Optimal control and energy storage for DC electric train systems using evolutionary algorithms
Publication Date
2021-12Journal Title
Railway Engineering Science
ISSN
2662-4753
Publisher
Springer Science and Business Media LLC
Volume
29
Issue
4
Pages
327-335
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Nallaperuma, S., Fletcher, D., & Harrison, R. (2021). Optimal control and energy storage for DC electric train systems using evolutionary algorithms. Railway Engineering Science, 29 (4), 327-335. https://doi.org/10.1007/s40534-021-00245-y
Description
Funder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266
Abstract
<jats:title>Abstract</jats:title><jats:p>Electrified 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>
Keywords
Article, Autonomous control, Intelligent transport systems, Energy optimisation, DC railway systems, Energy regeneration
Identifiers
s40534-021-00245-y, 245
External DOI: https://doi.org/10.1007/s40534-021-00245-y
This record's URL: https://www.repository.cam.ac.uk/handle/1810/329809
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.