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GDS International - Next - Generation - Utilities - Summit - US - 3


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Understanding the Potential of Smart Grid Data Analytics

Understanding the Potential of Smart Grid Data Analytics

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  • 1. JANUARY 2012Understanding the Potentialof Smart Grid Data Analytics A GTM Research Whitepaper
  • 2. Understanding the Potential of Smart Grid Data AnalyticsTABLE OF CONTENTS1 OVERVIEW: THE AMI DATA AND ANALYTICS OPPORTUNITY 32 PLANNING FOR DATA QUALITY 43 AMI DATA TYPES 5 3.1 Measurement Data Versus Events and Alerts 5 3.2 Power Consumption Data 6 3.3 Additional Data Types and Functions 6 3.4 Analytical Methods 6 3.4.1 Aggregations7 3.4.2 Correlations7 3.4.3 Trending8 3.4.4 Exception Analysis 8 3.4.5 Forecasts84 DATA AND ANALYTICS APPLICATIONS 9 4.1 Revenue Management 9 4.1.1 Load Forecasting 9 4.1.2 Theft Detection 9 4.1.3 Prepay9 4.1.4 Rate Plan Modeling 10 4.1.5 Demand Management 11 4.2 Consumer Engagement 11 4.2.1 Conservation Tips and Suggestions 11 4.2.2 Rate Plan Selection 12 4.2.3 Efficiency Program Measurement and Planning 12 4.3 Distribution Optimization 12 4.3.1 Outage Management 13 4.3.2 Distribution Network Planning 13 4.4 AMI Network Management 14 4.4.1 Service Level Management 14 4.4.2 Network and Device Configuration and Troubleshooting 14 4.5 The Future of Smart Grid Data Analytics 15Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 2
  • 3. Understanding the Potential of Smart Grid Data Analytics1 OVERVIEW: THE AMI DATA AND ANALYTICS OPPORTUNITYSmart meters present unprecedented opportunities to push the boundaries of grid visibilitybeyond substations and transformers and into the home. The potential benefits of advancedmetering infrastructure (AMI) go well beyond automating the meter-to-cash businessprocess. It is now possible to view and analyze consumption data in new ways for a plethoraof business applications, including capacity planning, demand management, rate designand reducing peak power consumption. Further, meters can also capture new metrics andreceive and execute remote commands. Examples include periodic voltage readings tosupport voltage optimization; remote connect/disconnect for service provisioning; outagealerts and power restoration notifications for automated outage management; on-demandregister reads by customer service representatives helping customers; and more.Organizations seeking to attain the maximum benefits from AMI need a data analyticsstrategy. The starting point is to understand the range of business applications AMI datacan enable or enhance. Once these opportunities are identified, planners can determinewhich ones make the most business sense to pursue, the supporting data requirements,and the analytical processes needed to turn raw data into actionable information forimproved decision-making.This whitepaper explores the AMI data analytics opportunity by highlighting the immediateopportunities smart meter data creates, as well as some of the data requirements andanalytical methods needed to capture them. It is intended as a useful planning guide forexecutives, senior managers, data architects, statisticians and other data managementprofessionals tasked with planning and executing strategic smart grid initiatives.Establishing a strategic data and analytics vision is paramount; however, the history of dataand analytics is littered with grand plans that have been bogged down under the weightof their own complexity. Wherever possible, we provide tips for getting smart grid dataanalytics up and running quickly and with a limited number of dependencies.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 3
  • 4. Understanding the Potential of Smart Grid Data Analytics2 PLANNING FOR DATA QUALITYGenerally speaking, high-quality data is accurate, timely and relevant. Specific requirementsfor collection frequency, latency (the lag time between data measurement and data use),and accuracy will vary depending on the data analytics application.For instance, accurate billing determinants need to be available within billing cycletimeframes, needs that can be satisfied by periodic data collection. Further, full-featuredmeter data management systems are able to deal with incomplete or inaccurate data via aprocess known as validation, estimating and editing (VEE). While the primary purpose ofVEE is to ensure data accuracy to generate billing determinants, VEE can also help ensurethat consumption data used for analytical purposes is clean and accurate.Voltage readings used for technical distribution optimization, on the other hand, presentdifferent data quality requirements. Here, timeliness takes higher priority, and instead ofperiodic polling, on-demand voltage readings can quickly be gathered and loaded intoan optimization engine.The key point is that data quality is relative and determined by the way the data will beused, as opposed to an overarching abstract principle; both of these considerations arediscussed further in this whitepaper.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 4
