Research on Distribution Network Resilience Assessment and Optimization Method Based on Flexible Resources

This paper aims to enhance the power restoration capability of critical nodes in an active distribution network under extreme weather and random fault conditions, proposing a flexible resource-based resilience evaluation method. By constructing four resilience indicators–distributed energy power support ratio, total power restoration time, mobile emergency power vehicle support rate, and energy loss percentage to systematically assesses the impact of flexible resources, such as distributed energy sources and mobile emergency power vehicles, on the power restoration capability of critical nodes. The TOPSIS method is used to determine the baseline values of these resilience indicators, and the multiple correlation coefficient is introduced to quantify the weights of resilience indicators under multiple random fault scenarios. The resilience of network nodes is analyzed, and based on the multiple correlation coefficient, the weights of network node resilience are determined to assess the overall power restoration capability of the distribution network under multiple random fault scenarios. The paper simulates the probability of flooding using a Gumbel cumulative distribution function, and applies the Monte Carlo method to simulate the fault recovery process of the IEEE 33-node model under random fault scenarios. Optimization suggestions for flexible resource configuration at weak nodes in scenarios with insufficient power restoration capacity are proposed, and the effectiveness of resilience improvement is simulated and analyzed.