Optimal Power Flow of Radial Networks and Its Variations: A Sequential Convex Optimization Approach

作     者：Wei, Wei Wang, Jianhui Li, Na Mei, Shengwei 

作者机构：Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gene Beijing 100084 Peoples R China Southern Methodist Univ Dept Elect Engn Dallas TX 75205 USA Argonne Natl Lab Energy Syst Div Argonne IL 60439 USA Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA 

出 版 物：《IEEE TRANSACTIONS ON SMART GRID》 (IEEE智能电网汇刊)

年 卷 期：2017年第8卷第6期

页      面：2974-2987页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Natural Science Foundation of China U.S. Department of Energy's Office of Electricity Delivery and Energy Reliability [DE-OE0000839] NSF CAREER Directorate For Engineering Div Of Electrical, Commun & Cyber Sys Funding Source: National Science Foundation 

主　　题：Bi-objective optimization convex optimization difference-of-convex programming maximum loadability optimal power flow radial network 

摘      要：This paper proposes a sequential convex optimization method to solve broader classes of optimal power flow (OPF) problems over radial networks. The non-convex branch power flow equation is decomposed as a second-order cone inequality and a non-convex constraint involving the difference of two convex functions. Provided with an initial solution offered by an inexact second-order cone programming relaxation model, this approach solves a sequence of convexified penalization problems, where concave terms are approximated by linear functions and updated in each iteration. It could recover a feasible power flow solution, which usually appears to be very close, if not equal, to the global optimal one. Two variations of the OPF problem, in which non-cost related objectives are optimized subject to power flow constraints and the convex relaxation is generally inexact, are elaborated in detail. One is the maximum loadability problem, which is formulated as a special OPF problem that seeks the maximal distance to the boundary of power flow insolvability. The proposed method is shown to outperform commercial nonlinear solvers in terms of robustness and efficiency. The other is the bi-objective OPF problem. A non-parametric scalarization model is suggested, and is further reformulated as an extended OPF problem by convexifying the objective function. It provides a single trade-off solution without any subjective preference. The proposed computation framework also helps retrieve the Pareto front of the bi-objective OPF via the e-constraint method or the normal boundary intersection method. This paper also discusses extensions for OPF problems over meshed networks based on the semidefinite programming relaxation method.