Economic dispatch strategy for wind-containing power systems considering cascading failures

The increasing integration of wind power into modern energy systems poses significant challenges for ensuring the security and reliability of operations. This paper employs historical wind power data alongside the genetic algorithm-backpropagation neural network to forecast wind power output. Additionally, it introduces a cascading failure prevention and control model that prioritizes economic efficiency. The model employs a two-tier optimization framework: The prevention stage optimizes the output potential of conventional generating units, ensuring that the wind power system’s safety margin adheres to predetermined constraints while minimizing operational costs. The inner layer computes the safety margin, which is subsequently used by the outer layer for constraint evaluation. Moreover, the paper proposes a solution algorithm based on the improved particle swarm optimization method to solve the preventive control model and validates the approach through simulations on the IEEE39 bus system. The results demonstrate that the proposed model effectively mitigates cascading failures while optimizing safety and minimizing costs.