When and How Should a Power Trader Engage in Arbitrage? Predict, then Contextually Optimize

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🔬研究者:Provides a novel decision framework combining prediction and contextual optimization for energy arbitrage, with empirical validation.

🏢実務担当者:Offers a practical bidding strategy for power traders to increase profits while managing risk, applicable to wind farms and hybrid plants.

🏛政策担当者:Illustrates how market design and flexible assets like electrolyzers can enhance renewable integration and market efficiency.

Electricity markets increasingly expose stochastic energy generators to arbitrage opportunities between the day-ahead and balancing markets, driven by widening price spreads. However, opportunistic bidding, deliberately deviating from the production forecast to exploit anticipated price spreads, carries significant risk, and existing frameworks rarely offer explainable, risk-aware decision support. We propose a predict-then-contextual-optimize framework that decomposes the day-ahead bidding decision into three explicit stages to decide, when to engage in arbitrage, in what direction, and to what extent. A probabilistic binary classifier with confidence thresholds determines whether the predicted price spread is sufficiently confident to justify an opportunistic bid. Otherwise, the trader defaults to an arbitrage-free bid equal to the power forecast. A linear decision policy learned for each class via contextual optimization determines the magnitude of the bid deviation from the power forecast. The framework accommodates both standalone renewable generation and hybrid power plants combining renewable generation with other assets, such as an electrolyzer. We evaluate the framework on a real wind farm in the European bidding zones DK1 and DE/LU using a rolling-window procedure and compare it against several benchmark bidding strategies. The results show that the proposed framework increases mean profit relative to an arbitrage-free benchmark, reaching an improvement of about 7% for the hybrid power plant in DK1. The largest gains occur when distributional drift between training and testing windows is low, while the co-located electrolyzer further increases arbitrage value by providing additional operational flexibility.

• semanticscholar https://www.semanticscholar.org/paper/eb971c7fdb92b41619f8277c9e70684d8d8a923afirst seen 2026-07-24 06:47:08