From earth2tech comes an article on California utilities struggling to get data to customers. The last paragraph brings up the challenge of payoff periods being out of line with available savings,

There are signs that the smart meter backlash is spreading beyond California —Duke Energy is being ordered by Indiana regulators to justify the costs of an 800,000 smart meter deployment in that state, and Dominion Virginia Power is delaying a $600 million smart meter rollout to do more testing, after state regulators questioned whether the meters will cost customers more in increased rates than they will help them save in reduced energy usage.

Building Systems Insight has relaunched and has a new website explaining our change of focus away from short-term measurement and verifications projects for one-time upgrades such as lighting retrofits.

BSI’s new focus is on monitoring total building critical resource use.

Critical resource monitoring includes energy measurement, logging and data mining.  But in addition, BSI’s data warehouse and analytics are available for watching gas, water, waste water, temperature and nearly any other sensor that produces a stream of raw data.

Building Systems Insight has events and alerts in development as well as enhancements to the time-series analysis tools released in August.

• Evolved slowly, as needed, over the last 4-5 decades
• Built to provide power from central generation facilities, not solar panels in New Jersey and the Southwest, or wind farms in North Dakota.
• Not built to re-direct the flow of energy effectively over large areas.
• No electrical energy storage
• Nor was it built to support an efficient market of energy trading (not the infrastructure nor the organizations)
• Not taking advantage of information technology for grid management

Although you wouldn’t know immediately based on the hype, only a small fraction (much less than 20%, the entire AMI and DR budget) of the spending will go to home energy monitoring and control [ref]. This spending makes sense based on the ratio of available savings from home energy monitoring/cost to deploy monitoring and control.

Robert Cialdini is implementing an interesting alternative to automated demand response in the home: publish comparative data on energy use to motivate changes in behavior.

Now Cialdini is applying that concept to energy consumption, with promising results. Positive Energy, a company that has drawn on his work (he’s the chief scientist), has created software that assesses energy usage by neighborhood. Results are sent to consumers on behalf of their local utility, praising you with a row of smiley faces (you’ve used 58 percent less electricity than your neighbors this month!) or damning you with none (you used 39 percent more electricity than your neighbors in the past 12 months, and it cost you $741 extra).

- From Greening With Envy, The Atlantic

The article goes on to explain the ideas behind “social proof” and tout the successes of the project.  This approach makes a lot of sense because:

1. Passive monitoring is boring.  No one but the most devoted energy geek is going to sit and watch their energy use numbers roll by in order to respond to “energy events” after the first few weeks.
2. Significant levels of automated demand response are irritating.  ”My washing machine stopped mid cycle because the energy company ran out of capacity?”
3. Monitoring and control is difficult to justify when the payback period on installation and monitoring systems from electricity bill savings is 5-10 years.

The aggregation and simple publishing idea from Cialdini seems practical–compatible with human psychology and cost effective.

WattsGoingDown BSI provides online access to real time energy use information for our clients and partners. In this case, verification is fairly simple but it illustrates the power of easily accessible, real time energy intelligence. Saturday morning, I grabbed as screen shot from BSI’s WattsGoingDown’s SaaS application.  It shows energy savings of 50%. The upgrade reduced lights-on power from 32 kilowatts to 16 kilowatts.  The upgrade happened over two different 2-day periods.  Looking at the plot, it isn’t hard to tell when these upgrades were going on…

Tagged: buildingsi, energy, energy monitoring, lighting retrofit ]]> http://blog.drskippy.com/2009/09/08/verification-of-energy-savings/feed/ 2 drskippy27 WattsGoingDown lighting retrofit energy savings http://blog.drskippy.com/2009/08/17/wattsgoingdown-software-release-beta-09/ http://blog.drskippy.com/2009/08/17/wattsgoingdown-software-release-beta-09/#comments Mon, 17 Aug 2009 17:15:40 +0000 Dr. Skippy http://blog.drskippy.com/?p=255 ]]>

Energy Intelligence

One of our bets at BSI WGD is that people can make better decisions about energy use when they have ready access to usage information.  For many of us, that statement seems obvious, maybe too obvious to bother writing down. For people trained to interpret data as stories about actions and outcomes, cause and effect, simple data can be highly motivating.

