Automated Network Decomposition and Relaxed Boundary-Consensus Distributed Power Flow

Power flow analysis is fundamental to power system planning, but repeated full network solutions become increasingly computationally demanding as system size and scenario volumes grow with the penetration of distributed energy resources. This paper develops and validates a partitioned power flow framework implemented in PSS®E, treated as a closed commercial solver without access to its internal Jacobian or solver state. The framework comprises three stages: network partitioning, automated case decomposition, and iterative boundary coordination. Spectral clustering based on real power flow, inverse reactance, and Z-bus electrical distance is compared with METIS multilevel graph partitioning, with METIS selected for its computational efficiency, partition balance, and independence from a prior power flow solution. The resulting bus to zone allocation is automatically converted into independently solvable regional cases using virtual reference buses at inter area boundaries. A master–worker scheme then solves the regional cases in parallel and iteratively reconciles duplicated interface voltage variables using an under relaxed boundary consensus update. The framework is validated against the centralised PSS®E Newton–Raphson solution using the IEEE 30-bus, IEEE 300-bus, and 6717-bus Texas synthetic systems. Mean voltage magnitude deviations remain below 0.00042 p.u. across the three systems, while the maximum deviations are 0.00005, 0.00293, and 0.03882 p.u., respectively; the larger Texas system deviations are confined to a small number of buses located predominantly near partition boundaries. Although the partitioned implementation does not provide a wall clock speedup on the four core workstation used in this study, the results demonstrate the feasibility of a fully automated domain-decomposed power flow workflow operating around an unmodified commercial solver and identify the accuracy and computational trade offs associated with boundary-based regional coordination.