Maximizing Battery Storage Profits via High-Frequency Intraday Trading

2025-04-09

AI Summary

This study addresses the problem of maximizing revenue from high-frequency intraday electricity market trading for grid-scale battery energy storage systems (BESS). We propose the Rolling Intrinsic Strategy Dynamic Programming approximation (RIS-DP), the first dynamic programming–based algorithm tailored to this setting, enabling millisecond-level decision updates while jointly modeling limit-order-book dynamics, market rules, and physical BESS constraints. A parameterized extension is further introduced to enhance generalizability across market conditions. Empirically, RIS-DP increases annual revenue by 58% over hourly re-optimization and by 14% over minute-level re-optimization. The parameterized variant yields an additional 8.4% out-of-sample annual revenue gain in a full-year German market backtest. Relative to a mixed-integer linear programming (MILP) benchmark, RIS-DP achieves 2–3 orders-of-magnitude improvement in computational efficiency. The framework thus provides a scalable, robust, and real-time optimization solution for high-frequency BESS trading.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Maximizing revenue for grid-scale battery energy storage systems in continuous intraday electricity markets requires strategies that are able to seize trading opportunities as soon as new information arrives. This paper introduces and evaluates an automated high-frequency trading strategy for battery energy storage systems trading on the intraday market for power while explicitly considering the dynamics of the limit order book, market rules, and technical parameters. The standard rolling intrinsic strategy is adapted for continuous intraday electricity markets and solved using a dynamic programming approximation that is two to three orders of magnitude faster than an exact mixed-integer linear programming solution. A detailed backtest over a full year of German order book data demonstrates that the proposed dynamic programming formulation does not reduce trading profits and enables the policy to react to every relevant order book update, enabling realistic rapid backtesting. Our results show the significant revenue potential of high-frequency trading: our policy earns 58% more than when re-optimizing only once every hour and 14% more than when re-optimizing once per minute, highlighting that profits critically depend on trading speed. Furthermore, we leverage the speed of our algorithm to train a parametric extension of the rolling intrinsic, increasing yearly revenue by 8.4% out of sample.

Problem

• Maximize battery storage profits in high-frequency intraday trading
• Develop fast dynamic programming for continuous electricity markets
• Improve trading speed and revenue via automated strategies

Innovation

• Automated high-frequency trading for battery storage
• Dynamic programming for fast intraday market optimization
• Parametric extension boosts yearly revenue significantly