Fine-Grained Scheduling Strategies and Optimization of Wind–Solar–Storage Powered Data Center Microgrids

This study presents a comprehensive collaborative optimization strategy for the efficient allocation of wind, solar, and energy storage capacities in data center microgrids. The proposed strategy systematically integrates multiple decision-making factors, including the intensity of renewable power generation, real-time electricity prices, and task completion times, with the dual objectives of minimizing total operational costs and carbon emissions while maximizing user satisfaction. To address the heterogeneous nature of data center workloads, the strategy differentiates between interactive and batch tasks, leveraging their respective scheduling flexibilities. Thirteen distinct time window models are employed to fully exploit temporal flexibility, enabling tasks to be adaptively shifted in response to renewable generation and price fluctuations. The optimization framework ensures that renewable energy is prioritized while meeting system reliability constraints. Simulation results under various capacity configurations validate that the proposed approach significantly improves renewable energy utilization, reduces electricity expenses, and enhances user-perceived service quality. By offering both a theoretical framework and practical implementation guidelines, this research provides valuable insights into capacity planning, operational scheduling, and sustainable management of next-generation data center microgrids.