IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v247y2022ics0360544222003784.html
   My bibliography  Save this article

Charging demand forecasting of electric vehicles considering uncertainties in a microgrid

Author

Listed:
  • Wu, Chuanshen
  • Jiang, Sufan
  • Gao, Shan
  • Liu, Yu
  • Han, Haiteng

Abstract

The currently increasing integration of electric vehicles (EVs) in microgrids (MGs) has gained significant attention. However, affected by the high uncertainties of weather, traffic, and driver behavior, the charging demand of EVs is difficult to forecast accurately. In this study, an optimal parameter forecasting method is presented to improve the forecasting accuracy of charging demand of EVs in an MG. For the methods of forecasting of EV status by sampling from probability distributions, this study modifies the optimal parameter values of probability distributions within fuzzy sets based on the feedback of EVs that have arrived in an MG. Fuzzy sets are utilized to limit the modification ranges of parameter values for the consideration of robustness. Moreover, the average values of multiple sampling results are calculated to improve the stability of forecasting results. Combined with the forecasted results, this study is executed over a rolling time horizon for energy management of EVs, ensuring that acceptable charge levels are reached at the disconnection times. Simulation results show that, compared with other state-of-the-art forecasting methods, the proposed forecasting method is highly effective in reducing forecasting errors of EVs and, hence, has better performance in regulating the charging of EVs in an MG.

Suggested Citation

  • Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003784
    DOI: 10.1016/j.energy.2022.123475
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222003784
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123475?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. Tan, Bifei & Chen, Haoyong, 2020. "Multi-objective energy management of multiple microgrids under random electric vehicle charging," Energy, Elsevier, vol. 208(C).
    2. Shojaabadi, Saeed & Abapour, Saeed & Abapour, Mehdi & Nahavandi, Ali, 2016. "Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties," Renewable Energy, Elsevier, vol. 99(C), pages 237-252.
    3. Heydarian-Forushani, E. & Golshan, M.E.H. & Shafie-khah, M., 2016. "Flexible interaction of plug-in electric vehicle parking lots for efficient wind integration," Applied Energy, Elsevier, vol. 179(C), pages 338-349.
    4. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    5. Colmenar-Santos, Antonio & Muñoz-Gómez, Antonio-Miguel & Rosales-Asensio, Enrique & López-Rey, África, 2019. "Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario," Energy, Elsevier, vol. 183(C), pages 61-74.
    6. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Tang, Xiaolin & Lang, Kun & Xin, Zongke & Brighton, James, 2019. "Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge," Energy, Elsevier, vol. 173(C), pages 667-678.
    7. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2019. "Two-stage stochastic optimization for cost-minimal charging of electric vehicles at public charging stations with photovoltaics," Applied Energy, Elsevier, vol. 242(C), pages 769-781.
    8. Rao, Yingqing & Yang, Jun & Xiao, Jinxing & Xu, Bingyan & Liu, Wenjing & Li, Yonghui, 2021. "A frequency control strategy for multimicrogrids with V2G based on the improved robust model predictive control," Energy, Elsevier, vol. 222(C).
    9. Jinquan, Guo & Hongwen, He & Jiankun, Peng & Nana, Zhou, 2019. "A novel MPC-based adaptive energy management strategy in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 175(C), pages 378-392.
    10. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2019. "Multiobjective robust fuzzy stochastic approach for sustainable smart grid design," Energy, Elsevier, vol. 176(C), pages 929-939.
    11. Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.
    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. Dan Zhou & Zhonghao Guo & Yuzhe Xie & Yuheng Hu & Da Jiang & Yibin Feng & Dong Liu, 2022. "Using Bayesian Deep Learning for Electric Vehicle Charging Station Load Forecasting," Energies, MDPI, vol. 15(17), pages 1-15, August.
    2. Zhang, Lei & Huang, Zhijia & Wang, Zhenpo & Li, Xiaohui & Sun, Fengchun, 2024. "An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing," Energy, Elsevier, vol. 299(C).
    3. Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
    4. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    5. Zhang, Fan & Lv, Huitao & Xing, Qiang & Ji, Yanjie, 2024. "Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles," Energy, Elsevier, vol. 290(C).
    6. Tostado-Véliz, Marcos & Jordehi, Ahmad Rezaee & Mansouri, Seyed Amir & Jurado, Francisco, 2023. "A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots," Energy, Elsevier, vol. 263(PD).

    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. Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.
    2. Yin, WanJun & Wen, Tao & Zhang, Chao, 2023. "Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism," Energy, Elsevier, vol. 263(PA).
    3. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    4. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    6. Tan, Kang Miao & Padmanaban, Sanjeevikumar & Yong, Jia Ying & Ramachandaramurthy, Vigna K., 2019. "A multi-control vehicle-to-grid charger with bi-directional active and reactive power capabilities for power grid support," Energy, Elsevier, vol. 171(C), pages 1150-1163.
    7. Xie, Shaobo & Qi, Shanwei & Lang, Kun & Tang, Xiaolin & Lin, Xianke, 2020. "Coordinated management of connected plug-in hybrid electric buses for energy saving, inter-vehicle safety, and battery health," Applied Energy, Elsevier, vol. 268(C).
    8. Duggal, Angel Swastik & Singh, Rajesh & Gehlot, Anita & Gupta, Lovi Raj & Akram, Sheik Vaseem & Prakash, Chander & Singh, Sunpreet & Kumar, Raman, 2021. "Infrastructure, mobility and safety 4.0: Modernization in road transportation," Technology in Society, Elsevier, vol. 67(C).
    9. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    10. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    11. Yang, Jie & Yu, Fan & Ma, Kai & Yang, Bo & Yue, Zhiyuan, 2024. "Optimal scheduling of electric-hydrogen integrated charging station for new energy vehicles," Renewable Energy, Elsevier, vol. 224(C).
    12. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    13. Motoaki, Yutaka & Yi, Wenqi & Salisbury, Shawn, 2018. "Empirical analysis of electric vehicle fast charging under cold temperatures," Energy Policy, Elsevier, vol. 122(C), pages 162-168.
    14. Peixiao Fan & Jia Hu & Song Ke & Yuxin Wen & Shaobo Yang & Jun Yang, 2022. "A Frequency–Pressure Cooperative Control Strategy of Multi-Microgrid with an Electric–Gas System Based on MADDPG," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    15. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Zhang, Jiye & Song, Pengyun, 2019. "Modeling and optimal energy management strategy for a catenary-battery-ultracapacitor based hybrid tramway," Energy, Elsevier, vol. 183(C), pages 1123-1135.
    16. Tan, Bifei & Chen, Simin & Liang, Zipeng & Zheng, Xiaodong & Zhu, Yanjin & Chen, Haoyong, 2024. "An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles," Applied Energy, Elsevier, vol. 356(C).
    17. Luo, Lizi & He, Pinquan & Gu, Wei & Sheng, Wanxing & Liu, Keyan & Bai, Muke, 2022. "Temporal-spatial scheduling of electric vehicles in AC/DC distribution networks," Energy, Elsevier, vol. 255(C).
    18. Yang, YeHa & Yang, SoYoung & Moon, HyungBin & Woo, JongRoul, 2024. "Analyzing heterogeneous electric vehicle charging preferences for strategic time-of-use tariff design and infrastructure development: A latent class approach," Applied Energy, Elsevier, vol. 374(C).
    19. Taghavifar, Hadi, 2021. "Fuel cell hybrid range-extender vehicle sizing: Parametric power optimization," Energy, Elsevier, vol. 229(C).
    20. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(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:247:y:2022:i:c:s0360544222003784. 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.