  • 5. Understanding the Potential of Smart Grid Data Analytics3 AMI DATA TYPESConventional electricity meters are simple devices that measure power consumption usinga running register. Smart meters vastly expand the available range of data and functionality.This is exciting, but at the same time, it can become confusing to wade through all thepossible permutations of data types, potential end uses and supporting data qualityrequirements. Two useful criteria to keep in mind are: 1) differences between measurementdata and events/alerts and 2) power consumption data vs. other types of data.3.1 Measurement Data Versus Events and AlertsBoth time-interval consumption data and register readings are types of measurement data.In terms of format, timing and structure, measurement data is predictable. Data collectioncan be scheduled and fulfilled via periodic polling at preset intervals, with batches of dataforwarded to a meter data repository for VEE.Events and alerts, on the other hand, are typically unscheduled messages that happenrandomly when an unusual situation is detected, such as a meter break-in as part of a theftattempt or an interruption in power delivery (i.e., an outage). Important alerts should berouted directly to the person and/or applications that need to know about them. A bestpractice is to use an enterprise service bus (ESB) messaging middleware with publish andsubscribe capabilities for multipoint broadcasts of critical messages, and to use complexevent processor (CEP) technology to quickly inspect messages, apply business rules anddetermine message routing.Although data management practices are, in general, quite different for measurementdata versus events and alerts, a common misconception is that analytics are limited tomeasurement data. To the contrary, event data can be very useful for analytics. A goodexample of this is the measurement of outages and reporting to regulators on quality ofservice using metrics such as SAIFI (System Average Interruption Frequency Index, orhow often the system-wide average customer experienced a power interruption in thereporting year) or MAIFI (Momentary Average Interruption Frequency, that is, the numberof momentary outages per customer per year).Measurement data can also be associated with an event and logged for analysis at a laterpoint. For instance, momentary voltage sags (i.e., power flickers) associated with metersalong a particular feeder can be logged and compared with transformer data to targetcostly vegetation management. Monetary savings from targeted maintenance dispatch canadd up quickly, yielding immediate payback from data analytics investments.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 5
  • 6. Understanding the Potential of Smart Grid Data Analytics3.2 Power Consumption DataThere are two basic types of power consumption data (as measured in kilowatt-hours orkWh): time-interval data and register reads. Register reads provide absolute values usefulfor billing purposes, while interval data provides more granular data (typically at 15-minuteor hourly intervals) for trending and analysis. Interval data is particularly useful for dataanalytics, since it is granular and neatly arrayed from a temporal standpoint.3.3 Additional Data Types and FunctionsExamples of new data available from smart meters include power quality data (e.g.,voltage, reactive power), outage alerts, and tamper alerts. Examples of new functionalityinclude the ability to deliver price signals and messages to devices inside the home andremote connect/disconnect for service provisioning.Conventions for collecting and managing power consumption data are well established.Determining whether, when and how to collect power quality data is a new smart gridfrontier. Planning is complicated by existing systems and processes. For instance, a turnkeyconservation voltage reduction (CVR) application may not be designed to accommodatevoltage readings from meters.Leveraging meter data across the organization is a give and take exercise of discoveringopportunities and collaborating to develop creative solutions. AMI pioneers cite a bestpractice of creating cross-functional task forces that bring experts from different businessunits (such as metering and operations) together to establish business requirements and tosolve technical problems like how to integrate new smart meter data with existing systems.3.4 Analytical MethodsCollecting accurate, timely and relevant data is the bedrock of any data analytics program,but the data needs to be put into an appropriate context to become useful information.Five fundamental analytical data transformations have immediate relevance to smart grid:aggregations, correlations, trending, exception analysis, and forecasting. Many high-valueanalytical processes combine several of these techniques as part of an overall analytical process.Each technique is discussed in more detail in the following sections, along with relevantsmart grid examples.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 6
