Differentially Private Decentralized Generator Maintenance and Operations for Power Networks

Abstract

Increasing sensor instrumentation has been critical to the improvement of power network operations and maintenance while challenges linked to data ownership and privacy remain. To this end, decentralized methods have been popular for their computational scalability while enabling data ownership by utility stakeholders. However, decentralization also requires sharing network flow estimates over public channels raising privacy concerns. In this paper, we propose a differential privacy driven, ADMM approach for decentralized, mixed integer optimization of operations and maintenance problems which preserves privacy of network flow estimates. We prove strong privacy guarantees by leveraging the linear relationship between the phase angles and flow. To address challenges associated with the mixed integer and dynamic nature of the problem, we introduce an exponential moving average based consensus mechanism to enhance convergence, coupled with a control chart based convergence criteria to improve stability. Our results demonstrate that our privacy preserving approach is robust to a range of noise levels and operational scenarios, delivering solutions on par with benchmark methods.