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Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

Author

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  • Desen Kirli

    (Institute for Energy Systems, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, UK)

  • Maximilian Parzen

    (Institute for Energy Systems, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, UK)

  • Aristides Kiprakis

    (Institute for Energy Systems, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, UK)

Abstract

The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is mainly due to the mitigation measures that include lockdown and work from home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-lockdown business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result in a net benefit for flexible stakeholders, such as consumers on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

Suggested Citation

  • Desen Kirli & Maximilian Parzen & Aristides Kiprakis, 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, MDPI, vol. 14(3), pages 1-25, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:635-:d:487722
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    References listed on IDEAS

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    1. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
    2. Stephen Snow & Richard Bean & Mashhuda Glencross & Neil Horrocks, 2020. "Drivers behind Residential Electricity Demand Fluctuations Due to COVID-19 Restrictions," Energies, MDPI, vol. 13(21), pages 1-20, November.
    3. Jun Maekawa & Bui Hien Hai & Sarana Shinkuma & Koji Shimada, 2018. "The Effect of Renewable Energy Generation on the Electric Power Spot Price of the Japan Electric Power Exchange," Energies, MDPI, vol. 11(9), pages 1-16, August.
    4. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.
    5. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1352-1362, August.
    6. Campillo, Javier & Dahlquist, Erik & Wallin, Fredrik & Vassileva, Iana, 2016. "Is real-time electricity pricing suitable for residential users without demand-side management?," Energy, Elsevier, vol. 109(C), pages 310-325.
    7. Hansen, Anca D. & Altin, Müfit & Margaris, Ioannis D. & Iov, Florin & Tarnowski, Germán C., 2014. "Analysis of the short-term overproduction capability of variable speed wind turbines," Renewable Energy, Elsevier, vol. 68(C), pages 326-336.
    8. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
    9. Emilio Ghiani & Marco Galici & Mario Mureddu & Fabrizio Pilo, 2020. "Impact on Electricity Consumption and Market Pricing of Energy and Ancillary Services during Pandemic of COVID-19 in Italy," Energies, MDPI, vol. 13(13), pages 1-19, July.
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    Cited by:

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    2. Zhiang Zhang & Ali Cheshmehzangi & Saeid Pourroostaei Ardakani, 2021. "A Data-Driven Clustering Analysis for the Impact of COVID-19 on the Electricity Consumption Pattern of Zhejiang Province, China," Energies, MDPI, vol. 14(23), pages 1-22, December.
    3. Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
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    5. Francis Mujjuni & Joyce Nyuma Chivunga & Thomas Betts & Zhengyu Lin & Richard Blanchard, 2022. "A Comparative Analysis of the Impacts and Resilience of the Electricity Supply Industry against COVID-19 Restrictions in the United Kingdom, Malawi, and Uganda," Sustainability, MDPI, vol. 14(15), pages 1-21, August.

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