Application of Artificial Intelligence in Wind Power New Energy Grid Integration Planning and Scheduling

Wind power integration into existing energy grids presents significant challenges in managing variable energy generation, grid stability, and scheduling optimization. Factors such as fluctuating wind power output, conventional grid generation, battery storage capacity, and varying load demands influence system performance in achieving effective energy distribution and integration. A novel bio-inspired resilient manta ray foraging (Res-MRF) optimization algorithm is applied to address these challenges in the actual-time planning and scheduling of power generation, storage, and load management in a wind-powered new energy grid. Initially, data were collected that comprise power generated by wind turbines. Then preprocessing using Z-score normalization is performed to standardize data effectively. Further, a maximum power point tracking algorithm based on the wind speed values from the dataset is implemented to predict the maximum power output. The suggested method is implemented using Python software. Finally, the proposed Res-MRF-based approach intelligently allocates resources, balances grid load, and reduces operational costs while ensuring sustainable energy delivery and user demand satisfaction. Extensive simulations validate the efficacy of the Res-MRF technique, demonstrating efficient reduction in total energy costs and improved grid efficiency, while ensuring that renewable energy resources are effectively utilized. These results highlight the potential of artificial intelligence-driven optimization in enhancing the integration of wind power into new energy grids.