Data. If one word describes the transformation from a utility to a Smart Grid, it would have to be “data.” Central to the concept of a Smart Grid is the overlay of the electric infrastructure with communications infrastructure paired with sensors, data collection and control devices, bringing data from the field to the utility back office, and sending control signals back out. Interval data from smart meters gets most of the attention these days, but a myriad of other devices will be deployed in the utility Smart Grid project, and each will produce data for specific purposes. The deluge of data flowing from these devices to utility back offices fits in with trends throughout the business world, denoted by the term “Big Data.”
Over the past decade, data sets have become so large that they are increasingly difficult to work with using traditional equipment, software, methods and approaches—this is the nature of Big Data. From web sites and blogs to smart phones, the proliferation of digital devices is driving new approaches to managing the deluge of data. And for utilities, smart meters bring the topic of data strategy to the fore. Generally, the first step in a Smart Grid transformation, the deployment of smart meters begins a flow of interval data that is only now beginning to confound utility IT managers as the first systems are fully deployed and operational. Consider the change in scale for utilities, as described in this excerpt from page 62, The Advanced Smart Grid.
“The change potential in automating revenue meter data collection with an Advanced Meter Infrastructure (AMI) is dramatic. Imagine moving from a single monthly meter read—twelve reads each year to produce a monthly bill—to a meter read every 15 minutes, to produce a bill, but to do so much more. Four reads per hour —15-minute interval reads – produces 96 reads each day. That’s eight times as many reads in a single day as was produced in a year with the manual, analog system. In a year, the number of meter reads will go from 12 to 35,040. And that’s just for a single account. For a utility with one million residential meters, the number of interval meter reads each year by that single utility will be over 35 billion. If each single meter produces 400 MB of data per year, then an electric utility with 1 million residential meters will have the challenge of managing 400 terabytes of new data each year. And that’s just for one mid-sized utility.”
The interval data from smart meters is managed by meter data management (MDM) software in order to produce data for digital billing systems. But beyond MDM lies a new area, wherein the utility takes its new asset—reams of data—and begins to analyze it for purposes beyond digital billing. Meter data analytics (MDA) is only now emerging from behind the shadow of MDM, but MDA holds tremendous potential for utilities not only in how they manage their utility, but also in how they work with their customers.
Since utility networks were first designed and deployed, the grid has been organized and built in a condition of relative ignorance—limited knowledge of grid conditions, inability to detect outages without manual processes, remote switching managers in distribution substations. Gradually, connectivity with the substation enabled automated operations with SCADA, but beyond the substation, along distribution feeders and out at the ends of the network at the meter level, utilities remained deaf, dumb and blind to grid status without a physical trip out into the field. In the next few years, smart meters and other devices in the distribution network will bring dramatic change, moving utilities from information scarcity to information abundance. The challenge for utilities will be adapting to this new condition and making the most of it—moving beyond management to mastery.
Abundance has positive connotations, but the rapid transition to Big Data will challenge utilities to develop robust strategies for managing and analyzing data, starting with Smart Grid architecture. Deliberate Smart Grid architecture design begins with customer use cases, moving on to process innovation, application selection, data flow design and infrastructure architecture. Data analytics will help to shape this process by contributing quantitative insights into consumer behavior, which will inform more qualitative consumer engagement programs.
Use cases, business process improvement, and grid modernization will go hand in hand, each driven and informed by analysis of the data flowing in from field systems. These three areas must be developed by utilities in iterative fashion, where data analytics help the utility to move along the maturity curve in a highly dynamic environment, taking readings and making adjustments in a continuous process of adaptation and self-improvement. First on the plate for utility managers will be gaining the internal competencies needed to manage the massive influx of data to MDM systems, to maintain reliability and revenue. More strategically, utility managers will need to find ways to use all that new-found data in new ways to generate new revenue, provide new services, engage with consumers in new ways, restructure internal departments, align to new business processes, hire to new skill sets, and re-architect the grid.
John Cooper, co-author of The Advanced Smart Grid, has recently joined the team at UtiliPoint and its sister company, Consonus, to further the goals and realize the vision he developed over the last 15 years as a Smart Grid pioneer and innovator, captured in this compelling and highly useful new book. John may be reached directly by e-mail at firstname.lastname@example.org.