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Market clearing of joint energy and reserves auctions using augmented payment minimization

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  • Amjady, N.
  • Aghaei, J.
  • Shayanfar, H.A.

Abstract

This paper presents the market clearing of joint energy and reserves auctions and its mathematical formulation, focusing on a possible implementation of the Payment Cost Minimization (PCM). It also discusses another key point in debate: whether market clearing algorithm should minimize offer costs or payment costs? An aggregated simultaneous market clearing approach is proposed for provision of ancillary services as well as energy, which is in the form of Mixed Integer Nonlinear Programming (MINLP) formulation. In the MINLP formulation of the market clearing process, the objective function (Payment cost or offer cost) are optimized while meeting AC power flow constraints, system reserve requirements and lost opportunity cost (LOC) considerations. The model is applied to the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS), and simulation studies are carried out to examine the effectiveness of each objective function.

Suggested Citation

  • Amjady, N. & Aghaei, J. & Shayanfar, H.A., 2009. "Market clearing of joint energy and reserves auctions using augmented payment minimization," Energy, Elsevier, vol. 34(10), pages 1552-1559.
    • Handle: RePEc:eee:energy:v:34:y:2009:i:10:p:1552-1559
    DOI: 10.1016/j.energy.2009.06.048
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    References listed on IDEAS

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    1. Yan, Joseph H. & Stern, Gary A., 2002. "Simultaneous Optimal Auction and Unit Commitment for Deregulated Electricity Markets," The Electricity Journal, Elsevier, vol. 15(9), pages 72-80, November.
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    Cited by:

    1. Partovi, Farzad & Nikzad, Mehdi & Mozafari, Babak & Ranjbar, Ali Mohamad, 2011. "A stochastic security approach to energy and spinning reserve scheduling considering demand response program," Energy, Elsevier, vol. 36(5), pages 3130-3137.
    2. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    3. Shayesteh, E. & Yousefi, A. & Parsa Moghaddam, M., 2010. "A probabilistic risk-based approach for spinning reserve provision using day-ahead demand response program," Energy, Elsevier, vol. 35(5), pages 1908-1915.
    4. Saez-Gallego, Javier & Morales, Juan M. & Madsen, Henrik & Jónsson, Tryggvi, 2014. "Determining reserve requirements in DK1 area of Nord Pool using a probabilistic approach," Energy, Elsevier, vol. 74(C), pages 682-693.
    5. Kim, M.K. & Park, J.K. & Nam, Y.W., 2011. "Market-clearing for pricing system security based on voltage stability criteria," Energy, Elsevier, vol. 36(2), pages 1255-1264.
    6. Motalleb, Mahdi & Thornton, Matsu & Reihani, Ehsan & Ghorbani, Reza, 2016. "A nascent market for contingency reserve services using demand response," Applied Energy, Elsevier, vol. 179(C), pages 985-995.
    7. Fernández-Blanco, Ricardo & Arroyo, José M. & Alguacil, Natalia, 2014. "Consumer payment minimization under uniform pricing: A mixed-integer linear programming approach," Applied Energy, Elsevier, vol. 114(C), pages 676-686.

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    1. Rimvydas Baltaduonis, 2006. "Efficiency in Deregulated Electricity Markets: Offer Cost Minimization vs. Payment Cost Minimization Auction," Working papers 2006-04, University of Connecticut, Department of Economics.
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