Paper: arXiv 2510.01956

Authors: Daniel Oeltz, Tobias Pfingsten

Abstract

Battery Energy Storage Systems (BESS) are a cornerstone of the energy transition, as their ability to shift electricity across time enables both grid stability and the integration of renewable generation. This paper investigates the profitability of different market bidding strategies for BESS in the Central European wholesale power market, focusing on the day-ahead auction and intraday trading at EPEX Spot. We employ the rolling intrinsic approach as a realistic trading strategy for continuous intraday markets, explicitly incorporating bid–ask spreads to account for liquidity constraints. Our analysis shows that multi-market bidding strategies consistently outperform single-market participation. Furthermore, we demonstrate that maximum cycle limits significantly affect profitability, indicating that more flexible strategies which relax daily cycling constraints while respecting annual limits can unlock additional value.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced optimization and stochastic modeling techniques to evaluate battery trading strategies, while demonstrating high empirical rigor with detailed analysis of liquidity constraints, bid-ask spreads, and robust backtesting on real Central European power market data.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>Optimize BESS bidding in Day-Ahead & Intraday markets"] --> B["Data & Inputs"]
  B --> C["Methodology<br/>Rolling Intrinsic Strategy with Bid-Ask Spreads"]
  C --> D{"Computations<br/>Single vs. Multi-Market Bidding"}
  D --> E["Key Findings & Outcomes"]
  
  subgraph B ["Inputs"]
      B1["EPEX Spot Data"]
      B2["Market Prices"]
      B3["Liquidity Constraints"]
  end

  subgraph E ["Outcomes"]
      E1["Multi-market strategies outperform single-market"]
      E2["Flexibility is crucial: relaxing daily limits unlocks value"]
      E3["Profitability tied to market access & cycling constraints"]
  end

Cited References (20)

  1. Deep H edging (2019)
  2. Deep Hedging of Green PPAs in Electricity Markets — → 2503.13056
  3. An Algorithm for Modelling Rolling Intrinsic Battery Trading on the Continuous Intraday Market (2024) — link
  4. The Value of Coordination in Multimarket Bidding of Grid Energy Storage (2023) — link
  5. Continuous Intraday Trading: An Open-Source Multi-Market Bidding Framework for Energy Storage Systems (2025) — link
  6. I ndex A usgleichsenergiepreis ( ID AEP ) (2022) — link
  7. T rading at EPEX SPOT (2025) — link
  8. Maximizing the value of batteries: A hands-on guide (2025) — link
and 12 more
  1. Electricity Balancing Guideline (2021) — link
  2. Annual Trading Results of 2024 (2025) — link
  3. Statistical Factsheet 2024 (2025) — link
  4. Calculation logic of balancing price (2025) — link
  5. Deployment of battery energy storage system for energy arbitrage applications (2016) — link
  6. The Value of Battery Energy Storage in the Continuous Intraday Market: Forecast vs. Perfect Foresight Strategies (2025) — link
  7. Optimal Bidding, Scheduling, and Deployment of Battery Systems in California Day-Ahead Energy Market (2016) — link
  8. Linear battery models for power systems analysis (2022) — link
  9. Maximizing Battery Storage Profits via High-Frequency Intraday Trading (2025) — link
  10. Smart optimization in battery energy storage systems: An overview (2024) — link
  11. EAO – A Framework for Optimizing Decentralized Portfolios and Green Supply (2021) — link
  12. The Energy Asset Optimization (EAO) package (2021–2025) — link

1 of 20 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.