Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Effective method for optimal allocation of distributed generation units in meshed electric power systems

Effective method for optimal allocation of distributed generation units in meshed electric power systems

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Improper placement of distributed generation (DG) units in power systems would not only lead to an increased power loss, but could also jeopardise the system operation. To avert these scenarios and tackle this optimisation problem, this study proposes an effective method to guide electric utility distribution companies (DISCOs) in determining the optimal size and best locations of DG sources on their power systems. The approach, taking into account the system constraints, maximises the system loading margin as well as the profit of the DISCO over the planning period. These objective functions are fuzzified into a single multi-objective function, and subsequently solved using genetic algorithm (GA). In the GA, a fuzzy controller is used to dynamically adjust the crossover and mutation rates to maintain the proper population diversity (PD) during GA's operation. This effectively overcomes the premature convergence problem of the simple genetic algorithm (SGA). The results obtained on IEEE 6-bus and 30-bus test systems with the proposed method are evaluated with the simulation results of the classical grid search algorithm, which confirm its robustness and accuracy. This study also demonstrates DG's economic viability relative to upgrading substation and feeder facilities, when the incremental cost of serving additional load is considered.

References

    1. 1)
      • J.A. Momoh . (2001) Electric power system applications of optimization.
    2. 2)
      • K.M. Miettinen . (1999) Nonlinear multiobjective optimization.
    3. 3)
    4. 4)
      • California Energy Commission: ‘California Distributed Energy Resources Guide’, 15 March 2010, www.energy.ca.gov/distgen/.
    5. 5)
    6. 6)
    7. 7)
      • Willis, H.L., Inc, A., Raleigh, N.C.: `Analytical methods and rules of thumb for modeling DG-distribution interaction', IEEE Power Engineering Society Summer Meeting, 2000, p. 1643–1644.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • W. El-Khattam , K. Bahtasharia , Y. Hegazy , M.M.A. Salama . Optimal investment planning for distributed generation in a competitive electricity market. IEEE Trans. Power Syst. , 3 , 1674 - 1684
    12. 12)
      • CIGRE WG 37-23: ‘Impact of increasing contribution of dispersed generation on the power system’. Final Report, 1998.
    13. 13)
    14. 14)
      • CIRED: Dispersed generation; preliminary report of CIRED. Int. Conf. Electricity Distribution, Working Group WG04, Brussels, Belgium, 1999.
    15. 15)
    16. 16)
    17. 17)
      • Gozel, T., Hocaoglu, M.H., Eminoglu, U., Balikci, A.: `Optimal placement and sizing of distributed generation on radial feeder with different static load models', Future Power Systems, Int. Conf., 2005, p. 1–6.
    18. 18)
      • Lee, M.A., Takagi, H.: `Dynamic control of genetic algorithms using fuzzy logic techniques', Fifth Int. Conf. on Genetic Algorithms, 1993, Urbana-Champaign, IL, p. 76–83.
    19. 19)
      • H. Willis , W. Scott . (2000) Distributed power generation: planning and evaluation.
    20. 20)
    21. 21)
      • R. Zimmerman , D. Gan . (1997) Matpower.
    22. 22)
    23. 23)
    24. 24)
      • U.S. Energy Information Administration: ‘Natural Gas Weekly Update’, 15 March 2010, http://tonto.eia.doe.gov/oog/info/ngw/ngupdate.asp.
    25. 25)
      • K. Wang . A new fuzzy genetic algorithm based on population diversity. Proc. 2001 IEEE Int. Symp. on Computational Intelligence in Robotics and Automation , 108 - 112
    26. 26)
    27. 27)
      • A. El-ela , S. Allam , M. Shatla . Maximal optimal benefits of distributed generation using genetic algorithms. Electr. Power Syst. Res. , 7 , 869 - 877
    28. 28)
      • Xu, H., Vukovich, G.: `A fuzzy genetic algorithm with effective search and optimization', Int. Joint Conf. on Neural Networks, 1993, p. 2967–2970.
    29. 29)
      • R.L. Haupt , S.E. Haupt . (1997) Practical genetic algorithms.
    30. 30)
    31. 31)
    32. 32)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2010.0199
Loading

Related content

content/journals/10.1049/iet-gtd.2010.0199
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address