Advertisement

Multi-Objective Zonal Reactive Power Market Clearing Model for Improving Voltage Stability in Electricity Markets Using HFMOEA

  • Ashish Saini
  • Amit Saraswat
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

This paper presents a development of a new multi-objective zonal reactive power market clearing (ZRPMC-VS) model for improving voltage stability of power system. In proposed multi-objective ZRPMC-VS model, two objective functions such as total payment function (TPF) for rective power support from generators and syncronus condensers and voltage stability enhancement index (VSEI) are optimized symultanously by satisfying various power system constraints using hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA). The results obtained using HFMOEA are comapared with a well known NSGA-II solution technique. This analysis helps the independent system operators (ISO) to take better decisions in clearing the reactive power market in competetive market environment.

Keywords

Zonal reactive power market reactive power market clearing prices hybrid fuzzy multi-objective evolutionary algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hao, S., Papalexopoulos, A.: Reactive power pricing and management. IEEE Transactions on Power Systems 12(1), 1206–1215 (1997)Google Scholar
  2. 2.
    Wang, Y., Xu, W.: An investigation on the reactive power support service need of power producers. IEEE Transactions on Power Systems 19(1), 586–593 (2004)CrossRefGoogle Scholar
  3. 3.
    Hao, S.: A Reactive Power Management Proposal for Transmission Operators. IEEE Transactions on Power Systems 18(4), 1374–1381 (2003)CrossRefGoogle Scholar
  4. 4.
    Bhattacharya, K., Zhong, J.: Reactive Power as an Ancillary Service. IEEE Transactions on Power Systems 16(2), 294–300 (2001)CrossRefGoogle Scholar
  5. 5.
    Zhong, J., Bhattacharya, K.: Toward a Competitive Market for Reactive Power. IEEE Transaction on Power Systems 17(4), 1206–1215 (2002)CrossRefGoogle Scholar
  6. 6.
    Zhong, J., Nobile, E., Bose, A., Bhattacharya, K.: Locallized reactive power markets using the concept of voltage control areas. IEEE Transactions on Power Systems 19(3), 1555–1561 (2004)CrossRefGoogle Scholar
  7. 7.
    Deb, K.: Multi-objective optimization using evolutionary algorithms. John Wiley and Sons, Inc., New York (2001)MATHGoogle Scholar
  8. 8.
    Abido, M.A., Bakhashwain, J.M.: Optimal VAR dispatch using a multi-objective evolutionary algorithm. Int. J. Electrical Power and Energy Systems 27(1), 13–20 (2005)CrossRefGoogle Scholar
  9. 9.
    Zhang, W., Liu, Y.: Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm. Electrical Power and Energy Systems 30, 525–532 (2008)CrossRefGoogle Scholar
  10. 10.
    Dai, C., Chen, W., Zhu, Y., Zhang, X.: Reactive power dispatch considering voltage stability with seekers optimization algorithm. Electric Power System Research 79, 1462–1471 (2009)CrossRefGoogle Scholar
  11. 11.
    Jeyadevi, S., Baskar, S., Babulal, C.K., Iruthayarajan, M.W.: Solving multiobjective optimal reactive power dispatch using modified NSGA-II. Electric Power and Energy Systems 33, 219–228 (2011)CrossRefGoogle Scholar
  12. 12.
    Rabiee, A., Shayanfar, H.A., Amjady, N.: Multiobjective clearing of reactive power market considering power system security. Applied Energy 86(9), 1555–1564 (2009)CrossRefGoogle Scholar
  13. 13.
    Kessel, P., Glavitsch, H.: Estimating the voltage stability of power systems. IEEE Transaction on Power Systems 1, 346–354 (1986)Google Scholar
  14. 14.
    Vyjayanthi, C., Thukaram, D.: Evaluation and improvement of generators reactive power margins in interconnected power systems. IET Generation. Transmission and Distribution 5(4), 504–518 (2011)CrossRefGoogle Scholar
  15. 15.
    Saraswat, A., Saini, A.: A Novel Hybrid Fuzzy Multi-Objective Evolutionary Algorithm: HFMOEA. In: Meghanathan, N., Chaki, N., Nagamalai, D. (eds.) CCSIT 2012, Part III. LNICST, vol. 86, pp. 168–177. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  17. 17.
    Reliability Test System Task Force. The IEEE reliability test system – 1996. IEEE Trans. Power Syst. 14(3), 1010–1020 (1999)Google Scholar
  18. 18.
    Rabiee, A., Shayanfar, H., Amjady, N.: Coupled energy and reactive power market clearing considering power system security. Energy Conversion and Management 50, 907–915 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Faculty of EngineeringDayalbagh Educational InstituteAgraIndia

Personalised recommendations