Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

作     者：Shixiong Fan Jianbo Guo Shicong Ma Lixin Li Guozheng Wang Haotian Xu Jin Yang Zening Zhao 

作者机构：China Electric Power Research InstituteBeijing 100192China the Beijing Huairou LaboratoryBeijing 101400China School of Engineeringthe University of GlasgowGlasgow G128QQUK 

出 版 物：《CSEE Journal of Power and Energy Systems》 (中国电机工程学会电力与能源系统学报（英文）)

年 卷 期：2024年第10卷第1期

页      面：1-12页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：supported by the National Key R&D Program of China(2018AAA0101500) 

主　　题：Artificial intelligence human-machine collaborative control human-machine hy brid intelligence optimization and evolution power grid dispatching and control 

摘      要：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 *** of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex *** of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this *** 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 *** technologies are discussed to achieve a thorough integration of human/machine ***,state-of-theart 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.