Estimating the maximum energy-saving potential based on IT load and IT load shifting

Zhu, Kai; Cui, Zhuo; Wang, Yabo; Li, Hailong; Zhang, Xiaojing; Franke, Carsten

doi:10.1016/j.energy.2017.07.092

Cited by 25 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…erefore, in terms of the energy efficiency optimization, we are interested in the thermal impact of various switch strategies [14,15]. e maximization of economic benefit for DCs is a complex problem because it is with many factors and restrictions.…”

Section: Related Workmentioning

confidence: 99%

“…It can be considered that the model is feasible. In order to evaluate the selected scheme, RHI (Return Heat Index) and RTI (Return Temperature Index) are introduced to conduct comprehensive evaluation [15,23], where RHI represents the utilization ratio of the air-cooling capacity of the rack, and RTI evaluates the air distribution in the DC. Details are shown in Table 1.…”

Section: Complexitymentioning

confidence: 99%

See 1 more Smart Citation

A Dynamic Thermal-Allocation Solution to the Complex Economic Benefit for a Data Center

et al. 2020

View full text Add to dashboard Cite

Data centers, which provide computing services and gain profits, are indispensable to every city in the information era. They offer computation and storage while consuming energy and generate thermal discharges. To maximize the economic benefit, the existing research studies on the data center workload management mostly leverage the dynamical power model, i.e., the power-aware workload allocation. Nevertheless, we argue that for the complex relationship between the economic benefit and so many attributes, such as computation, energy consumption, thermal distribution, cooling, and equipment life, the thermal distribution dominates the others. Thus, thermal-aware workload allocation is more efficient. From the perspective of economic benefits, we propose a mathematical model for thermal distribution of a data center and study which workload distribution could determinately change the thermal distribution in the dynamic data center runtime, so as to reduce the cost and improve the economic benefits under the guarantee of service provisioning. By solving the thermal environment evaluation indexes, RHI (Return Heat Index) and RTI (Return Temperature Index), as well as heat dissipation models, we define quantitative models for the economic analysis such as energy consumption model for the busy servers and cooling, energy price model, and the profit model of data centers. Numerical simulation results validate our propositions and show that the average temperature of the data center reaches the best values, and the local hot spots are avoided effectively in various situations. As a conclusion, our studies contribute to the thermal management of the dynamic data center runtime for better economic benefits.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

A Dynamic Thermal-Allocation Solution to the Complex Economic Benefit for a Data Center

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The ITE results in a much more intensive cooling load than typical equipment in commercial facilities such as office buildings. Previous research has demonstrated significant energy saving potential on both the ITE side [3] and the HVAC system side [4][5][6][7]. With more aggressive adoption of energy efficiency strategies, the electricity demand of data centers is projected to decrease by 45 percent compared to the current efficiency trends [1].…”

Section: Introductionmentioning

confidence: 99%

Prototype energy models for data centers

Sun

Luo

et al. 2021

Energy and Buildings

View full text Add to dashboard Cite

Data centers in the United States consume about two percent of the nation's electricity. Because heat gains from IT equipment drive cooling demand, data centers offer unique opportunities for energy savings. However, no prototype energy model for data centers is available in the suite of existing U.S. Department of Energy's Commercial Prototype Building Models. This study presented the development of two new data center prototype models and their implementation in OpenStudio and EnergyPlus. The small-size data center model represents a computer room in a building served by computer room air conditioners (CRACs); while the large-sized model represents stand-alone data centers served by computer room air handlers (CRAHs) with a central chiller plant. For each data center model, two levels of IT equipment (ITE) load density were considered, to cover the wide range of IT power density of data centers: 40 and 100 W/ft 2 (430 and 1,076 W/m 2) for the computer room, and 100 and 500 W/ft 2 (1,076 and 5,382 W/m 2) for the standalone data center. All other assumptions, such as building envelope, lighting, HVAC efficiencies and schedules, were based on the minimal requirements of ASHRAE Standard 90.1 at various vintages. We introduced a novel concept of supply and return air approach temperatures to capture the essential effects of non-uniform airflow and temperature distribution in data centers. The approach temperatures were pre-computed by computational fluid dynamics (CFD) simulations for various configurations of ITE loads and airflow containment management in data centers. A new feature was developed in EnergyPlus to implement the approach temperature method. A case study was conducted to demonstrate the use of the data center models. The two data center models cover all U.S. climate zones and can be used to evaluate energy saving measures for data centers, as well as to support development of data center energy efficiency codes and standards.

show abstract

“…In addition, usage of raised-floors and containment of hot aisles can improve the efficiency of the airflow optimization [16]. One more way to achieve energy saving is to flexibly adapt the utilization of the coolers to actual IT-load [17], as well, the local fans to the actual temperature of corresponding CPUs [18]- [20]. Finally, the free-cooling supplying the outside air directly to the server rooms is considered as an effective decision improving energy efficiency in regions with cold climate [21]- [23].…”

Section: Introductionmentioning

confidence: 99%