Big Data Focus - Smart Energy International · be your first targets for smart meter deployment. The value of addressing known grid issues can help justify accelerating your smart

Big Data Focus: Analytics

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METERING INTERNATIONAL ISSUE - 4 | 201356

BIG DATA – ANALYTICS

Metering International will be addressing the challenges of Big Data across all editions of the magazine, focusing on various elements of the Big Data spectrum. This particular edition focuses on some of the issues around Data Analytics and the benefits these can bring to the utility sector, along with some of the headaches executives are experiencing when it comes to understanding the role of analytics in their organization.

How Is THe uTIlITy InDusTry evolvIng?In a recent survey, utility companies were asked a number of questions with a view to determining the evolution of the industry and the amount of change that is happening within the industry. What does this change look like from the inside, and how are utilities adapting? What’s driving the decisions and strategies of utilities today?

Most of the responding utilities were distributors or municipal — but others were retailers, investor-owned, or cooperatives. The results can apply generally across the global utility industry.

wHo’s MAkIng THe MAjor DecIsIons? Utility business managers, followed by IT departments, are generally most involved with core decisions that drive adoption of new approaches to solving utility specific problem sets.

wHAT Do uTIlITIes wAnT? Across all geographical regions, utilities report that their main business motivators are delivering a high level of service and protecting assets. Increasingly, customer engagement is seen as an important way of maintaining excellent levels of service. As consumers become more educated around energy conservation, utilities are both challenged and ever-ready to deliver a solution that will meet needs and budgets.

Additional requirements of asset protection and loss reduction were key to the utilities’ needs. Protecting assets brings a twofold benefit for utilities and customers and taking timely steps to maintain infrastructure reduces outages. Addressing losses – and consequently the risks introduced with tampered assets – helps lower the impacts to businesses’ bottom line.

DATA AnD sHArIng InsIgHT cAn HelpNew technology brings great opportunity to capitalize on the value of data to drive better decisions, while simultaneously posing technical integration challenges that are unique to each utility’s operational environment.

The lack of visibility across a complex network of systems and applications poses the biggest challenge to utilities. Not knowing what is a systemic issue vs. “corner cases” leaves too much guesswork in the job of allocating resources.

wHy cHAnge cAn Be slow Customers expect services to be reliable, so utilities are risk averse when it comes to affecting operational flow. This makes it hard for utilities to decide when it is the right time to shift to a new way of doing business.

The costs of new solutions are one factor that may delay rollout, but the “unknown” of exactly which solution is best can delay decisions to acquire solutions. With the introduction of solutions for analytics and equipment load management, utilities are gaining new insight from big data. This yields faster analysis and even faster decisions to upgrade

equipment at risk of failing, as well as easier spotting of energy theft that impacts the rates that

all consumers pay for energy.

ThE ChANGING ROLE Of DATA IN ThE UTILITY:ThE fUTURE Of ANALYTICS

METERING INTERNATIONAL ISSUE - 4 | 2013 57


The power distribution network is essential infrastructure for any utility – yet most utilities have had remarkably few tools that provide insight into what is actually happening on their network at any given time. This has made it challenging for utilities to respond to underlying problems, adapt to consumer engagement and other systemic changes, and plan for the future.

they were lucky, they may have been able to differentiate between houses and apartments. It was on this scant micro basis that utilities made major decisions about their current and future distribution network.

With enough time and money, it is possible to add sensors to distribution network assets such as transformers and substations. however, many utilities have already rolled out smart meters or will start to do so soon. forward-thinking utilities are using that Smart Meter investment to provide real-time and detailed feedback on a distribution network’s performance, in addition to analysis on customers’ energy consumption.

Anything a utility might do to replace or expand physical grid infrastructure will typically cost significantly more than pragmatically implementing analytics. By comparison, AMI analytics is cheap and if such a small investment helps optimize hundreds of millions in investment, it makes sense to install smart meters and implement analytics up front to maximize business awareness and enable high value decisions based on relevant data.

here are three ways utilities can apply analytics to smart meter data to get more value out of their distribution network investments and preserve the reliability and quality of grid operations:

1. understand and manage the impact of renewable energy on the grid.

