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

Published version
Peer-reviewed

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Authors

Fletcher, D 
Harrison, R 

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>

Description

Funder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266

Keywords

Autonomous control, Intelligent transport systems, Energy optimisation, DC railway systems, Energy regeneration

Journal Title

Railway Engineering Science

Conference Name

Journal ISSN

2662-4745
2662-4753

Volume Title

29

Publisher

Springer Science and Business Media LLC