Distributed Optimization for Power Distribution Systems with Cyber-Physical Co-Simulation

In power distribution systems, the limitations of centralized optimal power flow (OPF) methods have led to the distributed computing paradigm. However, the primary shortcoming in the existing distributed OPF techniques is a large number of required communication rounds (in the order of 10² ~ 10³), i.e., macro-iterations among the distributed computing agents for solving one instance of the optimization problem, which requires expensive communication infrastructures. In this paper, a novel distributed optimization method for unbalanced power distribution networks is proposed to solve optimal power flow (OPF) that requires fewer macro-iterations. The developed innovations reduce the required number of communication-rounds for convergence by an order of magnitude. The algorithm is based on equivalent network approximation, where the radial topology of the distribution network is leveraged. The approach is validated using IEEE 123-bus test systems with both cyber and physical layers simulated using a co-simulation platform. The robustness of the algorithm is verified for different parameters of the communication network. The solutions from the distributed method were found to converge to the centralized OPF solutions.