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Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology

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  • Zare, Kazem
  • Moghaddam, Mohsen Parsa
  • Sheikh El Eslami, Mohammad Kazem

Abstract

This paper provides a technique to derive the bidding strategy in the day-ahead market for a large consumer that procures its electricity demand in both day-ahead market and a subsequent adjustment market. It is considered that hourly market prices are normally distributed and this correlation is modeled by variance–covariance matrix. The uncertainty of procurement cost is modeled using concepts derived from information gap decision theory which allows deriving robust bidding strategies with respect to price volatility. First Order Reliability Method is applied to construct the robust bidding curve. The proposed technique is illustrated through a realistic case study.

Suggested Citation

  • Zare, Kazem & Moghaddam, Mohsen Parsa & Sheikh El Eslami, Mohammad Kazem, 2010. "Demand bidding construction for a large consumer through a hybrid IGDT-probability methodology," Energy, Elsevier, vol. 35(7), pages 2999-3007.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:7:p:2999-3007
    DOI: 10.1016/j.energy.2010.03.036
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    References listed on IDEAS

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    1. Carraretto, Cristian, 2006. "Power plant operation and management in a deregulated market," Energy, Elsevier, vol. 31(6), pages 1000-1016.
    2. Sezgen, Osman & Goldman, C.A. & Krishnarao, P., 2007. "Option value of electricity demand response," Energy, Elsevier, vol. 32(2), pages 108-119.
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    Cited by:

    1. Nojavan, Sayyad & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2017. "Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program," Applied Energy, Elsevier, vol. 187(C), pages 449-464.
    2. Majidi, M. & Mohammadi-Ivatloo, B. & Soroudi, A., 2019. "Application of information gap decision theory in practical energy problems: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 157-165.
    3. Ben-Haim, Yakov & Osteen, Craig D. & Moffitt, L. Joe, 2013. "Policy dilemma of innovation: An info-gap approach," Ecological Economics, Elsevier, vol. 85(C), pages 130-138.
    4. Effenberger, Frank & Hilbert, Andreas, 2016. "Towards an energy information system architecture description for industrial manufacturers: Decomposition & allocation view," Energy, Elsevier, vol. 112(C), pages 599-605.
    5. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2016. "Prosumer bidding and scheduling in electricity markets," Energy, Elsevier, vol. 94(C), pages 828-843.
    6. Alipour, Manijeh & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2016. "Optimal risk-constrained participation of industrial cogeneration systems in the day-ahead energy markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 421-432.
    7. Mestre, Guillermo & Sánchez-Úbeda, Eugenio F. & Muñoz San Roque, Antonio & Alonso, Estrella, 2022. "The arithmetic of stepwise offer curves," Energy, Elsevier, vol. 239(PE).
    8. Yu, Dongmin & liu, Huanan & Bresser, Charis, 2018. "Peak load management based on hybrid power generation and demand response," Energy, Elsevier, vol. 163(C), pages 969-985.
    9. Behrangrad, Mahdi & Sugihara, Hideharu & Funaki, Tsuyoshi, 2012. "Integrating the cold load pickup effect of reserve supplying demand response resource in social cost minimization based system scheduling," Energy, Elsevier, vol. 45(1), pages 1034-1041.

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