Research on a data-driven evaluation model for assessing the capacity of distribution grids to accommodate distributed photovoltaic connections

A hosting capacity assessment model combining deep learning and robust chance-constrained optimization is proposed, enabling data-driven identification of the admissible operating region of AC/DC hybrid distribution networks under different PV penetration levels. Finally, the case studies verify that the proposed method can significantly improve assessment efficiency while maintaining accuracy, and intuitively reflect the impact of PV output uncertainty on the hosting capacity of AC/DC hybrid distribution networks, thereby providing effective support for the secure operation of distribution networks and the friendly integration of distributed PV.