Efficient Graph Partitioning under Resource Constraints: A Cutting-Plane Framework for Distribution Grids

TL;DR

This paper introduces a cutting-plane framework for optimal, resource-constrained graph partitioning that enables real-time network reconfiguration with guarantees on optimality and feasibility.

Contribution

It develops a novel iterative cutting-plane method for resource-aware network topology control with proven convergence and efficiency improvements.

Findings

Achieves median speedup of 57.5x in simulations.

Ensures radial connectivity and resource feasibility.

Demonstrates effectiveness on a 240-bus power grid.

Abstract

This paper presents an optimal network topology control framework using cutting-plane methods for efficient network partitioning with controllable edges. The objective is to enable real-time reconfiguration of interconnected sub-networks while ensuring radial connectivity, resource feasibility, and structured leader allocation, which are essential for distributed control, stability, and coordination. The problem is formulated as a mixed-integer program that integrates graph-theoretic constraints, resource flow, and network structural properties to enforce an operational hierarchy. To address the combinatorial complexity of cycle elimination and leader assignment, we propose an iterative cutting-plane framework that ensures convergence to an optimal and feasible network topology. Theoretical guarantees on optimality preservation, feasibility, and convergence are established, ensuring systematic elimination of infeasible configurations while maintaining distributed controllability. Simulations on a modified Iowa 240-bus power distribution grid demonstrate the framework's effectiveness in network reconfiguration under resource constraints. The approach achieves median and best-case speedups of 57.5x and over 64x in a 46-switch configuration, highlighting its applicability to other networked control systems.