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Analyzing power and dynamic traffic flows in coupled power and transportation networks

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  • Zhou, Zhe
  • Zhang, Xuan
  • Guo, Qinglai
  • Sun, Hongbin

Abstract

The increasing penetration of electric vehicles is tightly coupling the operation of power and transportation systems. The representation of the dynamics of network flows in response to real-time information and control strategies is a problem of considerable practical significance. However, most of work considers static network models that are only suitable for long-term planning horizons. This paper seeks to develop an integrated modeling framework for the real-time analysis and operation of the coupled systems. In particular, a dynamic traffic assignment model is exploited to account for the time-varying travel demand and flow dynamics. An accelerated diagonalization algorithm is proposed to compute the traffic flows in an efficient manner. Moreover, we show that the equilibrium flow pattern of the coupled systems can be characterized by a fixed-point problem, thus motivating a decentralized approach to attain the flow solutions. Numerical studies demonstrate both the spatial and temporal interconnections between the power and traffic flows. The comparison of network flow solutions based on the dynamic, semi-dynamic and static transportation network models is presented.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:135:y:2021:i:c:s1364032120303749
    DOI: 10.1016/j.rser.2020.110083
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    7. Clairand, Jean-Michel & González-Rodríguez, Mario & Kumar, Rajesh & Vyas, Shashank & Escrivá-Escrivá, Guillermo, 2022. "Optimal siting and sizing of electric taxi charging stations considering transportation and power system requirements," Energy, Elsevier, vol. 256(C).
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    9. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).

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