Hedging against Black Swans in Day-Ahead Energy Markets
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917018578452480 |
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| author | Aolaritei, Liviu Bangoura, Boubacar Bolognani, Saverio Lanzetti, Nicolas Dörfler, Florian |
| author_facet | Aolaritei, Liviu Bangoura, Boubacar Bolognani, Saverio Lanzetti, Nicolas Dörfler, Florian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14328 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hedging against Black Swans in Day-Ahead Energy Markets Aolaritei, Liviu Bangoura, Boubacar Bolognani, Saverio Lanzetti, Nicolas Dörfler, Florian Optimization and Control 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. |
| title | Hedging against Black Swans in Day-Ahead Energy Markets |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.14328 |