Are you ready to test your knowledge about electricity data and the privacy of that data?  And beyond Smart Grid discussions, as more devices are communications-enabled, the Internet of Things will produce enormous amounts of new data that can profoundly impact our privacy.  Here are a few questions:

1. Smart meters provide electricity data that lets utilities spy on consumers.  T/F 
2. My electricity data doesn’t have value to anyone but me.  T/F
3. A kilowatthour (kWh) can’t tell my utility exactly what appliances have been using electricity.  T/F
4. Utilities need to do more to ensure that my electricity data is protected.  T/F
5. The new Green Button initiative will
   1. Result in my electricity data being sold to the highest bidder
   2. Give me control over my electricity data and who may view or use it
   3. Automatically post my electricity data to my Google+, Facebook, and LinkedIn pages.

Here are the answers. 

1. False.  While smart meters can communicate the amount of electricity that you are consuming in your home, special hardware and software that you install within your home is needed to disaggregate the stream of electrons flowing into your home and break it down to what flows to individual components.  A smart meter can offer a more time-granular view of electricity consumption, and that data could allow you to infer that spikes or declines in use correspond to operation of specific equipment  – particularly the biggest guzzlers like clothes dryers, pool pumps, and heating/ventilation/air conditioning (HVAC ) systems.   There are companies that take smart meter data and create suggestions to help you reduce electricity use, but those suggestions are based on inference and analytics comparing your usage against a peer group with similar variables for location, size of home, number of occupants, etc.
2. False.  Your electricity consumption data has enormous potential value to you and to others.  For instance, think about how your internet search data has value to advertisers.  Similarly, analysis of your electricity data could reveal information that would be valuable to businesses that want to sell products or services to you.  If you choose to share your data with a company in exchange for any value-added services, you’ll want to obtain a detailed description of exactly how they use that data, how they protect that data from unauthorized access, and if they want the ability to sell that data (anonymized or not) to others.   
3. True.  A kilowatthour is a unit of measurement that is one kilowatt of power expended in one hour.  It can’t tell you or your utility what that kilowatt was used for, anymore than the miles per gallon (mpg) metric can tell you or your friendly state trooper how fast you’ve been driving your car or where you’ve been driving it.  You could make inferences about the lavishness of your lifestyle by a monthly kWh consumption compared to a peer group.  But a kWh number won’t tell you or your utility how much electricity was spent chilling your 3000 bottle wine collection.  You can get that information if you install special devices, but the utility will never know.
4. True.  Smart meters do collect more electricity consumption data than dumb meters.  That data can help us recognize the true total cost of operation (TCO) for our equipment and our lifestyles.  Utilities must re-examine their existing policies and practices to ensure that they can securely communicate and store data needed to continue the safe, reliable, and cost-effective delivery of electricity.  We already have too many horror stories about how insurance companies and retailers compromise personal, medical, and financial information.  We don’t want to see utilities or third party service providers making similar errors with our electricity data.  See this blog for more information about ongoing activities to help utilities incorporate the policies and best practices to protect consumers’ electricity data.
5. The correct answer is b.  The recently-launched Green Button initiative models the successful Blue Button initiative that makes it very easy for consumers to “have timely access to their own electricity data in consumer-friendly and computer-friendly formats.”   You own your electricity data, and you can choose who may have access to it (aside from the utility that has legitimate needs for “revenue-grade” data to accurately bill your electricity use.)  However, and this is a big caveat, as consumers we need to know how the companies with whom we share the data will use it  and protect it from unauthorized access or use.  Just as we have expectations that retailers secure our credit card information, we should have similar expectations of any companies that we allow to access our electricity data.

Twenty-four states have Energy Efficiency Resource Standards (EERS) that define annual energy efficiency targets sustained over time, not single events like Demand Response programs.  Each EERS mandates that energy consumption be reduced by a certain percentage through energy efficiency programs.  These programs can be aimed at residential, commercial, industrial, and agricultural customers, and incentives are tailored to meet different needs.  But unless the regulatory agencies that govern utilities provide support in the form of revenue recovery for these energy efficiency programs, utilities are naturally reluctant to invest time, money, and resources into reducing their revenues.   One form of support is a policy called decoupling, in which utilities are assured a rate of return that is equivalent to sales of electricity. 

