黄敏芳, 郝园媛, 王颜新. 数据驱动的电网系统突发事件应急响应决策方法[J]. 华北电力大学学报(社会科学版), 2023, 4(4): 61-70. DOI: 10.14092/j.cnki.cn11-3956/c.2023.04.007
引用本文: 黄敏芳, 郝园媛, 王颜新. 数据驱动的电网系统突发事件应急响应决策方法[J]. 华北电力大学学报(社会科学版), 2023, 4(4): 61-70. DOI: 10.14092/j.cnki.cn11-3956/c.2023.04.007
HUANG Min-fang, HAO Yuan-yuan, WANG Yan-xin. Data-driven Decision-making Method for Emergency Response to Power Grid System Emergencies[J]. JOURNAL OF NORTH CHINA ELECTRIC POWER UNIVERSITY(SOCIAL SCIENCES), 2023, 4(4): 61-70. DOI: 10.14092/j.cnki.cn11-3956/c.2023.04.007
Citation: HUANG Min-fang, HAO Yuan-yuan, WANG Yan-xin. Data-driven Decision-making Method for Emergency Response to Power Grid System Emergencies[J]. JOURNAL OF NORTH CHINA ELECTRIC POWER UNIVERSITY(SOCIAL SCIENCES), 2023, 4(4): 61-70. DOI: 10.14092/j.cnki.cn11-3956/c.2023.04.007

数据驱动的电网系统突发事件应急响应决策方法

Data-driven Decision-making Method for Emergency Response to Power Grid System Emergencies

  • 摘要: 为应对大型城市电网系统中的不确定性,以提高应急管理理论和方法的可操作性、提升电网系统应急决策的实时性和智能性为目标,本文提出了一种刻画电网系统中数据采集、数据存储与管理、数据分析、决策执行与评估、演练仿真技术五大部分功能的动态决策方法。首先,分析了电网系统大数据的特性,探讨了数据驱动的应急响应决策原理,并从技术场景与突发事件的演变过程两个维度部署了应急响应决策方法体系架构与功能。然后,阐述了多源数据融合技术、突发事件的演化建模技术、面向复杂大群体的智能优化决策技术三项关键技术的研究需求和实现策略。本文可为相关电力企业建立电力大数据环境下突发事件应急响应决策方法提供实操性强的指导。

     

    Abstract: In order to cope with the uncertainty in the large-scale urban power grid system, improve the operability of emergency management theories and methods, and improve the real-time and intelligence of emergency decision-making in the power grid system, this paper proposes a dynamic decision-making method to describe five major functions, including data acquisition, data storage and management, data analysis, decision execution and evaluation, and simulation technology. Firstly, the characteristics of big data in the power grid system are analyzed, the principle of data-driven emergency response decision-making is discussed, and the system architecture and functions of emergency response decision-making methods are deployed from the two dimensions of technical scenarios and the evolution process of emergencies. Then, the research requirements and implementation strategies of three key technologies are expounded, including multi-source data fusion technology, evolutionary modeling technology for emergencies, and intelligent optimization decision-making technology for complex large groups. This paper can provide practical guidance for relevant power companies to establish emergency response decision-making methods for emergencies in the power big data environment.

     

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