Managing Data to Maximize Smart Grid Benefits

Transcription

1 Managing Data to Maximize Smart Grid Benefits CONCLUSIONS PAPER Insights from a webinar hosted by Electric Light & Power Originally broadcast in November 2011 Featuring: Chet Geschickter, Senior Analyst of Smart Grid with GTM Research David Pope, Principal Solutions Architect for SAS Global High-Performance Computing Practice

2 SAS Conclusions Paper To view this webinar and other on-demand SAS webcasts, visit Select Webcasts from the list of resources in the right navigation and then choose Managing Data to Maximize Smart Grid Benefits.

3 Managing Data to Maximize Smart Grid Benefits Data management can become a common thread that unites the different business units within a utility from a customer service representative who talks with customers by phone to a line worker who checks asset health. Across the business, no matter what their position, utility personnel could make better decisions if they had access to more timely, accurate and complete information. Targeting IT groups which are often tasked with responsibility for delivering this data SAS produced the webinar Managing Data to Maximize Smart Grid Benefits in concert with Electric Light & Power. The webcast identified how utilities can create greater business insights through IT leaders, ensuring better data management. The webcast speakers counseled utilities to accept that the new volumes and types of smart grid data can significantly enhance their planning capabilities, inform better market segmentation and improve engagements with their customers. At the same time, the speakers acknowledged that all these new data inputs add to the complexity of data management. As every utility knows, it s difficult enough to manage each data source separately, considering the complexity of GIS, SCADA, smart meter and CIS systems. But try to manage them together and the challenges expand exponentially. While adding to complexity, the new volumes and types of smart grid data can significantly enhance utilities planning capabilities, inform better market segmentation and improve engagements with customers. The speakers noted that IT leaders in the utility industry need to figure out what they really want to do with all the data coming from the smart grid. And the webcast speakers confirmed that there is a lot they can do with the data so long as there is up-front realization that a business intent has to be clear in order to build the right architecture and data governance model to support it. The webinar helped further the evolution of the smart grid with featured comments from Chet Geschickter, Senior Analyst of Smart Grid with GTM Research, and David Pope, Principal Solutions Architect for the Global High-Performance Computing Practice at SAS. Combined, their observations provide guideposts for achieving the next levels of smart grid version 2. The Smart Grid Vision Reality Gap The utility industry s current version 1 conception of the smart grid means fault remediation and shorter outages, renewable power integration, grid optimization, and so on. The good news is that there are already conceptual architectures for some of these functions and applications. In this vision of smart grid 1.0, utilities are already deploying lots of new hard assets into the field, like smart meters, intelligent electronic devices and even synchrophasors on the transmission grid. These devices generate lots of data for which the current strategy is to collect and store, Geschickter said. Utilities are saying, OK, if we have meters, we need a meter data management system. If we ve got intelligent electronic devices (IEDs) we need a place to put metrics and data from those. If we have synchrophasors, we need some sort of data archiving. That much of their smart grid 1.0 strategy is clear to them. 1

