Abstract: Abstract: To fully harness the emergency power supply potential of distributed photovoltaics in the fault recovery stage of the distribution network， mitigate the impact of uncertainty in source-load output on the reliability of control schemes， this paper proposes an active distribution network dynamic fault recovery method based on a rolling optimization framework. Initially， employing the RTH-CNN-LSTM algorithm for short-term forecasting of distributed photovoltaic output to obtain predicted output during the fault recovery phase. Subsequently， considering the time required for fault line inspection， material constraints， and operational safety constraints of nodes， voltages， and power flow in the distribution network during the fault recovery phase， the fault recovery model for the distribution network is constructed from the perspectives of load recovery rate， strategic economics， and system reliability， integrating distributed photovoltaic forecast output， switch states， and network topology. Lastly， utilizing the rolling optimization framework， the Red-Tailed Hawk （RTH） optimization algorithm is employed to dynamically solve the fault recovery model， obtaining the optimal action strategy for line maintenance sequence， switch status， and load shedding amount. Using the IEEE 123-node test distribution network as a case study， simulation results demonstrate that the proposed rolling optimization method effectively reduces the impact of distributed photovoltaic output uncertainty on the effectiveness of recovery schemes， significantly enhancing the reliability and efficiency of fault recovery schemes.

Key words: Key words: active distribution network, photovoltaic prediction, fault recovery, network reconstruction, rolling optimization