Dynamic Energy Storage Control for Reducing Electricity Cost in Data Centers

As the scale of the data centers increases, electricity cost is becoming the fastest-growing element in their operation costs. In this paper, we investigate the electricity cost reduction opportunities utilizing energy storage facilities in data centers used as uninterrupted power supply units (UPS). Its basic idea is to combine the temporal diversity of electricity price and the energy storage to conceive a strategy for reducing the electricity cost. The electricity cost minimization is formulated in the framework of finite state-action discounted cost Markov decision process (MDP). We apply -Learning algorithm to solve the MDP optimization problem and derive a dynamic energy storage control strategy, which does not require any priori information on the Markov process. In order to address the slow-convergence problem of the -Learning based algorithm, we introduce a Speedy -Learning algorithm. We further discuss the offline optimization problem and obtain the optimal offline solution as the lower bound on the performance of the online and learning theoretic problem. Finally, we evaluate the performance of the proposed scheme by using real workload traces and electricity price data sets. The experimental results show the effectiveness of the proposed scheme.