Simulation-Integrated Distributed Optimal Power Flow for Unbalanced Power Distribution Systems

TL;DR

This paper introduces a scalable distributed optimal power flow algorithm for unbalanced power systems that leverages digital twin simulation within a cyber-physical co-simulation platform, reducing communication rounds and enhancing robustness.

Contribution

It presents a novel simulation-integrated distributed optimization method using digital twins, improving convergence and robustness in unbalanced power distribution systems.

Findings

Validated on IEEE 123-bus system

Robust under stressed communication conditions

Reduces communication rounds in distributed optimization

Abstract

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.