However, given the current economic downturn and a continued trend in improved energy efficiency in home and business appliances and equipment, utilities are facing a stall in consumption that mirrors the classic S-shaped curve of growth, and are at the point where growth slows.  There are no policy mechanisms like decoupling to protect utilities from these economic realities, but there are strategies that utilities can deploy to change their business model from revenue reliance on one service to a diversity of services and new metrics for consumer value. 

Utilities should consider new business models that are enabled by Smart Grid technologies as opportunities to protect and grow revenues even as electricity consumption falls.  Subscription-based services to manage electricity, such as Home Energy Management Systems (HEMS) software and devices are one possibility.  Similar to the business models successfully used by telecom carriers to increase the Average Revenue Per User or ARPU, an array of energy management services could help boost utility revenues.  But while ARPU is a useful metric for classic consumer/service provider relationships, it falls short in valuing some of the most exciting opportunities with the Smart Grid.  If we consider that electricity consumers may become prosumers – both consumers and producers of electricity or services, then we need a metric that reflects services that are sold back to a utility as well as purchased.  That metric is Lifetime Consumer Value (LCV). 

LCV accounts for the consumption and production values of a consumer.  For instance, a recent Texas law requires the state’s grid operator, ERCOT, to devise a market model whereby residential, commercial and industrial consumers can bid conservation (energy savings called negawatts) into the wholesale electricity market.  Sales of energy produced from customer-owned generation sources or energy storage assets may also be factored into LCV calculations.  This is a far different metric than kilowatthours consumed, and it requires significant re-engineering within utilities to create consumer-centric operations that can build lifetime consumer value.  I’ll be discussing how utilities can build consumer value at a webinar on October 10, and you’re invited to attend.

As mentioned in my previous blog, the rollout of Smart Grid projects supports a larger concept called the Internet of Things.  Utilities could eventually manage networks with device numbers in the millions to hundreds of millions.  These are many challenges to designing and running the best network architectures that can manage these numbers of devices.  They range from building in flexibility and scalability to absorb a dizzying array of devices that have different needs in terms of network speeds and responsiveness to successfully making necessary cultural and organizational changes within utilities to support these operations. 

In the past, utilities built application-specific networks, but utilities will be under pressure from policy-makers, constituents, or competitors (depending on the regulated nature of your electricity sector) to reduce operations costs.  To achieve that, these subnetworks will transition into combined or converged heterogeneous networks, and utilities will have to determine how to assign priorities to the vast amounts of data that are offered from different devices on these networks.  There will be interesting discussions about Quality of Service (QoS) requirements between various stakeholders, because often what is “mission-critical” to one group may not be so important to another.  For instance, the group responsible for billing will consider the meter data to measure kilowatthours to be mission-critical, but an operations manager will want last gasp signals from a meter, signaling an outage, to take priority.

The abilities to aggregate and correlate data along with business rules that allow decision-making as close to the decision point as possible will help these dynamic, flexible, and high-growth networks serve mission-critical and routine requirements as time-effectively as possible too.  Normalization of data – time-synchronized to ensure accurate reflections of activity – from different sources will be critical to delivering useful information. 

There are a number of practical benefits that advanced analytics offer to utility operators as they confront the new challenges that Smart Grids pose to them.  How much is it worth to a utility to proactively identify and address network congestion problems?  Probes and self monitoring network elements deliver high value Quality of Service (QoS) and bandwidth usage data that can help manage Smart Grid communication networks.  Analysis of network routing, reliability and performance statistics enable better management decisions.  Analytics can also determine predictive maintenance for departments focused on asset management, allowing them to schedule planned maintenance that extends the life of equipment rather than react to expensive failures of equipment and subsequent service outages.   

 As networks grow in diversity and complexity, managing Service Level Agreements (SLAs) will take on increasing importance – utilities will need to know which vendors are meeting their contractual obligations, and which ones aren’t.    Utilities not only have diverse communications networks, they have a range of vendors supplying equipment for their power and communications networks.  Many utilities lack the tools to track their negotiated agreements against actual performance.  Correlation of fact-based vendor service statistics enable utility companies to verify compliance claims to performance using historical fault records and service trends. 

The utility industry is beginning to explore the multiple, valuable uses of data analytics, and we’ll see an increase in the conferences and presentations devoted to the topic.  The best learning experiences will include careful attention to the lessons and best practices derived from other similar business sectors – such as communications service providers and retail/hospitality markets.

According to CASAGRAS  – an EU Framework 7 project for the Coordination and Support Action for Global RFID-related Activities and Standardization – the IoT is one of the pillars supporting the future networked society and structured on a foundation of future network infrastructure.  The IoT exists in nascent forms today – primarily islands of applications that relate to objects being identified and included in networked systems.  Some industrial processes could fall into this view, as well as smart buildings that have IP addressable devices down to the lighting fixtures.

The Smart Grid is a specialized example of the IoT, in which small to large networks connect devices and use embedded intelligence in the forms of sensing and control to deliver and manage electricity, minimizing or eliminating the need for human interactions to achieve those same objectives.  The Internet of Things has been viewed as a “metaphor for the universality of communication processes, for the integration of any kind of digital data and content, for the unique identification of real or virtual objects and for architectures that provide the ‘communicative glue’ among these components”, according to CASAGRAS.  But from a Smart Grid perspective, it’s easier to think of it as nested and overlapping networks.  A Home Area Network (HAN) is nested in a Neighborhood Area Network (NAN), a NAN is nested in a Field Area Network (FAN), and that FAN is part of the distribution grid of a utility.   

Utilities are rapidly investing in wireless and wired communications technologies and services to build out their Smart Grid projects.  Spending as a proportion of overall telecom budgets could double over the next five years, growing from 28% of telecom spending in 2011 to half (50%) of all telecom spending in 2016, according to research conducted by the Utilities Telecom Council (UTC)  this year.

While smart meter rollouts constitute a significant portion of utility investments in the USA, they are also investing in IEDs – intelligent electronic devices.  IEDs enable local and/or remote sensing and control of substation equipment at what is typically a machine to machine (M2M) level, and this is a primary reason why Smart Grids fit so well into IoT concepts.  The typical mid-sized utility in the USA has between 2000 to 5000 devices online today to provide SCADA communications, condition-based monitoring, and polling for event-specific data in their substations.  The proliferation of IEDs is one of the most interesting Smart Grid stories too – because embedding communications and intelligence in the distribution network offers new opportunities to utilities to monitor and manage their power networks, and thus improve overall reliability (decreased outages) at the least operational costs (fewer expensive remedial repairs).  

Although Smart Grids and the Internet of Things have a significant portion of activity that is M2M, all of these networks will require human interactions at different times, and those interactions will be based on data distilled into information and insights powered by advanced analytics solutions that are only now being deployed by utilities.  Next week’s blog will focus on how these networks can be managed to meet objectives that are set by the network owners. 

Energy consumption data provides sufficient information to describe patterns of behavior that could constitute remote surveillance.  Used appropriately, this is valuable information that can help consumers build awareness and make intelligent choices about energy consumption.  However, and this is a big caveat – this information could also be quite valuable to vendors and service providers who want to learn more about consumer habits, lifestyles, and choices in order to more effectively target marketing campaigns to them.   

Two sessions at the recent EnergySec Summit West addressed privacy issues, which are inextricably linked to security issues.  There is a growing body of work focused on the privacy protections for energy consumption data, including a recent California Public Utilities Commission (CPUC) ruling that considers primary uses of energy consumption data.  Primary uses include analysis of a consumer’s data to identify opportunities for energy savings through actions that range from shifting energy use to hours with cheaper rates and recommendations on replacement of inefficient devices coupled with rebate program information.  As defined by the CPUC, primary users are utilities, their authorized service providers, and consumers.  Secondary users of energy consumption data include appliance manufacturers, data aggregators, agencies, law enforcement and other governmental entities, and advertisers.  Just to be extra confusing, secondary users are also called third parties, and include service providers such as wired and wireless communications carriers. 

There are two troubling aspects to this ruling and to other ongoing work.   First, almost all of the discussion presumes that utilities own the energy consumption data and have the primary relationship with the consumer.  That may be true today, but perhaps future business models and technologies will offer new options in which third parties – those service providers –have the primary relationship with consumers, and do not use smart meters to obtain their data.  Utilities are in the background, delivering electricity, but no value-add services.  Secondly, although the CPUC ruling is quite good about requirements for utilities to make all data available to consumers, there is no direction about how consumers will be educated. 

This lack of guidance about the education of consumers is a real concern.  This is new data, and consumers need to be aware of the potentials for abuse.  It is quite likely that utilities will follow the lead of financial institutions and communications carriers, and produce densely worded privacy policies that appear as an annual insert in one of our bills.  How many consumers read those inserts in the mail or privacy pages on a website?  What we need is plain and simple communications that clearly state the value of this data, and consumer rights around it.  We need consensus around who is responsible to deliver this information, and how educational campaigns are funded.  Without effective education, we may be consigned to learn the hard way about what energy consumption data says about our behaviors within our homes – just like many early adopters of Facebook discovered in posting details about their lives.   Without effective education, we may gain insights, but lose the opportunities for financial compensation in providing access to our data to third parties.  Without effective education, we’ll know much more about how to protect ourselves from electrical hazards, but not the ones generated by energy consumption data. 

In separate interviews, Aclara, represented by Andy Zetlan, VP of Product Management, and PreClarity, represented by Bob Becklund, Co-founder, agreed that there are a couple of related challenges for utilities to intelligently incorporate analytics into operations at the distribution and consumption points in the electricity supply chain.  The first has to do with composition of organizations, and the second with approaches to problem solving, and how these challenges are addressed can have profound implications for the success of analytics solutions and any other Smart Grid initiatives.   

Utilities are commonly described as siloed organizations, meaning that departments like operations, engineering, marketing or regulatory relations work very independently of each other.  The introduction of communications networks to transmit the data that smart meters can deliver – also known as Advanced Metering Infrastructure* (AMI) networks – creates challenges for siloed organizations since different groups have expectations and requirements, Andy Zetlan noted.  For instance, the group responsible for billing needs a network that reliably delivers volumes of meter data – although it may not need to be at near real-time speeds.  An operations center may need relatively small streams of data that have little tolerance for latency or delays to transmission.  Building cost-effective communications networks in the distribution grid that can adequately satisfy both needs is the challenge.  Utilities that can bring all groups together to document their use cases – what they need the communications network to do – are taking the first steps to reducing, if not removing, those silos. 

Similarly, once the communications networks are in place, utilities also need to determine the value of data to build the proper analytics tools.  Use cases are a great way of helping to define requirements.  Some data may have no value, and other data may be immensely useful.  Marketing will find data that helps them predict the profiles of early adopters of DR programs to be extremely valuable, but this data doesn’t need to be delivered instantaneously.  On the flip side, the operations group might identify a combination of asset data with Geographic Information Systems (GIS) or spatial relationship data that helps them respond to outages faster, providing a higher prioritization for that data in terms of how it is loaded and stored for analysis.

The related challenge is in how utilities solve problems.  There are some aspects of utility operations that are unique to electric, gas, or water utilities, but when it comes to communications networks and data analytics, there’s a lot to be said for learning from the experiences of other business sectors.  Telecom companies have experimented with flat rate pricing to application-specific pricing, and companies like PreClarity have experience in designing analytics solutions that help regulatory and marketing groups determine the right pricing designs and programs.  Putting a different spin on consumer segmentation, Bob Becklund pointed out that the cable industry is similar to utilities in that what was once a uni-directional service (entertainment rather than electricity) is now transforming into bi-directional flows of communications or electricity.  That forces changes in how these companies will relate to consumers, and what they’ll need to know about them via analytics. 

Both PreClarity and Aclara also voiced similar themes about the needs to normalize data – which means providing a common synchronization of data so it can be used in the correct context.  Time-skewed data creates false relationships and erroneous conclusions that can undermine the value that correctly normalized data can provide to different utility users. 

Two different companies, but the same messages that utilities best serve themselves and their customers by finding new ways of doing business that start with addressing siloed operations and looking to other industries for parallel applications of how data analytics can improve operations, reduce costs, and improve consumer relationships.  

 *you can get a definition of AMI in the Smart Grid Dictionary 3^rd Edition.

But there is value in the new amounts of heretofore non-existent data from the grid edge that will be available as more Smart Grid technologies rollout into consumer residences and businesses.  There are killer apps in the analysis of energy use data, and data analytics solution providers with experience in business sectors like wireless and consumer retail have interesting ideas about the real values of consumption data.  The ideas documented here are based on conversations with Bob Becklund of PreClarity, co-founder of a data analytics solution firm working in these areas.  Keep in mind that there are still unsettled areas about energy consumption data and privacy, and it will take time to sort out consumer options to permit use of data by utilities or other entities.   But consumers, utilities, and service providers will find significant value in consumption data – particularly as it relates to behavior and lifestyle.

Utilities get smarter about consumers

Utilities could sift through consumer energy use data and perform correlations with demographic data such as zip code, housing and income data to develop profiles of highest value targets for demand response or energy efficiency programs.  Demand response (DR) programs reduce peak demand (electricity used when it is most expensive).   Analysis and simulations of DR program designs and consumer responses can help utilities save money in avoided costs of additional expensive generation, and participating consumers happily save money in programs that fit their lifestyles. 

Utilities could also take energy use data from residential consumers and correlate it with use of social media and preferred consumer interaction channels, and create new education outreach and marketing campaigns aimed at different populations.   One meter may reflect multiple consumers in a household, and each will have preferences for communication channels.  New campaigns could be launched via Facebook or Google + that are targeted to specific demographics for significantly less cost and time than the “one message fits all” practices found in billing inserts. 

Utilities are gradually moving to new pricing plans based on time of use (TOU) or sources of power (clean versus dirty). This is similar to the evolution of pricing within other markets such as the wireless and airline industries.  As pricing has grown more complex, these industries have demonstrated needs for information, insight, and visibility to “what-if” scenarios to support  business decisions, ensure regulatory compliance, and enhance consumer relationships.   Utilities will have similar needs for tools to help them manage a variety of pricing plans and programs and assist consumers in selecting the plans that make the most sense for them.  

Drivers get smarter about EV roaming

EVs have some strong similarities to mobile phones – consumers tend to take them everywhere.  In the early days of mobile phones, it did not take much distance to move from your zone and incur expensive roaming charges.  Could the same history repeat itself with EV charging plans?   Perhaps not, but roaming and charging at pay stations could create billing complexities for consumers, energy service providers, and utilities.  Data analytics expertise can assist in bringing transparency to all the charging data and the business rules behind roaming EV billing, as well as assist in trip planning or identifying the best charging locations.

Consumers get smarter about energy consumption data

Energy consumption data is all about behavior, and therefore needs privacy protections.  However, that being said, there are situations where consumers may find value in giving permission to utilities, energy service providers, or other third parties to access and analyze their energy consumption data.  For instance, appliance warranties in the future may look very different.   Instead of phoning up a repair shop once the refrigerator has died, a consumer may choose a warranty option that allows the manufacturer to monitor the refrigerator’s use of electricity, and analyze that data to predict and schedule maintenance calls that become proactive rather than reactive. 

Consumers may appreciate services that analyze energy use and break it down by appliance or device – similar to how credit card companies report spending patterns into dining, air travel, and other categories.  Seeing categories helps consumers plan budgets, and similar reports of appliance consumption could help accelerate retirement of inefficient, older appliances for more energy-efficient ones.   

We’re just at the beginning of a brave new world of energy consumption data and no, it’s not overkill.  Data analytics can turn this data into usable information.   Leveraging expertise and experiences of solution providers from related business sectors will expedite the discovery and deployment of these new killer apps for the Smart Grid.

The term dynamic is often used to describe the distribution grid, especially as it transforms into a Smart Grid.  First and foremost, there is a massive increase in the number of assets and devices that are monitored and/or remotely controlled.   Then the distribution grid is impacted by new plug loads such as electric vehicles (EVs) that could introduce new consumption patterns to existing grid models used in operations planning.  EVs and distributed generation and energy storage assets enable the distribution grid to accommodate bi-directional electricity flows – one of the key benefits of a fully deployed Smart Grid, but certain to add to operations management challenges.  Aging legacy equipment needs to maintain uptime.  And finally, smart meters offer the possibilities to collect data more often than the traditional and limited monthly meter read.   The grid must deliver power reliably, safely, and cost-effectively – and now it requires similar performance in the communications networks that monitor, collect, process and control remote equipment and devices on both networks.  Managing and making sense of all the data from all these devices on two different networks is a significant challenge for utilities – how to correlate, integrate, and analyze data to manage network + grid performance.

 The Smart Grid buildout has surpassed the capabilities of traditional utility data management solutions for distribution grids with little to no communications capabilities.  But from a data analytics perspective, the distribution grid shares common characteristics with another sector – specifically wireless service providers.  Wireless providers have networks that are even more complex than power grids, with large numbers of devices consuming voice, data, and video in different and sometimes very elaborate subscription plans.  While all of us typically get the same electric service and consume different quantities of kWh with minimal rate variability (although that will change over time), we have a range of choices in the subscription plans we buy from wireless providers. 

Therefore, it makes sense for utilities to turn to data analytics solution providers that have extensive big data experience with wireless providers for guidance to solve the toughest Smart Grid operational and business challenges.  One of these companies, PreClarity, shared several best practices to help utilities manage the data deluges hitting distribution grid operations centers.  According to Bob Becklund, principal and co-founder of PreClarity, a mission-critical objective for any service provider is to maintain the reliability of services – whether these are delivering electricity or bandwidth.  For example, a missing meter read could be the result of a meter transmitter failure, a power grid failure, or a wireless network failure.  With smart meters, data analysis must integrate data from the utility’s operational systems, communications network and the distribution grid, correlate very diverse data points, and present it as actionable information.  Fault location and identification analysis expedites realtime response resolution.  While that’s a great application for data analytics – the best practice is to use predictive data analytics to identify patterns and trends and proactively respond with corrective actions (predictive maintenance) before those faults occur and thus avoid critical power outages and customer  service issues.   Another best practice is the use of harmonized data models that accept and integrate data from a variety of devices and software applications for function-specific dashboard views and data visualizations.  For example, an operations center can use data visualizations to integrate and overlay high-resolution aerial or satellite maps with device status information and workforce applications and thus optimize deployment of field repair resources.  It’s a clear demonstration of the “picture worth a thousand words” principle, and one that makes sense in operations centers working to keep the lights on in homes and businesses.

A common concern for wireless and utility operators is how to future proof against anticipated growth rates in devices and data. When the first wireless networks were built, designers did not anticipate smart phones and tablets, streaming video or social networks.  Wireless networks experienced exponential data growth from 100Gbs of data per day to TBs of data per day, stressing some analytics applications while other systems were able to handle network and growth issues with marginal additions to their core platforms. The ones that gracefully scaled up in devices and data volumes exercised best practices in data architectural design – creating flexible and scalable data models that consider Event Driven Architectures (EDA) or Service-Oriented Architecture (SOA) decisions; real time event processing vs. historical analysis needs; data schemas to deal with diverse network elements; and the advantages of centralized vs. distributed architectures.  

So while there is a learning curve for utilities to deal with all the data that the Smart Grid delivers, the climb doesn’t have to be as steep or as high as it is sometimes made out to be.  There are other business sectors and solution providers like PreClarity that bring thought leadership and experience to share in the Smart Grid convergence of IT and OT and managing data deluges.   And there’s no doubt that Smart Grid data analytics – with their potentials to improve operational performance and reduce service downtime – will be killer apps in the distribution grid.

Sensing and communications technologies that help make the Smart Grid smart create massive volumes of data from thousands to millions of devices that range from meters to transformers, substations to transmission lines, and generation plants.  This data can give utilities new information to revolutionize their operations and improve grid reliability, stability, and efficiency while reducing costs.  But the new data could strain existing capacities of utilities to manage it – particularly in terms of resources and experiential knowledge.  To realize the full benefits of the Smart Grid, we will need data analytics solutions to help utilities deal with the daily deluges of data. 

Data analytics solutions are already in play in one part of the electrical grid.  The electrical grid is a supply chain that consists of generation, transmission, distribution, and consumption.  The transmission grid, which transports bulk power (electricity above 69kV), has been transforming into a smarter grid through remote wireless sensors and control devices that monitor line conditions and high-speed communications networks that transmit this data.  This Wide Area Situational Awareness (WASA) will help avoid future blackouts like the one that impacted the Northeast USA and Canada in August 2003, at a cost of billions of dollars to the two economies.   The US government decided that a little government investment to prevent future blackouts and economic disasters is a good thing, and encouraged and funded the North American SynchroPhasor Initiative (NASPI) to address it.  This project advances the deployment of technology and sharing information and experiences to enhance knowledge.  The remote sensors and control units are called phasor measurement units (PMUs) and take measurements at 30 times per second.  In contrast, the technology PMUs replace only took measurements once every 4 seconds.  Measurements are time-stamped to a common time reference to deliver a very accurate and comprehensive view of the transmission system. 

