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Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale

Author

Listed:
  • Yuqing Yang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China)

  • Weige Zhang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China)

  • Liyong Niu

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China)

  • Jiuchun Jiang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, No. 3 Shang Yuan Cun, Haidian District, Beijing 100044, China)

Abstract

Currently, electric taxis have been deployed in many cities of China. However, the charging unbalance in both temporal and spatial scale has become a rising problem, which leads to low charging efficiency or charging congestion in different stations or time periods. This paper presents a multi-objective coordinated charging strategy for electric taxis in the temporal and spatial scale. That is, the objectives are maximizing the utilization efficiency of charging facilities, minimizing the load unbalance of the regional power system and minimizing the customers’ cost. Besides, the basic configuration of a charging station and operation rules of electric taxis would be the constraints. To tackle this multi-objective optimizing problems, a fuzzy mathematical method has been utilized to transfer the multi-objective optimization to a single optimization issue, and furthermore, the Improved Particle Swarm Optimization (IPSO) Algorithm has been used to solve the optimization problem. Moreover, simulation cases are carried out, Case 1 is the original charging procedure, and Cases 2 and 3 are the temporal and spatial scale optimized separately, followed with Case 4, the combined coordinated charging. The simulation shows the significant improvement in charging facilities efficiency and users’ benefits, as well as the better dispatching of electric taxis’ charging loads.

Suggested Citation

  • Yuqing Yang & Weige Zhang & Liyong Niu & Jiuchun Jiang, 2015. "Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale," Energies, MDPI, vol. 8(2), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:2:p:1256-1272:d:45572
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    Citations

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    Cited by:

    1. Jean-Michel Clairand & Paulo Guerra-Terán & Xavier Serrano-Guerrero & Mario González-Rodríguez & Guillermo Escrivá-Escrivá, 2019. "Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies," Energies, MDPI, vol. 12(16), pages 1-22, August.
    2. Yuqing Yang & Weige Zhang & Jiuchun Jiang & Mei Huang & Liyong Niu, 2015. "Optimal Scheduling of a Battery Energy Storage System with Electric Vehicles’ Auxiliary for a Distribution Network with Renewable Energy Integration," Energies, MDPI, vol. 8(10), pages 1-18, September.
    3. Lixing Chen & Zhong Chen & Xueliang Huang & Long Jin, 2016. "A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways," Energies, MDPI, vol. 9(5), pages 1-18, May.
    4. Shyang-Chyuan Fang & Bwo-Ren Ke & Chen-Yuan Chung, 2017. "Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System," Energies, MDPI, vol. 10(7), pages 1-20, June.
    5. Lixing Chen & Xueliang Huang & Zhong Chen & Long Jin, 2016. "Study of a New Quick-Charging Strategy for Electric Vehicles in Highway Charging Stations," Energies, MDPI, vol. 9(9), pages 1-20, September.
    6. Zhan, Xingbin & Szeto, W.Y. & (Michael) Chen, Xiqun, 2022. "A simulation–optimization framework for a dynamic electric ride-hailing sharing problem with a novel charging strategy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    7. Yian Yan & Huang Wang & Jiuchun Jiang & Weige Zhang & Yan Bao & Mei Huang, 2019. "Research on Configuration Methods of Battery Energy Storage System for Pure Electric Bus Fast Charging Station," Energies, MDPI, vol. 12(3), pages 1-17, February.
    8. Jiang, C.X. & Jing, Z.X. & Cui, X.R. & Ji, T.Y. & Wu, Q.H., 2018. "Multiple agents and reinforcement learning for modelling charging loads of electric taxis," Applied Energy, Elsevier, vol. 222(C), pages 158-168.
    9. Paul Stewart & Chris Bingham, 2016. "Electrical Power and Energy Systems for Transportation Applications," Energies, MDPI, vol. 9(7), pages 1-3, July.

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