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

Abstract

Renewable energy sources (RES) are erratic, while its variability and intermittency limit energy supply once the grid is operating in islanded mode. High penetration rate challenges for renewable energy demands, such as frequency reserves reduction, voltage profile deterioration, inductive loading, and supply-demand balance aggregation/matching are difficult to achieve in practice.. However, the use of renewable energy resources complicates the optimization of nonlinear control variables. Metaheuristic approaches can efficiently solve high-dimensional economic dispatch (ED) issues. Renewables-incorporated ED problems are currently receiving a lot of attention as a way to deal with the challenges of an energy crisis and the environment, and they are being solved utilizing the PSO method. In this vein, the charging infrastructure required by electric vehicles is a fundamental challenge that must be addressed before large-scale deployment of RESs can take place. The thesis developed Particle Swarm Optimisation (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. The most important contribution of the thesis is the analysis and investigation of the optimization effect of PSO method for the inclusion in the economic dispatch of renewable energy generation plants in energy management strategies to select the nonlinear optimization control variables, objective function and techniques are considered highly capable of solving high-dimensional ED problems with less computational time. The EPD problems are solved by implementing the developed PSO algorithm simulation on grid-tied-RES diffusion to address supply-demand balance aggregation/matching and uncertainty costs for renewable generation to enable lower power demand from the energy storage systems (ESSs). The developed PSO method handles the co-optimization using a RES uncertainty cost functions using the B-loss transmission coefficient approach to estimate operational costs for allocated generation unit's power values. 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.