access icon openaccess Ordinal optimisation approach for complex distribution network reconfiguration

The increasing complexity of the distribution system makes the practical distribution network operation much difficult. This paper presents an ordinal optimisation (OO) approach to solve the distribution network reconfiguration, which is an NP- hard problem with discrete control variables. OO uses crude and computationally fast model to reduce the search space. The spirit of OO is to seek the good enough solution instead of the best with high probability. The proposed approach is validated with two practical distribution systems, TPC 84 node test system. The results are compared with Optimal Flow Pattern (OFP), Common Genetic Algorithm (CGA), Partheno Genetic Algorithm with Tree Structure Encoding technology (TSE-PGA) and Second-Order Cone Programming (SOCP).

Inspec keywords: optimisation; distribution networks; genetic algorithms

Other keywords: TPC 84 node test system; practical distribution network operation; distribution system; complex distribution network reconfiguration; OO; ordinal optimisation approach; practical distribution systems; discrete control variables; increasing complexity; NP- hard problem; search space

Subjects: Optimisation techniques; Optimisation techniques; Distribution networks; Distributed power generation; Monte Carlo methods

References

    1. 1)
      • 11. Jian, Z., Xiaodong, Y., Yubo, Y., et al: ‘A novel genetic algorithm based on all spanning trees of undirected graph for distribution network reconfiguration’, J. Mod. Power Syst. Clean Energy, 2014, 2, (2), pp. 143149.
    2. 2)
      • 2. Baran, M.E., Wu, F.F.: ‘Network reconfiguration in distribution systems for loss reduction and load balancing’, IEEE Trans. Power Deliv., 1989, 4, pp. 14011407.
    3. 3)
      • 22. Xing, H., Sun, X.: ‘Distributed generation locating and sizing in active distribution network considering network reconfiguration’, IEEE Access, 2017, 5, pp. 1476814774.
    4. 4)
      • 3. Shirmohammadi, D., Hong, H.W.: ‘Reconfiguration of electric distribution networks for resistive line losses reduction’, IEEE Trans. Power Deliv., 1989, 4, pp. 14921498.
    5. 5)
      • 19. Zimmerman, R.D., Murillo-Sánchez, C.E., Thomas, R.J.: ‘MATPOWER: steady-state operations, planning and analysis tools for power systems research and education’, IEEE Trans. Power Syst., 2011, 26, pp. 1219.
    6. 6)
      • 9. Zhenkun, L., Xingying, C., Kun, Y., et al: ‘Hybrid particle swarm optimization for distribution network reconfiguration’, Proc. CSEE, 2008, 28, (31), pp. 3541(in Chinese).
    7. 7)
      • 21. Wenjun, Z., Haozhong, C., Saiyi, W., et al: ‘Distribution network planning based on tree structure encoding partheno-genetic algorithm’, 2008 Third Int. Conf. Electric Utility Deregulation and Restructuring and Power Technologies. DRPT, 2008, pp. 13991406.
    8. 8)
      • 18. Min, X., Jin, Z., Wu, F.F.: ‘Multiyear transmission expansion planning using ordinal optimization’, IEEE Trans. Power Syst., 2007, 22, pp. 14201428.
    9. 9)
      • 14. Shin-Yeu, L., Yu-Chi, H., Ch'i-Hsin, L.: ‘An ordinal optimization theory-based algorithm for solving the optimal power flow problem with discrete control variables’, IEEE Trans. Power Syst., 2004, 19, pp. 276286.
    10. 10)
      • 16. Fangxing, L.: ‘Application of Ordinal Optimization for distribution system reconfiguration’, Power Systems, Conf. and Exposition, 2009. PSCE ‘09. IEEE/PES, Seattle, WA, USA, 2009, pp. 18.
    11. 11)
      • 4. Lin, W.M., Cheng, F.S., Tsay, M.T.: ‘Distribution feeder reconfiguration with refined genetic algorithm’, IEE Proc., Gener. Transm. Distrib., 2000, 147, pp. 349354.
    12. 12)
      • 10. Chiou, J.-P., Chang, C.-F., Su, C.-T., et al: ‘Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems’, IEEE Trans. Power Syst., 2005, 20, (2), pp. 668674.
    13. 13)
      • 1. Civanlar, S., Grainger, J.J., Yin, H., et al: ‘Distribution feeder reconfiguration for loss reduction’, IEEE Trans. Power Deliv., 1988, 3, pp. 12171223.
    14. 14)
      • 23. Xing, H., Fu, Y., Cheng, H.: ‘Active distribution network expansion planning integrating practical operation constraints,’’, Electr. Power Compon. Syst., 2017, 45, pp. 17951805.
    15. 15)
      • 7. Wenchuan, M., Jiaju, Q.: ‘An artificial immune algorithm to distribution network reconfiguration’, Proc. CSEE, 2006, 26, (17), pp. 2529(in Chinese).
    16. 16)
      • 6. Das, D.: ‘A fuzzy multiobjective approach for network reconfiguration of distribution systems’, IEEE Trans. Power Deliv., 2006, 21, pp. 202209.
    17. 17)
      • 17. Jabr, R., Pal, B.: ‘Ordinal optimisation approach for locating and sizing of distributed generation’, IET. Gener. Transm. Distrib., 2009, 3, pp. 713723.
    18. 18)
      • 20. Nara, K., Shiose, A., Kitagawa, M., et al: ‘Implementation of genetic algorithm for distribution systems loss minimum re-configuration’, IEEE Trans. Power Syst., 1992, 7, pp. 10441051.
    19. 19)
      • 13. Ho, Y.C., Zhao, Q.C., Jia, Q.S.: ‘Ordinal optimization soft optimization for hard problems’ (Springer, New York, NY, USA, 2007).
    20. 20)
      • 5. Su, C.-T., Lee, C.-S.: ‘Network reconfiguration of distribution systems using improved mixed-integer hybrid differential evolution’, IEEE Trans. Power Deliv., 2003, 18, pp. 10221027.
    21. 21)
      • 12. Niknam, T., Fard, A.K., Seifi, A.: ‘Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants’, Renew. Energy, 2012, 37, pp. 213225.
    22. 22)
      • 8. Genjun, C., Li, K.K., Guoqing, T.: ‘A tabu search approach to distribution network reconfiguration for loss reduction’, Proc. CSEE, 2002, 22, (10), pp. 2833(in Chinese).
    23. 23)
      • 15. Mori, H., Tani, H.: ‘A hybrid method of PTS and ordinal optimization for distribution system service restoration’, in 2003 IEEE Int. Conf. Systems, Man and Cybernetics, 2003, vol. 4, pp. 34763483..
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