Data Center Temperatures and the News

I use a variety of ways to track the news these days and few use traditional paper publications.  I mostly use web-based and, yes, cloud computing applications to manage the inflow of news. These primarily consist of Google Reader, Google alerts, Twitter, and email newsletters. I bring this up for two reasons:

#1. Because unlike physical magazines or journals I don’t actually own any of this content I read.  It’s all stored somewhere else, in various data centers around the world, waiting for me to access it.

#2. It’s a great example of how web-based tools can increase the efficiency of my life, and even decrease my energy footprint.

This latter point is important, because it’s what a lot of us people interested in data center efficiency stress all the time: we don’t want people to stop using data centers and the tools they provide, we just want data centers to follow best practices and minimize their energy footprint while providing the same level of service.

As an example of my news review practices, I just skimmed the titles of probably over a hundred articles in a pretty short time (~30 minutes). Of the few articles I actually skimmed or read, I found this interesting blog post on the data center temperature debate. This is not a blog I follow, or an issue I follow particularly closely, but after scouring my many news sources this is the article that caught my eye enough to post about.  It does a good job of explaining the issues around data center temperature setpoints and the arguments of whether they result in energy savings.  The bottom line, as usual, is that energy savings will likely depend on your specific situation – if it allows you to cool more with outside air it’ll likely help, if it just causes your server fans to kick into higher gear it might not.

I’m not going to take one side or the other on this issue, but what I think is interesting is this: for years, engineers have overdesigned everything. Structures are built with a factor of safety of many times the needed strength; capacities are built out well over requirements just in case more is needed later or something goes wrong; and, yes, data centers are kept much colder than they need to be just to be safe. In some cases these decisions are based more on superstition than sound engineering. One of the things computers and servers have given us is the ability to dial in and optimize some of these design points through computer aided design, advanced monitoring, automated controls, etc.

Here’s where it all comes together:  these new tools should allow a savvy data center operator to dial in the correct cooling level across the data center and not have to significantly overdesign the cooling system or setpoints.  Good monitoring should identify problems quickly or even before they occur.  Good controls should alter settings to compensate for potential problems.  The two together should be tuned to optimize efficiency and decrease costs.  I’d be willing to bet that for the majority of data centers there can be significant energy savings by increasing temperatures, but you need to use these tools to address potential hotspots and to determine if that increase should be 1 degree, 2 degrees or 10 degrees.  Too much or too little and there may not be savings, or worse, there could be an increase in energy use.

So, in summary, we continue to keep using computers and data centers to increase efficiencies across all aspects of the economy, and, of course, use these techniques on data centers to slow the energy growth of these tools themselves.

Now, if I could just get the Economist to forward my magazine subscription to my new address.  How could this possibly take 3 weeks in this day and age? Maybe that will be the subject of another post. No matter, I’ll just read it online…

Key Take-Aways of Colocation Research for Silicon Valley Power

Last fall EMI, under subcontract to Summit Blue Consulting (now a part of Navigant Consulting), performed a process evaluation for Silicon Valley Power (SVP) in Santa Clara California.  The process evaluation focused on identifying barriers to colocation data centers (or “colos”) participating in SVP’s energy efficiency programs.  Colos provide data center infrastructure to support other companies’ IT equipment – basically data center space for rent.  This space can be leased in units as small as half a rack and as big as thousands of square feet.  The latter situation, where whole rooms or even whole data centers are leased to one tenant, are known as “wholesale” colocation facilities.

Santa Clara has a dense concentration of data centers and colocation facilities.  This is largely due to their location in the heart of Silicon Valley, but also is due to their relatively low cost of power and high reliability of their power delivery. As a result of this high concentration, data centers are a main focus of the SVP energy efficiency programs, and in 2007-2008, roughly 60% of SVP’s energy savings came from data center related projects.

EMI conducted this research by performing: online research and a document review of appropriate reports, and in-depth interviews with colo managers from inside and outside SVP’s service territory and other industry experts.  Here are some of the main take-aways from EMI’s research into participation of colocation facilities in SVP’s programs.

