{"title":"面向大规模电网调度与控制的人机混合增强智能系统框架与关键技术","authors":"Shixiong Fan;Jianbo Guo;Shicong Ma;Lixin Li;Guozheng Wang;Haotian Xu;Jin Yang;Zening Zhao","doi":"10.17775/CSEEJPES.2023.00940","DOIUrl":null,"url":null,"abstract":"With integration of large-scale renewable energy, new controllable devices, and required reinforcement of power grids, modern power systems have typical characteristics such as uncertainty, vulnerability and openness, which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability, interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation, this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First, theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally, state-of-the-art and future development of HHI in power grid regulation are summarized, aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":6.9000,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375976","citationCount":"0","resultStr":"{\"title\":\"Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control\",\"authors\":\"Shixiong Fan;Jianbo Guo;Shicong Ma;Lixin Li;Guozheng Wang;Haotian Xu;Jin Yang;Zening Zhao\",\"doi\":\"10.17775/CSEEJPES.2023.00940\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With integration of large-scale renewable energy, new controllable devices, and required reinforcement of power grids, modern power systems have typical characteristics such as uncertainty, vulnerability and openness, which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability, interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation, this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First, theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally, state-of-the-art and future development of HHI in power grid regulation are summarized, aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.\",\"PeriodicalId\":10729,\"journal\":{\"name\":\"CSEE Journal of Power and Energy Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2023-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375976\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CSEE Journal of Power and Energy Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10375976/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CSEE Journal of Power and Energy Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10375976/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control
With integration of large-scale renewable energy, new controllable devices, and required reinforcement of power grids, modern power systems have typical characteristics such as uncertainty, vulnerability and openness, which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability, interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation, this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First, theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally, state-of-the-art and future development of HHI in power grid regulation are summarized, aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.
期刊介绍:
The CSEE Journal of Power and Energy Systems (JPES) is an international bimonthly journal published by the Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE (The Institute of Electrical and Electronics Engineers) Inc. Indexed by SCI, Scopus, INSPEC, CSAD (Chinese Science Abstracts Database), DOAJ, and ProQuest, it serves as a platform for reporting cutting-edge theories, methods, technologies, and applications shaping the development of power systems in energy transition. The journal offers authors an international platform to enhance the reach and impact of their contributions.