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-driven method

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,因此提出了一种实时调度策略旨在降低配电网的线损。文章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;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;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。

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,the deep bidirectional long and short-term memory network(BI-LSTM)is used 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.