Abstract
The escalating penetration of distributed photovoltaics (DPV) frequently triggers voltage violations and degrades the static voltage stability within distribution networks. To effectively mitigate these challenges, this paper proposes a dynamic hosting capacity (DHC) assessment method that incorporates the static voltage stability margin (SVSM). First, a set of key stress scenarios (KSS)—specifically capturing extreme conditions of “maximum reverse power flow” and “maximum forward heavy loading”—is generated utilizing non-parametric probabilistic forecasting. Next, an optimization model designed to maximize PV hosting capacity is formulated, innovatively enforcing the SVSM as a hard constraint to guarantee system robustness during heavy-load transitions. To overcome the intensive computational burden inherent to traditional physical verification, a hybrid solution framework is introduced. This framework integrates a deep neural network (DNN) surrogate model with an advanced particle swarm optimization (PSO) algorithm. By combining the offline learning of nonlinear SVSM boundaries with online rapid predictions and dual physical validation, the method achieves quasi-real-time capacity assessment. Case studies conducted on a modified IEEE 33-bus system demonstrate that the proposed framework not only uncovers the latent limitations imposed by evening peak voltage stability risks—which are typically neglected by conventional approaches—but also significantly accelerates computational efficiency while preserving rigorous evaluation accuracy.