access icon openaccess Optimal allocation of distributed generation and remote control switches for reliability enhancement of a radial distribution system using oppositional differential search algorithm

This is an open access article published by the IET under the Creative Commons Attribution-NoDerivs License (http://creativecommons.org/licenses/by-nd/3.0/)
Received 01/07/2015, Accepted 16/07/2015, Revised 10/07/2015, Published 31/07/2015

Reliability enhancement of power distribution system has attained much significance in the present competitive electricity market. Accordingly, methodologies to assess and improve distribution system reliability are also gaining much importance. This study proposes oppositional differential search (ODS) algorithm to solve reliability optimisation problem of radial distribution system. The objective of this study is to obtain the optimum number, size and location of distributed generation as well as optimal number and location of remote control switch simultaneously in radial distribution system in order to improve system reliability at a compromised cost. A multi-objective function has been formulated here. Differential search (DS) algorithm imitates the seasonal migration behaviour of an organism in search of efficiency of food areas. Opposition-based DS (ODS) algorithm has been used here to improve the quality of solution in minimum time. The proposed opposition-based DS (ODS) algorithm utilises opposition-based learning for Superorganism initialisation and also for iteration wise update operation. Simulation results obtained by ODS algorithm have been compared with that of DS algorithm and differential evolutionary algorithm. Simulation results reveal that ODS algorithm provides considerably superior performance, in terms of quality of solution obtained and computational efficiency.

Inspec keywords: power distribution reliability; computational complexity; optimisation; power markets; search problems; power generation reliability; distributed power generation

Other keywords: oppositional differential search algorithm; multiobjective function; computational efhciency; superorganism initialisation; distributed generation optimal allocation; radial power distribution system reliability enhancement; opposition-based learning; ODS algorithm; remote control switch; opposition-based DS algorithm; electricity market

Subjects: Distribution networks; Reliability; Optimisation techniques; Distributed power generation; Power system management, operation and economics

