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A real-time demand response market through a repeated incomplete-information game

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  • Motalleb, Mahdi
  • Annaswamy, Anuradha
  • Ghorbani, Reza

Abstract

Demand Response (DR) programs have been developed to help traditional power market to meet demand specially with increasing penetrations of renewable energies. This paper focuses on application of a game-theoretic framework to model competition between demand response aggregators to sell aggregated energy stored in storage devices directly to other aggregators in a market. This proposed market is cleared in each time interval of a day using a repeated game-theoretic framework. After finding optimal bidding strategies of the aggregators in each time interval, Dynamic Economic Dispatch (DED) is performed to update the dispatch of generators based on updated demand. Dynamic pricing has been considered in the proposed market framework in two forms: Real-Time Pricing (RTP) in each time interval of a day with updating demand and supply and Time-of-Use (TOU) with demand price-based scheduling through dynamic programing. The proposed method minimizes the fuel consumption and operation costs and optimally schedules the generation in grid's supply side. It also presents optimal prices during different periods simultaneously. Customers in light of the utility's optimal price minimize theirs electricity costs and optimally schedule their power consumption in order to participate in the DR market. The presented model is applied to IEEE 24-bus model.

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  • Motalleb, Mahdi & Annaswamy, Anuradha & Ghorbani, Reza, 2018. "A real-time demand response market through a repeated incomplete-information game," Energy, Elsevier, vol. 143(C), pages 424-438.
    • Handle: RePEc:eee:energy:v:143:y:2018:i:c:p:424-438
    DOI: 10.1016/j.energy.2017.10.129
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    1. Fotouhi Ghazvini, Mohammad Ali & Canizes, Bruno & Vale, Zita & Morais, Hugo, 2013. "Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market," Applied Energy, Elsevier, vol. 101(C), pages 667-677.
    2. Motalleb, Mahdi & Ghorbani, Reza, 2017. "Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices," Applied Energy, Elsevier, vol. 202(C), pages 581-596.
    3. 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.
    4. Nwulu, Nnamdi I. & Xia, Xiaohua, 2015. "Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs," Energy, Elsevier, vol. 91(C), pages 404-419.
    5. Su, Wencong & Huang, Alex Q., 2014. "A game theoretic framework for a next-generation retail electricity market with high penetration of distributed residential electricity suppliers," Applied Energy, Elsevier, vol. 119(C), pages 341-350.
    6. Min, C.G. & Kim, M.K. & Park, J.K. & Yoon, Y.T., 2013. "Game-theory-based generation maintenance scheduling in electricity markets," Energy, Elsevier, vol. 55(C), pages 310-318.
    7. Zhang, Ni & Yan, Yu & Su, Wencong, 2015. "A game-theoretic economic operation of residential distribution system with high participation of distributed electricity prosumers," Applied Energy, Elsevier, vol. 154(C), pages 471-479.
    8. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    9. Chassin, David P. & Rondeau, Daniel, 2016. "Aggregate modeling of fast-acting demand response and control under real-time pricing," Applied Energy, Elsevier, vol. 181(C), pages 288-298.
    10. Wang, Jianhui & Zhou, Zhi & Botterud, Audun, 2011. "An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand," Energy, Elsevier, vol. 36(5), pages 3459-3467.
    11. Reihani, Ehsan & Motalleb, Mahdi & Thornton, Matsu & Ghorbani, Reza, 2016. "A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture," Applied Energy, Elsevier, vol. 183(C), pages 445-455.
    12. Reihani, Ehsan & Motalleb, Mahdi & Ghorbani, Reza & Saad Saoud, Lyes, 2016. "Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration," Renewable Energy, Elsevier, vol. 86(C), pages 1372-1379.
    13. Yu, Mengmeng & Hong, Seung Ho, 2016. "Supply–demand balancing for power management in smart grid: A Stackelberg game approach," Applied Energy, Elsevier, vol. 164(C), pages 702-710.
    14. Kamalinia, Saeed & Shahidehpour, Mohammad & Wu, Lei, 2014. "Sustainable resource planning in energy markets," Applied Energy, Elsevier, vol. 133(C), pages 112-120.
    15. Wan, Rui & Boyce, John R., 2014. "Non-renewable resource Stackelberg games," Resource and Energy Economics, Elsevier, vol. 37(C), pages 102-121.
    16. Lorenczik, Stefan & Panke, Timo, 2016. "Assessing market structures in resource markets — An empirical analysis of the market for metallurgical coal using various equilibrium models," Energy Economics, Elsevier, vol. 59(C), pages 179-187.
    17. Liu, Zhen & Zhang, Xiliang & Lieu, Jenny, 2010. "Design of the incentive mechanism in electricity auction market based on the signaling game theory," Energy, Elsevier, vol. 35(4), pages 1813-1819.
    18. Lo Prete, Chiara & Hobbs, Benjamin F., 2016. "A cooperative game theoretic analysis of incentives for microgrids in regulated electricity markets," Applied Energy, Elsevier, vol. 169(C), pages 524-541.
    19. Lise, Wietze & Linderhof, Vincent & Kuik, Onno & Kemfert, Claudia & Ostling, Robert & Heinzow, Thomas, 2006. "A game theoretic model of the Northwestern European electricity market--market power and the environment," Energy Policy, Elsevier, vol. 34(15), pages 2123-2136, October.
    Full references (including those not matched with items on IDEAS)

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