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The Effects of Social Distancing on Electricity Demand Considering Temperature Dependency

Author

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  • Mohannad Alkhraijah

    (Center for Complex Systems, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia
    School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30313, USA)

  • Maad Alowaifeer

    (School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30313, USA
    Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31262, Saudi Arabia)

  • Mansour Alsaleh

    (Center for Complex Systems, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia)

  • Anas Alfaris

    (Center for Complex Systems, King Abdulaziz City for Science and Technology, Riyadh 11442, Saudi Arabia)

  • Daniel K. Molzahn

    (School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30313, USA)

Abstract

To mitigate the spread of the Novel Coronavirus (COVID-19), governments around the world have imposed social distancing policies ranging from minor social activity suspensions to full curfews. These social distancing policies have altered electricity consumption behaviors in numerous countries. Many governments imposed strict social distancing policies during a temperature transition season where the impacts of temperature variations are particularly important for the operation of the electric grid. This paper studies how strict social distancing policies affect the relationship between electricity demand and ambient temperature. We first review the expected short- and long-term impacts of social distancing on the electricity demand. We then present a case study on the electricity demand of the Kingdom of Saudi Arabia during strict social distancing policies. The results of this case study suggest that strict social distancing policies result in a stronger correlation between temperature and electricity demand compared to previous years. Additionally, we observe a reduction in the time required for the electricity demand to respond to temperature changes. Power system regulators can use the results in this paper to better design energy policies. The results can also be used by power system operators to more accurately forecast electricity demands and avoid inefficient and insecure operation of the electric grid.

Suggested Citation

  • Mohannad Alkhraijah & Maad Alowaifeer & Mansour Alsaleh & Anas Alfaris & Daniel K. Molzahn, 2021. "The Effects of Social Distancing on Electricity Demand Considering Temperature Dependency," Energies, MDPI, vol. 14(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:473-:d:481912
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    Cited by:

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    2. Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
    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.
    4. Shah, Muhammad Ibrahim & Kirikkaleli, Dervis & Adedoyin, Festus Fatai, 2021. "Regime switching effect of COVID-19 pandemic on renewable electricity generation in Denmark," Renewable Energy, Elsevier, vol. 175(C), pages 797-806.
    5. Štefan Bojnec & Alan Križaj, 2021. "Electricity Markets during the Liberalization: The Case of a European Union Country," Energies, MDPI, vol. 14(14), pages 1-21, July.
    6. Toshiyuki Sueyoshi & Youngbok Ryu & Ji-Young Yun, 2021. "COVID-19 Response and Prospects of Clean/Sustainable Energy Transition in Industrial Nations: New Environmental Assessment," Energies, MDPI, vol. 14(4), pages 1-30, February.
    7. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    8. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management," Energies, MDPI, vol. 16(4), pages 1-28, February.

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