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Electrical Engineering

, Volume 100, Issue 4, pp 2779–2788 | Cite as

Distribution network reinforcement planning in uncertain environment using stochastic multi-attribute utility analysis

  • Aleksandar Janjic
  • Zeljko Dzunic
  • Vladimir Djordjevic
Original Paper
  • 50 Downloads

Abstract

This paper considers a stochastic multiple criteria decision-making problem in power distribution system reinforcement planning. The problem with exhaustive set of alternatives, with different types of trade-offs among stochastic criteria, has been solved using the novel analytical methodology. The proposed methodology is based on discrete convolution of criteria probability distribution functions and OWA operators, modelling different types of criteria aggregation. After the new, aggregated probability distributions have been built for every alternative, they are ranked by the overall expected utility. The methodology allows the usage of compensatory aggregation, which is more suitable for conflicting criteria or the human aggregation behaviour. Finally, an example of decision on the network reinforcement of IEEE 34 bus test feeder illustrates the efficiency of this method.

Keywords

Stochastic multi-attribute utility Distribution network reinforcement OWA Discrete convolution 

Notes

Acknowledgements

This work was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia under Grant III 44006.

References

  1. 1.
    Ziari I, Ledwich G, Ghosh A, Platt G (2013) Optimal distribution network reinforcement considering load growth, line loss and reliability. IEEE Trans Power Syst 28(2):587–597CrossRefGoogle Scholar
  2. 2.
    El-Khattam W, Bhattacharya K, Hegazy Y, Salama MMA (2004) Optimal investment planning for distributed generation in a competitive electricity market. IEEE Trans Power Syst 19(3):1674–1684CrossRefGoogle Scholar
  3. 3.
    Koutsoukis NC, Georgilakis PS, Hatziargyriou ND (2014) A Tabu search method for distribution network planning considering distributed generation and uncertainties. In: International conference on probabilistic methods applied to power systems (PMAPS), pp 1–6Google Scholar
  4. 4.
    Bagheri A, Monsef H, Lesani H (2015) Integrated distribution network expansion planning incorporating distributed generation considering uncertainties reliability and operational conditions. Int J Electr Power Energy Syst 73:56CrossRefGoogle Scholar
  5. 5.
    Catrinu MD, Nordgård DE (2011) Integrating risk analysis and multi-criteria decision support under uncertainty in electricity distribution system asset management. Reliab Eng Syst Saf 96(6):663–670CrossRefGoogle Scholar
  6. 6.
    Ayuub B, Klir G (2006) Uncertainty modeling and analysis in engineering and science. CRC, Boca RatonCrossRefGoogle Scholar
  7. 7.
    Durbach IN, Stewart TJ (2012) Modeling uncertainty in multi-criteria decision analysis. Eur J Oper Res 223(1):1–14MathSciNetCrossRefGoogle Scholar
  8. 8.
    Leou R-C, Su C-L, Lu C-N (2014) Stochastic analyses of electric vehicle charging impacts on distribution network. IEEE Trans Power Syst 29(3):1055–1063CrossRefGoogle Scholar
  9. 9.
    Pudjianto D, Djapic P, Aunedi M, Kim C, Strbac G, Huang S, Infield D (2013) Smart control for minimizing distribution network reinforcement cost due to electrification. Energy Policy 52:76–84CrossRefGoogle Scholar
  10. 10.
    Bin Humayd AS, Bhattacharya K (2017) Distribution system planning to accommodate distributed energy resources and PEVs. Electr Power Syst Res 145:1CrossRefGoogle Scholar
  11. 11.
    Xiang Y, Liu J, Li F, Liu Y, Liu Y, Xu R, Su Y, Ding L (2016) Optimal active distribution network planning: a review. Electr Power Compon Syst 44:1075CrossRefGoogle Scholar
  12. 12.
    Hilber P, Miranda V, Matos M, Bertling L (2007) Multiobjective optimization applied to maintenance policy for electrical networks. IEEE Trans Power Syst 22(4):1675–1682CrossRefGoogle Scholar
  13. 13.
    Yang F, Chang CS (2009) Multiobjective evolutionary optimization of maintenance schedules and extents for composite power systems. IEEE Trans Power Syst 24(4):1694–1702CrossRefGoogle Scholar
