Optimal Scheduling of Grid-Connected Microgrids Using an Enhanced Generalized Normal Distribution Optimization
Received: 15 December 2025 | Revised: 13 January 2026 | Accepted: 23 January 2026 | Online: 4 April 2026
Corresponding author: Dieu Ngoc Vo
Abstract
The increasing penetration of microgrids in modern power systems has heightened the need for advanced operational strategies that can effectively manage the intermittency of renewable energy sources, load uncertainty, and complex operational constraints. Existing studies have extensively explored mathematical and metaheuristic optimization techniques for microgrid operation; however, many approaches still suffer from premature convergence and reduced effectiveness when applied to highly constrained, nonconvex scheduling problems. Hence, this research proposes an Enhanced Generalized Normal Distribution Optimization (EGNDO) for optimal microgrid scheduling, which comprises dispatchable Distributed Energy Resources (DERs), renewable generation units, and a Battery Energy Storage System (BESS). The scheduling problem was formulated to minimize the total operating cost, including the generation, start-up, electricity trading, and BESS degradation costs, while satisfying the technical and operational constraints. Three operational scenarios with varying grid pricing and power exchange policies were examined over a 24-h period. Simulation results on a modified IEEE 13-bus test system demonstrated that active and bidirectional grid participation can significantly reduce operating costs. Furthermore, comparative studies confirm that the proposed EGNDO consistently outperforms benchmark metaheuristic algorithms in terms of solution quality and robustness, highlighting its effectiveness for complex microgrid scheduling problems.
Keywords:
battery energy storage system, distributed energy resources, generalized normal distribution optimization, microgridDownloads
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Copyright (c) 2026 Tuan Trong Nguyen, An Quang Phan, Khoa Hoang Truong, Dieu Ngoc Vo, Thanh Van Tran
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