Paper: arXiv 2609.37903

Authors: Runyao Yu, Jochen L. Cremer, Pierre Pinson, Jalal Kazempour, Leo Semmelmann, Takuji Matsumoto, Derek W. Bunn

Abstract

Power systems with increasing variable renewable generation face greater uncertainty in scheduling and balancing. Intraday and balancing electricity markets facilitate position adjustments and real-time balancing close to delivery. As delivery approaches, continuous intraday market participants exposed to imbalance settlement adjust their positions by trading additional volumes to reduce their imbalance exposure. We conjecture that positions remaining open after intraday trading, together with demand and supply uncertainties affecting physical market participants, influence price formation in the balancing market. This cross-market interaction is, however, rarely studied. To understand this interaction, this paper uses probabilistic modeling to examine how intraday orderbook information reflects subsequent imbalance price formation in Germany and Austria. We compare orderbook representations based on open, high, low, close, and volume, Volume-Weighted Average Price (VWAP), and last mid price across multiple horizons. Each representation is evaluated using the self product, neighboring products, and the product from the neighboring country. We then compare the best orderbook setting with fundamental feature sets and their combinations, followed by an ablation study of the available training history. We show that VWAP with neighboring products provides the best performance in both countries. Combining orderbook and fundamental information reduces testing loss in Germany but increases it in Austria. Using all available observations provides the best overall performance, while excluding 2022 can reduce loss for extreme price samples. These results reveal and help explain country-dependent interactions between the intraday and balancing markets.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses probabilistic modeling and statistical comparisons, indicating a solid empirical approach. While the mathematical underpinnings are not explicitly detailed in the excerpt, the focus appears to be on applying existing methods to a novel problem in energy markets. The novelty lies in the cross-market interaction analysis and the comparison of orderbook representations.

Research Flowchart

  flowchart TD
    A[Research Goal: Understand Cross-Market Interaction & Influence of Intraday Orderbook on Imbalance Price] --> B{Key Methodology Steps};

    B --> C[Data/Inputs: Intraday Orderbook (Open, High, Low, Close, Volume, VWAP, Last Mid Price), Fundamental Features];
    C --> D[Computational Processes: Probabilistic Modeling, Orderbook Representation Evaluation (Self, Neighboring, Country Products), Best Setting Comparison, Ablation Study];

    D --> E[Key Findings/Outcomes: VWAP with Neighboring Products Best Performance (Germany & Austria)];
    E --> F[Key Findings/Outcomes: Orderbook + Fundamental Reduces Loss in Germany, Increases in Austria];
    F --> G[Key Findings/Outcomes: All Observations Best Performance; Excluding 2022 Reduces Loss for Extreme Prices];