Data Driven O&M Strategies with Wattch and Omnidian

Transcript

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If you're going to make a truck roll making sure that it is maximally valuable, the worst case is diagnostic truck rolls. If you have to roll a truck out to site to figure out what the problem is, then you're going back to fix it later. You probably don't have the right replacement part. It's about making sure that the right people come to site. Depends on what needs to be done. So by the time you roll a truck to site, you should know that the issue cannot be resolved remotely.

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We'll touch on that as well, and that you're bringing the right person in the right part the first time. And that's that's super that's super, super important.

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Are you speeding the energy transition here at the Clean Power Hour, our host, Tim Montague and John Weaver, bring you the best in solar, batteries and clean technologies every week. Want to go deeper into decarbonization. We do too.

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We're here to help you understand and command the commercial, residential and utility, solar, wind and storage industries. So let's get to it together. We can speed the energy transition.

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Welcome to supercharging solar O and M with data driven strategies for rapid growth. I'm Tim Montague, your host. I'm just thrilled to have Alex Nussey With Wattch and Trish Graf with Omnidian with us today as our expert panelists, and we're going to do a deep dive into how O and M is changing because of the advent of modern technologies, like the platform that Wattch has developed. So I look forward to learning more about that platform, and you can type your questions into the Q and A or the chat. I think the Q and A is preferred, if that works, do that, and then we will answer those, either on the fly or at the end, at, say, quarter of but we will get to your questions.

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And we really want to hear your questions. That's how you're going to get the most out of this webinar, is by asking us some questions with that, I will let Alex nussi introduce himself for the audience, please.

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Yeah, absolutely.

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Thank you, Tim. Super excited to be here, and thank you, Trish for joining. Really, really been looking forward to this for a while. My name is Alex Nussey.

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Mostly go by Nussey For those of you that have met me before, and I'm the co founder and CEO of Wattch. Wattch is a monitoring, intelligence and control platform for renewable energy, so solar, energy, storage, Ev, charging primarily, and we are we work very closely with O and M providers, both EPCs and dedicated owning providers to sort of help maintain the maximum performance on these assets. I guess that's my that's my background to doing this for about five years now, as much as I do love listening to myself talk, I'm going to reiterate what Tim said, please ask questions. Really want to make sure that we're sort of engaging with the audience. I think these things are always more fun when it's sort of a dialog. So thank you.

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Thank you. Alex and Trish Graff,

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hi Trish Graff, VP sales and strategic partnerships, and I'm nydian.

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Nydian is a technology forward, data driven operations and maintenance services company that provides performance assurance for our clients and partners on their PB assets and also their their battery storage assets. So thrilled to be here as well, and likewise, please bring the questions forward.

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Well, I'd love to give our audience a setting of the table.

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What is the state of the state of monitoring and analytics when it comes to distributed generation with with an emphasis on CNI and small utility solar.

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So Alex, why don't you set the table a little bit and then I also want to hear amidians Trish Graf's comments on where is the industry and what are some of the challenges that the industry is facing.

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That's a good question. I think that there's been so much focus for so long in the industry on the sort of leading edge of the growth curve. We're trying to get new assets on roofs and in the ground. And when I joined the industry, at least, it seemed like there wasn't as much attention on. In O and M, and at least especially not efficiency in O and M. But as these portfolios continue to grow, and importantly, start to age, it's really becoming a lot more important. And the kinds of problems that you could solve with sort of just man hours with a portfolio of five or 10 projects, it just doesn't hold up when you have a portfolio of hundreds of projects. I think you mentioned sort of CNI and small utility we would say like DG scale, distributed generation scale. I think that's certainly where wattch sees ourselves participating most heavily in the market. We do serve some utility scale customers, but the sort of core of our customers portfolios is in that large, commercial and industrial to small utility range. And I think that these are, I think the right way to put it is any site that is not large enough to warrant full time staff on site.

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If you're building a half gigawatt project, you probably have somebody there every hour that the sun is up. But that's not true with a distributed commercial portfolio, and that's where sort of leverage of Trish use, you know, the term data driven and technology enabled decision making. I think it's where it becomes. It's not just sort of the nice to have, it's required to do a good job at maintaining the sort of the high bar these assets. So I think our goal is to help the industry continue to scale without needing to scale headcount as quickly to maintain these, these growing portfolios, I could talk on that thread forever. And

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just for some context, we installed, I think, something like 35 gigawatts of solar in the US in 2023 and and Trish. What is the scale of of your business at Omnidian?

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You're one of the major O and M providers across the country in the DG space. But tell us a little bit about your portfolio and how you see the DG industry growing.

