Day-Ahead Bidding Strategies for Wind Farm Operators under a One-Price Balancing Scheme
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Abstract (arXiv)
We study day-ahead bidding strategies for wind farm operators under a one-price balancing scheme, prevalent in European electricity markets. In this setting, the profit-maximising strategy becomes an all-or-nothing strategy, aiming to take advantage of open positions in the balancing market. However, balancing prices are difficult, if not impossible, to forecast in the day-ahead stage and large open positions can affect the balancing price by changing the direction of the system imbalance. This paper addresses day-ahead bidding as a decision-making problem under uncertainty, with the objective of maximising the expected profit while reducing the imbalance risk related to the strategy. To this end, we develop a stochastic optimisation problem with explicit constraints on the positions in the balancing market, providing risk certificates, and derive an analytical solution to this problem. Moreover, we show how the price-impact of the trading strategy on the balancing market can be included in the ex-post evaluation. Using real data from the Belgian electricity market and an offshore wind farm in the North Sea, we demonstrate that the all-or-nothing strategy negatively impacts the balancing price, resulting in long-term losses for the wind farm. Our risk-constrained strategy, however, can still significantly enhance operational profit compared to traditional point-forecast bidding.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 May 2025.
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Citing papers per year: 2025: 1; 2026: 4.
5 papers.
- 2025Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Taskscited by 3
- 2026Decision-Aligned Evaluation of Uncertainty Quantificationcited by 1
- 2026Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensemblescited by 0
- 2026Neural Network-Assisted Model Predictive Control for Implicit Balancingcited by 0
- 2026Decision-Aware Training for Sample-Based Generative Modelscited by 0
- 2026Decision-Aware Training for Sample-Based Generative Models1 Jul 2026
- 2026Decision-Aligned Evaluation of Uncertainty Quantification25 Jun 2026
- 2026Neural Network-Assisted Model Predictive Control for Implicit Balancing2 Apr 2026
- 2026Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles13 Feb 2026
- 2025Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Tasks16 Dec 2025