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Managing the financial risks of electricity producers using options

Author

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  • Pineda, S.
  • Conejo, A.J.

Abstract

Electricity producers participating in electricity markets face risks pertaining to both selling prices and the availability of the production units. Among electricity derivatives, options represent an adequate instrument to manage these risks. In this paper, we propose a multi-stage stochastic model to determine the optimal selling strategy of a risk-averse electricity producer including options, forward contracts, and pool trading. A detailed case study highlights the advantages of an option vs. a forward contract to hedge against the financial risks related to pool prices and unexpected unit failures.

Suggested Citation

  • Pineda, S. & Conejo, A.J., 2012. "Managing the financial risks of electricity producers using options," Energy Economics, Elsevier, vol. 34(6), pages 2216-2227.
    • Handle: RePEc:eee:eneeco:v:34:y:2012:i:6:p:2216-2227
    DOI: 10.1016/j.eneco.2012.03.016
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Boroumand, Raphaël Homayoun & Goutte, Stéphane & Porcher, Simon & Porcher, Thomas, 2015. "Hedging strategies in energy markets: The case of electricity retailers," Energy Economics, Elsevier, vol. 51(C), pages 503-509.
    2. Pinheiro Neto, Daywes & Domingues, Elder Geraldo & Coimbra, António Paulo & de Almeida, Aníbal Traça & Alves, Aylton José & Calixto, Wesley Pacheco, 2017. "Portfolio optimization of renewable energy assets: Hydro, wind, and photovoltaic energy in the regulated market in Brazil," Energy Economics, Elsevier, vol. 64(C), pages 238-250.
    3. Shin, Hunyoung & Baldick, Ross, 2018. "Mitigating market risk for wind power providers via financial risk exchange," Energy Economics, Elsevier, vol. 71(C), pages 344-358.
    4. Apergis, Nicholas & Baruník, Jozef & Lau, Marco Chi Keung, 2017. "Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?," Energy Economics, Elsevier, vol. 66(C), pages 108-115.
    5. Yang, Lu, 2022. "Idiosyncratic information spillover and connectedness network between the electricity and carbon markets in Europe," Journal of Commodity Markets, Elsevier, vol. 25(C).
    6. Raimonda Martinkutė-Kaulienė, 2014. "Risk Factors in Derivatives Markets," Entrepreneurial Business and Economics Review, Centre for Strategic and International Entrepreneurship at the Cracow University of Economics., vol. 2(4), pages 71-83.
    7. Nikkinen, Jussi & Rothovius, Timo, 2019. "Market specific seasonal trading behavior in NASDAQ OMX electricity options," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 16-29.
    8. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    9. Fotouhi Ghazvini, Mohammad Ali & Faria, Pedro & Ramos, Sergio & Morais, Hugo & Vale, Zita, 2015. "Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market," Energy, Elsevier, vol. 82(C), pages 786-799.
    10. Shabanzadeh, Morteza & Sheikh-El-Eslami, Mohammad-Kazem & Haghifam, Mahmoud-Reza, 2016. "A medium-term coalition-forming model of heterogeneous DERs for a commercial virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 663-681.
    11. Gersema, Gerke & Wozabal, David, 2017. "An equilibrium pricing model for wind power futures," Energy Economics, Elsevier, vol. 65(C), pages 64-74.
    12. Xiaojia Guo & Alexandros Beskos & Afzal Siddiqui, 2016. "The natural hedge of a gas-fired power plant," Computational Management Science, Springer, vol. 13(1), pages 63-86, January.
    13. Seyedhossein, Seyed Saeed & Moeini-Aghtaie, Moein, 2022. "Risk management framework of peer-to-peer electricity markets," Energy, Elsevier, vol. 261(PB).
    14. Sánchez de la Nieta, Agustín A. & Paterakis, Nikolaos G. & Gibescu, Madeleine, 2020. "Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping," Applied Energy, Elsevier, vol. 266(C).
    15. Kaijian He & Hongqian Wang & Jiangze Du & Yingchao Zou, 2016. "Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology," Energies, MDPI, vol. 9(11), pages 1-11, November.
    16. Tat Andrejus Ngujen, 2018. "Electricity Price Forecasting Using Monte Carlo Simulation: The Case of Lithuania," Ekonomika (Economics), Sciendo, vol. 97(1), pages 76-86, January.

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    More about this item

    Keywords

    Electricity market; Option; Forward contract; Electricity producer; Risk; Multi-stage stochastic programming;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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