A Linear AC-OPF Formulation for Unbalanced Distribution Networks

作     者：Giraldo, Juan S. Vergara, Pedro P. Lopez, Juan Camilo Nguyen, Phuong Hong Pateralds, Nikolaos G. 

作者机构：Univ Twente EEMCS Fac Math Operat Res Grp NL-7522 NB Enschede Netherlands Delft Univ Technol Intelligent Elect Power Grids Grp NL-2628 CD Delft Netherlands Univ Estadual Campinas Sch Elect & Comp Engn BR-13083970 Campinas Brazil Eindhoven Univ Technol Energy Syst Grp NL-5612 AP Eindhoven Netherlands 

出 版 物：《IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS》 (IEEE工业应用汇刊)

年 卷 期：2021年第57卷第5期

页      面：4462-4472页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

主　　题：Substations Programming Computational modeling Scalability Reactive power Distribution networks Admittance Ac optimal power flow Euclidean norm approximation mixed-integer linear programming unbalanced distribution networks 

摘      要：Linear optimal power flow (OPF) formulations are powerful tools applied to a large number of problems in power systems, e.g., economic dispatch, expansion planning, state estimation, congestion management, electricity markets, among others. This article proposes a novel mixed-integer linear programming formulation for the ac-OPF of three-phase unbalanced distribution networks. The model aims to minimize the total energy production cost while guaranteeing the network s voltage and current magnitude operational limits. New approximations of the Euclidean norm, which is present in the calculation of nodal voltage and branch current magnitudes, are introduced by applying a linear transformation of weighted norms and a set of intersecting planes. The accuracy, optimality, feasibility, and scalability of the proposed linearizations are compared with common linear approximations in the literature using two unbalanced distribution test systems. The obtained results show that the proposed formulation is computationally more efficient (almost twice) while being as accurate and more conservative than the benchmarked approaches with maximum errors lower than 0.1%. Thus, its potential application in a variety of distribution systems operation and planning optimization problems is endorsed.