Citation :

IBK Sugirianta, IAD Giriantari, WG Ariastina, IB Alit Swamardika, IGNA Dwijaya Saputra, "New Hybrid Optimization Algorithm based on COA and WCA for Hybrid Microgrid System," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 4, pp. 196-208, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I4P115

Abstract

This paper shows the developments and evaluations of a hybrid optimization algorithm combining the Coyote Optimization Algorithm (COA) and the Water Cycle Algorithm (WCA) for energy management in hybrid microgrid systems. Hybrid microgrids, which are the integration of renewable energy sources such as solar panels and wind turbines with energy storage and diesel generators, require optimization to minimize both the Cost of Energy (COE) and the Loss of Power Supply Probability (LPSP). While COA is known for its strong exploration capabilities, and WCA excels in local exploitation, both algorithms face challenges in optimizing complex multi-objective systems. Simulation results using MATLAB demonstrate that the COA-WCA hybrid algorithm overcomes the limitations of the individual algorithms. The hybrid approach achieves faster convergence, improved stability, and higher efficiency, reducing the standard deviation of results by 61.8% compared to COA. Additionally, the COA-WCA hybrid produces the highest hypervolume (31.63174), which means that this system has the ability to explore a broader solution space and achieve more optimal solutions. In sensitivity analysis, the COA-WCA hybrid is more robust to variations in parameters such as fuel prices and weather conditions. This study shows the significant contribution to the development of more efficient and reliable energy management systems, especially for off-grid microgrid applications in remote locations.

Keywords

COA-WCA, COE, Hybrid Microgrid, LPSP, Metaheuristics, Multi-Objective Optimization.

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