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Autonomous Household Energy Management Based on a Double Cooperative Game Approach in the Smart Grid

Author

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  • Bingtuan Gao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, Jiangsu, China)

  • Xiaofeng Liu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Wenhu Zhang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Yi Tang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China
    Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Nanjing 210096, Jiangsu, China)

Abstract

Taking advantage of two-way communication infrastructure and bidirectional energy trading between utility companies and customers in the future smart grid (SG), autonomous energy management programs become crucial to the demand-side management (DSM). Most of the existing autonomous energy management schemes are for the scenario with a single utility company or the scenario with one-way energy trading. In this paper, an autonomous household energy management system with multiple utility companies and multiple residential customers is studied by considering the bidirectional energy trading. To minimize the overall costs of both the utility companies and the residential customers, the energy management system is formulated as a double cooperative game. That is, the interaction among the residential users is formulated as a cooperative game, where the players are the customers and the strategies are the daily schedules of their household appliances; and the interaction among the utility companies is also formulated as a cooperative game, where the players are the suppliers and the strategies are the proportions of the daily total energy they provide for the customers. Without loss of generality, the bidirectional energy trading in the double cooperative game is formulated by allowing plug-in electric vehicles (PEVs) to discharge and sell energy back. Two distributed algorithms will be provided to realize the global optimal performance in terms of minimizing the energy costs, which can be guaranteed at the Nash equilibriums of the formulated cooperative games. Finally, simulation results illustrated that the proposed double cooperative game can benefit both the utility companies and residential users significantly.

Suggested Citation

  • Bingtuan Gao & Xiaofeng Liu & Wenhu Zhang & Yi Tang, 2015. "Autonomous Household Energy Management Based on a Double Cooperative Game Approach in the Smart Grid," Energies, MDPI, vol. 8(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:7:p:7326-7343:d:52857
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    References listed on IDEAS

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    1. Ippolito, M.G. & Di Silvestre, M.L. & Riva Sanseverino, E. & Zizzo, G. & Graditi, G., 2014. "Multi-objective optimized management of electrical energy storage systems in an islanded network with renewable energy sources under different design scenarios," Energy, Elsevier, vol. 64(C), pages 648-662.
    2. Tadahiro Taniguchi & Koki Kawasaki & Yoshiro Fukui & Tomohiro Takata & Shiro Yano, 2015. "Automated Linear Function Submission-Based Double Auction as Bottom-up Real-Time Pricing in a Regional Prosumers’ Electricity Network," Energies, MDPI, vol. 8(7), pages 1-26, July.
    3. Bingtuan Gao & Wenhu Zhang & Yi Tang & Mingjin Hu & Mingcheng Zhu & Huiyu Zhan, 2014. "Game-Theoretic Energy Management for Residential Users with Dischargeable Plug-in Electric Vehicles," Energies, MDPI, vol. 7(11), pages 1-20, November.
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    Cited by:

    1. Wei Fan & Nian Liu & Jianhua Zhang, 2016. "An Event-Triggered Online Energy Management Algorithm of Smart Home: Lyapunov Optimization Approach," Energies, MDPI, vol. 9(5), pages 1-24, May.
    2. Xiaofeng Liu & Shijun Wang & Jiawen Sun, 2018. "Energy Management for Community Energy Network with CHP Based on Cooperative Game," Energies, MDPI, vol. 11(5), pages 1-18, April.
    3. Kai Ma & Yege Bai & Jie Yang & Yangqing Yu & Qiuxia Yang, 2017. "Demand-Side Energy Management Based on Nonconvex Optimization in Smart Grid," Energies, MDPI, vol. 10(10), pages 1-17, October.
    4. Danish Mahmood & Nadeem Javaid & Nabil Alrajeh & Zahoor Ali Khan & Umar Qasim & Imran Ahmed & Manzoor Ilahi, 2016. "Realistic Scheduling Mechanism for Smart Homes," Energies, MDPI, vol. 9(3), pages 1-28, March.

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