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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 871540, 8 pages
http://dx.doi.org/10.1155/2014/871540
Research Article

Improved Multiobjective Harmony Search Algorithm with Application to Placement and Sizing of Distributed Generation

1Power Distribution Department, Electric Power Research Institute of China, No. 15, Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, China
2School of Automation Science and Electrical Engineering, Beihang University, No. 37, Xuanyuan Road, Haidian District, Beijing 100191, China

Received 27 January 2014; Accepted 22 March 2014; Published 10 April 2014

Academic Editor: Her-Terng Yau

Copyright © 2014 Wanxing Sheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Y. T. Ma, D. K. Y. Yau, N. K. Yip, N. S. V. Rao, and J. Chen, “Stochastic steepest descent optimization of multiple-objective mobile sensor coverage,” IEEE Transactions on Vehicular Technology, vol. 61, no. 4, pp. 1810–1822, 2012. View at Publisher · View at Google Scholar
  2. K.-Y. Liao, C.-Y. Chang, and J. C.-M. Li, “A parallel test pattern generation algorithm to meet multiple quality objectives,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 30, no. 11, pp. 1767–1772, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Singh and K. S. Verma, “Multiobjective optimization for DG planning with load models,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 427–436, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Saffar, R. Hooshmand, and A. Khodabakhshian, “A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm,” Applied Soft Computing, vol. 11, no. 5, pp. 4021–4028, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. A. M. El-Zonkoly, “Optimal placement of multi-distributed generation units including different load models using particle swarm optimization,” Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 50–59, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. R. K. Singh and S. K. Goswami, “Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue,” International Journal of Electrical Power & Energy Systems, vol. 32, no. 6, pp. 637–644, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. Z.-H. Zhan, J.-J. Li, J.-N. Cao, J. Zhang, H. S.-H. Chung, and Y.-H. Shi, “Multiple populations for multiple objectives: a coevolutionary technique for solving multiobjective optimization problems,” IEEE Transactions on Cybernetics, vol. 43, no. 2, pp. 445–463, 2013. View at Publisher · View at Google Scholar
  8. Z. W. Geem, J. H. Kim, and G. V. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001. View at Google Scholar · View at Scopus
  9. S. Sivasubramani and K. S. Swarup, “Multi-objective harmony search algorithm for optimal power flow problem,” International Journal of Electrical Power & Energy Systems, vol. 33, no. 3, pp. 745–752, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Nekooei, M. M. Farsangi, H. Nezamabadi-pour, and K. Y. Lee, “An improved multi-objective harmony search for optimal placement of DGs in distribution systems,” IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 557–567, 2013. View at Publisher · View at Google Scholar
  11. H. B. Puttgen, P. R. MacGregor, and F. C. Lambert, “Distributed generation: semantic hype or the dawn of a new era?” IEEE Power and Energy Magazine, vol. 1, no. 1, pp. 22–29, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. M. F. Shaaban, Y. M. Atwa, and E. F. El-Saadany, “DG allocation for benefit maximization in distribution networks,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 639–649, 2013. View at Publisher · View at Google Scholar
  13. N. Khalesi, N. Rezaei, and M.-R. Haghifam, “DG allocation with application of dynamic programming for loss reduction and reliability improvement,” International Journal of Electrical Power & Energy Systems, vol. 33, no. 2, pp. 288–295, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. V. R. Pandi, H. H. Zeineldin, and W. Xiao, “Determining optimal location and size of distributed generation resources considering harmonic and protection coordination limits,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1245–1254, 2013. View at Publisher · View at Google Scholar
  15. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: improving the strength Pareto evolutionary algorithm,” TIK Report 103, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland, 2001. View at Google Scholar
  17. M. A. Abido, “Multiobjective evolutionary algorithms for electric power dispatch problem,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 315–329, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. P. Kessel and H. Glavitsch, “Estimating the voltage stability of a power system,” IEEE Transactions on Power Delivery, vol. 1, no. 3, pp. 346–354, 1986. View at Google Scholar · View at Scopus
  19. G. B. Jasmon and L. H. C. C. Lee, “Distribution network reduction for voltage stability analysis and loadflow calculations,” International Journal of Electrical Power & Energy Systems, vol. 13, no. 1, pp. 9–13, 1991. View at Publisher · View at Google Scholar · View at Scopus
  20. E. Zitzler, K. Deb, and L. Thiele, “Comparison of multiobjective evolutionary algorithms: empirical results,” Evolutionary Computation, vol. 8, no. 2, pp. 173–195, 2000. View at Publisher · View at Google Scholar · View at Scopus