Itron, Inc. (ITRI) Earnings Call Transcript & Summary

All right. Well, welcome, and thank you for joining today's press conference. I'm Alison Mallahan, Head of Corporate Communications at Itron. While we weren't able to gather as an industry last week at DistribuTECH, we didn't want to lose the opportunity to share some news with you that we had planned for the event. Today, we're going to discuss the launch of an exciting new set of solutions for the low voltage grid. But before we jump into that, let me share our agenda for today's press conference. I'm joined today by Stefan Zschiegner, our Vice President of Product Management for our outcomes business, and he's going to give an overview of our portfolio and the announcements of today's details. He'll be followed by Kevin Ferree with Itron's experience team, who's going to be doing a live demo of our low voltage grid applications. Following that demo, we'll have moderated Q&A, so be sure to be thinking of your questions. We have the Q&A field within WebEx where you can submit your questions, and we'll address those at the end of the press conference. This press conference is being recorded, and we will make the slides and recording available afterwards. So without further ado, I'm going to pass the ball over to Stefan to take us through the slides.

Thank you for your interest in our announcements here. So let me just proceed, and I know many of you are frequent followers and participant in our events. So clearly, a quick reminder, without going into too much details about Itron, as an industry leader with a long-term tenure in the utility space, I think we've made a significant progress over the last year. You can see, especially in the areas of managing and communicating and especially advancing our distributed intelligence mission. And I want to share a little bit more about these stats here. When you look at our outcomes business, which comprises of the software and services, including delivery, consulting and also, the managed services, we have made significant progress here. At this point, we have over 70 million endpoints under management worldwide used by over 1,200 utilities. And that's a very significant market position we have, and we really appreciate the loyalty of our customers to hand over a lot of mission-critical infrastructure services to our management. In the last year, especially, I'd like to point out that we made really significant progress in the area of distributed intelligence, and we'll talk about this more in this presentation. Especially, you can see we have transitioned really from early use to scale. We have a significant amount of meters deployed that are capable of -- and enable to provide distributed intelligence applications, 3.7 million. We have a lot of -- 6.2 million DI applications license, and we have a vibrant ecosystem, with now 4 applications at least, and more underway by partners and progress. Now, while we made a lot of progress on rolling out our distributed intelligence solutions, we also make steady progress across the board. And you can see also our traditional applications to enable AMI in terms of analytics and data management, have made significant progress over the last year, adding customers from large utilities, but also Temetra data collection and making significant progress across the world, with adding over 100 new customers. Now, as we see -- look at this, I would like to share with you, if I can advance this. So what's somewhat driving some of our customers here? And I think you have seen, these are similar slides you may have them yourself in terms of really summarizing the key drivers that you're familiar with, right? They are infrastructure changes that are disrupting -- and our customer base here with the adoption of distributed energy resources, EVs, solar adoption, that's really changing how the grid works and really drives a lot of grid modernization. In the center, you see that we are experiencing lot of extreme weather, and especially last year's event in Texas is still driving a lot of investments, now, in our -- in the utility customer base. And then, of course, as we are looking at coming out of COVID, our customers are very, very much increasing their focus and investment in engaging with the end customers and consumers in different ways, and it's becoming more important than ever. Also, decarbonization goals, safety and these other drivers drive investments. If you look at this very, very broad range of critical needs that drive our utility customers, at the end, we can boil it down to 2 reasons why these customers are coming to Itron, and that really drive investments. One, on the left side, you see the need to invest in grid safety, resiliency and reliability; and on the other side, you see the need to transform customer relationships. Now, in many cases, this is called AMI 2.0. And you can see from these use cases, this is much broader than meter-to-cash. We see that our customers shift a lot of their investments into the low-voltage distribution grid. And especially here, you can see that the realtime visibility and the ability to really look at the end of the distribution grid and the interface between the customer and the grid side is becoming really critical for them to deal with the drivers that I mentioned in the previous slide. In this world of AMI 2.0, every meter is becoming a grid sensor, a control point, and this is not only for new AMI deployments where utilities are moving from the first generation to the second generation of AMI, but also, we see a lot of interest in use case-driven scenarios to start to build in this infrastructure capability. So with this, this direction is one of the key reasons we're looking at why Itron really is in the loop on this is because we are enabling this interface with distributed intelligence, meaning edge compute that is built not only in the meters, but also in the network and in our -- in the back office infrastructure to provide a system solution that bridges the