{"title":"考虑突发的能源物联网能源调度的元宇宙框架设计","authors":"Yechen Han, Xing He, Haihong Zhang, Yuanjun Zuo, Wenjing Zhang, Qian Ai, Zhuo Chen","doi":"10.12688/digitaltwin.17873.1","DOIUrl":null,"url":null,"abstract":"The traditional scheduling approach, which primarily considers energy oligarchs like large-scale loads and large-scale generators, is challenged by the rapid rise of distributed energy resources (DERs) in energy internet of things (EIoT). Metaverse is emerging as one of the most promising technologies considering emergence from DERs in EIoT. Our work, from a macro perspective in the virtual space, provides a metaverse framework to harness the swarm intelligence that emerges from the aggregation behavior of massive diverse DERs in EIoT. The presented framework is built upon virtual twins, data science, systems theory, and 4th-Paradigm (data-intensive scientific discovery paradigm), enabling a novel energy scheduling mode. Our goal is to achieve data empowerment and intelligence improvement through data connectivity, virtual and real interaction, which will ultimately result in a new theory on complex system scheduling.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Metaverse Framework Designing for Energy Scheduling in Energy Internet of Things Considering Emergence\",\"authors\":\"Yechen Han, Xing He, Haihong Zhang, Yuanjun Zuo, Wenjing Zhang, Qian Ai, Zhuo Chen\",\"doi\":\"10.12688/digitaltwin.17873.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The traditional scheduling approach, which primarily considers energy oligarchs like large-scale loads and large-scale generators, is challenged by the rapid rise of distributed energy resources (DERs) in energy internet of things (EIoT). Metaverse is emerging as one of the most promising technologies considering emergence from DERs in EIoT. Our work, from a macro perspective in the virtual space, provides a metaverse framework to harness the swarm intelligence that emerges from the aggregation behavior of massive diverse DERs in EIoT. The presented framework is built upon virtual twins, data science, systems theory, and 4th-Paradigm (data-intensive scientific discovery paradigm), enabling a novel energy scheduling mode. Our goal is to achieve data empowerment and intelligence improvement through data connectivity, virtual and real interaction, which will ultimately result in a new theory on complex system scheduling.\",\"PeriodicalId\":29831,\"journal\":{\"name\":\"Digital Twin\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Twin\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12688/digitaltwin.17873.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Twin","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12688/digitaltwin.17873.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Metaverse Framework Designing for Energy Scheduling in Energy Internet of Things Considering Emergence
The traditional scheduling approach, which primarily considers energy oligarchs like large-scale loads and large-scale generators, is challenged by the rapid rise of distributed energy resources (DERs) in energy internet of things (EIoT). Metaverse is emerging as one of the most promising technologies considering emergence from DERs in EIoT. Our work, from a macro perspective in the virtual space, provides a metaverse framework to harness the swarm intelligence that emerges from the aggregation behavior of massive diverse DERs in EIoT. The presented framework is built upon virtual twins, data science, systems theory, and 4th-Paradigm (data-intensive scientific discovery paradigm), enabling a novel energy scheduling mode. Our goal is to achieve data empowerment and intelligence improvement through data connectivity, virtual and real interaction, which will ultimately result in a new theory on complex system scheduling.
期刊介绍:
Digital Twin is a rapid multidisciplinary open access publishing platform for state-of-the-art, basic, scientific and applied research on digital twin technologies. Digital Twin covers all areas related digital twin technologies, including broad fields such as smart manufacturing, civil and industrial engineering, healthcare, agriculture, and many others. The platform is open to submissions from researchers, practitioners and experts, and all articles will benefit from open peer review.
The aim of Digital Twin is to advance the state-of-the-art in digital twin research and encourage innovation by highlighting efficient, robust and sustainable multidisciplinary applications across a variety of fields. Challenges can be addressed using theoretical, methodological, and technological approaches.
The scope of Digital Twin includes, but is not limited to, the following areas:
● Digital twin concepts, architecture, and frameworks
● Digital twin theory and method
● Digital twin key technologies and tools
● Digital twin applications and case studies
● Digital twin implementation
● Digital twin services
● Digital twin security
● Digital twin standards
Digital twin also focuses on applications within and across broad sectors including:
● Smart manufacturing
● Aviation and aerospace
● Smart cities and construction
● Healthcare and medicine
● Robotics
● Shipping, vehicles and railways
● Industrial engineering and engineering management
● Agriculture
● Mining
● Power, energy and environment
Digital Twin features a range of article types including research articles, case studies, method articles, study protocols, software tools, systematic reviews, data notes, brief reports, and opinion articles.