Realistic Differentially-Private Transmission Power Flow Data Release

TL;DR

This paper introduces a novel differentially private method for releasing power flow data that enhances privacy protection, maintains data utility, and scales to large transmission networks, improving upon prior approaches.

Contribution

It proposes a new post-processing technique using public grid loss information, protects additional sensitive parameters, and demonstrates scalability to large power systems.

Findings

Achieves higher privacy guarantees than previous methods.

Maintains system solvability and data fidelity at large scale.

Successfully obfuscates data for a 4700-bus system.

Abstract

For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers. This critical challenge has recently been somewhat addressed in [1]. This paper significantly extends this existing work. First, we reduce the potential leakage information by proposing a fundamentally different post-processing method, using public information of grid losses rather than power dispatch, which achieve a higher level of privacy protection. Second, we protect more sensitive parameters, i.e., branch shunt susceptance in addition to series impedance (complete pi-model). This protects power flow data for the transmission high-voltage networks, using differentially private transformations that maintain the optimal power flow consistent with, and faithful to, expected model behaviour. Third, we tested our approach at a larger scale than previous work, using the PGLib-OPF test cases [10]. This resulted in the successful obfuscation of up to a 4700-bus system, which can be successfully solved with faithfulness of parameters and good utility to data analysts. Our approach addresses a more feasible and realistic scenario, and provides higher than state-of-the-art privacy guarantees, while maintaining solvability, fidelity and feasibility of the system.