数据驱动的考虑安全约束机组组合问题研究综述  

Review of Data-driven Security-constrained Unit Commitment

作　　者：杨楠[1] 郝俊聪 产雪振 张志 许丹 YANG Nan;HAO Juncong;CHAN Xuezhen;ZHANG Zhi;XU Dan(Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station,China Three Gorges University,Yichang 443002,China;State Grid Corporation of China,Beijing 100031,China;China Electric Power Research Institute,Beijing 100192,China)

机构地区：[1]梯级水电站运行与控制湖北省重点实验室(三峡大学),宜昌443002 [2]国家电网有限公司,北京100031 [3]中国电力科学研究院有限公司,北京100192

出　　处：《高电压技术》2023年第9期3654-3668,共15页High Voltage Engineering

基　　金：国家电网有限公司科技项目(5108−202355040A−1−1−ZN)。

摘　　要：安全约束机组组合(security-constrained unit commitment,SCUC)作为编制发电计划的核心环节,在电力系统优化调度等方面具有十分重要的意义。因此,该文首先从物理模型和求解方法简要概述了SCUC问题。然后,从多目标、多元化决策变量、不确定性、多时间尺度与多元约束条件5个方面梳理了物理模型驱动的SCUC的研究进展,并分析了此类方法所面临的挑战。同时,重点总结归纳了现有基于人工智能技术和数据驱动的SCUC问题的研究成果,并分析了不同类型方法的特点、优势和缺陷。最后提出了对未来基于数据驱动的SCUC研究方向的相关思考。As the key building block of power generation schedules,security-constrained unit commitment(SCUC)is of great significance in power system operation.Consequently,this paper first briefly outlines the SCUC problem from viewpoints of the physical model and the solving methods.Then,the research progress of SCUC driven by the physical model is reviewed from five aspects,namely,multi-objective,diversified decision variables,uncertainty,multi-time scale,and multi-variate constraints,with the knowledge of their underlying challenges.At the same time,the existing research results of SCUC problems based on artificial intelligence and data-driven are generalized,and the characteristics,advantages,and disadvantages of different types of methods are analyzed.Finally,the future research direction of data-driven SCUC is discussed.

关 键 词：安全约束机组组合 优化调度 模型驱动 人工智能 数据驱动 

分 类 号：TM73[电气工程—电力系统及自动化]