UK Smart Meter Roll Out: It’s All About The Data



Despite some 2.6 million smart meters already being installed in the UK, it is the data infrastructure that is causing delays with the further roll out of smart meters in the UK, according to a recent BBC article. This IT project is necessary to support the volume of data anticipated to come from the smart meter roll out that is being pushed by the government.

From the chart below you can see how many meters have been installed since 2012.  Higher volumes of data are already being collected which reinforces the need for this important IT project to be up and running as soon as possible.


(Chart and data available from the UK Department of Energy & Climate Change)

With news that the data infrastructure launch is pushed back until the autumn, what impact will this have?

How much data will smart meters generate?

To do a quick calculation on monthly meter reads from the potential smart meters across the UK, there would be around 53 million reads per month. By contrast, with smart meters that record data every 15 minutes, this could mean 96 reads a day from 53 million meters resulting in thousands of times more data being generated. This is obviously a rough estimation but gives an indication as to what the energy companies would be dealing with. This doesn’t include status messages from the meters which would add to the mass amount of data being generated.

Why is this so important, if smart meters are just about making billing automated and putting an end to manual meter reading? There is a lot more value within meter readings and status messages beyond billing.

The benefits of smart meters are clear for consumers: tracking how much energy you are using, monitoring the effect of changes that you have made to your energy consumption, and receiving accurate bills without having to submit a meter reading.

When applied properly, data helps energy companies to manage supply and demand in a much easier fashion. Energy companies benefit from analysing the data collected from the smart meters to enable new rates and business models, implement demand response programs, manage solar power panels in a better way and improve support for electric vehicles, to mention but a few.

To benefit from the thousands-fold growth in meter data, energy companies need analytics that locate the problems and opportunities hidden inside this massive amount of data. Smart meter analytics must be intelligent enough to do the heavy lifting for users, not just make it easier somehow for users to browse among millions of meters. Increasingly, analytics for this size of data set needs the intelligence and autonomy to make decisions independently.

Once the IT infrastructure is in the place, the UK energy companies can start pursuing the new value within smart meter data, analysing it to make better business decisions. All 53 million UK meters likely won’t be changed out by 2020, but that shouldn’t stop UK energy providers from using the smart meter data they already have, or will have soon.

(Image courtesy of rido / 123RF Stock Photo)


Can Analytics Make Large Power Transformers Immortal?


Large power transformer

Large power transformers (LPT) are the workhorses of the North American electric transmission grid, and many have lived past their life expectancy. The U.S. Department of Energy reports that average LPT is 40 years old; 70 percent of LPTs are 25 years or older. These assets are becoming a weak link in the chain of networked transmission assets and may be subject to catastrophic failure, including from severe weather.

If transmission systems fail, large-scale outages can occur. According to a different Department of Energy report, 85 percent of U.S. outages affecting 10,000 customers or more in 2015 were caused by weather or by asset failure. These outages cause customers economic and cost transmission organizations lost revenue and damaged reputation. Regulators are increasingly focused on loss-of-load probability and loss-of-load hours, both key reliability measures.

The Department of Energy also reports that LPTs cost up to $7.5 million dollars each, weigh up to 400 tons, and take up to 18 months to procure and install. The money and time required goes up significantly if new engineering is required. The cost of LPTs accounts for 15-50 percent of total transmission capital expenditures.

For all these reasons, there is an urgent need to understand the operational contingency of heavily loaded LPTs to manage and reduce outages, and to bridge the time until critical LPTs can be replaced.

With analytics you can make the most of what you have while planning for new assets. And with an 18 month delivery cycle, utilities need to start that analysis now.

The growing array of smart, connected devices in the transmission system generates large silos of data. That data can be useful in maintaining safe, reliable, affordable and sustainable transmission operations, but it cannot be correlated, analyzed and applied in a timely manner without advanced visual analytics.

Recently, Siemens and Space-Time Insight announced a partnership in part to tackle the issue of large power transformers.

Situational intelligence provides a number of ways to apply advanced analytics to silos of data for managing the current population of LPTs more effectively. Consider three scenarios:

  • By correlating and analyzing the health of LPTs along identified transmission corridors with demand forecasts and power dispatch schedules, transmission operators are able to prioritize the delivery of power using assets that are relatively healthier than other assets. This helps organizations increase grid reliability and make the most of their current assets.
  • By correlating weather forecasts, LPT health and forecasted energy demand, analytics gives transmission operators advanced warning of weather impacts on transmission assets so that they can respond accordingly to avoid outages and asset damage.
  • By forecasting the impact of removing some LPTs from service, analytics gives transmission planners better insight for planning and executing outages necessary for LPT maintenance, repair and replacement, potentially extending the life of these essential assets.

