Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid

• Published in: IEEE access Vol. 9; pp. 148144 - 148155
• Main Authors: Niu, Xiangyu, Nguyen, Hung Khanh, Sun, Jinyuan, Han, Zhu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; cloud computing; Computation; Computational modeling; Constraints; Contingency; Costs; Cryptography; distributed systems; Optimal power flow; Optimization; Performance evaluation; Power flow; Power systems; Privacy; privacy-preserving; Security; Servers; Smart grid
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3119618

In this paper, we present a distributed privacy-preserving quadratic optimization algorithm to solve the Security Constrained Optimal Power Flow (SCOPF) problem in the smart grid. The SCOPF problem seeks the optimal dispatch subject to a set of postulated constraints under the normal and contingency conditions. However, due to the large problem size and real-time requirement, a fast and robust technique is required to solve this problem. Moreover, due to privacy concerns, it is important that the data remains confidential and processed on local computers. Therefore, a fully privacy-preserving algorithm is proposed which performs computation directly over the encrypted SCOPF problem. The SCOPF is decomposed into smaller subproblems corresponding to individual pre-contingency and post-contingency cases using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. Both algorithms are presented for solving the SCOPF problem in a privacy-preserving and distributed manner. Security analysis shows that our algorithm can preserve both system confidentiality and data privacy. Performance evaluations validate the correctness and effectiveness of the proposed algorithm.