Research and Application of Artificial Intelligence Optimization Algorithm in Power Grid Planning

Smart grid planning relies heavily on research and application, but the issue of imprecise planning and positioning persists. The conventional bee colony algorithm fails to address the research and application challenges in smart grid planning adequately, resulting in unsatisfactory outcomes. Hence, this study proposes the adoption of an artificial intelligence optimization algorithm for power grid planning research and application, while also analyzing its effectiveness. Initially, the simulated annealing theory is employed to identify influential factors and categorize indicators based on planning research and application requirements, thereby minimizing interference factors. Subsequently, the simulated annealing theory is utilized to formulate a comprehensive planning research and application scheme using the artificial intelligence optimization algorithm, followed by an extensive analysis of the planning research and application results. MATLAB simulation results demonstrate that, under specific evaluation criteria, the artificial intelligence optimization algorithm surpasses the traditional bee colony algorithm in terms of accuracy and time required for planning research and application influencing factors.