Robust Energy Management and Workload Allocation in Islanded Data Center Microgrids with Data-Driven Risk-Tunable Approach

The rapid development of cloud computing, Big Data, and artificial intelligence has driven a surge in data center energy demand, while traditional power grids struggle to meet their high reliability and low-carbon requirements. To address this challenge, this paper proposes a co-optimization framework for islanded data center microgrids and develops a two-stage robust optimization model. The model leverages a coordinated control layer to centralize decision-making for the energy supply and computing resource layers, enabling joint optimization of energy management and workload allocation while minimizing total costs. For computing resources optimization, a cross-regional communication network is established to facilitate coordinated workload spatial allocation among geographically distributed data centers, enhancing server resource utilization and optimizing energy consumption. A quality of service constraint mechanism using dynamic voltage and frequency scaling technology is developed, alongside an average response time analysis based on queuing theory. To address uncertainties in wind power generation, load demand, and outdoor temperature, a data-driven risk-tunable modeling approach is proposed. The method constructs distributionally robust chance-constrained programming using Wasserstein distance, transforms it into a risk-bound optimization problem, and designs a polyhedral uncertainty set that adaptively adjusts to risk levels and sample sizes. Finally, case studies demonstrate the effectiveness of the proposed model in improving data center resource utilization, reducing energy consumption, and optimizing robustness adjustment capabilities.