Optimization of Renewable Energy based Hybrid Energy System using Evolutionary Computational Techniques

The sudden increase in global energy demandefor renewable energy resources. The global transition to renewable energy has emphasized the need for efficient, sustainable and cost-effective hybrid renewable energy system in the conventional power system. This study focuses on the optimization of HRES witeeh the aim objective of improving energy efficiency, sustainability and affordability. The proposed HRES which consists of standby diesel generator, wind turbines, battery storage system and photovoltaic system is designed to satisfy energy demands while reducing dependency on fossil fuels. The optimization of the power system is implemented with the eevolutionary computation techniques governed by particle swarm optimization and genetic algorithm in the MATLAB environment to coordinate the optimal power flow among several components of HRES. The techniques present in this research are based on the optimization of the total cost of the system and cost of energy of DG/PV/WT/BSS hybrid energy system. The hybridization of WT, PV and BSS in a single power system provides uninterrupted power supply to consumers at minimum CT of $11399 and $10906 as well as minimum COE of $0.1369/kWh and $0.1316/kWh by using GA and PSO. The findings show that the computational time to solve the problem by PSO is significantly less than that provided by GA. The optimal configuration has 72 PV panels (8.64 kW), 1 unit of WT (3 kW) and 76 battery systems (159.6 kWh) with computational time of 0.146831 s. The outcomes of the study demonstrate that HRES is a cost-effective solution to satisfy the power demand of the selected location and other regions based on similar meteorological data. The results obtained from the study align with Sustainable Development Goals by promoting clean energy access and fostering sustainable infrastructure development.