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Statistical Arbitrage and Information Flow in an Electricity Balancing Market

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  • Derek W. Bunn
  • Stefan O.E. Kermer

Abstract

Motivated by the events following a natural experiment in 2015, when the market rules for electricity spot trading were changed in Britain, we analyse the operational effects of market participants responding to price incentives for spillage and shortage positions in a single price, real-time market. We develop an analytical model for optimal real-time decisions by generators and speculators based upon forecasts of the conditional distribution of the total system imbalance between instantaneous supply and demand. From this, we examine the effects of time delays in information transparency for the consequent statistical arbitrage positions. We backtested this model empirically to the Austrian system imbalance settlements process within the German/Austrian integrated market. Results suggest that permitting additional intraday flexibility from a physical generator or a non-physical trader can be beneficial for the agents themselves, the system operator and market efficiency.

Suggested Citation

  • Derek W. Bunn & Stefan O.E. Kermer, 2021. "Statistical Arbitrage and Information Flow in an Electricity Balancing Market," The Energy Journal, , vol. 42(5), pages 19-40, September.
    • Handle: RePEc:sae:enejou:v:42:y:2021:i:5:p:19-40
    DOI: 10.5547/01956574.42.5.dbun
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    References listed on IDEAS

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    1. van der Veen, Reinier A.C. & Abbasy, Alireza & Hakvoort, Rudi A., 2012. "Agent-based analysis of the impact of the imbalance pricing mechanism on market behavior in electricity balancing markets," Energy Economics, Elsevier, vol. 34(4), pages 874-881.
    2. Boogert, Alexander & Dupont, Dominique, 2005. "On the effectiveness of the anti-gaming policy between the day-ahead and real-time electricity markets in The Netherlands," Energy Economics, Elsevier, vol. 27(5), pages 752-770, September.
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    Cited by:

    1. Deng, Sinan & Inekwe, John & Smirnov, Vladimir & Wait, Andrew & Wang, Chao, 2024. "Seasonality in deep learning forecasts of electricity imbalance prices," Energy Economics, Elsevier, vol. 137(C).

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