Multi-Objective Hybrid Optimization for Optimal Sizing of a Hybrid Renewable Power System for Home Applications

Md. Arif Hossain (), Ashik Ahmed, Shafiqur Rahman Tito, Razzaqul Ahshan (), Taiyeb Hasan Sakib and Sarvar Hussain Nengroo ()
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Md. Arif Hossain: Department of Electrical and Electronic Engineering, Islamic University of Technology, Dhaka 1212, Bangladesh
Ashik Ahmed: Department of Electrical and Electronic Engineering, Islamic University of Technology, Dhaka 1212, Bangladesh
Shafiqur Rahman Tito: Department of Software Engineering, University of Waikato, Hamilton 3216, New Zealand
Razzaqul Ahshan: Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University (SQU), Muscat 123, Oman
Taiyeb Hasan Sakib: Department of Electrical and Electronic Engineering, Brac University, Dhaka 1212, Bangladesh
Sarvar Hussain Nengroo: Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

Energies, 2022, vol. 16, issue 1, 1-19

Abstract: An optimal energy mix of various renewable energy sources and storage devices is critical for a profitable and reliable hybrid microgrid system. This work proposes a hybrid optimization method to assess the optimal energy mix of wind, photovoltaic, and battery for a hybrid system development. This study considers the hybridization of a Non-dominant Sorting Genetic Algorithm II (NSGA II) and the Grey Wolf Optimizer (GWO). The objective function was formulated to simultaneously minimize the total energy cost and loss of power supply probability. A comparative study among the proposed hybrid optimization method, Non-dominant Sorting Genetic Algorithm II, and multi-objective Particle Swarm Optimization (PSO) was performed to examine the efficiency of the proposed optimization method. The analysis shows that the applied hybrid optimization method performs better than other multi-objective optimization algorithms alone in terms of convergence speed, reaching global minima, lower mean (for minimization objective), and a higher standard deviation. The analysis also reveals that by relaxing the loss of power supply probability from 0% to 4.7%, an additional cost reduction of approximately 12.12% can be achieved. The proposed method can provide improved flexibility to the stakeholders to select the optimum combination of generation mix from the offered solutions.

Keywords: battery; hybrid renewable energy system (HRES); genetic algorithm (NSGA) II; grey wolf optimizer (GWO); non-dominant sorting; optimization; photovoltaics; wind energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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