Optimal control and energy storage for DC electric train systems using evolutionary algorithms

Authors
Fletcher, D 
Harrison, R 

Change log
Abstract

jats:titleAbstract</jats:title>jats:pElectrified railways are becoming a popular transport medium and these consume a large amount of electrical energy. Environmental concerns demand reduction in energy use and peak power demand of railway systems. Furthermore, high transmission losses in DC railway systems make local storage of energy an increasingly attractive option. An optimisation framework based on genetic algorithms is developed to optimise a DC electric rail network in terms of a comprehensive set of decision variables including storage size, charge/discharge power limits, timetable and train driving style/trajectory to maximise benefits of energy storage in reducing railway peak power and energy consumption. Experimental results for the considered real-world networks show a reduction of energy consumption in the range 15%–30% depending on the train driving style, and reduced power peaks.</jats:p>

Publication Date
2021
Online Publication Date
2021-07-24
Acceptance Date
2021-06-24
Keywords
Autonomous control, Intelligent transport systems, Energy optimisation, DC railway systems, Energy regeneration
Journal Title
Railway Engineering Science
Journal ISSN
2662-4745
2662-4753
Volume Title
29
Publisher
Springer Science and Business Media LLC