DPLib: A Standard Benchmark Library for Distributed Power System Analysis and Optimization

TL;DR

DPLib introduces a reproducible, open-source benchmark library tailored for distributed power system studies by combining a graph-based partitioning toolkit with MATPOWER-compatible multi-region test cases and ADMM-based DC/AC OPF solvers. The partitioning framework represents networks as graphs with Laplacian L = D − A, uses the first $k$ nonzero eigenvectors to form a spectral embedding, and applies $k$-means to create $k$ electrically meaningful regions, minimizing inter-regional tie-lines. Validation is performed with modular ADMM-based solvers (DC and AC OPF) operating on the partitioned data, demonstrating convergence and near-optimality (often with optimality gaps below 1%). The work provides a practical foundation for scalable, privacy-preserving distributed optimization in power systems and is maintained as an actively developed open-source project on GitHub.

Abstract

\textit{DPLib} is an open-source MATLAB-based benchmark library created to support research and development in distributed and decentralized power system analysis and optimization. Distributed and decentralized methods offer scalability, privacy preservation, and resilience to single points of failure, making them increasingly important for modern power systems. However, unlike centralized tools such as MATPOWER, no general-purpose, reproducible data library package currently exists for distributed power system studies. DPLib, available at \href{https://github.com/LSU-RAISE-LAB/DPLib.git}{GitHub}, fills this gap by providing a standard power system library featuring over 20 multi-region benchmark test cases of varying sizes, along with a graph-based partitioning toolkit that decomposes any MATPOWER test system into multiple electrically coherent regions. The partitioning toolkit, an easy-to-use MATLAB code, generates standardized \texttt{.mat} and \texttt{.m} files, along with region visualizations for intuitive understanding. We also provide modular, easy-to-use distributed optimal power flow (OPF) solvers: an alternating direction method of multipliers(ADMM)-based DC-OPF solver implemented in YALMIP, and an ADMM-based AC-OPF solver leveraging IPOPT. These solvers validate the generated test systems for distributed optimization applications. Numerical results validate the generated test cases, establishing DPLib as a foundation for reproducible distributed power system research.

Figures (23)

• Figure 1: Illustration of a partitioned power system
• Figure 2: Sequential ADMM loop for distributed OPF with scaled residuals and adaptive penalties
• Figure 3: Three-region topology for case200_tamu.
• Figure 4: primal residual (left) and optimality gap (right)
• Figure 5: primal residual (left) and optimality gap (right)