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

Paper: arXiv 2509.21337

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.

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (45)

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3. Betriebserfahrung und O ptimierung von G roßbatteriesystemen: E ine S imulationstudie zur O ptimierung des SoC -Managements (2020)
4. Gurobi Optimizer Reference Manual (2023) — link
5. JuMP: A Modeling Language for Mathematical Optimization (2017) — link
6. Elektrochemische Speicher: Superkondensatoren, Batterien, Elektrolyse-Wasserstoff, Rechtliche Rahmenbedingungen — link
7. Speicher: Für eine funktionierende Energiewende — link
8. Speicher: Flexibilität für eine erfolgreiche Energiewende — link

0 of 45 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.