Optimal Coordination and Penetration of Distributed Generation with Shunt FACTS Using GA/Fuzzy Rules

Mahdad, Belkacem;Srairi, Kamel;Bouktir, Tarek

  • Published : 2009.03.01


In recent years, integration of new distributed generation (DG) technology in distribution networks has become one of the major management concerns for professional engineers. This paper presents a dynamic methodology of optimal allocation and sizing of DG units for a given practical distribution network, so that the cost of active power can be minimized. The approach proposed is based on a combined Genetic/Fuzzy Rules. The genetic algorithm generates and optimizes combinations of distributed power generation for integration into the network in order to minimize power losses, and in second step simple fuzzy rules designs based upon practical expertise rules to control the reactive power of a multi dynamic shunt FACTS Compensator (SVC, STATCOM) in order to improve the system loadability. This proposed approach is implemented with the Matlab program and is applied to small case studies, IEEE 25-Bus and IEEE 30-Bus. The results obtained confirm the effectiveness in sizing and integration of an assigned number of DG units.


Distribution generation;Economic dispatch (ED);Genetic Algorithm;Fuzzy logic;SVC;FACTS;Reactive sensitivity index


  1. V. H. Mendez, J. Rivier, and T. Gomez, 'Assesment of energy distribution losses for increasing penetration of distribution generation,' IEEE Trans. Power Systems, vol. 21, n. 21, pp. 533-5400, May 2006
  2. P. N. Vovos, A. E. Kiprakis, A. R. Wallace, and G. P. Harrison, 'Centralized and distribution voltage control: impact on distribution generation penetration,' IEEE Trans. Power Systems, vol. 22, n. 1, pp. 476-683, February 2007
  3. B. Mahdad, T. Bouktir, K. Srairi, 'Flexible methodology based in fuzzy logic rules for reactive power planning of multiple shunt FACTS devices to enhance system loadability,' Power Engineering Society General Meeting, 2007. IEEE, 24-28 June 2007 Page(s):1-6, Digital Object Identifier Ob- ject Identifier 10.1109/PES.2007.385750
  4. R. C. Bansal, 'Optimization methods for electric power systems: an overview,' International Journal of Emerging Electric Power Systems, vol. 2, no. 1, pp. 1-23, 2005
  5. M. Huneault, and F. D. Galiana, 'A survey of the optimal power flow literature,' IEEE Trans. Power Systems, vol. 6, no. 2, pp. 762-770, May 1991
  6. A. Keane, M. O'Malley, 'Impact of distribution generation capacity on losses,' in Proc. IEEE, pp. 1-7, 2006
  7. C. Wang, and M. H. Nehrir, 'Analytical approaches for optimal placment of distribution generation sources in power systems,' IEEE Trans. Power Systems, vol. 19, no. 4, pp. 2068-2076, May 2004
  8. A. Hanif, M. A. Choudhry, 'Inversigation smooth power flow control for dispersed generator working parallel to the grid system on the level load,' in Proc.PSCE, pp. 2241-2248,2006
  9. A. Keane, M. O'Malley, 'Impact of distribution network constraints on distribution generation capacity,' in Proc. of 40th Inter national Universities power engineering conference, Cork, 2005
  10. B. Kuri, M. Redfern, and F. Li, 'Optimization of rating and positionning of dispersed generation with minimum network disruption,' In Proc. IEEE Power Eng. Soc. Gen. Meeting, Denver,CO, Juin 2004, pp. 2074-2078
  11. W. EI-Khaltam, K. Bhattacharya, Y. Hegazy, and M. M. A. Salama, 'Optimal investment planning of distributed generation in a competetive electricity market,' IEEE Trans. Power Syst. , vol. 19, no. 3, pp. 1674-1684, Aug. 2004
  12. G. P. Harrison, A. Piccolo, P. Siano, A. R. Wallace, 'Distributed generation capacity evaluation using combined genetic algorithm and OPF,' Intenational Journal of Emerging Electric Power Systems, vol. 8, Issue. 2, 2007
