Demand response acts as an effectual tool to better balance electricity demand and supply in the smart grid. The response to prices and incentives is described as "changes on electricity usage from conventional consumption patterns to end-use customers." In this paper, a hybrid approach is proposed for Electric Vehicle Based Grid connected to the distribution generation (DG). The proposed system is joined performance of student psychology optimization algorithm (SPOA) and AdaBoost algorithm, thus it known as SPOA 2 B approach. The major aspire of this study is "to minimize the peak power cutoff, voltage regulation, spin reserve for making the optimization mode ideally convex, and accurate second-order conic relaxations." Additionally, the SPOA 2 B method is a proficient deal by standard business solvers with allocation models, and then acquires least annualized costs. In this study, the electric vehicle charging station (EVCS) is linked to DG, and then it gives charging service to electric vehicles (EVs). When the EV appears at the charging station, it informs the EVCS operator of their own energy requirement with anticipated departure time. Every EVCS can capture the information of all EVs using an SPOA 2 B technique. By then, the proposed system is performed on MATLAB/ Simulink site and the efficiency is compared with other processes. The number of iteration, like, 100, 250, 500,
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