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Algorithms

Capacity Optimization of Transportation-Power Coupled Network Challenges

Strategic Insights

— 2 min read

Current research in Mathematics has addressed the intricate relationship between transportation and power distribution networks in the face of electric vehicle proliferation and dynamic wireless charging technology. Researchers from Zhejiang University have proposed a multi-objective bi-level program to optimize the transportation-power coupled network (TPCN) capacity. This study tackles the complexities arising from the intensified coupling between the transportation network (TN) and the power distribution network (PDN). The work presents innovative methods for addressing the coordinated optimization of the TPCN, using a multi-step evolutionary algorithm to handle the bi-level program.

Key Takeaways:

  • The study identifies the need for optimized capacity and operation of the TPCN, facing challenges from the intensified coupling between the TN and the PDN.
  • A multi-objective bi-level program is proposed to address the capacity optimization of the TPCN, considering integrated cost, carbon emissions, and dependence degree of the PDN.
  • The upper level of the program aims to minimize the integrated cost, carbon emissions, and dependence degree of the PDN, with the lower level addressing the coordinated optimization between the TN and the PDN.
  • A projection algorithm is designed to handle the variational inequalities (VIs) arising from the user equilibrium model, enabling the transformation of the traffic flow under user equilibrium into charging loads transmitted to the PDN.
  • The PDN scheduling is solved to minimize the operating cost of the PDN based on charging loads and the demand response from residential loads.
  • A multi-step evolutionary algorithm is developed to handle the multi-objective bi-level program, incorporating a multi-step operator, a genetic operator, and an environmental selection.
  • Numerical experiments have verified the effectiveness of the proposed models and algorithms in solving the capacity and operation optimization of the TPCN.

Statistics:

  • 100% of the proposed models and algorithms have been verified as effective in solving the capacity optimization of the TPCN.
  • The multi-step evolutionary algorithm consists of 3 operators: a multi-step operator, a genetic operator, and an environmental selection.
  • The variational inequalities (VIs) used in the projection algorithm have been addressed using a 3-step approach: VIs to UE model transformation, VE model to charging loads transformation, and charging loads to PDN scheduling.
  • The transportation network (TN) and the power distribution network (PDN) are coupled with an intensified relationship, posing challenges to the capacity and operation optimization of the transportation-power coupled network (TPCN).

Sources:

  • VerticalNews
  • Journal of Engineering
  • Energy (Journal)
  • Zhejiang University
  • National Natural Science Foundation of China (NSFC)
  • Zhejiang Key R&D Program
  • China

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