Robust Spatiotemporal Optimal Scheduling for Distributed Data Centers With Multiple Uncertainties

Geographically distributed data centers (DCs) offer significant spatiotemporal flexibility and are ideal candidates for demand response in power systems. This paper proposes a two-stage robust spatiotemporal optimal scheduling model for geographically distributed DCs that accounts for the uncertainty in both renewable energy generation and workload. In the first stage, a day-ahead scheduling approach is employed based on predicted workload and renewable energy generation values to determine pre-scheduling strategies. In the second stage, between the day-ahead and intraday periods, an iterative algorithm is introduced to calculate the number of reserved servers, thereby mitigating the workload overload issues arising from workload uncertainty. Power fluctuations resulting from the uncertainty of the predicted values are balanced using energy storage and generators. Finally, simulation results validate the effectiveness of the proposed algorithm in reducing overcapacity and enhancing the economic efficiency of the scheduling model.