含高渗透率分布式光伏配电网的线损预测方法

Method for Line Loss Prediction in Distribution Networks with High Penetration of Distributed Photovoltaic Systems

摘要: 针对含高渗透率分布式光伏的配电网线损预测问题，提出了一种基于BI-LSTM (Bidirectional Long-Short Term Memory，即双向长短期记忆网络)的线损预测方法：构建含分布式光伏配电网模型，并以改进高斯混合模型划分源荷典型运行场景，确定最佳场景数与聚类参数后，基于双向长短期记忆网络(BI-LSTM)构建线损预测模型实现高精度预测。通过某地区10 kV配电网实例，验证该方法在多场景下的有效性，并与传统极限学习机(ELM (Extreme Learning Machine))模型比较，证明其在预测精度与稳定性上更优，为电网降损和能效管理提供依据。

Abstract: Aiming at the line loss prediction problem in distribution networks with high penetration of distributed photovoltaics (PV), a line loss prediction method based on Bidirectional Long-Short Term Memory (BI-LSTM) is proposed. A distribution network model with distributed photovoltaics is constructed, and the typical source-load operation scenarios are divided by an improved Gaussian Mixture Model. After determining the optimal number of scenarios and clustering parameters, a line loss prediction model based on BI-LSTM is built to achieve high-precision prediction. The effectiveness of this method in multiple scenarios is verified through a 10 kV distribution network example in a certain area. Compared with the traditional Extreme Learning Machine (ELM) model, it is proven to be superior in prediction accuracy and stability, providing a basis for power grid loss reduction and energy efficiency management.