Energy-efficient Metro Train Operation Considering the Regenerative Energy: A Discrete Differential Dynamic Programming Approach

EasyChair Preprint 892

Abstract

With the increase of the operating mileage, a large amount of energy consumption generated by metro systems needs to be taken seriously. One of the effective ways to reduce the energy consumption is to collaboratively optimize the driving strategy and train timetable by considering the regenerative energy (RE). However, the dimensionality and computational time will increase accordingly in optimization as the number of operating trains rises. With the intention of tackling this problem by efficiently reducing dimensionality, the energy-efficient metro train operation problem is optimized in this paper by applying the discrete differential dynamic programming (DDDP) approach. Firstly, the model calculating the net energy consumption that takes into account the RE is formulated. Then, the optimization model will be transformed to a discrete decision problem by using Space-Time-Speed (STS) network methodology, and the corresponding solution will be obtained through the DDDP based algorithm. Finally, two case studies will be conducted in a metro network to illustrate the effectiveness of the proposed approach.

Keyphrases: Discrete differential dynamic programming, Energy reduction, Regenerative energy, Space-Time-Speed network methodology

@booklet{EasyChair:892,
  author    = {Junjie Lou and Xuekai Wang and Shuai Su and Tao Tang and Yihui Wang},
  title     = {Energy-efficient Metro Train Operation Considering the Regenerative Energy: A Discrete Differential Dynamic Programming Approach},
  doi       = {10.29007/wc97},
  howpublished = {EasyChair Preprint 892},
  year      = {EasyChair, 2019}}