The expensive cost and intermittent availability of renewable energy bring great challenges to its efficient utilization in green data centers. In this paper, we propose a new way to achieve an explicit trade-off between operational cost and carbon emission by dynamic storing off-site renewable energy in distributed data centers. We first formulate a constrained stochastic optimization problem for cost minimization of data centers. Then, by leveraging Lyapunov optimization theory, we design an online Carbon Capped Cost Minimization algorithm (CCCM) to achieve a near-optimal cost with rigorous mathematical proof. Specially, the decisions at each time slot are determined with an efficient iterative algorithm based on the Generalized Benders Decomposition (GBD) technique. Finally, extensive simulations are conducted to show the effectiveness of our algorithm. The results show that our algorithm can save about 6% total costs compared with the algorithm without offsite energy storage.

昂贵的成本和可再生能源的间歇性可用性给其在绿色数据中心的有效利用带来了巨大挑战。在本文中，我们提出了一种通过在分布式数据中心中动态存储场外可再生能源来实现运营成本与碳排放之间显式权衡的新方法。我们首先为数据中心的成本最小化制定一个约束随机优化问题。然后，利用李雅普诺夫（Lyapunov）最优化理论，设计了一种在线碳上限成本最小化算法（CCCM），以严格的数学证明来实现接近最佳的成本。特别地，每个时隙的决策都是基于通用Benders分解（GBD）技术的高效迭代算法确定的。最后，进行了广泛的仿真，以证明我们算法的有效性。结果表明，与没有异地能量存储的算法相比，我们的算法可以节省大约6％的总成本。