TY - GEN
T1 - FACTS devices allocation for congestion management considering voltage stability by means of MOPSO
AU - Wibowo, Rony Seto
AU - Yorino, Naoto
AU - Eghbal, Mehdi
AU - Zoka, Yoshifumi
AU - Sasaki, Yutaka
PY - 2009/12/16
Y1 - 2009/12/16
N2 - This paper proposes a method for solving congestion management problem by optimally allocating FACTS devices. The problem is approached by utilizing optimization method which optimizes generation and installation costs while satisfying voltage stability index. The main contribution of this paper is to provide pareto optimal solutions which describe previous objectives during congestion and after congestion removed. Moreover, the method is able to rank optimal location in relieving congestion, to describe feasibility of solutions and to show better solution in improving voltage stability. Therefore, it is valuable for decision maker in determining locations and sizes of devices which gaining the benefit. Due to the complexity of the problem, Multi Objective Particle Swarm Optimization (MOPSO) is utilized to optimize devices allocation as master sub-problem; Sequential Quadratic Programming (SQP) is used to solve the operation sub-problem, and Point of Collapse method is applied to calculate load margin during contingency. The effectiveness of this technique is demonstrated in modified IEEE 14 bus system.
AB - This paper proposes a method for solving congestion management problem by optimally allocating FACTS devices. The problem is approached by utilizing optimization method which optimizes generation and installation costs while satisfying voltage stability index. The main contribution of this paper is to provide pareto optimal solutions which describe previous objectives during congestion and after congestion removed. Moreover, the method is able to rank optimal location in relieving congestion, to describe feasibility of solutions and to show better solution in improving voltage stability. Therefore, it is valuable for decision maker in determining locations and sizes of devices which gaining the benefit. Due to the complexity of the problem, Multi Objective Particle Swarm Optimization (MOPSO) is utilized to optimize devices allocation as master sub-problem; Sequential Quadratic Programming (SQP) is used to solve the operation sub-problem, and Point of Collapse method is applied to calculate load margin during contingency. The effectiveness of this technique is demonstrated in modified IEEE 14 bus system.
KW - Congestion management
KW - FACTS devices allocation
KW - Multi objective particle swarm optimization
KW - Voltage stability
UR - http://www.scopus.com/inward/record.url?scp=76249119115&partnerID=8YFLogxK
U2 - 10.1109/TD-ASIA.2009.5357015
DO - 10.1109/TD-ASIA.2009.5357015
M3 - Conference contribution
AN - SCOPUS:76249119115
SN - 9781424452309
T3 - Transmission and Distribution Conference and Exposition: Asia and Pacific, T and D Asia 2009
BT - Transmission and Distribution Conference and Exposition
T2 - Transmission and Distribution Conference and Exposition: Asia and Pacific, T and D Asia 2009
Y2 - 26 October 2009 through 30 October 2009
ER -