NSGA-II and MOPSO Based Optimization for Sizing of Hybrid PV/ Wind / Battery Energy Storage System
Document Type
Article
Publication Date
1-1-2019
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.
Keywords
Cost of energy, Hybrid renewable energy system, Loss of power supply probability, MOPSO, Multi objectives NSGA_II
Divisions
fac_eng
Publication Title
International Journal of Power Electronics and Drive Systems
Volume
1
Issue
1
Publisher
Institute of Advanced Engineering and Science