customer with the utility side. And the key differences here in terms of use cases is because you can create a lot more higher resolution and near realtime insights and decision-making at the edge. The second reason is that you can enable local control and autonomously coordinate between devices at the edge. And at the end result, these enable new use cases and applications that were previously not possible. And we have talked in the past about this in other -- with you about our endorsements by some of the leading adopters, and I just referenced here Tampa Electric on the left side as a reminder of what we have talked about before. Now, when you look at this application, I put a schematic here to really look at this from the context of the low voltage grid. In this image, that is -- let me walk you through this step by step here. So at the end, you see the meters that are connecting and communicating peer-to-peer in the red arrows with each other, but also with devices on the low voltage grid that are on the grid side. It could be a transformer or it could be a grid sensor. It also enables the capabilities through WiFi connectivity using open standards such as 2030.5 to communicate with devices that are behind the meter, such as your EV or the solar implementation inverters and other devices. So with this in mind, now Itron is in a unique position to also connect through the network into the back office, through realtime control into the back office on the operations side, such as ADMS and SCADA, but also on the market side to really provide ancillary service into deregulated markets. Now, the difference is that here is that, uniquely, Itron has is built to connect and communicate and manage these millions and millions of endpoints while the other back-office systems are really designed for numbers that are orders of magnitude smaller. So we have an opportunity here to aggregate and integrate and extend the visibility for the central control systems to the edge. And that allows us to -- so it's a very differentiated approach in this way, but also, it allows the utilities to achieve really transformative results. Now, when you look at this and it drives more use cases. So you can now look at these use cases, and if you look at the bottom of the slide, from a grid edge operation, but also, from a demand response and energy resource management of how many of the infrastructure or consumer engagement point of view. Our customers, the utilities, not only bank on one of these applications to solve the low-voltage grid problems. But many of -- all of our utility customers actually balance and have all these types of programs and investments in place. They manage their portfolio and prioritize the cost across all of these with different use cases. So clearly, here, Itron has a unique opportunity with a single platform approach and multiple applications to really leverage investments across all the broader portfolio of choices that the utilities have to solve these low-voltage grid problems that are -- and challenges that are out there. So with this in mind and these opportunities in front of us to really help the utilities to really extend and create return on investments that are now shifting from instead of generation and transmission to the low voltage distribution grid, we now have the opportunity in announcing our product portfolio with 3 new solutions to address these challenges. These new solutions are the EV charging optimizer, the DER optimizer and the grid edge optimizer. And we addressed specific areas here that really are focus area for the utilities and opportunities for the utilities to provide the value of adding new demand -- distributed energy resources to deal with resiliency or to really optimize the adoption of new EV chargers and EV fleets, for example, in this image here. So let me walk you through this. So we have the EV charger optimizer, which is really a solution that optimizes the deployment and operation of EV charging, the [ charge ] customer side, with the EV charging operators, but also balance this with the utility needs. The DE optimizer optimizes the benefits for the resources and the impact on the grid. And that's basically our DERM solution that is unique in the sense that it not only looks at the customer side, but also on the grid side. And finally, the great edge optimizer optimizes the -- and provides operational visibility down to the endpoint and allows integration all the way into the operation system such as ADMS. Now, we have talked about this before that we enable all this with our distributed intelligence solution in our open and modular platform, but we also layered on a new layer of value here by really recognizing that we need to provide visibility into this realtime and added a realtime vision technology here that I will talk about in a minute. Let's go through this one by one. So first, we're talking about the EV charging optimizer. It's a unique first-of-a-kind solution that bridges EV charging operators and utilities. EV adoption is one of the fastest and highest growth changes that are really creating a transformational challenge for our utility customers. But with these solutions, what we have seen is that, basically, customers can save about 35% or more of the energy cost through managed charging instead of unmanaged charging. Also, with this solution, utilities can save more than 20% a year on grid infrastructure investments and ongoing management. So there's a significant saving just by reducing infrastructure investments, but if you analyze them and then also consider the ongoing investment, you get through a really breakthrough, 20% per year savings. That allows our utilities and our customers to address local constraints and really orchestrate the charging. And when you look at our press release that we have launched this morning, we also shared that we have -- we are offering an end-to-end solution here with a very strong ecosystem partner system that includes leading charging management partners