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Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system

Author

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  • Chapaloglou, Spyridon
  • Nesiadis, Athanasios
  • Iliadis, Petros
  • Atsonios, Konstantinos
  • Nikolopoulos, Nikos
  • Grammelis, Panagiotis
  • Yiakopoulos, Christos
  • Antoniadis, Ioannis
  • Kakaras, Emmanuel

Abstract

In this study, a novel algorithm for the management of the power flows of an islanded power system was developed, capable of simultaneously achieving steadier conventional unit operation and shaving the demand peak values, for the days of the year that present a night peak in their load curve. The under investigation system is composed of Diesel Generators, a PV farm and a Battery Energy Storage System (BESS) with the power system’s consumption to be relatively higher than its RES production. The proposed algorithm combines the use of a load forecasting methodology, a pattern recognition procedure and a custom optimal power flow scheduling algorithm. The prediction module was based on a feedforward artificial neural network, capable of short-term day ahead load forecasting. The forecasted day ahead load profile was then used as an input to the developed pattern recognition algorithm, in order to be classified based on its load curve shape (pattern). Subsequently, in case that the classification resulted in a clear night peak pattern, it was possible to estimate an hourly based trajectory for the diesel generators operation and derive the BESS charging setpoints, which result in the desired peak shaving and smoothing level simultaneously. In this way, it is possible to replace or substitute the highest power demand with stored renewable energy and to operate the diesel engines as steady as possible, diminishing the ramp up and the steep gradients before the night hours’ peak. The algorithm was integrated in the overall system model in APROS software, where dynamic simulations were performed. The simulation results proved that by applying the proposed algorithm, a combined effect of smoother diesel generator operation and peak shaving with renewable energy is achievable even with the absence of PV overproduction, diminishing the variability of the load to be covered from the conventional units. Such an operation aims at enabling diesel engines to be rated at a lower, than currently, maximum capacity while increasing the share of the renewable energy penetration into the grid.

Suggested Citation

  • Chapaloglou, Spyridon & Nesiadis, Athanasios & Iliadis, Petros & Atsonios, Konstantinos & Nikolopoulos, Nikos & Grammelis, Panagiotis & Yiakopoulos, Christos & Antoniadis, Ioannis & Kakaras, Emmanuel, 2019. "Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island’s power system," Applied Energy, Elsevier, vol. 238(C), pages 627-642.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:627-642
    DOI: 10.1016/j.apenergy.2019.01.102
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    18. Erick Alves & Santiago Sanchez & Danilo Brandao & Elisabetta Tedeschi, 2019. "Smart Load Management with Energy Storage for Power Quality Enhancement in Wind-Powered Oil and Gas Applications," Energies, MDPI, vol. 12(15), pages 1-15, August.
    19. Nieta, Agustín A. Sánchez de la & Ilieva, Iliana & Gibescu, Madeleine & Bremdal, Bernt & Simonsen, Stig & Gramme, Eivind, 2021. "Optimal midterm peak shaving cost in an electricity management system using behind customers’ smart meter configuration," Applied Energy, Elsevier, vol. 283(C).
    20. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    21. Qin, Peijia & Tan, Xianlin & Huang, Youbin & Pan, Mingming & Ouyang, Tiancheng, 2023. "Two-stage robust optimal scheduling framework applied for microgrids: Combined energy recovery and forecast," Renewable Energy, Elsevier, vol. 214(C), pages 290-306.
    22. Talaat, M. & Hatata, A.Y. & Alsayyari, Abdulaziz S. & Alblawi, Adel, 2020. "A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach," Energy, Elsevier, vol. 190(C).

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