With the scale of the power grid and past deficit of investment in transmission infrastructure, we will be dealing with aged LPTs for many years. Analytics gives us tools to make the most of the assets we currently have, but it won’t make LPTs immortal.


Augmented Reality For The Enterprise: A Use Case In Electrical Substation Field Service



Augmented reality can make a real impact in field service workers in almost any industry. For a specific example, let’s look at a field technician visiting a problematic transformer at a substation.

Currently field technicians download test plans and view them on a laptop or iPad. They have to remain near their car as they conduct spatial reasoning with regards to electrical circuits and their locations. They have to hold some of they learned and deduced in memory, meaning they are eating up more bandwidth of the part of their brain that holds memory, conducts planning and spatial reasoning or navigation. This forces them to continually reference back and forth between the computer screen, supporting documents, tools and the work site. Many of the artifacts they will use are situated at angles and distances where they must turn away and effectively interrupt the mental process of fixing their eyes on the object they plan to use to do their work so that they can do the reasoning. They are basically switching between numerous cognitive tasks.

Below is a screen grab from a video of one technician conducting such procedures.


The problem

Field technicians show up to substations and other work sites without knowing how to navigate the site. They also need to find the proper tools and documentation for completing maintenance, tests and repairs to assets.

They continually circulate their eyes between consulting site plans, asking site staff where assets are located, monitoring asset performance and completing their task checklist. Their hands are likely juggling multiple items that they need to set down in particular locations and keep track of in order to do the job.



With augmented reality, field service workers don glasses at the job site. The glasses give them an internal map of the site they are at. Locations of tools and supplies are flagged or highlighted. Digital documents needed to complete a task appear in their field of vision. They can see any chart for an asset by shifting their attention to the asset. Field workers won’t need to remember anything or fumble with tools and documentation simultaneously. They can just focus on performing the work.

Here’s an example of how a substation needing maintenance might appear with augmented reality.


  1. The menu helps decrease the total space filled by UI elements. Only the current UI element is up. Others appear wilted until workers turn gaze at it or access it with their hand or voice.
  2. This check list helps the user complete their work.
  3. The radar helps them navigate the station. The orange square shows the location of the nitrogen they will need.
  4. A capacity indicator of the transformer cylinder is off in the distance. Relative size is used to indicate importance. It isn’t up close because it isn’t exigent and spatial relationship is the primary representation in use.
  5. The chart shows year-over-year performance for the transformer. The orange year shows the current performance which fluctuates outside the range it should be in.
  6. The problematic area of the transformer is highlighted to draw attention to the user

This is not science fiction. It is reality. All the technologies and features listed in this use case are completely feasible with many of the headsets coming on market in 2016-2017. One example I love is the Meta headset which has gesture tracking among other awesome features.


Analytics and Vegetation


vegetation blog

Electric utilities, cable operators, pipeline companies, railroad, municipalities—all will tell you that it’s a jungle out there. Vegetation has a way of interacting with and interrupting the operations of technologically sophisticated and complicated networks. Even your wireless communication networks are not immune to the impacts of vegetation.

Vegetation causes trouble in several ways:

  • Falling onto assets, such as trees falling across roads, damaging them or rendering them unusable
  • Growing into assets, such as roots growing into sewer lines, lowering their performance or making them fail
  • Making contact with assets and causing malfunctions, such as tree limbs touching power lines and causing power outages or sparking fires
  • Allowing wildlife to contact assets and cause equipment failure, such as bushes helping squirrels enter substations and disrupt power operations
  • Obstructing rights of way such as roads, bridges, tunnels and waterways, for example reeds and seaweed clogging ship channels

Similarly, the lack of vegetation can also be a problem. Slopes that have lost their vegetation due to wildfires during times of drought become prone to erosion and landslides when rains finally return. If these areas are adjacent to roads, waterways, power lines, pipelines or other assets that you own or operate, sudden ground movement from erosion or landslide could damage your equipment or block access.

An asset-intensive organization can spend millions of dollar per year on spraying, trimming, pruning, removing and replanting vegetation. Its labor intensive work with costs that add up quickly. When you experience an unplanned event related to vegetation—tree fall, land slide, brush fire—your emergency costs pile up while services are interrupted.

