Driving data center PUE, efficiency

When developing data center energy-use estimations, engineers must account for all sources of energy use in the facility.

06/09/2015


This article has been peer-reviewed.Learning objectives

  • Understand how to measure energy efficiency in a data center.
  • Learn which systems affect power usage effectiveness (PUE).
  • Know how to determine data center reliability.

For the last decade, power usage effectiveness (PUE) has been the primary metric in judging how efficiently energy is used in powering a data center. PUE is a simple energy-use ratio where the total energy of the data center facility is the numerator, and the energy use of the information technology (IT) systems is the denominator. PUE values theoretically run from 1 to infinity. But in real-life operations, well-designed, operated, and maintained data centers typically have PUE values between 1.20 and 1.60. Extremely low energy-use data centers can have a PUE of 1.10. Keep in mind that PUE can never be less than 1.0.

Future flexibility and scalability will keep long-term ownership costs low. This is especially important because IT systems evolve on a lifecycle of 12 to 18 months. This, however, can lead to short-term over-provisioning of power and cooling systems until the IT systems are fully built out. And even at a fully built-out stage, the computers, storage, and networking equipment will experience hourly, daily, weekly, and monthly variations depending on the type of computing performed. This double learning curve of increasing power usage over time plus ongoing fluctuations of power use can make the design and operation of these types of facilities difficult to optimize.

Figure 1: This graph shows the daily energy consumption of a typical data center for a 1-year period. All graphics courtesy: HP Data Center Facilities ConsultingThe concept of how PUE is calculated is relatively straightforward. However, putting the concept into practice requires a detailed approach, making sure to consider all elements that affect data center energy use. In addition, when conducting an energy-use simulation and analysis to determine PUE for a data center, it is important to include all available relevant information (at least what is known at the time of the study) in the simulation (see Figure 1). If specific input parameters are not known, industry standard values can be used, such as the minimum energy-efficiency ratings defined in ASHRAE 90.1: Energy Standard for Buildings Except Low-Rise Residential Buildings. Examples (not a complete list) include:

 

1. Overall system design requirements: These requirements generally describe a mode of operation or sequence of events needed to minimize energy use while maintaining the prerequisite conditions for the IT equipment.

  • a. Type of economizer cycle
  • b. If water, describe the control sequence and parameters to be measured and controlled for successful execution of the sequence (maximum/minimum outdoor temperatures and humidity levels).
  • c. If air, describe the control sequence and parameters to be measured and controlled for successful execution of the sequence (maximum/minimum outdoor temperatures and humidity levels).

2. Indoor environmental conditions: Depending on the indoor temperature and humidity parameters, significant amounts of energy can be saved by increasing the supply air temperature and lowering the humidity level. Determining the data center environmental conditions is an important step in the process:

  • a. Supply air temperature
  • b. Return air temperature
  • c. Minimum and maximum moisture content (grains of water per kilogram of air).

3. Power and efficiency parameters for systems and equipment

  • a. Air-handling unit fans
  • b. Compressors
  • c. Cooling system pumps
  • d. Heat-rejection system pumps
  • e. Heat-rejection fans
  • f. Lighting
  • g. Other miscellaneous electrical loads.

4. Efficiency of power-delivery systems

  • a. Incoming electricity transformers losses
  • b. Uninterruptible power supply (UPS) losses
  • c. Power distribution unit (PDU) losses
  • d. Wiring losses

5. IT load

  • a. What is the IT system operational load compared to design load?
  • b. Most power and cooling systems run less efficiently at partial load.
  • c. Most data centers never reach full power-use potential, so the facility will run at partial load virtually the entire life of the facility.

6. Building envelope

  • a. Increased/decreased internal moisture due to vapor migration
  • b. Heating of non-data center spaces (loading docks, exit doors, vestibule)

7. Climate

  • a. Analyze more than 8,760 hr (the number of hours in a year) using ASHRAE international weather data (IWEC2)
  • b. Must consider a full year of weather data, hour-by-hour, to see trends in energy use
  • c. Extreme weather data, n-year return period values of extreme dry-bulb temperature where n = 5, 10, 20, 50 years.

8. Reliability requirements

  • a. System efficiency drops with higher reliability (generally).
  • b. Cooling and power systems must be carefully designed to optimize reliability requirements with partial load performance.
  • c. If multiple modules are needed for reliability, the equipment can also be used as a way to keep energy use to a minimum during partial load.

9. Operating schedules

  • a. Data center facilities will have variable use for systems like lighting and miscellaneous power.
  • b. Based on actual use, the energy use of different systems will vary from facility to facility, especially a lights-out facility.

10.  IT systems and equipment

  • a. Arguably one of the most important factors that control the energy-use outcome
  • b. IT systems: traditional air-cooled, water-cooled, rear-door heat exchanger, fan-powered chimney, high-temperature air/water
  • c. High-temperature air/water will often lead to a chiller-less cooling system, which uses heat-rejection equipment to cool only the IT equipment
  • d. Density (in watts per square foot/meter) of equipment will drive cooling and power solutions and ultimately energy use.
  • e. Efficiency and turn-down ratio of servers
  • f. Efficiency solutions such as virtualization and cloud.

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