Privacy-Preserving Distributed Average Observers in Distribution Systems with Grid-Forming Inverters

Information security is critical for the safe and secure operation of distribution systems. Distributed averaging has been frequently utilized to coordinate multiple inverter-interfaced distributed generators (DGs) within the distribution grid. Unfortunately, state-of-the-art dynamic consensus-based average observers lead to loss of privacy due to neighboring information exchange, i.e., the local states of each DG that contain private information could be inferred by the neighboring DGs. In this paper, to avoid privacy disclosure and guarantee the effectiveness of distributed averaging, a privacy-preserving distributed average observer is proposed. The proposed observer adopts an algorithm-based approach in privacy preservation. Compared to the existing distributed privacy-preserving algorithms in the literature, the proposed observer achieves accurate averaging and does not introduce additional restrictions on the communication network topology. Two-fold obfuscation is implemented to mask the true values of one agent's local states from its neighbors during the average seeking. Particularly, the true values are randomly deviated at each agent locally before publishing, and the true values of the deviations are further masked using dynamic weights that vary randomly. Additionally, the proposed observer supports 'plug-and-play' functionality and is robust against communication delays. The proposed observer is implemented on hardware controllers, and its observation and privacy-preserving performance are validated in a controller hardware-in-the-loop (HIL) testbed.