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A data-driven approach to urban charging facility expansion based on bi-level optimization: A case study in a Chinese city

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  • Cao, Jianing
  • Han, Yuhang
  • Pan, Nan
  • Zhang, Jingcheng
  • Yang, Junwei

Abstract

This paper addresses the optimization of urban charging facilities expansion in response to the growing charging demand and proposes a data-driven bi-level optimization framework. The proposed framework combines a mixed-integer programming model for siting and capacity planning, an improved sparrow search algorithm (ISSA) for hyperparameter optimization of the CNN-BiLSTM model, and the application of Benders decomposition with Lagrangian relaxation using Gurobi for solving the model. The proposed approach is empirically evaluated through a case study conducted in an administrative area of a Chinese city, using comprehensive charging records and other relevant information. The results demonstrate that the ISSA consistently outperforms other algorithms, showing an average improvement of 23.97 % in root mean square error, 19.25 % in average absolute percentage error, and 1.50 % in the determination coefficient. The modified Diebold-Mariano test confirms the significant advantage of ISSA. Based on the model analysis, the study recommends constructing 20 new charging stations in the case city, with more than half equipped with fast chargers. This strategy ensures that 95 % of the area can find a charging station within 5 min while also ensuring the operator's profitability. The research findings provide effective support for investment decisions related to charging facilities.

Suggested Citation

  • Cao, Jianing & Han, Yuhang & Pan, Nan & Zhang, Jingcheng & Yang, Junwei, 2024. "A data-driven approach to urban charging facility expansion based on bi-level optimization: A case study in a Chinese city," Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224013021
    DOI: 10.1016/j.energy.2024.131529
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    1. Dimitris Bertsimas & Shimrit Shtern & Bradley Sturt, 2023. "A Data-Driven Approach to Multistage Stochastic Linear Optimization," Management Science, INFORMS, vol. 69(1), pages 51-74, January.
    2. Zhang, Junxia & Li, Xingmei & Jia, Dongqing & Zhou, Yuexin, 2023. "A Bi-level programming for union battery swapping stations location-routing problem under joint distribution and cost allocation," Energy, Elsevier, vol. 272(C).
    3. Park, Junseok & Moon, Ilkyeong, 2023. "A facility location problem in a mixed duopoly on networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    4. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    5. Dayi Qu & Shaojie Wang & Haomin Liu & Yiming Meng, 2022. "A Car-Following Model Based on Trajectory Data for Connected and Automated Vehicles to Predict Trajectory of Human-Driven Vehicles," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    6. Cordeau, Jean-François & Furini, Fabio & Ljubić, Ivana, 2019. "Benders decomposition for very large scale partial set covering and maximal covering location problems," European Journal of Operational Research, Elsevier, vol. 275(3), pages 882-896.
    7. Prince Aduama & Zhibo Zhang & Ameena S. Al-Sumaiti, 2023. "Multi-Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model," Energies, MDPI, vol. 16(3), pages 1-14, January.
    8. Hengle Qin & Jun Xiao & Dongdong Ge & Linwei Xin & Jianjun Gao & Simai He & Haodong Hu & John Gunnar Carlsson, 2022. "JD.com: Operations Research Algorithms Drive Intelligent Warehouse Robots to Work," Interfaces, INFORMS, vol. 52(1), pages 42-55, January.
    9. Sahar Koohfar & Wubeshet Woldemariam & Amit Kumar, 2023. "Prediction of Electric Vehicles Charging Demand: A Transformer-Based Deep Learning Approach," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    10. Okan Arslan & Oya Ekin Karaşan & Ridha Mahjoub & Hande Yaman, 2019. "A Branch-and-Cut Algorithm for the Alternative Fuel Refueling Station Location Problem with Routing," Transportation Science, INFORMS, vol. 53(4), pages 1107-1125, July.
    11. Wang, Ning & Tian, Hangqi & Wu, Huahua & Liu, Qiaoqian & Luan, Jie & Li, Yuan, 2023. "Cost-oriented optimization of the location and capacity of charging stations for the electric Robotaxi fleet," Energy, Elsevier, vol. 263(PC).
    12. Tao, Yuechuan & Qiu, Jing & Lai, Shuying & Sun, Xianzhuo & Zhao, Junhua & Zhou, Baorong & Cheng, Lanfen, 2022. "Data-driven on-demand energy supplement planning for electric vehicles considering multi-charging/swapping services," Applied Energy, Elsevier, vol. 311(C).
    13. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    14. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    15. Zhang, Jianghua & Long, Daniel Zhuoyu & Li, Yuchen, 2023. "A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    16. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    17. Loni, Abdolah & Asadi, Somayeh, 2023. "Data-driven equitable placement for electric vehicle charging stations: Case study San Francisco," Energy, Elsevier, vol. 282(C).
    18. Liu, Bingchun & Song, Jiangji & Wang, Qingshan & Xu, Yan & Liu, Yifan, 2023. "Charging station forecasting and scenario analysis in China," Transport Policy, Elsevier, vol. 139(C), pages 87-98.
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