极端天气下基于风险决策的新型电力供电弹性规划方法研究

陈玉州; 曾凡明; 黄小丹; 许志伟; 土邓曲培

陈玉州,曾凡明,黄小丹,许志伟,土邓曲培.极端天气下基于风险决策的新型电力供电弹性规划方法研究[J].电测与仪表,2026,63(7):46-58.
CHEN Yuzhou,ZENG Fanming,HUANG Xiaodan,XU Zhiwei,TUDENG Qupei.Research on a new elastic planning method for power supply based on risk decision-making under extreme weather conditions[J].Electrical Measurement & Instrumentation,2026,63(7):46-58.

极端天气下基于风险决策的新型电力供电弹性规划方法研究

Research on a new elastic planning method for power supply based on risk decision-making under extreme weather conditions

DOI：10.19753/ j.issn1001-1390.2026.07.005

中文关键词: 极端天气风险决策新型电力供电弹性规划供需失衡风险深度主动学习两阶段规划弹性不足率

英文关键词:extreme weather conditions, risk decision-making, new type of electricity, elastic power supply planning, risk of supply-demand imbalance, deep active learning, two-stage planning, elasticity deficiency rate

基金项目:国网科技项目编号 SCXZJY00QJJS2500008

作者中文名	作者英文名	单位
陈玉州	CHEN Yuzhou	国网西藏电力有限公司经济技术研究院, 拉萨 850000
曾凡明	ZENG Fanming	国网西藏电力有限公司, 拉萨 850000
黄小丹	HUANG Xiaodan	福建亿榕信息技术有限公司, 福州 350003
许志伟	XU Zhiwei	国网西藏电力有限公司经济技术研究院, 拉萨 850000
土邓曲培	TUDENG Qupei	国网西藏电力有限公司经济技术研究院, 拉萨 850000

摘要点击次数: 153

中文摘要:

极端天气是多维气象参数时空耦合作用形成的复杂现象。传统方法通常基于单因素或简化假设,难以准确表征多参数间的耦合关系,导致对极端天气下电力系统风险场景的识别不够全面、评估结果滞后。这一局限性使得现有规划方法难以基于动态风险进行协同优化,在极端天气的全过程中难以实现安全性与经济性兼顾的系统弹性提升。为此,文中提出一种基于风险决策的供电弹性规划方法。融合多源气象数据与电网历史故障信息,构建气象因素与电力系统状态关联矩阵,通过引入阈值偏移系数建立气象-电力映射边界,精准识别极端天气风险场景。采用基于 Copula 理论的逆变换法与蒙特卡洛抽样,构建多维气象因素联合分布的概率量化模型,准确捕捉气象参数间的尾部依赖特性,并综合设备服役年限、极端环境及运行状态构建时变故障概率模型,以此为输入构建深度主动学习模型,运用日失负荷量动态评估系统的供需失衡风险,依据风险等级启动弹性规划决策;以极端天气风险规避与弹性提升为导向,构建两阶段供电弹性规划模型,协同优化系统弹性不足率、成本及负荷供应比例等为目标。结果显示,在暴风雪场景下,经两阶段规划后,该方法将系统日失负荷量严格控制在150 MW 警戒阈值以内(最高140 MW),储能与新能源装机占比分别提升至5.61%与38.90%,系统弹性不足率降至18%,负荷供应比例达95%,有效增强了系统应对极端天气的动态韧性与供电弹性。

英文摘要:

Extreme weather is a complex phenomenon formed by the spatiotemporal coupling of multiple meteorological parameters. Traditional methods, which are usually based on single factors or simplified assumptions, struggle to accurately characterize the coupling relationships among multiple parameters. This results in an incomplete identification of power system risk scenarios under extreme weather and a lag in assessment results. This limitation makes it difficult for existing planning methods to perform collaborative optimization based on dynamic risks, and it is hard to enhance system resilience while balancing safety and economy throughout the full course of extreme weather events. Therefore, this paper proposes a method of power supply elastic planning based on risk decision. Integrating multi-source meteorological data and historical fault information of power grid, the correlation matrix between meteorological factors and power system state is constructed, and the meteorological power mapping boundary is established by introducing threshold offset coefficient to accurately identify extreme weather risk scenarios. This paper adopts the inverse transformation method based on Copula theory and Monte Carlo sampling, a probability quantization model for the joint distribution of multi-dimensional meteorological factors is constructed to accurately capture the tail dependence between meteorological parameters, and a time-varying failure probability model is constructed based on the service life of the equipment, extreme environment and operation status. With this as the input, a deep active learning model is constructed. The daily load loss is used to dynamically evaluate the risk of supply and demand imbalance of the system, and the flexible planning decision is initiated according to the risk level. Guided by extreme weather risk aversion and flexibility improvement, a two-stage power supply flexibility planning model is built to jointly optimize the system elasticity deficiency rate, cost and load supply ratio. Results show that under a blizzard scenario, this method strictly controls the system's daily load loss within the 150 MW alert threshold, with a maximum of 140 MW; the shares of energy storage and new energy installations increase to 5.61% and 38.90%, respectively; the system resilience insufficiency rate drops to 18%, and the load supply ratio reaches 95%, effectively enhancing the dynamic robustness and power supply resilience of system in response to extreme weather.

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