such as TMH or the Microgrid Labs. But also, we have telemetric partnerships in there like Geotab and infrastructure partnerships like AWS. So very excited about this new solution, and we are in the process of engaging and rolling out this to customers. In fact, we are deploying this to customers starting in the first half of this year. And for example, some of the customers have plans to not only look at a small fleet today or they look at their internal fleet, but they have plans to expand this from the tens of units of buses, for example, to about 5,000 or 10,000 buses by the year 2030. So we've seen significant opportunities and really are excited about this new capability in our portfolio. The second solution is the grid edge optimizer. And this is a way that includes most of the DI applications we have talked about in the last year and more. It allows our customers to really monitor and get visibility all the way to the end points of the low voltage grid. And here, we have made a lot of progress over the last year. We have over -- we have now 4 applications deployed and over 2 million applications downloaded and ran on meters to date. So the next deployment of the applications has already started in terms of new applications beyond the first 4, and we also have expanded our deployment at new customers at this point, and we expect to share with you progress updates throughout the year. In terms of the business case that our customers are looking for to drive this, you have seen probably at multiple occasions, our overall AMI business case. For this presentation, I pull it out, that a key part of the AMI business case is really the DI and the DI applications and the return our customers can see. We have actually detailed calculations on each of these applications. And typically, what we see that our customers can experience a return on investment that's significant over what they're looking for in other applications with the rates that are exceeding 4 to 1. So the solution is available now, of course, and is scaling not only in North America, but also in Asia Pacific at this time. The third one is we are launching the DER Optimizer. And that really is optimizing the benefits of the DERs and the impact on the grid. And that's unique about Itron. So we're really looking at this one as an expansion of our intelliSOURCE DERMS platform. So as you may recall, our intelliSOURCE DERMS platform is deployed at over 20 utilities in North America. We have over 1 gigawatt of capacity under management and active utility programs. So it's a very well-established platform, and we are building -- expanding this into the DER optimizer with new devices that we entered to this with open standard approaches such as inverters with an open ADR, but also leveraging the 2030.5 technology standard. We do have also realtime monitoring and provide analytics to really forecast the DER impacts to the grid and manage this program end-to-end like we have done in the demand response programs before. So very excited about that new capability as we are bringing this to customers. And in fact, we do have a partner customer engaged and are starting to roll out capabilities for this customer this quarter. This program will run over 4 years, and it's really focused on enrolling and managing EV charging for cars that are in residences or in C&I facilities. And provide potential cost savings for the customer, but also cost savings for the utility, as you may have seen from other materials that just even 3 or 4 EV chargers under the same service transformer can cause overload situations if not managed correctly. So they provide a risk to the grid, but they also create an opportunity if we manage correctly to expand the capability and capacity without costly infrastructure upgrades. Finally, as an enabler, I wanted to just mention that we have basically put in place from the ground up a new technology, Digital Twin-based, we call it realtime vision, that is complementing our distributed intelligence at the edge. It allows us to really integrate with AI and machine learning to provide contextual insights, and we built it from the ground up, where we leverage proven and scalable enterprise-grade technology that is used by other industries in the telecommunication space for hundreds of millions of endpoints. It's highly compatible and is really designed to scale and provides really also flexible deployment option, and we will use this first with our EV charging optimizer. But very excited about that new capability that we're bringing out, in order to enable these new solutions. Finally, I'd like to also provide a note to the new partners that we have onboarded in our ecosystem. Our ecosystem expansion is absolutely critical for us in order to provide more innovation and more choice to our customers and also, basically leverage and basically the value of our platform, enable more time-to-market. Recent additions include Grid4c that we announced in the second half of last year. And they are delivering predictable insights to utilities based on low disaggregation, meaning to look at the profile of the currents and voltages at the meter and decipher what appliances are behind the meter; when are they on, when they are off, what are they consuming, and therefore, with more data, provide more insights that may create actions and enable actions by the utilities. Second, we have a new partner also just announced NET2GRID. NET2GRID offers a leading-edge artificial intelligence, machine learning services. Also, for energy consumption of these loads and also to provide more consumer engagement this way. So a very hot topic for our customers. And both of them are ready to use and the utility engagements are very active for both at this point. So another set of news I wanted to share with you. So with that, I wanted to wrap up the news section here and would like to hand it over to Kevin for a demonstration. So you kind of get a feel on how these new solutions actually operate for our customers.

