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

Optimal distributed generation location using mixed integer non-linear programming in hybrid electricity markets

Optimal distributed generation location using mixed integer non-linear programming in hybrid electricity markets

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

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

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.

  • Author(s): A. Kumar 1  and  W. Gao 2
    • View affiliations
    • Affiliations: 1: Department of Electrical Engineering, National Institute of Technology, Kurukshetra, India
      2: Department of Electrical and Computer Engineering, Centre for Energy Systems Research, Tennessee Tech University, USA
  • Source: Volume 4, Issue 2, February 2010, p. 281 – 298
    DOI:  10.1049/iet-gtd.2009.0026 , Print ISSN 1751-8687, Online ISSN 1751-8695
© The Institution of Engineering and Technology
Published

This study presents mixed integer non-linear programming (MINLP) approach for determining optimal location and number of distributed generators in hybrid electricity market. For optimal location of distributed generation (DG), first the most appropriate zone has been identified based on real power nodal price and real power loss sensitivity index as an economic and operational criterion. After identifying the suitable zone, mixed integer non-linear programming approach has been applied to locate optimal place and number of distributed generators in the obtained zone. The non-linear optimisation approach consists of minimisation of total fuel cost of conventional and DG sources as well as minimisation of line losses in the network. The pattern of nodal real and reactive power prices, line loss reduction and fuel cost saving has been obtained. The results have also been obtained for pool electricity market model for comparison. The impact of demand variation on the results has also been obtained for both the market models. The proposed MINLP-based optimisation approach has been applied for IEEE 24 bus reliability test system.

Inspec keywords: nonlinear programming; power system simulation; power system reliability; power markets

Other keywords: mixed integer non-linear programming; hybrid electricity markets; real power loss sensitivity index; distributed generation optimal location; IEEE 24 bus reliability test system; real power nodal price; non-linear optimisation

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

References

    1. 1)
    2. 2)
      • ‘Impact of increasing contribution of dispersed generation on power system – final report’. CIGRE WG 37–23, September 1998.
    3. 3)
    4. 4)
    5. 5)
      • (1998) Distributed power generation: a strategy for a competitive energy industry.
    6. 6)
      • Rosehart, W., Nowicki, E.: `Optimal placement of distributed generation', Proc. 14th PSC Conf., 2002, p. 1–5.
    7. 7)
    8. 8)
      • (1999) MATLAB and GAMS: interfacing optimization and visualization software.
    9. 9)
    10. 10)
      • D. Gautam , N. Mithulananthan . Optimal DG placement in deregulated electricity market. Electr. Power Syst. Res. , 1627 - 1636
    11. 11)
      • N. Acharya , P. Mahat , N. Mithulananthan . An analytical approach for DG allocation in primary distribution network. Electr. Power Energy Syst. , 669 - 678
    12. 12)
      • Electric Power Research Institute web-page, April 2008, http://www.epri.com/gg/newgen/disgen/index.html.
    13. 13)
      • Celli, G., Pillo, F.: `Optimal distributed generation allocation in MV distribution networks', Proc. IEEE PES Conf. on PICA, 2001, p. 81–86.
    14. 14)
      • G.A. Estevez , R.P. Behnke , R.T. Avila , L.S. Vargas . A competitive market integration model for distributed generation. IEEE Trans. Power Syst. , 4
    15. 15)
      • J.A.P. Lopes , N. Hatziargyriou , J. Mutale , P. Djapic , N. Jenkins . Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities. Electr. Power Syst. Res. , 1189 - 1203
    16. 16)
      • Nara, K., Hayashi, Y., Ikeda, K., Ashizawa, T.: `Application of tabu search to optimal placement of distributed generators', Proc. IEEE PES Winter Meeting, 2001, p. 288–294.
    17. 17)
      • J.W.M. Cheng , F.D. Galiana , D.T. McGills . Studies of bilateral contracts with respect to steady state security in a deregulated environment. IEEE Trans. Power Syst. , 3 , 1020 - 1025
    18. 18)
    19. 19)
      • A report prepared by the reliability test system task force of the applications of probability methods subcommittee. IEEE Trans. Power Appar. Syst. , 2047 - 2054
    20. 20)
      • A.K. Sharma . New secure bilateral transaction matrix determination using AC distribution factors and impact of TCPAR and TCSC on its pattern. Electr. Power Compon. Syst. , 921 - 943
    21. 21)
      • I.O. Elgerd . (1971) Electric energy system theory.
    22. 22)
    23. 23)
      • M. Donkelaar . A survey of solutions and options for integration of distributed generation in electricity supply systems. J. Energy Environ. , 2 , 323 - 332
    24. 24)
    25. 25)
      • M. Shahidepour , M. Alomoush . (2001) Restructured electrical power systems, operation, trading, and volatility.
    26. 26)
      • Cano, E.B.: `Using fuzzy optimization for distributed generation allocation', Proc. Power Engineering, Large Engg. System Conf., 10–12 October 2007.
    27. 27)
      • A. Brooke , D. Kendrick , A. Meeraus , R. Raman , R.E. Rasenthal . (1998) A user's guide, GAMS software.
    28. 28)
      • Dugan, R.C., Price, S.K.: `Issues for distributed generations in the US', Proc. IEEE PES, Winter Meeting, January 2002, 1, p. 121–126.
    29. 29)
    30. 30)
      • M. Sadighizadeh , A. Rezazadeh . Using genetic algorithm for distributed generation allocation to reduce losses and improve voltage profile. Proc. World Acad. Sci. Eng. Technol. , 251 - 256
    31. 31)
      • Porkar, S., Abbaspour-Tehrani Fard, A., Saadate, S.: `An approach to distribution system planning by implementing distributed generation in a deregulated electricity market', Proc. Power System Large Engg. System Conf., 10–12 October 2007, p. 90–95.
    32. 32)
      • G.P. Harrison , A. Piccolo , P. Siano , A.R. Wallace . Hybrid GA and OPF evaluation of network capacity for distribution generation connections. Electr. Power Energy Syst. , 392 - 398
    33. 33)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2009.0026
Loading

Related content

content/journals/10.1049/iet-gtd.2009.0026
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address