Decentralized and Secure Generation Maintenance with Differential Privacy

TL;DR

This paper introduces a differential privacy approach for decentralized power system maintenance optimization, ensuring privacy of network flow estimates while maintaining solution quality and computational robustness.

Contribution

It presents a novel differential privacy method tailored for mixed integer power system optimization, with enhanced convergence and stability mechanisms.

Findings

Solution quality comparable to non-private benchmarks

Effective privacy guarantees via linear flow-angle relationship

Robust performance across various noise levels and scenarios

Abstract

Decentralized methods are gaining popularity for data-driven models in power systems as they offer significant computational scalability while guaranteeing full data ownership by utility stakeholders. However, decentralized methods still require sharing information about network flow estimates over public facing communication channels, which raises privacy concerns. In this paper we propose a differential privacy driven approach geared towards decentralized formulations of mixed integer operations and maintenance optimization problems that protects network flow estimates. We prove strong privacy guarantees by leveraging the linear relationship between the phase angles and the flow. To address the 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 experimental results obtained on the IEEE 118 bus case demonstrate that our privacy preserving approach yields solution qualities on par with benchmark methods without differential privacy. To demonstrate the computational robustness of our method, we conduct experiments using a wide range of noise levels and operational scenarios.