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Congestion management in hybrid power markets using modified Benders decomposition

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  • Esmaili, Masoud
  • Ebadi, Fatemeh
  • Shayanfar, Heidar Ali
  • Jadid, Shahram

Abstract

Congestion management is one of critical tasks in electric power markets. Although congestion management using a centralized optimal power flow may give an efficient solution, it lacks enough transparency in a competitive power market for its participants about congestion related costs. On the other hand, clearing the market and mitigating congestion separately may not result in an efficient solution from cost viewpoint. In this paper, a two-stage method using a modified Benders decomposition technique is proposed for congestion management in hybrid electricity markets including pool and bilateral transactions. An innovative convergence criterion and a new type of the sub-problem are proposed in using Benders decomposition in congestion management. The proposed method not only gives the efficient solution, but also provides enough transparency about congestion cost. Results of testing the method on the New-England test system are presented and discussed in detail. The results confirm the efficiency of the proposed congestion management method.

Suggested Citation

  • Esmaili, Masoud & Ebadi, Fatemeh & Shayanfar, Heidar Ali & Jadid, Shahram, 2013. "Congestion management in hybrid power markets using modified Benders decomposition," Applied Energy, Elsevier, vol. 102(C), pages 1004-1012.
    • Handle: RePEc:eee:appene:v:102:y:2013:i:c:p:1004-1012
    DOI: 10.1016/j.apenergy.2012.06.019
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    References listed on IDEAS

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    1. Schroeder, Andreas, 2011. "Modeling storage and demand management in power distribution grids," Applied Energy, Elsevier, vol. 88(12), pages 4700-4712.
    2. Niknam, Taher & Khodaei, Amin & Fallahi, Farhad, 2009. "A new decomposition approach for the thermal unit commitment problem," Applied Energy, Elsevier, vol. 86(9), pages 1667-1674, September.
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    4. Esmaili, Masoud & Amjady, Nima & Shayanfar, Heidar Ali, 2011. "Multi-objective congestion management by modified augmented [epsilon]-constraint method," Applied Energy, Elsevier, vol. 88(3), pages 755-766, March.
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    Cited by:

    1. Karl-Kiên Cao & Kai von Krbek & Manuel Wetzel & Felix Cebulla & Sebastian Schreck, 2019. "Classification and Evaluation of Concepts for Improving the Performance of Applied Energy System Optimization Models," Energies, MDPI, vol. 12(24), pages 1-51, December.
    2. Nesamalar, J. Jeslin Drusila & Venkatesh, P. & Raja, S. Charles, 2016. "Energy management by generator rescheduling in congestive deregulated power system," Applied Energy, Elsevier, vol. 171(C), pages 357-371.
    3. Tabandeh, Abbas & Abdollahi, Amir & Rashidinejad, Masoud, 2016. "Reliability constrained congestion management with uncertain negawatt demand response firms considering repairable advanced metering infrastructures," Energy, Elsevier, vol. 104(C), pages 213-228.
    4. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2016. "Managing the risk of uncertain wind power generation in flexible power systems using information gap decision theory," Energy, Elsevier, vol. 114(C), pages 846-861.
    5. Hosseini, Seyyed Ahmad & Amjady, Nima & Shafie-khah, Miadreza & Catalão, João P.S., 2016. "A new multi-objective solution approach to solve transmission congestion management problem of energy markets," Applied Energy, Elsevier, vol. 165(C), pages 462-471.

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