• Aggravated Barriers – Since providing reliable data center space is a colo’s main business, some of the barriers to energy efficiency for typical data centers are aggravated in the colocation facilities’ case.  These include an extreme focus on reliability, with little interest in energy efficiency.  It also creates an extreme case of the split incentive, because the people paying the power bill and the IT purchasers work for completely different companies, so there is little motivation to invest in more efficient equipment.
• Pricing Models of Colos Affect Investment in Efficiency– Different colocation companies have different methods for splitting up charges for power, cooling and space.  While data centers become more constrained by power and cooling (and less constrained by space), some colocation facilities are moving away from space-based charges and more towards charging directly for power and cooling, which helps create more of an incentive for their customers to save energy.
• Difficulty in Reaching the Colocation Facilities’ Customers –SVP, like other utilities, is restricted to only offer incentives to their customers of record.  This is necessary as it allows SVP to be able to recoup the incentive amounts if the measure does not stay in place for the contracted five-year period, and therefore the measure does not deliver the full five years of savings SVP claims for the measure.  As a result, SVP cannot give incentives to the colocation facilities’ customers, as these customers do not pay SVP for their power, but instead the cost of power is bundled with their colo charges.  This is a major barrier for many utilities to getting colocation customers to participate in virtualization incentives, for example.
• Lack of Expertise for Completing Calculations – Some facilities indicated that they did not have the engineering expertise on staff to complete the necessary calculations to receive incentives, as operating colos are basically “a couple IT guys with a sales department.” SVP offers support to fill out the applications, but some potential participants were not aware of this, so this was an area where better communication of the program offerings could help increase participation from companies that need this support.

All in all, the evaluation found that SVP’s focus on data centers has been very successful and that they are undertaking a lot of efforts to help overcome these barriers such as: emphasizing new construction where the barriers and inertia to energy efficiency are not as great, and offering technical support where it is needed.  Other opportunities lie in collaborating with other utilities to identify new opportunities (e.g., for prescriptive measures which simplify the application process), and investigating new ways to get to colo customers.  Although there are many barriers in place for colocation facilities, this is a large data center market, it is growing rapidly, and it is worth having progressive utilities like SVP continue to push to develop programs and processes to overcome these barriers.

The full report (available here) offers more detail on the colocation market and barriers to their participation in energy efficiency programs.

How big can data centers be? How about 19 football fields?

Today I realized that my understanding of how large data centers can get was significantly understated.  This realization came as I reviewed Data Center Knowledge’s special report on the world’s largest data centers.  I have previously used #5, Microsoft’s Chicago data center, as an example of one of the largest, but was shocked to realize that the largest is almost 60% bigger.  Another interesting result is that seven of the top ten are colocation facilities.  This is significant because it is often difficult to get colocation facilities to engage in energy efficiency programs, especially after they’re operational. The other three – and the only corporate data centers in the top 10 – are all Microsoft facilities. A number of the facilities (including the largest) are also buildings converted to data centers from other uses.  Since these are not purpose-built data centers, my guess would be that they are probably not ideally designed in terms of efficiency.

I’m also disappointed to see that relatively few have energy use or even power capacity listed.  In an environment where power is starting to dominate as the primary constraint on data center growth, wouldn’t it make sense to track a list of the largest data centers in terms of energy use?

Here are some other highlights from the report:

#10. SuperNAP (407,000 SF) – Number ten is notable mostly for its power consumption.  At 250 MW capacity it boasts densities of up to 1,500 w/SF, made possible through advanced cooling using “a high-density T-SCIF (Thermal Separate Compartment in Facility) containment system to fully separate the hot and cold aisles.”

#7. i/o Data Centers Phoenix ONE (538,000 SF) – This one just seems to keep popping up, with “enormous rooftop array of solar panels that will eventually generate as much as 4.5 megawatts of power for the data center, and a thermal storage system that will allow i/o Data Centers to run chillers for its cooling systems at night when power rates are lower.”

#6. Microsoft’s Dublin Data Center (550,000 SF) – This one operates 100% of the time on outside air through the use of economizers and “Microsoft says it can run its server rooms at temperatures of up to 95 degrees F (35 degrees Celsius),” which should give it an efficiency advantage.

#5. Microsoft Chicago Data Center (700,000 SF) – A large portion of this data center consists of double-stacked 40-foot shipping containers that are each filled with up to 2,000 servers.  Containers make the system highly scalable and efficient.

#1. Digital Realty Trust Lakeside Technology Center (1.1 M SF, 100+ MW of power) – In Chicago, this data center used to house the printing presses for the Yellow Book and the Sears Catalog. It was converted to telecom use in 1999 and is now 2^nd largest power customer for Commonwealth Edison.

Some people might wonder why a bohemoth such as Google doesn’t show up on this list? Well, it seems that Google likes to focus on many data centers together on a campus, while Microsoft tends to go big, and the report only looks at individual buildings not campuses.

So how big are these?  Let’s put it in perspective:

1.1  million square feet is equivalent to just over 19 football fields

250 MW is equivalent to the average power use of about 200,000 American homes

These numbers really speak to the massive amount of computing needed in modern society, but this is actually not where the majority of energy use from data centers come from.  According to the US EPA’s 2007 report to Congress, only 38% of data center energy use in the US comes from “enterprise-class” data centers of greater than 5,000 SF.  The remaining 62% is used in the smaller data centers, which means that these smaller data centers offer the largest overall chance for energy savings in this industry.

Should Utilities Look at Data Centers to Achieve Increasing Efficiency Goals?

The market for energy efficiency is increasing, and more states and Public Utility Comissions (PUCs) are jumping on the bandwagon every day.  But where are these new electricity savings going to come from?

I recently stumbled on this ACEEE report (released in March 2009 and available here) covering the increased energy efficiency goals of many states.  As the paper summary says, “In just the last few years, energy efficiency has evolved from being largely a token gesture or a ‘public benefits’ set-aside, to being a top-priority utility system resource.”  As a result, many states (including Minnesota, Illinois, Ohio, New York, Maryland and Vermont) were increasing their yearly efficiency goals to 1.5% – 2.0% a year, when “the very few top performing states in the nation were only achieving savings in the area of 0.8% per year.”  This fact makes these new savings goals look very aggressive.  In my mind this is a great thing because efficiency is the cleanest and cheapest form of capacity.

There are a few other interesting findings from this report, which reaffirm a couple persistent issues and trends in this industry:

1. “Energy efficiency spending was relatively balanced between the residential and non-residential sectors (median across the states of 44% and 56% respectively), but that savings were relatively skewed toward the non-residential sector (63% non- residential).”
2. “Also striking was the extent to which the lighting end use dominated the savings accomplishments, accounting for nearly two-thirds of all savings in the states which had disaggregated data available.  In the residential sector alone, lighting accounted for between 63% and 92% of reported savings.”

So let us summarize and distill what we have learned so far:

1. States are looking very aggressively to energy efficiency as part of their resource planning, with rapidly increasing goals.
2. The bulk of this savings is coming from non-residential (e.g., Agricultural, Commercial, and Industrial) measures.
3. Lighting makes up the vast majority of achieved savings.

So this begs my initial question – where are these new savings going to come from?

As the price of efficient lighting comes down, and customers are increasingly happy with the quality of new efficient lamp designs, how are utilities going to continue to squeeze savings out of an increasingly saturated market?  Like squeezing juice from an orange, at some point the effort needed increases faster than the juice keeps coming.  To make matters worse for utility programs, as the federal government gets in the mix, new legislation could eventually phase out much of the inefficient lighting.  This increases the baseline lighting efficiency and makes it more difficult to claim large savings for lighting projects.

Since the majority of savings typically come from the non-residential sector, it seems logical to focus on these industries for more savings. And, where better to look than some of the most energy dense facilities there are – data centers!  Studies indicate that data centers are up to 40 times more energy intensive than typical office buildings and that savings potential can run from 25% – 50% per facility.  This all adds up to a very concentrated opportunity for energy savings.  Sure there are a number of challenges to utility incentives in this space, but what other industries and facility types are utilities going to look to in order to achieve these kind of increased savings goals? To me, this speaks to a large need to investigate and to learn to overcome the challenges to utilities creating incentives for energy efficiency in data centers.

Utility Sponsored Incentives for Data Center Efficiency

One of the big barriers we see to energy efficiency in the data center market is a knowledge gap between utilities that are new to the data center market and data center operators who are not necessarily fluent in energy efficiency or in the language of utilities.  Last week I presented at AFCOM’s 30^th Data Center World Conference in Nashville, TN.  I was there to share the research we’ve done at EMI into what utilities out there are offering data center efficiency incentives and to try and help close this gap.

Part of the presentation was focused on trying to get the data center operators to understand the utility mindset – what motivates utilities and what makes sense for them to offer money for energy efficiency. This is one of my favorite slides, because it attempts to answer one of the questions I get the most from non utility/EE folks:

I love this question because it really gets to the heart of the economics of energy efficiency.  In the end, it often comes down to this singular point made here by Bruce Folsom, the director of energy efficiency programming at the utility Avista in Eastern Washington State, “Our energy future is about using the resources we have wisely, and energy efficiency remains our lowest-cost resource.”

To further this goal of reducing the knowledge gap between utilities and data center operators, I attempt to explain incentives as trying to influence you to implement a project, or to help motivate a transition of an idea into an action. This transition is illustrated here:

The presentation includes explanations of how utility incentives can reduce payback times for energy efficiency projects and increase the ROI of these projects.  In addition, I do a run down of incentives offered for data centers and examples of utilities offering these incentives.  One breakdown I explore is where different incentives are applied within the data center, as illustrated in this slide:

I finish with a list of steps for data center operators to engage with their utility to pursue these incentives:

1. Become familiar with the utility’s programs

• Check your utility’s web site for information on available programs and contact information
• Contact your utility or your account manager to discuss available programs/incentives

2. Identify projects

• Schedule an energy audit or technical assistance from utility (where available)
• Find projects relevant for incentives

3. Confirm Projects

• Fill out any applicable pre-application paperwork to confirm relevance and incentive amounts

4. Perform pre-inspection with utility (where applicable)

5. Install measure

6. Perform post-inspection (where applicable)

• Calculate savings and incentive amount

7. Apply for incentive or rebate

So that’s my attempt to distill my hour long presentation into a blog post.  I was really pleased by the reaction at Data Center World, which speaks for the need for people to help plug these gaps in communication and knowledge.  I had a number of utilities in the room, a few consultants and some data center managers, and the question and answer period turned into more of a discussion between utility folks and managers.  That’s what I like to see.

I would definitely be interested in any feedback on what I’ve included here, or in any information readers have on available programs.  We’re attempting to fill out a matrix of available programs by utility so any information would be greatly appreciated.  Also if anyone is interested in the full presentation let me know.  You can always reach me at ajhoward (at) emi1.com

Based on the reaction I will be attempting to update the presentation and will resubmitting my abstract to hopefully speak at the next Data Center World Conference in Las Vegas in October.  Thanks!

ENERGY STAR for UPS

Speaking of ENERGY STAR, the EPA released a framework document for the newly announced Uninterruptable Power Supplies (UPS) specification last week.  UPS, like computer power supplies before them, lack industry standard measurement procedures to specify their efficiency.  As the market for energy efficient data center equipment grows UPS makers seem to be increasingly marketing the efficiency of their devices, but manufacturers usually specify 100% load – a condition that a UPS will never actually operate in because many UPS are critically underloaded.  Also similar to server power supplies, many UPS are operated in redundant configurations where the load is split between two UPS in the case that one fails.  This means that a UPS in this configuration could only hit 50% load, max.  The efficiency of power conversion equipment tends to fall off below 50% load, so it’s important to measure and specify the efficiency of loads below 50%, because this is where a lot of this equipment is actually running.

To illustrate the point, here’s a chart of power supply efficiency curves from when I was working on the server specification, which I stole from the ENERGY STAR website:

For servers, EPA specified efficiency all the way down to the 10% load condition because available data indicated that that’s where a lot of the redundant power supplies were being operated.  My guess is that ENERGY STAR will be doing a similar thing with UPS, and then the industry will have a way to compare the efficiency of different UPS solutions across much of their operating range. This should be a great help to utilities looking to get verifiable savings through offering incentives or rebates for more efficient UPS.

EPA is also continuing the trend of pushing for standardized reporting requirements (through a power and performance data sheet) and for real-time power and temperature reporting over a standard network.  This is also similar to the V1.0 server specification and what is being proposed for data center storage equipment. EPA is looking to add similar requirements for all data center equipment so that data centers can be operated more efficiently when the managers have better access to data on what’s actually happening in their data center. The power and performance data sheet will also be helpful for proving the specifications of equipment when applying for rebates and incentives.

Interested stakeholders can download the new documents here, and offer comments by April 2, 2010.

Demand Response (DR) for Reduced Peak Power in Data Centers

One interesting approach to demand reduction is the idea or demand response, or “DR” programs.  The New York Times recently had this article on Idaho Power’s approach to DR.  The article includes this explanation of what DR is:

This concept, called demand response, has gained traction in utility circles. In essence, it involves paying users to make small sacrifices when there is an urgent need for extra power (the “peak”). The utility can then rely on cutting some demand on its system at crucial times — and, in theory, avoid the cost of building a new plant just to meet those peak needs.

There are many opportunities for demand response in data centers. EMI did a process evaluation for the California Emerging Technologies Program (ETP).  During this project, EMI prepared a number of case studies on different technologies assessed by the ETP. One such case study was on an “Auto-DR” technology.  My colleague who worked on this passed on this report on a joint effort between PG&E and LBNL’s Demand Response Research Center (DRRC) on an a similar Auto-DR pilot program in the summer of 2006. During the pilot program, they setup locally participating businesses to have automated controls to lower their energy consumption in response to demand response signals from PG&E. Of the 24 facilities that participated in the pilot, an office/data center had highest achieved demand reduction for a single event at 363 kW and highest average for 294 kW. In this instance the the DR strategies used at the data center site included: duct static pressure increase, Supply Air Temp (SAT) increase, fan VFD limit, chilled water (CHW) temp increase, and cooling valve limit. The chart below from the report shows how high the demand savings was for the office/data center (all the way on the left) compared to other sites.

The office data center also had the lowest payback period at 0.4 years for implementing the Auto-DR.

Following the project, the DRRC published this data sheet with information on the DR potential of data centers.  The sheet makes some interesting points including that “savings can be higher than those in other industries because reducing server loads simultaneously reduces cooling and other equipment loads.”

Here are some of the other methods the DRRC recommends in their fact sheet:

–      Dynamically shift load onto fewer servers using virtualization.

–      Migration of load to another location (i.e. another data center).

–      Temporarily raise set-point temperatures.

–      Use backup reserves such as ice storage or chilled-water storage for cooling.

PG&E is still running the Auto-DR program along with the other large California IOUs which also have programs.

“Energy Savings” versus “Demand Reduction”

A funny thing in the efficiency and utility segment is the constant confusion between power and energy, or between kilowatts and kilowatt-hours. Even among engineers who clearly know the difference, it’s interesting that people are still constantly confusing the two, or at least use them interchangeably in situations where you really can’t.  A sure way to insult an energy engineer is to say they “can’t tell a kW from a kWh.

Technically, power (kW) is an instantaneous measure of the rate in which you’re using energy, while energy is a cumulative measure of how much of a resource you’re using.  Confused?!

Another way to think of it is that our resources, such as coal or natural gas, store a certain amount of energy. The power you draw dictates the rate at which you are using this energy.  If you draw more power you’ll use up your coal more quickly.  If you use less power your coal will last longer.  Obviously using less power is a good thing because your resources will last longer.

Turns out this distinction between power and energy is very important in the utility industry, and therefore effects how they run their conservation programs.  As residential customers, most people are used to being charged by the kWh, a measure of cumulative energy used.  A residential utility meter therefore measures the cumulative kWh you use in a given month, which is what the utility charges you for.

One of the chief goals of conservation programs is to reduce the peak power draw (kW) on the system, and not necessarily the overall energy used.  The peak demand is what dictates how many power plants need to be running to service a population.  Utilities that are trying to delay the construction of new power plants will look to reduce the peak power demand from their customers.  For this reason, large energy users (such as commercial and industrial customers) will pay not only for the energy usage, but also for their peak demand usage.  This gives a clear pricing signal to the customer to reduce peak demand.

