Dynamic directed graph convolution network based ultra‐short‐term forecasting method of distributed photovoltaic power to enhance the resilience and flexibility of distribution network

Abstract

Accurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on the reliability and resilience of the distribution network. However, most of the current photovoltaic power forecasting methods suffer from two key problems: (1) ignoring the asymmetric influence relationship among DPV sites; (2) lack of consideration of dynamic spatiotemporal correlation among DPV sites. As a result, these methods are unable to fully adapt to the characteristics of DPV, making it challenging to directly apply the existing forecasting methods to improve the accuracy of DPV power forecasting. To conquer this limitation, a dynamic directed Graph Convolution Neural Network (DDGCN) is applied to regional DPV ultra‐short‐term power forecasting. Unlike the conventional Graph Convolution Neural Network (GCN) based forecasting methods, the proposed method improves GCN to process the directed graph. On this basis, to capture the dynamic and directed adjacency relationship among graph nodes, a temporal attention mechanism is proposed and combined with the directed GCN model. In this way, the dynamic and asymmetric/directed relationships among DPV sites can be taken into account. It is worth noting that the DPVs’ adjacency relationship can be constructed without any prior knowledge by end‐to‐end model training. The simulation experiment proves that the prediction accuracy can be further improved by taking into account the dynamic directed relationship among the sites via a real DPV power dataset.