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Electricity Demand Elasticity, Mobility, and COVID-19 Contagion Nexus in the Italian Day-Ahead Electricity Market

Author

Listed:
  • Carlo Andrea Bollino

    (Perugia Energy Environment Research Center, 06123 Perugia, Italy
    KAPSARC, Riyadh 11672, Saudi Arabia)

  • Maria Chiara D’Errico

    (Department of Economics, University of Perugia, 06123 Perugia, Italy)

Abstract

The magnitude of the impact of the pandemic on key variables, such as electricity demand, mobility of people and number of COVID-19 hospitalization cases, has been unprecedented. Existing economic models have not estimated the impact of sucokh events. This paper fills this gap, investigating the nexus among electricity demand elasticity, shifting behaviors of mobility and COVID-19 contagion with econometric estimation techniques. Firstly, using the single bids to purchase recorded in the Italian day-ahead wholesale electricity market in 2020, we estimate hourly electricity demand and price elasticity directly from short-run consumer behavior. Then, we analyze the effects of the main aspects of the pandemic, the health situation and the mobility contraction at the national level, on the estimated price elasticities. The period of heavy lockdown between 10 March and 3 June recorded a reduction in the price elasticity of electricity demand. However, when the pandemic broke out again at the beginning of October, elasticity increased, highlighting how companies and economic activities had adopted countermeasures to avoid the arrest of the economy and, consequently, the sharp contraction in electricity demand.

Suggested Citation

  • Carlo Andrea Bollino & Maria Chiara D’Errico, 2022. "Electricity Demand Elasticity, Mobility, and COVID-19 Contagion Nexus in the Italian Day-Ahead Electricity Market," Energies, MDPI, vol. 15(20), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7501-:d:939921
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    References listed on IDEAS

    as
    1. Simona Bigerna and Carlo Andrea Bollino, 2014. "Electricity Demand in Wholesale Italian Market," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    2. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    3. Bollino, Carlo Andrea, 1987. "Gaids: a generalised version of the almost ideal demand system," Economics Letters, Elsevier, vol. 23(2), pages 199-202.
    4. Faheem Jan & Ismail Shah & Sajid Ali, 2022. "Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis," Energies, MDPI, vol. 15(9), pages 1-15, May.
    5. Qu, Hui & Duan, Qingling & Niu, Mengyi, 2018. "Modeling the volatility of realized volatility to improve volatility forecasts in electricity markets," Energy Economics, Elsevier, vol. 74(C), pages 767-776.
    6. Halbrügge, Stephanie & Buhl, Hans Ulrich & Fridgen, Gilbert & Schott, Paul & Weibelzahl, Martin & Weissflog, Jan, 2022. "How Germany achieved a record share of renewables during the COVID-19 pandemic while relying on the European interconnected power network," Energy, Elsevier, vol. 246(C).
    7. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
    8. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    9. Maria Chiara D'Errico & Carlo Andrea Bollino, 2015. "Bayesian Analysis of Demand Elasticity in the Italian Electricity Market," Sustainability, MDPI, vol. 7(9), pages 1-22, September.
    10. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-326, June.
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