Privacy-preserving distributed state estimation in smart grid.

This paper studies a privacy-preserving distributed state estimation (SE) problem in smart grid systems with an AC power flow model. To enhance the precision of SE, a sensor data fusion approach is employed to integrate the sampled data from supervisory control and data acquisition (SCADA) and phasor measurement units (PMUs). Then, through iterative information exchange among local and neighboring areas, all interconnected areas can achieve consensus on unbiased estimations while avoiding the issue of excessively large data dimensionality caused by centralized data acquisition. In addition, the proposed approach combines with the average consensus algorithm, significantly reducing the computational burden and enabling real-time distributed SE, and the incorporation of differential privacy further mitigates potential privacy leakage risks during the information transmission process. Simulation results on IEEE 14-bus and IEEE 118-bus systems demonstrate that the proposed approach not only achieves excellent estimation accuracy but also significantly reduces the convergence time, while maintaining enhanced the capability of privacy-preserving. • The model developed in this research is more closely with the operational patterns of real-world systems. • The distributed algorithm proposed in this paper does not rely on local observability or a central coordinator. • The proposed internal-merger and external-modification (IMEM) algorithm significantly reduces the number of iterations during simulation. • Differential privacy further enhances the security of local private data. [ABSTRACT FROM AUTHOR]