  14. 14.
    Berredo RC, Ekel PY, Martini JSC, Palhares RM, Parreiras RO, Pereira JG Jr (2011) Decision making in fuzzy environment and multicriteria power engineering problems. Int J Electr Power Energy Syst 33(3):623–632CrossRefGoogle Scholar
  15. 15.
    Barin A, Pozzatti LF, Canha LN, Machado RQ, Abaide AR, Arend G (2010) Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning. Int J Electr Power Energy Syst 32(10):1157–1164CrossRefGoogle Scholar
  16. 16.
    Pan J, Rahman S (1998) Multiattribute utility analysis with imprecise information: an enhanced decision support technique for the evaluation of electric generation expansion strategies. Electr Power Syst Res 46:101–109CrossRefGoogle Scholar
  17. 17.
    Janjic A, Savic S, Velimirovic L, Nikolic V (2015) Renewable energy integration in smart grids-multicriteria assessment using the fuzzy analytical hierarchy process. Turk J Electr Eng Comput Sci 23:1896–1912CrossRefGoogle Scholar
  18. 18.
    Kazmi SAA, Shahzad MK, Ryeol Shin D (2017) Multi-objective planning techniques in distribution networks: a composite review. Energies 10:208CrossRefGoogle Scholar
  19. 19.
    Daim T et al (eds) (2013) Research and technology management in the electricity industry, green energy and technology. Springer, LondonGoogle Scholar
  20. 20.
    Zhag P, Lee ST (2004) Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion. IEEE Trans Power Syst 19(1):676–682CrossRefGoogle Scholar
  21. 21.
    Villanueva D, Feijóo AE, Pazos JL (2014) An analytical method to solve the probabilistic load flow considering load demand correlation using the DC load flow. Electr Power Syst Res 110:1–8CrossRefGoogle Scholar
  22. 22.
    Su CL (2005) Probabilistic load-flow computation using point estimate method. IEEE Trans Power Syst 20(4):1843–1851MathSciNetCrossRefGoogle Scholar
  23. 23.
    Brown R (2008) Electric power distribution reliability. CRC Press, Boca RatonCrossRefGoogle Scholar
  24. 24.
    Evans DL, Leemis LM (2004) Algorithms for computing the distributions of sums of discrete random variables. Math Comput Model 40(13):1429–1452MathSciNetCrossRefGoogle Scholar
  25. 25.
    Martel J, D’Avignon G (1982) Projects ordering with multicriteria analysis. Eur J Oper Res 10(1):56–69CrossRefGoogle Scholar
  26. 26.
    D’Avignon G, Vincke P (1988) An outranking method under uncertainty. Eur J Oper Res 36:311–321CrossRefGoogle Scholar
  27. 27.
    Zaras K (2004) Rough approximation of a preference relation by a multi-attribute dominance for deterministic, stochastic and fuzzy decision problems. Eur J Oper Res 159(1):196–206MathSciNetCrossRefGoogle Scholar
  28. 28.
    Wu D, Olson DL (2008) A comparison of stochastic dominance and stochastic DEA for vendor evaluation. Int J Prod Res 46(8):2313–2327CrossRefGoogle Scholar
  29. 29.
    Lahdelma R, Hokkanen J, Salminen P (1998) SMAA—stochastic multiobjective acceptability analysis. Eur J Oper Res 106(1):137–143CrossRefGoogle Scholar
  30. 30.
    Lahdelma R, Salminen P (2001) SMAA-2: stochastic multicriteria acceptability analysis for group decision making. Oper Res 49(3):444–454CrossRefGoogle Scholar
  31. 31.
    Keeney RL, Raiffa H (1993) Decisions with multiple objectives—preferences and value tradeoffs. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  32. 32.
    Yager R (1988) On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190CrossRefGoogle Scholar
  33. 33.
    IEEE Distribution Planning Working Group Report (1991) Radial distribution test feeders. IEEE Trans Power Syst 6(3):975–985CrossRefGoogle Scholar
  34. 34.
    Janjic A, Popovic D (2007) Selective maintenance schedule of distribution networks based on risk management approach. IEEE Trans Power Syst 22(2):597–604CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Electronic Engineering NišUniversity of NišNišSerbia
  2. 2.University of NišNišSerbia
  3. 3.Electric Power Industry of SerbiaBelgradeSerbia

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