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

Jul 16, 2026

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Electricity markets increasingly create arbitrage opportunities between day-ahead and balancing prices for stochastic generators, yet opportunistic bids that deviate from production forecasts remain risky and lack explainable, risk-aware decision support. A predict-then-contextual-optimize framework decomposes bidding into three stages: decide when to engage, whether to go long or short, and how much to deviate. Probabilistic confidence thresholds trigger arbitrage or fall back to an arbitrage-free forecast bid. Contextual optimization learns deviation magnitudes by class and is evaluated on a European wind farm and hybrid plant with up to ~7% mean profit improvement in DK1, especially under low training-testing drift, with added electrolyzer flexibility.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages32

Reading Time1 h 21 min

Introduction

Literature Review

arXiv :2607 .0735 1v 1 [ cs .CE] 8 Jul 2026 When and How Should a Power Trader Engage in Arbitrage? Predict, then Contextually Optimize Yannick Heiser 1 , Jalal Kazempour 1 , Farzaneh Pourahmadi 1 1Department of Wind and Energy Systems, Technical University of Denmark. 1 Email: {yahei, jalal, [farpour](farpour}@dtu.dk)[}](farpour}@dtu.dk)[@dtu.dk](farpour}@dtu.dk) 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. 1 Introduction A stochastic energy generator, e.g., a wind farm, typically sells a majority of its energy for a delivery period 􀁃 in the day-ahead electricity market at price 􀁟D􀁃A (=C/MWh) . One day prior to physical delivery 􀁃, it needs to decide on the amount of energy 􀀿 D􀁃A (MWh) to bid into the day-ahead market, given a production forecast ˆ􀀥W􀁃 (MWh) and assuming a marginal production cost of =C0/MWh. Given the realized production 􀀥W􀁃 after the delivery period 􀁃, any physical deviation from the contracted energy 􀀿B􀁃 = 􀀥W􀁃 − 􀀿 D􀁃A is settled post-delivery in the balancing market at price 􀁟B􀁃 (=C/MWh), which may be lower than, equal to, or higher than 􀁟D􀁃A , depending on the system need, i.e., whether thereis a power deficit or surplus in the system. If the trader bids its production forecast ˆ􀀥W􀁃, we refer to this as an arbitrage-free bid, since the trader does not intentionally exploit a forecasted price difference between the two markets. By contrast, if the trader deliberately bids above the forecast to benefit from a positive price spread Δ􀁟􀁃 = 􀁟D􀁃A − 􀁟B􀁃, we call this an opportunistic long arbitrage bid. If the trader bids below the forecast to benefit from a negative price spread, we call this an opportunistic short arbitrage bid. However, both market prices (􀁟D􀁃A , 􀁟B􀁃) are uncertain at the time of decision making in the day-ahead stage, introducing substantial risk to opportunistic bidding strategies. Recent market developments further motivate this problem, as increasing renewable penetration, higher balancingprice volatility, and ongoing changes in balancing market design make arbitrage opportunities between the day-ahead and balancing markets more relevant. At the same time, these trends also make opportunistic arbitrage decisions riskier and more difficult. Therefore, an effective trading strategy needs to account for the u

FAQs

What does “arbitrage-free” mean for a power trader in the day-ahead stage?

An arbitrage-free bid submits the power forecast amount without intentionally exploiting a predicted price difference between day-ahead and balancing markets.

How does the proposed framework decide when to engage in arbitrage?

A probabilistic binary classifier with confidence thresholds checks whether the predicted day-ahead-to-balancing price spread is sufficiently confident; otherwise, it defaults to the arbitrage-free bid equal to the production forecast.

What factors drive the largest performance gains in the evaluation results?

Gains are largest when distributional drift between training and testing rolling windows is low, and the co-located electrolyzer increases arbitrage value by adding operational flexibility.

Jul 16, 2026

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