Multi-Objective MPSO/GA Optimization of an Autonomous PV-Wind Hybrid Energy System

Abstract

This article presents a study of the energy efficiency and the optimal sizing of an autonomous hybrid energy system (PV-wind-battery) as a power source for a typical household in an isolated village in Adrar, Algeria using the multi-objective Particle Swarm Optimization (MPSO) algorithm and Genetic Algorithm (GA). This study presents a new approach to obtain an optimal configuration and sizing of the main components integrated into the autonomous hybrid system (PV/wind) which meets the requirements of the desired system. In the first phase, the reliability criterion (LPSP) is met with the lowest Energy Cost (EC) value (min Total Net Present Cost (TNPC)). The required storage bank must have a low rate of aging in order to extend the battery life, which contributes to the reduction of the overall cost of the system. Due to the inability to meet the needs demanded by random load profiles, we have incorporated a complementary approach to home insulation to significantly improve energy efficiency. In order to improve the thermal comfort of the house and reduce the energy consumed by air conditioning and heating, a thermal and energy study on this house has been evaluated using TRNSYS 18 simulation software for the overall thermal and energy behavior of a habitat and its environment in dynamic regime. The simulation results show that the thermal insulation of the walls reduces energy consumption by 13.39% in the case of heating and by 10% in the case of air conditioning. Assuming a 10% decrease in energy needs for heating and air conditioning, we observe its positive effect on optimizing the battery lifetime.