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Dynamic Energy Storage Control for Reducing Electricity Cost in Data Centers

机译：动态储能控制可降低数据中心的用电成本

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摘要

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 Q-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 Q-Learning based algorithm, we introduce a Speedy Q-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.
机译：随着数据中心规模的扩大，电力成本已成为其运营成本中增长最快的要素。在本文中，我们研究了在不间断电源设备（UPS）中使用数据中心中的储能设施来降低电费的机会。其基本思想是将电价的时间多样性和储能相结合，以构想降低电费的策略。电力成本最小化是在有限状态作用折现成本马尔可夫决策过程（MDP）的框架内制定的。我们应用Q学习算法来解决MDP优化问题，并得出动态储能控制策略，该策略不需要有关马尔可夫过程的任何先验信息。为了解决基于Q学习的算法的慢收敛问题，我们引入了快速Q学习算法。我们进一步讨论离线优化问题，并获得最优的离线解决方案，作为在线和学习理论问题性能的下限。最后，我们通过使用实际工作量跟踪和电价数据集来评估所提出方案的性能。实验结果表明了该方案的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第1期|380926.1-380926.13|共13页
  • 作者

    Zhang Shuben; Yang Jian; Shi Youkang; Wu Xiaomin; Ran Yongyi;

  • 作者单位

    Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230027, Peoples R China.;

    Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230027, Peoples R China.;

    Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230027, Peoples R China.;

    Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230027, Peoples R China.;

    Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230027, Peoples R China.;

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