All right. Well, thank you, Stefan. Let me get set up here and share my content. Okay. So hello, everyone. My name is Kevin Ferree, and I work on our product showcasing team. And I've got some demos that we wanted to share with you in light of this announcement to give you a better feel for what Stefan has been talking about, see if it sometimes helps solidify concepts. And so these are some demos that we developed that we wanted to show at DistribuTECH, so you're getting a first look here at them. I'm going to start here with the low-voltage visibility control demo. So this helps really level set the problems that Stefan was describing that our utility customers have been facing in recent times. This is a small map, a quick look at a view of the low-voltage distribution system under substations. So we have the substation, 3 transformers and a number of meters, residential meters in this case. And when you talk about visibility into the voltage and the service that's being provided to the customers, utilities have a great insight at the substation level. So they're managing that and have been managing that for a very long time, using things like SCADA, but that's just too expensive to push down lower closer to the home. One reason is that -- well, we'll get to the reasons why we help in that area, but let's continue looking around. As we traverse out to the transformers, we don't have very much insight at all. So the icons are grayed out. We might know where they're physically with our GIS system. We probably know their transformer rating, what they're set up to handle as far as load, but that's about it. We go down to the meter, and with AMI, there's some data that's available there, but demand and consumption values, metered cash is a primary responsibility in the AMI world. Maybe some voltage data, probably have some outage statuses. You'll know whether they're up or down based on whether you're getting outage alarms, but that's about it. And that provides huge blind spots in today's grid, because while we were talking about all this, this customer didn't know that 2 out of 3 of these transformers were currently experiencing issues. The transform left was overloaded and the transformer on the right is in an over-voltage situation, which can happen with too much excess solar. So why are these issues possibly happening more often, and Stefan hinted at it, but as EV and solar penetration becomes more prevalent, these issues are beginning to become more prevalent as well. So knowing and having information is every day becoming more important for our utility customers. And so the first part of the solution for this is distributed intelligence. And we've been talking about distributed intelligence for several years now, and that really does bring the first level of defense here. It brings insight that the utility doesn't have otherwise. So what I'm going to do is I'm going to deploy some DI apps down to these meters, and you can see that represented by the emboldened icons with the plus sign. And we'll just focus on one of these meters right now to see what's going on. And so we've deployed 4 apps to these meters: location awareness, transformer load monitoring, EV awareness, and solar awareness. And with these apps running on the meters, again, consuming that 1-second data, they're analyzing and creating results that are giving insight now to the utility that they did not have before. Location awareness figures out the transformer-to-meter relationships in near realtime, figures out the phase of power, feeder relationships. And all that gets sent to the back office, and it informs the utility to these relationships, which is a building block to some of the other apps. In fact, I'm not going through this slide any longer, let's just go ahead and go through our menu here so that we can talk about them individually. The installed distributed apps are these 4. And so we're going to just focus on location awareness now. Again, we now know the relationship between the transformers and the meters, which is very important to know how they're electrically connected because that, again, enables things like transformer load monitoring. So as we focus on now transformer load monitoring, our icons have brightened a bit for our transformers. We have green and red now. We know where problems are occurring. And so if we click on one of these now, we can see that yes, about 3 hours ago, an overloaded situation occurred on this transformer. If we go to the other one, a similar amount of time to go, a high voltage situation has occurred from over-voltage and excess solar. So again, this insight is now providing the utility with the information to the actual issues happening in their area. EV awareness is another app that's very important. And again, this is running on the meter, looking behind the meter into the home, and we can detect aggregate EV loads in the home. And so in this case, we've got the icons and meters bright if it's detected EV. It's grayed