基于数据驱动的有源配电网实时调度降损策略

夏革非; 丁智涵; 于长任; 张慧敏; 张海峰; 吴乃月

夏革非,丁智涵,于长任,张慧敏,张海峰,吴乃月.基于数据驱动的有源配电网实时调度降损策略[J].电测与仪表,2022,59(12):103-109.
XIA Gefei,DING Zhihan,YU Changren,ZHANG Huimin,zhanghaifeng,WU Naiyue.Real time scheduling strategy for loss reduction of active distribution network based on data drive method[J].Electrical Measurement & Instrumentation,2022,59(12):103-109.

基于数据驱动的有源配电网实时调度降损策略

Real time scheduling strategy for loss reduction of active distribution network based on data drive method

DOI：10.19753/j.issn1001-1390.2022.12.014

中文关键词: 有源配电网数据驱动线损分析深度双向长短时记忆网络实时调度

英文关键词:active distribution network(ADN), data-driven, line loss analysis, deep bidirectional long-term and short-term memory network(BI-LSTM), real-time scheduling

基金项目:国家重点研发计划项目(2016YFB0900100)

作者中文名	作者英文名	单位
夏革非	XIA Gefei	国网冀北电力有限公司承德供电公司
丁智涵	DING Zhihan	国网冀北电力有限公司承德供电公司
于长任	YU Changren	国网冀北电力有限公司承德供电公司
张慧敏	ZHANG Huimin	北京中恒博瑞数字电力科技有限公司
张海峰	zhanghaifeng	北京大学动力中心
吴乃月	WU Naiyue	北京中恒博瑞数字电力科技有限公司

摘要点击次数: 1411

中文摘要:

农村配网通常接入有较大容量的小水电等分布式电源,在向上级电网反送电能的过程中造成了大量的线损,因此本文提出了一种实时调度策略旨在降低配电网的线损。首先,考虑分布式电源的有功无功控制以及有载调压分接头的控制,基于支路潮流模型建立了配电网调度降损模型；进一步,通过构造高维随机矩阵,从配电网运行时间序列数据中提取能够表征运行状态的特征作为输入,对配电网历史调节策略进行热编码作为输出；然后利用深度双向长短时记忆网络学习配电网特征与网络降损策略之间的函数映射关系,建立基于数据深度学习驱动的有源配电网实时调度降损模型。最后,基于实际有源配电网系统进行了仿真,仿真结果表明所提出的实时调度算法能在保证小水电上网收益的前提下,优化小水电的出力曲线,提高分布式电能的就地消纳率,从而降低了网损。

英文摘要:

The rural distribution network is usually connected to the distributed power supply with large capacity, such as small hydropower, which causes a lot of line loss in the process of reverse power transmission to the superior grid. Therefore, this paper proposes a real-time scheduling strategy to reduce the line loss of the distribution network. Firstly, considering the active and reactive power control of distributed generation and the control of on load tap changer, a loss reduction model of distribution network scheduling is established based on the branch power flow model; secondly, by constructing a high-dimensional random matrix, the characteristics that can represent the operation state are extracted from the operation time series data of distribution network as the input, and the historical regulation strategy of distribution network is thermally coded as the output; Then, using deep bidirectional long and short-term memory network to learn the function mapping between the characteristics of distribution network and the network loss reduction strategy, a real-time loss reduction model of active distribution network based on data deep learning driving is established. Finally, based on the actual active distribution system simulation, the simulation results show that the proposed real-time scheduling algorithm can optimize the output curve of small hydropower, improve the local absorption rate of distributed power, and reduce the network loss on the premise of ensuring the income of small hydropower network.

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