Carbon-Aware Electricity Cost Minimization for Sustainable Data Centers

2017 ◽  
Vol 2 (2) ◽  
pp. 211-223 ◽  
Author(s):  
Hui Dou ◽  
Yong Qi ◽  
Wei Wei ◽  
Houbing Song
2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Shuben Zhang ◽  
Jian Yang ◽  
Youkang Shi ◽  
Xiaomin Wu ◽  
Yongyi Ran

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 applyQ-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 theQ-Learning based algorithm, we introduce a SpeedyQ-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.


2019 ◽  
Vol 11 (18) ◽  
pp. 4937 ◽  
Author(s):  
Jing Ni ◽  
Bowen Jin ◽  
Shanglei Ning ◽  
Xiaowei Wang

The energy consumption of fast-growing data centers is drawing attentions from not only energy organizations and institutions all over the world, but also charity groups, such as Greenpeace, and research shows that the power consumption of air conditioning makes up a large proportion of the electricity cost in data centers. Therefore, more detailed investigations of air conditioning power consumption are warranted. Three types of airflow distributions with different aisle layouts (the open aisle, the closed cold aisle, and the closed hot aisle) were investigated with Computational Fluid Dynamics (CFD) methods in a typical data center of four rows of racks in this study. To evaluate the results of thermal and bypass phenomenon, the temperature increase index (β) and the energy utilization index (ηr) were used. The simulations show that there is a better trend of the β index and ηr index both closed cold aisle and closed hot aisle compared with free open aisle. Especially with high air flow rate, the β index decreases and the ηr index increases considerably. Moreover, the results prove the closed aisles (both closed cold aisle and closed hot aisle) can not only significantly improve the airflow distribution, but also reduce the mixture of cold and heat flow, and therefore improve energy efficiency. In addition, it proves the design of the closed aisles can meet the increasing density of installations and our simulation method could evaluate the cooling capacity easily.


2014 ◽  
Vol 1 (3) ◽  
pp. 255-264 ◽  
Author(s):  
Liang Yu ◽  
Tao Jiang ◽  
Yang Cao ◽  
Qi Qi

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