  • 7. Understanding the Potential of Smart Grid Data Analytics3.4.1 AggregationsSimply put, an aggregation is a summary of data using set criteria. Because smart meterdata is atomistic (i.e., it is associated with a metering endpoint), it can be aggregated indifferent ways to serve planning purposes. For instance, the meters connected to individualtransformers can be aggregated together to identify transformer loading patterns.Combining homes or businesses into demand response pools to deliver sizable demandreductions (or ‘negawatts’) is another aggregation supported by smart meters. ‘Virtualmeters’ are arbitrary user-defined aggregations that combine data from multiple metersthat share a common characteristic. A typical virtual meter aggregation combines meterswith common linear relationships to support distribution planning and analysis (e.g.,common substations, feeders or transformers).3.4.2 CorrelationsCorrelations identify statistical relationships between related data that are useful forbuilding predictions. A basic smart meter correlation is the relationship between outdoorair temperature and power consumption. The fact that heat waves drive spikes in powerconsumption is well known. Statistical correlation using time-interval consumption datamakes it possible to build algorithms that predict the size of demand spikes using forecasttemperature. Correlations can also be built using multiple variables (i.e., multivariatecorrelations). For instance, cloud cover, humidity and time of day can be added to theequation to further refine peak predictions. The ability to align data temporally, spatially,or across other attributes is important for building correlations. For instance, instead ofrelying on measures such as daily minimum, maximum and average temperatures for anentire metropolitan area, the collection of nearby 15-minute time-interval temperature datais far more powerful for building weather/power consumption correlations. In fact, dataanalytics pioneers are using this type of data to build analytical models that measure theenergy efficiency of individual commercial properties.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 7
  • 8. Understanding the Potential of Smart Grid Data Analytics3.4.3 TrendingTrending is one of the most basic forms of analytics, and it can be an obvious win forimproving customer relations and service quality using smart meter data. A web pagethat shows customers a simple consumption data trend line can help them relate powerconsumption to household activity. The ability to overlay multiple trend lines together isalso valuable for purposes such as comparing consumption across similar seasons andtimes of day. Trending is a useful analytical process for any time series data.3.4.4 Exception AnalysisExceptions are unexpected or abnormal conditions. A missing meter read, for instance, isan exception event. The ability to analyze exceptions over time is valuable for identifyingproblems in communications and measurement infrastructure, as well as in the distributiongrid. Equipment failure is useful for homing in on a subset of data for other forms ofanalysis. In the case of a blown transformer, it may be useful to build a historical trend oftransformer loading prior to the failure. Once pre-failure patterns are identified, they canbe used to build predictive algorithms useful for preventing future failures. Trending ofexception events can also help identify component degradation or operational breakdowns.3.4.5 ForecastsForecasts are predictions of future events or values using historical data. For instance,a forecast of power consumption for a new residential subdivision can be created usinghistorical data from similar homes. Forecasts can also be built using correlation data.For instance, a forecast could be as simple as predicting one incremental megawatt ofpower consumption for each one-degree rise in summer temperature above 78 degreesFahrenheit, or it could be a more granular and sophisticated set of algorithms that forecastmaintenance expenses based on the age of equipment, utilization trends and past servicetrends for similar equipment.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 8