Many countries are aggressively promoting the distributed generation of renewable energy on customer premises through economic incentives. This can not only serve to dramatically shift load profiles; it also means excess energy is being returned to the grid.

Reverse load is a potentially destabilizing force, which one European utility executive recently compared to “water flowing

3 WAYS ANALYTICS ShEDS LIGhTS ON “BLACk BOx” DISTRIBUTION NETWORkSBy krishan Gupta, Director of Product Management, eMeter

fortunately, applying analytics to smart meter data can help utilities operate more smoothly and avoid considerable expenses associated with distribution, in the short and long term. for decades, all distribution network performance information came from SCADA (supervisory control and data acquisition) systems that monitor delivery of power out to substations. This allowed utilities to make very rough estimates and form perpetually outdated models of load curves for any particular class of users – if

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Answers for infrastructure and cities.



uphill”. This introduces the additional risk of transformer overload in high-concentration areas. More importantly, power generated by solar panels tends to present power quality problems that often add stress to distribution assets. Solar panels produce DC current and are equipped with inverters to interface with the AC power grid. however, it’s difficult to accurately synchronize renewable energy well with the phase and harmonics power already present on the grid. Applying analytics to data from smart meters on the premises of solar-producing customers can help utilities monitor reverse load and power quality issues. You can add up the entire load from distributed generation in a section of the network, calculate whether power is flowing back onto the grid, and analyze how total transformer loading is subsequently affected. Also, you can detect power quality problems at their source, where it’s a much easier problem to solve. In the bigger picture, insight provided by analytics can help utilities work more effectively with regulators to determine the best locations to allow or incentivize renewable energy, as well as how much distributed generation can be safely handled by existing distribution equipment.

2. Monitor equipment stress and minimize outages. Your power grid’s environment is constantly changing. Utilities used to annually track the construction of new homes and other buildings to predict future load, but the truth is far more nuanced. Customers move, add air conditioning, or expand their homes. Weather patterns are ever changing. Advancing technology such as electric vehicles, increased use of home electronics and wireless communications, and more solar panels all affect the stability of the power grid.

costs of truck rolls, staff time, potential safety and insurance implications, and customer dissatisfaction. Plus, making it easier to stay within regulatory limits on allowable outages can help utilities improve relationships with regulators.

3. Detecting energy theft. for decades, most European countries have assumed that energy theft was not a significant problem. Now, as more European utilities have conducted smart meter pilot projects, they have uncovered and begun to recognize the true scope of this problem – especially exacerbated in recent years by the increase in illegal marijuana growing operations.

historically, utilities spotted energy theft by examining bills for occupied properties recording zero energy consumption. In response, energy thieves became more creative in concealing their energy use. for instance, they might bypass only the load for equipment to grow marijuana, while leaving more normal loads associated with the premises connected to the meter. Or they might swap meters with another property (a smart meter has a unique ID, but not GPS, so the meter does not transmit information concerning its location). Or they might bypass the meter on a timer only when grow lights are turned on. Smart meter analytics can help spot the sophisticated class of energy thieves. More importantly, analytics software can do this automatically by constantly learning how to spot new patterns that might indicate theft, helping utilities identify which premises warrant further investigation, and integrating the results of investigations.

Since energy theft often results in prosecutions and subpoenas, analytics can also help utilities document the decisions and actions taken in investigations to substantiate and justify why a particular customer’s premises was targeted, how the investigation was conducted, and the results.

To achieve these benefits, you need to have smart meters installed in strategic locations in the field, coupled with analytics applied from the start. Consider the potential benefits of analytics in the context of distribution issues your utility already faces – such as certain transformers that are routinely overloaded, or regions where a large amount of solar generation is being installed. These might be your first targets for smart meter deployment.

