Enhancing State Estimation Accuracy in Smart Grids with Low-Rank Tensor Ring-Based Electric Vehicle Data Completion

With the widespread adoption of electric vehicles(EVs), their load has posed new challenges for state estimation in smart distribution networks. Specifically, in practice, the low-resolution load measurement data from EVs often cannot be aligned with the timestamps of smart grid measurement devices. To address this issue, this paper proposes a method for state estimation in distribution networks based on EV load data completion. More specifically, we first convert the EV load data into a high-dimensional tensor structure and treat the missing data as the parts to be completed. To achieve this, we introduce a tensor ring rank minimization method based on an alternating minimization algorithm. Simulation results show that the proposed method achieves high data reconstruction accuracy, significantly enhancing the accuracy of state estimation in distribution networks.