Optimal Scheduling Strategy of Electric Vehicle Cluster Based on Index Evaluation System

Abstract

Aiming at the randomness of current electric vehicle (EV) users’ participation in the vehicle-to-grid (V2G) scheduling, this paper establishes an electric vehicle indicators evaluation model and proposes an optimal scheduling strategy to achieve optimal EV cluster scheduling. By analyzing the influence of various information declared by electric vehicle users on the dispatching plan made by aggregators, and by taking the declared dispatching power, users’ credit, battery loss and users’ participation as evaluation indicators, the evaluation index model of electric vehicle cluster is established. The weight of each index is determined based on the optimal combination weight method of accelerated genetic algorithm. At the same time, this paper compares and analyzes various scenarios under different weights to obtain the scheduling order of each electric vehicle in the cluster. Combined with the requirements of power system scheduling plan, this paper further determines the schedulable ability of each aggregator in each period. The example simulation shows that the comprehensive weights adopted in this paper not only can exclude the fleets with poor credit, but can also eliminate the fleets with low declared power. The proposed strategy can reasonably and effectively dispatch electric vehicle cluster to participate in the scheduling plan by comprehensively considering the influence of multiple indicators.