Optimal Sustainable Energy Management for Isolated Microgrid: A Hybrid Jellyfish Search-Golden Jackal Optimization Approach

Dilip Kumar (), Yogesh Kumar Chauhan, Ajay Shekhar Pandey, Ankit Kumar Srivastava, Raghavendra Rajan Vijayaraghavan, Rajvikram Madurai Elavarasan () and G. M. Shafiullah
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Dilip Kumar: Department of Electrical Engineering, Institute of Engineering and Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya, Faizabad 224001, India
Yogesh Kumar Chauhan: Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur 228118, India
Ajay Shekhar Pandey: Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur 228118, India
Ankit Kumar Srivastava: Department of Electrical Engineering, Institute of Engineering and Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya, Faizabad 224001, India
Raghavendra Rajan Vijayaraghavan: Automotive Department, Harman Connected Services India Pvt. Ltd., Bengaluru 560066, India
Rajvikram Madurai Elavarasan: School of Engineering and Energy, College of Science, Technology, Engineering & Mathematics, Murdoch University, Perth, WA 6150, Australia
G. M. Shafiullah: School of Engineering and Energy, College of Science, Technology, Engineering & Mathematics, Murdoch University, Perth, WA 6150, Australia

Sustainability, 2025, vol. 17, issue 11, 1-40

Abstract: This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications.

Keywords: EMS; solar photovoltaic (PV); wind turbine (WT); battery system (BS); microturbine (MT); MPPT; tip speed ratio (TSR); pitch angle control; JSO-GJO algorithm; energy optimization; cost optimization (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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