References

    1. 1)
    2. 2)
    3. 3)
      • 19. Wang, J., Carvalho, P.M.s., Kirtley, J.: ‘Emergency reconfiguration and distribution system planning under the single-contingency policy’. Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, Washington, DC, 16–20 January 2012, pp. 1–5, doi: 10.1109/ISGT.2012.6175678.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
      • 40. Mailisia, A.R.: ‘Investigating the application of opposition-based ideas to ant algorithms’. Master's thesis, University of Waterloo, Ontario, Canada, 2007.
    15. 15)
      • 6. Cox, P.W.: ‘Self-healing networks: performance improvement by automated switching algorithm’. Proc. 20th Int. Conf. Electricity Distribution (CIRED 2009), 2009, pp. 1–8.
    16. 16)
    17. 17)
      • 26. Ali, I., Thomas, M.S., Kumar, P.: ‘Effect of loading pattern on the performance of reconfigured medium size distribution system’. IEEE Power India Conf., Murthal, 19–22 December 2012, pp. 1–6, doi: 10.1109/PowerI.2012.6479503.
    18. 18)
      • 37. Ventresca, M., Tizhoosh, H.: ‘Improving the convergence of back propagation by opposite transfer functions’. IEEE Int. Joint Conf. on Neural Networks, Vancuover, BC, 16–21 July 2006, pp. 4777–4784, doi: 10.1109/IJCNN.2006.247153.
    19. 19)
      • 1. Billinton, R., Allan, R.N.: ‘Reliability evaluation of power system’ (Springer, New York, USA, 1996, 2nd edn.).
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
      • 24. Zidan, A., Sadani, A.F.I.: ‘Effect of network configuration on maximum loadability and maximum allowable DG penetration in distribution systems’. IEEE Electrical Power & Energy Conf. (EPEC), Halifax, NS, 21–23 August 2013, pp. 1–6, doi: 10.1109/EPEC.2013.6802952.
    25. 25)
      • 43. Haiping, M., Xieyong, R., Baogen, J.: ‘Oppositional ant colony optimization algorithm and its application to fault monitoring’. Proc. 29th Chinese Control Conf. (CCC), July 2010, pp. 3895–3898.
    26. 26)
      • 7. Wagner, T.: ‘Impact of remote controlled switches on distribution grid recovering process’. Master Degree Project, School of Electrical Engineering, Royal Institute of Technology (KTH), 2010.
    27. 27)
      • 32. Mirjalili, H.-R., Sedighianaraki, A.–R., Haghifam, M.–R.: ‘A new method for loss reduction based on simultaneous DG placement and network reconfiguration’. 19th Iranian Conf. Electrical Engineering (ICEE) 2011, Tehran, 17–19 May 2011, pp. 1–6.
    28. 28)
    29. 29)
    30. 30)
      • 9. Asr, F.T., Kazemi, A.: ‘Determining optimum location of automated switches in distribution network’. Electric Power Conf. (EPEC2008), Canada, 6–7 October 2008, pp. 1–6, doi: 10.1109/EPC.2008.4763358.
    31. 31)
      • 41. Ergezer, M., Simon, D., Du, D.: ‘Oppositional biogeography based optimization’. Proc. of the 2009 IEEE Int. Conf. on Systems, Man, and Cybernetics, San Antonio, TX, USA, October, 2009, pp. 1009–1014, doi: 10.1109/ICSMC.2009.5346043.
    32. 32)
    33. 33)
    34. 34)
    35. 35)
      • 23. Zou, K., Keerthipala, W.W.L., Perera, S.: ‘SAIDI minimization of a remote distribution feeder’. Proc. of the 2007 Australasian Universities Power Engineering Conf. (AUPEC 2007), Perth, Australia, 9–12 December 2007, pp. 342–346.
    36. 36)
      • 38. Wang, H., Li, H., Liu, Y., Li, C., Zeng, S.: ‘Opposition-based particle swarm algorithm with Cauchy mutation’. IEEE Congress on Evolutionary Computation, Singapore, 25–28 September 2007, pp. 4750–4756, doi: 10.1109/CEC.2007.4425095.
    37. 37)
    38. 38)
    39. 39)
      • 36. Tizhoosh, H.: ‘Opposition-based learning: a new scheme for machine intelligence’. Proc. of the Int. Conf. on Computational Intelligence for Modelling Control and Automation, Australia, 28–30 November 2005, pp. 695–701, doi: 10.1109/CIMCA.2005.1631345.
    40. 40)
    41. 41)
      • 12. Viotto Romero, M.E., Wesz da Silva, L.G., Mantovani, J.R.S.: ‘Optimal switch allocation for automatic load transfer in distribution substations’. IEEE Trondheim PowerTech, Trondheim, 19–23 June 2011, pp. 1–7, doi: 10.1109/PTC.2011.6019208.
    42. 42)
      • 25. Elsaiah, S., Benidris, M., Mitra, J.: ‘Reliability improvement of power distribution system through feeder reconfiguration’. Int. Conf. Probabilistic Methods Applied to Power Systems (PMAPS), 2014, Durham, 7–10 July 2014, pp. 1–6, doi: 10.1109/PMAPS.2014.6960676.
    43. 43)
    44. 44)
      • 3. Miu, K.N., Chiang, H.D., Yuan, B., Darling, G.: ‘Fast service restoration for large-scale distribution systems with priority customers and constraints’. 20th Int. Conf. Power Industry Computer Applications, Columbus, OH, 11–16 May 1997, pp. 3–9.
    45. 45)
      • 34. Zidan, A., Al-Abri, R.S., El-Saadany, E.F.: ‘Load model effect on distributed generation allocation and feeders’ reconfiguration in unbalanced distribution systems’. Proc. of the 8th IEEE GCC Conf. and Exhibition (GCCCE), Muscat, Oman, 1–4 February 2015, pp. 1–5, doi: 10.1109/IEEEGCC.2015.7060021.
    46. 46)
    47. 47)
      • 42. Civicioglu, P., 2011. http://www.pinarcivicioglu.com/ds.htmlS, accessed 02 October, 2011.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2015.0097
Loading

Related content

content/journals/10.1049/joe.2015.0097
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading