IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v213y2020ics0360544220319897.html
   My bibliography  Save this article

A coordinated charging scheduling method for electric vehicles considering different charging demands

Author

Listed:
  • Zhou, Kaile
  • Cheng, Lexin
  • Wen, Lulu
  • Lu, Xinhui
  • Ding, Tao

Abstract

The uncoordinated charging of large amounts of electric vehicles (EVs) can lead to a substantial surge of peak loads, which will further influence the operation of power system. Therefore, this study proposed a coordinated charging scheduling method for EVs in microgrid to shift load demand from peak period to valley period. In the proposed method, the charging mode of EVs was selected based on a charging urgency indicator, which can reflect different charging demand. Then, a coordinated charging scheduling optimization model was established to minimize the overall peak-valley load difference. Various constraints were considered for slow-charging EVs, fast-charging EVs, and microgrid operation. Furthermore, Monte Carlo Simulation (MCS) was used to simulate the randomness of EVs. The results have shed light on both the charging modes selection for EV owners and peak shaving and valley filling for microgrid operation. As a result, this model can support more friendly power supply-demand interaction to accommodate the increasing penetration of EVs and the rapid development of flexible microgrid.

Suggested Citation

  • Zhou, Kaile & Cheng, Lexin & Wen, Lulu & Lu, Xinhui & Ding, Tao, 2020. "A coordinated charging scheduling method for electric vehicles considering different charging demands," Energy, Elsevier, vol. 213(C).
  • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220319897
    DOI: 10.1016/j.energy.2020.118882
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220319897
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.118882?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. Di Giorgio, Alessandro & Liberati, Francesco, 2014. "Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models," Applied Energy, Elsevier, vol. 128(C), pages 119-132.
    2. Schuller, Alexander & Flath, Christoph M. & Gottwalt, Sebastian, 2015. "Quantifying load flexibility of electric vehicles for renewable energy integration," Applied Energy, Elsevier, vol. 151(C), pages 335-344.
    3. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    4. Sousa, Tiago & Morais, Hugo & Soares, João & Vale, Zita, 2012. "Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints," Applied Energy, Elsevier, vol. 96(C), pages 183-193.
    5. Xu, Min & Meng, Qiang & Liu, Kai & Yamamoto, Toshiyuki, 2017. "Joint charging mode and location choice model for battery electric vehicle users," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 68-86.
    6. Yang, Jun & He, Lifu & Fu, Siyao, 2014. "An improved PSO-based charging strategy of electric vehicles in electrical distribution grid," Applied Energy, Elsevier, vol. 128(C), pages 82-92.
    7. Lefeng, Shi & Qian, Zhang & Yongjian, Pu, 2013. "The reserve trading model considering V2G Reverse," Energy, Elsevier, vol. 59(C), pages 50-55.
    8. Luo, Yugong & Zhu, Tao & Wan, Shuang & Zhang, Shuwei & Li, Keqiang, 2016. "Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems," Energy, Elsevier, vol. 97(C), pages 359-368.
    9. Ons Sassi & Ammar Oulamara, 2017. "Electric vehicle scheduling and optimal charging problem: complexity, exact and heuristic approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 519-535, January.
    10. Rui Miao & Wenjie Huang & Donghao Pei & Xiyao Gu & Zefeng Li & Jie Zhang & Zhibin Jiang, 2016. "Research on lease and sale of electric vehicles based on value engineering," International Journal of Production Research, Taylor & Francis Journals, vol. 54(18), pages 5361-5380, September.
    11. Han, Sekyung & Han, Soohee & Aki, Hirohisa, 2014. "A practical battery wear model for electric vehicle charging applications," Applied Energy, Elsevier, vol. 113(C), pages 1100-1108.
    12. Song-Man Wu & Hu-Chen Liu & Li-En Wang, 2017. "Hesitant fuzzy integrated MCDM approach for quality function deployment: a case study in electric vehicle," International Journal of Production Research, Taylor & Francis Journals, vol. 55(15), pages 4436-4449, August.
    13. Zhang, Tianyang & Pota, Himanshu & Chu, Chi-Cheng & Gadh, Rajit, 2018. "Real-time renewable energy incentive system for electric vehicles using prioritization and cryptocurrency," Applied Energy, Elsevier, vol. 226(C), pages 582-594.
    14. González, L.G. & Siavichay, E. & Espinoza, J.L., 2019. "Impact of EV fast charging stations on the power distribution network of a Latin American intermediate city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 309-318.
    15. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
