NSGA-II and MOPSO Based Optimization for Sizing of Hybrid PV/ Wind / Battery Energy Storage System

Hlal, Mohamed Izdin and Ramachandaramurthya, Vigna K. and Padmanaban, Sanjeevikumar and Kaboli, Hamid Reza and Pouryekta, Aref and Tuan Abdullah, Tuan Ab Rashid (2019) NSGA-II and MOPSO Based Optimization for Sizing of Hybrid PV/ Wind / Battery Energy Storage System. International Journal of Power Electronics and Drive Systems, 1 (1). pp. 463-478. ISSN 2088-8694

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Abstract

This paper presents a Stand-alone Hybrid Renewable Energy System (SHRES) as an alternative to fossil fuel based generators. The Photovoltaic (PV) panels and wind turbines (WT) are designed for the Malaysian low wind speed conditions with battery Energy Storage (BES) to provide electric power to the load. The appropriate sizing of each component was accomplished using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques. The optimized hybrid system was examined in MATLAB using two case studies to find the optimum number of PV panels, wind turbines system and BES that minimizes the Loss of Power Supply Probability (LPSP) and Cost of Energy (COE). The hybrid power system was connected to the AC bus to investigate the system performance in supplying a rural settlement. Real weather data at the location of interest was utilized in this paper. The results obtained from the two scenarios were used to compare the suitability of the NSGA-II and MOPSO methods. The NSGA-II method is shown to be more accurate whereas the MOPSO method is faster in executing the optimization. Hence, both these methods can be used for techno-economic optimization of SHRES.

Item Type: Article
Uncontrolled Keywords: Cost of energy; Hybrid renewable energy system; Loss of power supply probability; MOPSO; Multi objectives NSGA_II
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 17 Jan 2019 02:46
Last Modified: 17 Jan 2019 02:46
URI: http://eprints.um.edu.my/id/eprint/20030

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