Skip to main content

Performance Study of a New Modified Differential Evolution Technique Applied for Optimal Placement and Sizing of Distributed Generation

  • Conference paper
Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8297))

Included in the following conference series:

  • 2203 Accesses

Abstract

Determination of optimal placement and sizing of Distributed Generations (DGs) is one of the important tasks in power system operation. Several conventional as well as heuristics techniques like particle swarm optimization, differential evolution etc. have been applied to solve the problem. But one of the major drawback of these techniques are the improper selection of user defined parameters for optimal solution. Improper selection of the parameters may even lead to premature convergence. A new modified differential evolution technique based algorithm is proposed in this paper for the solution of optimal sizing and location of distributed generation to avoid premature convergence. The proposed algorithm is applied on IEEE 14 and 30 bus systems to verify its effectiveness. The results obtained by the proposed method are compared with other methods. It is found that the results obtained by the proposed algorithm are superior in terms of cost and losses.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Electric Power Research Institute, http://www.epri.com/gg/newgen/disgen/index.html

  2. CIGRE, “Impact of increasing contribution of dispersed generation on the power system”, CIGRE Study Committee no 37, Final Report (September 1998)

    Google Scholar 

  3. Distributed Generation in Liberalized Electricity Markets, International Energy Agency/OECD. IEA, Paris, p. 19 (2002)

    Google Scholar 

  4. Ackermann, T., Andersson, G., Sodder, L.: Distributed generation: a definition. Elect. Power Syst. Res. 57, 195–204 (2001)

    Article  Google Scholar 

  5. Ghosh, S., Ghoshal, S.P., Ghosh, S.: Optimal sizing and placement of distributed generation in a network system. Electrical Power and Energy Systems 32, 849–856 (2010)

    Article  Google Scholar 

  6. Al Abri, R.S., El-Saadany, E.F., Atwa, Y.M.: Optimal Placement and Sizing Method to Improve the Voltage Stability Margin in a Distribution System Using Distributed Generation. IEEE Trans. on Power Systems 28(1) (February 2013)

    Google Scholar 

  7. Abdi, S., Afshar, K.: Application of IPSO-Monte Carlo for optimal distributed generation allocation and sizing. Electrical Power and Energy Systems 44, 786–797 (2013)

    Article  Google Scholar 

  8. Prenc, R., Škrlec, D., Komen, V.: Distributed generation allocation based on average daily load and power production curves. Electrical Power and Energy Systems 53, 612–622 (2013)

    Article  Google Scholar 

  9. García, J.A.M., Mena, A.J.G.: Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm. Electrical Power and Energy Systems 50, 65–75 (2013)

    Article  Google Scholar 

  10. Nayak, M.R., Dash, S.K., Rout, P.K.: Optimal Placement and Sizing of Distributed Generation in Radial Distribution System Using Differential Evolution Algorithm. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds.) SEMCCO 2012. LNCS, vol. 7677, pp. 133–142. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  11. Vijayakumar, K., Jegatheesan, R.: Optimal Location and Sizing of DG for Congestion Management in Deregulated Power Systems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds.) SEMCCO 2012. LNCS, vol. 7677, pp. 679–686. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  12. Storn, R., Price, K.: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley (CA): International Computer Science Institute. Technical report TR-95-012 (1995)

    Google Scholar 

  13. Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  14. Kaelo, P., Ali, M.M.: A numerical study of some modified differential evolution algorithms. European Journal of Operational Research 169(3), 1176–1184 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  15. Amjady, N., Sharifzadeh, H.: Solution of non-convex economic dispatch problem considering valve loading effect by a new Modified Differential Evolution algorithm. Electrical Power and Energy Systems 32, 893–903 (2010)

    Article  Google Scholar 

  16. El Ela, A.A.A., Abido, M.A., Spea, S.R.: Differential evolution algorithm for optimal reactive power dispatch. Electric Power Systems Research 81, 458–464 (2011)

    Article  Google Scholar 

  17. Pai, M.A.: Techniques in power system analysis. Tata McGraw-Hill Publishing Company Limited, New Delhi (2006)

    Google Scholar 

  18. Wallach, Y.: Calculations & Programs for Power System Networks. Prentice-Hall, Inc., Englewood Cliffs

    Google Scholar 

  19. Yokoyama, R., Bae, S.H., Morita, T., Sasaki, H.: Multiobjective optimal generation dispatch based on probability security criteria. IEEE Trans. on Power Systems 3, 317–324 (1988)

    Article  Google Scholar 

  20. Durga, G., Nadarajah, M.: Optimal DG placement in deregulated electricity market. Electric. Power Syst. Res. 77, 1627–1636 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Kumar, S., Pal, D., Mandal, K.K., Chakraborty, N. (2013). Performance Study of a New Modified Differential Evolution Technique Applied for Optimal Placement and Sizing of Distributed Generation. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-03753-0_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03752-3

  • Online ISBN: 978-3-319-03753-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics