In the era of the digital economy, the rapid development of cloud computing and artificial intelligence industries has made computing power an increasingly valuable strategic resource. However, the energy consumption and carbon emissions generated by computing applications are also rising sharply. In this context, the development of green computing has become an industry consensus and a necessity of our times. Optimizing the scheduling of computing resources has emerged as a critical approach to reducing energy consumption, lowering costs, and improving efficiency. This paper focuses on four specific optimization problems related to computing resource scheduling: peak-shifting scheduling of computational tasks, load balancing of containers, autoscaling of clusters, and uniform deployment of mixed services. For each of these optimization problems, we present corresponding mathematical models and optimization algorithms. Furthermore, we introduce an intelligent computing power scheduling system designed for industrial applications, along with the challenges faced during its implementation. This scheduling system has been successfully applied to various scenarios within Ant Group, including big data computing and database management, delivering significant benefits in terms of carbon neutrality and energy savings. The results demonstrate that the system not only reduces energy consumption but also enhances operational efficiency, offering a practical solution for enterprises to achieve sustainability goals. Finally, this paper discusses the challenges of computing power scheduling in the era of large AI models, where the growing complexity and scale of computations demand even more innovative approaches.