MARCH/APRIL 2015 EXELON’S CHRIS CRANE: Innovative Solutions Drive Performance 32 WORKFORCE DEVELOPMENT STRATEGIES 46 MANAGING BIG DATA equipment and machinery; transformers; substations; distribution feeders; and power generation and control components. Once sensored, these devices become remotely machine addressable, meaning information can be sent and received across a computer network. Taking smart meters as one example, it is clear that the smart grid is communicate, with lasting effects on advancing apace. As of 2014, nearly professional and leisure activities. 400 million smart meters have been With the genesis of the smart grid, installed globally, according to Navigant Bell’s predictions from four decades Research. That number will more than ago are having a direct impact on double in the coming decade. Repretoday’s energy systems. The National senting a fraction of the sensors on the Academy of Engineers identified the grid infrastrucelectric grid as the ture, smart meter most significant As utilities adapt from installations serve scientific achieveas a proxy for the ment of the 20th managing a relatively small penetration and century. The smart number of non-communicating growth rate of grid will be the devices to connecting hundreds the smart grid. largest and most of millions of diverse sensored The “smart” complex machine devices, data volumes are sensored devices ever conceived in and of themand will likely prove expanding exponentially. selves provide one of the most little utility. They significant sciensimply provide the capability to remotely tific achievements of the 21st century. sense and/or change a device’s state. It is estimated that as much as For example, is the device operative $2 trillion is being invested this decade or inoperative? If operative, at what in upgrading the power infrastructure temperature, voltage, or amperage? globally to add sensors to the devices It might allow us to know the amount throughout the grid, creating part of of energy that the device has conwhat has come to be known as the sumed or recorded over some period IoT. These newly sensored things or of time or is consuming in real time. devices are the basis for the physical Collectively, these devices generate infrastructure of the smart grid and massive amounts of information—an include smart meters; thermostats; increase of six orders of magnitude from home appliances; heating, ventilation, before the connected grid. As utilities and air conditioning equipment; factory information technology The Internet of Energy BY THOMAS M. SIEBEL T he “Internet of Things” (IoT) is creating a buzz across industries. It describes the integration of an advanced, interconnected information backbone into the functioning of physical devices, systems, and infrastructures. It is the convergence of the virtual and the physical. At a basic level, IoT applies the Internet as we know it to wirelessly connect machines, devices, systems, and other “things.” IoT will be transformational, and Gartner predicts that, by 2020, 25 billion things will be connected in industries ranging from automotive to food and beverage services. In the energy industry, the momentum of IoT is having a tremendous impact as the grid becomes sensored, connected, and smarter. The “Internet of Energy” applies the premise of IoT to the infrastructure of the grid and is driving the acceleration of the dynamic smart grid. The Advent of the Smart Grid Nearly 40 years ago, a Harvard sociologist predicted the advent of the Information Age. Years before the invention of the Internet, minicomputer, personal computer, and smart phone, Daniel Bell authored “The Coming of Post-Industrial Society.” He predicted that information and communications technology would cause a fundamental change in the structure of the global economy— a change as significant as the Industrial Revolution. The Information Age, as we know it today, describes the free, nearly instantaneous transfer and access of data. It predicated the preeminence of the “knowledge worker” and resulted in the emergence and continued growth of information technology (IT). It drives ubiquitous changes in the ways we Thomas M. Siebel is the chairman and CEO of C3 Energy. He also was the founder, chairman, and CEO of Siebel Systems, which became a leader in application software before merging with Oracle Corporation in 2006. 54 ELECTRIC PERSPECTIVES | www.eei.org/ep The “Internet of Energy” is driving the acceleration of the dynamic smart grid. information technology C3 Energy Data analytics solutions for utilities must integrate massive amounts of disparate data, apply sophisticated multilayered analytics, and provide highly usable portals that generate actionable real-time insights. adapt from managing a relatively small number of non-communicating devices to connecting hundreds of millions of diverse sensored devices, data volumes are expanding exponentially. To effectively aggregate and manage this influx of data, utilities require next-generation technologies to integrate, process, apply analytics, and intuitively visualize the data and analytic results in a way that drives business outcomes through a common data- and intelligence-driven solution. Next-Generation Technologies By applying technologies and techniques commonly used by Google, Amazon, Netflix, and Twitter in the consumer industry, utilities can collect and aggregate the sum of increasing volumes of data to correlate and scientifically analyze all of the information generated by the smart grid infrastructure in real time. Computer science techniques, including elastic cloud computing, machine learning, and social human-computer interaction models, are now being applied to challenges utilities have faced for years, such as 56 ELECTRIC PERSPECTIVES | www.eei.org/ep algorithm to refine it. As a result, over managing the operational health of time, it “learns” and evolves so that advanced metering infrastructure (AMI) the analyses generated are increasassets and preventing revenue loss ingly accurate, reflecting real-world due to theft and meter malfunctions. conditions specific to the utility. Utilities can apply the same techMachine learning is used to classify nology concepts that Twitter uses to assets at high risk of failure, segment process 15 million tweets per second customers for targeted marketing or Netflix uses to stream more than campaigns, identify one billion hours non-technical loss of videos per Leveraging not only machine (which includes