The value of addressing known grid issues can help justify accelerating your smart meter deployment, since it enhances the economics of this investment beyond straightforward meter-to-cash benefits as a matter of course.

The key is to implement smart meters as soon as possible, in strategic locations, and apply analytics right from the start – don’t treat analytics as an afterthought. If you wait until you’ve implemented meter-to-cash and finished your multi-year installation of meters in order to add analytics, you’ve sacrificed substantial savings and operational benefits. Shed some light on your distribution grid now and the data will prove that this decision was invaluable. MI

Smart meter data and analytics allow utilities to create “virtual meters” on specific assets to assess what’s happening with load on every individual piece of distribution assets. They can track transformer loading at smaller intervals so they’ll know exactly which transformers, fuses, switches, and other distribution assets are getting overloaded, and for how long. They will also be able to estimate the likely risk of outages and the impact to the useful life of equipment.

Effectively and continuously managing stress on distribution equipment and appropriately timing the maintenance or expansion of assets does far more than save the costs associated with replacing failed transformers. Outages also entail the significant

ABouT THe AuTHor:As Director of Product Management at eMeter, A Siemens Business, krishan leads Analytics products as well as the EnergyIP Smart Grid Platform. krishan brings a breadth of Big Data and Analytics experience from several industries to this position. Prior to eMeter, krishan held leadership roles at Cisco, Symantec, and Silicon Valley startup, Underlying. In these positions, krishan drove Analytics efforts in Social Media, Retail, and Cloud Automation.

www.emeter.com



Analysis of data is a process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making.

It sounds like such a simple thing – collect data, analyse it and draw conclusions from the analysis. Right? But where do utilities start with data analytics, what kind of information can they utilitse and what kind of insights can they be drawing from analytics? More importantly, what does a data analyst look like and where do you find them?

Reports would suggest that while utilities understand in principal what benefits utility analytics can bring to their business, many are finding the implementation and planning of the analytics to be more challenging. This is due to a variety of reasons including:• No clear strategy for analytics development• Lack of internal skills• Siloed nature of utilities• Privacy and data security concerns.

Perhaps the most important element of an effective data analytics programme is this – take the data and give it to the right people. A strong data base is only as good as the people analysing it. But what does it take to secure the right people for your utility analytics programme?

Current estimates are that there will be a shorfall of 140 000 analytics jobs by 2018 in the US, with reports indicating that in the UK alone, the demand for data experts will be more than 69 000 by 2017. Many utilities don’t have the budgets to pay the high salaries expected from some data analysts, and yet others worry that while an expert may be good at analysing the data, if they are not seeing the business case in the analysis, the efforts will go largely to waste.

It is therefore important that utilities have a good understanding of the skills required for data analysis, preperation and management, and identify the skills gaps within the organisation. A list of development needs must be prepared, along with a list of people who could potentially benefit from upskilling or retraining.

According to an article in the Harvard Business Review by Thomas Davenport and DJ Patil, in October 2012, data analytics could be “the sexiest job of the 21st-century.”

Interesting research by Talent Analysts, a firm specialising in recruitment and analytics, indicates that the number one hiring mistake made today when recruiting analytics staff is hiring for technical skills alone. While it is essential that your analytics staff have technical skills, there is more to effective recruitment than technical skills alone.

Research has indicated that hiring purely for technical skills can be limiting because it doesn’t take into consideration the other elements of a good data analyst. According to Talent Analyst, one of the most important things to consider when hiring a data analyst or putting together a data dream team is the mindset of the individuals involved. High priority qualities to look for are curiosity and creativity. Other key traits include task orientation, discipline, high level of attention to detail, process driven and

able to work collaboratively and independently. Analysts need to have the mindset for solving complex problems, identifying the business case, and then being able to relate the numbers and the supporting story within the organisation. Communciation skills are vitally important – while much of the analysts’ time will be spent ‘crunching numbers’, they also need to be able to explain their analysis to people who may not understand where they have drawn their conclusions from.

