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The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey

Author

Listed:
  • Yukseltan, E.
  • Kok, A.
  • Yucekaya, A.
  • Bilge, A.
  • Aktunc, E. Agca
  • Hekimoglu, M.

Abstract

The rapid spread of COVID-19 has severely impacted many sectors, including the electricity sector. The reliability of the electricity sector is critical to the economy, health, and welfare of society; therefore, supply and demand need to be balanced in real-time, and the impact of unexpected factors should be analyzed. During the pandemic, behavioral restrictions such as lockdowns, closure of factories, schools, and shopping malls, and changing habits, such as shifted work and leisure hours at home, significantly affected the demand structure. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the estimated impact of the restrictions on total demand and daily demand profile. A modulated Fourier Series Expansion evaluates deviations from normal conditions in the aggregate demand and the daily consumption profile. The aggregate demand shows a significant decrease in the early phase of the pandemic, during the period March–June 2020. The shape of the daily demand curve is analyzed to estimate how much demand shifted from daytime to night-time. A population-based restriction index is proposed to analyze the relationship between the strength and coverage of the restrictions and the total demand. The persistency of the changes in the daily demand curve in the post-contingency period is analyzed. These findings imply that new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches in the future. The long-term policy implications for the energy transition and lessons learned from the COVID-19 pandemic experience are also presented.

Suggested Citation

  • Yukseltan, E. & Kok, A. & Yucekaya, A. & Bilge, A. & Aktunc, E. Agca & Hekimoglu, M., 2022. "The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey," Utilities Policy, Elsevier, vol. 76(C).
  • Handle: RePEc:eee:juipol:v:76:y:2022:i:c:s0957178722000248
    DOI: 10.1016/j.jup.2022.101359
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    1. 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.
    2. Andrew Leach & Nic Rivers & Blake Shaffer, 2020. "Canadian Electricity Markets during the COVID-19 Pandemic: An Initial Assessment," Canadian Public Policy, University of Toronto Press, vol. 46(S2), pages 145-159, August.
    3. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    4. De Felice, Matteo & Alessandri, Andrea & Catalano, Franco, 2015. "Seasonal climate forecasts for medium-term electricity demand forecasting," Applied Energy, Elsevier, vol. 137(C), pages 435-444.
    5. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    6. 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.
    7. Torrini, Fabiano Castro & Souza, Reinaldo Castro & Cyrino Oliveira, Fernando Luiz & Moreira Pessanha, Jose Francisco, 2016. "Long term electricity consumption forecast in Brazil: A fuzzy logic approach," Socio-Economic Planning Sciences, Elsevier, vol. 54(C), pages 18-27.
    8. Bašta, Milan & Helman, Karel, 2013. "Scale-specific importance of weather variables for explanation of variations of electricity consumption: The case of Prague, Czech Republic," Energy Economics, Elsevier, vol. 40(C), pages 503-514.
    9. Kentaka Aruga & Md. Monirul Islam & Arifa Jannat, 2020. "Effects of COVID-19 on Indian Energy Consumption," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    10. Liu, Xiaolei & Lin, Zi, 2021. "Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory," Energy, Elsevier, vol. 227(C).
    11. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    12. Andersen, F.M. & Larsen, H.V. & Gaardestrup, R.B., 2013. "Long term forecasting of hourly electricity consumption in local areas in Denmark," Applied Energy, Elsevier, vol. 110(C), pages 147-162.
    13. Prol, Javier López & O, Sungmin, 2020. "Impact of COVID-19 Measures on Short-Term Electricity Consumption in the Most Affected EU Countries and USA States," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 23(10).
    14. Werth, Annette & Gravino, Pietro & Prevedello, Giulio, 2021. "Impact analysis of COVID-19 responses on energy grid dynamics in Europe," Applied Energy, Elsevier, vol. 281(C).
    15. Lu, Hongfang & Ma, Xin & Ma, Minda, 2021. "A hybrid multi-objective optimizer-based model for daily electricity demand prediction considering COVID-19," Energy, Elsevier, vol. 219(C).
    Full references (including those not matched with items on IDEAS)

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    2. Pizarro-Irizar, Cristina, 2023. "Is it all about supply? Demand-side effects on the Spanish electricity market following Covid-19 lockdown policies," Utilities Policy, Elsevier, vol. 80(C).
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    5. Agata Jaroń & Anna Borucka & Rafał Parczewski, 2022. "Analysis of the Impact of the COVID-19 Pandemic on the Value of CO 2 Emissions from Electricity Generation," Energies, MDPI, vol. 15(13), pages 1-15, June.

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