access icon free Heuristic optimisation for network restoration and expansion in compliance with the single-contingency policy

© The Institution of Engineering and Technology
Received 09/05/2017, Accepted 25/07/2017, Revised 29/06/2017, Published 26/07/2017

Since failure of high- to medium-voltage (MV) transformers affect a large number of consumers, they are usually built with a redundancy to guarantee service restoration in the case of a single contingency. The redundancy can be provided by a backup transformer or by transferring the load to a neighbouring substation in case of failure. While a load transfer allows for more efficient use of transformers in normal operation, it also requires the MV network to be dimensioned with redundant transmission capability for the contingency case. In this study, the authors present their approach to determine if it is profitable in the long term to remove a backup transformer in intrinsically safe substations and handle resupply with load transfer to neighbouring substations instead. To this end, they compare the cost of the necessary network expansion to the costs of a backup transformer. They introduce an iterated local search algorithm for the calculation of emergency switching plans as well as expansion of the MV network. The methodology is applied to a large real MV network group, where reinforcing the network to allow a load transfer is cost efficient compared with the existing backup transformer in three of four substations.

Inspec keywords: power transformers; failure analysis; power system restoration; redundancy; optimisation; transformer substations

Other keywords: redundant transmission capability; network restoration; MV transformer failure; iterated local search algorithm; network expansion; high-to-medium-voltage transformers; heuristic optimisation; single-contingency policy; emergency switching plan calculation; service restoration; substation

Subjects: Reliability; Optimisation techniques; Power system management, operation and economics; Substations; Transformers and reactors

