access icon free Optimal sizing and sitting of DG with load models using soft computing techniques in practical distribution system

In the deregulated power market environment, distributed generation (DG) is an effective approach to manage performance, operation and control of the distribution system. Methods available in the literature for DG planning are often not able to simultaneously provide technical and economical benefits. Therefore an effective methodology is developed to improve the technical as well as economical benefits as compared with the existing approaches. This study reports the optimal installation of multi-DG in the standard 33-bus, 69-bus radial distribution systems and 54-bus practical radial distribution system. Several performance evaluation indices such as active and reactive power loss indices, voltage deviation index, reliability index and shift factor indices are used to develop a novel multi-objective function (MOF). A new set of equations is developed for representing different practical load models. A novel MOF has been solved to find optimal sizing and placement of DGs using genetic algorithm and particle swarm optimisation technique. The comparative result analysis is also discussed for both techniques. The result analysis reveals that system losses, energy not supplied, system MVA intakes are reduced, whereas available transfer capability, voltage profile, reliability and cost benefits are improved for the case with-DGs in the distribution system.

Inspec keywords: electrical installation; reactive power; power markets; particle swarm optimisation; distributed power generation; power distribution reliability; genetic algorithms

Other keywords: distributed generation; genetic algorithm; 69-bus radial distribution systems; soft computing techniques; optimal installation; load models; optimal sizing; 33-bus radial distribution systems; reliability index; optimal sitting; multiobjective function; optimal placement; deregulated power market environment; voltage deviation index; particle swarm optimisation; reactive power loss index; 54-bus practical radial distribution system; shift factor index

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

References

    1. 1)
    2. 2)
      • 2. Goldberg, D.E.: ‘Genetic algorithms in search, optimization and machine learning’ (Addison-Wesley publishers, 1989, 1st edn.).
    3. 3)
    4. 4)
    5. 5)
      • 8. Zimmerman, R.D., Murillo-Sanchez, C.E.: ‘Matpower4.1’, December 2011. Available at: http://www.pserc.cornell.edu//matpower/.
    6. 6)
    7. 7)
      • 35. Price, W.W., Casper, S.G., Nwankpa, C.O., et al: ‘Bibliography on load models for power flow and dynamic performance simulation’, IEEE Power Eng. Rev., 1995, 15, (2), p. 70.
    8. 8)
    9. 9)
    10. 10)
      • 38. Mishra, M.: ‘Optimal placement of DG for loss reduction considering DG models’. IEEE Int. Conf. on Electrical, Computer and Communication Technologies (ICECCT), 2015, 5–7 March 2015, pp. 16, doi: 10.1109/ICECCT.2015.7225996.
    11. 11)
    12. 12)
    13. 13)
      • 4. Kennedy, J., Eberhart, R.C.: ‘Particle swarm optimization’. IEEE Int. Conf. on Neural Networks Proc., November/December 1995, vol. 4, pp. 19421948, doi: 10.1109/ICNN.1995.488968.
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
      • 5. Eberhart, R.C., Kennedy, J.: ‘A new optimizer using particle swarm theory’. Proc. Sixth Int. Symp. on Micro Machine and Human Science (Nagoya, Japan), IEEE Service Center, Piscataway, NJ, 4–6 October 1995, pp. 3943, doi: 10.1109/MHS.1995.494215.
    25. 25)
    26. 26)
      • 1. Holland, J.H.: ‘Adaptation in natural and artificial systems’ (The University of Michigan Press, Ann Arbor, 1975).
    27. 27)
    28. 28)
      • 40. Behera, S.R., Dash, S.P., Panigrahi, B.K.: ‘Optimal placement and sizing of DGs in radial distribution system (RDS) using Bat algorithm’. Int. Conf. on Circuit, Power and Computing Technologies (ICCPCT), 2015, 19–20 March 2015, pp. 18, doi: 10.1109/ICCPCT.2015.7159295.
    29. 29)
    30. 30)
    31. 31)
      • 3. Haupt, R.L., Haupt, S.E.: ‘Practical genetic algorithms’ (published byJohn Wiley & Sons, inc., Hoboken, New Jersey, published simultaneously in Canada, 2004, 2nd edn.).
    32. 32)
    33. 33)
    34. 34)
    35. 35)
      • 29. Patra, S.B., Mitra, J., Ranade, S.J.: ‘Micro-grid architecture: a reliability constrained approach’. IEEE Pmtower Engineering Society General Meeting|, 2005, IEEE, pp. 23722377.
    36. 36)
      • 6. Bohre, A.K., Agnihotri, G., Dubey, M.: ‘Hybrid butterfly based particle swarm optimization for optimization problems’. First Int. Conf. on Networks and Soft Computing (ICNSC), 2014, 19–20 August 2014, pp. 172177, doi: 10.1109/CNSC.2014.6906650.
    37. 37)
    38. 38)
    39. 39)
      • 28. Mitra, J., Patra, S.B., Ranade, S.J., et al: ‘Reliability-specified generation and distribution expansion in micro-grid architectures’, WSEAS Trans. Power Syst., 2006, 1, (8), pp. 14461453.
    40. 40)
    41. 41)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2015.1034
Loading

Related content

content/journals/10.1049/iet-gtd.2015.1034
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading