2011 · 441 citations · 15 references
EngineeringOperations ManagementEnergy ManagementEnergy SystemsOptimizationEnergy Management SystemsEnergy StoragePower ControlPower ManagementPower SystemsUninterruptible Power Supply SystemsData Center Energy OptimizationEnergy Storage OptimizationEnergy Storage System OptimizationLyapunov ControlLyapunov FunctionsLyapunov StabilityOptimal Power ControlStorage Capacity
Electricity bills comprise a major share of data‑center operating costs, and using UPS units as energy storage rather than only as fail‑over devices offers a new way to cut these expenses. The study aims to determine how UPS‑based storage can be leveraged to lower the time‑average utility bill in a data center. An online control algorithm based on Lyapunov optimization is proposed, which optimally exploits UPS storage without requiring prior knowledge of workload or price statistics. The algorithm’s performance gap shrinks as storage capacity grows, and the work establishes a new direction for data‑center power management.
Since the electricity bill of a data center constitutes a significant portion of its overall operational costs, reducing this has become important. We investigate cost reduction opportunities that arise by the use of uninterrupted power supply (UPS) units as energy storage devices. This represents a deviation from the usual use of these devices as mere transitional fail-over mechanisms between utility and captive sources such as diesel generators. We consider the problem of opportunistically using these devices to reduce the time average electric utility bill in a data center. Using the technique of Lyapunov optimization, we develop an online control algorithm that can optimally exploit these devices to minimize the time average cost. This algorithm operates without any knowledge of the statistics of the workload or electricity cost processes, making it attractive in the presence of workload and pricing uncertainties. An interesting feature of our algorithm is that its deviation from optimality reduces as the storage capacity is increased. Our work opens up a new area in data center power management.
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