Joint Bidding on Intraday and Frequency Containment Reserve Markets

TL;DR

The paper addresses multi-market participation for battery energy storage systems under rising renewable variability by proposing a joint bidding framework that couples primary frequency reserve (FCR) with continuous intraday market (IDM) trading. It develops a MILP-based rolling intrinsic (RI) trading method for IDM and a learning-based classifier to allocate capacity across FCR and IDM, validated via an extensive out-of-sample backtest on German market data. Results show the learned classifier strategy (LCS) delivering substantial profit gains, narrowing the gap to a perfect foresight benchmark to about 4%, and outperforming naïve and static strategies. The approach demonstrates practical viability for high-frequency, multi-market decision-making in BESS operations and offers a foundation for further enhancements such as reinforcement learning for IDM and endogenous FCR pricing.

Abstract

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.

Figures (9)

• Figure 1: Order of events on any given trading day for EPEX day-ahead and intraday markets and the FCR auctions in Germany.
• Figure 2: Required activation of reserve power for a bid of $P_{bid}$ MW as a function of the frequency deviation $\Delta f$.
• Figure 3: Illustration of the limit order book clearing mechanism. Source GrKuWo24.
• Figure 4: Two extreme price patterns for one day.
• Figure 5: The left panel shows the relationship between the bid size in the FCR and the duration ratio, while the right panel depicts the how the capacity to power ratio affects the profits in the IDM.