A HLBDA, GA, and COA for optimal operation of distributed energy resources

Bilal Naji Alhasnawi, Sabah Mohammed Mlkat Almutoki, Hayder Khenyab Hashim, Abdellatif M Sadeq, Ali Qasim Almousawi, Basil H Jasim, Raad Z Homod, Firas Faeq K Hussain, Mahmood A Al-Shareeda, Alžběta Dočekalová and Vladimír Bureš

PLOS ONE, 2026, vol. 21, issue 1, 1-30

Abstract: Although renewable energy sources offer enormous potential to improve environmental sustainability, maximizing economic benefits inside microgrids requires resolving their intermittency and irregularity. A viable alternative is to combine energy storage with renewable energy technologies. This article introduced a energy management system for hybrid renewable power plants that includes fuel cells, wind turbines, solar cells, battery energy storage devices, and micro-turbines. Optimization problem is formulated as Hyper Learning Binary Dragonfly Algorithm (HLBDA) for optimizing economic benefits and with objectives of minimizing operating costs and pollutant gas emissions. Suggested model is compared with existing methods like Genetic Algorithms (GA), and Crayfish Optimization Algorithm (COA). Also, stochastic framework is considered suitable solution for achieving optimal operation point in microgrids to cope with uncertain parameters. According to the simulation results, suggested method proves reductions in overall system costs and pollutant gas emissions. The proposed system achieved significant superiority across all indicators. In the area of cost reduction, the algorithms demonstrated remarkable progress. The algorithms achieved significant improvements in cost reduction compared to genetic algorithm (GA). HLBDA algorithm achieved a 12.4% cost saving compared to GA, and the COA algorithm showed a 3.24% improvement in cost reduction. In the area of carbon emission reduction, the algorithms also showed significant progress: the HLBDA algorithm recorded the highest emission reduction rate at 9.54%, and the COA algorithm showed a 2.40% improvement in emission reduction.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0340259

DOI: 10.1371/journal.pone.0340259

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