Optimization Scheduling Strategy for New Energy Grids Based on Computer Intelligent Algorithms

The efficient and stable operation of the new energy grid has become the focus of power system research due to the transformation of the global energy structure and the rapid development of new energy technology. This paper proposes a new energy grid optimization scheduling strategy based on computer intelligent algorithm. By analyzing the existing new energy grid scheduling problems and combining the superiority of intelligent algorithms, this paper adopts PSO-GA (Particle Swarm Optimization-Genetic Algorithm) based algorithm for optimal scheduling research, and conducts comparative experiments with LP (Linear Programming) and DP (Dynamic Programming) algorithms, and comparative experiments are carried out with LP (Linear Programming) and DP (Dynamic Programming) algorithms. First, in the stability assessment, the PSO-GA algorithm has a standard deviation of 0.05 Hz for frequency fluctuation and 2 V for voltage fluctuation. Second, in the scheduling efficiency assessment, the PSO-GA algorithm has a response time of 1.6 MW, and a load matching degree of 0.98. In the final cost-benefit assessment, the PSO-GA algorithm has an average total operating cost of $5000, and a unit energy cost is $50/MW. From the data conclusions, PSO-GA algorithm outperforms the traditional LP and DP algorithms in improving grid stability, dispatch efficiency and cost-effectiveness.