month to intemeasurement grate, aggregate, learning but also a full stack of errors, recording and analyze the tools associated with the science errors, theft, and massive amounts of big data, analytics enabled by timing differences), of incoming data. the smart grid provide efficiencies and predict future One such powerful across the energy value chain. load, among many computer sciother applications. ence technique is For example, like a machine learning, credit card company can use historical or the ability for computers to learn spending data to flag potential fraud, without being explicitly programmed. utilities can use a variety of historical Machine learning simply uses matheand real-time data to identify cases of matical equations known as algorithms energy theft. Baltimore Gas and Electric that can learn from data. The algoCompany (BGE) proved this use case rithms are trained on historical data when it deployed the C3 Revenue to make predictions. After predictions Protection™ application across its full are made, actual confirmed results two-million-meter service territory. In are fed back into a machine-learning six months, the solution identified more than 8,000 new cases of potential theft, higher than its original goal. Leveraging not only machine learning but also a full stack of tools associated with the science of big data, analytics enabled by the smart grid provide efficiencies across the energy value chain. Examples far outreach revenue protection and include: Z real-time pricing signals to energy consumers; Z management of sophisticated energy efficiency and demand response programs; Z conservation of energy use; Z reduction of fuel necessary to power the grid; Z real-time reconfiguration of the power network around points of failure; Z instantaneous recovery from power interruptions; Z accurate prediction of load; Z efficient management of distributed generation capacity; Z rapid recovery from damage inflicted by weather events and system failures; Z reduction of fuel needed to power the grid; and Z substantial reduction in adverse environmental impacts. Data Analytics Solutions Data analytics solutions for utilities must integrate massive amounts of disparate data, apply sophisticated multilayered analytics, and provide highly usable portals that generate actionable realtime insights. Utilities need end-to-end system visibility across supply-side and demand-side smart grid operations. Data analytics enable grid operators to realize dramatic advances in safety, reliability, cost efficiency, and environmental benefits by correlating and analyzing all of the dynamics and interactions associated with the end-to-end power infrastructure as a fully interconnected and sensored network, including current and predicted demand, consumption, electric vehicle load, distributed generation capacity, technical and non-technical losses, weather reports and forecasts, and generation capacity across the entire value chain. In another example from BGE, the utility used the C3 AMI Operations™ application to identify 3,600 meter health issues with 99-percent accuracy in order to streamline critical maintenance on AMI assets. data management system, validated theft-case data, external weather data, and Google for address verification. Leveraging investments in analytical algorithms, machine learning, data International Impact integration, and cloud-scale infrastrucThe growth of the smart grid and the ture, Enel deployed 55 unique and necessity for next-generation anasophisticated energy flow analytics lytics solutions are not limited to the to identify anomalous meter activUnited States. The European market ity. The company is leveraging these is seeing strong drivers for analytics, analytics to execute rule-based and including the increased number of smart machine-learning algorithms to unlock meter deployments in Italy, the United insights from both batch and streamKingdom, France, and Spain, and the ing data, and to generate increasingly European Union’s recommendation targeted and accurate results. of 80-percent smart meter penetraThe initial deployment proved that tion in member countries by 2020. the data-analytics solution could readily Enel, a leading integrated player in handle Enel’s smart grid data processthe world’s power and gas markets with ing and aggregation needs. Based on the largest customer base (61 million) the results from the one-million-meter among its Eurodemonstration, pean peers, Enel and C3 is deploying dataEnergy are working By 2006, Enel had installed analytics solutions to expand the 32 million smart meters across to enable smart deployment of Italy; Enel has since deployed grid and smart city this solution more a total of approximately services. A smart widely across the grid pioneer, Enel group’s distribu40 million smart meters in was the first utility tion network. Enel Europe, representing more in the world to also is installing than 80 percent of the total replace traditional additional applismart meters on the continent. electromechanical cations to expand meters with digital on what will be the smart meters, largest deploya major operation carried out among ment of software-as-a-service Enel’s entire Italian customer base. By smart grid analytics in the world. 2006, Enel had installed 32 million Leading utilities are driving innosmart meters across Italy; Enel has vation toward the Internet of Energy. since deployed a total of approximately They are at the cutting edge of tech40 million smart meters in Europe, nology advancements and are realrepresenting more than 80 percent of izing significant returns by applying the total smart meters on the continent. data-analytics solutions that combine To optimize the monitoring of energy the sciences of cloud-scale computflows, Enel has deployed one of C3 ing, advanced smart grid analytics, Energy’s data-analytics applications and machine learning to the benefit of across one million meters in Italy. Impletheir communities, consumers, stakemented in less than eight months, the holders, and the environment. solution ultimately identified 93 percent of Enel’s already-known operational issues through the initial deployment. For Enel, C3 Energy integrated, normalized, and aggregated more than 50 billion rows of data from 11 Enel sources, including the customer information system, billing system, work order system, outage management system, producer system, meter MARCH | APRIL 2015 57
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