References

    1. 1)
      • 7. Santos, A.C., Delbem, A.C.B., London, J.B.A., et al: ‘Node-depth encoding and multiobjective evolutionary algorithm applied to large-scale distribution system reconfiguration’, IEEE Trans. Power Syst., 2010, 25, (3), pp. 1254–1265.
    2. 2)
      • 9. Morelato, A.L., Monticelli, A.J.: ‘Heuristic search approach to distribution system restoration’, IEEE Trans. Power Deliv., 1989, 4, (4), pp. 2235–2241.
    3. 3)
      • 22. Seifi, H., Sepasian, M.S.: ‘Electric power system planning – issues, algorithms and solutions’ (Springer, Berlin, Germany, 2011).
    4. 4)
      • 23. Lourenço, H.R., Martin, O.C., Stützle, T.: ‘Iterated Local Search’, in Glover, F., Kochenberger, G.A. (Eds.): ‘Handbook of Metaheuristics’ (Springer, Boston, MA, USA, 2003), pp. 320–353. Available at http://dx.doi.org/10.1007/0-306-48056-5_11.
    5. 5)
      • 15. Sahoo, N.C., Ganguly, S., Das, D.: ‘Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization’, Swarm Evol. Comput., 2012, 3, pp. 15–32.
    6. 6)
      • 18. Scheidler, A., Thurner, L., Kraiczy, M., et al: ‘Automated grid planning for distribution grids with increasing PV penetration’. 6th Int. Workshop on Integration of Solar Power into Power Systems, Vienna, Austria, 2016.
    7. 7)
      • 2. Leung, L.C., Khator, S.K., Pence, J.: ‘Optimal transformer allocation under single-contingency’, IEEE Trans. Power Syst., 1996, 11, (2), pp. 1046–1051.
    8. 8)
      • 17. Keko, H., Skok, M., Skrlec, D.: ‘Solving the distribution network routing problem with artificial immune systems’. Proc. of the 12th IEEE Mediterranean Electrotechnical Conf. (MELECON), Dubrovnik, Croatia, 2004, vol. 3, pp. 959–962.
    9. 9)
      • 13. Miranda, V., Ranito, J.V., Proenca, L.M.: ‘Genetic algorithms in optimal multistage distribution network planning’, IEEE Trans. Power Syst., 1994, 9, (4), pp. 1927–1933.
    10. 10)
      • 10. Matsumoto, K., Sakaguchi, T., Kafka, R.J., et al: ‘Knowledge-based systems as operational aids in power system restoration’, Proc. IEEE, 1992, 80, (5), pp. 689–697.
    11. 11)
      • 8. Toune, S., Fudo, H., Genji, T., et al: ‘A reactive tabu search for service restoration in electric power distribution systems’. 1998 IEEE Int. Conf. on Evolutionary Computation Proc. IEEE World Congress on Computational Intelligence, Anchorage, AK, USA, 1998, pp. 763–768.
    12. 12)
      • 11. González, A., Echavarren, F.M., Rouco, L., et al: ‘Reconfiguration of large-scale distribution networks for planning studies’, Int. J. Electr. Power Energy Syst., 2012, 37, (1), pp. 86–94.
    13. 13)
      • 5. Luan, W.P., Irving, M.R., Daniel, J.S.: ‘Genetic algorithm for supply restoration and optimal load shedding in power system distribution networks’, IEEE Proc., Gener. Transm. Distrib., 2002, 149, pp. 145–151.
    14. 14)
      • 21. Thurner, L., Scheidler, A., Schäfer, F., et al: ‘pandapower – convenient power system modeling and analysis based on pypower and pandas’ (University of Kassel and Fraunhofer Institute for Wind Energy and Energy System Technology, 2017). Available at http://pandapower.readthedocs.io/en/v1.4.0/_downloads/pandapower.pdf.
    15. 15)
      • 14. Camargo, V., Lavorato, M., Romero, R.: ‘Specialized genetic algorithm to solve the electrical distribution system expansion planning’. 2013 IEEE Power Energy Society General Meeting, Vancouver, Canada, 2013, pp. 1–5.
    16. 16)
      • 19. Zmijarević, Z., Skok, M., Keko, H., et al: ‘A comprehensive methodology for long-term planning of distribution networks with intrinsic contingency support’. 18th Int. Conf. and Exhibition on Electricity Distribution, CIRED, Turin, Italy, 2005. pp. 1–5.
    17. 17)
      • 1. Schlabbach, J., Rofalski, K.H.: ‘Power system engineering: planning, design, and operation of power systems and equipment’ (Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, 2008).
    18. 18)
      • 6. Augugliaro, A., Dusonchet, L., Sanseverino, E.R.: ‘Multiobjective service restoration in distribution networks using an evolutionary approach and fuzzy sets’, Int. J. Electr. Power Energy Syst., 2000, 22, (2), pp. 103–110.
    19. 19)
      • 20. Perrier, N., Agard, B., Baptiste, P., et al: ‘A survey of models and algorithms for emergency response logistics in electric distribution systems. Part i: reliability planning with fault considerations’, Comput. Oper. Res., 2013, 40, (7), pp. 1895–1906. Available at http://www.sciencedirect.com/ science/article/pii/S0305054813000245.
    20. 20)
      • 12. Brown, R.E., Hanson, A.P., Willis, H.L., et al: ‘Assessing the reliability of distribution systems’, IEEE Comput. Appl. Power, 2001, 14, (1), pp. 44–49.
    21. 21)
      • 4. Aoki, K., Kuwabara, H., Satoh, T., et al: ‘Outage state optimal load allocation by automatic sectionalizing switches operation in distribution systems’, IEEE Trans. Power Deliv., 1987, 2, (4), pp. 1177–1185.
    22. 22)
      • 3. Wang, J., Carvalho, P.M.S., Kirtley, J.: ‘Emergency reconfiguration and distribution system planning under the single-contingency policy’. IEEE PES Innovative Smart Grid Technologies (ISGT), 2012, Washington, DC, USA, 2012, pp. 1–5.
    23. 23)
      • 16. Cossi, A.M., Silva, L.G.W.D., Lazaro, R.A.R., et al: ‘Primary power distribution systems planning taking into account reliability, operation and expansion costs’, IET Gener. Transm. Distrib., 2012, 6, (3), pp. 274–284.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.0729
Loading

Related content

content/journals/10.1049/iet-gtd.2017.0729
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading