文章摘要
李 萌,陈云龙,刘继彦,王者龙,刘夏丽,周兴华.基于粒子群优化BP神经网络多元客户负荷可调节潜力动态评估[J].电力需求侧管理,2024,26(5):82-87
基于粒子群优化BP神经网络多元客户负荷可调节潜力动态评估
Dynamic assessment of multi-customer load adjustable potential of BP neural network based on particle swarm
投稿时间：2024-04-05  修订日期：2024-06-20
DOI：10. 3969 / j. issn. 1009-1831. 2024. 05. 013
中文关键词: 多元客户负荷可调节潜力动态评估  影响机理分析  特征选择  评估结果校验
英文关键词: dynamic assessment of multi-customer load adjustable potential  influence mechanism analysis  feature selection  evaluation result verification
基金项目:国家电网有限公司科技项目（5108-202218280A-2-0-XG）
作者单位
李 萌 国网山东省电力公司 营销服务中心（计量中心）,济南 250000 
陈云龙 国网山东省电力公司 营销服务中心（计量中心）,济南 250000 
刘继彦 国网山东省电力公司,济南 250000 
王者龙 国网山东省电力公司,济南 250000 
刘夏丽 北京中恒博瑞数字电力科技有限公司,北京 100038 
周兴华 北京中恒博瑞数字电力科技有限公司,北京 100038 
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中文摘要:
      “双碳”及新型电力系统背景下,新能源装机容量逐年提升,数据中心、5G基站等新型负荷持续增长,用户侧负荷形态与负荷特征发生重大变化,同时叠加外界环境等因素影响,电力供需形势严峻。为保障电网安全稳定运行,深入挖掘用户负荷可调节潜力,提出一种基于粒子群优化BP神经网络多元客户负荷可调节潜力动态评估方法。分析多元客户负荷可调节潜力影响机理,构建特征标签体系,利用灰色关联度分析法选取可调节潜力相关特征,搭建可调节潜力评估模型,实现多元客户负荷调控能力评估,并根据实际响应结果校验评估模型准确性,滚动更新用户负荷、需求响应数据,实现用户侧可调节潜力的动态评估,使得模型能够适应不断变化的用户行为。
英文摘要:
      Under the background of“dual carbon”and new power systems, the installed capacity of new energy has increased year by year,new loads such as data centers and 5G base stations have continued to grow, and the user-side load patterns and load characteristics have undergone major changes, while superposing the influence of external environment and other factors, and the power supply and demand situation is grim. In order to ensure the safe and stable operation of power grid and tap the user’s adjustable potential, a dynamic evaluation method of multi-element customer load adjustable potential based on BP neural network based on particle swarm optimization is proposed.The influence mechanism of the adjustable potential of multi-customer load is analyzed, the feature label system is constructed, the relevant features of the adjustable potential are selected by the grey relational degree analysis method, and the adjustable potential assessment model is built to realize the assessment of the load control ability of multi-customer load. The accuracy of the assessment model is verified according to the actual response results, and the user load and demand response data are regularly updated. Dynamic evaluation of userside adjustability potential enables the model to adapt to changing user behavior.
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