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Power-traffic coordinated operation for bi-peak shaving and bi-ramp smoothing – A hierarchical data-driven approach

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  • Jiang, Huaiguang
  • Zhang, Yingchen
  • Chen, Yuche
  • Zhao, Changhong
  • Tan, Jin

Abstract

The power distribution system and urban transportation system are two networked system bare their own operation constraints, such peak load in power systems and traffic congestion in transportation system. With the increasing number of electrical vehicles and charging/discharging stations, two systems are become tightly coupled. However, to optimize the two systems target using electrical vehicles as decision control variables cannot be easily solved using a uniformed optimization frame work. Thus we propose a hierarchical optimization approach to address this problem, which consists of a higher and a lower level. In the higher level, the power distribution system and urban transportation system are treated together to minimize the social cost. Meanwhile, the electrical vehicles and the charging/discharging stations are treated as customers to minimize their own expenditures. Then, an equilibrium is designed to determine the optimal charging/discharging price. In the lower level, the models of power distribution system and urban transportation system are developed to provide a detailed analysis. Specifically, in power distribution system, the three-phase unbalanced optimal power flow problem is relaxed with the semidefinite relaxation programming, and solved with alternating direction method of multiplier. A dynamic user equilibrium problem is formulated for the urban transportation system. For electrical vehicles, the state of charge is considered to optimize the charging/discharging schedule and reduce the impacts of power distribution systems. We conducted the simulation and numerical analysis using the IEEE 8500-bus distribution system and the Sioux Falls system with about 10,000 cars. The results demonstrate the feasibility and effectiveness of the proposed approach.

Suggested Citation

  • Jiang, Huaiguang & Zhang, Yingchen & Chen, Yuche & Zhao, Changhong & Tan, Jin, 2018. "Power-traffic coordinated operation for bi-peak shaving and bi-ramp smoothing – A hierarchical data-driven approach," Applied Energy, Elsevier, vol. 229(C), pages 756-766.
    • Handle: RePEc:eee:appene:v:229:y:2018:i:c:p:756-766
    DOI: 10.1016/j.apenergy.2018.06.021
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    3. Wei Dai & Zhihong Zeng & Cheng Wang & Zhijie Zhang & Yang Gao & Jun Xu, 2024. "Non-Iterative Coordinated Optimisation of Power–Traffic Networks Based on Equivalent Projection," Energies, MDPI, vol. 17(8), pages 1-21, April.
    4. 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).
    5. Geng, Lijun & Lu, Zhigang & He, Liangce & Zhang, Jiangfeng & Li, Xueping & Guo, Xiaoqiang, 2019. "Smart charging management system for electric vehicles in coupled transportation and power distribution systems," Energy, Elsevier, vol. 189(C).
    6. Lv, Si & Wei, Zhinong & Chen, Sheng & Sun, Guoqiang & Wang, Dan, 2021. "Integrated demand response for congestion alleviation in coupled power and transportation networks," Applied Energy, Elsevier, vol. 283(C).
    7. Kou, Yu & Bie, Zhaohong & Li, Gengfeng & Liu, Fan & Jiang, Jiangfeng, 2021. "Reliability evaluation of multi-agent integrated energy systems with fully distributed communication," Energy, Elsevier, vol. 224(C).
    8. Zhou, Ze & Liu, Zhitao & Su, Hongye & Zhang, Liyan, 2022. "Integrated pricing strategy for coordinating load levels in coupled power and transportation networks," Applied Energy, Elsevier, vol. 307(C).
    9. Fang, Xin & Hodge, Bri-Mathias & Jiang, Huaiguang & Zhang, Yingchen, 2019. "Decentralized wind uncertainty management: Alternating direction method of multipliers based distributionally-robust chance constrained optimal power flow," Applied Energy, Elsevier, vol. 239(C), pages 938-947.

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