4 SAS Conclusions Paper What isn t clear is how the utility goes from collecting and storing all the data that those new assets generate and then translating that collection effort into a future world of smart grid benefits, or smart grid 2.0. Unfortunately the road map to get to these applications and functions, and what exactly they will mean for utilities and the customers they serve, is much less clear. It s a rabbit-out-of-the-hat trick that may or may not work, Geschickter said. Geschickter asked viewers to consider a case study from Northeast Utilities. At the very outset of its smart grid build-out, Northeast Utilities defined four things it wanted to accomplish through its investment: improve reliability; improve system efficiency; effectively integrate distributed generation into the system; and do advanced asset management. As part of its visioning process, Northeast Utilities created a smart grid business architecture that mapped and aligned smart grid benefits into the organization in terms of quantifiable benefits. Improving reliability meant reducing outage duration and the number of customers affected by outages. It also meant developing accurate knowledge of individual customer outage and restoration times and events. Each objective had its own set of quantifiable metrics to measure performance across all phases of smart grid deployment. The Smart Grid Value Machine Obviously every organization will have its set of metrics for determining what they want to gain from their smart grid effort, and each will have a different business architecture associated with achieving those business goals in their smart grid journey, Geschickter said. But when the utility endeavors to identify the benefits it wants to achieve, it establishes a different context. The utility says, We re going to continue to collect device data, but we ll also get process metrics along the way as well, like how long the average outage lasts and how immediately we notify customers of that outage. The utility can then determine how well it is performing in the process of keeping callers proactively informed. The business metrics flow from that, including losses occurring from outage duration, fading, etc. In Geschickter s view, the smart grid then becomes a primary tool in measuring performance, the core component of a performance optimization loop that creates a smart grid value machine. The process starts with business objective metrics; analysis of those metrics sparks any number of related actions intended to improve over the initial objectives. Every step can be repeated for continuing performance gains. I call this vision a smart grid value machine. We re not here to serve the grid. The grid is here to serve us. Chet Geschickter Smart Grid Analyst, GTM Research This smart grid value machine helps the utility derive more value more quickly, in a more systematic fashion than ever before. So the smart grid serves the utility. 2

5 Managing Data to Maximize Smart Grid Benefits Smart Grid Analytics Improves Performance Even more exciting, Geschickter said, are the prospects for new value emanating from in-memory data processing tools and the ability to process gigabytes and even terabytes of data in real time so that the utility can spot problems and identity opportunities almost immediately, as they occur. By setting this as a goal, the business creates a real-time data analytics architecture that enables the utility to move toward the convergence of information technology with operations technology. It improves and optimizes specific things like load forecasting, planning new tariffs, dynamic pricing, conservation voltage reduction and renewables integration, Geschickter said. It s the smart grid value machine. Big Data, Big Analytics, Big Value Webinar speaker David Pope organized his remarks around the concepts of architecture and analytics. Through its enterprise architecture, each and every utility s IT department can enable the value of analytics and use them in every operation of the utility, he said. People are using the terms big data and big analytics to describe the new environment for the volume, variety and velocity of data that is being created, Pope said. We would like to add the term value to the definition because if the utility works to use analytics they can actually find the value in the big data. The trick, Pope said, is identifying what s relevant. That s difficult because relevancy changes over time or at the time of query. The only way that you can keep track of and deliver the kind of information needed is by using analytics on all parts of the data at every stage. By processing gigabytes and even terabytes of data in real time, utilities can spot problems and identify opportunities almost immediately, as they occur. Through its enterprise architecture, each and every utility s IT department can enable the value of analytics and use them in every operation of the utility. David Pope Principal Solutions Architect at SAS Global High-Performance Computing Practice We call it stream it, score it, store it and apply analytics to the data in stream so you know which pieces to store where, and what parts may become valuable in the future. With this in mind, flexibility becomes a key architectural consideration, Pope said. In this context, with regard to all the information that is being generated by all the new machines and all the smart meters that are being deployed, the utility can t create value unless it has a flexible system. For example, the utility can direct smart grid data into a siloed data warehouse where it is seldom used. Or it can set up the data to become available to the utility s existing transactional systems. From an IT perspective, the current platform will need to be changed to handle the volume coming through, but the data will be used for more than just reporting or statistics. It needs to be configured for analytics. Analytics and the New Normal Pope encouraged utilities to view analytics as more than hindsight reporting. It s definitely not the same as statistics, like determining the average power consumption of a building or customer. Pope cautioned that statistical averages don t mean much if one customer or building is spiking and disrupting the data that is used to draw conclusions. 3

