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Welcome to the Electricity-Data-Pipeline 👋

Open In Colab

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.

Publications

Paper

link to paper

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

Dataset

link to dataset

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.

Using Electricity-Data-Pipeline

There are multiple ways to use the Electricity-Data-Pipeline:

  1. Google Colab: Click Open In Colab to open our example notebooks.

  2. Please check the Prerequisites section below and proceed with your chosen interpreter.

Highlights

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)

Prerequisites

Before you begin, ensure you have met the following requirements:

  • You have installed python 3 and 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 in your_machine\Electricity-Data-Pipeline)
  1. Register on Elexon's website a new user - here

Click on sign-in -> register

  1. Follow the instructions here

Log-in -> Click on my profile -> Copy the scripting key

  1. Paste the API Key in the api_key.txt file
  2. Check that your API Key is saved correctly
  • Please check the example notebooks for more detailed information.

Installing

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

More info about datasets and BMRS

For more info about the BMRS and its user guide - here

Contact

If you want to contact me you can reach me at desen.kirli@ed.ac.uk.

License

Shield: CC BY 4.0

This work is licensed under a Creative Commons Attribution 4.0 International License.

CC BY 4.0

Acknowledgement

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/)