Predicting electricity imbalance prices and volumes: capabilities and opportunities

Browell, J. and Gilbert, C. (2022) Predicting electricity imbalance prices and volumes: capabilities and opportunities. Energies, 15(10), 3645. (doi: 10.3390/en15103645)

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Electricity imbalance pricing provides the ultimate incentive for generators and suppliers to contract with one another ahead of time and deliver against their obligations. As delivery time approaches, traders must judge whether to trade-out a position or settle it in the balancing market at the as-yet-unknown imbalance price. Forecasting the imbalance price (and related volumes) is therefore a necessity in short-term markets. However, this topic has received surprisingly little attention in the academic literature despite clear need by practitioners. Furthermore, the emergence of algorithmic trading demands automated forecasting and decision-making, with those best able to extract predictive information from available data gaining a competitive advantage. Here we present the case for developing imbalance price forecasting methods and provide motivating examples from the Great Britain’s balancing market, demonstrating forecast skill and value.

Item Type:Articles
Additional Information:This work was funded by the EPSRC Supergen Energy Networks Hub (EP/S00078X/1), the EPSRC Innovation Fellowship held by JB (EP/R023484/1 and EP/R023484/2), the Energy Technology Partnership, and Scottish Power, SSE, and University of Strathclyde through the latter’s Technology and Innovation Centre Low Carbon Power and Energy program.
Keywords:Balancing market, imbalance, real-time market, probabilistic forecasting, electricity price forecasting.
Glasgow Author(s) Enlighten ID:Browell, Dr Jethro
Creator Roles:
Browell, J.Conceptualization, Methodology, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review and editing, Visualization, Supervision, Project administration, Funding acquisition
Authors: Browell, J., and Gilbert, C.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Energies
ISSN (Online):1996-1073
Published Online:16 May 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Energies 15(10): 3645
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
315958System-wide Probabilistic Energy ForecastingJethro BrowellEngineering and Physical Sciences Research Council (EPSRC)EP/R023484/2M&S - Statistics