Design of Optimal Power Distribution Networks Using Multiobjective Genetic Algorithm | SpringerLink
Skip to main content

Design of Optimal Power Distribution Networks Using Multiobjective Genetic Algorithm

  • Conference paper
KI 2005: Advances in Artificial Intelligence (KI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3698))

Included in the following conference series:

  • 819 Accesses

Abstract

This paper presents solution of optimal power distribution networks problem of large-sized power systems via a genetic algorithm of real type. The objective is to find out the best power distribution network reliability while simultaneously minimizing the system expansion costs. To save an important CPU time, the constraints are to be decomposing into active constraints and passives ones. The active constraints are the parameters which enter directly in the cost function and the passives constraints are affecting the cost function indirectly as soft limits. The proposed methodology is tested for real distribution systems with dimensions that are significantly larger than the ones frequently found in the literature. Simulation results show that by this method, an optimum solution can be given quickly. Analyses indicate that proposed method is effective for large-scale power systems. Further, the developed model is easily applicable to n objectives without increasing the conceptual complexity of the corresponding algorithm and can be useful for very large-scale power system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Lee Willis, H., Tram, H., Engel, M.V., Finley, L.: Optimization applications to power distribution. IEEE Computer Applications in Power 8(4), 12–17 (1995)

    Article  Google Scholar 

  2. Gonen, T., Foote, B.L.: Distribution-System Planning Using Mixed-Integer Programming. Proceeding IEE 128(Pt. C, No. 2), 70–79 (1981)

    Google Scholar 

  3. Adams, R.N., Laughton, M.A.: Optimal planning of power networks using mixed-integer programming Part I static and time-phaseed network synthesis. Proc. IEE, 121(2), 139–147 (1974)

    Google Scholar 

  4. Thompson, G.L., Wal1, D.L.: A Branch and Bound model for Choosing Optimal Substation Locations. IEEE Trans. PAS, 2683–2688 (May 1981)

    Google Scholar 

  5. Adams, R.N., Laughton, M.A.: A dynamic programming network flow procedure for distribution system planning. Presented at the IEEE Power Industry Computer Applications Conference (PICA) (June 1973)

    Google Scholar 

  6. Ponnaviakko, M., Parkasa Rao, K.S., Venkata, S.S.: Distribution System Planning through a Quadratic Mixed Integer Programming Approach. IEEE Trans. PWRD, 1157–1163 (October 1987)

    Google Scholar 

  7. Brauner, G., Zobel, M.: Knowledge Based Planning of Distribution Networks. IEEE Trans Power Delivery 5(3), 1514–1519 (1990)

    Article  Google Scholar 

  8. Chen, J., Hsu, Y.: An Expert System for Load Allocation in Distribution Expansion Planning. IEEE Trans Power Delivery 4(3), 1910–1917 (1989)

    Article  Google Scholar 

  9. Goswami, S.K.: Distribution System Planning Using Branch Exchange Technique. IEEE Trans. On Power System 12(2), 718–723 (1997); Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning. Addison Wesley, (1989)

    Google Scholar 

  10. Ramírez-Rosado, I.J., Bernal-Agustín, J.L.: Genetic algorithms applied to the design of large power distribution systems. IEEE Trans. On Power Systems 13(2), 696–703 (1998)

    Article  Google Scholar 

  11. Ramfrez-Rosado, I.J., Giinen, T.: Pseudodynamic Planning for Expansion of Power Distribution Systems. IEEE Transactions on Power Systems 6(1), 245–254 (1991)

    Article  Google Scholar 

  12. Partanen, J.: A Modified Dynaniic Programming Algorithm for Sizing Locating and Timing of Feeder Reinforcements. IEEE Tram. on Power Delivery 5(1), 277–283 (1990)

