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

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.

• Record URL:
  https://doi.org/10.1007/s40534-021-00245-y
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/26624745
• Supplemental Notes:
  • © 2021 Sam Nallaperuma et al.
• Authors:
  • Nallaperuma, Sam
  • 0000-0002-4947-5870
  • Fletcher, David
  • Harrison, Robert
• Publication Date: 2021-7

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 327–335
• Serial:
  • Railway Engineering Science
  • Volume: 29
  • Issue Number: 4
  • Publisher: Springer Nature
  • ISSN: 2662-4745
  • Serial URL: https://www.springer.com/journal/40534

Subject/Index Terms

• TRT Terms: Electric railroads; Energy consumption; Energy storage systems; Environmental impacts; Optimization
• Subject Areas: Energy; Railroads; Vehicles and Equipment;

Filing Info

• Accession Number: 01788839
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 18 2021 12:14PM