DC Microgrid Enhancement via Chaos Game Optimization Algorithm

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Detalles Bibliográficos
Publicado en:	Processes vol. 13, no. 7 (2025), p. 2042-2070
Autor principal:	Heikal, Abdelrahman S
Otros Autores:	Diaaeldin Ibrahim Mohamed, Badra, Niveen M, Attia, Mahmoud A, Badr, Ahmed O, Omar Othman A. M., EL-Ebiary, Ahmed H, Kang, Hyun-Soo
Publicado:	MDPI AG
Materias:	National Institute of Standards & Technology Software Parameter identification Distributed generation Mathematical analysis Infrastructure Energy industry Communication Emissions Optimization techniques Root-mean-square errors Electricity generation Renewable resources Electric power Optimization Systems stability Energy storage Energy resources Energy consumption Controllers Cost control Emission standards Optimization algorithms Mathematical models Efficiency
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	Microgrids are increasingly being adopted as alternatives to traditional power transmission networks, necessitating improved performance strategies. Various mathematical optimization techniques are used to determine optimal controller parameters for these systems. These optimization methods can generally be categorized into natural, biological, and engineering-based approaches. In this research, the authors evaluated and compared several optimization techniques to enhance the secondary controller of DC microgrids, focusing on reducing operating time and minimizing error rates. Optimization tools were utilized to identify the optimal gain control parameters, aiming to achieve the best possible system performance. The enhanced controller response enables quicker recovery to steady-state conditions during sudden disturbances. The root-mean-square error (RMSE) served as a performance metric, with the proposed approach achieving a 15% reduction in RMSE compared to previous models. This improvement contributes to faster response times and lower energy consumption in microgrid operation.
ISSN:	2227-9717
DOI:	10.3390/pr13072042
Fuente:	Materials Science Database