Optimal Distribution of Reactive Power based on Shark Smell Optimization with Pareto Criterion in Power System

Authors

semnan university

Abstract

In recent years, the electrical industrial convert to a competitive market in some of the countries which is named a deregulated systems. To maintain the stability of power grids and reliable power transmission, in addition to providing active power, reactive power supply is also needed to improve the safety system should be optimized by the operation of the power system. In this paper, we proposed a new solution to management the reactive power which is presented as a long term and non-linear optimization problem. Hence, the planning and improving the power market based on optimal distribution of reactive power has been presented as an optimization problem. The mentioned problem is solved by a new meta-heuristic algorithm which is based on shark smell abilities named Shark Smell Optimization (SSO). This algorithm improved with Pareto criterion to increase the abilities of proposed strategy in nonlinear problem with different constrains. The effectiveness of the proposed method has been applied over tow power system case studies through the comparison with other techniques.

Keywords

References

 
[1]    Baughman, M. L., and Siddiqi, S. N., (1991), “Real-time Pricing of Reactive Power: Theory and Case Study Results,” IEEE Transactions on Power Systems. vol. 6, no. 2, pp. 23-9.
[2]    Baughman, M. L., Siddiqi, S. N., and Zarnikau, J. W., (1997), “Advanced Pricing in Electrical Systems. I. Theory,” IEEE Transactions on Power Systems, vol. 12, no. 1, pp. 489-95.
[3]    Baughman, M. L., Siddiqi, S. N., and Zarnikau, J. W., (1997), “Advanced Pricing in Electrical Systems. II. Implications,” IEEE Transactions on Power Systems, vol. 12, no. 1, pp. 496-502.
[4]    Li, Y. Z., and David, A. K., (1993), “Pricing Reactive Power Conveyance,” IEE-Proceedings-C- (Generation, Transmission-and-Distribution) vol. 140, no. 3, pp.174-80.
[5]    Li, Y. Z., and David, A. K., (1994), “Wheeling Rates of Reactive Power Flow under Marginal Cost Pricing,” IEEE Transactions on Power Systems. vol. 9, no. 3, pp.1263-9.
[6]    Jong-Bae P,Ki-Song L; Joong-Rin S; Lee, K.Y, (2005), ”Particle swarm optimization for economic dispatch considering the generator constraints”, IEEE Trans. on Power Syst., vol. 20, no. 1, pp. 34 – 42.
[7]    Hosam K. Youssef and Khaled M. El-Naggar, (2000), ”Genetic based algorithm for security constrained power system economic dispatch”, Electric Power Systems Research, vol. 53, no. 1, pp. 47-51.
[8]    Granville. S., Pereira. M.V.F., Monticelli. A., (1988), ‘An integrated methodology for VAR sources planning’, IEEE Trans. Power Syst., vol. 3, no. 2, pp. 549–557.
[9]    Estevam. C.R.N., Rider. M.J., Amorim. E., Mantovani. J.R.S., (2010), ” Reactive power dispatch and planning using a non-linear branch-and-bound algorithm,” IET Gener. Transm. Distrib., vol. 4, no. 8, pp. 963–973.
[10]                         Abedinia. O., Amjady. N., Ghasemi. A., Hejrati. Z., (2013), Solution of Economic Load Dispatch Problem via Hybrid PSO-TVAC and BFA Techniques, European Transaction on Electrical Power, Vol. 23, Issue 8, pp. 1504–1522.
Journal of Modeling in Engineering
Volume 14, Issue 45
June 2016
Pages 133-144

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History

  • Receive Date: 28 January 2017
  • Revise Date: 16 September 2017
  • Accept Date: 28 January 2017

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