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Volume 2 , Issue 1 , PP: 42-52, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammed A. Saeed 1 * , Amal H. Alharbi 2
Doi: https://doi.org/10.54216/MOR.020104
Smart electrical grids (SGs) have emerged to advance the management of power systems by solving issues such as voltage instability, reactive loads, power loss, and the integration of renewable energy resources. This review focuses on the applicability of metaheuristic algorithms to energy distribution systems, improve operation, and overcome the repercussions affecting the environment and overall costs. PSO, GA, and GWO have been identified for their effectiveness in dealing with the complexity of PS due to the nonlinear and dynamic nature of today's energy systems. The review also addresses the extension of methods in machine learning for enhancing load forecasting and real-time energy control, which are key factors for shifting to innovative and renewable energy systems. Based on the literature review of the state of the art over the last five years, this research highlights some achievements and limitations. It provides recommendations for further directions in advancing Smart Grid algorithms. These results highlight the use of meta-heuristics in redesigning processes that offer optimal, reliable and sustainable energy facilities.
Metaheuristic Algorithms , Smart Grids , Energy Management Systems , Renewable Energy Integration , Load Forecasting , Optimization Techniques
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