Distribution System Expansion Planning

  • Gregorio Muñoz-Delgado
  • Javier Contreras
  • José M. Arroyo
Chapter
Part of the Power Systems book series (POWSYS)

Abstract

The widespread growth of distributed generation (DG), mainly due to its numerous operational and planning benefits and to the penetration of renewable energy, inevitably requires the inclusion of this kind of generation in distribution planning models. This chapter addresses the multistage expansion planning problem of a distribution system where investments in the distribution network and in DG are jointly considered. The optimal expansion plan identifies the best alternative, location, and installation time for the candidate assets. The incorporation of DG in distribution system expansion planning drastically increases the complexity of the optimization process. In order to shed light on the modeling difficulties associated with the co-optimized planning problem, a deterministic model is presented first. The model is driven by the minimization of the net present value of the total cost including the costs related to investment, maintenance, production, losses, and unserved energy. As a relevant feature, radiality conditions are specifically tailored to accommodate the presence of DG in order to avoid the islanding of distributed generators and the issues associated with transfer nodes. Since a large portion of DG relies on non-dispatchable renewable-based technologies, the uncertainty associated with the high variability of the corresponding energy sources needs to be properly characterized in the planning models. Based on the previous deterministic model, uncertainty is incorporated using a stochastic programming framework. Within such a context, the uncertainty featured by renewable-based generation and demand is characterized through a set of scenarios that explicitly capture the correlation between uncertainty sources. The resulting stochastic program is driven by the minimization of the total expected cost. Both deterministic and stochastic optimization problems are formulated as mixed-integer linear programs for which finite convergence to optimality is guaranteed and efficient off-the-shelf software is available. Numerical results illustrate the effective performance of the approaches presented in this chapter.

Keywords

Distributed generation Distribution system planning Multistage Network expansion Stochastic programming Uncertainty 

