# Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets

arXiv: https://arxiv.org/abs/1811.06113 · HTML: https://mlsanity.com/paper/1811.06113/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

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

- [Differentially Private Algorithms for Synthetic Power System Datasets](https://mlsanity.com/paper/2303.11079/) (2023)
- [Unsupervised Congestion Status Identification Using LMP Data](https://mlsanity.com/paper/2411.10058/) (2024)
- [Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1](https://mlsanity.com/paper/2101.02456/) (2021)
- [How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory](https://mlsanity.com/paper/2305.02485/) (2023)
- [Feature-driven reinforcement learning for photovoltaic in continuous intraday trading](https://mlsanity.com/paper/2510.16021/) (2025)

## Abstract (arXiv)

We consider the process of bidding by electricity suppliers in a day-ahead market context where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent prices.