |
|---|---|---|---|
| Carbon intensity_source | |||
| Mix_Other | 0.006730 | 0.006343 | 0.006455 |
| Biomass_AT | 0.000331 | 0.000301 | 0.000302 |
| Fossil_Brown_coal/Lignite_AT | 0.000000 | 0.000000 | 0.000000 |
| Fossil_Coal-derived_gas_AT | 0.000000 | 0.000000 | 0.000000 |
| Fossil_Gas_AT | 0.017903 | 0.016169 | 0.016806 |
| ... | ... | ... | ... |
| Other_renewable_IT | 0.000000 | 0.000000 | 0.000000 |
| Solar_IT | 0.000000 | 0.000000 | 0.000000 |
| Waste_IT | 0.000000 | 0.000000 | 0.000000 |
| Wind_Offshore_IT | 0.000000 | 0.000000 | 0.000000 |
| Wind_Onshore_IT | 0.000000 | 0.000000 | 0.000000 |
101 rows × 3 columns
#############
impacts for Human carcinogenic toxicity:
| 2017-02-01 00:00:00 | 2017-02-01 01:00:00 | 2017-02-01 02:00:00 | |
|---|---|---|---|
| Human carcinogenic toxicity_source | |||
| Mix_Other | 0.000449 | 0.000423 | 0.000430 |
| Biomass_AT | 0.000023 | 0.000021 | 0.000021 |
| Fossil_Brown_coal/Lignite_AT | 0.000000 | 0.000000 | 0.000000 |
| Fossil_Coal-derived_gas_AT | 0.000000 | 0.000000 | 0.000000 |
| Fossil_Gas_AT | 0.000129 | 0.000116 | 0.000121 |
| ... | ... | ... | ... |
| Other_renewable_IT | 0.000000 | 0.000000 | 0.000000 |
| Solar_IT | 0.000000 | 0.000000 | 0.000000 |
| Waste_IT | 0.000000 | 0.000000 | 0.000000 |
| Wind_Offshore_IT | 0.000000 | 0.000000 | 0.000000 |
| Wind_Onshore_IT | 0.000000 | 0.000000 | 0.000000 |
101 rows × 3 columns
#############
impacts for Fine particulate matter formation:
| 2017-02-01 00:00:00 | 2017-02-01 01:00:00 | 2017-02-01 02:00:00 | |
|---|---|---|---|
| Fine particulate matter formation_source | |||
| Mix_Other | 0.000010 | 0.000009 | 0.000009 |
| Biomass_AT | 0.000001 | 0.000001 | 0.000001 |
| Fossil_Brown_coal/Lignite_AT | 0.000000 | 0.000000 | 0.000000 |
| Fossil_Coal-derived_gas_AT | 0.000000 | 0.000000 | 0.000000 |
| Fossil_Gas_AT | 0.000006 | 0.000005 | 0.000005 |
| ... | ... | ... | ... |
| Other_renewable_IT | 0.000000 | 0.000000 | 0.000000 |
| Solar_IT | 0.000000 | 0.000000 | 0.000000 |
| Waste_IT | 0.000000 | 0.000000 | 0.000000 |
| Wind_Offshore_IT | 0.000000 | 0.000000 | 0.000000 |
| Wind_Onshore_IT | 0.000000 | 0.000000 | 0.000000 |
101 rows × 3 columns
#############
impacts for Land use:
| 2017-02-01 00:00:00 | 2017-02-01 01:00:00 | 2017-02-01 02:00:00 | |
|---|---|---|---|
| Land use_source | |||
| Mix_Other | 0.000194 | 0.000182 | 0.000186 |
| Biomass_AT | 0.001016 | 0.000926 | 0.000926 |
| Fossil_Brown_coal/Lignite_AT | 0.000000 | 0.000000 | 0.000000 |
| Fossil_Coal-derived_gas_AT | 0.000000 | 0.000000 | 0.000000 |
| Fossil_Gas_AT | 0.000066 | 0.000060 | 0.000062 |
| ... | ... | ... | ... |
| Other_renewable_IT | 0.000000 | 0.000000 | 0.000000 |
| Solar_IT | 0.000000 | 0.000000 | 0.000000 |
| Waste_IT | 0.000000 | 0.000000 | 0.000000 |
| Wind_Offshore_IT | 0.000000 | 0.000000 | 0.000000 |
| Wind_Onshore_IT | 0.000000 | 0.000000 | 0.000000 |
101 rows × 3 columns
Group per country
The following piece of code suggests a basic visualization of the Carbon intensity category, grouping the results per country of origin of the tracked electricity.
def compute_per_country(results):
"""Function to group results per country"""
countries = np.unique([c.split("_")[-1] for c in results.columns]) # List of countries
per_country = []
for c in countries:
cols = [k for k in results.columns if k[-3:]==f"_{c}"]
per_country.append(pd.Series(results.loc[:,cols].sum(axis=1), name=c))
return pd.concat(per_country,axis=1)
gwp_per_country = compute_per_country(impacts['Carbon intensity']) # Group Carbon intensity index impacts per country
gwp_per_country.plot.area(figsize=(12,4), legend='reverse', color=['r','w','y','b','c','k'],
title="Some visualization of the Carbon intensity index aggregated per country"); # Build the graph
Group per production type
The following piece of code suggests a basic visualization of the Carbon intensity category, grouping the results per technology of origin of the tracked electricity.
def compute_per_type(results):
"""Function to group datasets per type of unit, regardless of the country of origin"""
unit_list = np.unique([k[:-3] if k[-3]=="_" else k for k in results.columns]) # List the different production units
per_unit = []
for u in unit_list:
cols = [k for k in results.columns if k[:-3]==u] # collect the useful columns
per_unit.append(pd.Series(results.loc[:,cols].sum(axis=1), name=u)) # aggregate
return pd.concat(per_unit,axis=1)
es13_per_type = compute_per_type(impacts['Carbon intensity']) # Group Carbon intensity index impacts per country
es13_per_type.plot.area(figsize=(12,8), legend='reverse',
title="Some visualization of the Carbon intensity index aggregated per source"); # Build the graph
