An Adaptive Grid Frequency Support Mechanism for Energy Management in Cloud Data Centers

Grid frequency support is one of the most challenging problems, since minor variations in it may lead to huge financial losses. This problem becomes even more challenging with the horizontal and vertical expansion of modern cloud data centers (DCs). In the past, several efforts have been made to manage frequency deviations using flywheels, commercial buildings, electric vehicles, and renewable energy resources. However, these are not adequate due to their complex operations. To fill these gaps, in this paper, we propose the usage of cloud DCs and uninterruptible power supply (UPS) units for the effective frequency regulation. This is achieved by designing a 2-layer hierarchical control scheme for optimal segregation of the regulation signals amongst the DCs and UPS batteries. The proposed scheme determines the scheduling policy for jobs at DCs; along with the charging and discharging cycles of the UPS batteries. The job scheduling is carried out with respect to MapReduce tasks in accordance with the regulation signals with minimal service level adherence violations. Additionally, the sustainability of DCs is also supported through active participation of UPSes during peak hours. The overall frequency support problem in the considered setup involves multiple objectives under multiconstraint environment. Thus, it has been divided in two subproblems, which are addressed individually using either integer linear programming or mixed integer linear programming. In a nutshell, the proposed scheme serves dual purposes, i.e., manages frequency fluctuations and sustains DCs during peak hours. It has been validated using data traces taken from OpenCloud Hadoop cluster and Pennsylvania New Jersey Maryland. The results obtained prove the effectiveness of the proposed scheme for frequency support and DC's sustainability.