It is important therefore to consider your data analyst as someone who combines raw talent (curiosity and creativity) with a number of skills.

According to Sheldon Glody, project manager at San Diego Gas and Electric, “the process of finding people with the mathematical brain power and skill sets to translate these use cases into analytics that provide significant value, is a real challenge.” A recent report indicates that utilities are looking for people that possess the ability to build models that are able to analyse the data and understand how to apply it to real business cases. Says Sabyasachi Chandra of TATA consultancy services, “the challenge for utility managers integrating data from systems that are traditionally disparaged is that it calls for staffing that can manage not only these systems, but staffing with the ability to integrate all of the data and convert that data into meaningful analysis. Utilities will typically have some skill pieces required to do this, but really do they have the entire capability?”

E-skills UK analysed data from IT Jobs Watch to identify the top big data roles and skills and identified the top data-focused roles as business intelligence consultant, data architect, business analyst, business intelligence architect and business intelligence analyst.Process skills required for these roles included business intelligence, NoSQL, data warehouse and big data; while experience of Oracle BI EE, MongoDB, MySQL, Hadoop, Informatica and Amazon EC2 were most important.

As utilities across the globe are implementing technologies which are giving them access to more and more data, utilities are having to come up with strategies and plans to manage this information and use it for improving business processes and customer experience.

The Big Data and Utilities report highlights that the industry is now moving to a clearer reactive analysis (both descriptive and diagnostic) incorporating a much higher volume of historical data, with more complexity, and analyzing it much more quickly. Knowing what to look for, utilities can also yield insights from real-time information streams. Finally, with historical and real-time data at hand, utilities are also beginning to look forward with predictive and prescriptive analytics, creating real value to proactively mitigate potential operational problems before they arise.”

It is estimated that utilities will spend more than $1.1 billion on analytics this year, and that spend is likely to increase four fold by the end of 2020. However, in a report by CapGemini and IDC Energy in 2013 it says, “currently, most analytics are project based. Utilities need to focus on long-term success by leveraging analytics across the operational infrastructure. They will be well served by developing a strategy that recognizes where analytics can provide the most business value and then leverage the appropriate tools and templates to recognize value.“ MI

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In 2004 the Essential Services Commission of Australia mandated smart meters for 2.6 million residential and small business electricity customers in the state of Victoria. The rollout commenced in 2009 and is due for completion at the end of 2013.

The programme, which was met with some resistance by consumers and consumer advocacy groups, has resulted in a re-think of the regulations around mandated smart meter rollouts, and at the time of writing, new proposals are being considered to move toward a model where smart metering is driven by the market instead of by regulation.

SmArT VS bASIC mETErSIn February this year AGL Energy Limited launched My AGL IQ®, a national online energy monitoring tool for residential, small business, commercial and industrial customers.

Using Smart Meter Analytics (SMA) and a HANA in memory database, My AGL IQ® allows consumers to track, monitor and plan personal energy usage based on a number of parameters and across meter and energy types.

AGL markets and sells natural gas, electricity and energy related products and services to approximately 3.5 million residential, small business and commercial Australian customers.

According to Owen Coppage, AGL Chief Information Officer, the My AGL IQ® programme can be used by consumers for gas, electricity or solar usage, utilising either smart or basic meters. The difference of course, is that the level of granular information available to those with standard meters varies to that offered to smart meter customers (at AGL, smart meters are read daily, while basic meters are read quarterly). Given the huge disparity between the amount of information available from smart meters vs that available from the basic meters, AGL created algorithms that provide forecasting of energy use so the company was able to provide something that was of value to a customer with a basic meter, even if the information was not as sophisticated as that provided to someone with a smart meter.