  • 9. Understanding the Potential of Smart Grid Data Analytics4 DATA AND ANALYTICS APPLICATIONSSmart grid data and analytics will revolutionize the way power is managed, delivered, andsold. This section of the whitepaper examines four categories of analytical applications:revenue management, customer engagement, distribution optimization and AMI networkmanagement. Specific examples of AMI data analytics appear in each of the relevant sections.4.1 Revenue ManagementKey revenue management applications include load forecasting, theft detection, prepay,rate plan modeling, and demand management.4.1.1 Load ForecastingThe ability to accurately predict loads supports multiple utility business processes,including power generation, power trading, capacity planning, and demand management.AMI data revolutionizes load forecasting by providing granular point-of-consumption data.This granular data is useful for building forecasts in a variety of contexts: to determinepower flow loads on specific parts of the distribution infrastructure, to aggregateconsumption up to locational marginal pricing nodes (LMPs) on the transmission grid insupport of power trading, and to plan load shed events (preferably demand response and/or dynamic pricing, not rolling blackouts). Suffice it to say that improved load forecasting isa killer analytic app for the smart grid, and time-interval data is the fuel that feeds it.4.1.2 Theft DetectionAMI supports theft detection in a number of ways. The first is the eliminationof electromechanical meters that can be tampered with to slow or even reverseregister values. Switching over to new, accurate digital meters quickly weeds outelectromechanical meter bandits.Potential theft or technical losses can also be identified by comparing smart meter datawith measurements from upstream sensors attached to transformers or feeders. Simplecheck-sum comparisons identify power loss. If total consumption at the meter level isless than at the feeder or transformer level, then field technicians can be dispatched toinvestigate the cause of the power loss, including inspection for bypass connections, metertampering, or a technical condition (such as runaway current in underground lines).4.1.3 PrepayTogether, theft detection and prepay can be considered the killer smart grid apps foremerging markets. Thanks to pay-as-you-go mobile phone plans, prepay is well establishedin emerging markets. Prepay has strong appeal wherever electrification is high butconsumer borrowing (and credit) is low. Offering prepay power purchase plans has multiplebenefits in these “cash societies.” First, prepay is comfortable and familiar to consumers.Second, prepay helps utilities limit exposure to credit risk. Finally, if the utility offering aprepay plan provides consumers with appropriate analytical tools, consumers can moreCopyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 9
  • 10. Understanding the Potential of Smart Grid Data Analyticseffectively manage power purchases and, most importantly, avoid service interruption. Itis important to note that, even in established economies, some utilities have reported thatprepay plans are quite popular as a way for consumers to manage power budgets.Data analytics, proactive communication and ease of purchase are all key success factorsfor prepay plans. The ability to notify consumers that they need to replenish their accountbalances is critical. This can be done by comparing remaining balances with the rate ofconsumption. More rigorous analytics improve subscriber account management. Forinstance, historical consumption patterns can be analyzed to estimate power consumption“burn rates” for comparison against remaining balances in order to estimate the number ofdays before account balances are depleted. Proactive communication via the channel(s) ofsubscriber’s choosing (phone, text, web portal, email, etc.) make it easy for consumers totrack their account’s standing.4.1.4 Rate Plan ModelingMany utilities are actively piloting and rolling out new variable price structures. Examplesinclude time-of-use or dynamic pricing as a way to reduce peak consumption. Smartmeter data can be used to analyze and plan different rate structures, while adheringto requirements like revenue neutrality or non-discrimination against low-incomedemographics or the elderly.Rate planning makes intensive use of data analytics. A typical sequence is: • Pricing pilot design. Determining the overall objectives of the pilot, what is to be tested and the desired findings. • Planning rate structures. Using historical consumption data, including timeframes where peak demand occurred, as well as data from the design and results of other pilots (especially peak reduction results of different rate structures and their impact on utility bills). • Selecting pilot participants. Sample size and participant diversity needs to be sufficient for segmentation analysis. Examples of data useful for segmentation include the heating and cooling systems for various homes (e.g., central air conditioning versus window air conditioners versus no system), high income versus low income, home office workers versus commuters, etc. • Gathering and analyzing pilot data. This includes the effectiveness of various pricing plans in reducing peak consumption and their impact on bills. • Submitting proposed rate structures for regulatory approval. This includes supporting analytics that prove out the benefits of the new rate plan for ratepayers and the utility. • Marketing new rate structures. This also entails providing consumers with tools and analysis to pick the rate structure that is most beneficial for them (see Rate Plan Selection in the Consumer Engagement section below).Advanced statistical analysis, such as cluster analysis, is needed to develop market segmentation.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 10
  • 11. Understanding the Potential of Smart Grid Data Analytics4.1.5 Demand ManagementDemand management program managers can use data analytics to forecast peaks andto plan demand management events, including when and where to call peak demandevents and who to include (for instance, an entire service territory may be called on toreduce generating requirements, while a smaller metropolitan area can ease distributionconstraints). Power consumption before, during and after a peak event also needs to betracked and analyzed, especially for peak time rebate programs that reward customersfor not consuming power. In this case, an analytical process for identifying the amount ofnon-consumption to pay is needed in order to prevent people from ‘gaming’ the system byratcheting up demand just prior to an event. Analytics of consumption patterns are alsouseful for validating demand reductions claimed by third-party demand response serviceproviders and for general market operations in open demand response markets.4.2 Consumer EngagementSharing smart meter consumption data with customers opens new opportunities foractively engaging consumers in energy management. A secure web portal wherecustomers can log in and view consumption trends is an obvious starting point, but carefulconsideration needs to be given to the overall user experience. How easy is it to log in?Can a forgotten user name or password be retrieved? Can the user select the timeframeof interest and perform comparisons between time periods? These basics are merely astarting point for delivering more advanced analytics like benchmark comparisons withother similar households and proactive recommendations for how to save energy.Consumer engagement is emerging as a lynchpin requirement for utilities implementing smartgrid and facing questions about what consumer benefit is accruing from smart grid initiatives.Consumer web portals with analytics capabilities are now a must-have requirement. Moreadvanced consumer engagement tools enabled by data analytics are discussed below.4.2.1 Conservation Tips and SuggestionsBehind-the-scenes profiles and automated algorithms are the ‘secret sauce’ for targetedpower conservation tips and recommendations. Profile data includes home square footageand data about major heating and cooling systems and other electrical appliances. Thisdata can be gathered using forms that customers voluntarily complete or external datasources like tax assessor databases. Examples of advanced analytics for generating targetedrecommendations include correlations between consumption and weather data thatmay lead to a recommendation for weather sealing or more efficient heating and coolingsystems, and trending to identify excessive baseline power consumption from “vampireappliances” like PCs, set-top boxes and chargers that consume power even when not in use.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 11
  • 12. Understanding the Potential of Smart Grid Data Analytics4.2.2 Rate Plan SelectionInstead of forcing customers to adopt a new pricing model, many utilities (and the publicutility commissions that regulate them) are taking a more cautious opt-in approach wherecustomers elect to enroll in a new rate plan, such as dynamic pricing or time-of-use pricing.This is an important economic decision for customers who will expect bill savings in returnfor adopting a new plan. Customer decision-support tools can aid the opt-in decisionprocess. An obvious analytics requirement is the ability for customers to model a new rateplan using historical consumption to determine whether the plan would have saved themmoney. However, since the point of many of these pricing plans is to spur peak-time usereductions that are a departure from past consumption behavior, additional analytical toolsare needed to help customers understand the impacts of different actions, such as settingthermostats higher during a critical peak pricing event. A ‘best-plan selector tool’ can alsorecommend pricing plans using a wizard interface supported by back-end algorithms.The end goal for rate plan modeling and selection is a win-win: to coach consumers toadopt the best plans for their pocketbook – and for utility cost to serve.4.2.3 Efficiency Program Measurement and PlanningBecause of its multivariate nature, measuring the effectiveness of energy efficiencyprograms is a complex undertaking. For instance, if a customer participated in a