DRL-Based Adaptive Energy Management for Hybrid Electric Storage Systems Under Dynamic Spatial-Temporal Traffic in Urban Rail Transits

Guannan Li, Siu Wing Or

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Hybrid electric storage systems (HESSs) of stationary batteries and supercapacitors have received increasing attention to the reduction of power/energy redundancy from their single-form counterparts. The real-time traffic in fast-expanding urban rail transit networks (URTNs) brings considerable challenges of time-varying, high-power, and large-scale regenerative braking energy recovery to the HESS energy management. This paper proposes an adaptive energy management method for effectively operating HESSs in URTNs by considering complex and dynamic spatial-temporal traffic characteristics and utilizing deep reinforcement learning. First, a Copula-based multi-dimensional spatial-temporal passenger flow correlation model is developed to generate dynamic multi-train operation scenarios. Second, the HESS energy management problem is formulated as a Markov decision process, where a dueling double deep Q-network algorithm is used to optimize a coordinated energy management strategy composed of voltage threshold adjustments and power distributions between batteries and supercapacitors for minimizing the URTN operation cost. Third, comparative studies are analyzed to verify the performance of the proposed method using real-world subway line data. The results demonstrate the superior performance of the proposed method in improving the energy-saving effect and economic benefits of the HESSs.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665471640
DOIs
Publication statusPublished - Dec 2023
Event2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023 - Wollongong, Australia
Duration: 3 Dec 20236 Dec 2023

Publication series

Name2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

Conference

Conference2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
Country/TerritoryAustralia
CityWollongong
Period3/12/236/12/23

Keywords

  • Adaptive energy management
  • deep reinforcement learning
  • dynamic spatial-temporal traffic
  • hybrid electric storage systems
  • urban rail transit networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Safety, Risk, Reliability and Quality

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