Analytics: The Next Step on the Road to the Smart Grid

Smart Grids are among the top of priorities for electric utilities and the communities they serve around the world.  There is a lot of activity.  Dept of Energy Report 2013In the US alone a recent Department of Energy report revealed that investment in smart grid projects has resulted in almost $7 billion in total economic output, benefiting a wide variety of industrial sectors and creating 47,000 jobs. On the other hand, at a recent seminar at MIT on the Smart Grid the panelists were both enthusiastic for the long run and skeptical or worried in the short run, especially in the consumer sphere.

The panelists from NSTAR, Schneider Electric, Peregrine and FirstFuel said that 22% of load on the grid is consumer load. Smart Grid design and capability goals include the ability to measure, control and bill at the circuit level inside a home or business, but the Smart Grid may have small economic impact there. According to the panel the best estimates of consumer savings from Smart Grid are $100/year.

In the realm of Small/Medium buildings there are substantive potential benefits but the panelists say not enough to have an Energy Manager on staff to make the changes, do the engineering, the monitoring, and the implementation. There isn’t enough benefit for them to focus on it.  If the building and all the tenants don’t act then the Smart Grid benefits will be hard to capture.

Given these challenges what is the next step for Smart Grid?  Some of the answer can be found in a recent article on how good ideas spread by Atul Guwande. He is a surgeon, a writer, and a public-health researcher.  Atul GuwandeIn this article he compares the lightning-fast spread of the invention of ether-based anesthesia with the long, slow adoption of clean operating rooms, washed hands, fresh gowns and Listerine.  In brief, anesthesia solved a problem that doctors and hospitals had with screaming and thrashing patients, emotionally draining surgical procedures.  Doctors wanted a change.  With antiseptics and cleanliness, the dangers were un-seen to the doctors, involved a lot of procedural changes, and solved a problem only the patients had; survival. For antiseptics the change only came, more than 30 years after the invention, when German doctors took it upon themselves to treat surgery as science. Science needed precision and cleanliness. This included white gowns, masks, antiseptics, fresh gloves, clean rooms. After a long dormancy the now-obvious idea “went viral.”

Consumer level investments and benefits from the Smart Grid don’t appear to be ready, yet.  Regulators and providers and distributors of power are looking for the returns.  Looking to consumer solutions is a bit like starting the computer revolution in the late 1950’s with personal computers. It didn’t and couldn’t happen that way.

In business, IT has gone through multiple eras in the way it transforms then supports an enterprise – think mainframes then client server then the internet and now mobility, big data, the cloud: I-SMAC.  Within each era, as with anything else in life, the first systems built are those with big payoffs.  For the Smart Grid this is in the industrial, the corporate, and the municipal, state and federal forms of consumption.

When we talk to utility companies the current focus area related to the Smart Grid relates to data.  Just as in other industries like pharmaceuticals, the grids, transformers, meters and controllers already deployed are producing more data than companies can deal with, and it will get worse.  Newer equipment is being installed or existing equipment being outfitted with more and better sensors.  Data can be captured in smaller and smaller time increments, isolated to smaller and smaller grid footprints.  All of the analysis done produces more meta data and the opportunities to learn yet more.  As a client says, utilities are not struggling with connectivity [to devices] as much as they are struggling with analysis of device borne data.

In addition to the volume of data there are myriad data analysis techniques that can be applied. Common predictive modeling techniques include classification trees and linear and logistic regression to leverage underlying statistical distributions to estimate future outcomes. New, more CPU-intensive techniques, such as advancements in neural networks, can mimic the way a biological nervous system, such as the brain, processes information.  Which to use, when and why?  Utility executives say they have only started using a very few out of the many techniques at their disposal.

A small delay, or speeding up, of an energy buy, can greatly change the profitability of that trade.  Small adjustments in voltage delivered, by time of day, can greatly change the economics of delivery and, if done properly, without materially affecting use.  Knowing which of these and other actions to take, precisely and specifically when, requires significant expansion of analytic activities by utilities.  But it is well worth it.  Expect much more of this long before your electricity provider asks permission to alter the fan speed on your refrigerator.

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