References

  1. 1.
    A. Gómez-Expósito, A.J. Conejo, C. Cañizares, Electric Energy Systems. Analysis and Operation (CRC Press, Boca Raton, FL, USA, 2009)Google Scholar
  2. 2.
    W.H. Kersting, Distribution System Modeling and Analysis, 3rd edn. (CRC Press, Boca Raton, FL, USA, 2012)MATHGoogle Scholar
  3. 3.
    H.L. Willis, Power Distribution Planning Reference Book, 2nd edn. (Marcel Dekker Inc, New York, NY, USA, 2004)CrossRefGoogle Scholar
  4. 4.
    P.S. Georgilakis, N.D. Hatziargyriou, A review of power distribution planning in the modern power systems era: models, methods and future research. Electr. Power Syst. Res. 121, 89–100 (2015)CrossRefGoogle Scholar
  5. 5.
    N. Jenkins, R. Allan, P. Crossley, D. Kirschen, G. Strbac, Embedded Generation (The Institution of Engineering and Technology, London, UK, 2000)CrossRefGoogle Scholar
  6. 6.
    A. Keane, L.F. Ochoa, C.L.T. Borges, G.W. Ault, A.D. Alarcon-Rodriguez, R.A.F. Currie, F. Pilo, C. Dent, G.P. Harrison, State-of-the-art techniques and challenges ahead for distributed generation planning and optimization. IEEE Trans. Power Syst. 28(2), 1493–1502 (2013)CrossRefGoogle Scholar
  7. 7.
    W. El-Khattam, Y.G. Hegazy, M.M.A. Salama, An integrated distributed generation optimization model for distribution system planning. IEEE Trans. Power Syst. 20(2), 1158–1165 (2005)CrossRefGoogle Scholar
  8. 8.
    R. Viral, D.K. Khatod, Optimal planning of distributed generation systems in distribution system: a review. Renew. Sust. Energ. Rev. 16(7), 5146–5165 (2012)CrossRefGoogle Scholar
  9. 9.
    H. Falaghi, C. Singh, M.-R. Haghifam, M. Ramezani, DG integrated multistage distribution system expansion planning. Int. J. Electr. Power Energy Syst. 33(8), 1489–1497 (2011)CrossRefGoogle Scholar
  10. 10.
    M. Gitizadeh, A.A. Vahed, J. Aghaei, Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms. Appl. Energy 101, 655–666 (2013)CrossRefGoogle Scholar
  11. 11.
    M. Sedghi, M. Aliakbar-Golkar, M.-R. Haghifam, Distribution network expansion considering distributed generation and storage units using modified PSO algorithm. Int. J. Electr. Power Energy Syst. 52, 221–230 (2013)CrossRefGoogle Scholar
  12. 12.
    M. Shivaie, M.T. Ameli, M.S. Sepasian, P.D. Weinsier, V. Vahidinasab, A multistage framework for reliability-based distribution expansion planning considering distributed generations by a self-adaptive global-based harmony search algorithm. Reliab. Eng. Syst. Saf. 139, 68–81 (2015)CrossRefGoogle Scholar
  13. 13.
    A. Tabares, J.F. Franco, M. Lavorato, M.J. Rider, Multistage long-term expansion planning of electrical distribution systems considering multiple alternatives. IEEE Trans. Power Syst. 31(3), 1900–1914 (2016)CrossRefGoogle Scholar
  14. 14.
    C.L.T. Borges, V.F. Martins, Multistage expansion planning for active distribution networks under demand and distributed generation uncertainties. Int. J. Electr. Power Energy Syst. 36(1), 107–116 (2012)CrossRefGoogle Scholar
  15. 15.
    A. Bagheri, H. Monsef, H. Lesani, Renewable power generation employed in an integrated dynamic distribution network expansion planning. Electr. Power Syst. Res. 127, 280–296 (2015)CrossRefGoogle Scholar
  16. 16.
    R. Hemmati, R.-A. Hooshmand, N. Taheri, Distribution network expansion planning and DG placement in the presence of uncertainties. Int. J. Electr. Power Energy Syst. 73, 665–673 (2015)CrossRefGoogle Scholar
  17. 17.
    H. Arasteh, M.S. Sepasian, V. Vahidinasab, P. Siano, SoS-based multiobjective distribution system expansion planning. Electr. Power Syst. Res. 141, 392–406 (2016)CrossRefGoogle Scholar
  18. 18.
    S.F. Santos, D.Z. Fitiwi, M. Shafie-Khah, A.W. Bizuayehu, C.M.P. Cabrita, J.P.S. Catalão, New multistage and stochastic mathematical model for maximizing RES hosting capacity–part I: problem formulation. IEEE Trans. Sustain. Energy 8(1), 304–319 (2017)CrossRefGoogle Scholar
  19. 19.
    S.F. Santos, D.Z. Fitiwi, M. Shafie-Khah, A.W. Bizuayehu, C.M.P. Cabrita, J.P.S. Catalão, New multi-stage and stochastic mathematical model for maximizing RES hosting capacity–part II: numerical results. IEEE Trans. Sustain. Energy 8(1), 320–330 (2017)CrossRefGoogle Scholar
  20. 20.
    G. Muñoz-Delgado, J. Contreras, J.M. Arroyo, Joint expansion planning of distributed generation and distribution networks. IEEE Trans. Power Syst. 30(5), 2579–2590 (2015)CrossRefGoogle Scholar
  21. 21.
    G. Muñoz-Delgado, J. Contreras, J.M. Arroyo, Multistage generation and network expansion planning in distribution systems considering uncertainty and reliability. IEEE Trans. Power Syst. 31(5), 3715–3728 (2016)CrossRefGoogle Scholar
  22. 22.
    S. Haffner, L.F.A. Pereira, L.A. Pereira, L.S. Barreto, Multistage model for distribution expansion planning with distributed generation–part I: problem formulation. IEEE Trans. Power Deliv. 23(2), 915–923 (2008)CrossRefGoogle Scholar
  23. 23.
    R.C. Lotero, J. Contreras, Distribution system planning with reliability. IEEE Trans. Power Deliv. 26(4), 2552–2562 (2011)CrossRefGoogle Scholar
  24. 24.
    L. Blank, A. Tarquin, Engineering Economy, 7th edn. (McGraw-Hill, New York, NY, USA, 2012)Google Scholar
  25. 25.
    P.C. Paiva, H.M. Khodr, J.A. Domínguez-Navarro, J.M. Yusta, A.J. Urdaneta, Integral planning of primary-secondary distribution systems using mixed integer linear programming. IEEE Trans. Power Syst. 20(2), 1134–1143 (2005)CrossRefGoogle Scholar
  26. 26.
    M. Lavorato, J.F. Franco, M.J. Rider, R. Romero, Imposing radiality constraints in distribution system optimization problems. IEEE Trans. Power Syst. 27(1), 172–180 (2012)CrossRefGoogle Scholar
  27. 27.
    S.P. Bradley, A.C. Hax, T.L. Magnanti, Applied Mathematical Programming (Addison-Wesley, Reading, MA, USA, 1977)Google Scholar
  28. 28.
    S. Binato, M.V.F. Pereira, S. Granville, A new Benders decomposition approach to solve power transmission network design problems. IEEE Trans. Power Syst. 16(2), 235–240 (2001)CrossRefGoogle Scholar
  29. 29.
    J.R. Birge, F. Louveaux, Introduction to Stochastic Programming, 2nd edn. (Springer, New York, NY, USA, 2011)CrossRefGoogle Scholar
  30. 30.
    L. Baringo, A.J. Conejo, Wind power investment within a market environment. Appl. Energy 88(9), 3239–3247 (2011)CrossRefGoogle Scholar
  31. 31.
    V. Miranda, J.V. Ranito, L.M. Proença, Genetic algorithms in optimal multistage distribution network planning. IEEE Trans. Power Syst. 9(4), 1927–1933 (1994)CrossRefGoogle Scholar
  32. 32.
    Y.M. Atwa, E.F. El-Saadany, M.M.A. Salama, R. Seethapathy, Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans. Power Syst. 25(1), 360–370 (2010)CrossRefGoogle Scholar
  33. 33.
    Red Eléctrica de España (2017) [Online], Available: https://www.esios.ree.es/en
  34. 34.
    ENERCON, ENERCON wind energy converters: products overview (July 2010) [Online], Available: http://www.enercon.de
  35. 35.
    KYOCERA SOLAR Europe (2017) [Online], Available: http://www.kyocerasolar.eu
  36. 36.
  37. 37.
    GAMS Development Corporation (2017) [Online], Available: http://www.gams.com

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Gregorio Muñoz-Delgado
    • 1
  • Javier Contreras
    • 1
  • José M. Arroyo
    • 1
  1. 1.E.T.S. de Ingenieros Industriales, Universidad de Castilla-La ManchaCiudad RealSpain

Personalised recommendations