This paper proposes a distributed wind and photovoltaic power generation modeling method based on swarm intelligence to deal with the uncertainty and complexity problems caused by the connection of distributed photovoltaic and wind power generation systems to the power grid. This method uses a differential evolution algorithm to optimize the search process through chromosome crossover and collaboration between individuals and the survival of the fittest mechanism. The core steps of the algorithm include: population initialization, mutation operation, crossover operation and selection operation. By analyzing the behavioral characteristics of photovoltaic systems and wind power generation systems, and using physical models and statistical models to predict them, a mathematical model of distributed wind and solar power sources was constructed. Research shows that this method can significantly improve the output prediction accuracy and grid dispatch efficiency of distributed photovoltaic and wind power generation systems, solve technical problems caused by distributed power supply access, and enhance the reliability and stability of the power system.