# Day-Ahead Bidding Strategies for Wind Farm Operators under a One-Price Balancing Scheme

arXiv: https://arxiv.org/abs/2505.05153 · HTML: https://mlsanity.com/paper/2505.05153/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2025.

## Built on by (most cited first, 5 of 5)

- [Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Tasks](https://mlsanity.com/paper/2512.14779/) (2025)
- [Decision-Aligned Evaluation of Uncertainty Quantification](https://mlsanity.com/paper/2606.26990/) (2026)
- [Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles](https://mlsanity.com/paper/2602.13010/) (2026)
- [Neural Network-Assisted Model Predictive Control for Implicit Balancing](https://mlsanity.com/paper/2604.01805/) (2026)
- [Decision-Aware Training for Sample-Based Generative Models](https://mlsanity.com/paper/2607.01171/) (2026)

## 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.