Predictive Electricity Cost Minimization Through Energy Buffering in Data Centers

Abstract

More and more cloud computing services are handled by different Internet operators in distributed Internet data centers (IDCs), which incurs massive electricity costs. Today, the power usage of data centers contributes to more than 1.5% market share of electricity consumption across the United States. Minimization of these costs benefits cloud computing operators, and attracts increasing attentions from many research groups and industrial sectors. Along with the deployment of smart grid, the electrical real-time pricing policy promotes power consumers to adaptively schedule their electricity utilization for lower operational costs. This paper proposes a novel approach to enable electrical energy buffering in batteries to predictively minimize IDC electricity costs in smart grid. Batteries are charged when electricity price is low and discharged to power servers when electricity price is high. A power management controller is used per battery to arbitrate the charging and discharging actions of the battery. The controller is designed as a MPC-based (model predictive control) controller. To this end, an MPC power minimization problem is formulated based on a discrete state-space model with states of battery power level and cost. Extensive simulation results demonstrate the effectiveness of our approach based on real-life electricity prices in smart grid.

Optimal scheduling of a microgrid with demand response resources

“…Recently, the relevant literature has indicated that there also is increasing interest in the optimal operation of data centres [12, 13, 17–23]. The models proposed in the technical literature are related mainly to the control of only the data centre's energy consumption in order to improve efficiency and to give them the potential for participating in a demand‐response program [19–23]. In [19], the benefits of integrating data centres into demand‐response schemes were discussed.…”

“…In [22], a load‐control method for data centres was presented that was based on both the data network and the electrical network with the aim of controlling power usage associated with participation in the demand‐response program. In [23], an approach was proposed to enable electrical energy buffering in batteries to predictably minimise data centres’ electricity costs in smart grids. In this approach, the batteries are charged when the price of electricity is low, and they are discharged to power servers when the price of electricity is high.…”

Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework

“…Afterwards, a new approach regarding electricity cost reduction problem presents the internet payload requests and SG dynamic electricity pricing mechanism [27]. This approach also discusses the predictive cost control for the smart charging on both sides: (1) battery energy for the servers and (2) electricity from the power grid.…”

A Survey: To Harness an Efficient Energy in Cloud Computing

“…This is due to two reasons. One is manual faults like improper scheduling and work overload [5,10] and other is due to hike in monetary cost of electricity [13]. The manual faults can be resolved by using monitoring and virtualization [12].…”