A lot of utility energy efficiency programs will focus on reducing demand and will pay incentives based on reductions in peak kW – not kWh savings.  Austin Energy and Southern California Edison are two examples I’ve found of utilities that base some incentives on kW reduction.  Often this reduction needs to happen during times of peak demand to be eligible for incentives. Typically peak demand occurs in the middle of the afternoon on a hot summer day when everyone is running the AC. In contrast, here in the Pacific Northwest almost no one has AC but most people have electric heat, so demand peaks on cold winter days.

To illustrate this concept, here’s a peak load curve on a natural gas plant I found at natgas.info.

This topic came to mind as I’ve been preparing my materials for my talk at AFCOM, where I will be summarizing data center utility incentives to data center managers and IT professionals. I’m seeking different ways to explain these concepts in simplified terms so that industry members can understand the utilities’ motivation in running conservation programs.

This whole concept reminded me of a neat article I read a while back about an Arizona data center taking advantage of time-of-use pricing.  The data center has installed a system that makes ice at night and uses that ice during the day to cool the data center.  While systems like this don’t necessarily produce energy savings, they create a significant amount of peak demand reduction by shifting that demand to off peak hours (this approach is often referred to as “load shifting”).  The local utility (Arizona Public Service Co.) charges only 2¢ per kWh for off peak energy and 13¢ per kWh for on peak energy.  In this way, the company can save 11¢ per kWh (or 85%) on their energy costs by shifting the demand to off-peak hours (this is probably not quite true since there are likely some loses associated with the load shifting but you get the point).

One of the attractive things about the “smart grid” or “smart meters” is the ability for utilities to offer different prices based on time of use to residential customers so that consumers can reduce their individual peak demand.  This would help further flatten the load profile, reduce the number of power plants needed to service the population and help make electricity production more efficient.

I’m sure that I, like others, will continue to interchange the words energy and power when talking about conservation, but it’s often important to recognize that difference when identifying opportunities to increase efficiency in the system.

Data-Driven Prescriptive Incentives for Data Centers

I’ve spent the last few weeks reviewing information on utility data center energy efficiency programs for a presentation I’m putting together for AFCOM Data Center World, and have been struck by how few prescriptive programs (or “deemed measures”) are available for data center equipment. The few programs that do exist seem to vary widely and are distributed among different utilities around the country.

The most important information you need for creating these programs are data on which to base your assumptions and calculations, including, data on the typical products in the market (or the baseline) and data on the more efficient offerings.  The delta between these two establishes the energy savings on which to base an incentive.  This was similar in my previous work developing specifications for the ENERGY STAR program for the EPA – the biggest problem was access to quality data of sufficient quantity to really understand the energy use of the products. This is a great strength of the ENERGY STAR program, because as a trusted third party they are able to pull in data from a number of different current, and sometimes future, products to get a real sense of how energy is used across different manufacturers.  When they are pulled together these EPA data sets often seem to be some of the best publically available data sets on the energy consumption of these products – a resource that is useful for the program, as well as for other advocates outside the program.

It seems that the utility industry lacks a similar mechanism to collect sufficient data to develop these prescriptive incentive programs for data center equipment.  The primary source of data seems to be data collected through demonstrations and custom incentive applications.  But the utility industry needs a large amount of data to maintain confidence that the prescriptive programs will deliver actual energy savings that they can reliably claim for their programs. However, the utility industry, like ENERGY STAR itself, has only recently taken the plunge into the data center industry.  A lot of programs have a random assortment of incentives they’ve given out for data centers – an efficient UPS here, an economizer there, a few virtual servers in the mix – and do not seem to be reaching the critical mass needed to gather the quantity of information needed to effectively develop prescriptive programs.

I think this turns into a chicken and egg problem. Utilities sometimes find it hard to get traction on their data center programs because they do not have the prescriptive programs that make it easy for the customer to participate, but without the data from participation in the programs they do not have the information needed to develop the prescriptive programs.  As usual, available data seems to be a bottleneck.  What is needed are some central depositories of data with mechanisms to develop intelligent incentives based on that data.  A lot of different groups and organizations have the potential to work toward this goal (and I believe are doing so), and it is an important goal as more prescriptive incentives would certainly help capture some of the energy savings potential which we all know exists in this industry. I’d be really interested to hear about any potential efforts in this area, so if anyone knows about anything fill me in!