6 SAS Conclusions Paper Instead, analytics is foresight, Pope said. It s using data mining, predictive models, regressions, decision trees and neural networks to find the best possible scenarios of what can happen, or avoid the worst possible scenarios for what might happen. In the smart grid environment common today, Pope said that people in the IT department are uniquely positioned to enable analytics because they are most responsible for data governance the flow of data through their systems. If you re in IT running the infrastructure, you don t have to know how to do forecasting or analytics. Your job is to operationalize that insight. For IT, it s all about enabling analytics by building an infrastructure to support an analytical workload versus a transactional database or query workload, Pope said. Bridging the Gap for Big Data and Big Analytics Pope thinks that some current thinking common in the utility industry seems confined to old ways of viewing the limitations on capabilities. The common view among utilities today goes like this: OK, well, you have to choose one or the other, big data or big analytics. The point is that these can be handled in a flexible infrastructure if the utility starts to combine the two together. Truly, you don t want to pick one at the expense of the other. Pope suggested that high-performance computing techniques like using a grid of computers to help analyze or collect data and then surface that information will persuade the industry to change its perceptions. He provided some examples of the benefits of using high-performance computing analytics capabilities for petabytes of data using case studies from companies in various industries. He recounted how Catalina Marketing, which creates grocery store coupon scan solutions, improved from a 1 percent redemption rate to a 10 percent rate by advancing from using a rules-based acceptance model to using basic analytics, like a regression of the predictive model. The company further increased its redemption rate from 10 percent to 25 percent by applying more high-end analytics, like data mining, regression, decision trees and neural networks. When applied to utility industry challenges, those kinds of high-performance grid computing analytics capabilities can help drive better utility insight and better performance for the grid, Pope said. Analytics is foresight. It s using data mining, predictive models, regressions, decision trees and neural networks to find the best possible scenarios of what can happen, or avoid the worst possible scenarios for what might happen. David Pope Principal Solutions Architect at SAS Global High-Performance Computing Practice Catalina Marketing improved from a 1 percent redemption rate to a 10 percent rate by advancing from using a rulesbased acceptance model to using basic analytics. The redemption rate then rose from 10 percent to 25 percent when the company began applying more high-end analytics. The value for utilities is applying those same kinds of analytical techniques to big utility data. Utilities can use these new capabilities to prevent failures they might not have known about before because they wouldn t have been able to analyze the signals coming across the network quickly enough to detect a problem that was starting to occur. 4

7 Managing Data to Maximize Smart Grid Benefits Pope emphasized that analytics should be viewed as reusable assets. You might bring analytics in to perform fault tolerance. But then you ll likely find out that the marketing group can use that to figure out what s the best service or product that they can offer to certain customers with this smart meter. Because of analytics applied to meter data, marketers can determine that there is an 85 percent chance that a certain customer will like a new service plan because others with similar usage patterns have responded positively. Closing Thoughts Pope verified Geschickter s view that it takes effort and planning for utilities to develop an infrastructure that supports analytical insight. But failure to do so without the new analytical insights available to them might cause them to lose competitive market share or invite skeptics on a public utility commission to question the organization s investments in smart grid technologies and any rate change requests. By creating an architecture that gives IT the ability to offer easy, wide-ranging access to data, the utility can add analytics and all its benefits to the day-to-day work of the utility, including the creation and provision of reliable energy and advanced customer service. About the Presenters Chet Geschickter Senior Analyst of Smart Grid at GTM Research In the course of his job, Geschickter follows trends in data management, data analytics and the enterprise, and has written reports on meter data management, home energy management systems and smart enterprise utility architecture. GTM Research is a subsidiary of Green Tech Media, an organization that provides daily news coverage of renewable energy and smart grid developments. David Pope Principal Solutions Architect at SAS Global High-Performance Computing Practice Pope has more than 20 years of experience working with or at SAS, in roles ranging from R&D to MIS, SAS Solutions OnDemand, and sales and marketing. His background in data integration and business intelligence combined with his expertise in using statistics, predictive modeling and forecasting have enabled him to develop the necessary skills to suggest ways to improve existing processes, and to describe new and innovative ways to solve business issues. Pope graduated magna cum laude from North Carolina State University with a BS in industrial engineering and a certificate of computer programming. For More Information For related SAS white papers: SAS for Smart Grid Data Management How Do You Put Smart into the Smart Grid? To learn more, visit 5

8 About SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 50,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW. SAS Institute Inc. World Headquarters To contact your local SAS office, please visit: SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright 2012, SAS Institute Inc. All rights reserved _S76864_0112