There are two important leaps here: (1) data tells a story and (2) motivations aren’t too conflicted.  At BSI WGD, one of our goals is to create a platform for Web access to real-time energy use information that tells the story clearly.  The other is to put the data and the story of energy use into a context where motivations are clear.

This is why we aren’t in the residential energy monitoring business.  The motivations in this space aren’t clear. The utilities have a motive to sell energy at capacity, but not over.  So demand shedding during high-use periods isn’t related to conservation of energy, but rather, to controlling outage and capital costs (new power generation capabilities).  At other times, the utility does better by providing energy use incentives.

Another misalignment exists on the consumer side of residential energy monitoring. Users may be motivated to save energy in order to save the environment or to save money.  But the amount of energy used in an average household hardly justifies the cost of energy monitoring and “the smart grid” for an individual user. And users have been pretty clear that they don’t really want the wash cycle to stop in the middle because the utility needs a break.

A friend recently suggested that one clear commercial benefit of the smart grid will be smart, networked appliances sharing information about consumer behavior for targeted advertising–that seems like uncomplicated motivation.  Maybe an idea like this has motivated some large players to get in middle of smart appliance networks in the home even in the face of not payback on energy savings for the comsumer. Imagine a networked refrigerator with a bar code reading laser at the door.  Every item in your refrigerator, the number of times it is removed, now long it lasts, when you are running low, etc all in a database for King Soopers to mine for your next promotional coupon…

Energy Output from Solar Panel for a single day (somewhat cloudy)

At BSI WGD, we are working to provide direct results so consumers of energy for changing their behavior. In a few weeks, we will release BSI WGD beta 0.90 of the online application to our current customers. Among the capabilities: Real time energy use 5 min and daily averages Power Factor and other diagnostic information Peak use periods/devices mining User defined data roll up for any number and combination of monitored devices Compare use profiles over time

What is a “Smart Grid?” Or nationally speaking, what is THE “Smart Grid?”  What is smart about it?  I am not going to survey the smart grid, but rather hope to point to and contrast two fundamental ideas behind the smart grid.

To start, I propose drawing a distinction between two concepts behind the Smart Grid :

(1) information access and transparency
(2) centralized control structures

Behind idea (1), people know more about energy consumption, load, demand, pricing and they know it faster and cheaper.

The idea underlying (2) is a bit more abstract.  The concept here is that only a central player has knowledge of the effects of the collective behavior of separate energy consumers and that this central player can make decisions about individual behaviors to make the system more efficient. This is often the illusion of centralized planning and control of complex systems. It usually results in systems that are optimized until they become brittle.

Our experience is pretty consistent that sufficiently complex systems are rarely understood and managed successfully in terms of what engineers think of as (linear) feed back and control.  We like to think that free markets are the proven answer to centralized and planned economies and that the history of the last 100 years has proven our side.  I think of these systems as “top-down.”

I propose that what we want from our national power grid is a system that is efficient enough and resilient.  And that that resilience will come from a bottom-up smart grid. A key attribute of resilient systems is that at various scales of the system, decisions contribute to the survival of the local and global system at the same time.

Notice that we violated this idea with a mortgage system in which mortgage brokers provided mortgages to consumers and sold them to investors on the same day, assuming no risk of default. Not surprisingly, mortgage brokers stopped being concerned with default, putting the entire system in peril.

These two ideas regarding information access and central control should be seen as entire Worlds apart.  The repeated conflation of the two ideas in the current thinking on energy distribution and consumption is dangerous. It seems clear from a systems engineering perspective that central control ought to be based on adequate information.  Adequate information coming in the form of appropriate models of system behavior and relevant access to real-world data.  From repeated experience, it is also clear that people and companies will hide information to maintain control and power as defined by success outside the system the information is about (e.g. Enron).

Robust, emergent, adaptive systems, on the other hand, may rely on the similar looking “information access and transparency” structures to the top-down systems, but have none of the centralized, coordinated control structures of the top-down system. Instead, these bottom-up systems rely on essentially local (in both proximity and meaning) decisions, where the resilient structure of the system emerges from the behavior of individual agents.

