Optimized Operation of Standalone Battery Energy Storage Systems in the Cross-Market Energy Arbitrage Business

ArXiv ID: 2509.21337 “View on arXiv”

Authors: Luis van Sandbergen

Abstract

The provision of renewable electricity is the foundation for a sustainable future. To achieve the goal of sustainable renewable energy, Battery Energy Storage Systems (BESS) could play a key role to counteract the intermittency of solar and wind generation power. In order to aid the system, the BESS can simply charge at low wholesale prices and discharge during high prices, which is also called energy arbitrage. However, the real-time execution of energy arbitrage is not straightforward for many companies due to the fundamentally different behavior of storages compared to conventional power plants. In this work, the optimized operation of standalone BESS in the cross-market energy arbitrage business is addressed by describing a generic framework for trading integrated BESS operation, the development of a suitable backtest engine and a specific optimization-based strategy formulation for cross-market optimized BESS operation. In addition, this strategy is tested in a case study with a sensitivity analysis to investigate the influence of forecast uncertainty. The results show that the proposed strategy allows an increment in revenues by taking advantage of the increasing market volatility. Furthermore, the sensitivity analysis shows the robustness of the proposed strategy, as only a moderate portion of revenues will be lost if real forecasts are adopted.

Keywords: Energy Arbitrage, Battery Energy Storage Systems (BESS), Renewable Energy, Wholesale Electricity Markets, Optimization Strategy

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail
  • Why: The paper uses advanced optimization techniques like Mixed-Integer Linear Programming (MILP) and Rolling Horizon frameworks, indicating high mathematical complexity. It also includes a dedicated backtest engine, sensitivity analysis, and case study with real-world data applications, showing substantial empirical rigor.
  flowchart TD
    A["Research Goal: Optimize<br>Standalone BESS Operation<br>for Cross-Market Arbitrage"] --> B["Input Data:<br>Historical Wholesale<br>Electricity Prices"]
    B --> C["Methodology:<br>Development of Optimization<br>Strategy & Backtest Engine"]
    C --> D{"Computational Process:<br>Forecast Price & Optimize<br>Charge/Discharge Scheduling"}
    D --> E["Case Study Execution<br>with Sensitivity Analysis"]
    E --> F["Outcome: Increased Revenue<br>via Market Volatility Exploitation"]
    E --> G["Outcome: High Robustness<br>Minimal Revenue Loss with Real Forecasts"]