The annual DistribuTECH conference is right around the corner, this year at the San Diego Convention Center from January 31 – February 2. With over 11,000 attendees from 78 countries and over 500 exhibiting companies, DistribuTECH is the place to be for those even mildly interested in energy transmission and distribution.
Spacetime will be there, this year hosting pre-scheduled meetings in room 3946. (Schedule your meeting here.)
You can also see Spacetime’s advanced analytics in action on the exhibit floor in our partners’ booths.
Asset Intelligence integrated with Siemens Spectrum Power
Distribution Intelligence integrated with Sentient AMPLE Platform
Distribution Intelligence integrated with Live Data RTI Platform
Visit our partners and see the future of advanced analytics for the internet of things today. Register for DistribuTECH or downloada free exhibit hall pass.
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.
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.
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.
While human populations shift, migrate and grow, the amount of fresh water remains constant. (The only way to make large quantities of fresh water is desalination, which is energy-intensive and practical only near the ocean.) To serve more people from the same supply of water, communities increasingly are turning to conservation measures.
In times of drought, communities employ water usage restrictions such as banning car washing and limiting lawn watering to certain days of the week or month. These rules rely on observable behaviors that aren’t easily enforced unless the utility employs smart meters or water cops—or both.
Analytics provide water utilities with unique ways to encourage participation in conservation without smart meters and water cops. Here are a couple of examples.
Pinpoint water conservation opportunities
Some utilities offer financial incentives such as loans or rebates for people willing to take the conservation steps of removing their lawn. According to the U.S. Environmental Protection Agency, landscape irrigation nationwide accounts for nearly one-third of all residential water use, totaling nearly 9 billion gallons per day. Which customers are likely to participate?
A first step for utilities is identifying the areas where such a lawn removal program can be successful. Geo-spatial analytics identify portions of the utility service territory where home footprint is small relative to lot size, which shows customers with a potentially high percentage of irrigable land. Locating such lots in areas with less tree cover shows yards that may require more watering in hot months. Sorting a list of these houses by the size of their seasonally-adjusted water bill shows you customers who might be motivated to lower their water bills by replacing some grass with hardy, native perennial shrubs and ground covers.
You might want to filter out houses with pools and hot tubs, to prevent those large water uses from skewing your results. Also, you could focus on households without children living at home. These households might lack built-in labor for mowing and be paying a lawn care company as well as high water bills.
Make conservation a game
Gamification applies concepts such as skills, challenges, points and rankings to non-game contexts such as conservation. Gamification brings out the competitor in us and can make conservation if not fun, then at least a little more rewarding and less boring.
One simple conservation game simply shows you how your water use compares with houses geographically close to you and with households that resemble yours in terms of lot size, people in household, and number of bathrooms. Improving your standing relative to those like you earns you points and entitles you to prizes.
Another, more active game may be to progress through levels of conservation skill, knowledge and savings by accepting certain challenges. Challenges could include doing a home water audit using the utility’s online tools, installing free low-flow showerheads from the utility, or cashing in rebates on low-flow toilets. You receive points as you complete challenges and lower your consumption. Analytics show you how you compare with your neighbors who are and aren’t playing the game. Achieving certain levels in the game might entitle you to prizes such as gift cards or bill rebates.
Fairly simple analytics run behind the scenes in these games to award points and keep score. More sophisticated analytics might be used to recruit players for the game and to target the challenges and rewards to more closely match the water saving needs and opportunities of their neighborhood and household type.
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.
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.
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.
This check list helps the user complete their work.
The radar helps them navigate the station. The orange square shows the location of the nitrogen they will need.
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.
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.
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.
Pipeline accidents in Allentown, Pennsylvania in 2011 and Sissonville, West Virginia in 2012 destroyed homes, caused death and injury, and reminded us how critical careful gas transmission and distribution really is. We are accustomed to natural gas reliably powering our homes and businesses, but even those responsible for getting it there can take that system for granted.
Safety and aging infrastructure are top concerns of natural gas executives surveyed by Black & Veatch in 2015, and with good cause. Replace just five percent of a room’s air with natural gas and the atmosphere becomes explosive. According to a U.S. Department of Transportation report, nearly one-third of natural gas distribution pipelines in the U.S. were built before 1970. More than 50,000 miles of these older pipes were welded together with outdated techniques that are prone to failure.
Despite these known conditions and obvious hazards, one-third of respondents to the Black & Veatch survey did not have a resilience plan in place for their natural gas operations four years after the Pennsylvania and West Virginia accidents. Fifty-four percent of respondents agreed with the statement that “a formal risk-based planning approach has not yet been undertaken to my knowledge.”
Why should so many utilities executives be without a risk-based plan, when the consequences of risk are so high?
Much of our natural gas infrastructure is hidden underground, which means it is often out of sight and thus out of mind, contributing to our becoming inured to problems. Gas utilities have developed clever methods such as pigging, hydro testing, and cathodic inspection for measuring and maintaining the health of pipes. Those clever methods generate multiple, disparate sources of data related about assets. Today, many utilities are flooded with data but no closer to fresh and useful insights based on that data.
Situational intelligence offers a powerful approach to analyzing those data sources and quantifying the risk present in natural gas assets. By correlating, analyzing and visualizing data related to an asset’s age, condition, location, network relationships, and operating history, situational intelligence provides a method for making decisions based on the likelihood of asset failure and the consequences should failure occur.
With this specific understanding of risk, natural gas managers and executives can prioritize maintenance, repair, refurbishment and replacement work to focus first on the most critical assets. This approach drives down risk faster than following a time-based or even condition-based approach to asset planning and operations.
We can’t fully eliminate risk, but we now have the analytics approaches to understand, quantify and lower risk to help prevent future pipeline accidents.
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.
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.
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.
In a previous post, I discussed the counter-intuitive notion that water utilities can gain more value from their tight budgets by increasing energy efficiency. A more straightforward approach to freeing up money is improving capital efficiency, of which situational intelligence plays a part.
Capital efficiency is much like energy efficiency—getting more results or value from the same resources. Capital efficiency questions a water utility might ask include
Where in my distribution network can I get the most leak reduction for the least amount of construction cost?
Considering demographics, land prices, construction budgets, and projected future demand, which site is best for a new water storage facility to cost effectively serve future demand?
Can I postpone a new water treatment plant for 10 more years without jeopardizing safe, reliable and affordable service?
These complex questions regarding where and when to take action regarding water infrastructure are ideally suited for analysis using situational intelligence, due to three distinguishing features of situational intelligence:
Data integration breaks down silos across the utility to represent the full range of capital-related challenges and considerations
Spatial-temporal-nodal analytics accounts for the full context of capital investment decisions
Intuitive visualizations present the results of analytics in ways that people across the utility can immediately grasp and act on
Capital efficiency is important because water utilities need to invest a lot of money, and soon, to maintain the services that customers expect.
In 2013, the American Society of Civil Engineers’ report card gave the United States a grade of D+ for drinking water and a D for waste water systems. They cite a 2007 report from the Environmental Protection Agency that the U.S. water system needs $334.8 billion invested over 20 years.
Saving just one percent of that amount through capital efficiency would yield $3.5 billion in savings. Those billions would go a long way to keeping water utility margins healthy and water rates affordable as capital expenditures rise to fund needed work. Situational intelligence can deliver that one percent savings by providing fast, reliable answers to specific capital efficiency questions related to repairing, refurbishing, replacing and expanding water infrastructure.