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Analyzing and Forecasting Zonal Imbalance Signs in the Italian Electricity Market

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  • Francesco Lisi
  • Enrico Edoli

Abstract

In this paper, within the Italian electricity market, we analyse the features and the dynamics of the imbalance sign, defined as the sign of the algebraic sum of energy bought and sold by the national Transmission and System Operator during the real-time balancing of the electric network. The analyses provide evidence that the probability of having a positive (negative) sign exhibits a serial dependence structure and a dependence on the load periods, as well as on past history. Based on this evidence, we build a suitable model for zonal sign dynamics, and we use it for an out-of-sample forecasting exercise concerning the probability of a positive imbalance sign, nt. The results show that the zonal imbalance sign is ‘predictable.’ An economic evaluation of the benefits of using the proposed model is also provided.

Suggested Citation

  • Francesco Lisi & Enrico Edoli, 2018. "Analyzing and Forecasting Zonal Imbalance Signs in the Italian Electricity Market," The Energy Journal, , vol. 39(5), pages 1-20, September.
    • Handle: RePEc:sae:enejou:v:39:y:2018:i:5:p:1-20
    DOI: 10.5547/01956574.39.5.flis
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    References listed on IDEAS

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    1. Bosco, Bruno & Parisio, Lucia & Pelagatti, Matteo, 2012. "Strategic bidding in vertically integrated power markets with an application to the Italian electricity auctions," Energy Economics, Elsevier, vol. 34(6), pages 2046-2057.
    2. Simona Bigerna, Carlo Andrea Bollino and Paolo Polinori, 2016. "Market Power and Transmission Congestion in the Italian Electricity Market," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    3. Startz, Richard, 2008. "Binomial Autoregressive Moving Average Models With an Application to U.S. Recessions," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 1-8, January.
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    1. F. Durante & A. Gatto & F. Ravazzolo, 2024. "Understanding relationships with the Aggregate Zonal Imbalance using copulas," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(2), pages 513-554, April.
    2. Kaiyao Jiang & Yuji Yamada, 2025. "A Comprehensive Analysis of Imbalance Signal Prediction in the Japanese Electricity Market Using Machine Learning Techniques," Energies, MDPI, vol. 18(11), pages 1-28, May.
    3. 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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