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Online Air-Conditioning Energy Management under Coalitional Game Framework in Smart Community

Author

Listed:
  • Wei Fan

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Nian Liu

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Jianhua Zhang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Jinyong Lei

    (Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510080, China)

Abstract

Motivated by the potential ability of air conditioning (A/C) units in demand response, this paper explores how to utilize A/C units to increase the profit of a smart community. A coalitional game between the households and the load serving entity (LSE) in a smart community is studied, where the LSE joins by selling renewable energy to householders and providing an energy saving service to them through an A/C controller. The A/C controller is designed to reduce the amount of electricity purchased from the main grid by controlling A/C units. An online A/C energy management algorithm is developed, based on Lyapunov optimization, that considers both the A/C energy consumption and the thermal comfort level of consumers. In order to quantify the contribution of A/C units, the Shapley value is adopted for distribution of the reward among the participating householders and the LSE, based on their contribution. The simulation result verifies the effectiveness of the proposed coalitional game for a smart community and the algorithm for A/C.

Suggested Citation

  • Wei Fan & Nian Liu & Jianhua Zhang & Jinyong Lei, 2016. "Online Air-Conditioning Energy Management under Coalitional Game Framework in Smart Community," Energies, MDPI, vol. 9(9), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:689-:d:76903
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    References listed on IDEAS

    as
    1. Graditi, G. & Ippolito, M.G. & Telaretti, E. & Zizzo, G., 2016. "Technical and economical assessment of distributed electrochemical storages for load shifting applications: An Italian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 515-523.
    2. Liu, Nian & Tang, Qingfeng & Zhang, Jianhua & Fan, Wei & Liu, Jie, 2014. "A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids," Applied Energy, Elsevier, vol. 129(C), pages 336-345.
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    Cited by:

    1. Antti Alahäivälä & Matti Lehtonen, 2016. "Increasing the Benefit from Cost-Minimizing Loads via Centralized Adjustments," Energies, MDPI, vol. 9(12), pages 1-13, November.
    2. Giovanni Pau & Mario Collotta & Antonio Ruano & Jiahu Qin, 2017. "Smart Home Energy Management," Energies, MDPI, vol. 10(3), pages 1-5, March.
    3. 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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