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Yeah, and perfect dovetail off all the pieces that Alex brought forward is we're seeing a growth in DG for a couple of reasons to bring back to your earlier point, Tim is, is, you know, the IPPS, EPCs owner operators are looking to grow their megawatts. And large scale. Utility scale projects have their own set of challenges and issues can take a longer time. So the shift to deploy for a utility scale owner, more DG has really been driven by the cycle can be quicker, right? We can get megawatts up faster with that. As Alex said, the challenge is, well, how do you resource? How 100 a 500 megawatt solar, utility scale solar, looks very different when you have 500 megawatts of DG. These projects are scattered nationwide. They're not large enough to warrant a full time resource at that asset. And so what we've done in in our offer and our focus is to support the fast growth scalability of large, scattered, disperse DG portfolios, so that anyone that's coming into the market can resource up quickly. You've got an asset in Hawaii or one in Puerto Rico with on Midian, but doesn't matter, we can support you immediately. So, and we're seeing that too, like with acquisitions, right? There's a lot of buying and selling happening in in our in our space, and for an owner operator, IPP, that's looking to acquire projects to help grow their portfolio, our goal then is to make that really simple for them, so they don't have to add headcount to manage all those projects.

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Yeah, so we've got this context of rapid growth and limited resources, human power, time, money, and we're trying to keep these assets performing optimally. We're trying to have an efficient machine to monitor and analyze the data coming out of the field. When do we need to roll a truck? When do we need to reboot an inverter? And it's just a big, hairy problem, and it's growing very fast, so when you guys think about these challenges that we've kind of put our finger on, and you, Alex and your and your company are developing this, this kind of next generation platform for monitoring and analytics that will lead to better O and M, more productive operations. Tell us a little bit about how you see that landscape and and where are you really leaning in to.

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You know, this, this piece of a data driven O and M, I.

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So I think that there's two sort of steps to maintaining this, this diverse portfolio like this, and the first is driven by the fact that not only are all of these assets sort of geographically physically distributed, they're also very heterogeneous. None of them are the same. They all, you know, especially when you have sort of a network of VPCs building for one owner, you're going to see different modules, different inverters to everything at every one of these projects. And as they age. For these smaller projects, you probably don't have a strategic reserve of replacements. You're probably buying new parts that don't actually match. So now you have multiple brands at one site five years in. So these every project is unique from every other project in the portfolio, and they're scattered around. So the first step is getting a sort of single pane of glass, a bird's eye view over the whole portfolio that makes it easy to draw conclusions and analyze information very easily. And that's sort of what we see as our first responsibility is get data from the field into the operator's hands in a way that is completely standardized signal. We call it our universal data model, but transform all the information as it's coming in, so that you can pivot on it as easily as possible. The second piece of that is finding sort of signal in the noise we capture for a good size commercial, like a large commercial industrial project, will capture something like a billion data points in a year from that from that site, and no human is going to go through and look at all of that data, right?

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So it's, it's that sort of where the machines step in to crunch that down and help you take the first step, tell you where to look with the end goal of helping you identify as closely as possible precisely what changes could be made to improve performance, or what has happened to cause a reduction in performance. Sort of, from there it comes the, you know, famously hard problem of the traveling salesman. How do you actually get to all these sites? Who goes? When do they go? And that's, that's not something that we winner is involved in.

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But in terms of, sort of, I think the intelligence piece is all about making it as easy as possible for operators to identify and size issues. Is this a $50 a day problem, or is this a $5,000 a day problem? You know, it's sort of localizing that and then making sure you know what you need to bring with you when you go out. Do you need your replacement part? Do you need this kind of personnel on what's, what's the issue before you roll the truck? I'll

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stop there for a second, yeah, and Trish, you know, I'm curious what Omnidian thinks about how assets are performing overall, and then what is the the value add, right when you onboard a more sophisticated platform like Wattch, yeah.

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So the first part of that is, you know, our job as the custodian. I mean, we we take care of our clients assets as if they're our own right. We are. We measure ourselves on performance. So, as Alex said, like the components to really identifying that is the data that's coming out and the analytics that sit on top to help really sort through all the components and the noise. Is this, it really a problem that needs to be addressed. Is this really? Is this affecting performance? Or is this something in when you're starting a project, you need to take into account for your true, expected energy, right? Where in the US is this project located?