    Article  Google Scholar 

  13. Ramirez-Rosado, I.J., Bemal-Agustfn, J.L.: Optimization of the Power Distribution Netwoik Design by Applications of Genetic Algorithms. International Journal of Power and Energy Systems 15(3), 104–110 (1995)

    Google Scholar 

  14. Ramirez-Rosado, I.J., Adams, R.N.: Multiobjective Planning of the Optimal Voltage Profile m Electric Power Distribution Systems. International Journal for Computation and Mathematics in Electrical and Electronic Engineering 10(2), 115–128 (1991)

    Article  Google Scholar 

  15. Zhu, J.z.: Optimal reconfiguration of electrical distribution network using the refined genetic algorithm. Electric Power System Research 62, 37–42 (2002)

    Article  Google Scholar 

  16. Partanen, J.: A modified dynamic programming algorithm for sizing locating and timing of feeder reinforcements. IEEE Trans. on Power Delivery 5(1), 277–283 (1990)

    Article  Google Scholar 

  17. Gönen, T., Ramírez-Rosado, I.J.: Optimal multi-stage planning of power distribution systems. IEEE Trans. on Power Delivery PWRD-2(2), 512–519 (1987)

    Article  Google Scholar 

  18. Sundhararajan, S., Pahwa, A.: Optimal Selection of Capacitors for Radial Distribution System Using A Genetic Algorithm. IEEE Trans. On Power System 9(3), 1499–1507 (1994)

    Article  Google Scholar 

  19. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989); ISBN: 0201157675

    MATH  Google Scholar 

  20. Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. In: Genetic Algorithms and Evolutionary Computation. Kluwer Academic Publishers, NewYork (2002)

    Google Scholar 

  21. Obayashi, S.: Pareto genetic algorithm for aerodynamic design using the Navier-Stokes equations. In: Quagliarella, D., Périaux, J., Poloni, C., Winter, G. (eds.) Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, pp. 245–266. John Wiley & Sons, Ltd, Trieste (1997)

    Google Scholar 

  22. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3, 257–271 (1999)

    Article  Google Scholar 

  23. Bentley, P.J., Wakefield, J.P.: Finding acceptable solutions in the pareto-optimal range using multiobjective genetic algorithms. In: Proceedings of the Second Online World Conference on Soft Computing in Engineering Design and Manufacturing (WSC2), vol. 5, pp. 231–240 (1998)

    Google Scholar 

  24. Deb, K., Mohan, M., Mishra, S.: A fast multi-objective evolutionary algorithm for finding well-spread pareto-optimal solutions, Technical Report 2003002, Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur, Kanpur, PIN 208016, India (2003)

    Google Scholar 

  25. Parmee, I.C., Watson, A.H.: Preliminary airframe design using co-evolutionary multiobjective genetic algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference 1999, pp. 1657–1665 (1999)

    Google Scholar 

  26. De Jong, K.: Lecture on Coevolution, Theory of Evolutionary Computation. H.-G. Beyer et al (chairs), Max Planck Inst. for Comput. Sci. Conf. Cntr., Schloß Dagstuhl, Saarland, Germany (2002)

    Google Scholar 

  27. Michalewicz, Z., Nazhiyzth, G.: Genocop III: A Co-evolutionary Algorithm for Numerical Optimization Problems with Nonlinear Constraints. Proceedings of the Second IEEE ICEC, Perth, Australia (1995)

    Google Scholar 

  28. Michalewicz, Z., Jaikow, C.: Handling Constraints in Genetic Algorithms. In: Proceedings of Fourth ICGA, pp. 151–157. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hadi, A., Rashidi, F. (2005). Design of Optimal Power Distribution Networks Using Multiobjective Genetic Algorithm. In: Furbach, U. (eds) KI 2005: Advances in Artificial Intelligence. KI 2005. Lecture Notes in Computer Science(), vol 3698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551263_17

Download citation

  • DOI: https://doi.org/10.1007/11551263_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28761-2

  • Online ISBN: 978-3-540-31818-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics