NSGA-II and MOPSO based optimization for sizing of hybrid PV/wind/battery energy storage system

• Published in: International Journal of Power Electronics and Drive Systems Vol. 10; no. 1; p. 463
• Main Authors: Hlal, Mohamed Izdin, Ramachandaramurthya, Vigna K., Padmanaban, Sanjeevikumar, Kaboli, Hamid Reza, Pouryekta, Aref, Tuan Abdullah, Tuan Ab Rashid bin
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.03.2019
• Subjects: Batteries; Classification; Electric power supplies; Energy storage; Fossil fuels; Genetic algorithms; Hybrid systems; Meteorological data; Multiple objective analysis; Panels; Particle swarm optimization; Photovoltaic cells; Sizing; Solar cells; Sorting algorithms; Wind speed; Wind turbines
• ISSN: 2088-8694; 2088-8694
• DOI: 10.11591/ijpeds.v10.i1.pp463-478

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.