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You need to account for weather and things like that. And then the second piece is for us, it's, you know, taking partnering and having a das provider, a data acquisition system such as Wattch, really makes our jobs easier. We act as an extension to our client's team, right so we're technical asset performance managers, and having the right data with the right frequency, with the right components built in really allows our platform, then we have our own proprietary platform that sits on top of the Wattch data to further enhance the components of what is the actionable item that needs to happen here? Does it need to happen? Does it justify, as Alex said, like if this is a $50 problem, but the cost to send a truck is going to be 1000 the math doesn't work. And so we take the information from from a provider such as watch, which has an amazing platform. Also we have performance engineers that look at it to help determine how much of the performance is this affecting, to really consult with the client on what is the best choice for this asset, and you know your OPEX or the portfolio, I think

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we like the word bionics here at Wattch. I think it's. Not, it's not totally machines. It's creating tools that help the right people who are who are actually doing the job coding leverage. But by likes is one of our like Wattch words here. So

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I remember in our in our pre show discussion, that I think of this in terms of dashboards and humans. And, you know, in a perfect world, the dashboard is greatly facilitating how humans then sort through the information coming in from the field and prioritize the information, and as much as possible, it's automating that for me and saying, Okay, here's the greens, here's the yellows, and here's the reds. The reds are needing immediate attention, and that's your priority, human in the in the DAS center, or the knock right? And then they're deploying assets, whether that's remotely or boots on the ground to fix the problem and get those reds to be greens. How do you guys see that? And how does Wattch facilitate that progression from bits and bytes coming from the field to a computer screen to a human army, human army. I like,

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like that. I think the sort of the separation where we at least draw our line of demarcation is we're really looking at the technical performance element. So if you have, you know, a $500 problem and two $200 problems, you know, or two $300 problems, but you can hit them both in one truck roll, you know, maybe it actually makes sense to do that one instead, right? It's, it's it's when you, when you identify and size the problems, and I'll talk more about that in a second. But that's, that's sort of where we start, and then it comes into the real world of humans and where they are today and and what they're doing.

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That's where, sort of managing the army is something that we, we work with fantastic folks like Omnidan. For us, we think it's getting that green, yellow, red, is our primary responsibility, and we kind of start with that. Trish used the word expected production, or expected energy. So a common term in the industry, that's where we've spent a ton of our engineering resources on, is on developing this model. We call it digital twin, but you can call it whatever you'd like. To tell you what a system should be doing at any at any moment. And while there's a top line there, you know, on a monthly basis, what should I be getting?

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There's also the drill down, which, in my opinion, is where the real utility starts to come from. Is it's not just at the site level, not just at the inverter level, but like getting down to every string on every mvpt of each inverter, you know, oh, is an RSD failed? Like, can we detect that? Because the voltage in the string has gone down, you know, is is this misconnected? Is there soiling, reducing the current? And we have a set of workflow tools within the product that help you sort of say, Hey, look at the top line. Your site's only producing 95% of what it should be. All of that is attributable to one of these inverters. And it's actually an issue on strings two and three. And then it's like, okay, go, point the right direction and tell you start here. That's sort of why that is, sort of humans come in.

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Was it a mistake that didn't get caught at commissioning? Was there a hailstorm recently? And that's there's a fine line with any dashboarding tool between Automated Insights and then still giving the operator access to the raw data. You really want those to live side by side, so that the operators know where to start, but then can jump in and look at as high fidelity information as they want to sort of help draw those conclusions.

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I don't know if that actually answered your question,

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but Trish, you're you know, Omnidian is inheriting portfolios at various stages of of their lifespan, from newly energized to have been around the block operating for some years, and then you're charged with improving performance, and you're actually guaranteeing performance at some level to your customers. But tell us a little bit. You've got some wonderful portfolios that are great examples of how big and hairy this problem is, you know, portfolios of 100 plus megawatts of DG projects that are spread over multiple states. And I don't, I don't, I don't cherish that responsibility. It's, it's a massive responsibility that means real, you know, real revenue potentially lost for your customers. Tell us a little bit about how omnidian tackles this, and why did you, you know, onboard Wattch, and what is the end result that a customer can expect when they work with omnidian. I. Um, with the benefit of Wattch underneath it, yep.

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So I'll take that in kind of chunks. You're exactly right, taking on large portfolios of as as Alex said, no two are alike. As far as you know, the components and the system type and size scattered all over different ages, different custodians, to Tim. So you may have a portion of the portfolio that was under great care and great Wattch, pun intended, and some that were not, and I think which starts with when we're aligning with our partners, our clients around you know we're our job is to, yes, ensure performance and increase performance. The first piece is aligning at, well, what? What do you expect this system to do now? And as Alex said, we're watching Omnidian are very closely aligned on building a digital twin. We absolutely need to make sure we see your system as it's truly been designed and built. So it starts with, there's opportunities to Tim still definitely in the marketplace for these older projects, to really kind of calibrate on what should it be doing? Good news is we get there. We have our performance verification tests that we do systems online for 90 days. Sometimes that can create a lot of heartburn with their clients, because they'll have had a number in mind about what this should be, should be doing, and 90 to 120, days later, we're getting that actual real data on this is what it is. So worst case scenario, our calibration point happens after that, performance verification, testing, and then our commitment, as you mentioned, also with our performance guarantee, is that we've aligned on a number, and we'll offer a 95% non weather adjusted performance guarantee. No one else does it, very different than an availability guarantee.

