{"ID":2854438,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.14328","arxiv_id":"2510.14328","title":"Hedging against Black Swans in Day-Ahead Energy Markets","abstract":"Renewable generators must commit to day-ahead market bids despite uncertainty in both production and real-time prices. While forecasts provide valuable guidance, rare and unpredictable extreme events (so-called black swans) can cause substantial financial losses. This paper models the nomination problem as an instance of optimal transport-based distributionally robust optimization (OT-DRO), a principled framework that balances risk and performance by accounting not only for the severity of deviations but also for their likelihood. The resulting formulation yields a tractable, data-driven strategy that remains competitive under normal conditions while providing effective protection against extreme price spikes. Using four years of Finnish wind farm and market data, we demonstrate that OT-DRO consistently outperforms forecast-based nominations and significantly mitigates losses during black swan events.","short_abstract":"Renewable generators must commit to day-ahead market bids despite uncertainty in both production and real-time prices. While forecasts provide valuable guidance, rare and unpredictable extreme events (so-called black swans) can cause substantial financial losses. This paper models the nomination problem as an instance...","url_abs":"https://arxiv.org/abs/2510.14328","url_pdf":"https://arxiv.org/pdf/2510.14328v1","authors":"[\"Liviu Aolaritei\",\"Boubacar Bangoura\",\"Saverio Bolognani\",\"Nicolas Lanzetti\",\"Florian Dörfler\"]","published":"2025-10-16T06:03:27Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}