Optimization Design in Wind Farm Distribution Network

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)


Nowadays, wind energy has an important role in the challenges of clean energy supply. It is the fastest growing energy source with a increasing annual rate of 20%. This scenario motivate the development of an optimization design tool to find optimal layout for wind farms. This paper proposes a mathematical model to find the best electrical interconnection configuration of the wind farm turbines and the substation. The goal is to minimize the installation costs, that include cable cost and cable installation costs, considering technical constraints. This problem corresponds to a capacitated minimum spanning tree with additional constraints. The methodology proposed is applied in a real case study and the results are compared with the ground solution.


Distribution networks wind farm optimization capacitated minimum spanning trees hop-indexed formulations 


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  1. 1.
    Carreno, E.M., Moreira, N., Romero, R.: Distribution network reconfiguration using an efficient evolutionary algorithm. In: IEEE Power Engineering Society General Meeting, Tampa, Florida, USA (June 2007)Google Scholar
  2. 2.
    Fan, J.Y., Zhang, L., McDonald, J.D.: Distribution network reconfiguration: single loop optimization. IEEE Trans. Power Systems 11(3), 1643–1647 (1996)CrossRefGoogle Scholar
  3. 3.
    Borozan, V., Rajicic, D., Ackovski, R.: Improved method for loss minimization in distribution networks. IEEE Trans. Power Systems 10(4), 1420–1425 (1995)CrossRefGoogle Scholar
  4. 4.
    Li, K., Chen, G., Chung, T., Tang, G.: Distribution planning using a rule-based expert system approach. In: IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies, Hong Kong (April 2004)Google Scholar
  5. 5.
    McDermott, T.E., Drezga, I., Broadwater, R.P.: A heuristic nonlinear construtive method for distribution system reconfiguration. IEEE Trans. Power Systems 14(2), 478–483 (1999)CrossRefGoogle Scholar
  6. 6.
    Shirmohammadi, D., Hong, H.: Reconfiguration of electric distribution networks for resistive line losses reduction. IEEE Trans. Power Delivery 4(2), 1492–1498 (1989)CrossRefGoogle Scholar
  7. 7.
    Bouchard, D., Chikhani, A., John, V.I., Salama, M.M.A.: Applications of hopfield neural-networks to distribution feeder reconfiguration. In: Applications of Neural Networks to Power Systems, IEEE ANNPS, Japan, pp. 311–316 (1993)Google Scholar
  8. 8.
    Kim, H., Ko, Y., Jung, K.H.: Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems. IEEE Trans. Power Delivery 8(3), 1356–1366 (1993)CrossRefGoogle Scholar
  9. 9.
    Nara, K., Shiose, A., Kitagawa, M., Ishihara, T.: Implementation of genetic algorithm for distribution systems loss minimum re-configuration. IEEE Transactions on Power Systems 7(3), 1044–1051 (1992)CrossRefGoogle Scholar
  10. 10.
    Mendoza, J., Lopez, R., Morales, D., Lopez, E., Dessante, P., Moraga, R.: Minimal loss reconfiguration using genetic algorithms with restricted population and addressed operators: real application. IEEE Transactions on Power Systems 21(2), 948–954 (2006)CrossRefGoogle Scholar
  11. 11.
    Zhu, J.Z.: Optimal reconfiguration of electrical distribution network using the refined genetic algorithm. Electric Power Systems Research 62(1), 37–42 (2002)CrossRefGoogle Scholar
  12. 12.
    Miranda, V., Ranito, J., Proenca, L.: Genetic algorithms in optimal multistage distribution network planning. IEEE Trans. Power Systems 9(4), 1927–(1933)CrossRefGoogle Scholar
  13. 13.
    Grady, S., Hussaini, M., Abdullah, M.: Placement of wind turbines using genetic algorithms. Renewable Energy 30(2), 259–270 (2005)CrossRefGoogle Scholar
  14. 14.
    González, J.S., Rodriguez, A.G.G., Mora, J.C., Santos, J.R., Payan, M.B.: Optimization of wind farm turbines layout using an evolutive algorithm. Renewable Energy 35(8), 1671–1681 (2010)CrossRefGoogle Scholar
  15. 15.
    Braz, H.D.M., Melo, G.H.S.V., Souza, B.A., de Souza, A.C.Z.: Planejamento da rede coletora de um parque de geração eólica usando algoritmos genéticos. In: Simpósio Brasileiro de Sistemas Elétricos, UFCG, Brasil, July 17-19, pp. 1–6 (2006)Google Scholar
  16. 16.
    Wang, C., Yao, G., Wang, X., Zheng, Y., Zhou, L., Xu, Q., Liang, X.: Reactive power optimization based on particle swarm optimization algorithm in 10kv distribution network. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part I. LNCS, vol. 6728, pp. 157–164. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Abdelaziz, A., Osama, R., El-Khodary, S., Panigrahi, B.: Distribution systems reconfiguration using the hyper-cube ant colony optimization algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part II. LNCS, vol. 7077, pp. 257–266. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Jeon, Y., Kim, J., Kim, J., Shin, J., Lee, K.Y.: An efficient simulated annealing algorithm for network reconfiguration in large-scale distribution systems. IEEE Trans. Power Delivery 17(4), 1070–1078 (2002)CrossRefGoogle Scholar
  19. 19.
    Thompson, G.L., Wall, D.L.: A branch and bound model for choosing optimal substation locations. IEEE Power Engineering PER-1(6), 69–70 (1981)CrossRefGoogle Scholar
  20. 20.
    Fangdong, W., Han, L., Fangdong, W., Buying, W.: Substation optimization planning based on the improved orientation strategy of voronoi diagram. In: 2010 2nd International Conference on Information Science and Engineering (ICISE), pp. 1563–1566 (2010)Google Scholar
  21. 21.
    Magnanti, T.L., Wolsey, L.A.: Chapter 9 optimal trees. In: Ball, M., Magnanti, T., Monma, C., Nemhauser, G. (eds.) Network Models. Handbooks in Operations Research and Management Science, vol. 7, pp. 503–615. Elsevier (1995)Google Scholar
  22. 22.
    Gouveia, L., Martins, P.: The capacitated minimum spanning tree problem: revisiting hop-indexed formulations. Computers & OR 32, 2435–2452 (2005)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CIO – Centro de Investigação OperacionalLisbonPortugal
  2. 2.INESC–TEC Technology and Science (formerly INESC Porto, UTAD pole)PortoPortugal
  3. 3.Escola de Ciências e TecnologiaUniversidade de Trás-os-Montes e Alto DouroVila RealPortugal

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