Indian Journal of Science and Technology

Open Access Open Access  Restricted Access Subscription Access

Implementation of Lagrangian Optimization Model for Optimal Power Flow in Power System


Affiliations
1 Department of Mathematics, Sathyabama University, Chennai – 119, India
 

This paper proposed the Lagrangian optimization model for Optimal Power Flow (OPF) problem. It is designed by relaxing the constraints from the Quadratic Programming (QP) problem. The objective of this model is to minimize the total cost of active power generation. The solution of QP is obtained by different optimization techniques like Particle Swarm Optimization (PSO) method, Genetic Algorithm (GA), Differential Evolution (DE) algorithm. In this paper, the optimum value of QP is obtained by the proposed model and it is compared with other methods PSO, GA, DE. The results of the methods have been tested through the standard IEEE 30 bus system. Based on numerical calculations and graphical representation, the optimal generation cost for OPF can be achieved.

Keywords

Optimal Power Flow, Genetic Algorithm, Particle Swarm Optimization, Lagrangian Model
User

  • Chen SD and Chen JF (1997) A new algorithm based on the Newton-Raphson approach for real-time emission dispatch. Electric power Sys. Res. 40,137-141.

  • Chung TS and Ge Shaoyun (1997) A recursive L-p based approach for optimal capacitor allocation with cost-benefit consideration. Electric power Sys.Res. 39,129-136.

  • Chung TS and YZ Li (2001) A hybrid GA approach for OPF with consideration of FACTS devices. IEEE power Engg. Rev. pp: 47-50.

  • Cui-Ru Wang, He-Jinyuan, Zhi-Qian Huang, Jiang-Wei Zhang and Chen-Jun Sun (2005) A modified particle swarm optimization algorithm and its application in optimal power flow problem. 4th Int.Conf. Machine learning and cybernetics.Guangzhon. pp: 2885-2889.

  • David C Walters and Gerald B Sheble (1992) Genetic algorithm solution of economic dispatch with valve point loading.IEEE/PES. 92SM, 414-413.

  • Grudinin N (1998) Reactive power optimization using successive quadratic programming method. IEEE Trans. Power Syst. 13(4), 1219-1225.

  • Iwan Santoso N and Owen T Tan (1990) Neural-net based real time control of capacitors installed on distribution systems. IEEE Trans. Power delivery. 5(1), 266-272.

  • Laboto E, Rouco L, Navarrete MI,Casanova R and Lopez G (2001) An LP-based optimal power flow for transimission losses and generator reactive margins minimization. Proc. IEEE Porto power tech conf.Portugal.

  • Maheswari S and Vijayalakshmi C (2011) Design and analysis of optimal power flow for power system using lagrangian relaxation technique. Elixir Appl. MathS.38, 4430-4437.

  • Maheswari S and Vijayalakshmi C (2011) Optimization model for electricity distribution system control using communication system by lagrangian relaxation technique. CiiT Int. J. Wireless Commun.3 (3), 183-187,2011.

  • Momoh A (1989) A generalized quadratic-based model for optimal power flow,989 IEEE. pp: 261-267.

  • Pudjianto D, Ahmed S and Strbac G (2002) Allocation of VAR support using LP and NLP based optimal power flows. IEEE Proc. Gener. Tranom. Distrib .149(4), 377-383.

  • Somasundaram P, Kuppusamy K and Devi RPK (2004) Evolutionary programming based security constrained optimal power flow. Electric Power Sys. Res. 72, 1377145.

  • Tong X and Lin M (2005) Semismooth Newton-type algorithms for solving optimal power flow problems. Proc. IEEE/PES Transmission and distribution Conf. Dalian, China. pp:1-7.

  • Torres GL and Quintana VH (2002) A jacobian smoothing non linear complementarily method for solving non linear optimal power flows. Proc.14th PSCC. Sevilla, Session 41, paper 1, pp. 117.

  • Wei Yan J, Yu DC, Yu and Bhattarai K (2006) A new optimal reactive power flow model in rectangular form and its solution by predictor corrector primal dual interior point method. IEEE Trans. Power Sys. 21(1),61-67.

  • Xiaoying D, Xifan W, Yonghua S and Jian G (2002) The interior point branch and cut method for optimal power flow, 2002 IEEE. pp: 6511655.

  • Yoshida H, Kawata K, Fukuyam Y et al. (2000) A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans. power Sys.15(4), 1232- 1239.

  • Yu IK and Song YH (2001) A novel short-term generation scheduling technique of thermal units using ant colony search algorithms. Electrical Power & Energy Sys. 23, 471-479.


Abstract Views: 449

PDF Views: 116




  • Implementation of Lagrangian Optimization Model for Optimal Power Flow in Power System

Abstract Views: 449  |  PDF Views: 116

Authors

S. Maheswari
Department of Mathematics, Sathyabama University, Chennai – 119, India
C. Vijayalakshmi
Department of Mathematics, Sathyabama University, Chennai – 119, India

Abstract


This paper proposed the Lagrangian optimization model for Optimal Power Flow (OPF) problem. It is designed by relaxing the constraints from the Quadratic Programming (QP) problem. The objective of this model is to minimize the total cost of active power generation. The solution of QP is obtained by different optimization techniques like Particle Swarm Optimization (PSO) method, Genetic Algorithm (GA), Differential Evolution (DE) algorithm. In this paper, the optimum value of QP is obtained by the proposed model and it is compared with other methods PSO, GA, DE. The results of the methods have been tested through the standard IEEE 30 bus system. Based on numerical calculations and graphical representation, the optimal generation cost for OPF can be achieved.

Keywords


Optimal Power Flow, Genetic Algorithm, Particle Swarm Optimization, Lagrangian Model

References





DOI: https://doi.org/10.17485/ijst%2F2012%2Fv5i6%2F30472