Optimal Scheduling of Data Center Clusters with Distributed and Shared Energy Storage Under a Carbon Trading Mechanism
The rapid growth in data storage and computing demand has substantially increased the energy consumption and carbon emissions of the data center (DC). Energy storage systems facilitate multi-source energy coordination, while DCs are playing an expanding role in the demand response (DR) program. This paper proposes an optimal energy and workload dispatch model for a DC cluster (DCC) integrating distributed energy storage (DES) and shared energy storage (SES) under the carbon trading mechanism. A Mixed-Integer Nonlinear Programming (MINLP) model is formulated to minimize the economic objective, including the electricity-related cost and carbon trading cost. Case study results show that, compared with a DES-only configuration, the DCC incorporating both DES and SES can achieve a 46.19% reduction in cost and a 54.02% reduction in carbon emission. DR participation yields further reductions in both cost and carbon emission. The scalability of the proposed model is validated by evaluating its computational performance across DCC instances with varying numbers of constituent DCs. The study also examines the effects of carbon prices and energy storage capacities on DCC performance. A higher carbon price reduces carbon emissions but increases costs, whereas larger DES and SES capacities reduce both. The proposed model proves solvable and well-behaved across a wide range of renewable energy generation conditions and computing workloads. This research provides practical implications for the sustainable development of DCs under the carbon trading mechanism.