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Статья

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Ahmed M. NassefComputers and Automatic Control Engineering Department, Faculty of Engineering, Tanta University, Tanta 31733, EgyptMohammad Ali AbdelkareemDepartment of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesHussein M. MaghrabieFaculty of Engineering, South Valley University, Qena 83523, EgyptAhmad BaroutajiSchool of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK
2023en
ABI

Аннотация

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field.

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