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A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach

Author

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  • Talaat, M.
  • Hatata, A.Y.
  • Alsayyari, Abdulaziz S.
  • Alblawi, Adel

Abstract

Load management represents one of the main constraints when considering smart grids. In addition, load management is a significant challenge for power system security operations. The smart techniques in load management endow the system with the ability to restore its normal or stable operation after being subjected to any disturbances. When the load exceeds the generation, the system stability is affected, which leads to cascade outages and shutdown of the major parts of the power system, causing the frequency decay effect. Fast load shedding (LS) is the best way to avoid cascading outages and power system blackouts. This paper proposes an innovative, accurate, reliable and fast under-frequency load shedding (UFLS) technique based on the grasshopper optimization algorithm (GOA). LS is considered in this research as a constrained optimization problem. The objective function is to minimize the amount of load shed while maximizing the lowest swing frequency at all stages. To validate the proposed GOA-based UFLS, a comparison with adaptive, particle swarm optimization (PSO) and genetic algorithm (GA) UFLS is carried out for different disturbances. Two different case study systems are considered to test the accuracy and reliability of the proposed algorithm: the IEEE 9-bus and 39-bus systems. The proposed GOA and PSO are coded using the MATLAB environment. Different operating cases involving outage of multiple generators and increasing load are implemented to validate the proposed GOA. The DigSilent power factory software is used as a platform for simulating the power system under study when subjected to different disturbance levels. The results verify the accuracy and reliability of GOA in minimizing the amount of load shed and maximizing the lowest swing frequency while satisfying the constraints. Moreover, GOA achieves a faster solution than PSO and GA.

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  • 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).
    • Handle: RePEc:eee:energy:v:190:y:2020:i:c:s0360544219321188
    DOI: 10.1016/j.energy.2019.116423
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    2. Santiago Bustamante-Mesa & Jorge W. Gonzalez-Sanchez & Sergio D. Saldarriaga-Zuluaga & Jesús M. López-Lezama & Nicolás Muñoz-Galeano, 2024. "Data for Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection," Data, MDPI, vol. 9(6), pages 1-8, June.
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    5. Talaat, M. & Farahat, M.A. & Mansour, Noura & Hatata, A.Y., 2020. "Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach," Energy, Elsevier, vol. 196(C).
    6. Antans Sauhats & Andrejs Utans & Jurijs Silinevics & Gatis Junghans & Dmitrijs Guzs, 2021. "Enhancing Power System Frequency with a Novel Load Shedding Method Including Monitoring of Synchronous Condensers’ Power Injections," Energies, MDPI, vol. 14(5), pages 1-21, March.
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    10. Sheha, Moataz & Mohammadi, Kasra & Powell, Kody, 2021. "Techno-economic analysis of the impact of dynamic electricity prices on solar penetration in a smart grid environment with distributed energy storage," Applied Energy, Elsevier, vol. 282(PA).

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