Yesterday, the EPA released their long-awaited carbon rules that aim to reduce US carbon by 30% by 2030.
While there will of course be plenty of partisan bickering about jobs versus our burning planet, the more interesting issue question is how states will meet the carbon mandates given the flexibility in the EPA draft regulations.
In most states, energy efficiency will be the cheapest method to reduce carbon emissions. But these EPA rules should be a catalyst to dramatically scale energy efficiency via #OpenEnergyMarkets.
Our friends at EnergySavvy made some great points in a blog post yesterday:
Even though the U.S. is on a positive energy efficiency trend – annualized electricity savings are up 82% in 5 years and utility programs saved 126 Terawatt hours in 2012 – the rate of spending has outstripped savings. Spending was up 155% over the same period to $6.9B in 2012.
This spending is almost 100% government and utility run "programs" that involve a considerable amount of planning, bureaucracy and inefficiency. To scale energy efficiency, therefore, the paradigm of command-and-control energy efficiency "programs" must end, and "program administrators" need to transition to "market administrators".
This starts with #OpenEnergyData, but also requires #OpenEnergyMarkets, which can be generally defined as the ability for third parties to get paid for generating verified energy savings.
These markets exist in a fairly robust manner in the Commercial & Industrial demand response space, with companies like EnerNoc managing load reductions for large manufacturing plants and businesses, getting paid by Independent System Operators like PJM that are in charge of managing the grid.
In the residential sector, there are a few models of how #OpenEnergyMarkets can be created and structured. Perhaps the most advanced market is the California ISO, which has created rules enabling third parties to monetize peak demand reductions from residential homes. So far the most innovative company taking advantage of this market is Ohmconnect, which frames their business model this way:
But while the CAISO market is a great model for residential peak demand reductions, it does not yet cover energy efficiency, or the reduction of energy regardless of time.
The CT Class III REC market provides one example of an energy efficiency market in action. In this market, third parties can apply to the CT Public Utilities Regulatory Authority (PURA) to get a project certified, and eligible to sell credits to electricity suppliers that are required to buy these RECs. Unfortunately, it is difficult for residential projects not run by the utility companies to qualify, as only 4/35 approved projects are residential, and those are all for lighting.
Connecticut's challenge is an ad-hoc process for measurement & verification (M&V), which puts pressure on the regulators to only approve things that have already been approved in previous processes. This makes things easier for regulators, but much harder for innovative companies trying to create new models for saving energy.
Illinois, on the other hand, represents perhaps the most mature energy efficiency market in the country today. Despite relatively low energy prices, Illinois enables third parties to bid energy efficiency programs to the Illinois Power Agency (IPA), which much be accepted as long as they are cost-effective. This simple, but powerful rule was embedded in Illinois' 2011 smart grid bill to mandate that the IPA procure:
Cost-effective energy efficiency programs or measures that are incremental to those included in energy efficiency and demand response plans
There are now more third-party programs than utility-administered programs for ComEd, the biggest electric utility in the state.
This third-party program structure can eventually turn into #OpenEnergyMarkets if policymakers, regulators and utilities can design transparent protocols for measuring savings that meet the standard the EPA has set:
To reach this standard will require a thoughtful process that leverages the market templates that exist today, a commitment to #OpenEnergyData, and the adoption of a "market creation" mentality that rewards a new generation of companies dedicated to saving energy.
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How existing audit software make things harder for Sealed
One thing that we've struggled with at Sealed is how to make it clear to homeowners how much money Sealed makes from the guarantee.
The truth is we take 20 to 25 percent of the "average actual" savings, which translates to $200 to $250 a year per home for a typical project that saves a home $1,000 a year.
However, the inaccuracy of existing audit software makes it difficult to explain this clearly. Because the audit tools generally over-predict savings, sometimes quite dramatically, our prospective customers often believe we are making much more money than we are.
In our pilot area of Long Island, for example, average projected savings are $1,300 a year, average actual savings are $1,000 and average guaranteed savings are $750 to $800. So based on the audit report, the homeowner thinks Sealed is making $500 a year or more (we wish!), when the truth is we are making less than half of that.