out if we're not detecting it. And of course, we also can determine what the actual kilowatt values are being -- the consumption being used by those EVs. Solar awareness is very much similar. It can detect it, to tell when it's there, when it's not. And we can also determine the actual generation that the solar system is creating, and we can also tell how much is being exported back onto the grid. Again, all these numbers gets sent back to the back office and give utility insight now into solving these new, and now more increasingly ever every day, more common issues. Now, distributed intelligence is only part of the battle. And so with this information, you certainly can roll trucks, you certainly could roll out fires as you find out about them. But really, the power and the long-term strategy is in finding a realtime control mechanism to handle these issues. And so that brings me to my final chapter. We're going to focus now just on this 1 transformer and the meters. And what we are doing now, what we're announcing today is our strategy in providing realtime control for our customers. And that is with the optimizer family products that we're announcing today. So we're going to enable our realtime modules, DER optimizer, grid edge optimizer, the EV charger optimizer, and that's going to expand some new icons in the screen. So previously, we were just talking about distributed intelligence. The actual apps running on the meters, providing information and insight to really collect and make intelligence from. But these realtime modules are now doing is they're providing another level where we can now go back and protect assets and make decisions and actions that we couldn't do necessarily with just the apps. What you see with these new icons are -- these square icons, they represent the actual physical devices at the home. So when we're looking at the thermostat, that's a real item in the home that we could control to reduce loads in the microgrid. The solar inverter is another icon that you see there. And if it's gray, it means we didn't detect it; if it's blue, it's lit up, that means it is being detected. The cars as well. We know when, how many there are, how much is being charged. But now, we actually, with the DER optimizer, for example, can reach out send commands, and actually control loads to protect utility assets. All of these things are within the limits of permission and things like that, obviously. But it is the next level in our ability or our enablement of our customers to control and take this long-term strategy. So this is the introduction to just help you understand the relationship between all these concepts, understand what the value proposition is here. But I want to now go into our demo of the EV charging optimizer, and I'll end with this. Again, just helping you understand the concepts involved. So this is just 1 use case that the EV charging optimizer can handle and meet. And so -- but it's simple and basic enough that you can get the gist. So we're looking at a microgrid. This time, it's more of a commercial situation. We have our transformer, we have a couple of meters, C&I meters, and then we have -- it looks like a solar farm and EV depot. Let's go ahead and focus on these one by one. So the transformer, obviously, it's a secondary transformer. Nothing special there. We can see its rating is 2 MVA. We're just using megawatts because we're going to do something in a moment where -- just to keep it simple. But we have a grocery store here. We can see the meter is being read. We know though, through history that the peak demand for this place could be up to 1.5 megawatts. We have a small retail outlet over on the other side of the parking lot. Their peak demand is much smaller, generally tops out at 0.2 megawatts. We have a solar implementation. Actually, the grocery store is pretty progressive. They have solar farm on top of their roof. And the peak generation capability of this system is about 1 megawatt. And finally, we have a fleet EV depot that, again, it could draw at a peak about 1 megawatt. Now, just so it happens though that this EV depot has been newly upgraded, has been expanded. And so when they had originally negotiated with the utility about how much they might be needing for energy and power and the utility size of the transformer, well, they've upgraded, and now that might cause a little bit of a problem. So what we want to do now is just analyze, let's add up the numbers, let's bring a scoreboard up, and we're going to quickly just see. Do we have enough supply capacity to handle the needs at this -- in this microgrid? So I'm going to go ahead and add the 2 megawatts of the transformer to our scoreboard, so you can see what's happening here. Our supply capacity is up to 2 megawatts right now. I'm going to quickly click on all of the loads. And so we can see though, if all those loads draw their peak at the same time, we're over the capacity of the transformer. We're now at 2.7 megawatts, which would