OpTING FOR ENHANCED ANALYTICS pROvIDES vICTORIA CONSUMERS wITH INCREASED INSIGHTS INTO ENERGY USAGE

In short: How the use of an enhanced metering analytics programme is bringing AGL Energy closer to their customers



when AGL decided to develop an energy monitoring solution, they had a choice between buying an ‘off the shelf’ product from an overseas vendor, or developing the solution in house. However, the overseas products only catered to smart meter residential customers, which was just a subset of the AGL customer base. To provide a compelling online proposition to residential and small business customers with all meter types, AGL realized that the solution had to be built inhouse.

“If you have a smart meter, you are at the top end of the information we can provide back to you, in comparison to whether you have a basic meter, but we didn’t want to arbitrarily carve those customers with basic meters out of our offering,” says Coppage.

According to Coppage this is the biggest step. “Given the environment we live in now, service providers are driving customers toward seeking out an internet based experience, because it is changing the relationship that the customer has with a service provider. The relationship changes from one managed by the service provider to one that allows the customer more control and more say over when things happen for them.”

Coppages stresses that the additional data being created by the meter polling goes to the core of the organisation. Due to the type of business that they operate, they have many physical assets and power stations. However, what is vitally important to them is how they engage with their customers and Coppage firmly believes that in this instance, more data is better than less data.

Of course, the challenge is the ability to manage that data and the programme is a move to being just a little more sophisticated and speaks to the concept of dealing with averages vs dealing with specifics.

“with the technology available today you can be more customer specific and hence more relevant to the customer, as opposed to dealing with an ‘average customer’. people like to be individuals rather than just being thought of as an average – just “one of a number,” is Coppage’s feeling.

AGL has about 20-25% market share and Coppage knows they need to win and retain customers – that they don’t have any customers by right. He is the first to acknowledge that companies such as AGL rely on the consumer customer base, but that they have to win them and they have to retain them.

The analytics programme was launched in February 2013. As Australia heads into summer, load will change due to additional power being used for air conditioning and increased use of pool pumps and such like.

“We didn’t want to arbitrarily carve those customers with basic

meters out of our offering.”

Individual users can select up to 40 attributes which reflect their unique usage situation. For example, what time they are most often at home, how many people in the family, if they use electricity or gas to heat water, if they have insulation or underfloor heating, lots of windows, a concrete or wooden home. Each of these attributes allows the consumer to compare themselves with other users in similar situations and geographies. In addition to usage patterns in the home, the programme records local weather conditions so people can track usage according to weather patterns as well. These attributes allow customers to benchmark their usage against similar households, and see how they compare in relation to the average as well as the most efficient similar homes.

Coppage doesn’t feel the project has been live long enough to establish any significant energy saving behavioural changes – but says the starting point to any change is providing better information about what people’s usage is, and then allowing them to determine where they are going to position themselves. with information comes the opportunity for people to learn and be more informed.

He continues: “when people have information presented to them that is reasonably granular, over a period of time they intuitively come to know how they consume energy. The question is really whether people will adjust their usage once they have this information. After a period, there is potentially an acceptance that ‘this is how I use electricity’ and they don’t generally go back to check their usage once this level of acceptance has been reached. However, giving them the information allows them to be more in touch with their environment.”

As interaction with utilities and other service providers changes to a more internet based service, and customers move from traditional way of engaging, Coppage’s experience tells him there is a natural pull toward technology – as consumers look for the opportunities provided by more information, programmes such as My AGL IQ® have higher acceptance rates. For AGL it’s a natural extension to the service they provide their customers, especially since the biggest change – moving from paper bills and traditional interaction to an online experience – has already occurred.

“You have to be able to partner and

collaborate to create value.”

LESSonS If you ask Coppage what lesson he has taken from the rollout and development of the My AGL IQ® he will tell you it’s all about partnerships. “You can’t do it by yourself. You have to learn to work with others – you have to be able to partner and collaborate to create value for mutual benefit and my experience is quite difficult for utilities to understand.” He is referring specifically to his qualifications as an IT specialist – a skill not traditionally associated with all aspects of utility service, but one that is becoming more and more part of the skill set that makes up the utility staffing requirements.

