基于数据驱动的非全实时观测配电网无功优化方法

现阶段配电网中量测设备覆盖率较低,只有部分节点的负荷数据可以实时采集得到,因此在配电网中进行实时无功优化时无法使用基于潮流计算的优化方法。考虑到以上情况,文章提出了一种基于数据驱动的非全实时观测配电网无功优化方法。该方法基于历史运行数据使用最优潮流离线生成无功优化策略,并通过训练神经网络构建可实时量测节点负荷数据和无功优化策略间的映射关系,实现对非全实时观测配电网的实时无功优化。最后基于改造的IEEE 33节点系统,将所提方法与传统九区图无功优化方法作对比,验证了所提方法的有效性。

Reactive Power Optimization of Partial Real-Time Visible Distribution Network Based on Data Driven

At present, the coverage of measuring equipment in distribution network is low, so only part of the nodes’ load data can be collected in real time. This situation makes it impossible to use the optimization based on power flow calculation in the real-time reactive power optimization of distribution network. Considering the above situation, this paper proposes a data-driven reactive power optimization method based on partial real-time visible distribution network. According to the historical operation data, the optimal power flow is used to generate the reactive power optimization strategy offline, and the mapping between the real-time measured node load data and the reactive power optimization strategy is established by training the neural network to realize the real-time reactive power optimization of the partial real-time visible distribution network. Finally, in the modified IEEE 33-bus system, the proposed method is compared with the 9-zone diagram method to verify the effectiveness of the proposed method.