Research on Dynamic Optimization Scheduling of New Energy AGC Units Based on Improved Genetic Algorithm

In order to improve the power stability of new energy AGC (Automatic Generation Control) units and reduce the dynamic regulation amount, this study proposes a dynamic optimization scheduling method for new energy AGC units based on the improved genetic algorithm. The objective function with the average value of equivalent load variation as the core was constructed. Considering the collaborative operation characteristics of wind power, photovoltaic power and energy storage comprehensively, a mathematical model that takes into account both system stability and economy was established. Aiming at the defect that the traditional genetic algorithm is prone to fall into local optimum, an improved strategy of dynamically adjusting the crossover probability and mutation probability is innovatively proposed. In the early stage of the algorithm, a high crossover probability is adopted to promote global search, and in the later stage, the mutation probability is increased to enhance the local optimization ability, effectively balancing the convergence speed of the algorithm and the solution accuracy. The experimental results show that the method proposed in this paper is superior to the comparison schemes in both power fluctuation control and dynamic regulation quantity optimization. It can significantly improve the dispatching performance of new energy AGC units, reduce the power fluctuation amplitude and regulation cost, thereby effectively enhancing the stability and operational efficiency of the power system.