Coppage concludes: “For the My AGL IQ® programme, we have a number of partners: two IT partners and two strategic partners but most importantly, our Retail and Merchant businesses. You can’t just rely on one partner to do everything. It must be a collaborative effort through a community of partners.” MI



Transformer metering and recently developed theft analytics provide a dramatic new capability to use AMI meter data to detect, locate and characterize virtually all energy theft without field investigations. It provides a continuous, system-wide view of theft. Adding the revenue recovered from systematically eliminated theft can turn a marginal AMI business case into a very compelling one.

Electricity theft is a world-wide problem. Every utility has it to some extent. The energy theft rate can range from a benign 2% or less all the way up to a malignant 25% or more. Figure 1 shows a compilation of theft rates by country from various published sources.

Very high theft rates, say 20% and above, may be indicative of social problems that need to be addressed before energy theft can be. At the recent Metering Latin American 2013 conference in Sao Paulo Brazil, for example, the emphasis was on how to deal with gangs, militia, and armed homeowners when it came to disconnecting customers for theft. Locating the theft was reportedly not a problem.

At the other extreme, utilities in countries with very small theft rates, say 1 or 2%, do not see energy theft to be a big enough problem to warrant their attention. The losses are tolerable and their utility commissions typically allow them to recover energy theft through their rates. They therefore have no incentive to go after energy theft. The lowest cost approach to theft, even for the honest rate payers here, may very well be to do nothing.

Most utilities are somewhere in the middle though, with energy theft rates that are worth pursuing and without widespread social issues that would impede their revenue protection efforts. They have the will to stop the energy theft but don’t have the tools. Most utilities are also moving to Smart Metering. New analytics are now emerging that work with smart meter data and have the potential to revolutionize revenue protection. We can now move from a tip-centric approach to revenue protection to one that covers a utility’s entire distribution system and locates virtually all energy theft right down to the individual customer.

This new theft identification approach has a hardware and software component to it. The hardware layer consists of adding a revenue meter to every pole-top or pad-mount distribution transformer in the system or, in sparse or low customer density areas, adding revenue metering to distribution lines themselves. These new meters allow us to do an energy inventory with the customers served from the distribution transformers or between adjacent meters on the feeder. This is a system-wide net that detects theft in any and all inventory “zones”. It identifies which inventory zones have no theft and require no further action, and which have theft and need further investigation. Initially it was thought that field investigators could take it from here and find the customers that were stealing. However, experience has shown that this frequently does not work. Investigators “follow the electrons”, so to speak. Thieves have found that if they turn off their theft load, investigators have nothing left to track and go home empty-handed. We need analytics, and this is where an understanding of theft mechanisms comes in.

THEFT IDENTIFICATION AND AMIBy Brent Hughes, Consultant, MBH Consulting

Figure 1 - Energy Theft Rate by Country



A number of theft mechanisms don’t require much effort to find. For instance, turning the meter upside down would make an old analog meter run backwards. Smart meters make this easy to find by having separate registers for energy delivered and energy received. Removing a meter and inserting jumpers in the meter socket for a number of days used to be a way to steal energy. Smart meters make this also easy to find by not recording any consumption for the hours and days in question and logging an “outage” event for the duration of the theft. Meter tampering, such as opening the meter’s voltage coil, is also easy to see with smart meter data. New analytics are now available to help with the remaining two theft mechanisms that have always been the most difficult to find – namely bypasses (jumpers around the meter) and taps (connections ahead of the meter).

BypAss ThefT AnAlysIsBypasses are simply wires or jumpers that are connected in parallel with the meter. Together, the meter and the bypass jumpers form a current divider whereby the customer’s total load divides itself between these two parallel paths. The fraction of the customer’s load that goes through the meter is determined by the resistances of the meter and the bypass jumpers, and its numerical value remains constant regardless of the magnitude of the load. The customer can therefore give themselves whatever discount they think they can get away with – say 50% – by virtue of their choice of jumper wire size.

Bypasses are a “set it and forget it” theft method mechanism. It’s stealing all the time and works with the customer’s entire load.

Solving the bypass problem involves analyzing multiple hours of customer and transformer load profile data for the inventory zone with the theft. Figure 2 shows a set of data that is without theft. The transformer and downstream customers are listed by row and different sets of load profile data are listed by column. In the example shown we have 15-minute load profile data (the cells with white background). Thirty or sixty minute data could also be used.

this way customers with bypasses are identified and the amount that they are stealing is quantified.

The tap theft that was simulated has also been identified as load that is “left over” after bypass solution constants have been applied (9th row, olive highlighting). However, since tap theft is not registered on customer meters it cannot be associated with any particular customer. It can only be identified as to the load interval in which it occurs. Identifying tap theft down to the customer level requires a different analytic technique.

For the example shown of 16 customers served from a transformer, the solution required less than a day’s worth of load profile data to solve. More data may be required if many of the 15-minute load profile data sets are practically the same. It should also be noted that the load profile data does not have to be contiguous. The only requirement is that the bypasses be present in all the load profile intervals being analyzed.

ConneCTIvITy AnAlysIsThe bypass solution also lends itself to solving another very practical problem, which is the connectivity issue: i.e. which customers are supplied from the transformer under study. Even though many utilities have geographic information systems (GIS) that purportedly contain this information, it is not always up to date or accurate. If the data set being analyzed includes customers that are not supplied by the transformer in question, they will be identified as having solution constants of zero. The ability to determine connectivity analytically rather than through field investigation is a tremendous benefit.

This new analytical technique can therefore identify single or multiple bypass thefts using just the ordinary load profile data available from virtually all smart meters. It can do so even with some tap theft present and without knowing beforehand which customers are actually served from the transformer under study. This is an extremely powerful tool and yet it is very easy to use. Simply give it a day’s worth of load profile data and the analysis routine does the rest.

TAp ThefT AnAlysIsThe last remaining challenge is tap solution. Since tap theft occurs ahead of the meter, the meter does not register any of the theft. Energy consumption readings therefore cannot be used as the basis for finding taps. The only available clue as to the whereabouts of a tap theft is voltage and the method used to find it is to look for nodes where the voltage is lower than what one would expect given the metered customer load and the admittance matrix for the secondary distribution network. Right away the requirement for accurate voltage measurements is a clear necessity. It is also clear that this time we will be using a power flow calculation to relate loads and voltages. As such we now need instantaneous (snapshot) voltage and load measurements.

Figure 2 - Bypass Analysis - “No theft” base case

Figure 3 - Bypass Analysis - Simulated thefts found

Starting from this “no theft” case two bypasses are simulated by multiplying all of customer #4’s readings by 0.5 and all of customer #13’s readings by 0.333 (Figure 3 – 3rd column, light blue highlighting). To further complicate the problem a 2kWh tap theft is simulated in load profile intervals 5 through 10 (Figure 3 – 4th row, light blue highlighting).

Solving for the bypasses yields a set of solution constants (2nd column, olive highlighting) which are the multipliers that have to be applied to each customer’s data in order to make the transformer and customer readings balance. Multipliers that are greater than 1 denote customers with bypasses. In our case here, customer #4’s data must be multiplied by a factor of roughly 2 to “undo” the simulated bypass of 0.5. Similarly, customer #13’s data must be multiplied by roughly 3 to undo the simulated bypass of 0.333. In



The tap analysis results can be enhanced by also plotting the distribution system voltage profile obtained assuming there is no theft (Figure 6 – blue line). It illustrates the sensitivity of voltage to tap loads. Taps close to the customer’s meters create voltage drops of roughly 0.2 Volts/kW. Taps on the secondary distribution system, on the other hand, create voltage drops of only 0.05 Volts/kW or so. The 10kW tap on the secondary distribution line therefore produces a 0.5 voltage drop which is equal to a 0.2% measurement error – typical for new smart meters. While tap theft is easily identified it does require highly accurate voltage measurement data to locate it.