Thoughts from PG&E’s Former Data Center Efficiency Program Manager

I’m getting caught up on some news from over the holidays (happy new year by the way!) and came across this two part interview with Mark Bramfitt, the former program manager for PG&E’s High Tech program which includes data center efficiency projects (found here: Part 1, Part 2).  Mark has been very vocal over the years in spreading information on PG&E’s ground breaking programs in this area and has been a great ambassador to the high tech and utility industries alike on data center utility incentives.

EMI performed the process evaluation of the PG&E’s High Tech Program (which can be found here) and I got to know Mark initially through his support for my work with the EPA on the ENERGY STAR Computer Server specifications.

Scouring through the two interviews I found a number of interesting points from Mark.

On Barriers to Program Adoption:

In my discussions with utilities across the U.S., this is probably the single biggest barrier to program adoption – they can’t find firms who can do the calculations, or resources to appropriately evaluate and review them.

What has slowed us down, I think, is that the IT industry and IT managers had essentially no experience with utility efficiency programs three years ago. It simply has taken us far longer than we anticipated to get the utility partnership message out there to the IT community.

These two quotes emphasize the fact that there’s a gap of knowledge (and talent) between the utility industry and the high tech companies that equip and run data centers.  On the utility front – there is a gap in knowledge about the IT industry.  The fast pace of technological innovation and quick growth in this industry presents challenges in finding or developing the expertise to implement effective programs (including performing the necessary calculations and analysis).  On the high tech company front – there is a gap in knowledge about how to identify and leverage these new programs and efficiently perform the analysis and calculations to receive the incentives.  My conversations with industry members on both sides highlight these frustrations, and the ultimate success of data center efficiency programs will hinge on closing these gaps in the coming years.

New Opportunities

On the retrofit side, we’re seeing interest in air flow management measures as the hot spot, perhaps because customers are getting the message that the returns are great, and it is an easy way to extend the life and capacity of existing facilities.

Metering and monitoring systems lead people to make simple changes, and can directly measure energy savings in support of utility incentive programs. We also like that some systems are moving beyond just measurement into control of facility and IT equipment, and to the extent that they can do so, we can provide incentive funding to support implementation.

There is a lot of room for potential growth from the basic programs currently offered by utilities.  Mark points out one of the areas of low hanging fruit is with air flow management.  This can include simple efforts like blanking panels or more advanced efforts like switching to hot/cold aisle containment.  The challenges here are in confirming the energy savings, which is where the expertise mentioned above is needed.

Metering and monitoring is always a hot topic of conversation in this industry.  Future and current efforts for quantifying energy efficiency gains rely on access to quality data, so the implementation of better measurement and monitoring would be a big a boost to future energy efficiency projects. The challenge, again is quantifying concrete savings from these measures.

These two points bring up what I see as a main challenge to the utility industry in simply and reliably quantifying the energy savings from these measures so they can use incentives to drive these right behaviors.  It’s a significant challenge and will take some creative thinking. Another of Mark’s points emphasizes the potential outcome of these challenges:

That being said, utilities in California are under tremendous pressure to deliver energy efficiency as cost effectively as possible, so some of the industry leadership activities undertaken by PG&E may have to be de-emphasized, and we may not be able to afford to develop new programs and services if they won’t deliver savings.

To get over this hurdle, the industry needs to think creatively on how to efficiently (i.e. cost effectively) justify incentives and programs that help drive the right behavior.  There’s a large potential for energy savings in this industry, and utilities should have a role in driving these behaviors, we just have to continue to push to find effective models for doing this.

Future Growth of the Industry

PG&E is not seeing the level of new data center construction that we had in ’07 and ’08, but the collocation community tells me demand is exceeding supply by 3-to-1. They just can’t get financing to build new facilities.

This last point emphasizes that despite the credit crunch, demand remains high.  Once some capital is freed up we should continue to see rapid growth of the industry and increased opportunities to effect change through intelligent incentives.