End-Edge-Cloud Collaboration-Based EVs Aggregator Control Method for Multiple Frequency Regulation Considering User Charging Demand
Lei Xu, Chunxia Dou, Dong Yue, Wei Guo, Nan Zhao, Houjun Li
Abstract
This article proposes an end-edge-cloud collaboration-based electric vehicles (EVs) aggregator control method for multiple frequency regulation (MFR), encompassing both primary (PFR) and secondary frequency regulation (SFR). Firstly, a frequency regulation capacity (FRC) assessment method is proposed, taking into account the user charging demand and battery degradation during MFR. Then, in the edge-cloud collaboration, a FRC based MFR task calculation method for charging stations (CSs) is proposed, which includes dynamic droop control for PFR and proportional power-sharing for SFR. Additionally, in the edge-edge collaboration, a consensus-based SFR redistribution method is developed to address the power overrun issue of MFR in a distributed way, arising from the different time scales between PFR and SFR. Finally, in end-edge collaboration, a two-stage model predictive control (TMPC) is proposed to achieve balanced MFR task allocation from CS to EVs, considering the different FRC and response characteristic between fast and slow charging. The validity of this method is verified by simulation.