Hedging Against Black Swans In Day-Ahead Energy Markets

Renewable generators must commit to day-ahead market bids despite uncertainty in both production and realtime 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 OTDRO consistently outperforms forecast-based nominations and significantly mitigates losses during black swan events.