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

2026-07-08

AI Summary

This study addresses the arbitrage opportunities available to electricity traders between day-ahead and balancing markets, where naive deviations from generation forecasts entail high risks and existing approaches lack interpretable, risk-aware decision mechanisms. The authors propose a novel “post-prediction contextual optimization” framework that explicitly decomposes arbitrage decisions into three sequential stages: whether to arbitrage, the direction of deviation, and its magnitude. A probabilistic binary classifier, governed by a confidence threshold, triggers arbitrage; otherwise, bids follow the original forecast. Each stage employs contextual optimization to learn linear policies that determine optimal deviation levels. Integrating risk-aware forecasting with interpretable decision-making, the method demonstrates an average profit increase of approximately 7% for hybrid power plants equipped with electrolyzers, as validated on real-world wind farm data from the Danish DK1 and German/Luxembourgish DE/LU bidding zones, thereby highlighting the value of coordinated arbitrage strategies.

Abstract

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.

Problem

• arbitrage
• electricity markets
• risk-aware decision
• stochastic generation
• opportunistic bidding

Innovation

• predict-then-optimize
• contextual optimization
• probabilistic classification
• arbitrage bidding
• hybrid power plant