A Privacy-Preserving State Estimation Scheme for Smart Grids

With the appearance of electric energy market deregulation, there exists a growing concern over the potential privacy leakage of commercial data among competing power companies where data sharing is essential in the applications such as smart grid state estimation. Most of the existing solutions are either perturbation-based or conventional cryptography-based where a trusted central 3rd party would often be required. This article proposes privacy-preserving state estimation protocols for DC and AC models. The proposed idea is to distribute the overall task of the system state estimation into sub-tasks which can be performed by local sub-grid operators with their private data. A masking method is designed inside a homomorphic encryption scheme which is then used to ensure both the input and output data privacy during the collaboration process among individual sub-task players. Security is achieved via the computationally indistinguishable post-quantum security guaranteed by a levelled homomorphic encryption scheme over real numbers and the differential privacy of the output estimated states provided by the Laplace mechanism perturbation integrated into the masking linear transformation. Simulation results are presented to demonstrate the validity of our proposed privacy-preserving system state estimation protocols.