BusIness CAseThe business case for theft identification is unique to every utility; never-the-less a high-level example is quite revealing. Figure 7 is a high-level business case for a utility with 1 million customers, a $0.20/kWh tariff rate, and a modest 5% theft rate.

ABouT The AuThoR:Brent Hughes retired from BC Hydro after 32 years of service in Analytical Studies, Transmission Planning, R&D, and lastly Revenue Metering where he focussed on AMI and electricity theft detection. He is now offering theft identification consulting services through MBH Consulting Ltd. Brent has a Masters of Applied Science in Electrical Engineering from the University of British Columbia. He is a Senior Member of the Institute of Electrical and Electronics Engineers and a registered Professional Engineer in the Province of British Columbia, Canada.

ABouT The CoMpAny:EnerGuard Analytics is part of MBH Consulting Ltd. It is focussed on developing and marketing new analytical techniques to locate electricity theft. If you would like to try the theft elimination techniques mentioned in this article, EnerGuard Analytics and SensorLink are setting up field trials. www.energuardanalytics.com

Figure 5 - Tap Theft - Solution Voltage Profile with Measured Customer Voltages

Figure 6 - Tap Theft - Voltage Profile With and Without Tap Theft

Figure 7 - AMI plus Theft Identification “High-Level” Business Case

Taps are an “à la carte” theft mechanism whereby the energy to run only those loads that are connected ahead of the meter is stolen. Theft is therefore intermittent, meaning energy is only being stolen when the tap connected loads are turned on. This could be a customer’s entire load but this would appear suspicious. More often it is just a single large load that the customer doesn’t want to pay for such as an air conditioner, pool heater, or marijuana grow lights. The tap solution is a two-stage process involving “reverse” and “forwards” power flow calculations to find where unmetered tap load must be added in order to make the power flow solution agree as closely as possible with the measured voltages.

Figure 4 shows the one-line diagram for a sample tap theft problem in an underground secondary distribution system with two tap thefts (10kW at customer#4 and 20kW and line#3). Figure 5 shows the measured customer voltages (black asterisks) and the results of the tap analysis (red squares with connecting lines). The red circles show where the analytics has correctly located and quantified the two tap thefts despite the uncertainty caused by voltage measurement error in the customer meters.

The tap analytics is based on measurement and calculation. It can be fully automated to where it simply accepts the measured instantaneous load data, voltage data and the pre-calculated admittance matrix, and it returns the location and magnitude of any tap theft. No operator intervention, interpretation or inference is required to reach a solution.

Figure 4 - Tap Theft – One-line Diagram

Assuming an average 10:1 customers-to-transformer ratio the one-time cost to supply and install transformer meters could be as low as $50M. This is a small fraction of the $280M lost every year to energy theft. Adding a transformer meter “layer” to an existing AMI project therefore makes tremendous sense. For that matter, the entire AMI plus transformer meter project (AMI+) cost of $223M could be paid for by the first year’s avoided theft. Adding the revenue recovered from systematically eliminated theft turns a marginal AMI business case into a very compelling one.

For people that steal electricity, the certainty of being caught and prosecuted is a powerful deterrent. Transformer metering and new patent-pending theft analytics provide a dramatic new capability to detect, locate and characterize virtually all energy theft without highly visible field investigations. This allows us to finally cast off our historic reliance on tips from customers and employees that only find a small fraction of the total energy theft. Transformer metering and theft analytics give us eyes that see all the theft on the entire distribution system and a mechanism to permanently eradicate it. The bottom line is that the theft identification system will find and enable elimination of theft so quickly that it simply won’t be worth the time and effort to set up for an energy theft. MI

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Big Data Focus - Smart Energy International · be your first targets for smart meter deployment. The value of addressing known grid issues can help justify accelerating your smart