# Multi-market value-stacking: Battery control for combined imbalance participation and non-uniform FCR bidding

arXiv: https://arxiv.org/abs/2605.23964 · HTML: https://mlsanity.com/paper/2605.23964/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 6 of 6)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning](https://mlsanity.com/paper/1711.03127/) (2017)
- [Impact of Low Rotational Inertia on Power System Stability and Operation](https://mlsanity.com/paper/1312.6435/) (2013)
- [Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism](https://mlsanity.com/paper/2401.00015/) (2023)
- [Coordinated Trading Strategies for Battery Storage in Reserve and Spot Markets](https://mlsanity.com/paper/2406.08390/) (2024)
- [Joint Bidding on Intraday and Frequency Containment Reserve Markets](https://mlsanity.com/paper/2510.03209/) (2025)

## Abstract (arXiv)

The growing share of Renewable Energy Sources (RES) in modern power systems increases both grid imbalances and frequency deviations, reinforcing the need for ancillary services such as Frequency Containment Reserve (FCR) and passive balancing. Battery Energy Storage Systems (BESS) are well-suited for these services, but prior research typically relies on uniform FCR bids that remain constant throughout the control period. Such static bids fail to fully exploit BESS flexibility, as they do not balance the trade-off between reserving energy for FCR delivery and using it for imbalance arbitrage, limiting the achievable value in value-stacking settings. To address this limitation, we propose a two-stage control framework for the European context that introduces non-uniform FCR bids. In the first stage, we derive a time-varying bid sequence using data-driven Monte Carlo (MC) optimization. In the second stage, a Deep Reinforcement Learning (DRL) agent leverages the residual flexibility for real-time imbalance trading while proactively managing the State of Energy (SoE) to ensure compliance with FCR requirements. The framework is presented as a proof of concept, highlighting the potential benefits of time-varying bidding strategies. By incorporating daily cycle budgets and time-varying reserve commitments, our approach achieves a 7.56% profit increase compared to uniform baselines. These results show that non-uniform bidding can unlock additional value by more effectively aligning reserve obligations with rapidly changing imbalance opportunities.