热浪和寒潮事件下源荷中长期变化及置信区间模拟与分析  

Simulation and Analysis of Medium-and Long-term Changes and Confidence Intervals in Power Sources and Loads Under Heat Waves and Cold Waves

作　　者：白婕 秦晓辉 丁保迪 赵明欣 刘洋[2] BAI Jie;QIN Xiaohui;DING Baodi;ZHAO Mingxin;LIU Yang(China Electric Power Research Institute,Beijing 100192,China;Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Key Laboratory of Land Surface Pattern and Simulation,Beijing 100101,China)

机构地区：[1]中国电力科学研究院有限公司,北京市100192 [2]中国科学院地理科学与资源研究所陆地表层格局与模拟院重点实验室,北京市100101

出　　处：《电力系统自动化》2025年第4期141-151,共11页Automation of Electric Power Systems

基　　金：国家电网有限公司科技项目(5100-202155466A-0-0-00)。

摘　　要：气候变化加剧,而区域尺度极端温度事件下风光等出力和负荷的日变化特征及关系尚不明确。为此,文中采用独立模型模拟天气影响负荷及风光水电出力,通过Copula函数计算出力联合概率与置信区间,并基于气候模式数据预估了中国华北、西南典型省份在“碳达峰”(2030年)时,热浪、寒潮天气下电源出力和负荷日变化特性及电力供需关系。文中提出了预测天气影响光伏出力的boosting集成学习模型,并采用历史实测数据校准,验证显示实际极端天气下的日平均误差为1.27%,平均绝对误差显著低于其他集成学习方法。中长期预估显示,2030年华北、西南典型省份在热浪和寒潮日晚间易出现电力供不应求,并给出了相关区域未来极端温度事件下电力供需的峰值和时间等指标。The climate change is intensifying,while the daily variation characteristics and relationships of wind,photovoltaic,and other renewable energy outputs and loads under regional-scale extreme temperature events remain unclear.Therefore,this paper uses independent models to simulate the impact of weather on load and wind,photovoltaic,and hydro power outputs.The joint probability and confidence intervals of outputs are calculated using Copula functions.Based on climate model data,the daily variation characteristics of power output and load as well as the power supply-demand relationship are estimated under heat waves and cold waves in typical provinces of North China and Southwest China by 2030,the target year for China’s carbon emission peak.A novel boosting ensemble learning model is proposed to predict the impact of weather on photovoltaic output,and calibrated with historical measured data.The validation shows that the model achieves a mean daily output error of 1.27%under actual extreme weather conditions,and the mean absolute error is significantly lower than other ensemble learning methods.The medium-and long-term forecast indicates that in 2030,typical provinces in North China and Southwest China are prone to experience power supply shortages during the evening hours of days under heat waves and cold waves,and indicators such as peak values and time of power supply and demand under extreme temperature events in the relevant regions in the future are given.

关 键 词：光伏发电 热浪 寒潮 极端天气 源荷特性 置信区间 电力供需 

分 类 号：TM615[电气工程—电力系统及自动化]