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Optimal retailer bidding in a DA market – a new method considering risk and demand elasticity

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  • Hajati, Maryam
  • Seifi, Hossein
  • Sheikh-El-Eslami, Mohamad Kazem

Abstract

This paper presents a new method to determine the optimal demand function for a retailer in power markets. It assumes that the retailer purchases the energy from either the day-ahead or the regulation market and sells it to the end users through fixed and/or real-time pricing contracts. The load is assumed to be price sensitive and the retailer to be price-taker. Through participation in the market and managing its risk, the retailer attempts to maximize its profit. The proposed method is tested on a typical power market.

Suggested Citation

  • Hajati, Maryam & Seifi, Hossein & Sheikh-El-Eslami, Mohamad Kazem, 2011. "Optimal retailer bidding in a DA market – a new method considering risk and demand elasticity," Energy, Elsevier, vol. 36(2), pages 1332-1339.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:2:p:1332-1339
    DOI: 10.1016/j.energy.2010.11.006
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    References listed on IDEAS

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    1. E. J. Anderson & A. B. Philpott, 2002. "Optimal Offer Construction in Electricity Markets," Mathematics of Operations Research, INFORMS, vol. 27(1), pages 82-100, February.
    2. Green, Richard J & Newbery, David M, 1992. "Competition in the British Electricity Spot Market," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 929-953, October.
    3. Greening, Lorna A., 2010. "Demand response resources: Who is responsible for implementation in a deregulated market?," Energy, Elsevier, vol. 35(4), pages 1518-1525.
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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. Silva, Hendrigo Batista da & Santiago, Leonardo P., 2018. "On the trade-off between real-time pricing and the social acceptability costs of demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1513-1521.
    3. Chen, Yue & Wei, Wei & Liu, Feng & Shafie-khah, Miadreza & Mei, Shengwei & Catalão, João P.S., 2018. "Optimal contracts of energy mix in a retail market under asymmetric information," Energy, Elsevier, vol. 165(PB), pages 634-650.
    4. Nojavan, Sayyad & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2017. "Robust bidding and offering strategies of electricity retailer under multi-tariff pricing," Energy Economics, Elsevier, vol. 68(C), pages 359-372.
    5. Fotouhi Ghazvini, Mohammad Ali & Faria, Pedro & Ramos, Sergio & Morais, Hugo & Vale, Zita, 2015. "Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market," Energy, Elsevier, vol. 82(C), pages 786-799.
    6. Shafie-khah, Miadreza & Parsa Moghaddam, Mohsen & Sheikh-El-Eslami, Mohamad Kazem & Rahmani-Andebili, Mehdi, 2012. "Modeling of interactions between market regulations and behavior of plug-in electric vehicle aggregators in a virtual power market environment," Energy, Elsevier, vol. 40(1), pages 139-150.
    7. Kharrati, Saeed & Kazemi, Mostafa & Ehsan, Mehdi, 2016. "Equilibria in the competitive retail electricity market considering uncertainty and risk management," Energy, Elsevier, vol. 106(C), pages 315-328.

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