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Co-optimized operation of electrified transportation systems considering mixed human and automated traffic flows

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  • Li, Jia
  • Li, Bin
  • Liu, Zhitao
  • Su, Hongye

Abstract

With the rapid advancement of autonomous driving technology, autonomous vehicles (AVs) are increasingly integrated into everyday transportation. However, the centralized control characteristic of AVs adds complexity to the interaction between the transportation network (TN) and the power distribution network (PDN) in electrified transportation systems (ETSs), which, in turn, affects the operational costs of ETSs. While substantial progress has been made in the co-optimization of TN and PDN, further research is necessary to develop optimization models that consider the characteristics of AVs and human-driven vehicles (HVs). This study presents a multi-period co-optimization model to optimize ETSs that incorporate AVs. In this model, AVs operate according to the system optimal (SO) principle, while HVs follow the user equilibrium (UE) principle. By transforming the problem into an equivalent optimization model and using a piecewise linear approximation method, the original nonlinear complementarity problem is reformulated as a mixed-integer linear programming problem, thereby improving its tractability. To enhance social welfare and improve the stability of the PDN, a bidirectional differential charging price (BDCP) scheme is introduced. This scheme independently manages charging prices for bi-directional traffic flows on the same road segment. Additionally, the PDN, which incorporates renewable energy sources, is modeled as an alternating current optimal power flow problem with second-order cone programming relaxation. An iterative method is developed to jointly optimize traffic distribution and optimal power flow. Simulation results on two test systems validate the proposed model’s effectiveness and advantages.

Suggested Citation

  • Li, Jia & Li, Bin & Liu, Zhitao & Su, Hongye, 2025. "Co-optimized operation of electrified transportation systems considering mixed human and automated traffic flows," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225014781
    DOI: 10.1016/j.energy.2025.135836
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    References listed on IDEAS

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