The Analysis of Multi-Scale Collaborative Optimization Scheduling for Electric Vehicle Clusters
TL;DR
A distributed data privacy protection framework tailored for multiscale collaborative optimization of electric vehicle clusters was proposed, which mitigated single-point failures and trust issues commonly found in centralized scheduling systems.
Abstract
As large-scale electric vehicle clusters become increasingly integrated into grid-wide collaborative scheduling, the cross-domain flow of massive user data introduces serious privacy risks. To address these challenges, this study proposed a distributed data privacy protection framework tailored for multiscale collaborative optimization of electric vehicle clusters. The framework mitigated single-point failures and trust issues commonly found in centralized scheduling systems. The proposed approach combined hierarchical federated learning with adaptive differential privacy to establish a three-tier collaborative architecture—vehicle, station, and cloud. At the charging station level, local models were trained with perturbed gradients, where an adaptive noise injection mechanism enforced (ε,δ)-differential privacy. At the cloud level, a multitimescale optimization model was employed: in the day-ahead stage, the globally aggregated hierarchical-federated-learning model predicted the schedulable capacity of electric vehicle clusters.