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Game-Theoretic Energy Management for Residential Users with Dischargeable Plug-in Electric Vehicles

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  • Bingtuan Gao

    (School of Electrical Engineering, Southeast University, No. 2, Sipailou, Nanjing 210096, Jiangsu, China)

  • Wenhu Zhang

    (School of Electrical Engineering, Southeast University, No. 2, Sipailou, Nanjing 210096, Jiangsu, China)

  • Yi Tang

    (School of Electrical Engineering, Southeast University, No. 2, Sipailou, Nanjing 210096, Jiangsu, China)

  • Mingjin Hu

    (School of Electrical Engineering, Southeast University, No. 2, Sipailou, Nanjing 210096, Jiangsu, China)

  • Mingcheng Zhu

    (School of Electrical Engineering, Southeast University, No. 2, Sipailou, Nanjing 210096, Jiangsu, China)

  • Huiyu Zhan

    (School of Electrical Engineering, Southeast University, No. 2, Sipailou, Nanjing 210096, Jiangsu, China)

Abstract

The plug-in electric vehicle (PEV) has attracted more and more attention because of the energy crisis and environmental pollution, which is also the main shiftable load of the residential users’ demand side management (DSM) system in the future smart grid (SG). In this paper, we employ game theory to provide an autonomous energy management system among residential users considering selling energy back to the utility company by discharging the PEV’s battery. By assuming all users are equipped with smart meters to execute automatic energy consumption scheduling (ECS) and the energy company can adopt adequate pricing tariffs relating to time and level of energy usage, we formulate an energy management game, where the players are the residential users and the strategies are their daily schedules of household appliance use. We will show that the Nash equilibrium of the formulated energy management game can guarantee the global optimization in terms of minimizing the energy costs, where the depreciation cost of PEV’s battery because of discharging and selling energy back is also considered. Simulation results verify that the proposed game-theoretic approach can reduce the total energy cost and individual daily electricity payment. Moreover, since plug-in electric bicycles (PEBs) are currently widely used in China, simulation results of residential users owing household appliances and bidirectional energy trading of PEBs are also provided and discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:7499-7518:d:42454
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    References listed on IDEAS

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    1. Luis M. Abadie & José M. Chamorro, 2014. "Valuation of Wind Energy Projects: A Real Options Approach," Energies, MDPI, vol. 7(5), pages 1-38, May.
    2. Chen, Zhisong & Su, Shong-Iee Ivan, 2014. "Photovoltaic supply chain coordination with strategic consumers in China," Renewable Energy, Elsevier, vol. 68(C), pages 236-244.
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    Cited by:

    1. Kicsiny, R., 2019. "Differential game model with discretized solution for distributing heat produced by solar heating systems," Renewable Energy, Elsevier, vol. 140(C), pages 330-340.
    2. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    3. Muhammad Aziz & Takuya Oda & Takashi Mitani & Yoko Watanabe & Takao Kashiwagi, 2015. "Utilization of Electric Vehicles and Their Used Batteries for Peak-Load Shifting," Energies, MDPI, vol. 8(5), pages 1-19, April.
    4. Danish Mahmood & Nadeem Javaid & Sheraz Ahmed & Imran Ahmed & Iftikhar Azim Niaz & Wadood Abdul & Sanaa Ghouzali, 2017. "Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation," Energies, MDPI, vol. 10(3), pages 1-25, March.
    5. 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.
    6. Jun Liu & Jinchun Chen & Chao Wang & Zhang Chen & Xinglei Liu, 2020. "Market Trading Model of Urban Energy Internet Based on Tripartite Game Theory," Energies, MDPI, vol. 13(7), pages 1-24, April.
    7. Yuttana Kongjeen & Krischonme Bhumkittipich, 2018. "Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis," Energies, MDPI, vol. 11(6), pages 1-16, June.
    8. Georgiou, Giorgos S. & Christodoulides, Paul & Kalogirou, Soteris A., 2019. "Real-time energy convex optimization, via electrical storage, in buildings – A review," Renewable Energy, Elsevier, vol. 139(C), pages 1355-1365.
    9. Fernandez, Edstan & Hossain, M.J. & Nizami, M.S.H., 2018. "Game-theoretic approach to demand-side energy management for a smart neighbourhood in Sydney incorporating renewable resources," Applied Energy, Elsevier, vol. 232(C), pages 245-257.
    10. Esther Salmeron-Manzano & Francisco Manzano-Agugliaro, 2018. "The Electric Bicycle: Worldwide Research Trends," Energies, MDPI, vol. 11(7), pages 1-16, July.
    11. Teng Liu & Yuan Zou & Dexing Liu & Fengchun Sun, 2015. "Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 8(7), pages 1-18, July.
    12. Sam Weckx & Reinhilde D'hulst & Johan Driesen, 2015. "Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration," Energies, MDPI, vol. 8(5), pages 1-22, May.
    13. Tiago Pinto & Zita Vale & Isabel Praça & E. J. Solteiro Pires & Fernando Lopes, 2015. "Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning," Energies, MDPI, vol. 8(9), pages 1-26, September.

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