Pricing and hedging wind power prediction risk with binary option contracts
Journal
Energy Economics
Subject
Management Science and Operations
Publishing details
Authors / Editors
Thakur J;Hesamzadeh M R;Date P;Bunn D
Biographies
Publication Year
2023
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
Available on ECCH
No
