IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v271y2023ics0360544223004826.html
   My bibliography  Save this article

Orderly charging strategy of electric vehicle based on improved PSO algorithm

Author

Listed:
  • Du, Wenyi
  • Ma, Juan
  • Yin, Wanjun

Abstract

With the increasing penetration of electric vehicles (EVs), the harmful impact caused by EV's disorderly charging becomes larger. Aiming for mitigating the impact of disorderly charging on the grid and improving the user's satisfaction, this paper firstly performs the Monte Carlo simulation (MCS) to obtain the distribution information of EVs' disorderly charging. Then an improved particle swarm optimization (PSO) algorithm is presented to model the orderly charging strategy. In order to maintain the diversity of the population better, a rotation matrix is utilized to yaw particle's search direction slightly in the improved PSO. And by adjusting the inertia weight index and learning factor, the problems of poor local optimization ability and premature convergence of the original PSO is alleviated. Finally, the proposed approach is verified by a practical engineering case. The outcome demonstrates that the proposed orderly charging strategy can significantly lower the charging cost and peak-valley difference.

Suggested Citation

  • Du, Wenyi & Ma, Juan & Yin, Wanjun, 2023. "Orderly charging strategy of electric vehicle based on improved PSO algorithm," Energy, Elsevier, vol. 271(C).
  • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004826
    DOI: 10.1016/j.energy.2023.127088
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223004826
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.127088?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hao, Ying & Dong, Lei & Liang, Jun & Liao, Xiaozhong & Wang, Lijie & Shi, Lefeng, 2020. "Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid," Renewable Energy, Elsevier, vol. 155(C), pages 1191-1210.
    2. Tao, Ye & Huang, Miaohua & Chen, Yupu & Yang, Lan, 2020. "Orderly charging strategy of battery electric vehicle driven by real-world driving data," Energy, Elsevier, vol. 193(C).
    3. Askarzadeh, Alireza, 2014. "Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran," Energy, Elsevier, vol. 72(C), pages 484-491.
    4. Yi, Tao & Cheng, Xiaobin & Peng, Peng, 2022. "Two-stage optimal allocation of charging stations based on spatiotemporal complementarity and demand response: A framework based on MCS and DBPSO," Energy, Elsevier, vol. 239(PC).
    5. Yin, WanJun & Ming, ZhengFeng & Wen, Tao, 2021. "Scheduling strategy of electric vehicle charging considering different requirements of grid and users," Energy, Elsevier, vol. 232(C).
    6. Hui Hwang Goh & Lian Zong & Dongdong Zhang & Wei Dai & Chee Shen Lim & Tonni Agustiono Kurniawan & Kai Chen Goh, 2022. "Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network," Energies, MDPI, vol. 15(5), pages 1-25, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Fractional-order long-term price guidance mechanism based on bidirectional prediction with attention mechanism for electric vehicle charging," Energy, Elsevier, vol. 293(C).
    2. Zhang, Yagang & Pan, Zhiya & Wang, Hui & Wang, Jingchao & Zhao, Zheng & Wang, Fei, 2023. "Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach," Energy, Elsevier, vol. 283(C).
    3. Meng, Weiqi & Song, Dongran & Huang, Liansheng & Chen, Xiaojiao & Yang, Jian & Dong, Mi & Talaat, M., 2024. "A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response," Energy, Elsevier, vol. 289(C).
    4. Wu, Hongbin & Lan, Xinjie & He, Ye & Wu, Andrew Y. & Ding, Ming, 2025. "Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation," Applied Energy, Elsevier, vol. 378(PA).
    5. Abdelfattah, Wael & Abdelhamid, Ahmed Sayed & Hasanien, Hany M. & Rashad, Basem Abd-Elhamed, 2024. "Smart vehicle-to-grid integration strategy for enhancing distribution system performance and electric vehicle profitability," Energy, Elsevier, vol. 302(C).
    6. Haoyu Pan & Junhui Gong, 2023. "Application of Particle Swarm Optimization (PSO) Algorithm in Determining Thermodynamics of Solid Combustibles," Energies, MDPI, vol. 16(14), pages 1-16, July.
    7. Zhong Guan & Hui Wang & Zhi Li & Xiaohu Luo & Xi Yang & Jugang Fang & Qiang Zhao, 2024. "Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm," Energies, MDPI, vol. 17(7), pages 1-20, April.
    8. Wang, Haifeng & Liao, Yate & Zhang, Jiarui & Cai, Ziwen & Zhao, Yun & Wang, Weijun, 2024. "Optimization of shared energy storage configuration for village-level photovoltaic systems considering vehicle charging management," Energy, Elsevier, vol. 311(C).