The “smarts” in the Smart Grid will come from agents acting on readily accessible (cheap, timely, relevant) information (#1 above) and not centralized, top-down control (#2 above).  Mixing the two ideas is confusing us, distracting us and slowing us down. A smart grid will emerge as a robust system relying on alignment between local and global objectives of the energy consumers and produces.

Where's the USB jack on this thing?

WattsGoingDown Building Systems Insight requires a low cost solution for putting energy measurements on the Internet. Our short-term plan is to use off-the-shelf hardware to create and deploy systems that send energy use data to our servers every minute or two.  A key driver of the software-as-a-service (SaaS) energy monitoring business model will be the balance of the savings realized by customers offset by the cost of the initial installation and WGD’s BSI’s monthly monitoring fees.

When the costs are justified purely by energy savings, monitoring system costing a few hundreds of dollars per monitored circuit only makes sense for higher-voltage, high current systems, or for circuits that are representative of many in the building or facility.

This is because, for a single circuit, the power used scales as voltage and current,  P= V x I . This is why business models centered on home energy monitoring are difficult to justify based on energy savings alone–customers have to be data geeks to make them work.  A typical home circuit runs at 120 volts and up to 20 amps.  This gives a maximum capacity of 2400 Watts per circuit (about two hair dryers).

Over a year, a home owner might run this circuit at capacity a quarter of the time (365 days x 6 hrs/day = 2200 hours).  At $0.10 per kWH, the total cost of running this example circuit is 2200 hours x 2400 Watts x 0.10 cents/kWH = $528.  If energy monitoring enables savings of 25% (aggressive!), you can pay no more than $132 per circuit for the hardware and monitoring to reach 1-year break even.

Without utility or government subsidy, the Tendril system costs quite a bit more. The Kill-A-Watt can be purchased for about $35 but provides no logging, network or analytics.  There is a clever hack, the tweet-a-watt, that adds a wireless Xbee module to the Kill-A-Watt, but you’ll need to be comfortable with soldering and analyzing the raw data yourself. The typical commercial network/analytic-enabled solutions run $750+ (e.g. WattNode + PLC).

So one challenge will be to create low-cost, dependable web-enabled measuring devices, devices so cheap that there will be no hesitation to install them and no motivation to remove them.

One of the challenges (and advantages!) of our ubiquitous use of energy in the form of electricity is the separation of energy generation from consumption.  We generate electricity where there is readily available gravitational, wind, chemical etc. energy then  transport it to high concentrations of people.

For the system to be efficient and robust, the network must have generation, routing and consumption optimizations at many scales.  More on this later, but this requires all parties–including end users–to have appropriate and complete data about energy flows.

Watts Going Down‘s Building Systems Insight tag line is “Energy Intelligence.”  We think energy intelligence lets the users of electrical power self-organize to use energy more efficiently and effectively for their businesses.  Here are some principles we hold to that will enable energy intelligence:

• Transparency.  It has been lacking in our banking system to dire consequences.  It is lacking in the details of your monthly power bills.  One number for energy and one number for the price is not transparent information.  How can you make better usage decision on that?
• Moving information is more efficient than moving energy.  This is true today, and BSI WDG will widen the gap in the coming months.
• Decision support.  Numbers are worth much if they can’t be assembled into a meaningful story.  We don’t learn by concentrating harder on numbers, we learn by integrating what numbers mean into our experience.
• Energy monitoring is infrastructure. Current solutions are meant to be spot checks.  They are temperamental, expensive and designed to be temporary.  BSI WGD is building a system that is hosted by BSI WGD, uses low-cost Internet-connected sensors and provides the up-time, security and continuously growing capability of a hosted ASP solution dedicated to energy intelligence.

Customers considering retrofits to improve lighting or HVAC efficiency, planning to add alternate energy sources such as a PV system, or who need to control peak energy demand costs, will find real time feedback essential.

This business is green, but closely tied to the mainstream ways energy is consumed and purchased now. I am enthusiastic about businesses in this space as they represent an opportunity to realize improvements in efficiency and optimize usage patterns within the current system. We will concentrate on projects with payback periods of a few to 18 months.

More to come…