基于非侵入式负荷监测的居民侧灵活性资源评估方法

李俊楠; 何心铭; 周慧娟; 肖渝舰; 刘云飞; 赵雯雯; 臧天磊

李俊楠,何心铭,周慧娟,肖渝舰,刘云飞,赵雯雯,臧天磊.基于非侵入式负荷监测的居民侧灵活性资源评估方法[J].电测与仪表,2024,61(6):148-158.
LI Junnan,HE Xinming,ZHOU Huijuan,XIAO Yujian,LIU Yunfei,ZHAO Wenwen,ZANGTianlei.Assessment method of residential-side flexibility resources based on non-intrusive load monitoring[J].Electrical Measurement & Instrumentation,2024,61(6):148-158.

基于非侵入式负荷监测的居民侧灵活性资源评估方法

Assessment method of residential-side flexibility resources based on non-intrusive load monitoring

DOI：10.19753/j.issn1001-1390.2024.06.020

中文关键词: 非侵入式负荷监测时域卷积网络门控循环单元灵活性资源评估

英文关键词:non-intrusive load monitoring, time-domain convolutional networks, gated cyclic units, flexibility resource assessment

基金项目:国家自然科学基金项目（No. 51907097）

作者中文名	作者英文名	单位
李俊楠	LI Junnan	国网河南省电力公司营销服务中心
何心铭	HE Xinming	国网河南省电力公司营销服务中心
周慧娟	ZHOU Huijuan	国网河南省电力公司营销服务中心
肖渝舰	XIAO Yujian	四川大学电气工程学院
刘云飞	LIU Yunfei	四川大学电气工程学院
赵雯雯	ZHAO Wenwen	四川大学电气工程学院
臧天磊	ZANGTianlei	四川大学电气工程学院

摘要点击次数: 693

中文摘要:

居民用户具备巨大的灵活性潜力，充分挖掘并合理利用居民侧灵活性资源有助于提升电网的灵活性。文章利用低频功率数据和深度学习模型，提出了一种基于非侵入式负荷监测的居民侧灵活性资源评估方法。文中采用功率波动-跳变事件检测算法，实现对电器用电事件的定位和功率数据获取。将时域卷积网络(time convolutional networks，TCN)和门控循环单元(gated recurrent unit，GRU)相结合，借助TCN的数据特征提取能力和GRU的非线性拟合能力，构建TCN-BiGRU负荷识别算法，以有效区分不同电器的用电负荷。利用负荷识别结果对用户总功率信号进行分解，建立设备状态矩阵、设备概率矩阵和设备习惯使用区间矩阵，获取各个电器的用电信息，分析用户用能行为，得到居民侧灵活性资源评估详细结果。通过实际居民用户数据，验证了所提方法的实际有效性。基于所提方法所得的灵活性资源评估结果可为居民需求侧响应提供辅助决策。

英文摘要:

Residential users have huge flexibility potential, and the full exploiting and reasonable utilizing residential-side flexibility resources can help to improve the flexibility of the power grid. In this paper, an assessment method of residential-side flexibility resources based on non-intrusive load monitoring is proposed through using low-frequency power data and deep learning models. A power fluctuation-skipping event detection algorithm is used to realize the localization of appliance power events and power data acquisition. Time convolutional networks (TCNs) and gated recurrent units (GRUs) are combined to construct a TCN-BiGRU load recognition algorithm with the help of the data feature extraction capability of TCNs and the nonlinear fitting capability of GRUs to efficiently differentiate the electricity loads of different appliances. The load identification results are used to decompose the total power signal of users, establish the equipment state matrix, equipment probability matrix and equipment habitual use interval matrix, obtain the power consumption information of each appliance, analyze the energy consumption behavior of users, and obtain the detailed results of the flexibility resource assessment on the residential side. The practical effectiveness of the proposed method is verified by actual residential user data. The flexibility resource assessment results obtained based on the proposed method can provide auxiliary decision-making for residential demand-side response.

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