The application of Computing Power Network (CPN) in wide-area environments offers users efficient and flexible computational power supply services. To meet users’ computing demands while minimizing brown energy consumption, this paper proposes a Green Energy-Aware Hybrid Service Scheduling (GEHS) strategy. A detailed model encompassing computation, communication, and energy consumption is constructed, incorporating a differentiated cost strategy based on green energy supply states. By considering task computational preferences and latency sensitivity, the model intelligently schedules computing resources in CPN, leveraging heterogeneous resources (such as CPU and GPU) to handle diverse types of computational tasks. The proposed GEHS algorithm establishes a balance between task Quality of Service (QoS) and brown energy consumption, with experimental simulations validating the algorithm’s convergence and effectiveness. This optimized scheduling strategy ultimately achieves an optimal balance between resource utilization and green energy usage efficiency.