基于神经网络的光伏集成主动配电网动态等值

As the distribution systems (DSs) are transitioning into active distribution networks (ADNs) with increasing photovoltaic (PV) penetration, computationally tractable dynamic models become necessary for real-time analysis and control. This article presents a two-stage data-driven modeling framework based on NARX type recurrent neural networks (RNNs) to develop dynamic equivalent models (DEMs) for smart PV systems and PV-integrated ADNs. Initially, DEMs are developed to capture the nonlinear power dynamics of residential PV systems with ancillary voltage support. The framework is further extended to autonomously capture substation-level ADN power exchange across varying PV penetration levels. The practical implementation of the DEMs is supported by integration strategy that allows seamless coupling of the data-driven models with conventional modeling counterparts. The proposed approach is validated on modified IEEE 123-node and 8500-node test feeders under unbalanced loading and dynamic environmental profiles. Case studies on 123-node feeder show that the RNN-based DEM of ADN achieves average MSEs of 4.12 10^-4 p.u. for active power and 7.04 10^-4 p.u. for reactive power, while maintaining robustness across PV penetration levels up to 60%. Importantly, the proposed DEM provides over 1000 simulation time speed-up compared to detailed model-based PV-integrated ADNs for the larger test feeder, offering a scalable solution for both DSOs and TSOs in dynamic power flow studies.