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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A Model for Transition to IoE in Manufacturing

In a recent interview, executives from Robert Bosch GmbH and McKinsey discussed the Internet of Everything (IoE) and its impact on manufacturing.  They described significant changes to the production process and to the management of supply chains from this “fourth industrial revolution.”  The IoE allows for the interconnection of factories within and across regions and the exposure or “display” of the status of each component of each product for each customer via each distribution method.  Sensors in machines and in components will be able to keep universally in synch about what has to be done, what has been done and how well it was done.

A global decentralization of production control is now possible. Creating this reality will require new forms of intercompany and interdisciplinary collaboration.  The buyer, seller and distributor will all be involved in product design, engineering, and logistics.

GE Industrial InternetToday, physical flows and financial flows and information flows are different for manufacturing.  The IoE vision has them increasingly fusing together.  This transformation to what GE calls the Industrial Internet begs a set of questions: In this future how will orders be placed and with whom?  Who or what verifies the accuracy of an order or a deliverable across a network of suppliers, manufacturers and distributors that is formed, of an instant, down to the level of at an order at a time?

In this coming future state information, via the cloud, will be real-time available to all concerned parties.  The decisions to be made based on this information will be subtle, situation-sensitive, and so voluminous and time dependent that people won’t be making them. Algorithms running in machine-machine (M2M) systems will.  On first consideration this all seems overwhelmingly complicated.  We’ll need a model, an example to build from, on how to make the transition.  It turns out we have one.

Changing the trading cycles for Wall Street are recent, real examples that provide a roadmap for the manufacturing transition.  In that world the number of days allowed to settle a trade, the “settlement cycle,” has undergone major transitions. The most notable was from 5 days to 3 days, so-called T+5 to T+3, occurred in 1995. That change required almost every firm in the US to make some changes to their processing flows and systems.  Since the move to T+3 various exchanges have made further improvements towards T+1.  The table below shows some of the major changes, the before and after, that were accomplished:

T-5 to T-1 Table

T+1, even if never mandated, can be viewed as an example of industry opportunity through dislocation. At some level, IoE capabilities can enable dramatic cycle time gains by unlinking end-to-end dependencies (e.g. I no longer need to “affirm” trades based upon evaluating “confirm trade” messages). Some entities/roles will become more independent, some more dependent. Some may disappear if they no longer add value.

The parallels for manufacturing in an Internet of Everything world are clear (though some elements used in trading may not be used here or at the same level of emphasis).  Cross-industry governance will be needed on the format and import of transactions, acceptable technical modes of sending and receiving the messages, management of the quality and timing of the messages both in content and technically, and how to handle disputes.

Douglas Brockway
doug.brockway@returnonintelligence.com

Ira Feinberg
ira.feinberg@returnonintelligence.com

July 16, 2015

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You Are HERE

Now what?..

The Internet of Everything has recently joined Big Data Analytics, Social and Mobile technologies and the Cloud as subjects that one can bring up in a general business or social situation and be reasonably sure people will know what it is or quickly understand it.  What is also becoming generally understood is that these elements are connected.  We call them I-SMAC.  They feed on each other and the combinations are creating new businesses and “disrupting” old ones.

That there is a new opportunity, or, if you’re of a different mind-set, a new threat, raises the question among business leaders, “where are we and what should we be doing?”  There’s a framework that dates back to the days before “Enterprise IT” was called Enterprise IT that can help.  First laid out in a Harvard Business Review article in the mid-70’s, the “Stages Theory” proposes four “growth processes” that managers can use to track the evolution of IT in support of business.

On the “Demand Side” are included the Using Community, their use, participation and understanding of technology, and the “Applications Portfolio”, now including both applications and services, that make up the functional, now including process and analytic capabilities that an organization (or market) does or could use.

Growth Processes

On the “Supply Side” are the Resources brought to bear:  technologies, personnel inside and, now, outside the organization, and other elements like facilities and supplies, along with Management Practices which range from strategy and governance through development and support to daily operation and break/fix.

On a cross-industry basis the Applications Portfolio for I-SMAC is still in an early stage.  In some companies and industries, like retail bookselling or personal photography, it has passed the early experimentation stage and a full ramp up in capability is underway.  In no case are these portfolios “mature” Stage IV portfolios. Over recent months we have seen a subtle but clear shift in the awareness of I-SMAC opportunities.  Still, the Using community tends to be either unaware or artificially enthusiastic or doubtful and combative.  This is consistent with the early stage nature of the portfolios.  Lots of promise but not yet enough history to show unquestioned benefit.

For the most parts the Resources being brought to bear are new and rapidly changing.  There is a very short half-life of the preferred vendor or technology for a given task, or there is not yet an implicit and emerging standard, in most cases.  The staff, in-house or in service providers, are skilled in what they are working on but, as the technologies around them are kaleidoscopically changing, are having to spend large amounts of time keeping up.  Management Practices are currently updates-with-Band-Aids of what went before.  The best way to build I-SMAC systems and to manage them at scale is not yet proven.

You Are HERE Stages

What should you do in your case?  First, set a baseline that reflects your industry or market overall and shows the position of your company.  However detailed and analytic you wish or need to make it, the baseline should cover the state of each of the Growth Processes.  You will typically find that they are at a similar stage, but not identical.  Spend more think time if one growth process is Stage III and one Stage I.  Such mis-matches are trouble.  Do a compare-and-contrast analysis between your status and an industry synopsis.  Make decisions about whether you are ahead or behind and what you should do about it.

Second, with a “light touch,” explore the I-SMAC efforts underway within your company today. This basic inventory, by the way, is a Stage II practice. You are building organizational awareness of how you are trying to take advantage of, or face down a threat from I-SMAC.  You need to know what these efforts are, but as they are almost certainly early-Stage efforts you need to avoid the urge to pull the plug because you can’t yet see the mature market value. Make sure you’re in the game. If you are not at least trying to make use of some combination of Internet of Everything generated Data via Mobile platforms, leveraging Social technology via the Cloud you are exposed to competitors and new entrants who will.

Douglas Brockway
July 15, 2013
doug.brockway@returnonintelligence.com

 

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