Open AccessRecover feasible solutions for SOCP relaxation of optimal power flow problems in mesh networks
Author(s) -
Tian Zhuang,
Wu Wenchuan
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6015
Subject(s) - mathematical optimization , relaxation (psychology) , second order cone programming , regular polygon , quadratically constrained quadratic program , convex optimization , quadratic growth , quadratic programming , power flow , quadratic equation , computer science , feasible region , mathematics , power (physics) , electric power system , algorithm , psychology , social psychology , physics , geometry , quantum mechanics
The AC optimal power flow (OPF) problem is essential for the schedule and operation of power systems. Convex relaxation methods have been studied and used extensively to obtain an optimal solution to the OPF problem. When the exactness of convex relaxations is not guaranteed, it is important to recover a feasible solution for the convex relaxation methods. This paper presents an alternative convex optimisation (ACP) approach that can efficiently recover a feasible solution from the result of second‐order cone programming (SOCP) relaxed OPF in mesh networks. The OPF problem is first formulated as a difference‐of‐convex programming (DCP) problem, then efficiently solved by a penalty convex concave procedure (CCP). By using the solution of a tightened SOCP OPF as an initial point, the proposed algorithm is able to find a global or near‐global optimal solution to the AC OPF problem. Numerical tests show that the proposed method outperforms those semi‐definite programming (SDP) and quadratically constrained quadratic programming (QCQP)–based algorithms.