Privacy-Preserving Obfuscation for Distributed Power Systems
This addresses privacy concerns for distributed power system operators when sharing sensitive load data, though it represents an incremental application of existing privacy techniques to a specific domain.
This paper tackles the challenge of releasing privacy-preserving load data for decentralized power systems while maintaining feasibility in Optimal Power Flow (OPF) problems, proposing a distributed algorithm based on ADMM that guarantees differential privacy and high data fidelity while satisfying AC power flow constraints.
This paper considers the problem of releasing privacy-preserving load data of a decentralized operated power system. The paper focuses on data used to solve Optimal Power Flow (OPF) problems and proposes a distributed algorithm that complies with the notion of Differential Privacy, a strong privacy framework used to bound the risk of re-identification. The problem is challenging since the application of traditional differential privacy mechanisms to the load data fundamentally changes the nature of the underlying optimization problem and often leads to severe feasibility issues. The proposed differentially private distributed algorithm is based on the Alternating Direction Method of Multipliers (ADMM) and guarantees that the released privacy-preserving data retains high fidelity and satisfies the AC power flow constraints. Experimental results on a variety of OPF benchmarks demonstrate the effectiveness of the approach.