IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9364-d999701.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9364/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9364/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ana Pavlićević & Saša Mujović, 2022. "Impact of Reactive Power from Public Electric Vehicle Stations on Transformer Aging and Active Energy Losses," Energies, MDPI, vol. 15(19), pages 1-24, September.
    2. Hasan, Mudassar & Arif, Muhammad & Naeem, Muhammad Abubakr & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021. "Time-frequency connectedness between Asian electricity sectors," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 208-224.
    3. Wan, Yang & He, Shi, 2021. "Dynamic connectedness of currencies in G7 countries: A Bayesian time-varying approach," Finance Research Letters, Elsevier, vol. 41(C).
    4. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Konstandatos, Otto & Rai, Alan, 2021. "Wind generation and the dynamics of electricity prices in Australia," Energy Economics, Elsevier, vol. 103(C).
    5. Do, Hung Xuan & Nepal, Rabindra & Jamasb, Tooraj, 2020. "Electricity market integration, decarbonisation and security of supply: Dynamic volatility connectedness in the Irish and Great Britain markets," Energy Economics, Elsevier, vol. 92(C).
    6. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2021. "An analysis of investor behaviour and information flows surrounding the negative WTI oil price futures event," Energy Economics, Elsevier, vol. 104(C).
    7. Jinwang Ma & Jingran Feng & Jun Chen & Jianing Zhang, 2024. "Volatility Spillover from Carbon Prices to Stock Prices: Evidence from China’s Carbon Emission Trading Markets," JRFM, MDPI, vol. 17(3), pages 1-24, March.
    8. Apergis, Nicholas & Pan, Wei-Fong & Reade, James & Wang, Shixuan, 2023. "Modelling Australian electricity prices using indicator saturation," Energy Economics, Elsevier, vol. 120(C).
    9. Andrés E. Díez & Mauricio Restrepo, 2021. "A Planning Method for Partially Grid-Connected Bus Rapid Transit Systems Operating with In-Motion Charging Batteries," Energies, MDPI, vol. 14(9), pages 1-22, April.
    10. Marco Toledo-Orozco & Eddy Bravo-Padilla & Carlos Álvarez-Bel & Diego Morales-Jadan & Luis Gonzalez-Morales, 2023. "Methodological Evaluation to Integrate Charging Stations for Electric Vehicles in a Tram System Using OpenDSS—A Case Study in Ecuador," Sustainability, MDPI, vol. 15(8), pages 1-26, April.
    11. Godin, Frédéric & Ibrahim, Zinatu, 2021. "An analysis of electricity congestion price patterns in North America," Energy Economics, Elsevier, vol. 102(C).
    12. Jan Niklas Buescher & Daria Gottwald & Florian Momm & Alexander Zureck, 2022. "Impact of the COVID-19 Pandemic Crisis on the Efficiency of European Intraday Electricity Markets," Energies, MDPI, vol. 15(10), pages 1-21, May.
    13. Nam Hoai Nguyen & Quynh T. Tran & Thao V. Nguyen & Nam Tran & Leon Roose & Saeed Sepasi & Maria Luisa Di Silvestre, 2023. "A Method for Assessing the Feasibility of Integrating Planned Unidirectional EV Chargers into the Distribution Grid: A Case Study in Danang, Vietnam," Energies, MDPI, vol. 16(9), pages 1-16, April.
    14. Nadja Klein & Michael Stanley Smith & David J. Nott, 2023. "Deep distributional time series models and the probabilistic forecasting of intraday electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 493-511, June.
    15. Gustavo Leite Gonçalves & Raphael Abrahão & Paulo Rotella Junior & Luiz Célio Souza Rocha, 2022. "Economic Feasibility of Conventional and Building-Integrated Photovoltaics Implementation in Brazil," Energies, MDPI, vol. 15(18), pages 1-16, September.
    16. Abdullah, Mohammad & Abakah, Emmanuel Joel Aikins & Wali Ullah, G M & Tiwari, Aviral Kumar & Khan, Isma, 2023. "Tail risk contagion across electricity markets in crisis periods," Energy Economics, Elsevier, vol. 127(PB).
    17. Luo, Keyu & Ye, Yong, 2024. "How responsive are retail electricity prices to crude oil fluctuations in the US? Time-varying and asymmetric perspectives," Research in International Business and Finance, Elsevier, vol. 69(C).
    18. Zhao, Yihang & Zhou, Zhenxi & Zhang, Kaiwen & Huo, Yaotong & Sun, Dong & Zhao, Huiru & Sun, Jingqi & Guo, Sen, 2023. "Research on spillover effect between carbon market and electricity market: Evidence from Northern Europe," Energy, Elsevier, vol. 263(PF).
    19. Sikorska-Pastuszka, Magdalena & Papież, Monika, 2023. "Dynamic volatility connectedness in the European electricity market," Energy Economics, Elsevier, vol. 127(PA).
    20. Pokpong Prakobkaew & Somporn Sirisumrannukul, 2022. "Practical Grid-Based Spatial Estimation of Number of Electric Vehicles and Public Chargers for Country-Level Planning with Utilization of GIS Data," Energies, MDPI, vol. 15(11), pages 1-19, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9364-:d:999701. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.