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Effect of Daily Forecasting Frequency on Rolling-Horizon-Based EMS Reducing Electrical Demand Uncertainty in Microgrids

Author

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  • Giuseppe La Tona

    (Consiglio Nazionale delle Ricerche (CNR), Istituto di Ingegneria del Mare (INM), via Ugo La Malfa 153, 90146 Palermo, Italy)

  • Maria Carmela Di Piazza

    (Consiglio Nazionale delle Ricerche (CNR), Istituto di Ingegneria del Mare (INM), via Ugo La Malfa 153, 90146 Palermo, Italy)

  • Massimiliano Luna

    (Consiglio Nazionale delle Ricerche (CNR), Istituto di Ingegneria del Mare (INM), via Ugo La Malfa 153, 90146 Palermo, Italy)

Abstract

Accurate forecasting is a crucial task for energy management systems (EMSs) used in microgrids. Despite forecasting models destined to EMSs having been largely investigated, the analysis of criteria for the practical execution of this task, in the framework of an energy management algorithm, has not been properly investigated yet. On such a basis, this paper aims at exploring the effect of daily forecasting frequency on the performance of rolling-horizon EMSs devised to reduce demand uncertainty in microgrids by adhering to a reference planned profile. Specifically, the performance of a sample EMS, where the forecasting task is committed to a nonlinear autoregressive network with exogenous inputs (NARX) artificial neural network (ANN), has been studied under different daily forecasting frequencies, revealing a representative trend relating the forecasting execution frequency in the EMS and the reduction of uncertainty in the electrical demand. On the basis of such a trend, it is possible to establish how often is convenient to repeat the forecasting task for obtaining increasing performance of the EMS. The obtained results have been generalized by extending the analysis to different test scenarios, whose results have been found coherent with the identified trend.

Suggested Citation

  • Giuseppe La Tona & Maria Carmela Di Piazza & Massimiliano Luna, 2021. "Effect of Daily Forecasting Frequency on Rolling-Horizon-Based EMS Reducing Electrical Demand Uncertainty in Microgrids," Energies, MDPI, vol. 14(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1598-:d:516385
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    References listed on IDEAS

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    1. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
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    Cited by:

    1. Aguilar, Diego & Quinones, Jhon J. & Pineda, Luis R. & Ostanek, Jason & Castillo, Luciano, 2024. "Optimal scheduling of renewable energy microgrids: A robust multi-objective approach with machine learning-based probabilistic forecasting," Applied Energy, Elsevier, vol. 369(C).
    2. Maria Carmela Di Piazza, 2022. "Recent Developments and Trends in Energy Management Systems for Microgrids," Energies, MDPI, vol. 15(21), pages 1-6, November.
    3. Maria Carmela Di Piazza, 2021. "Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids," Energies, MDPI, vol. 14(24), pages 1-3, December.
    4. La Tona, G. & Luna, M. & Di Piazza, M.C., 2024. "Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 63-75.

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