Joint Bidding on Intraday and Frequency Containment Reserve Markets

ArXiv ID: 2510.03209 “View on arXiv”

Authors: Yiming Zhang, Wolfgang Ridinger, David Wozabal

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.

Keywords: Battery Energy Storage, Mixed Integer Linear Programming, Frequency Reserve Market, Rolling Intrinsic, Classifier Strategy, Commodities (Electricity)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical modeling, including mixed-integer linear programming (MILP) and a rolling intrinsic algorithm, which represents significant mathematical complexity. It is heavily data-driven, featuring a comprehensive out-of-sample backtest over more than one year of real market data with specific profit improvements and a comparison to a theoretical perfect foresight benchmark, indicating high empirical rigor.

  flowchart TD
    A["Research Goal: Optimize BESS Participation in Joint Intraday & Frequency Reserve Markets"] --> B["Methodology: Learning Classifier Strategy + Rolling Intrinsic Algorithm"]
    B --> C["Data: 1+ Year of Historical German Market Data"]
    C --> D["Process: Mixed Integer Linear Programming & State of Charge Recovery"]
    D --> E["Outcomes: Backtested via Out-of-Sample Simulation"]
    E --> F["Findings: +4% vs Best Static Strategy, +3% vs Dynamic Benchmark, 4% vs Perfect Foresight"]