Maximizing Battery Storage Profits via High-Frequency Intraday Trading

Paper: arXiv 2504.06932

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.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 9.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced dynamic programming and MILP formulations to optimize high-frequency trading, placing it in the high math category. It is empirically rigorous, featuring a detailed backtest using a full year of German order book data, algorithmic speed validation, and published code for reproducibility.

Research Flowchart

  flowchart TD
  A["Research Goal: Maximize BESS Revenue in Continuous Intraday Markets"] --> B{"Input: German Order Book Data & BESS Parameters"}
  B --> C["Core Methodology: Dynamic Programming Approximation of Rolling Intrinsic Strategy"]
  C --> D["Computational Process: Real-time Optimization for Every LOB Update"]
  D --> E{"Comparison: Re-optimization Frequency"}
  E --> F["Hourly Re-opt: Baseline"]
  E --> G["Minute Re-opt: 14% Lower Revenue"]
  E --> H["Proposed HFT (Instant): Highest Revenue"]
  H --> I["Key Outcomes: 58% > Hourly & +8.4% with Parametric Extension"]

Cited References (49)

1. ISEA Battery Revenue Index (2025) — link
2. Intraday Markets for Power: Discretizing the Continuous Trading? (2016) — link
3. Enhancing Battery Storage Energy Arbitrage With Deep Reinforcement Learning and Time-Series Forecasting (2024) — link
4. Evaluating the Impact of Model Accuracy for Optimizing Battery Energy Storage Systems (2025) — link
5. Joint Bidding on Intraday and Frequency Containment Reserve Markets (2025) — link
6. Smart Meter Data Analytics: Practical Use-Cases and Best Practices of Machine Learning Applications for Energy Data in the Residential Sector (2023) — link
7. BESS Revenue Index 1H (2024) — link
8. Electric vehicle battery prices are expected to fall almost 50% by 2026 (2023) — link

0 of 49 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.