Control method of wide-area distributed household regenerative electric heating clusters

被引：0
|
作者：
Yang G. [1 ,2 ]
Wang W. [1 ]
Yao H. [3 ]
Guo X. [2 ]
Yuan T. [3 ]
机构：
[1] School of Electrical Engineering, Xinjiang University, Urumqi
[2] State Grid Xinjiang Electric Power Co.，Ltd., Urumqi
[3] School of Electrical Engineering, Dalian University of Technology, Dalian
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2023年 / 43卷 / 08期
基金：
中国国家自然科学基金;
关键词：
convolutional neural network; flexible load; particle swarm optimization algorithm; regenerative electric heating;
D O I：
10.16081/j.epae.202303009
中图分类号：
学科分类号：
摘要：
Aiming at the problems of large dimension and difficult regulation of the spatial and temporal distribution characteristics of wide-area distributed household regenerative electric heating（REH） load for new energy consumption modulation，a REH control method based on historical data is proposed. According to the information such as user house parameters，REH type，and so on，the REH users are clustered to reduce the solving dimension of the strategy. Considering the user comfort and the REH operation constraints，the corresponding objective functions are established according to different control objectives. The particle swarm optimization algorithm is used to optimize the historical power consumption strategy of each REH cluster. The relationship between the learning state characteristics and the power consumption strategy of REH is studied by using the convolutional neural network，and the real-time operation strategy of REH is generated based on this convolutional neural network. Based on the power generation data of a region in winter，Monte Carlo method is used to simulate the power demand of 10 000 REH units and the simulation analysis is carried out to verify the effectiveness of the proposed method. The results show that the proposed method can effectively promote the consumption of new energy，reduce the heating cost of users and smooth load fluctuation under the premise of meeting the timeliness of strategy generation and user comfort. © 2023 Electric Power Automation Equipment Press. All rights reserved.
引用
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页码：56 / 62
页数：6
相关论文
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