AccScience Publishing / JES / Online First / DOI: 10.36922/JES025500034
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ORIGINAL RESEARCH ARTICLE

Supervisory control of an inverter-based microgrid using the Grey Wolf Optimization algorithm

Bhinal Mehta1* ,  Darshan Khimoria1
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1 Department of Electrical Engineering, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India
JES, 025500034 https://doi.org/10.36922/JES025500034
Received: 9 December 2025 | Revised: 8 January 2026 | Accepted: 2 February 2026 | Published online: 18 March 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

For an inverter-based microgrid to function, supervisory control is necessary. This study proposes a Grey Wolf Optimization (GWO)-based method to enhance microgrid supervisory control. The GWO algorithm proficiently searches the solution space to determine an optimal control strategy that minimizes voltage and frequency errors, maximizes renewable energy integration, and enhances microgrid stability while fulfilling operational limits. The study establishes an objective function, maps the optimized controller parameters to the voltage and frequency error signals, and coordinates control actions for microgrid operation. Simulation results show that the GWO-based supervisory control strategy achieves superior voltage and frequency regulation, faster settling response, and reduced overshoot compared with Particle Swarm Optimization (PSO) and genetic algorithm (GA) employing roulette-wheel selection, supporting GWO as a highly effective and robust optimization technique for supervisory control of inverter-based microgrid systems.

Keywords
Droop control
Microgrid supervisory control
Grey Wolf Optimization
Particle Swarm Optimization
Genetic algorithm
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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