Enhancing Scalability of Optimal Kron-based Reduction of Networks (Opti-KRON) via Decomposition with Community Detection

TL;DR

This paper introduces an improved Opti-KRON method that uses community detection to decompose large electrical networks, enabling scalable and efficient Kron-based reductions with significant node reduction in power system analysis.

Contribution

It extends the existing Opti-KRON approach by integrating community detection, allowing parallel processing of sub-graphs for scalable network reduction in large power systems.

Findings

Achieves 80-95% network reduction in large test cases

Statistically outperforms previous Kron-based methods

Enables scalable power system analysis with reduced computational complexity

Abstract

Electrical networks contain thousands of interconnected nodes and edges, which leads to computational challenges in some power system studies. To address these challenges, we contend that network reductions can serve as a framework to enable scalable computing in power systems. By building upon a prior AC "Opti-KRON" formulation, this paper presents a DC power flow formulation for finding network reductions that are optimal within the context of large transmission analysis. Opti-KRON previously formulated optimal Kron-based network reductions as a mixed integer linear program (MILP), where the number of binary variables scaled with the number of nodes. To improve the scalability of the Opti-KRON approach, we augment the MILP formulation with a community detection (CD) technique that segments a large network into smaller, disjoint, but contiguous sub-graphs (i.e., communities). For each sub-graph, we then (in parallel) apply MILP-based along with a new cutting plane constraint, thus, enhancing scalability. Ultimately, the new DC-based Opti-KRON method can achieve a 80-95\% reduction of networks (in terms of nodes) while statistically outperforming other CD- and Kron-based methods. We present simulation results for the IEEE RTS-96 and the 2383-bus Polish networks.