access icon free Probabilistic load flow using the particle swarm optimisation clustering method

© The Institution of Engineering and Technology
Received 12/05/2017, Accepted 17/09/2017, Revised 05/08/2017, Published 21/09/2017

In this study, a clustering scheme based on the particle swarm optimisation (PSO) algorithm is used for probabilistic load flow calculation in the presence of wind generations. In this method, input random variables are first clustered in several groups according to their similarity and then a representative is assigned to each group by the PSO algorithm; finally, the deterministic load flow is performed. Using this technique, computational time is meaningfully decreased, while an acceptable level of accuracy is achieved. The IEEE 57-bus and IEEE 118-bus test systems were selected for the case study to demonstrate the performance of the proposed method. The results were compared with those of the Monte Carlo as well as K-means clustering methods from the accuracy and computation time points of view. Simulation results show that the introduced method significantly reduced the computational burden while keeping a high level of accuracy.

Inspec keywords: probability; load flow; wind power plants; pattern clustering; particle swarm optimisation

Other keywords: PSO algorithm; probabilistic load flow calculation; IEEE 57-bus test system; deterministic load flow; Monte Carlo method; k-means clustering method; wind generation; IEEE 118-bus test system; particle swarm optimisation clustering method

Subjects: Optimisation techniques; Wind power plants; Other topics in statistics

References

    1. 1)
      • 24. Vander Merwe, D. W., Engelbrecht, A. P.: ‘Data clustering using particle swarm optimization’. Proc. 2003 Congress on Evolutionary Computation, 2003, vol. 1, pp. 215–220.
    2. 2)
      • 14. Aien, M., Ramezani, R., Ghavami, S. M.: ‘Probabilistic load flow considering wind generation uncertainty’, Eng. Technol. Appl. Sci. Res., 2011, 1, pp. 126–132.
    3. 3)
      • 3. Aien, M., Fotuhi-Firuzabad, M., Aminifar, F.: ‘Probabilistic load flow in correlated uncertain environment using unscented transformation’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 2233–2241.
    4. 4)
      • 17. Hong, Y.-Y., Lin, F.-J., Yu, T.-H.: ‘Taguchi method-based probabilistic load flow studies considering uncertain renewables and loads’, IET Renew. Power Gener., 2016, 10, (2), pp. 221–227.
    5. 5)
      • 23. Kuo, R., Zulvia, F.: ‘Automatic clustering using an improved particle swarm optimization’, J. Ind. Intell. Inf., 2013, 1, (1), pp. 46–51.
    6. 6)
      • 15. Ai, X., Wen, J., Wu, T., et al: ‘A discrete point estimate method for probabilistic load flow based on the measured data of wind power’, IEEE Trans. Ind. Appl., 2013, 49, (5), pp. 2244–2252.
    7. 7)
      • 19. Galloway, S., Elders, I., Burt, G., et al: ‘Optimal flexible alternative current transmission system device allocation under system fluctuations due to demand and renewable generation’, IET Gener. Transm. Distrib., 2010, 4, pp. 725–735.
    8. 8)
      • 5. Mohammadi, M., Shayegani, A., Adaminejad, H.: ‘A new approach of point estimate method for probabilistic load flow’, Int. J. Electr. Power Energy Syst., 2013, 51, pp. 54–60.
    9. 9)
      • 7. Zhang, L., Cheng, H., Zhang, S., et al: ‘Probabilistic power flow calculation using the Johnson system and Sobol's quasi-random numbers’, IET. Gener. Transm. Distrib., 2016, 10, (12), pp. 3050–3059.
    10. 10)
      • 16. Oke, O. A., Thomas, D.W., Asher, G.M., et al: ‘Probabilistic load flow for distribution systems with wind production using unscented transform method’. IEEE PES Innovative Smart Grid Technologies (ISGT), Anaheim, CA, USA, April 2011, pp. 1–7.
    11. 11)
      • 12. Wu, W., Wang, K., Li, G., et al: ‘Probabilistic load flow calculation using cumulants and multiple integrals’, IET Gener. Transm. Distrib., 2016, 10, (7), pp. 1703–1709.
    12. 12)
      • 2. Ackermann, T.: ‘Wind power in power systems’ (John Wiley, Chichester, West Sussex, England, 2005).
    13. 13)
      • 13. Morales, J., Perez-Ruiz, J.: ‘Point estimate schemes to solve the probabilistic power flow’, IEEE Trans. Power Syst., 2007, 22, (4), pp. 1594–1601.
    14. 14)
      • 10. Brucoli, M., Torelli, F., Napoli, R.: ‘Quadratic probabilistic load flow with linearly modelled dispatch’, Int. J. Electr. Power Energy Syst., 1985, 7, (3), pp. 138–146.
    15. 15)
      • 1. Karki, R., Hu, P., Billinton, R.: ‘A simplified wind power generation model for reliability evaluation’, IEEE Trans. Energy Convers., 2006, 21, (2), pp. 533–540.
    16. 16)
      • 22. Xu, R., WunschII, D.: ‘Survey of clustering algorithms’, IEEE Trans. Neural Netw., 2005, 16, (3), pp. 645–678.
    17. 17)
      • 11. Zhang, P., Lee, S.: ‘Probabilistic load flow computation using the method of combined cumulants and Gram–Charlier expansion’, IEEE Trans. Power Syst., 2004, 19, (1), pp. 676–682.
    18. 18)
      • 25. [Online]. Available: http://www.ee.washington.edu/research/pstca/pf14/pg_tca14bus.html.
    19. 19)
      • 18. Galvani, S., Banna Sharifian, M., Tarafdar Hagh, M.: ‘Unified power flow controller impact on power system predictability’, IET Gener. Transm. Distrib., 2014, 8, (5), pp. 819–827.
    20. 20)
      • 20. Billinton, R., Allan, R.: ‘Reliability evaluation of power systems’ (Plenum Press, New York, 1984).
    21. 21)
      • 6. Usaola, J.: ‘Probabilistic load flow with wind production uncertainty using cumulants and Cornish–Fisher expansion’, Int. J. Electr. Power Energy Syst., 2009, 31, (9), pp. 474–481.
    22. 22)
      • 9. Allan, R., Léite da Silva, A.: ‘Probabilistic load flow using multi linearisations’. IEE Proc. C, Gener. Transm. Distrib., 1981, vol. 128, no. 5, pp. 280–287.
    23. 23)
      • 4. Chen, P., Chen, Z., Bak-Jensen, B.: ‘Probabilistic load flow: a review’. Third Int. Conf. on Electric Utility Deregulation and Restructuring and Power Technologies, 2008 (DRPT 2008), 2008, pp. 1586–1591.
    24. 24)
      • 21. Gan, G., Ma, C., Wu, J.: ‘Data clustering theory, algorithms, and Applications’ (Society for Industrial and Applied Mathematics, 2007).
    25. 25)
      • 8. Oke, O.A., Thomas, D.W.P., Asher, G.M.: ‘A new probabilistic load flow method for systems with wind penetration’. IEEE Trondheim PowerTech, 2011, pp. 1–6.
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