Savings Over-Prediction
There are three main reasons why most energy audit software over-predict savings.
The first is simply that it's very hard. As Mike Rogers relayed from #BScamp conference:
Some things that are hard to model: foundation heat loss, infiltration, wall and attic heat loss, window loss and gain, and HVAC performance. In other words, it’s hard to model homes, especially existing homes!
Michael Blasnik among others have demonstrated how inaccurate most building models are, and how greater model complexity simply leads to greater error.
The second type of error comes from entering and analyzing billing data. Most audit software leverages historical billing data, but can still overstate actual usage, both overall and within specific building systems.
The root of this problem is disaggregation, or breaking down the bill into its component parts, namely "base" and "weather-variable" load (e.g. heating & cooling). For example, if you are calculating the savings from a new high efficiency hot water system, you probably want to know how much energy is currently being used for hot water so that you can accurately model the impact of a more efficient system.
Sounds simple, but when analyzing bills some audit software does non-intuitive things like assume the usage data entered is entirely for weather-variable load, and then making a separate calculation for base load (e.g. water heating). This causes the software to over-predict savings because it doesn't correctly interpret the billing data, adding estimated base load to actual total usage.
For example, for a standard oil to gas conversion, Conservation Services Group's Real Home Analzyer software will often predict a home will save more oil than it uses. Try explaining that to a homeowner!
The third type of error is the "true-up" process to align billing data with the audit models, a blend of the first two problems. Because the audit models don't often line up with actual data, auditors go back into the software to change the model until it roughly matches the real data.
While this "true-up" process is better than nothing, the truth is that reality is often suspended (e.g. unrealistic indoor temperature settings) and sometimes the software will literally allow auditors to put in a fudge factor. PSD's TREAT software, for example, has a "usage adjustment multiplier" for hot water demand that can be adjusted until the model is "trued-up".
What Can be Done?
So we have a situation where there is savings over-prediction from modeling, billing analysis and "true-up" error. What is to be done!
First, we have to distinguish between accuracy and precision. Today, audit software is neither accurate (correct on average) nor precise (consistent across homes).
The first priority should be to make the software accurate so as not to systematically over-predict savings. Inaccurate savings estimates do a disservice to homeowners, contractors and ratepayers. And of course it also makes it harder for Sealed.
To improve the accuracy of audit software, a few things need to happen:
Stop blaming "behavior" and the homeowner. There's no evidence for dramatic behavioral change post-efficiency improvement.
Stop creating incentives for contractors to treat the software like a video game to hit arbitrary cost-effectiveness benchmarks (in other words move from TRC to UCT)
Most importantly, measure the actual results and adjust the models accordingly.
The first step is admitting this is a problem. Matt Golden, Michael Blasnik, and Mike Rogers, among other industry leaders, are confronting this issue head on, but we need the entire industry to try and improve audit software accuracy. As detailed in the last post, the key is #openenergydata, which requires a big push for regulators and other stakeholders to leverage existing data sets.
The precision problem is where Sealed comes in. As we've shown, homes are difficult to model, period. So while it should be possible to get things right "on average", average savings don't help an individual homeowner. As Mike Rogers tweeted today in reference to LBL's HES software:
"Lots of scatter but both above and below the line" Sorry, that is no help in individual homes!
Sealed solves this problem by guaranteeing the savings, smoothing out the risk across a large sample size of homes.
But while we can be honest with homeowners, it makes our job more difficult when the Sealed savings guarantee is compared to over-stated savings projections. Turns out, it's hard to be honest.
Fight the good (but boring) fight: regulators and #openenergydata
The concept of open data is very powerful in today's world. We all see the opportunities and progress made by leveraging data sets to make better decisions and improve our lives.
In the context of clean energy, #openenergydata is becoming increasingly salient. There is now a whole industry called "cleanweb" that leverages data to reduce resource dependencies.
Government has embraced this movement at the federal, state and local level, releasing data sets of government buildings, transportation systems and, increasingly, commercial buildings.