There are vegetation management systems available to organizations today. Maybe you use one. These mainly target the management of scheduled activities, routes and workers. They are useful, and can be augmented to be more valuable by integrating intelligence about actual and potential problems into the scheduling of trim activity. Advanced analytics will identify the areas most in need for trimming or other management and also optimize overall crew schedules so that your vegetation management processes and costs to improve reliability and safety and lower operational costs.

A situational intelligence approach to understanding your vegetation challenges and potential problems maps vegetation’s proximity to your networks, predicts how vegetation will grow and interact with those networks over time, and prioritizes the geographic locations and network sections most susceptible to vegetation problems.

Data about tree and plant species, microclimates, past and future rain fall, time of year, and other variables informs growth models that improve your vegetation management schedules. By applying analytics to this data, you can prioritize your work more effectively to address true problem areas and not just the next assignment in the vegetation management cycle.

By working differently, working smarter, you can optimize your vegetation operations budget and make your networks and assets more reliable.

Copyright: alephcomo / 123RF Stock Photo


Wind, Coal, Or Natural Gas? Dispatching Electricity with Situational Intelligence


According to the U.S. Energy Information Agency, use of renewable energy sources such as wind and solar power in the United States doubled in just six years. From 2007 to 2013, renewables grew from three to six percent of the electricity generation mix.

Six percent isn’t a big part of the overall mix, but the intermittent nature of these energy sources poses additional challenge for an already complex problem: when to use the various types of generation to meet demand?

Consider these complications:

  • Despite recent advances in battery technology, it’s difficult and expensive to storage large quantities of electricity, as Bill Gates recently noted. This means energy needs to be made on demand and consumed in real-time.
  • It takes time to vary the production of energy. Large nuclear, coal and gas generators can’t be turned on and off like light switches.
  • The sunny doesn’t always shine and the wind doesn’t always blow, which makes scheduling renewables difficult.
  • In addition, regulations outline the criteria for selecting what generation sources to use: lowest cost, most reliable, least polluting.
  • Once electricity is produced, it must be delivered across transmission lines. Produce too much electricity in the area served by a single transmission line and you’re liable to create congestion on the line.

This whole problem what type of generation to use when and where is known as the dispatching of electricity.

As readers of this blog know, situational intelligence applications are ideal solutions for these complex what-where-when problems. Situational intelligence applications employ spatial-temporal-nodal analytics to solve simultaneously for what, when and where.

Using forecasted energy demand, generation availability, weather and other variables along with the constraints of reliability, cost, pollution and location, spatial-temporal-nodal analytics solve the dispatch problem and visualize the needed generation mix.

This mix then guides power brokers and planners, transmission companies, regulators and others in delivering in real time the safe, reliable and affordable power on which we rely.


What’s The Big Deal About Distributed Energy Resources?


Energy stories are dominating the news—not just oil prices, but electric cars, home energy storage systems, rooftop solar power, even Supreme Court rulings about demand response.

These devices and programs that move power on and off the distribution grid are collectively known as distributed energy resources (DERs), and they are a hot topic:

  • The DistribuTECH conference happening this month offers 60+ tracks related to DERs, distribution automation, demand response, energy efficiency, renewables integration, microgrids and energy storage.
  • The Supreme Court recently affirmed demand response.
  • New York and California have specific plans for supporting and exploiting DERs.
  • Nevada and Arizona are waging battles over solar energy and net metering.
  • Hawaii is pushing for 100 renewable energy by 2045, with 30 percent of homes on Oahu already using solar power.

Why are DERs such a big deal? Because it matters where DERs are located, what they connect to, and how and when they operate in real-time and in the future.

This increased focus about where, when and how things are operating on the distribution grid is a new mindset for utilities. Originally, the distribution grid was conceived and designed for a one-way flow of power from the utility’s central generation to consumers. DERs turn that pattern on its head.

The location of DERs matters because of their impact on the distribution grid. DERs like electric vehicles and energy storage devices draw large amounts of power from the grid. When the grid is overworked like this, voltage levels drop below acceptable levels, which leads to flickering lights, momentary outages, and eventually black outs. Other DERs such as rooftop solar panels can put too much power onto the grid. When this happens, voltage rises above acceptable levels, which leads to burnt out equipment and eventually power outages.

The time of day when DERs operate matters because of the impact on the prevailing assumptions about power consumption throughout a typical day. Historically, business hours have been the period of the heaviest power consumption, with afternoons / early evenings seeing the peak use. The current generation and distribution system has been designed around this consumption pattern. DERs disrupt this pattern. Solar power is most productive during sunny business hours, which means far less need for central generation. As soon as the sun goes down, however, solar goes away and suddenly lots of central generation is needed online in a hurry, at the busiest time of the day for energy use.

On the other hand, batteries and electric vehicles are charged most cost-effectively when rates are low, usually at night. Wind farms can be quite productive at night, which aligns well with energy storage. But wind farms are by their nature intermittent. Get enough large devices charging on a calm night and suddenly it isn’t necessarily the period of lowest generation and cheapest power.

Analyzing where, when and how things are operating is the unique strength and benefit of situational intelligence. DERs are one reason why situational intelligence has gained such a hold in the utilities space.

Soon you’ll see news stories about utilities adopting new business models and revenues streams based on making the increasingly dynamic distribution grid work smoothly and fairly for all participants, instead of just selling kilowatt hours to users. Just remember, you read it here first.


Utility 3.0? We’re not at 2.0 yet!


If you think that the grid is getting more dynamic as we move towards Utility 2.0 (as I wrote about in a previous post), just what till you see Utility 3.0.

“Wait!” you say. “We’re not even at 2.0!”

And you’re right. In the United States, a recent report by the Federal Energy Regulatory Commission (FERC) estimates that less than one-third of energy consumers have smart meters. Other places, such as Italy, have a much higher penetration rate of smart meters, but overall we are still just at the start of a ubiquitous smart grid.

Still, Utility 2.0 looks to be a transitional state that prepares the way for an even more dynamic energy system which is expected to evolve into what many now call  the “Transactive Energy Market.”

With Utility 3.0, the energy distribution system will consist of numerous microgrids, and an even larger network of nano grids at the consumer level, that encompass centralized and decentralized energy sources. Our current utility-scale power plants run on nuclear, hydro and fossil fuel won’t go away overnight. The current generation of wind farms and utility solar will be functional for a long time. But added to their ranks will be numerous distributed energy resources such as residential solar, community solar and batteries of many different capacities, functions and locations.

Here’s what it will look like, according to a Gridwise infographic:

Gridwise diagram

At the heart of all of this will be energy transactions between consumers, suppliers of all sizes, and energy markets from single people living in apartments to power plants and industrial sites.

What you know today as your local electric utility will likely morph into a distribution system operator, or DSO since we love industry abbreviations. In the infographic, the DSO sits squarely in the center. DSOs will operate and maintain the network (wires that move energy between buyers and sellers), and also the mechanism for reconciling all those energy transactions.

Think of this utility evolution as the difference between telecommunications companies (telcos) before Internet Protocol (IP) became widespread, and telcos today. Before the Internet, telcos were network operators (wires and airwaves) and the services provided on those networks were limited to what telcos offered (local calling, long-distance, three-way calling, call waiting, etc). Consumers were captive to those services and providers.  With the emergence of IP, telcos faced a critical decision: do they continue to operate the wires, or do they create a platform on top of the wires with the ability to create and extract new value that might even combine third-party services with theirs? Or, do they turn their core services into commodities and let third party service providers extract the value from new, niche products?

Just like the telcos, utilities in the era of Utility 3.0 need to new ways to create value, including extracting value from enabling the transactive energy market.  Keeping supply and demand balanced on a grid will be more complicated than moving bits across the Internet, for several reasons:

  • One, reliability and safety are at the core of energy distribution network value.  The health and reliability of the grid depends on the balance of supply and demand, especially given the fact that many more suppliers are selling into the energy market using the distribution network.
  • Two, every buy order must be filled, or else people will go without power.
  • Three, it will matter where a transaction takes place, since it involves physically moving a commodity (electricity) at a specific place and time.  In the internet transaction, it doesn’t much matter where the parties are located, other than for legal and tax reasons.

The complex web of new services, service providers, and transactions will spawn new capabilities layered above the existing network, in what is called the Transactive Energy Platform.  At the core of this new platform resides real-time and even predictive analytics that account for the time, place and network location of production, consumption and transaction. This new platform must  dynamically optimize the network services and energy production and distribution assets based on reliability, cost, and market pricing at all levels of the distribution network. That will be the role of situational intelligence in Utility 2.0 enabling a successful transformation to Utility 3.0 and the transactive energy market.  That’s what I’ll talk about in my next post.



SI World 2015: Learn about Intelligent Energy Storage


Utility-scale energy storage is disrupting the electricity industry, which means it’s disrupting just about everything.

