The power grid library for benchmarking AC optimal power flow algorithms

作     者：Babaeinejadsarookolaee, Sogol Birchfield, Adam Christie, Richard D. Coffrin, Carleton DeMarco, Christopher Diao, Ruisheng Ferris, Michael Fliscounakis, Stéphane Greene, Scott Huang, Renke Josz, Cédric Korab, Roman Lesieutre, Bernard Maeght, Jean Molzahn, Daniel K. Overbye, Thomas J. Panciatici, Patrick Park, Byungkwon Snodgrass, Jonathan Zimmerman, Ray 

作者机构：Electrical and Computer Engineering University of Wisconsin-Madison WI United States Electrical and Computer Engineering Texas A&M University TX United States Electrical and Computer Engineering University of Washington WA United States Advanced Network Science Initiative Los Alamos National Laboratory NM United States AI & System Analytics GEIRI North America San JoseCA United States Computer Sciences University of Wisconsin-Madison WI United States  Paris France Electricity Infrastructure Group Pacific Northwest National Laboratory RichlandWA United States Industrial Engineering and Operations Research Columbia University New YorkNY United States Electrical Engineering Silesian University of Technology Gliwice Poland Electrical and Computer Engineering Georgia Institute of Technology GA United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Applied Economics and Management Cornell University IthacaNY United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2019年

核心收录：

主　　题：Convex optimization 

摘      要：In recent years, the power systems research community has seen an explosion of novel methods for formulating the AC power flow equations. Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the primary method for evaluating these emerging methods. However, it is often difficult to directly compare these studies due to subtle differences in the AC-OPF problem formulation as well as the network, generation, and loading data that are used for evaluation. To help address these challenges, this IEEE PES Task Force report proposes a standardized AC-OPF mathematical formulation and the PGLib-OPF networks for benchmarking AC-OPF algorithms. A motivating study demonstrates some limitations of the established network datasets in the context of benchmarking AC-OPF algorithms and a validation study demonstrates the efficacy of using the PGLib-OPF networks for this purpose. In the interest of scientific discourse and future additions, the PGLib-OPF benchmark library is open-access and all the of network data is provided under a creative commons license. Copyright © 2019, The Authors. All rights reserved.