But despite all of this momentum, the most important residential data remains locked by one of the most powerful (but boring) stakeholders: regulators.
A great example of the power of regulation to inhibit clean technology innovation is the current struggle by Tesla to sell cars directly to consumers. You may think this is a no-brainer, but outdated laws and regulations mandate that car companies sell through authorized car dealerships (as if we needed more of an excuse to hate car dealerships). It would be like if a t-shirt company was not allowed to set up their own store (goodbye Gap!).
In the clean energy space, the most important regulators are the Public Utility Commissions (PUCs). PUCs were set up at the beginning of the 20th century to manage natural monopolies that required large amounts of capital, particularly the electric power grid. In the last 20-30 years, however, PUCs have also been put in charge of managing "demand-side management" (DSM) programs that reduce the need for more energy supply. Billions of dollars are spent each year to reduce energy, which almost all of that spent by states and utilities, but regulated by PUCs.
Specifically, PUCs sign off on the DSM budgets, the savings claimed and the incentives received by utilities and other parties. The PUCs also control what data gets collected, analyzed and published. Typically, the only information the public gets is high-level evaluation reports that estimate savings from different DSM programs.
These reports support the PUC's statutory requirements, but do a disservice to the broader market that wants to leverage data from these programs to improve consumer offerings.
For example, detailed information about actual savings by different population groups helps Sealed determine an appropriate savings guarantee for homeowners. Other companies are using data to better target solar and efficiency offerings to consumers by combining lots of data sources together.
To build and scale these new approaches to clean energy, however, important pieces of data are needed:
Building and demographic data for homes
Clean energy program participation
Pre and post energy usage
These data sets generally all exist in separate places and are protected by privacy regulations. It is rare that any single entity gets access to more than one type of data, and then usually only under strict non-disclosure agreements (NDAs).
To truly scale clean energy adoption through #openenergydata strategies, therefore, state PUCs need to come up with new rules (and funding) to enable utilities to share this data. A recent report by the New York Moreland Commission created in response to Hurricane Sandy reflects this sentiment in the context of creating a new master energy efficiency database:
The Commission is sensitive to the customers’ right to privacy; however, it is incumbent upon the PSC to create an environment that optimizes use of ratepayer funds in the administration of clean energy programs.
Sharing data in a more open way not only provides the opportunities for new cleanweb companies to grow and thrive, but it also ensures accountability for public money spent to incentivize clean energy.
To implement the #openenergydata systems and regulations, however, public pressure must be brought to bear on the PUCs. PUC commissioners are generally not elected, but they do take into account comments, testimony and are not, of course, immune to public opinion.
The clean energy community needs to make creating change through PUCs a priority. To date, most of the clean energy public efforts have been spent with elected officials at the city and legislative levels. While these should be continued, much more energy needs to be geared towards regulatory efforts.
We should all take inspiration from companies like Tesla that are making a large (public) push to drive these issues of regulation into the mainstream. They are currently collecting hundreds of thousands of online signatures to fight the outdated laws and regulations that prevent them from selling cars.
Viva la #openenergydata revolution!
Update:
The Advanced Energy Economy Institute (AEEI) recently published a great portal to help find public utility commissioners in all 50 states
I just finished my first Affordable Comfort Institute (ACI) conference, and my head is spinning (and not just because of the parties).
Despite the weird name, ACI is the largest home performance conference, and attracts a healthy mix of contractors, program folks and vendors (audit software, new insulation technologies, etc.). As a reminder, "home performance" is the term generally associated with efficiency improvements that create an energy reduction of 20%+.
It was a great experience, and I learned a ton about the challenges and opportunities of home performance across the country. Below are a few of the takeaways.
Many contractors don't sell with energy savings
One of the things that struck me the most was how many contractors are selling home performance without talking about savings. This is mostly due to necessity for those in areas with low energy prices. Instead, they are selling on comfort, health and safety, with any projected energy savings being a nice by-product, but not a buying justification.
