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Multi-Party Energy Management for Networks of PV-Assisted Charging Stations: A Game Theoretical Approach

Author

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  • Nian Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Minyang Cheng

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Li Ma

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    Power Distribution Department, China Electric Power Research Institute, Beijing 100192, China)

Abstract

Motivated by the development of electric vehicles (EVs), this paper addresses the energy management problem for the PV-assisted charging station (PVCS) network. An hour-ahead optimization model for the operation of PVCS is proposed, considering the profit of the PVCS, the local consumption of the photovoltaic (PV) energy and the impacts on the grid. Moreover, a two-level feasible charging region (FCR) model is built to guarantee the service quality for EVs and learning-based decision-making is designed to assist the optimization of the PVCS in various scenarios. The multi-party energy management problem, including several kinds of energy flows of the PVCS network, is formulated as a non-cooperative game. Then, the strategies of the PVCSs are modeled as the demand response (DR) activities to achieve their own optimization goals and a two-level distributed heuristic algorithm is introduced to solve the problem. The simulation results show that the economic profit of the network is increased by 6.34% compared with the common time of use (TOU) prices approach. Besides, the percentage of the PV energy in total charging load (PPTCL) and load rate are promoted by 28.93% and 0.3125, respectively, which demonstrates the validity and practicability of the proposed method.

Suggested Citation

  • Nian Liu & Minyang Cheng & Li Ma, 2017. "Multi-Party Energy Management for Networks of PV-Assisted Charging Stations: A Game Theoretical Approach," Energies, MDPI, vol. 10(7), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:905-:d:103384
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    References listed on IDEAS

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    1. Angelamaria Massimo & Marco Dell'Isola & Andrea Frattolillo & Giorgio Ficco, 2014. "Development of a Geographical Information System (GIS) for the Integration of Solar Energy in the Energy Planning of a Wide Area," Sustainability, MDPI, vol. 6(9), pages 1-15, August.
    2. Liu, Nian & Chen, Zheng & Liu, Jie & Tang, Xiao & Xiao, Xiangning & Zhang, Jianhua, 2014. "Multi-objective optimization for component capacity of the photovoltaic-based battery switch stations: Towards benefits of economy and environment," Energy, Elsevier, vol. 64(C), pages 779-792.
    3. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    4. Nian Liu & Minyang Cheng, 2017. "Effectiveness Evaluation for a Commercialized PV-Assisted Charging Station," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
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    Cited by:

    1. Su Su & Yong Hu & Tiantian Yang & Shidan Wang & Ziqi Liu & Xiangxiang Wei & Mingchao Xia & Yutaka Ota & Koji Yamashita, 2018. "Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities," Energies, MDPI, vol. 11(2), pages 1-19, February.
    2. Liaqat Ali & S. M. Muyeen & Hamed Bizhani & Arindam Ghosh, 2019. "Comparative Study on Game-Theoretic Optimum Sizing and Economical Analysis of a Networked Microgrid," Energies, MDPI, vol. 12(20), pages 1-14, October.

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