中国电机工程学报2024，Vol.44Issue(z1)：131-145,15.DOI:10.13334/j.0258-8013.pcsee.240977
电网优化调度的模型-数据-知识融合方法研究评述及展望
Review and Prospects on Model-data-knowledge Combined Methodology for Power Grid Optimization Dispatch
摘要
Abstract
Due to vast time-varying uncertain factors and nonlinearity in the new-type power system,the complexity of power grid optimization dispatch has dramatically increased.By fully utilizing the complementary characteristics of physical models,data-driven or knowledge experience,it is expected to achieve an improvement in the efficiency and flexibility of power grid optimization dispatch decisions.Firstly,new developments related to uncertainty dispatch,data-driven dispatch,and knowledge experience have been separately summarized in this paper.Secondly,the connotation of three methodologies'combination is analyzed,the combination architectures are mainly divided into master-slave mode and peer-to-peer mode,and the relevant research work are reviewed respectively.Finally,the existing problems in Model-data-knowledge combined methodology are analyzed,future research directions are put forward and discussed from four aspects:quantitative evaluation of the effect of Model-Data-Knowledge Combined Methodology,proactively choose and deduction of trustworthy scheduling knowledge,reliable intelligent decision-making of multi-mode fusion in power grid scheduling,and autonomous optimization evolution.关键词
模型驱动/数据驱动/知识经验/融合方式/电力优化调度Key words
model-driven/data-driven/knowledge experience/combined methodology/power grid optimization dispatch分类
信息技术与安全科学引用本文复制引用
王珂,万祥宽,王继业,李亚平,徐云贵,ASAD WAQAR..电网优化调度的模型-数据-知识融合方法研究评述及展望[J].中国电机工程学报,2024,44(z1):131-145,15.基金项目
国家重点研发计划项目(2022YFB2403400) （2022YFB2403400）
国家自然科学基金项目(52377092).National Key R&D Program of China(2022YFB2403400) （52377092）
Project Supported by National Natural Science Foundation of China(52377092). （52377092）
