Skip to main content
SpringerLink
Log in

An Efficient MVMO-SH Method for Optimal Capacitor Allocation in Electric Power Distribution Systems

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

Included in the following conference series:

  • 2152 Accesses

Abstract

This paper proposes an efficient method for optimal capacitor allocation in electric power distribution systems. The proposed method is based on a new metaheuristic method, MVMO (Mean Variance Mapping Optimization) that makes use of information on the mean and variance of archives through the mapping function. The optimal capacitor allocation is aimed at minimizing the active power network losses under some constraints with capacitor banks. The mathematical formulation results in a combinatorial optimization problem. In this paper, MVMO-SH is proposed to evaluate better solutions. It introduces swarm intelligence into MVMO that finds out better solutions with the mean and variance obtained from the archives. It improves the performance of MVMO with the multi-point search and multi-parent crossover. The proposed method is successfully applied to the IEEE 69-bus and 119-bus electric power distribution systems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • 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

References

  1. Duran, H.: Optimum number, location and size of shunt capacitors in radial distribution feeders a dynamic programming approach. IEEE Trans. Power Apparatus Syst. PAS-87(9), 1769–1774 (1968)

    Article  Google Scholar 

  2. Grainger, J.J., Lee, S.H.: Optimum size and location of shunt capacitors for reduction of losses on distribution feeders. IEEE Trans. Power Apparatus Syst. PAS-100(3), 1105–1118 (1981)

    Article  Google Scholar 

  3. Grainger, J.J., Civanlar, S.: Volt/Var control on distribution systems with lateral branches using shunt capacitors and voltage regulators parts I, II and III. IEEE Trans. Power Apparatus Syst. PAS-104(11), 3278–3297 (1985)

    Article  Google Scholar 

  4. Baran, M.E., Wu, F.F.: Optimal capacitor placement on radial distribution systems. IEEE Trans. on Power Delivery 4(1), 725–734 (1989)

    Article  Google Scholar 

  5. Baran, M.E., Wu, F.F.: Optimal sizing of capacitors placed on a radial distribution system. IEEE Trans. Power Deliv. 4(1), 735–743 (1989)

    Article  Google Scholar 

  6. Chiang, H.D., Wang, J.C., Cockings, O., Shin, H.D.: Optimal capacitor placements in distribution systems: parts I and II. IEEE Trans. Power Deliv. 5(2), 634–649 (1990)

    Article  Google Scholar 

  7. Sundhararajan, S., Pahwa, A.: Optimal selection of capacitors for radial distribution system using a genetic algorithm. IEEE Trans. Power Syst. 9(3), 1499–1507 (1994)

    Article  Google Scholar 

  8. Gallego, R.A., Monticell, A.J., Romero, R.: Optimal capacitor placement in radial distribution networks. IEEE Trans. Power Syst. 16(4), 630–637 (2001)

    Article  Google Scholar 

  9. Oo, N.W.: A comparison study on particle swarm and evolutionary particle swarm optimization using capacitor placement problem. In: IEEE 2nd International Power and Energy Conference 2008, Johor Baharu, Malaysia, pp. 1208–1211 (2008)

    Google Scholar 

  10. Erlich, I., Venayagamoorthy, G.K., Worawat, N.: A mean-variance optimization algorithm. In: IEEE CEC 2010, Shanghai, China, pp. 1–6 (2010)

    Google Scholar 

  11. Erlich, I., Nakawiro, W., Martinez, M.: Optimal dispatch of reactive sources in wind farms. In: IEEE PES General Meeting 2011, Detroit, MI, USA, pp. 1–7 (2010)

    Google Scholar 

  12. Nakawiro, W., Erlich, I., Rueda, L.: A novel optimization algorithm for optimal reactive power dispatch: a comparative study. In: 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, Weihai, China, pp. 1555–1561 (2011)

    Google Scholar 

  13. Rueda, J.L., Loor, R., Erlich, I.: MVMO for optimal reconfiguration in smart distribution systems. In: 9th IFAC Symposium on Control of Power and Energy Systems, New Delhi, India, pp. 276–281 (2015)

    Google Scholar 

  14. Rueda, J.L., Erlich, I.: Hybrid mean-variance mapping optimization for solving the IEEE-CEC 2013 competition problems. In: IEEE CEC 2013, Cancun, Mexico, pp. 1664–1671 (2013)

    Google Scholar 

  15. Rueda, J.L., Erlich, I.: Testing MVMO on learning-based real-parameter single objective benchmark optimization problems. In: IEEE CEC 2015, Sendai, Japan, pp. 1025–1032 (2015)

    Google Scholar 

  16. Chiang, H.D., Jumeau, R.J.: Optimal network reconfigurations in distribution systems: parts I and II. IEEE Trans. Power Deliv. 5(4), 1902–1909 (1990)

    Article  Google Scholar 

  17. Zhang, D., Fu, Z., Zhang, L.: An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems. Electr. Power Syst. Res. 77, 685–694 (2007)

    Article  Google Scholar 

  18. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE IJCNN 1995, Perth, Australia, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  19. Miranda, V., Fonseca, N.: EPSO-best-of-two-words meta-heuristic applied to power system problems. In: IEEE CEC 2003, Newport Beach, CA, USA, vol. 2, pp. 1080–1085 (2003)

    Google Scholar 

  20. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE SMC 1997, Orlando, FL, USA, vol. 5, pp. 4104–4108 (1997)

    Google Scholar 

  21. Chiang, H.D.: A decoupled load flow method for distribution power network: algorithms, analysis and convergence study. Electr. Power Energy Syst. 13(3), 130–138 (1991)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Network Design, Meiji University, Nakano, Japan

    Hiroyuki Mori & Hiromitsu Ikegami

Authors
  1. Hiroyuki Mori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiromitsu Ikegami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroyuki Mori .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Mori, H., Ikegami, H. (2017). An Efficient MVMO-SH Method for Optimal Capacitor Allocation in Electric Power Distribution Systems. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61833-3_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation