Comparison of Multiple Attribute Decision-Making Methods—TOPSIS and PROMETHEE for Distribution Systems

  • S. G. KambleEmail author
  • K. Vadirajacharya
  • U. V. Patil
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)


Distribution system (DS) is considered the weakest link in the power system with 5–13% technical losses. Utilities are under pressure to trim down these losses, improve reliability, and power quality of supply to consumers in the deregulated, competitive environment. This has attracted researchers again for reconfiguration with many alternatives available for decision-making such as losses, power factor, voltage profile, cost, and reliability indices like SAIFI, SAIDI, CAIFI, AENS, etc. Multi-Attribute Decision-Making (MADM) is one such popular method available for decision-making which deals with problems through a number of qualitative and quantitative criteria in reconfiguration. In this paper, MADM methods like TOPSIS and PROMETHEE are proposed for finding the compromised best configuration by considering loss minimization, and reliability indices from available alternatives. Two examples are furnished in this paper to show the effectiveness of the methods.


Distribution system reconfiguration Loss minimization Multi-attribute decision-making TOPSIS PROMETHEE 


  1. 1.
    Shirmohammadi, D., Hong, H.W.: Reconfiguration of electric distribution networks for resistive line losses reduction. IEEE Trans. Power Syst. 4(2), 1492–1498 (1989)CrossRefGoogle Scholar
  2. 2.
    Kamble, S.G., Patil, U.V.: Performance improvement of distribution systems by using PROMETHEE—multiple attribute decision making method. In: International Conference on Communication and Signal Processing (ICCASP-2016)Google Scholar
  3. 3.
    Kamble, S.G., Patil, U.V., Vadirajacharya, K.: Decision making in distribution system using different MADM methods. In: National Conference on Recent trends in Electrical Engineering at Government college of Engineering, KaradGoogle Scholar
  4. 4.
    Kamble, S.G., Vadirajacharya, K., Patil, U.V.: Decision making in distribution systems using improved AHP-PROMETHEE method. In: IEEE International Conference on Computing Methodologies and Communication (ICCMC 2017)Google Scholar
  5. 5.
    Kamble, S.G., Vadirajacharya, K., Patil, U.V.: Application of improved TOPSIS method for decision making in distribution system. In: IEEE 2nd International Conference on Inventive Computation Technologies (ICICT 2017)Google Scholar
  6. 6.
    IEEE Guide for Electric Power Distribution Reliability Indices, IEEE Std. 1366-2003, May 2004Google Scholar
  7. 7.
    Venkata Rao, R.: Decision Making in the Manufacturing Environment, pp. 27–41. Springer London Limited (2007)Google Scholar
  8. 8.
    Venkata Rao, R., Patel, B.K.: Decision making in the manufacturing environment using an improved PROMETHEE method. Int. J. Product. Res. 1–18 (2009), iFirstGoogle Scholar
  9. 9.
    Venkata Rao, R.: Decision Making in the Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods, vol. 2, Springer Series in Advanced Manufacturing, Springer LondonGoogle Scholar
  10. 10.
    Triantaphyllou, E., Shu, B., Nieto Sanchez, S., Ray, T.: Multi-criteria decision making: an operations research approach. In: Webster, J.G. (ed.) Encyclopedia of Electrical and Electronics Engineering. Wiley, New York, NY (1998)Google Scholar
  11. 11.
    Paterakis, N.G., Mazza, A., Santos, S.F., Erdinç, O., Chicco, G., Bakirtzis, A., Catalão, J.P.S.: Multi-objective reconfiguration of radial distribution systems using reliability indices. IEEE Trans. Power Syst. 31, 1048–1062 (2016)CrossRefGoogle Scholar
  12. 12.
    Zhang, T., Zhang, Guangquan, Jun, M.A., Lu, J.: Power distribution system planning evaluation by a fuzzy multi-criteria group decision support system. Int. J. Comput. Intell. Syst. 3(4), 474–485 (2010)CrossRefGoogle Scholar
  13. 13.
    Espie, P., Ault, G.W., Burt, G.M., McDonald, J.R.: Multiple criteria decision making techniques applied to electricity distribution system planning. IEE Proc.-Gener. Transm. Distrib. 150(5), 527–535 (2003)CrossRefGoogle Scholar
  14. 14.
    Wong, S., Bhattacharya, K., Fuller, J.D.: Electric power distribution system design and planning in a deregulated environment. IET Gener. Transm. Distrib. 3(12), 1061–1078 (2009)CrossRefGoogle Scholar
  15. 15.
    Mazza, A., Chicco, G., Russo, A.: Optimal multi-objective distribution system reconfiguration with multi criteria decision making-based solution ranking and enhanced genetic operators. Int. J. Elect. Power Energy Syst. 54, 255–267 (2014)CrossRefGoogle Scholar
  16. 16.
    Pohekar, S.D., Ramachandran, M.: Application of multi-criteria decision making to sustainable energy planning—a review. Renew. Sustain. Energy Rev. 8, 365–381 (2004). 369CrossRefGoogle Scholar
  17. 17.
    Vitorino, R.M., Jorge, H.M., Neves, L.P.: Multi-objective optimization using NSGA-II for power distribution system reconfiguration. Int. Trans. Electr. Energy Syst. (2013) (Wiley Online Library ( Scholar
  18. 18.
    Baran, M.E., Wu, F.F.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv. 4(2), 1492–1498 (1989)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • S. G. Kamble
    • 1
    Email author
  • K. Vadirajacharya
    • 1
  • U. V. Patil
    • 2
  1. 1.Dr. Babasaheb Ambedkar Technological UniversityLonereIndia
  2. 2.Government College of EngineeringKaradIndia

Personalised recommendations