I'm not sure how I feel about this. On the one hand, I'm very impressed by their ability to sell without savings. On the other hand, it does seem to limit the market penetration significantly. Most contractors I talked to said they love to sell on energy savings when they can, and would like to offer a guarantee as well (hear hear!).
But it did remind me that energy efficiency is doing much more than lowering energy bills. It is solving real people's problems and therefore there is no way you can ONLY sell the energy savings (if you do you're selling yourself short).
There is a lot of diversity in business and program models
Another big thing that stuck out was how much diversity there is in business and program models across the country. Some contractors rely heavily on financing while others have their customers pay everything in cash. Some areas have heavy market involvement by the "program" people (state / non-profit / utility folks), while others rely much more on the contractors. Some contractors only do home performance, while others have it as a side business. Some programs try to create proactive engagement, while others are focused on leveraging reactive engagement (e.g. furnace breaks).
While a lot of this diversity is appropriate to different market circumstances, my own take is that models that empowered the contractors worked a lot better than others. The contractors are the ones on the ground developing the relationship with the homeowners, and it always seems awkward when there is another party involved. The other side of this is a fear that unscrupulous contractors will take advantage of programs without producing results.
To overcome these challenges, there needs to be more dialogue with the contractors to create systems that engender flexibility with accountability. More on this in another blog post.
#OpenEnergyData is starting to come, but not fast enough
The last big thing that made an impression on me was the emergence of robust data sets that are finally quantifying the savings, realization rates (actual savings divided by projected savings) and variability.
The DOE is taking an important leadership role on aggregating this data through the Building Performance Database (BPD) and the National Renewable Energy Lab (NREL) with the Building America Field Data Repository (BAFDR). There are also a number of programs that are investing in gathering and analyzing their own data. These are all amazing efforts, and Sealed is obviously going to leverage them all if we can.
The disappointing observation, however, was that there did not seem to be the appropriate sense of urgency in allowing access to these data sets. The BPD in particular has a policy of only allowing derivative analysis, not being able to analyze the raw (anonymized) data. You can see Sealed's comments to the BPD here. The bottom line is they should be asking their data partners to allow the raw data to be shared.
The BAFDR (which will theoretically feed into BPD) looks to also have a ton of promise. NREL has gathered a good amount of data around home performance results, and are starting to use it to test the accuracy of various software tools (so far looks like Michael Blasnik's "SIMPLE" model works best). But I was told that it might be "several years" until this data can be analyzed by anyone other than the government, a proposition that seems patently ridiculous given the value it can serve in the private market.
Various programs (weatherization, ARRA, etc.) are also starting to collect post-retrofit data, analyzing the overall realization rates and variance, and sharing them with the wider community. I'm going to talk about the takeaways from the data in another blog post, but it is very encouraging to see grassroots efforts to collect and share this data.
In concert with more data is a new standardized data protocol (HPXML) that will hopefully be deployed soon in New York, Virginia and Arizona.
Big thanks to the community
The last observation from ACI is just how great of a community it is. The people working in home performance are truly passionate about what they are doing, and it shows. These are men and women who have literally sacrificed everything to work in an industry that is rarely profitable (today), extremely frustrating and technically challenging.
I felt welcomed by a family this week, one that I couldn't be more excited to become more a part of. Special thanks to Dan Kartzman and the Powersmith crew for letting me tag along to various events (and delicious dinners), and introducing me to various HP folks. Another big shoutout to Aaron Goldfeder and the Energy Savvy team for throwing some awesome parties, and bringing some needed common sense (and data frameworks) to the industry. And thanks to Will Schweiger of Bablyon for data and smokeless cigarette education; Damian Hodgkinson, a Rochester-based contractor (and newly elected Efficiency First board member) who gave me some invaluable feedback on selling HP in the field; and Peter Troast, HP marketer extraordinaire, who imparted wisdom as always and also uncovered by #nerdcrush on his old Friends of the Earth co-worker Amory Lovins. Thanks also to Nate Adams, Coby Rudolph, Brian Kurtz and many others who made this week awesome.
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