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From Intraday Orderbook to Imbalance Price: Understanding Cross-Market Interaction

Author

Listed:
  • 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.

Suggested Citation

  • Runyao Yu & Jochen L. Cremer & Pierre Pinson & Jalal Kazempour & Leo Semmelmann & Takuji Matsumoto & Derek W. Bunn, 2026. "From Intraday Orderbook to Imbalance Price: Understanding Cross-Market Interaction," Papers 2609.37903, arXiv.org.
  • Handle: RePEc:arx:papers:2609.37903
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    References listed on IDEAS

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    1. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1533-1547.
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    3. Vighnesh Natarajan Ganesh & Derek Bunn, 2024. "Algorithmic trading of real-time electricity with machine learning," Quantitative Finance, Taylor & Francis Journals, vol. 24(11), pages 1545-1559, November.
    4. Simon Hirsch & Florian Ziel, 2024. "Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution," The Energy Journal, , vol. 45(3), pages 87-124, May.
    5. Runyao Yu & Julia Lin & Derek W. Bunn & Jochen Stiasny & Wentao Wang & Yujie Chen & Tara Esterl & Peter Palensky & Jochen L. Cremer, 2026. "A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting," Papers 2605.09061, arXiv.org.
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