    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. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    2. Powell, Siobhan & Vianna Cezar, Gustavo & Apostolaki-Iosifidou, Elpiniki & Rajagopal, Ram, 2022. "Large-scale scenarios of electric vehicle charging with a data-driven model of control," Energy, Elsevier, vol. 248(C).
    3. Zhang, Chengquan & Kitamura, Hiroshi & Goto, Mika, 2024. "Feasibility of vehicle-to-grid (V2G) implementation in Japan: A regional analysis of the electricity supply and demand adjustment market," Energy, Elsevier, vol. 311(C).
    4. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    5. Wang, Yuanyuan & Chi, Yuanying & Xu, Jin-Hua & Yuan, Yongke, 2022. "Consumers’ attitudes and their effects on electric vehicle sales and charging infrastructure construction: An empirical study in China," Energy Policy, Elsevier, vol. 165(C).
    6. Sedghi, Mahdi & Ahmadian, Ali & Aliakbar-Golkar, Masoud, 2016. "Assessment of optimization algorithms capability in distribution network planning: Review, comparison and modification techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 415-434.
    7. Wang, Kaijie & Ma, Yuncong & Wei, Fanrong & Lin, Xiangning & Li, Zhengtian & Dawoud, Samir Mohammed, 2025. "Resilient market bidding strategy for Mobile energy storage system considering transfer uncertainty," Applied Energy, Elsevier, vol. 377(PC).
    8. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    9. Qi Huang & Aihua Jiang & Yu Zeng & Jianan Xu, 2022. "Community Flexible Load Dispatching Model Based on Herd Mentality," Energies, MDPI, vol. 15(13), pages 1-18, June.
    10. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    11. Müller, Mathias & Blume, Yannic & Reinhard, Janis, 2022. "Impact of behind-the-meter optimised bidirectional electric vehicles on the distribution grid load," Energy, Elsevier, vol. 255(C).
    12. Liwen Zhu & Jun He & Lixun He & Wentao Huang & Yanyang Wang & Zong Liu, 2022. "Optimal Operation Strategy of PV-Charging-Hydrogenation Composite Energy Station Considering Demand Response," Energies, MDPI, vol. 15(16), pages 1-23, August.
    13. António Gomes Martins & Luís Pires Neves & José Luís Sousa, 2023. "Electricity Demand Side Management," Energies, MDPI, vol. 16(16), pages 1-3, August.
    14. Qingbo Tan & Zhuning Wang & Wei Fan & Xudong Li & Xiangguang Li & Fanqi Li & Zihao Zhao, 2022. "Development Path and Model Design of a New Energy Vehicle in China," Energies, MDPI, vol. 16(1), pages 1-15, December.
    15. Wang, Guotao & Liao, Qi & Wang, Chang & Liang, Yongtu & Zhang, Haoran, 2022. "Multiperiod optimal planning of biofuel refueling stations: A bi-level game-theoretic approach," Renewable Energy, Elsevier, vol. 200(C), pages 1152-1165.
    16. Tonni Agustiono Kurniawan & Mohd Hafiz Dzarfan Othman & Xue Liang & Muhammad Ayub & Hui Hwang Goh & Tutuk Djoko Kusworo & Ayesha Mohyuddin & Kit Wayne Chew, 2022. "Microbial Fuel Cells (MFC): A Potential Game-Changer in Renewable Energy Development," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    17. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
    18. Alireza Pourdaryaei & Mohammad Mohammadi & Mazaher Karimi & Hazlie Mokhlis & Hazlee A. Illias & Seyed Hamidreza Aghay Kaboli & Shameem Ahmad, 2021. "Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market," Energies, MDPI, vol. 14(19), pages 1-28, September.
    19. Li, Xinyu & Cao, Yue & Yan, Fei & Li, Yuzhe & Zhao, Wanlin & Wang, Yue, 2022. "Towards user-friendly energy supplement service considering battery degradation cost," Energy, Elsevier, vol. 249(C).
    20. Robby Dwianto Widyantara & Muhammad Adnan Naufal & Poetro Lebdo Sambegoro & Ignatius Pulung Nurprasetio & Farid Triawan & Djati Wibowo Djamari & Asep Bayu Dani Nandiyanto & Bentang Arief Budiman & Muh, 2021. "Low-Cost Air-Cooling System Optimization on Battery Pack of Electric Vehicle," Energies, MDPI, vol. 14(23), pages 1-14, November.

    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:eee:energy:v:271:y:2023:i:c:s0360544223004826. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.