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Bargaining-based cooperative energy trading for distribution company and demand response

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  • Fan, Songli
  • Ai, Qian
  • Piao, Longjian

Abstract

This paper studies the energy trading among flexible demand response aggregators (DRAs) and a distribution company (Disco) with self-owned generators. Instead of the conventional non-cooperative game based approach, the trading problem is formulated as a bargaining based cooperative model, where Disco and DRAs collaboratively decide the amounts of energy trade and the associated payments. This cooperative interaction can be beneficial to both Disco and DRAs, by reducing the aggregated peak demand and increasing the potential cost savings. The increased benefits from cooperation are fairly allocated among these participants, based on the Nash bargaining theory. Compared with the non-cooperative game based approach, the proposed bargaining cooperative model can further improve the benefits of Disco and DRAs. Moreover, the bargaining outcome can maximize the social welfare of the system. Considering the privacy and autonomy issues of participants, we utilize a decentralized solution to solve the bargaining problem, with minimum information exchange. Numerical studies demonstrate the effectiveness of the bargaining -based cooperative framework, and also show the improvement of benefits of the system.

Suggested Citation

  • Fan, Songli & Ai, Qian & Piao, Longjian, 2018. "Bargaining-based cooperative energy trading for distribution company and demand response," Applied Energy, Elsevier, vol. 226(C), pages 469-482.
    • Handle: RePEc:eee:appene:v:226:y:2018:i:c:p:469-482
    DOI: 10.1016/j.apenergy.2018.05.095
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    References listed on IDEAS

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    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. Rieger, Alexander & Thummert, Robert & Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang, 2016. "Estimating the benefits of cooperation in a residential microgrid: A data-driven approach," Applied Energy, Elsevier, vol. 180(C), pages 130-141.
    3. 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.
    4. Fotouhi Ghazvini, Mohammad Ali & Soares, João & Horta, Nuno & Neves, Rui & Castro, Rui & Vale, Zita, 2015. "A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers," Applied Energy, Elsevier, vol. 151(C), pages 102-118.
    5. 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.
    6. Jin, Ming & Feng, Wei & Marnay, Chris & Spanos, Costas, 2018. "Microgrid to enable optimal distributed energy retail and end-user demand response," Applied Energy, Elsevier, vol. 210(C), pages 1321-1335.
    7. Zenginis, Ioannis & Vardakas, John S. & Echave, Cynthia & Morató, Moisés & Abadal, Jordi & Verikoukis, Christos V., 2017. "Cooperation in microgrids through power exchange: An optimal sizing and operation approach," Applied Energy, Elsevier, vol. 203(C), pages 972-981.
    8. Ericson, Torgeir, 2009. "Direct load control of residential water heaters," Energy Policy, Elsevier, vol. 37(9), pages 3502-3512, September.
    9. Wang, Ge & Zhang, Qi & Li, Hailong & McLellan, Benjamin C. & Chen, Siyuan & Li, Yan & Tian, Yulu, 2017. "Study on the promotion impact of demand response on distributed PV penetration by using non-cooperative game theoretical analysis," Applied Energy, Elsevier, vol. 185(P2), pages 1869-1878.
    10. 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.
    11. Behboodi, Sahand & Chassin, David P. & Djilali, Ned & Crawford, Curran, 2018. "Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets," Applied Energy, Elsevier, vol. 210(C), pages 1310-1320.
    12. Kai Ma & Congshan Wang & Jie Yang & Qiuxia Yang & Yazhou Yuan, 2017. "Economic Dispatch with Demand Response in Smart Grid: Bargaining Model and Solutions," Energies, MDPI, vol. 10(8), pages 1-17, August.
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