What is it about?
This publication is a systematic review that provides an overview of the use of metaheuristic algorithms in optimizing electric power systems. It focuses on different paradigms within electric power systems, such as microgrids and smart grids, power flow, power quality, load forecasting, and renewable energies. The review analyzes the most commonly used metaheuristics in these paradigms, including Particle Swarm Optimization, Gray Wolf Optimizer, Genetic Algorithms, Cuckoo Search, and Differential Evolution. It also discusses the search operators used in these metaheuristics and provides recommendations for future research in the field. The review aims to support researchers and practitioners interested in furthering the optimization of electric power systems using metaheuristic algorithms.
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Why is it important?
This work is important because it fills a gap in the literature by focusing on metaheuristics in electric power system applications, which has not been extensively studied before. The review provides a general panorama of paradigms such as Renewable Energies, Load Forecasting, Power Flow, Microgrids and Smart grids, and Power Quality. It analyzes the most employed metaheuristics in these paradigms, such as Particle Swarm Optimization, Gray Wolf Optimizer, Genetic Algorithms, Cuckoo Search, and Differential Evolution. The review also classifies the metaheuristics from a more formal perspective based on their search operators. The insights and recommendations provided in this work can support researchers and practitioners interested in furthering the field of electric power systems optimization.
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This page is a summary of: A systematic review of metaheuristic algorithms in electric power systems optimization, Applied Soft Computing, January 2024, Elsevier,
DOI: 10.1016/j.asoc.2023.111047.
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