freeCFL program and also signed up for a home energy audit, how much savings should beattributed to each program? Limited consumption data compounds the difficulty. Forinstance, one monthly kWh consumption number makes it difficult or impossible to createa detailed timeline that connects consumption reductions with actions taken at a specificpoint in time or to adjust for externalities, such as a heat wave or cold snap. For instance,one would expect that weatherization improvements would yield significant powerconservation during extreme weather events, something that can only be measured withthe benefit of time interval consumption data.The energy efficiency audit community is just beginning to recognize the opportunitiesfor improvement made possible by smart meter data. We expect auditors and efficiencyprogram managers to become important customers for smart meter data.4.3 Distribution OptimizationLeading utilities are beginning to identify opportunities to optimize power distributionmanagement using smart meter data. Two immediate opportunities are in the areasof outage management and distribution network planning. An additional longer-termopportunity to wield smart meter voltage data for conservation voltage reduction isdiscussed further in the Futures section, which concludes this whitepaper.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 12
  • 13. Understanding the Potential of Smart Grid Data Analytics4.3.1 Outage ManagementEnhanced outage management ranks as the first true real-time application for smartmeters. But using ‘last-gasp’ meter outage alerts to drive real-time outage notifications intoan outage management system is not a plug-and-play endeavor. When it comes to outagenotifications, smart meters can be very ‘noisy’ over reporters. Examples include momentaryinterruptions in power caused by line flicker (vegetation brushing against power lines, forinstance), or outage ‘message storms’ caused by thousands of meters all reporting powerinterruptions as part of a larger outage. Real-time analysis (best performed by a complexevent processor, or CEP) is needed to handle both contingencies.An example of a CEP in action would be rolling thousands of outage alerts up to a commonupstream node on the distribution grid (such as a common feeder or a substation) tocreate one master outage instance and to support targeted crew dispatch. Anotherexample is a business rule that suppresses the creation of a new outage incident if anoutage restoration alert quickly follows an outage alert – a business rule that eliminatesspurious reporting of line flickers as outages.4.3.2 Distribution Network PlanningSmart meter data can be used to improve distribution network planning. Historically,distribution sizing is a very conservative exercise where planners err on the side ofovercapacity, absent any detailed data on utilization trends, especially for transformers.Smart meter data can be aggregated to reflect the transformers they are connected to,and then utilization can be compared to the capacity of the transformer to build detailedcapacity utilization trend analysis.Examples of questions that this type of analysis can answer include: What percentageof the time is a transformer operating within 10 percent of its peak rating? Are therecertain times of day or times of year when transformers are nearing overload? What is theminimum size transformer that could be used to replace an aging transformer?Utilization patterns can also be compared against pre-failure data for similar transformersto begin building proactive asset maintenance and failure prevention.In summary, distribution network planning and analysis using smart meter data is the firstwave of distribution optimization enabled by smart meter data analytics.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 13
  • 14. Understanding the Potential of Smart Grid Data Analytics4.4 AMI Network ManagementSavings garnered through remote meter reading can quickly disappear if extensivemanual troubleshooting is needed to configure and manage the AMI network. Any datacommunications network needs to be proactively managed and administered, and AMInetworks are no exception.Going forward, personnel responsible for ensuring the reliability of the end-to-end meter datacollection process are going to need access to data about network performance and reliability.4.4.1 Service Level ManagementA strong end-to-end AMI solution includes data about meter and AMI networkperformance, including response times, data packet losses, message retries, communicationoutages and device failures. All of this data needs to be rolled up to create an overview ofAMI service levels. Key metrics include network reliability and on-time message deliveryperformance. Service level metrics can be used to support planning decisions, such asdetermining where to invest in more capacity and which applications or types of dataneed to be throttled back to ensure