Pricing and hedging wind power prediction risk with binary option contracts

Jagruti Thakur, Mohammad Reza Hesamzadeh, Paresh Date and Derek Bunn

Energy Economics, 2023, vol. 126, issue C

Abstract: In markets with a high proportion of wind generation, high wind outputs tend to induce low market prices and, alternatively, high prices often occur under low wind output conditions. Wind producer revenues are affected adversely in both situations. Whilst it is not possible to directly hedge revenues, it is possible to hedge wind speed with weather insurance and market prices with forward derivatives. Thus combined hedges are offered to the wind producers through bilateral arrangements and as a consequence, the risk managers of wind assets need to be able to forecast fair prices for them. We formulate these hedges as binary option contracts on the combined uncertainties of wind speed and market price and provide a new analysis, based upon machine learning classification, to forecast fair prices for such hedges. The proposed forecasting model achieves a classification accuracy of 88 percent and could therefore aid the wind producers in their negotiations with the hedge providers. Furthermore, in a realistic example, we find that the predicted costs of such hedges are quite affordable and should therefore become more widely adopted by the insurers and wind generators.

Keywords: Wind power; Forecasting; Hedging; Quanto options; Deep learning; Multi-class classification; Risk management (search for similar items in EconPapers)
JEL-codes: C13 C15 G17 L11 L21 L23 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:126:y:2023:i:c:s0140988323004589

DOI: 10.1016/j.eneco.2023.106960

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