数据驱动的综合能源系统运行优化方法研究综述  

Review of research of data-driven methods on operational optimization of integrated energy systems

作　　者：陈龙[1,2] 韩中洋[1,2] 赵珺 王伟[1,2] CHEN Long;HAN Zhong-yang;ZHAO Jun;WANG Wei(School of Control Science and Engineering,Dalian University of Technology,Dalian 116024,China;Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学控制科学与工程学院,辽宁大连116024 [2]大连理工大学工业装备智能控制与优化教育部重点实验室,辽宁大连116024

出　　处：《控制与决策》2021年第2期283-294,共12页Control and Decision

基　　金：国家重点研发计划项目(2017YFA0700300)。

摘　　要：构建清洁、低碳、安全、高效和可持续的现代能源体系被列为我国国家能源发展战略.综合能源系统(integrated energy system,IES)包含多类能源的产生、传输、转换、存储以及分配等过程,其综合管控与协同优化是在系统工艺与装备相对完备的情况下提升能源效率、降低成本、保护环境的关键技术,可为我国构建低碳可持续发展的能源运行模式,特别是工业园区的能源管控提供技术基础.随着大数据与机器学习技术的发展,一系列数据驱动的方法在IES相关研究中相继出现,其研究重点涵盖了IES的建模、评估以及动态调度等内容.鉴于此,综述数据驱动方法应用于以上几个方面的国内外研究现状,详细分析当前研究中亟需解决的科学问题与技术挑战,在此基础上探讨数据驱动的IES运行优化研究的未来发展方向.Building a clean,low-carbon,safe,efficient and sustainable energy system has been listed as one of the national energy development strategies in China.The integrated energy systems(IES)integrates the processes of multiple energy generation,transmission,conversion,storage and distribution.The collaborative management and optimization for multi-energy resources is the key technology to raise energy efficiency,reduce costs and protect environments under a certain configuration of technologies and equipment,which provides the basis for forming a low-carbon sustainable energy operation mode,especially for the industrial parks.With the development of the big data and machine learning technologies,a series of data-driven methods have occurred in the field of the research on IES,covering the modeling,assessment and operational optimization of the IES,etc.The studies and issues of these aforementioned aspects are reviewed in detail,and the ongoing scientific problems and technical challenges that need to be further investigated are also presented.In addition,the future research directions of the data-driven methods for the IES operational optimization are discussed.

关 键 词：综合能源系统 数据驱动 建模 评估 运行优化 机器学习 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]