摘要
居民用电行为受多种因素影响,如生活习惯、天气变化、电价波动、突发事件等,这些因素使得用户行为模式具有高度的不确定性和动态变化性,进一步增加了居民负荷需求预测的复杂性。为此,研究低碳约束下智能电网居民负荷需求响应动态预测方法。从经济性、环保性和社会性3个方面选取9种居民负荷需求响应的影响因素,作为基于灰色理论构建的多因素GM(1,1)预测模型输入数据,累加运算、驱动因子计算和累减还原计算三步骤实现居民负荷需求响应动态预测,并将其模型优化成等维新息GM(1,1)模型,降低预测精度受旧数据影响,实现动态预测。实验结果显示:该方法能有效预测并引导居民采取节能措施,显著降低电网负荷和污染物排放,同时在预测负荷峰谷差方面表现出更高的准确性,可为智能电网调度提供了重要参考。
The electricity consumption behavior of residents is influenced by various factors,such as lifestyle habits,weather changes,electricity price fluctuations,emergencies,etc.These factors make user behavior patterns highly uncertain and dynamically changing,further increasing the complexity of predicting residential load demand.Therefore,a dynamic prediction method for residential load demand response in smart grids under low-carbon constraints is studied.Nine influencing factors of residential load demand response were selected from three aspects:economic,environmental,and social.These factors were used as input data for a multi factor GM(1,1)prediction model based on grey theory.The dynamic prediction of residential load demand response was achieved through three steps:accumulation,driving factor calculation,and cumulative reduction calculation.The model was optimized into an equal dimensional new information GM(1,1)model to reduce the influence of old data on prediction accuracy and achieve dynamic prediction.The experimental results show that this method can effectively predict and guide residents to take energy-saving measures,significantly reduce power grid load and pollutant emissions,and demonstrate higher accuracy in predicting load peak valley differences,which can provide important reference for smart grid dispatch.
作者
黄雅君
周企慧
沈京京
李亦农
夏周祺
仇成
HUANG Yajun;ZHOU Qihui;SHEN Jingjing;LI Yinong;XIA Zhouqi;QIU Cheng(State Grid Shanghai Electric Power Company Economic and Technological Research Institute,Shanghai 200233,China;State Grid Shanghai Electric Power Company Songjiang Power Supply Company,Shanghai 201600,China)
出处
《自动化与仪器仪表》
2025年第10期45-48,53,共5页
Automation & Instrumentation
基金
国网上海市电力公司经济技术研究院技术服务项目资助(PW202401PW28)。
关键词
低碳约束
智能电网
居民负荷
需求响应
动态预测
low-carbon constraints
smart grid
residential load
demand response
dynamic prediction
