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Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach

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  • Yu, Mengmeng
  • Hong, Seung Ho

Abstract

This paper proposes a novel incentive-based demand response model from the view of a grid operator to enable system-level dispatch of demand response resources. The model spans three hierarchical levels of a grid operator, multiple service providers, and corresponding customers. The grid operator first posts an incentive to service providers, who will then invoke sub-programs with enrolled customers to negotiate quantities of demand reduction via providing service provider incentives. In view of this hierarchical decision-making structure, a two-loop Stackelberg game is proposed to capture interactions between different actors. The existence of a unique Stackelberg equilibrium that provides optimal system solutions is demonstrated. Simulation results show that the proposed approach is effective in helping compensate system resource deficiency at minimum cost.

Suggested Citation

  • Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
  • Handle: RePEc:eee:appene:v:203:y:2017:i:c:p:267-279
    DOI: 10.1016/j.apenergy.2017.06.010
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    References listed on IDEAS

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    1. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    2. Jingshuang Shen & Chuanwen Jiang & Bosong Li, 2015. "Controllable Load Management Approaches in Smart Grids," Energies, MDPI, vol. 8(10), pages 1-16, October.
    3. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    4. Ghasemi, A. & Shayeghi, H. & Moradzadeh, M. & Nooshyar, M., 2016. "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management," Applied Energy, Elsevier, vol. 177(C), pages 40-59.
    5. Hung-po Chao, 2011. "Demand response in wholesale electricity markets: the choice of customer baseline," Journal of Regulatory Economics, Springer, vol. 39(1), pages 68-88, February.
    6. Patteeuw, Dieter & Bruninx, Kenneth & Arteconi, Alessia & Delarue, Erik & D’haeseleer, William & Helsen, Lieve, 2015. "Integrated modeling of active demand response with electric heating systems coupled to thermal energy storage systems," Applied Energy, Elsevier, vol. 151(C), pages 306-319.
    7. 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.
    8. Shafie-khah, M. & Kheradmand, M. & Javadi, S. & Azenha, M. & de Aguiar, J.L.B. & Castro-Gomes, J. & Siano, P. & Catalão, J.P.S., 2016. "Optimal behavior of responsive residential demand considering hybrid phase change materials," Applied Energy, Elsevier, vol. 163(C), pages 81-92.
    9. Yu, Mengmeng & Lu, Renzhi & Hong, Seung Ho, 2016. "A real-time decision model for industrial load management in a smart grid," Applied Energy, Elsevier, vol. 183(C), pages 1488-1497.
    10. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    11. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
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