# Learn to Bid as a Price-Maker Wind Power Producer

arXiv: https://arxiv.org/abs/2503.16107 · HTML: https://mlsanity.com/paper/2503.16107/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 61 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Survey of Contextual Optimization Methods for Decision Making under Uncertainty](https://mlsanity.com/paper/2306.10374/) (2023)
- [Stochastic optimization with decision-dependent distributions](https://mlsanity.com/paper/2011.11173/) (2020)
- [Performative Prediction: Past and Future](https://mlsanity.com/paper/2310.16608/) (2023)
- [Efficient Contextual Bandits with Continuous Actions](https://mlsanity.com/paper/2006.06040/) (2020)
- [Stochastic Bandits with Context Distributions](https://mlsanity.com/paper/1906.02685/) (2019)
- [Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests](https://mlsanity.com/paper/2003.00359/) (2020)
- [Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning](https://mlsanity.com/paper/2012.08101/) (2020)
- [Contextual Games: Multi-Agent Learning with Side Information](https://mlsanity.com/paper/2107.06327/) (2021)
- [Probabilistic forecasting of German electricity imbalance prices](https://mlsanity.com/paper/2205.11439/) (2022)
- [Tracking Most Significant Shifts in Nonparametric Contextual Bandits](https://mlsanity.com/paper/2307.05341/) (2023)
- [Algorithmic Bidding for Virtual Trading in Electricity Markets](https://mlsanity.com/paper/1802.03010/) (2018)
- [Feature-driven Improvement of Renewable Energy Forecasting and Trading](https://mlsanity.com/paper/1907.07580/) (2019)
- [Online Decision Making for Trading Wind Energy](https://mlsanity.com/paper/2209.02009/) (2022)
- [Learning to Bid in Forward Electricity Markets Using a No-Regret Algorithm](https://mlsanity.com/paper/2404.03314/) (2024)

## Abstract (arXiv)

Wind power producers (WPPs) participating in short-term power markets face significant imbalance costs due to their non-dispatchable and variable production. While some WPPs have a large enough market share to influence prices with their bidding decisions, existing optimal bidding methods rarely account for this aspect. Price-maker approaches typically model bidding as a bilevel optimization problem, but these methods require complex market models, estimating other participants' actions, and are computationally demanding. To address these challenges, we propose an online learning algorithm that leverages contextual information to optimize WPP bids in the price-maker setting. We formulate the strategic bidding problem as a contextual multi-armed bandit, ensuring provable regret minimization. The algorithm's performance is evaluated against various benchmark strategies using a numerical simulation of the German day-ahead and real-time markets.