# Joint Bidding on Intraday and Frequency Containment Reserve Markets

arXiv: https://arxiv.org/abs/2510.03209 · HTML: https://mlsanity.com/paper/2510.03209/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding](https://mlsanity.com/paper/2004.05940/) (2020)
- [Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage using Approximate Dynamic Programming](https://mlsanity.com/paper/1402.3575/) (2014)
- [An Artificial Intelligence Framework for Bidding Optimization with Uncertainty in Multiple Frequency Reserve Markets](https://mlsanity.com/paper/2104.01865/) (2021)
- [Profit-Maximizing Planning and Control of Battery Energy Storage Systems for Primary Frequency Control](https://mlsanity.com/paper/1604.00952/) (2016)
- [The Influence of Frequency Containment Reserve Flexibilization on the Economics of Electric Vehicle Fleet Operation](https://mlsanity.com/paper/2107.03489/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multi-market value-stacking: Battery control for combined imbalance participation and non-uniform FCR bidding](https://mlsanity.com/paper/2605.23964/) (2026)

## Abstract (arXiv)

As renewable energy integration increases supply variability, battery energy storage systems (BESS) present a viable solution for balancing supply and demand. This paper proposes a novel approach for optimizing battery BESS participation in multiple electricity markets. We develop a joint bidding strategy that combines participation in the primary frequency reserve market with continuous trading in the intraday market, addressing a gap in the extant literature which typically considers these markets in isolation or simplifies the continuous nature of intraday trading. Our approach utilizes a mixed integer linear programming implementation of the rolling intrinsic algorithm for intraday decisions and state of charge recovery, alongside a learned classifier strategy (LCS) that determines optimal capacity allocation between markets. A comprehensive out-of-sample backtest over more than one year of historical German market data validates our approach: The LCS increases overall profits by over 4% compared to the best-performing static strategy and by more than 3% over a naive dynamic benchmark. Crucially, our method closes the gap to a theoretical perfect foresight strategy to just 4%, demonstrating the effectiveness of dynamic, learning-based allocation in a complex, multi-market environment.