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Particle Swarm Optimization Technique for Equalization of EV Load with Variable Wind Power Generation
Objectives: An Electric Vehicle (EV) charging station supplies electrical energy for the charging of Electric Vehicles. As the plug-in hybrid electric vehicle is expanding, there is a growing need for widely distributed publicly accessible charging stations. This paper defines Optimization method for equalize/match charging schedule of Electric vehicle with dynamic wind power availability. Methods/Statistical Analysis: Optimal charging cost and average running time are the major considerable constraints at equalization of dynamic wind power with EV load. Depending upon the remaining parking time, the EVs are aggregated to reduce the size of the problem. The proposed model innovatively incorporates the degree of equalization between EV charging load and Wind power into the objective function. Estimation of EV parking time affects the charging schedule, so it is a considerable factor for optimization. Findings: Particle Swarm Optimization (PSO) technique can optimize/reduce the scheduling problems and it can equalize dynamic behavior of wind power generation with respect to EV loads. Applications/Improvements: Computational efficiency and the average running time show the validation of the proposed technique.
Electric vehicle, Wind power generation (WPG), Particle Swarm Optimization (PSO), smart grid.
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