    16. Liu, Dunnan & Xiao, Bowen, 2018. "Exploring the development of electric vehicles under policy incentives: A scenario-based system dynamics model," Energy Policy, Elsevier, vol. 120(C), pages 8-23.
    17. Zheng, Yanchong & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2018. "A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid," Applied Energy, Elsevier, vol. 217(C), pages 1-13.
    18. Jian, Linni & Zheng, Yanchong & Shao, Ziyun, 2017. "High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles," Applied Energy, Elsevier, vol. 186(P1), pages 46-55.
    19. Su, Wencong & Chow, Mo-Yuen, 2012. "Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck," Applied Energy, Elsevier, vol. 96(C), pages 171-182.
    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. Muhammad Usman & Wajahat Ullah Khan Tareen & Adil Amin & Haider Ali & Inam Bari & Muhammad Sajid & Mehdi Seyedmahmoudian & Alex Stojcevski & Anzar Mahmood & Saad Mekhilef, 2021. "A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization," Energies, MDPI, vol. 14(17), pages 1-16, August.
    2. Norouzi, Mohammadali & Aghaei, Jamshid & Pirouzi, Sasan & Niknam, Taher & Fotuhi-Firuzabad, Mahmud, 2022. "Flexibility pricing of integrated unit of electric spring and EVs parking in microgrids," Energy, Elsevier, vol. 239(PB).
    3. Himadry Shekhar Das & Md Nurunnabi & Mohamed Salem & Shuhui Li & Mohammad Mominur Rahman, 2022. "Utilization of Electric Vehicle Grid Integration System for Power Grid Ancillary Services," Energies, MDPI, vol. 15(22), pages 1-15, November.
    4. Zhang, Gang & Wen, Jiaxing & Xie, Tuo & Zhang, Kaoshe & Jia, Rong, 2023. "Bi-layer economic scheduling for integrated energy system based on source-load coordinated carbon reduction," Energy, Elsevier, vol. 280(C).
    5. Hemmatpour, Mohammad Hasan & Rezaeian Koochi, Mohammad Hossein & Dehghanian, Pooria & Dehghanian, Payman, 2022. "Voltage and energy control in distribution systems in the presence of flexible loads considering coordinated charging of electric vehicles," Energy, Elsevier, vol. 239(PA).
    6. Liu, Lu & Zhou, Kaile, 2022. "Electric vehicle charging scheduling considering urgent demand under different charging modes," Energy, Elsevier, vol. 249(C).
    7. 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).
    8. Vulfovich, A. & Kolesnik, S. & Baimel, D. & Gutman, M. & Geftler, A. & Kuperman, A., 2022. "Output characteristics modeling and experimental verification of secondary-uncompensated inductive power delivery link operating without feedback," Energy, Elsevier, vol. 252(C).
    9. Liao, Wei & Xiao, Fu & Li, Yanxue & Zhang, Hanbei & Peng, Jinqing, 2024. "A comparative study of demand-side energy management strategies for building integrated photovoltaics-battery and electric vehicles (EVs) in diversified building communities," Applied Energy, Elsevier, vol. 361(C).
    10. Muhammad Ahsan Khan & Akhtar Hussain & Woon-Gyu Lee & Hak-Man Kim, 2023. "An Incentive-Based Mechanism to Enhance Energy Trading among Microgrids, EVs, and Grid," Energies, MDPI, vol. 16(17), pages 1-23, September.
    11. Song, Yanqiu & Shangguan, Lingzhi & Li, Guijun, 2021. "Simulation analysis of flexible concession period contracts in electric vehicle charging infrastructure public-private-partnership (EVCI-PPP) projects based on time-of-use (TOU) charging price strateg," Energy, Elsevier, vol. 228(C).
    12. Ma, Shao-Chao & Yi, Bo-Wen & Fan, Ying, 2022. "Research on the valley-filling pricing for EV charging considering renewable power generation," Energy Economics, Elsevier, vol. 106(C).
    13. Darhovsky, Yegal & Mellincovsky, Martin & Baimel, Dmitry & Kuperman, Alon, 2021. "A novel contactless, feedbackless and sensorless power delivery link to electromagnetic levitation melting system residing in sealed compartment," Energy, Elsevier, vol. 231(C).
    14. Matteo Ravasio & Gian Paolo Incremona & Patrizio Colaneri & Andrea Dolcini & Piero Moia, 2021. "Distributed Nonlinear AIMD Algorithms for Electric Bus Charging Plants," Energies, MDPI, vol. 14(15), pages 1-17, July.
    15. Zhao, Zhonghao & Lee, Carman K.M. & Ren, Jingzheng, 2024. "A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile," Applied Energy, Elsevier, vol. 355(C).
    16. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    17. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
    18. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).
    19. Wu, Ji & Su, Hao & Meng, Jinhao & Lin, Mingqiang, 2023. "Electric vehicle charging scheduling considering infrastructure constraints," Energy, Elsevier, vol. 278(PA).
    20. Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
    21. Zhou, Yuekuan, 2022. "Transition towards carbon-neutral districts based on storage techniques and spatiotemporal energy sharing with electrification and hydrogenation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    22. 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).