be a problem. But fortunately, we do have our solar farm here from the grocery store, and we'll go ahead and add that 1 megawatt to the total, but that does have an asterisk. That requires good weather, it requires clear skies. So we are all clear as long as the weather is good, which is better than nothing, I guess, but let's go ahead and continue forward. We're going to now quickly look though, as long as this thing is well, we can look at the UI here for the product, focusing on the EV depot. And we're looking at a graph of the charging schedule. So part of it is historical and part of it is forecasted, but it's the same every day. The charging schedule is sort of something that this particular fleet depot is just trying to get it done in 4 hours. They're charging most of their fleet at one time. So it's over quickly, but it is close to the capacity of the depot itself. And I would just want you to understand, though, that they do the same thing every day in general, and they've negotiated that with utility and it generally works. And when we look at the graph for the transformer and we fold that EV depot data in with the rest of the non-EV load from the other customers in this microgrid, we can see, again, as long as solar is available, we're going to be under the rating of the transformer and there should not be any issues. However, what happens when forecast change, when the weather changes? And so the nightly news is coming on, and the weatherman has some news for us, an update to the forecast for tomorrow, and it looks like it's going to be heavy rain all day, which, of course, that's not good for solar generation. So with the forecast being changed overnight, that might cause some problems with our microgrid here. Well, the good thing is that the EV charging optimizer, it never sleeps. And so it's running and working the whole time. And so we're going to go through the mind of the optimizer as it begins to analyze changes. It's importing the latest weather data. And again, not to oversimplify, but this is really integrated, sophisticated weather data that we then pair with our machine learning to dial in the forecast. And so what you see here is just a generalization for you to help appreciate, where we had a previous forecast with 100% of solar expectation, we were fine. In the left graph, we were not going to go over the rating of the transformer. But with our revised forecast, based on the change in the weather pattern, we're now looking at a 0.1 megawatt capacity from the solar farm. So the reduction now is going to cause the total peak of our day to go over the rating of the transformer, again, back to the 2.7 megawatt, which now, that's a problem. We don't want that to happen. Again, loss of life on the transformer is a costly thing for the utility for this to happen. So again, this is all happening, automated. The charging optimizer are working again on the problem for us. And so let's go ahead and continue forward. Once this has been realized by the optimizer, it then communicates with the charging management system of the fleet depot. It sends a request for that charging schedule to be changed and how to change it in order to meet the requirements that the forecast is predicting. We can see on our scoreboard that they have acknowledged this and they updated their charging schedule. We now only hit a peak of 0.4 megawatts at the fleet depot, and that brings us into harmony now so that we won't be going over the rating of the transformer and we won't be incurring loss of life conditions on expensive assets. Now, all this happened while you the utility employee were sleeping. You had no concept of this occurring. And so you come into the work the next day, you grab your cup of coffee, you just happened to log into the optimize -- the EV charging optimizer system, just to take a look around, see what's going on. You look at the EV depot and lo and behold, you look at now the forecasted charging schedule has changed. It's not what it was and usually is every single day. In fact, you can see what's happened here. We've now delayed some of the charging. The gratification is not all upfront. So we're smoothing that peak out, not making a huge request at any time. And now, because of the spreading out of the charging schedule, when we look at the transformer graph, bringing in the other loads again, we can see that even with the reduction in capacity or output from the solar farm, we don't go over the rating of the transformer because we've spread out that usage across more of the day. Now, again, this is the end of the demo. It's just a simple example using one depot. But in reality, utilities will be using this forecasting capability across the broader grid. It's important to note that this can be applied even to substations where multiple depots might be involved. But I hope it helps you understand and see the value that's being provided by the EV charging optimizer. And I guess with that, I'll hand it back to you, Alison.

