# Optimal Battery Participation in Frequency Regulation Markets

arXiv: https://arxiv.org/abs/1710.10514 · HTML: https://mlsanity.com/paper/1710.10514/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer](https://mlsanity.com/paper/2109.01659/) (2021)
- [Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach](https://mlsanity.com/paper/2404.17683/) (2024)
- [Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices](https://mlsanity.com/paper/2506.06824/) (2025)
- [Electric Arc Furnaces Scheduling under Electricity Price Volatility with Reinforcement Learning](https://mlsanity.com/paper/2512.09293/) (2025)

## Abstract (arXiv)

Battery participants in performance-based frequency regulation markets must consider the cost of battery aging in their operating strategies to maximize market profits. In this paper we solve this problem by proposing an optimal control policy and an optimal bidding policy based on realistic market settings and an accurate battery aging model. The proposed control policy has a threshold structure and achieves near-optimal performance with respect to an offline controller that has complete future information. The proposed bidding policy considers the optimal control policy to maximize market profits while satisfying the market performance requirement through a chance-constraint. It factors the value of performance and supports a trade-off between higher profits and a lower risk of violating performance requirements. We demonstrate the optimality of both policies using simulations. A case study based on the PJM regulation market shows that our approach is effective at maximizing operating profits.