Estimating data center PUE
When estimating data center energy use, consider applying these five analysis techniques.
Socrates said, “The more I learn, the more I learn how little I know.” This quote can correlate strongly with the topic of estimating and analyzing data center energy use. Despite many years of experience, a definitive, detailed approach on how to estimate power usage effectiveness (PUE) in a project’s early design phases is extremely elusive.
Reports and articles typically focus on measuring energy use after a facility is operational. The Green Grid Association and ASHRAE offer a wealth of information on how to perform energy compliance analyses and how to use the data to determine PUE (see “For further reading”). However, applying this knowledge to an energy use simulation process takes a lot of reverse engineering and postulations on how the data center will actually function.
Even after making the appropriate assumptions and creating the energy model, the fact remains that most data centers are an amalgamation of standard commercial office space (corridors, offices, conference rooms, restrooms, and loading docks) and technology spaces (data halls, communication rooms, UPS/electrical rooms, generator rooms, and air handling rooms). To further complicate matters, there often is no clear differentiation between the spaces included in the PUE calculation and ones that are not. Also, it is typical to see power and cooling systems that serve multiple zones of a facility, obscuring a solution even more.
According to The Green Grid, "In mixed-use data centers, shared ancillary services, such as common lobbies, common bathrooms, and elevators may be excluded from the energy use boundary. However, ancillary services that are dedicated to the data center must be included (e.g., lobby, bathrooms, office spaces that are dedicated to the data center operation)." While this offers some direction, it becomes clear that the energy engineer responsible for determining the estimated energy use and data center PUE must make significant assumptions. PUE is based on annual energy usage—the average of the energy use over 8,760 hours, but the peak PUE is also an essential metric when examining the greatest hourly power demand. It should be noted that to keep the nomenclature consistent, there technically isn’t a peak PUE because PUE is measured in energy use, not demand. However, knowing snapshot PUE values over the course of a year is helpful when analyzing which systems use the highest amount of power, and when.
There are three common arrangements for projects that require pre-design phase energy use approximations:
- Stand-alone data center: includes the entire building
- Stand-alone data center: includes data halls, UPS/battery rooms, and communication rooms; excludes offices, corridors, and infrastructure rooms
- Data center inside a commercial building: includes all technology spaces and only office spaces that directly support the data center.
Clearly, there are many combinations of and variations on these three arrangements that can be developed, and each results in a different energy use and PUE estimate. Until there is a regulated approach and strict definition of how to determine which areas and associated power and cooling systems should be included in the PUE calculation, there will be, at best, inconsistent approaches and, at worst, gaming in order to get the most attractive PUE. The importance of this standardization becomes elevated when data center users ask that the PUE be guaranteed before the development of data center construction documents (see Figure 1). The guarantee is typically tied to financial penalties and shared savings depending on the data center’s energy use performance, so documenting assumptions and referring to relevant industry standards is vital for inclusion in the contractual documents.
The purpose of this article is to expose conditions that will influence estimated energy use; it is not intended to provide solutions simply because there are too many different parameters and circumstances to cover. However, the following items illustrate analysis techniques that can be applied toward particular situations. Undoubtedly, this list will continue to grow as we learn.
- Continuous cooling UPS losses
- Separate, smaller UPS for other IT loads
- Miscellaneous power and lighting usage schedules
- Elevated supply air temperatures
- Partial IT load.
Continuous cooling UPS losses
The combination of high-reliability, high-density, and high-temperature data center operation has led to the use of continuous cooling (CC) systems. The HVAC and electrical system design, program requirements, and project budget will drive the amount of cooling equipment required to have continuous operation in the event of a power interruption until on-site generation equipment is fully energized. For water-based cooling systems, the pumps will often be on the CC system to circulate water that is still cold due to its high thermal capacity. In this situation, the computer room air handler or air handling unit fans must also be on the CC system.
Less common is to have the vapor compression equipment on the CC system, due to the large power demand of the compressors. Because much of the support systems and equipment for the IT gear in the data center are located in areas outside the data center proper, it is necessary to make an assessment of the operational continuity requirements of electrical gear and other equipment that can be adversely affected by exposure to prolonged elevated temperatures. The outcome of this assessment will inform the engineers if cooling equipment outside the data center must be on the CC system.
As in the other UPS systems, the CC system will have losses that will vary by the amount of cooling load and associated power demand. This is where the use of economization systems not only reduces the amount of energy used by the cooling system, but also reduces the amount of losses generated by the CC system (see Figure 2). When modeling this aspect of the data center, good practice dictates that these electrical loss variations be modeled as accurately as possible as to not overestimate energy use by assuming that the power required by the cooling system does not change throughout the year.
Separate, smaller UPS for other IT loads
The UPS system in the data center is arguably one of the most critical components of the electrical system to protect the IT equipment from power anomalies and to provide ride-through time in the event of a loss of power. Each type of UPS equipment will have a distinct efficiency curve that depicts how the efficiency changes with changes in the IT load. Based on this curve, the energy engineer can predict how much additional energy is required of the air-conditioning system due to the UPS inefficiencies.
Less apparent is the use of smaller UPS systems for IT loads that might be located outside the data center proper. The equipment might be for communication devices, security, building management, and in healthcare settings, medical IT equipment used for medical records and imaging. First, it will need to be determined if this equipment will be considered as a part of the primary IT load, thereby going in the denominator of the PUE equation. Second, the inefficiency of this (undoubtedly smaller) UPS system will need to be included in the PUE calculation as part of the annual energy use. The primary UPS system will have different operating characteristics (efficiency at part loads) and should not be used as a proxy for the smaller UPS system.
- Events & Awards
- Magazine Archives
- Oil & Gas Engineering
- Salary Survey
- Digital Reports
Annual Salary Survey
Before the calendar turned, 2016 already had the makings of a pivotal year for manufacturing, and for the world.
There were the big events for the year, including the United States as Partner Country at Hannover Messe in April and the 2016 International Manufacturing Technology Show in Chicago in September. There's also the matter of the U.S. presidential elections in November, which promise to shape policy in manufacturing for years to come.
But the year started with global economic turmoil, as a slowdown in Chinese manufacturing triggered a worldwide stock hiccup that sent values plummeting. The continued plunge in world oil prices has resulted in a slowdown in exploration and, by extension, the manufacture of exploration equipment.
Read more: 2015 Salary Survey