动态分时电价下居民用户需求响应基线负荷预测方法

胡可心; 李康平; 刘春阳; 纪陵; 米增强

胡可心,李康平,刘春阳,纪陵,米增强.动态分时电价下居民用户需求响应基线负荷预测方法[J].电测与仪表,2025,62(9):62-72.
Kexin Hu,Kangping Li,Chunyang Liu,Ling Ji,Zengqiang Mi.A baseline load forecasting method for residential demand response under dynamic time-of-use electricity price[J].Electrical Measurement & Instrumentation,2025,62(9):62-72.

动态分时电价下居民用户需求响应基线负荷预测方法

A baseline load forecasting method for residential demand response under dynamic time-of-use electricity price

DOI：10.19753/j.issn1001-1390.2025.09.007

中文关键词: 分时电价需求响应基线负荷预测电价差

英文关键词:time-of-use electricity price, demand response, baseline load, forecasting, electricity price difference

基金项目:国家重点研发计划政府间国际科技创新合作重点专项（2018YFE0122200）；国家自然科学基金青年基金（52107103）；中国华电集团“揭榜挂帅”制项目（CHDKJ21-01-107）

作者中文名	作者英文名	单位
胡可心	Kexin Hu	华北电力大学电力工程系
李康平	Kangping Li	上海交通大学智慧能源创新学院
刘春阳	Chunyang Liu	华北电力大学电力工程系
纪陵	Ling Ji	国电南京自动化股份有限公司
米增强	Zengqiang Mi	华北电力大学电力工程系

摘要点击次数: 73

中文摘要:

准确预测用户基线负荷（customer baseline load, CBL）对于确定激励型需求响应调用需求及用户响应量化具有重要意义。近年来，随着动态分时电价政策的逐渐落地，居民用户的用电行为将变得更加复杂多变。现有CBL预测方法未考虑电价波动带来的影响，预测结果存在较大误差。为此，提出了一种适应动态分时电价的居民CBL预测方法。文中将居民用户在动态分时电价下的用电行为建模成家庭能量优化管理问题，通过调整用户舒适度和经济性两个指标之间的权重，模拟生成不同偏好用户在动态分时电价下的负荷数据，从待预测时段的峰谷属性以及其与邻近时段的电价差两个方面考虑电价波动对CBL的影响，并搭配历史负荷作为输入特征，对用户CBL进行预测。将文章方法与传统CBL预测方法进行了对比，结果表明所提方法能有效提升动态分时电价下CBL预测效果。

英文摘要:

Accurately forecasting the customer baseline load (CBL) is of great significance for the incentive-based demand response in terms of determining the scheduling demand and quantifying the customer response. In recent years, with the gradual implementation of the dynamic time-of-use electricity price policy, the power consumption behavior of residential customers will become more complex and changeable. The impact of price fluctuation is not considered in the existing CBL forecasting methods, so there are large errors in the forecasting results. Therefore, a CBL forecasting method for residential customers suitable for dynamic time-of-use electricity price is proposed. The electricity consumption behavior of residential customers under the dynamic time-of-use electricity price is modeled as a household energy optimization management problem. By adjusting the weight between the two indicators of customer comfort and economy, the load data of customers with different preferences under the dynamic time-of-use electricity is simulated. Secondly, the impact of electricity price fluctuations on CBL is considered from the peak-valley attributes of the period to be forecasted and the electricity price difference between the current period and the adjacent period. The peak-valley attributes and price difference are extracted as input features together with the historical load to forecast residential CBL. The method proposed in this paper is compared with the traditional CBL forecasting method, and the results show that the proposed method can effectively improve the CBL forecasting effect under the dynamic time-of-use electricity price.

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