Distributed State Estimation in Digital Distribution Networks Based on Proximal Atomic Coordination
Zhelin Liu, Shiyuan Gao, Peng Li, Haoran Ji, Wei Xi, Hao Yu, Jianzhong Wu, Chengshan Wang · IEEE Transactions on Instrumentation and Measurement · 2022
With the emerging digitalization technologies represented by edge computing, distribution networks are gradually transforming into digital distribution networks (DDNs). The realization of edge computing drives the distributed operation of DDNs, where multiple areas exchange boundary information through edge computing devices. Benefitting from the data acquisition and computing capacity of edge computing devices, it is feasible to perform accurate and real-time state estimation on the edge side. Aiming at the state perception with edge computing devices in DDNs, this paper proposes a distributed state estimation (DSE) method based on the proximal atomic coordination (PAC) algorithm. Firstly, based on convex relaxation optimization, the state estimation model is converted into a positive semidefinite programming model to solve the nonconvexity caused by nonlinear measurements, which ensures the accuracy and convergence of state estimation. Then, a DSE method based on the PAC algorithm is proposed to exchange information of each area, which reduces the computation time and realizes the efficient state estimation on the edge side. The model and the effectiveness of the proposed method are numerically demonstrated on the modified PG&E 69-node system and the test case from a practical pilot in Guangzhou, China.