Optimization of grid connected electric vehicle charging and discharging using Taguchi gray relational analysis

Abstract

In order to reduce emissions, considerable changes have been made to the power dispatching process. These changes have allowed for multi-level coordination of the transmission network as well as the progressive implementation of source grid monitoring and control. Uncontrolled charging and discharge of large electric vehicles (EVs) will have a negative impact on the network of distribution, but grouping EVs and promoting controlled charging and discharge will reduce the cost of the distribution network. As a result, it is crucial to design a schedule control system that is appropriate for both charging and discharging electric vehicles. A set-up framework for capacity for distribution based on the synchronous load rate and the area power usage plan was developed for charging loads in various locations using Taguchi and Gray relational evaluation. In this experimental design, the vehicle’s ECR, range, and battery stress were considered performance measures, while the control strategy, bank SOC, and UC Os were taken into account as control factors. Peak-to-valley disparities and load changes in distribution networks might be successfully decreased, and unnecessary spending could be eliminated, with the usage of this paradigm. The findings demonstrate that the suggested grid optimization for electric vehicles using Taguchi Gray relational analysis may successfully maintain the voltage offset within the permitted deviation from the voltage range and successfully delay the requirement to invest in the allocation network. The Taguchi orthogonal matrix was used for optimization and analyses were performed using Computational Fluid Dynamics (CFD). The maximum temperature, standard deviation of the surface temperature, and pressure loss at the base were taken into account as evaluation criteria, and the outputs were evaluated using Minitab software. Since there is more than one result parameter, an optimization study was carried out with Taguchi-based multi-response Gray Relational Analysis.