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Transmission congestion management with integration of wind farm: a possible solution methodology for deregulated power market

Author

Listed:
  • Sadhan Gope

    (Mizoram University)

  • A. K. Goswami

    (National Institute of Technology Silchar)

  • P. K. Tiwari

    (National Institute of Technology Silchar)

Abstract

Congestion management (CM) work is a challenging task for researchers working in the field of power transmission sector. In this paper, an appreciable effort has been made to eliminate the line congestion by integrating wind farm in the system. To reschedule the conventional generators for achieving best optimal solution, moth flame optimization (MFO) algorithm is implemented here. Generator sensitivity factors and bus sensitivity factors are respectively used to reschedule the generators and to optimally locate the wind farm in deregulated power system. To test the performance and check the effectiveness of the proposed CM approach, modified IEEE 30 bus test system and modified 39 bus New England test system are used here. Further after obtained details results, the competitive performance of MFO algorithm is compared and verified with others optimization algorithms like artificial bee colony, firefly algorithm and ant lion optimizer algorithms in terms of rescheduling amount, rescheduling cost and active power losses.

Suggested Citation

  • Sadhan Gope & A. K. Goswami & P. K. Tiwari, 2020. "Transmission congestion management with integration of wind farm: a possible solution methodology for deregulated power market," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 287-296, April.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-019-00856-z
    DOI: 10.1007/s13198-019-00856-z
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    References listed on IDEAS

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    1. Sood, Yog Raj & Singh, Randhir, 2010. "Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources," Renewable Energy, Elsevier, vol. 35(8), pages 1828-1836.
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    4. Afshar, Karim & Ghiasvand, Farshad Shamsini & Bigdeli, Nooshin, 2018. "Optimal bidding strategy of wind power producers in pay-as-bid power markets," Renewable Energy, Elsevier, vol. 127(C), pages 575-586.
    5. Peesapati, Rajagopal & Yadav, Vinod Kumar & Kumar, Niranjan, 2018. "Flower pollination algorithm based multi-objective congestion management considering optimal capacities of distributed generations," Energy, Elsevier, vol. 147(C), pages 980-994.
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