Research on Timetable Optimization for Charging Capacity Reduction in Supercapacitor-powered Urban Rail Transit
Abstract
For supercapacitor-powered urban rail transit, its fast charging energy supply mode and possible scenario of multiple vehicles charging simultaneously may incur intermittent high-power charging demand to the grid, challenging the safety and economy of the whole system. In this paper, we adjust dwell duration of vehicles at each station to minimize the superposition of charging power resulted from simultaneous stops of vehicles, thereby reducing the capacity of the substation as well as the power stress of the grid. Firstly, in analogy with the related concept in the electric power system, the coincidence factor in urban rail transit is defined to describe the superposition of charging power. Then a timetable optimization model with dwell duration as the decision variable is developed to minimize the maximum coincidence factor and Particle Swarm Optimization is implemented to find the optimal solution. Furthermore, a case study is presented based on the data from Haizhu Line in Guangzhou, China. The result shows that the optimized timetable can reduce the maximum coincidence factor by 20% in comparison with the current used timetable.
Keywords
urban rail transit, timetable optimization, supercapacitor, charging capacity reduction, Particle Swarm Optimization
DOI
10.12783/dteees/iceee2018/27835
10.12783/dteees/iceee2018/27835
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