    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. Zhou, Kaile & Cheng, Lexin & Lu, Xinhui & Wen, Lulu, 2020. "Scheduling model of electric vehicles charging considering inconvenience and dynamic electricity prices," Applied Energy, Elsevier, vol. 276(C).
    2. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2016. "Low carbon technologies as providers of operational flexibility in future power systems," Applied Energy, Elsevier, vol. 168(C), pages 724-738.
    3. Jian, Linni & Zheng, Yanchong & Xiao, Xinping & Chan, C.C., 2015. "Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid," Applied Energy, Elsevier, vol. 146(C), pages 150-161.
    4. Jian, Linni & Zheng, Yanchong & Shao, Ziyun, 2017. "High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles," Applied Energy, Elsevier, vol. 186(P1), pages 46-55.
    5. Wei Li & Jiekai Shi & Hanyun Zhou, 2024. "Coordinated Charging Scheduling Approach for Plug-In Hybrid Electric Vehicles Considering Multi-Objective Weighting Control in a Large-Scale Future Smart Grid," Energies, MDPI, vol. 17(13), pages 1-17, June.
    6. Liu, Lu & Zhou, Kaile, 2022. "Electric vehicle charging scheduling considering urgent demand under different charging modes," Energy, Elsevier, vol. 249(C).
    7. Zheng, Yanchong & Niu, Songyan & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2019. "Integrating plug-in electric vehicles into power grids: A comprehensive review on power interaction mode, scheduling methodology and mathematical foundation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 424-439.
    8. Dingyi Lu & Yunqian Lu & Kexin Zhang & Chuyuan Zhang & Shao-Chao Ma, 2023. "An Application Designed for Guiding the Coordinated Charging of Electric Vehicles," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    9. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    10. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
    11. Kacperski, Celina & Ulloa, Roberto & Klingert, Sonja & Kirpes, Benedikt & Kutzner, Florian, 2022. "Impact of incentives for greener battery electric vehicle charging – A field experiment," Energy Policy, Elsevier, vol. 161(C).
    12. Paterakis, Nikolaos G. & Gibescu, Madeleine, 2016. "A methodology to generate power profiles of electric vehicle parking lots under different operational strategies," Applied Energy, Elsevier, vol. 173(C), pages 111-123.
    13. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    14. He, Lifu & Yang, Jun & Yan, Jun & Tang, Yufei & He, Haibo, 2016. "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, Elsevier, vol. 168(C), pages 179-192.
    15. Wang, Yubo & Shi, Wenbo & Wang, Bin & Chu, Chi-Cheng & Gadh, Rajit, 2017. "Optimal operation of stationary and mobile batteries in distribution grids," Applied Energy, Elsevier, vol. 190(C), pages 1289-1301.
    16. Shang, Yitong & Liu, Man & Shao, Ziyun & Jian, Linni, 2020. "Internet of smart charging points with photovoltaic Integration: A high-efficiency scheme enabling optimal dispatching between electric vehicles and power grids," Applied Energy, Elsevier, vol. 278(C).
    17. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2016. "Power demand control scenarios for smart grid applications with finite number of appliances," Applied Energy, Elsevier, vol. 162(C), pages 83-98.
    18. Yang, Jun & He, Lifu & Fu, Siyao, 2014. "An improved PSO-based charging strategy of electric vehicles in electrical distribution grid," Applied Energy, Elsevier, vol. 128(C), pages 82-92.
    19. Li, Bin & Dong, Xujun & Wen, Jianghui, 2022. "Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour," Energy, Elsevier, vol. 243(C).
    20. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).

    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:213:y:2020:i:c:s0360544220319897. 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.