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Coordinated pricing of coupled urban Power-Traffic Networks: The value of information sharing

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  • Sheng, Yujie
  • Guo, Qinglai
  • Chen, Feng
  • Xu, Luo
  • Zhang, Yang

Abstract

The growing demand for the fast charging of electric vehicles has created stronger interdependency between the operation of urban traffic networks and power distribution networks. This paper investigates the necessity and feasible mechanism of coordinated pricing in coupled urban power-traffic networks. The retail charging pricing of the power distribution network operator and congestion pricing of the urban traffic network operator will simultaneously affect the routing and charging decision choices for electric vehicles. Then, the response of multiclass vehicles—i.e., the aggregated traffic flow and charging load—will in return affect the operation performance—i.e., power loss and traffic congestion—of the coupled networks. However, due to operators’ differing pricing goals and limited access to user information, the actual response for vehicles might deviate from the respective expectation of the two operators under isolated pricing. With the growing penetration of electric vehicles, such deviation might become more evident, calling for a focus on pricing coordination. To investigate the necessity of coordinated pricing, the Stackelberg game framework is employed to model the pricing interactions between operators (leaders) and vehicles (followers). Accounting for privacy issues, a decentralized coordination framework of the two networks is proposed for information sharing. The numerical test results confirm the negative impacts of isolated pricing and demonstrate the effectiveness of coordinated pricing. Sensitivity analysis is conducted to examine the influence of critical parameters: e.g., charging electric vehicle penetration and total travel demand.

Suggested Citation

  • Sheng, Yujie & Guo, Qinglai & Chen, Feng & Xu, Luo & Zhang, Yang, 2021. "Coordinated pricing of coupled urban Power-Traffic Networks: The value of information sharing," Applied Energy, Elsevier, vol. 301(C).
  • Handle: RePEc:eee:appene:v:301:y:2021:i:c:s0306261921008217
    DOI: 10.1016/j.apenergy.2021.117428
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    References listed on IDEAS

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    1. Zhou, Zhe & Zhang, Xuan & Guo, Qinglai & Sun, Hongbin, 2021. "Analyzing power and dynamic traffic flows in coupled power and transportation networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
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    Cited by:

    1. Zhou, Guanyu & Dong, Qianyu & Zhao, Yuming & Wang, Han & Jian, Linni & Jia, Youwei, 2023. "Bilevel optimization approach to fast charging station planning in electrified transportation networks," Applied Energy, Elsevier, vol. 350(C).
    2. Li, Zepeng & Wu, Qiuwei & Li, Hui & Nie, Chengkai & Tan, Jin, 2024. "Distributed low-carbon economic dispatch of integrated power and transportation system," Applied Energy, Elsevier, vol. 353(PA).
    3. Winschermann, Leoni & Bañol Arias, Nataly & Hoogsteen, Gerwin & Hurink, Johann, 2023. "Assessing the value of information for electric vehicle charging strategies at office buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    4. Sheng, Yujie & Zeng, Hongtai & Guo, Qinglai & Yu, Yang & Li, Qiang, 2023. "Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations," Applied Energy, Elsevier, vol. 348(C).
    5. Chen, Yuanyi & Hu, Simon & Zheng, Yanchong & Xie, Shiwei & Hu, Qinru & Yang, Qiang, 2024. "Coordinated expansion planning of coupled power and transportation networks considering dynamic network equilibrium," Applied Energy, Elsevier, vol. 360(C).
    6. Zhou, Ze & Liu, Zhitao & Su, Hongye & Zhang, Liyan, 2023. "Planning of static and dynamic charging facilities for electric vehicles in electrified transportation networks," Energy, Elsevier, vol. 263(PE).

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