Data-Driven Detection and Capacity Estimation of User-Side Photovoltaics in Active Distribution Networks

With the increasing integration of low-voltage distributed photovoltaics (PVs), effective nonintrusive detection methods of user-side PV have become critical for the operators of active distribution networks. However, existing methods are mostly offline, requiring long-term data accumulation and lacking rapid response capability. This study proposes a data-driven method for fast detection and capacity estimation of user-side PVs. A cloud-edge collaborative framework is constructed to maximize the utilization of data features from smart meters. Within this framework, a lightweight rolling detection and capacity estimation method is proposed, reducing the required data volume and thus enhancing efficiency. A validation model based on historical data is developed in addition to ensuring the reliability of estimation results. Case studies verify that, with only 4 h of data, the proposed method can attain accuracies similar to existing methods that require multiple days of data. In addition, it can capture the uncertain variations in PV capacity within a short time, enabling operational applications such as PV monitoring and control in active distribution networks.