  Distributed optimization methods have been extensively applied for the
optimization of electric power distribution systems, especially for grid-edge
coordination. Existing distributed optimization algorithms applied to power
distribution systems require many communication rounds among the distributed
agents and may pose convergence challenges in difficult nonlinear settings. The
communication network parameters also significantly impact the algorithm's
performance. In this paper, we propose a scalable, equivalent network
approximation-based, distributed optimization algorithm that employs simulation
within optimization using the system's digital twin (DT) to solve the optimal
power problems (OPF) for a three-phase unbalanced distribution system. The
proposed approach is implemented using a cyber-physical co-simulation platform
to validate the robustness of the proposed distributed algorithm under stressed
communication. The proposed approach is thoroughly validated using the IEEE
123-bus test system.
