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

Abstract

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.

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Item Type:	Article
Authors/Creators:	Nallaperuma, S. Fletcher, D. https://orcid.org/0000-0002-1562-4655 Harrison, R.
Copyright, Publisher and Additional Information:	© 2021 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Autonomous control; Intelligent transport systems; Energy optimisation; DC railway systems; Energy regeneration
Dates:	Published: December 2021 Published (online): 24 July 2021 Accepted: 24 June 2021
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Funding Information:	Funder Grant number Engineering and Physical Science Research Council EP/N022289/1
Depositing User:	Symplectic Sheffield
Date Deposited:	28 Jul 2021 09:53
Last Modified:	09 Mar 2022 12:12
Status:	Published
Publisher:	Springer Nature
Refereed:	Yes
Identification Number:	10.1007/s40534-021-00245-y
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:176583

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

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