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Analyzing and Forecasting Zonal Imbalance Signs in the Italian Electricity Market

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  • Francesco Lisi and 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, pt. 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 and Enrico Edoli, 2018. "Analyzing and Forecasting Zonal Imbalance Signs in the Italian Electricity Market," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    • Handle: RePEc:aen:journl:ej39-5-lisi
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    Cited by:

    1. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    2. Eicke, Anselm & Ruhnau, Oliver & Hirth, Lion, 2021. "Electricity balancing as a market equilibrium: An instrument-based estimation of supply and demand for imbalance energy," Energy Economics, Elsevier, vol. 102(C).
    3. Eicke, Anselm & Ruhnau, Oliver & Hirth, Lion, 2021. "Electricity balancing as a market equilibrium," EconStor Preprints 233852, ZBW - Leibniz Information Centre for Economics.
    4. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    5. Fianu, Emmanuel Senyo & Ahelegbey, Daniel Felix & Grossi, Luigi, 2022. "Modeling risk contagion in the Italian zonal electricity market," European Journal of Operational Research, Elsevier, vol. 298(2), pages 656-679.
    6. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    7. Karakoyun, Ece Cigdem & Avci, Harun & Kocaman, Ayse Selin & Nadar, Emre, 2023. "Deviations from commitments: Markov decision process formulations for the role of energy storage," International Journal of Production Economics, Elsevier, vol. 255(C).
    8. Goodarzi, Shadi & Perera, H. Niles & Bunn, Derek, 2019. "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, Elsevier, vol. 134(C).
    9. Sinan Deng & John Inekwe & Vladimir Smirnov & Andrew Wait & Chao Wang, 2023. "Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices," Working Papers 2023-03, University of Sydney, School of Economics.
    10. Eicke, Anselm & Ruhnau, Oliver & Hirth, Lion, 2020. "Electricity balancing as a market equilibrium: Estimating supply and demand of imbalance energy," EconStor Preprints 223062, ZBW - Leibniz Information Centre for Economics.
    11. Kaneko, Nanae & Fujimoto, Yu & Hayashi, Yasuhiro, 2022. "Sensitivity analysis of factors relevant to extreme imbalance between procurement plans and actual demand: Case study of the Japanese electricity market," Applied Energy, Elsevier, vol. 313(C).

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    JEL classification:

    • F0 - International Economics - - General

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