4.8 Article

An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer

Journal

APPLIED ENERGY
Volume 264, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2020.114770

Keywords

Metro energy conservation; Regenerative braking; Transfer waiting time

Funding

  1. National Natural Science Foundation of China [51765006]
  2. Natural Science Foundation of Guangxi Province of China [2017GXNSFDA198012]
  3. Key Project of Science and Technology of Guangxi
  4. Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund [19-050-44-S015]
  5. Innovation Project of Guangxi Graduate Education [YCSW2019033]

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Energy consumption by metro trains has attracted considerable attention due to economic and environmental concerns. Passengers want convenient travel that takes less time. Based on the requirements of reduced train costs and more passenger comfort, the train control strategy and time parameters need to be adjusted properly. This paper proposes an integrated method for the minimum energy consumption of metro trains and minimum transfer waiting time cost for transfer passengers. First, we used a four-stage control strategy to optimize the train trajectory and generated a power matrix. Then, a tab encoding method was established for the regenerative energy calculation on the basis of the power matrix. To obtain the transfer waiting time of passengers, an arrival time matrix was built that was inspired by the matching operation of the regenerative energy. The genetic algorithm was employed for optimization because of its good searching ability, and the matrix form in this model was suitable for computer programming. Finally, two metro lines from the Nanning rail transit system were selected for the case study. The results show that the proposed method has good efficiency for energy conservation and decreased the transfer waiting time between two metro lines. Further research can be conducted by considering more kinds of passengers from the travel flow in the future.

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