Particle swarm optimization method for energy management of the hybrid system of an electric vehicle charging station

The thesis developed Particle Swarm Optimization (PSO) method for energy management of the hybrid system of an electric vehicle charging station (EVCS). The PSO provide the best value for uncertainty cost functions for both RESs and electric vehicle charging stations considering active power loss, reactive power loss operation cost, power flow, and voltage deviation in the thesis. The power generation problem for committed generators is scheduled to meet obligatory load demand while satisfying the inequality and equality constraints. The thesis provides economic power dispatch (EPD) and optimal power flow (OPF) optimization solutions based on uncertainty costs for renewable generation and its application on economic dispatch. PSO algorithms in the thesis applied uncertainty cost function with and without RESs, test cases where EVCSs loading were integrated with optimally sized RESs in the IEEE 14-bus, IEEE 30-bus and IEEE 118-bus distribution testbed. The developed PSO methods and algorithms can be useful for the resolution of numerous energy management problems in smart grid applications, provincial and national control centers, and research and educational institutions.