Wonderful. Thank you so much, Kevin. It's always really helpful to see the technology in action and how we're helping utilities with the influx of EVs and solar and really bringing that picture back home.

So we're going to move into our Q&A portion. [Operator Instructions] So Stefan, first up is what are the drivers for creating this optimizer solution portfolio?

Yes. So when we look at the drivers for this, I mean, you can see that the EV adoption, as an example, is going up, and very, very fast these days with more electric vehicles launched every week or so in the news. And not only in residential, but also in commercial. Solar adoption is not slowing down. But also, you can see the weather drivers and the need for consumer engagement that we mentioned at the beginning. So I think all of these kind of create almost an impossible situation for our utility customers where the need for change and driving innovation is becoming more urgent. And in fact, we are talking with our utility partners and have the discussions. And this is reality, right? We see their planning cycles decreasing and their need to invest accelerating so they're looking for innovative solutions here.

Our next question is, so what technology is required to power the optimizer software and services? And is distributed intelligence a requirement as a part of that?

Yes. So that's a very good question. So clearly, our solutions are designed to be -- to work with the standard AMI deployments that exists. We clearly -- some of them work the AMI agnostic, but DI does make -- provide a higher level of fidelity, a higher level of responsiveness, and as Kevin and the demo for example shared, also, better grid sensor to measure. So clearly, we help our customers to get started without DI, but DI deployments do provide an additional value for the utilities to resolve problems.

Great. That's really helpful. Next, we have a question about do any of these solutions help predict future capacity requirements?

Yes. So I think when we look at the solutions, for example, the grid edge optimizer. One of the key targets is not only the operational department in the utility grid operations, but also the grid planning. Also, the -- you can, for example, use also the DER optimizer and the analytics part that we are deploying with our early customers. And look at this to refine your time-of-use programs, so there's an opportunity to not only do grid planning or investment planning, but also to do rate planning and other. Clearly, these insights are important tools for various departments within the utility.

Great. What communication protocol is used between the EV optimizer and the transformer?

EV optimizer and the transformer. Yes, so that's another very good question. So we have -- when you look at this, the transformer itself is not necessarily a communicating device, right? So what you see here in the demo, we are deducting and measuring and using the meters as the grid sensor in order to deduct what the transformer sees because the transformer itself is not an intelligent device. Now, there are grid sensors we can collaborate with that some of the utilities deploy. And then, grid sensors would provide a more complete picture because then, you can correlate what does the transformer see with what you see and measure with the AMI meters at the endpoint. And you can correlate and sum up, okay, does the sum of all the measurements I do at the households match what I'm reading at the transformer level. And that's important for the utilities, too. So some of them deploy sensors at the transformers as well as leveraging AMI in order to correlate.

Okay. Great. What do you mean by realtime? This person asked, I thought that you do edge intelligence for near realtime?

Yes. So that's very good. So clearly, realtime means -- depending on different solutions and applications, mean different things. So basically, when we look at this, we look at this from a use case basis. So for many DERM solutions, below 5 minutes, it's realtime, and then scaling down to 1 minute, the responsiveness of 1 minute is sufficient. If we look at the grid optimization and responsiveness to really be consistent with what the grid assets see and what the grid control system is expecting, you need to be in the 5- to 10-second range. But for some of the responsiveness to really help immediate response, you need to be even in the sub-second range. So we -- and therefore, when we talk about near realtime on the distributed intelligence, we're talking about near realtime in the sub -- second or sub-second phase to respond to an event immediately on site at the premise where the meter is installed. Now, when we talk about our realtime analytics and control system in the back office, we clearly have the vision to scale it down to the second range. But for many use cases right now, 5 minute, 1 minute or 30 seconds is sufficient.

Okay, great. Another question, why would utilities select Itron for these solutions?

Yes, that's an excellent question as well. So I mean, at the end, we have talked about this in the summary here. We're clearly in a unique position here to combine the customer side and the grid side. So clearly, that's a unique reason that -- and to really integrate this because this is where all the change and the problems happen at the edge of the distribution grid. Now, overall, Itron has a long heritage to provide end-to-end solutions and really have a platform that is highly scalable, very secure and reliable to integrate meters with networks and back office communication and services. And finally, with this combined, these solutions show you that you basically then enable to accelerate the time-to-value and impact for the utility. So I think, yes. So these would be the primary reasons I would mention.