Storage on this scale means that energy no longer needs to be consumed the moment it’s produced. That balancing act places huge stress on the technology, markets and policies that drive the industry. Now, excess energy—such as roof-top solar on a sunny day, wind power during blustery weather, or base-load generation at night—can be absorbed into giant batteries and used during times of peak demand.

Energy storage has its own challenges, though, that turn out to be situational intelligence applications. Where should you locate your batteries for maximum grid reliability? When should you charge or discharge batteries? If you know the solar or wind forecast and your fossil-fuel generation capabilities, how do you optimize your fuel mix for low carbon, low prices and high reliability?

Dr. Hiroshi Hanafusa of NEC will discuss the issues of batteries, cloud networks, services and big data analytics that drive the economic value of energy storage. If you’re curious about how storage will transform the powered world as we know it, don’t miss this session.

This year, SI World takes place Wednesday, October 28, in New Orleans. Registration is still open.


Situational Intelligence for Cyber Security


Network security

Cyber attacks aimed at U.S. businesses and government entities are being launched from various sources, including sophisticated hackers, organized crime, and state-sponsored groups. These attacks are advancing in scope and complexity.

The electrical industry is uniquely singled out as a target for cyber attacks.   The Department of Homeland Security Industrial Control Systems Cyber Emergency Response Team (ICS-CERT) reports that in the first half of 2013 some 53% of all reported cyber attacks were on the energy sector, followed in prevalence by 32% on Critical Manufacturing, and 5% each for the next most targeted sectors (communications and transportation).

The electric utility industry is also unique among critical infrastructure sectors in having a mandatory and enforceable reliability and cyber security standards regime. Under the existing regime, the electric power industry works closely with various government agencies on securing the power system. Additionally, utilities actively implement cyber security measures on their own, and help develop reliability standards with the North American Electric Reliability Corporation (NERC).

Imminent cyber threats require quick action and flexibility that can come only from close collaboration with the government and emergency response protocols that are planned and practiced before a disaster strikes. It also takes situational awareness of a variety of data to isolate and prevent or recover from an event. Increasingly the focus is on defense in depth coupled with resiliency when a vulnerability is exploited.

Getting all the stakeholders that are responding to a cyber event on the same page requires access to intuitive and comprehensive visualization of the problem, drawn from analytics that span all relevant data sources. Also helpful is quick access to various response mechanisms–operational controls, social media, first responder communications—integrated with the analytics and visualization:

Situational intelligence applications show promise as an approach to gather all data sources and stakeholders together into a coherent approach to cyber event preparation, prevention, assessment, recovery and documentation.

John Di Stasio is President of the Large Public Power Council.


Water, Energy and Situational Intelligence



Although rain falls freely from the sky (except in California, but that is a subject for another post), water utilities consume a lot of energy doing things like pumping water into water towers.

According to the Alliance to Save Energy, energy is among the top three costs borne by water utilities, often coming second to labor. In developing countries where labor costs are low, energy is usually the highest utility cost.

Energy consumption related to water production, distribution and consumption include

  • Pumping from wells, streams, lakes and oceans
  • Desalinating salt water
  • Operating treatment plants
  • Pumping to and from water towers and across elevation zones
  • Maintaining proper pressure in pipes
  • Heating and chilling water for end use

Driving down energy consumption lowers costs for water utilities and their customers, plus provides the related energy utilities with additional kilowatt hours to meet other growing demands.

You can reduce costs by optimizing production and distribution according to the forecasted supplies of water and energy. Because energy prices are dynamic, varying the time that you treat and pump water makes a difference. The fact that water is much easier to store than energy also creates opportunity. You can produce, pump and store water while rates are low, and then distribute it later.

This sort of optimizing requires analysis of current and forecasted supply, demand and cost of both water and energy. Other relevant factors include the weather and the relative elevation of water production, storage and consumption sites. Pulling all these data sources together into a predictive solution that provides actionable insight requires analysis across time, territory and network, a substantial analytical task at which situational intelligence excels.

The Alliance to Save Energy presents several case studies of energy efficiency for water. One example comes from Pune, a city of more than 3.5 million people in India. By focusing on energy efficiency, the Pune Municipal Corporation reduced their energy consumption by 3.8 million kWh per year. That produced annual savings of $336,000.

Capital is often in short supply for water utilities, especially in the US, so the annual savings are certainly welcome. As a bonus, energy efficiency measures also allowed Pune to deliver 10 percent more water without adding any new water production capacity, saving further capital. For a utility, that’s almost like cash falling from the sky.