Optimization Strategy of Unit Peak Load and Frequency Regulation Based on Improved Particle Swarm Optimization Algorithm

With the ever—increasing complexity of electric power systems, he peak and frequency regulation capabilities of the units have become a key element in maintaining the safe and stable operation of the power system. However, when faced with large-scale and highly complex power grid scheduling challenges, traditional unit peak shaving and frequency regulation optimization strategies often expose problems of poor efficiency and unsatisfactory results. Given the shortcomings of the current optimization strategy for unit peak shaving and frequency regulation, this paper innovatively proposes an optimization strategy relying on the enhanced particle swarm optimization algorithm, aiming to significantly improve the efficiency and effectiveness of unit peak shaving and frequency regulation. The paper begins by elaborating on the research background, objectives, and significance of unit peak shaving and frequency regulation optimization, and deeply analyzes the limitations of traditional optimization strategies. In the methodology section, the improved particle swarm optimization algorithm will be introduced into the field of unit peak shaving and frequency regulation optimization. By constructing a fitness function to evaluate the superiority and inferiority of scheduling schemes, while fully considering constraints such as unit output limitations and ramp rates, the feasibility of the schemes can be ensured. After repeated iterative optimization, the optimal unit scheduling scheme was finally obtained. At the end of the paper, the effectiveness of the proposed strategy was verified through experiments, and the experimental results were discussed in depth. The experimental data shows that during the entire testing process, the optimization of peak shaving and frequency regulation of the unit showed small fluctuations, and the peak shaving depth remained between 40% and 50%, and showed a slight upward trend in the later stage. Compared with traditional strategies, the optimization strategy for unit peak shaving and frequency regulation based on improved particle swarm optimization algorithm performs better in improving peak shaving and frequency regulation efficiency and effectiveness, and has extremely high practical application value.