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Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System

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  • Lijuan Sun

    (Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

  • Menggang Chen

    (Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

  • Yawei Shi

    (Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

  • Lifeng Zheng

    (Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

  • Songyang Li

    (Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

  • Jun Li

    (Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

  • Huijuan Xu

    (State Nuclear Electric Power Planning Design & Research Institute Chongqing Co., Ltd., Chongqing 401121, China)

Abstract

As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles.

Suggested Citation

  • Lijuan Sun & Menggang Chen & Yawei Shi & Lifeng Zheng & Songyang Li & Jun Li & Huijuan Xu, 2022. "Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System," Energies, MDPI, vol. 15(24), pages 1-13, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9364-:d:999701
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    References listed on IDEAS

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    1. Najmat Celene Branco & Carolina M. Affonso, 2020. "Probabilistic Approach to Integrate Photovoltaic Generation into PEVs Charging Stations Considering Technical, Economic and Environmental Aspects," Energies, MDPI, vol. 13(19), pages 1-18, September.
    2. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    3. Daniel Betancur & Luis F. Duarte & Jesús Revollo & Carlos Restrepo & Andrés E. Díez & Idi A. Isaac & Gabriel J. López & Jorge W. González, 2021. "Methodology to Evaluate the Impact of Electric Vehicles on Electrical Networks Using Monte Carlo," Energies, MDPI, vol. 14(5), pages 1-16, February.
    4. Theron Smith & Joseph Garcia & Gregory Washington, 2022. "Novel PEV Charging Approaches for Extending Transformer Life," Energies, MDPI, vol. 15(12), pages 1-17, June.
    5. George Konstantinidis & Emmanuel Karapidakis & Alexandros Paspatis, 2022. "Mitigating the Impact of an Official PEV Charger Deployment Plan on an Urban Grid," Energies, MDPI, vol. 15(4), pages 1-18, February.
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