  13. B. Mahdad, T. Bouktir, K. Srairi, 'Methodology based in practical fuzzy rules coordinated with asymetric dynamic compensation applied to the unbalanced distribution network,' International Review of Electrical Engineering (IREE), vol. 3, no. 2, pp. 145-153 (2007), ISSN 1827-6660, Praise Worthy Prize, Italy
  14. A. G. Bakistzis, P. N. Biskas, C. E. Zoumas, and V. Petridis, 'Optimal power flow by enhanced genetic algorithm,' IEEE Trans. Power Systems, vol. 17, no. 2, pp. 229-236, May 2002
  15. L. Davis, 'Adapting operator probabilities in genetic algorithms,' in Proc. 3rd Int. Conf. Genetic Algorithms Applications, J. Schaffer, Ed., SanMateo, CA, June 1989, pp.61-69
  16. H. N. Ng, M. M. A. Salama, and A.Y. Chikhani, 'Capacitor allocation by approximate reasoning: fuzzy capacitor placament,' IEEE Trans. Power Delivery, vol. 15, no. 1, pp. 393-398, January 2000
  17. H. N. Ng, M. M. A. Salama and A.Y. Chikhani, 'Classification of capacitor allocation techniques,' IEEE Trans. Power Delivery, vol. 15, no. 1, pp. 387-392, January 2000
  18. T. Yalcinoz, H. Altun, and M. Uzam, 'Economic dispatch solution using genetic algorithm based on arithmetic crossover,' in Proc. IEEE Porto Power Tech. Conf, Porto, Portugal, Sep. 2001
  19. M. Todorovski, and D. RajiCic, 'An initialization procedure in solving optimal power flow by genetic algorithm,' IEEE Trans. Power Systems, vol. 21, no. 2, pp. 480-487, May 2006
  20. K. Iba, 'Reactive power optimization by genetic algorithm,' IEEE Trans. Power Systems, vol. 9, no. 2, pp. 685-692, May1994
  21. L. L. Lai, 1. T. Ma, R. Yokoyama, and M. Zhao, 'Improved genetic algorithm for optimal power flow under both normal and contingent operation states,' Elec. Power Energy Syst., vol. 19, no. 5, pp. 287-292, 1997
  22. M. E. H. Golshan, and S. A. Arefifar, 'Distribution genartion, reactive sources and network-configuration planning for power and energy-loss reduction,' lEE Proc.-Gener. Transm. Distrib., vol. 153, no. 2, pp. 127-136, March 2006
  23. C. R. Feurt-Esquivel, E. Acha, Tan SG; JJ. Rico, 'Efficient object oriented power systems software for the analysis of large-scale networks containing FACTS controlled branches,' IEEE Trans. Power Systems, vol. 13, no. 2, pp. 464-472, May 1998
  24. T. Bouktir, L. Slimani, B. Mahdad, 'Optimal power dispatch for large scale power system using stochastic search algorithms,' International Journal of Power and Energy Systems, vol. 28, no. 1, pp. 1-10 2008
  25. L. Slimani, T. Boluktir, 'Economic power dispatch of power system with pollution control using multiobjective ant colony optimization,' International Journal of Power and Computational Intelligence Research, vol. 03,no.2,pp.145-153,2007
  26. A. Saini, D. K. Chaturvedi, A. K. Saxena, 'Optimal power flow solution: a GA-Fuzzy system approach,' International journal of emerging electric power systems, vol. 5, Issue. 2, pp. 1-21,2006
  27. C. A. Canizares, 'Power flow and transient stability models of FACTS controllers for voltage and angle stability studies,' IEEE Proceeding, 2000
  28. B. Sttot and J. L. Marinho, 'Linear programming for power system network security applications,' IEEE Trans. Power Apparat. Syst., vol. PAS-98, pp. 837-848, May/June 1979
  29. O. Alsac and B. stott. 'Optimal load flow with steady state security,' IEEE Trans. Power Appara. Syst., pp. 745-751, May-June 1974
  30. L. L. Lai, J. T. Ma, R. Yokoyama, and M. Zhao, 'Improved genetic algorithm for optimal power flow under both normal and contingent operation states,' Elec. Power Energy Syst., vol. 19, no. 5, pp. 287-292,1997
  31. G. Dhaoyun, and T. S. Chung, 'Optimal active power flow incorporating FACTS devices with power flow constraints,' Electrical Power & Energy Systems, vol.20, no.5, pp. 321-326, 1998
  32. M. Chis, M.M.A. Salama, and S. Jayaram, 'Capacitor placement in distribution systems using heuristic search strategies.' IEE Proceedings Generation. Transmission and Distribution. vo. 114, n. 2, pp. 225-230, May 1997