Cybersecurity-Aware Distributed Optimization for Optimal Power Flow

Abstract

Distributed optimization algorithms have many attractive features for coordinating systems with multiple agents, as they allow multiple agents to collaborate in solving large-scale optimization problems while maintaining their autonomy. However, distributed algorithms may be vulnerable to cyberattacks due to their dependency on communication. This dissertation proposes a general cybersecurity-aware distributed optimization implementation framework for solving optimal power flow problems. The proposed framework increases the resiliency of distributed optimization against cyberattacks and data manipulation. The main contributions of the dissertation are (1) development of an open-source framework to expedite the process of testing and experimenting with distributed optimization algorithms, (2) benchmarking multiple distributed algorithms with various optimal power flow models in the presence of nonideal communication via an extensive empirical analysis, (3) investigation of cyberattack threats on distributed optimization and proposition of cyberattack detection models, (4) development of a mitigation strategy for cyberattacks and communication failures via formulating and solving a robust optimization problem, and (5) development of a fault-tolerant distributed termination method that prevents faulty termination caused by cyberattacks or communication errors.