{"title":"考虑储能装置部署的高速列车节能运行图优化","authors":"Yuzhao Zhang , Shenyingze Gao , Xuan Ji","doi":"10.1016/j.jestch.2025.102197","DOIUrl":null,"url":null,"abstract":"<div><div>This study proposes an energy-efficient operation diagram optimization method for high-speed trains considering the deployment of energy storage devices. A hybrid PSO-SA algorithm is developed to solve the model, incorporating constraints such as departure times, dwell durations, and safety headways. Validated on the Baoji-Lanzhou section, results demonstrate an immediate regenerative energy utilization rate of 47.26 %, rising to 62.79 % with energy storage deployment. The hybrid algorithm reduces computation time by 74.59 % compared to traditional SA algorithm while maintaining solution quality. Compared with the GA algorithm, the optimal result obtained by the PSO-SA algorithm improved by 4.76 %. Economic analysis highlights prioritizing storage deployment in key power supply zones for optimal cost-effectiveness, offering actionable strategies for sustainable railway operations. This research provides theoretical and practical insights into energy-efficient high-speed rail systems.</div></div>","PeriodicalId":48609,"journal":{"name":"Engineering Science and Technology-An International Journal-Jestech","volume":"71 ","pages":"Article 102197"},"PeriodicalIF":5.4000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy-efficient operation diagram optimization for high-speed trains considering energy storage device deployment\",\"authors\":\"Yuzhao Zhang , Shenyingze Gao , Xuan Ji\",\"doi\":\"10.1016/j.jestch.2025.102197\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study proposes an energy-efficient operation diagram optimization method for high-speed trains considering the deployment of energy storage devices. A hybrid PSO-SA algorithm is developed to solve the model, incorporating constraints such as departure times, dwell durations, and safety headways. Validated on the Baoji-Lanzhou section, results demonstrate an immediate regenerative energy utilization rate of 47.26 %, rising to 62.79 % with energy storage deployment. The hybrid algorithm reduces computation time by 74.59 % compared to traditional SA algorithm while maintaining solution quality. Compared with the GA algorithm, the optimal result obtained by the PSO-SA algorithm improved by 4.76 %. Economic analysis highlights prioritizing storage deployment in key power supply zones for optimal cost-effectiveness, offering actionable strategies for sustainable railway operations. This research provides theoretical and practical insights into energy-efficient high-speed rail systems.</div></div>\",\"PeriodicalId\":48609,\"journal\":{\"name\":\"Engineering Science and Technology-An International Journal-Jestech\",\"volume\":\"71 \",\"pages\":\"Article 102197\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Science and Technology-An International Journal-Jestech\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2215098625002526\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Science and Technology-An International Journal-Jestech","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2215098625002526","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Energy-efficient operation diagram optimization for high-speed trains considering energy storage device deployment
This study proposes an energy-efficient operation diagram optimization method for high-speed trains considering the deployment of energy storage devices. A hybrid PSO-SA algorithm is developed to solve the model, incorporating constraints such as departure times, dwell durations, and safety headways. Validated on the Baoji-Lanzhou section, results demonstrate an immediate regenerative energy utilization rate of 47.26 %, rising to 62.79 % with energy storage deployment. The hybrid algorithm reduces computation time by 74.59 % compared to traditional SA algorithm while maintaining solution quality. Compared with the GA algorithm, the optimal result obtained by the PSO-SA algorithm improved by 4.76 %. Economic analysis highlights prioritizing storage deployment in key power supply zones for optimal cost-effectiveness, offering actionable strategies for sustainable railway operations. This research provides theoretical and practical insights into energy-efficient high-speed rail systems.
期刊介绍:
Engineering Science and Technology, an International Journal (JESTECH) (formerly Technology), a peer-reviewed quarterly engineering journal, publishes both theoretical and experimental high quality papers of permanent interest, not previously published in journals, in the field of engineering and applied science which aims to promote the theory and practice of technology and engineering. In addition to peer-reviewed original research papers, the Editorial Board welcomes original research reports, state-of-the-art reviews and communications in the broadly defined field of engineering science and technology.
The scope of JESTECH includes a wide spectrum of subjects including:
-Electrical/Electronics and Computer Engineering (Biomedical Engineering and Instrumentation; Coding, Cryptography, and Information Protection; Communications, Networks, Mobile Computing and Distributed Systems; Compilers and Operating Systems; Computer Architecture, Parallel Processing, and Dependability; Computer Vision and Robotics; Control Theory; Electromagnetic Waves, Microwave Techniques and Antennas; Embedded Systems; Integrated Circuits, VLSI Design, Testing, and CAD; Microelectromechanical Systems; Microelectronics, and Electronic Devices and Circuits; Power, Energy and Energy Conversion Systems; Signal, Image, and Speech Processing)
-Mechanical and Civil Engineering (Automotive Technologies; Biomechanics; Construction Materials; Design and Manufacturing; Dynamics and Control; Energy Generation, Utilization, Conversion, and Storage; Fluid Mechanics and Hydraulics; Heat and Mass Transfer; Micro-Nano Sciences; Renewable and Sustainable Energy Technologies; Robotics and Mechatronics; Solid Mechanics and Structure; Thermal Sciences)
-Metallurgical and Materials Engineering (Advanced Materials Science; Biomaterials; Ceramic and Inorgnanic Materials; Electronic-Magnetic Materials; Energy and Environment; Materials Characterizastion; Metallurgy; Polymers and Nanocomposites)