Performance Comparison of Metaheuristic and Hybrid Algorithms Used for Energy Cost Minimization in a Solar–Wind–Battery Microgrid

Seyfettin Vadi, Merve Bildirici and Orhan Kaplan ()
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Seyfettin Vadi: Department of Electrical and Electronics Engineering, Gazi University, Ankara 06500, Turkey
Merve Bildirici: Graduate School of Natural and Applied Sciences, Gazi University, Ankara 06500, Turkey
Orhan Kaplan: Department of Electrical and Electronics Engineering, Gazi University, Ankara 06500, Turkey

Sustainability, 2025, vol. 17, issue 19, 1-27

Abstract: The integration of renewable energy sources has become a strategic necessity for sustainable energy management and supply security. This study evaluates the performance of eight metaheuristic optimization algorithms in scheduling a renewable-based smart grid system that integrates solar, wind, and battery storage for a factory in İzmir, Türkiye. The algorithms considered include classical approaches—Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), the Whale Optimization Algorithm (WOA), Krill Herd Optimization (KOA), and the Ivy Algorithm (IVY)—alongside hybrid methods, namely KOA–WOA, WOA–PSO, and Gradient-Assisted PSO (GD-PSO). The optimization objectives were minimizing operational energy cost, maximizing renewable utilization, and reducing dependence on grid power, evaluated over a 7-day dataset in MATLAB. The results showed that hybrid algorithms, particularly GD-PSO and WOA–PSO, consistently achieved the lowest average costs with strong stability, while classical methods such as ACO and IVY exhibited higher costs and variability. Statistical analyses confirmed the robustness of these findings, highlighting the effectiveness of hybridization in improving smart grid energy optimization.

Keywords: distributed generation; metaheuristic algorithms; smart grids; optimization; renewable energy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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