bandwidth is available for critical data (like collectingconsumption data to support the meter-to-cash process).4.4.2 Network and Device Configuration and TroubleshootingThe scope and scale of AMI networks in terms of the number of end-node devices (i.e.,smart meters) is unprecedented. The device management challenge that smart meterscreate should not be underestimated. Each meter needs to be meticulously tracked,including make and model, warranty information, firmware release and configurationsettings. The same holds true for head end systems (also known as data collectors) thatinterface with the meter. Further, the accuracy of each meter needs to be validated toensure ratepayers are treated fairly. All of this data needs to be tracked and managed.Key reports and analytics about smart meters include the number of meters on a specificversion of firmware, the current configurations for all smart meters, problem meters andnetwork segments that are unreliable and causing missing or inaccurate readings, and soforth. Analytics data supports asset management tasks like planning upgrades (e.g., addingrepeaters to fix intermittently dark network segments), swapping out bad equipment, andfuture purchase decisions.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 14
  • 15. Understanding the Potential of Smart Grid Data Analytics4.5 The Future of Smart Grid Data AnalyticsThe analytics opportunities smart meters present are just beginning to be identified. As thiswhitepaper illustrates, time-interval consumption data from smart meters alone presentsa plethora of opportunities ranging from planning new dynamic pricing rate structures todistribution equipment sizing, load forecasting, theft detection and more. But time-intervaldata is merely the tip of the smart grid data analytics iceberg. Smart meters are capable ofproducing additional types of data, each opening a range of new opportunities and uses.Here is a brief summary of additional types of smart meter data and some of the analyticsopportunities we expect to emerge in the near future:• Voltage data. With smart meters, it is now possible to collect voltage readings from the edge of the distribution network. This data can be collected and matched with other voltage readings further upstream in the distribution network, then analyzed to optimize voltage regulation. Voltage conservation can be used for technical demand response and/ or to improve overall power delivery efficiency.• Power quality data. Reactive power readings from smart meters can be captured and analyzed to measure power quality and to determine adjustments in the distribution network to reduce power harmonics, increase delivery efficiency, and provide a high- quality product to customers.• Peak demand readings. Time-interval consumption data follows a time-based sampling methodology. Within any given timeframe, there will be a maximum draw of power – in other words, a peak demand reading. Peak demand data can be analyzed to learn more about consumption patterns, including the identification of ‘heavy-hitter’ appliances like pool pumps, central air conditioners, electric hot water heaters, and in the future, electric vehicles.• Home area networks (HAN). Many of the dynamic pricing trials underway include a HAN component that connects energy management systems inside the home with utility systems. While the ultimate vision is some form of elegant machine-to-machine (M2M) interaction to achieve peak reduction, some level of analytics by utility personnel will be necessary to orchestrate power consumption. It will be necessary to be able to analyze the portfolio of available customer load assets at any given time and to interrogate their current status (potentially including thermostat settings and current indoor temperatures, for instance), including modeling the amount of power made available by different actions. Although HANs will be outside of utility direct control, it will still be desirable to create data records for them and to make them ready to receive communications (provisioning and commissioning).• Electric vehicles. Electric vehicles are a new type of consumption asset – a mobile device that can draw power at multiple points across the grid. Data analytics will enable utilities to visualize and analyze EV charging trends, create new charging plans, and to identify changes in distribution sizing and planning necessary to accommodate these new power-hungry devices.Utilities that embrace smart meter data analytics as a core business function will reapsubstantial rewards in the form of improved business operations, more effective revenuemanagement, and technical improvements in power delivery. Data analytics unlocks smartgrid potential and turns opportunity into business reality.Copyright © 2012 Greentech Media Inc., eMeter. All Rights Reserved. 15
  • 16. For more info please visit A joint white paper produced by: A Greentech Media Company