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Hedging electricity price volatility using nuclear power

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  • Mari, Carlo

Abstract

The analysis presented in this paper aims to put in some evidence the role of nuclear power as hedging asset against the volatility of electricity prices. The unpredictability of natural gas and coal market prices as well as the uncertainty in environmental policies may affect power generating costs, thus enhancing volatility in electricity market prices. The nuclear option, allowing to generate electricity without carbon emissions, offers the possibility to reduce the volatility of electricity prices through optimal diversification of power generating technologies. This paper provides a methodological scheme to plan well diversified “portfolios” of generating capacity that minimize the electricity price risk induced by random movements of fossil fuels market prices and by unpredictable fluctuations of carbon credits prices. The analysis is developed within a stochastic environment in which the dynamics of fuel prices as well as the dynamics of carbon credits prices is assumed to evolve in time according to well defined Brownian processes. Starting from market data and using Monte Carlo techniques to simulate generating cost values, the hedging argument is developed by selecting optimal portfolio of power generating technologies using a mean–variance approach.

Suggested Citation

  • Mari, Carlo, 2014. "Hedging electricity price volatility using nuclear power," Applied Energy, Elsevier, vol. 113(C), pages 615-621.
    • Handle: RePEc:eee:appene:v:113:y:2014:i:c:p:615-621
    DOI: 10.1016/j.apenergy.2013.08.016
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    References listed on IDEAS

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    Cited by:

    1. Gwang Goo Lee & Sung-Won Ham, 2023. "Prediction of Carbon Price in EU-ETS Using a Geometric Brownian Motion Model and Its Application to Analyze the Economic Competitiveness of Carbon Capture and Storage," Energies, MDPI, vol. 16(17), pages 1-13, August.
    2. Leurent, Martin & Jasserand, Frédéric & Locatelli, Giorgio & Palm, Jenny & Rämä, Miika & Trianni, Andrea, 2017. "Driving forces and obstacles to nuclear cogeneration in Europe: Lessons learnt from Finland," Energy Policy, Elsevier, vol. 107(C), pages 138-150.
    3. Turkson, Charles & Liu, Wenbin & Acquaye, Adolf, 2024. "A data envelopment analysis based evaluation of sustainable energy generation portfolio scenarios," Applied Energy, Elsevier, vol. 363(C).
    4. Lynch & John Curtis, 2016. "The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis," Applied Economics, Taylor & Francis Journals, vol. 48(2), pages 133-151, January.
    5. Benalcazar, Pablo & Komorowska, Aleksandra & Kamiński, Jacek, 2024. "A GIS-based method for assessing the economics of utility-scale photovoltaic systems," Applied Energy, Elsevier, vol. 353(PA).
    6. Costa, Oswaldo L.V. & de Oliveira Ribeiro, Celma & Rego, Erik Eduardo & Stern, Julio Michael & Parente, Virginia & Kileber, Solange, 2017. "Robust portfolio optimization for electricity planning: An application based on the Brazilian electricity mix," Energy Economics, Elsevier, vol. 64(C), pages 158-169.
    7. Stringer, Thomas & Joanis, Marcelin & Abdoli, Shiva, 2024. "Power generation mix and electricity price," Renewable Energy, Elsevier, vol. 221(C).
    8. Guglielmo D’Amico & Fulvio Gismondi & Filippo Petroni, 2020. "Insurance Contracts for Hedging Wind Power Uncertainty," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
    9. Carlo Mari, 2018. "CO 2 Price Volatility Effects on Optimal Power System Portfolios," Energies, MDPI, vol. 11(7), pages 1-18, July.
    10. Barsha Nibedita & Mohd Irfan, 2022. "Non-linear cointegration between wholesale electricity prices and electricity generation: an analysis of asymmetric effects," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(1), pages 285-303, February.
    11. Carlo Lucheroni & Carlo Mari, 2021. "Internal hedging of intermittent renewable power generation and optimal portfolio selection," Annals of Operations Research, Springer, vol. 299(1), pages 873-893, April.
    12. Jakub Ochmann & Grzegorz Niewiński & Henryk Łukowicz & Łukasz Bartela, 2024. "Potential for Repowering Inland Coal-Fired Power Plants Using Nuclear Reactors According to the Coal-to-Nuclear Concept," Energies, MDPI, vol. 17(14), pages 1-21, July.
    13. Locatelli, Giorgio & Invernizzi, Diletta Colette & Mancini, Mauro, 2016. "Investment and risk appraisal in energy storage systems: A real options approach," Energy, Elsevier, vol. 104(C), pages 114-131.
    14. Wu, Jung-Hua & Huang, Yun-Hsun, 2014. "Electricity portfolio planning model incorporating renewable energy characteristics," Applied Energy, Elsevier, vol. 119(C), pages 278-287.
    15. Carlo Lucheroni & Carlo Mari, 2018. "Optimal Integration of Intermittent Renewables: A System LCOE Stochastic Approach," Energies, MDPI, vol. 11(3), pages 1-21, March.
    16. Vithayasrichareon, Peerapat & MacGill, Iain F., 2014. "Incorporating short-term operational plant constraints into assessments of future electricity generation portfolios," Applied Energy, Elsevier, vol. 128(C), pages 144-155.
    17. Lucheroni, Carlo & Mari, Carlo, 2017. "CO2 volatility impact on energy portfolio choice: A fully stochastic LCOE theory analysis," Applied Energy, Elsevier, vol. 190(C), pages 278-290.

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