# Learning to Bid in Forward Electricity Markets Using a No-Regret Algorithm

arXiv: https://arxiv.org/abs/2404.03314 · HTML: https://mlsanity.com/paper/2404.03314/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2024.

## Built on by (most cited first, 3 of 3)

- [Learn to Bid as a Price-Maker Wind Power Producer](https://mlsanity.com/paper/2503.16107/) (2025)
- [Feature-driven reinforcement learning for photovoltaic in continuous intraday trading](https://mlsanity.com/paper/2510.16021/) (2025)
- [Learning to Bid in FCR Markets: A Best-of-Both-Worlds Approach](https://mlsanity.com/paper/2605.31070/) (2026)

## Abstract (arXiv)

It is a common practice in the current literature of electricity markets to use game-theoretic approaches for strategic price bidding. However, they generally rely on the assumption that the strategic bidders have prior knowledge of rival bids, either perfectly or with some uncertainty. This is not necessarily a realistic assumption. This paper takes a different approach by relaxing such an assumption and exploits a no-regret learning algorithm for repeated games. In particular, by using the a posteriori information about rivals' bids, a learner can implement a no-regret algorithm to optimize her/his decision making. Given this information, we utilize a multiplicative weight-update algorithm, adapting bidding strategies over multiple rounds of an auction to minimize her/his regret. Our numerical results show that when the proposed learning approach is used the social cost and the market-clearing prices can be higher than those corresponding to the classical game-theoretic approaches. The takeaway for market regulators is that electricity markets might be exposed to greater market power of suppliers than what classical analysis shows.