Great. Our next question has to do with the EV charging optimizer. Can you share more background related to the business benefits?

Yes. Maybe that's a great opportunity for me to maybe to introduce one of the team members here. Mark Brady is leading the -- our EV charging business. So maybe, Mark, that would be a great opportunity to maybe share some answers here.

Yes. Can you hear me?

Absolutely.

Okay. Thanks, Stefan. Yes, so as Stefan mentioned, I'm in charge of the EV charging product. And so yes, some of the benefits are really the bridge that -- that presence at the grid edge that Stefan mentioned. So the benefits are twofold. One is to the end customer. So the fleet charging operator. And so that's all about -- and they're really concerned about 2 things. They're concerned about ensuring their vehicles are charged when they need to be. That's their first constraint. So you think about a UPS or an Anheuser-Busch, they just want to get their goods to where they're supposed to get. So they have a commercial need that they need to meet, so that's their first constraint. The second is managing their new fuel costs, which is their utility bill. And so that's all about managing demand charges, managing time of use rates. And so there's a lot of value to extract from just those 2 things. But in the future, it's all about interoperability with the grid. And so we've heard a lot in the industry about vehicle to grid. And so those markets are early in terms of where they're at, but there will be a value in the future of grid interoperability between batteries in the vehicle and what the grid's needs are. So basically, there's an optimization that needs to happen from a constraint standpoint and then an economic standpoint for that end customer. And so that's -- and we're just -- with the software solution, we're just taking a piece of that value we're providing. So -- that savings we're providing. Now, on the grid side, it's fairly similar in nature, right? So grid has different constraints. So they're thinking about ensuring the health of their grid. And so again, there's savings from whether it's just putting hardware in the ground or managing wear and tear on the grid in terms of depreciation. So we're trying to avoid accelerated depreciation from overages or peaks. And then the third one is really avoiding really peak energy purchases for the utility. And so again, with our software, we optimize that equation for the grid operator. And our goal with the software is just to take a piece of that savings that, that grid operator sees. So again, we have modules for the grid operator, software modules for the grid operator and software modules for the end customer. They can be fully integrated or broken apart for each of those individual stakeholders. So hopefully, that helps answer the question.

Yes. Thank you, Mark. And this question about the business benefits for this are really, really top of mind for not only for this audience, but also for the utilities, as there is an urge to really learn about these economics. So stay tuned. We are working on more deliverables, marketing materials to really -- with partners and independent references to really help educate. So stay tuned for more material to come in this area. So great questions. And thank you, Mark.

Yes. Thank you, both. We have one last question. Are any utilities deploying this technology?

I think the answer is yes. So we have customers lined up, working on the deployment plans that will come up -- will start in the middle of the year. So we are working through the schedules at this point.

Yes. And you'll see some announcements come out in the coming time period around that as well.

Great. Well, Stefan, I'll let you close this out here.

Yes, no. I think thank you for your attention and your time today here. We are super excited about our mission and how we are taking part in making this a more resourceful world. I mean at the end, the purpose is still at the center of what we do. I think we are uniquely positioned to really help -- solve a lot of these challenges our utility customers are facing. And so thank you for your time and your support. I think there's a lot more resources that are available. Maybe Alison, you want to walk us through that and to close it out. Thank you.

Yes. So I'm sharing on my screen some additional resources that we have. We published a blog yesterday that gets into a little bit more detail about the solution set. And then we included links to this morning's press release as well as the Net2Grid partnership announcement from last week, and then links to a couple of the websites that have recently launched around this web pages. So we will be sharing the recording and the slides so that you can easily access all of this information. We're also available, if you have further questions, would like a more in-depth briefing, we're happy to follow up and schedule some one-on-one time with you as well. And I thank you for your time. I appreciate you being here and hearing from us on our news. And we look forward to connecting in May at DistribuTECH. Have a great day. Bye.

Thank you. Thank you, Alison.