Voltage Stability Constrained Optimal Power Flow Using NSGA-II

Sandeep Panuganti, Preetha Roselyn John, Durairaj Devraj, Subhransu Sekhar Dash

DEAN (Research), Kalasalingam University, Krishnankoil, India.
Department of Electrical and Electronics Engineering, Sri RamaswamyMemorial,Chennai, India.

DOI: 10.4236/cweee.2013.21001                

Abstract

Voltage stability has become an important issue in planning and operation of many power systems. This work includes multi-objective evolutionary algorithm techniques such as Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm II (NSGA II) approach for solving Voltage Stability Constrained-Optimal Power Flow (VSC-OPF). Base case generator power output, voltage magnitude of generator buses are taken as the control variables and maximum L-index of load buses is used to specify the voltage stability level of the system. Multi-Objective OPF, formulated as a multi-objective mixed integer nonlinear optimization problem, minimizes fuel cost and minimizes emission of gases, as well as improvement of voltage profile in the system. NSGA-II based OPF-case 1-Two objective-Min Fuel cost and Voltage stability index; case 2-Three objective-Min Fuel cost, Min Emission cost and Voltage stability index. The above method is tested on standard IEEE 30-bus test system and simulation results are done for base case and the two severe contingency cases and also on loaded conditions.

Keywords

Voltage Stability; Optimal Power Flow; Multi Objective Evolutionary Algorithms

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	N. Srinivas and K. Deb, “Multi-Objective Optimization Using Nondominated Sorting Ingenetic Algorithms,” Technical Report, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, 1993.
[2]	N. Srinivas and K. Deb, “Multi-Objective Optimization Using Nondominated Sortingin Genetic Algorithms,” Evo lutionary Computation, Vol. 2, No. 3, 1994, pp. 221-248. doi:10.1162/evco.1994.2.3.221
[3]	A. J. Wood and B. F. Wollenberg, “Power Generation Operation and Control,” John Wiley & Sons, Inc., New York, 1996.
[4]	M. S. Kumari, “Enhanced Genetic Algorithm Based Computation Technique for Multi-Objective, Optimal Power Flow Solution,” Electrical Power and Energy Systems, Vol. 32, No. 6, 2010, pp. 736-742.
[5]	K. O. Alawode, A. M. Jubril and O. A. Komolafe, “Multi Objective Optimal Power Flow Using Hybrid Evolutiona ry Algorithm,” International Journal of Electrical Power & Energy Systems Engin, Vol. 3, No. 3, 2010, p. 196.
[6]	D. Devaraj and J. P. Roselyn, “Improved Genetic Algorithm for Voltage Security Constrained Optimal Power Flow Problem,” International Journal of Energy Technology and Policy, Vol. 5, No. 4, 2007, pp. 475-488. doi:10.1504/IJETP.2007.014888
[7]	H. Sadat, “Power Systems Analysis,” McGraw Hill Publication, New Delhi, 1997.
[8]	O. Alsac, and B. Scott, “Optimal Load Flow with Steady State Security,” IEEE Transactions on Power Systems, Vol. PAS-93, No. 3, 1974, pp.745-751. doi:10.1109/TPAS.1974.293972
[9]	H. W. Dommel and W. F. Tinney, “Optimal Power Flow Solutions,” IEEE Transactions on Power Apparatusand Systems, Vol. PAS-87, No. 10, 1968, pp. 1866-1876.
[10]	S. Dhanalakshmi, S. Kannan, K. Mahadevan and S. Bas kar, “Application of Modified NSGA-II Algorithm to Combined Economicand Emission Dispatch Problem,” International Journal of Electrical Power & Energy Systems, Vol. 33, No. 4, 2011, pp. 992-1002.
[11]	R. He, G. A. Taylor and Y. H. Song, “Multi-Objective Optimal Reactive Power Flow including Voltage Security and Demand Profile Classification,” International Journal of Electrical Power & Energy Systems, Vol. 30, No. 5, 2008, pp. 327-336.
[12]	K. O. Alawode, A. M. Jubril and O. A. Komolafe, “Multi Objective Optimal Reactive Power Flow Using Elitist Nondominated Sorting Genetic Algorithm: Comparison and Improvement,” Journal of Electrical Engineering & Technology, Vol. 5, No. 1, 2010, pp. 70-78.