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A Model Predictive Control for the Dynamical Forecast of Operating Reserves in Frequency Regulation Services

Author

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  • Pavlos Nikolaidis

    (Department of Electrical Engineering, Cyprus University of Technology, P.O. Box 50329, 3603 Limassol, Cyprus)

  • Harris Partaourides

    (Department of Electrical Engineering, Cyprus University of Technology, P.O. Box 50329, 3603 Limassol, Cyprus)

Abstract

The intermittent and uncontrollable power output from the ever-increasing renewable energy sources, require large amounts of operating reserves to retain the system frequency within its nominal range. Based on day-ahead load forecasts, many research works have proposed conventional and stochastic approaches to define their optimum margins for reliability enhancement at reasonable production cost. In this work, we aim at delivering real-time load forecasting to lower the operating-reserve requirements based on intra-hour weather update predictors. Based on critical predictors and their historical data, we train an artificial model that is able to forecast the load ahead with great accuracy. This is a feed-forward neural network with two hidden layers, which performs real-time forecasts with the aid of a predictive model control developed to update the recommendations intra-hourly and, assessing their impact and its significance on the output target, it corrects the imposed deviations. Performing daily simulations for an annual time-horizon, we observe that significant improvements exist in terms of decreased operating reserve requirements to regulate the violated frequency. In fact, these improvements can exceed 80% during specific months of winter when compared with robust formulations in isolated power systems.

Suggested Citation

  • Pavlos Nikolaidis & Harris Partaourides, 2021. "A Model Predictive Control for the Dynamical Forecast of Operating Reserves in Frequency Regulation Services," Forecasting, MDPI, vol. 3(1), pages 1-14, March.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:1:p:14-241:d:519135
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    References listed on IDEAS

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    Cited by:

    1. Pavlos Nikolaidis, 2023. "Solar Energy Harnessing Technologies towards De-Carbonization: A Systematic Review of Processes and Systems," Energies, MDPI, vol. 16(17), pages 1-39, August.
    2. Pavlos Nikolaidis & Andreas Poullikkas, 2022. "A Thorough Emission-Cost Analysis of the Gradual Replacement of Carbon-Rich Fuels with Carbon-Free Energy Carriers in Modern Power Plants: The Case of Cyprus," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    3. Sonia Leva, 2022. "Editorial for Special Issue: “Feature Papers of Forecasting 2021”," Forecasting, MDPI, vol. 4(1), pages 1-3, March.

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