An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids

Upasana Lakhina, Nasreen Badruddin (), Irraivan Elamvazuthi (), Ajay Jangra, Truong Hoang Bao Huy and Josep M. Guerrero
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Upasana Lakhina: Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Nasreen Badruddin: Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Irraivan Elamvazuthi: Department of Electrical and Electronics Engineering, Institute of Health and Analytics, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Ajay Jangra: Department of Computer Science and Engineering, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra 136119, India
Truong Hoang Bao Huy: Department of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, Republic of Korea
Josep M. Guerrero: Centre of Research on Microgrids, Department of Energy Technology, Aalborg University, P.O. Box 159 Aalborg, Denmark

Mathematics, 2023, vol. 11, issue 9, 1-24

Abstract: A microgrid is an autonomous electrical system that consists of renewable energy and efficiently achieves power balance in a network. The complexity in the distribution network arises due to the intermittent nature of renewable generation units and varying power. One of the important objectives of a microgrid is to perform energy management based on situational awareness and solve an optimization problem. This paper proposes an enhanced multi-objective multi-verse optimizer algorithm (MOMVO) for stochastic generation power optimization in a renewable energy-based islanded microgrid framework. The proposed algorithm is utilized for optimum power scheduling among various available generation sources to minimize the microgrid’s generation costs and power losses. The performance of MOMVO is assessed on a 6-unit and 10-unit test system. Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms for multi-objective optimization.

Keywords: energy management; microgrids; multi-objective multi-verse optimizer algorithm; optimization; power scheduling; stochastic generation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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