A Hybrid Optimization Approach for Optimal Energy Management and Economic Dispatch in Multi-Microgrid Systems with Hydrogen-Based Energy Storage

Prashant SinghDepartment of Electrical Engineering, Sardar Patel University, Balaghat, IndiaSuresh Kumar TandekarDepartment of Electrical Engineering, Sardar Patel University, Balaghat, IndiaAjay Shyam KunwarDepartment of Electrical Engineering, Sardar Patel University, Balaghat, IndiaNaresh SapateDepartment of Electrical Engineering, Sardar Patel University, Balaghat, India

Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 758-765

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 31-05-2026

doi Logo doi.org/10.47001/IRJIET/2026.105101

Abstract

The integration of hydrogen-based energy storage systems (HESS) into multi-microgrid networks presents both unprecedented opportunities and significant optimization challenges for modern power systems. This paper proposes a novel hybrid optimization approach combining the Grey Wolf Optimizer (GWO) with Particle Swarm Optimization (PSO) for optimal energy management and economic dispatch in interconnected multimicrogrid systems equipped with hydrogen production, storage, and fuel cell technologies. The proposed Hybrid Grey Wolf-Particle Swarm Optimization (HGWPSO) algorithm addresses the complex, non-linear, and multi-objective nature of the energy management problem by leveraging the exploration capabilities of GWO and the exploitation strengths of PSO. A comprehensive mathematical model is developed that incorporates renewable energy sources, conventional generators, battery energy storage systems, and hydrogen-based storage including electrolyzers, hydrogen tanks, and fuel cells. The proposed methodology is validated through extensive simulations on a test system comprising three interconnected microgrids. Comparative analysis demonstrates that the HGWPSO algorithm achieves a 12.7% reduction in operational costs compared to conventional PSO, 9.3% improvement over standard GWO, and 15.8% cost savings compared to genetic algorithm-based approaches. Furthermore, the hydrogen storage system contributes to a 23.4% improvement in renewable energy utilization and reduces curtailment by 31.2%.

Keywords

Multi-microgrid systems, hydrogen energy storage, economic dispatch, hybrid optimization, Grey Wolf Optimizer, Particle Swarm Optimization, renewable energy integration.


Citation of this Article

Prashant Singh, Suresh Kumar Tandekar, Ajay Shyam Kunwar, & Naresh Sapate. (2026). A Hybrid Optimization Approach for Optimal Energy Management and Economic Dispatch in Multi-Microgrid Systems with Hydrogen-Based Energy Storage. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 758-765.

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