This synchrophasor data helps utilities, Independent System Operators (ISOs), Regional Transmission Operators (RTOs), power generators and transmission companies identify stresses to the grid and take actions that ensure continued operations.  The challenges for the operations centers are twofold – first to focus on finding the exceptional data that could signal a pending or imminent failure.  It’s the equivalent of going from reading an x-ray to reading an MRI – there’s a lot more to look at, but you still have to understand what you are looking at.  Second, since this data hasn’t existed before, NASPI participants are in the interesting role of discovery – identifying new applications that leverage real-time and historical data. 

Data analytics solutions are the critical linchpins to make sense of all this data.  Data analysis automates sifting through vast amounts of synchrophasor samplings to perform real-time alerts for immediate actions to ensure grid reliability.  Data analysis also creates “baselines” using historical data to detect trends or patterns that will help grid operators identify pending failures and take corrective actions.  Beyond these applications, analytics also aid in power system planning and modeling to integrate both traditional and intermittent renewable sources of generation into the grid and in the forensic analysis of failures.   

The initial analytics results are encouraging, and the fact that there is a coordinated effort led by NASPI expedites the learning process for participants to successfully transform new data into meaningful information.  The transmission part of the electrical grid is well on its way to fulfilling the promise of the Smart Grid using communications technology and intelligent devices to improve grid reliability and stability.  The distribution and consumption links in the electrical supply chain face different operational challenges, with implications for the rollout of data analytics killer apps.  Those distinctions are explored in next week’s blog. 

This energy consumption data may benefit from a new approach to the growing awareness of the value of personal data.  There’s an interesting organization called the Personal Data Ecosystem Consortium that promotes the idea that “individuals control their own data by enabling a thriving network of businesses around personal data stores and services.”  I like the idea of an ecosystem that lets me benefit from my data.  After all, if it is valuable enough for grocery store chains to entice me to share it in exchange for cents off of items, then perhaps there are other ways I can gain value from my data.  The same could be true for energy consumption data.  My energy consumption data could have value to utilities and to other companies that could offer me solutions that range from home energy audit services to more energy-efficient appliances. 

If you are of an age to remember green stamps or other early loyalty programs, you’ll recall that you received these stamps when you shopped at certain grocery stores.  After accumulating books filled with stamps, you redeemed them at special merchandise centers.  It was a family activity as children pasted stamps into the books and parents scanned the catalogs for redemption options.  Companies like People Power could take their initial solution and extend it into the personal monetization of energy data by building a common rewards platform that is based on this green stamps model (which still exists as green points).  Use of a common rewards platform as part of any HEMS solution would assist utilities in their efforts to incorporate gamification into their websites. 

For example, utilities could use that rewards platform and award points to consumers for participation in web-based energy awareness games, energy efficiency programs, demand response (DR) programs, or other Smart Grid-enabled programs.   Like the green stamps of years ago, participation can involve entire households – especially using gamification and social gaming to build data, knowledge, and desired consumer energy behaviors.  Point awards could be redeemed for merchandise or services from local businesses that participate in these programs.  Services could include energy efficiency upgrades, HVAC maintenance and other consumer activities that reduce overall electricity demand for utilities.  Merchandise could range from energy-efficient appliances and light bulbs to EV charging stations and solar panels.   

For utilities, the benefits include increased consumer participation in programs which result in reduced need for new generation facilities, reduced operating costs, and reduced CO2 emissions.  For local businesses, the benefits include more consumer transactions and increased loyalty.  Consumers enjoy the monetization of their data in the form of tangible products or services that reduce their energy bills, keep their rates down, and reduce CO2 emissions.  They might also have some fun in the process.  Of course, we must ensure that personal energy consumption data has appropriate privacy safeguards, and recommendations are being developed through the efforts of the National Institute of Standards and Technology (NIST). 

Unlike search, consumer purchase, and social network data, we have the opportunity to create a different value model for the monetization of energy consumption data.  The benefits of a new value model for energy consumption data can directly accrue to individuals, communities, businesses, and achieve environmental and energy security objectives too.  It’s an opportunity worth exploring.