Electricity-Data-Pipeline is a tool that allows people in the field of energy systems to extract the Great Britain electricity market (a.k.a. balancing mechanism) data. The origin of the data is Balancing Mechanism Reporting Service BMRS.
This tool was developed as a means of easier data extraction for our paper on the impact of the first COVID-19 lockdown on the electricity system here.
The dataset used in this publication is also made available on DataShare. Hence, if you are interested in the same datasets, please click here.
Kirli, Desen; Parzen, Maximilian; Kiprakis, Aristides. 2021. "Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability" Energies 14, no. 3: 635. https://doi.org/10.3390/en14030635
Kirli, Desen; Kiprakis, Aristides; Parzen, Max. (2021). Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability, 2019-2020 [dataset]. University of Edinburgh. School of Engineering. Institute for Energy Systems. https://doi.org/10.7488/ds/2979.
There are multiple ways to use the Electricity-Data-Pipeline:
-
Google Colab: Click
to open our example notebooks.
-
Please check the Prerequisites section below and proceed with your chosen interpreter.
The <Electricity-Data-Pipeline> offers ready-to-use functions to import different sets of electricity data.
Below is a list of all functions defined so far:
Table 1: List of the quick BMRS helper functions
Electricity-Data-Pipeline Quick Functions |
Description | Resolution | Inputs |
|---|---|---|---|
demand() |
Rolling System Demand | 5 min | demand(start_date = 'YYYY-MM-DD', end_date = 'YYYY-MM-DD', save_to_csv = False) |
temperature() |
Average Daily Temperature in Britain | Daily | " |
generation() |
Half-hourly Generation by Fuel Type | Halfhourly (30 min) | " |
frequency() |
System Frequency | 15 sec | " |
initial_demand_national() |
Initial National Demand Out-turn | Halfhourly (30 min) | " |
initial_demand_transmission() |
Initial Transmission System Demand Out-turn | Halfhourly (30 min) | " |
demand_forecast_national() |
National Demand Forecast | Halfhourly (30 min) | " |
demand_forecast_transmission() |
Transmission System Demand Forecast | Halfhourly (30 min) | " |
imbalance_volume() |
Imbalance Volume | Halfhourly (30 min) | " |
loss_of_load() |
Loss of Load and De-rated Margin | Halfhourly (30 min) | " |
imbalance_price() |
Imbalance Price | Halfhourly (30 min) | " |
derived_system_data() |
Derived System Data | Halfhourly (30 min) | " |
extract_data() |
Uses BMRS data label and tries different methods | depends on dataset of choice | extract_data(report_name = 'TEMP', start_date = 'YYYY-MM-DD', end_date = 'YYYY-MM-DD', save_to_csv = True) |
Table 2: List of the data extractions functions for a week or longer periods.
Electricity-Data-Pipeline Function for Weekly/Long-term Imports |
Description | Range | Inputs |
|---|---|---|---|
extract_data_weekly() |
Extracts data for a week from the start_date using the function names from the table above | Fixed - Weekly | extract_data_weekly(func_name = demand , start_date = 'YYYY-MM-DD', save_to_csv = True) |
extract_data_range() |
Extracts data for long timeframes | Variable | extract_data_range(func_name = temperature, start_date = 'YYYY-MM-DD', end_date = 'YYYY-MM-DD', save_to_csv = False) |
extract_data_range_with_BMRS_label() |
Same as above but using BMRS report names rather than the function names from the table above | Variable | data_extract_range_with_BMRS_label(report_name = 'TEMP', start_date = 'YYYY-MM-DD', end_date = 'YYYY-MM-DD', save_to_csv = False) |
Before you begin, ensure you have met the following requirements:
- You have installed
python 3and followed the instructions to download the required packages - Your working directory is set to
your_machine\Electricity-Data-Pipeline - You obtained an API key and pasted it in
api_key.txt(lives inyour_machine\Electricity-Data-Pipeline)
- Register on Elexon's website a new user - here
Click on
sign-in->register
- Follow the instructions here
Log-in -> Click on
my profile-> Copy thescripting key
- Paste the API Key in the
api_key.txtfile - Check that your API Key is saved correctly
- Please check the example notebooks for more detailed information.
To install , follow these steps:
Download as zipped folder from top RHS
or
git clone https://github.com/desenk/Electricity-Data-Pipeline.git
then if using Anaconda
$ conda create --name <env name> --file requirements.txt
or just using pip
$ pip install -r requirements.txt
For more info about the BMRS and its user guide - here
If you want to contact me you can reach me at desen.kirli@ed.ac.uk.
Shield:
This work is licensed under a Creative Commons Attribution 4.0 International License.
Contains BMRS data © Elexon Limited copyright and database right [2021] [license link] (https://www.elexon.co.uk/operations-settlement/bsc-central-services/balancing-mechanism-reporting-agent/copyright-licence-bmrs-data/)