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So we're focused that that inherently drives our activity set, and our focus on ensuring performance right system can be available at 10 o'clock at night. It doesn't matter. It can't produce anything. So all of these, these components, lead to setting up Omnidian As we are fully driven, and we've got skin in the game now with the performance guarantee that we're going to work with you to ensure we're taking the right actions for the right the right act problems that are really affecting performance. To go back to what Alex said earlier, there can be faults and alarms and and if you have somebody internally on the client side that's watching these they're probably not trained like a performance engineer would be to be to to understand what is this really affecting. We see a lot that without that true, the right data with the right analytics. And then, as you said, Tim, the human who's then assessing all of that to determine, does this justify going out to make, to make a change of repair, a fix you could be sending trucks every time a beat goes off and you are upside down pretty quickly. And so for that with Wattch, what has been great for our partnerships with our shared clients, is that we know, from a data acquisition standpoint, how Wattch is, how they're grabbing the data, the different time stamps that they're grabbing it from, which components they're looking at. And as Alex said, string level information, all of that. Those details help us do better on the performance end, on making the right decisions for the asset.

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So there's, there's three big buckets that we're talking about here, efficient resource allocation and prioritization, remote issue detection and diagnosis and minimizing false alarms and missed problems. Alex, tell us about those buckets, and if you can give a real world example, you know that would be, that would be amazing. But how does, how does watch see this world, and how does it come into play in the real world?

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Yeah, yeah, that's definitely can talk about that.

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I think the three buckets you mentioned, I will let Trish mostly speak to number one, efficient resource allocation and prioritization. And I'll actually, I want to zoom out for a second. We didn't mention this up at the front, but I think that bucket to their remote issue detection and diagnosis is something where Omnidian and Wattch overlap. Omnidian obviously works with a lot of das providers. Wattch obviously works with a lot of on. Folks, I think we both really enjoy working with each other, because we have this sort of shared ethos that makes it very high leverage when we when we have these shared clients, particularly high leverage so that that second bucket, remote issue detection and diagnostics is is something that we sort of have, especially to back after that the diagnosis we have some some overlap in I will touch on a couple of real world examples here. I think we see issues come up in sites two times. They're sort of in the ongoing operation, which is what everyone thinks about, the new problems that emerge. And then there's commissioning, making sure that Trish used the phrase as built, making sure that you're as built is actually what was as built, especially in commercial getting a authoritative representation of what was installed on site and how it was installed can sometimes be a painful process, but it's essential in order to right size expectations with the long term owner. So I'll start a little bit couple of real world examples. And if you don't mind sharing that those slides I sent you, I'd show a couple of different items from that.

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Sure. Slide One is on screen now, cool, yeah, so if you don't

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mind jumping to the third slide there, okay, it's my little face, actually, yeah. So this is just one of the ways as an example of a sort of scorecard that Wattch provides for an inverter. So we look at its at the very top level. We have the overall health score, as we call it, that actual versus expected, or actual versus ideal energy. So this inverter is very healthy, and we sort of analyze an inverter in three stages, mostly by looking at the DC inputs from that photovoltaic array. So the first is, and I actually realize you can't see my mouse, but I'm looking at this first table here, the sort of ideal versus measured voltage. And this is a great example of sort of automating steps for human users to have help give them the most leverage possible. So that measured voltage column is an average of the input voltage on each of the strings for that inverter over the course of a day, and then that ideal voltage is what our digital twin says the voltage should be. This takes into account a number of calculations. We're looking at the irradiance at the pyranometer. We're transposing that onto the array. We use the IV curve of the module and its pan file, and we essentially simulate what its voltage should be. And then when we compare those, we can see that they very roughly match. Now if you go down one slide, you'll see, here's an example of another inverter where they don't match.

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In this case, MPPT is four and five. Looks like we basically swapped those two strings. And in this case, that actually has a pretty minimal you can see 96% health score in the top minimal impact. But depending on how those are switched and how many you've put on, each string, can have a material impact on the overall site performance. And this is, this is something that within, you know, 10 or 20 minutes of turning the site on, you can come look at this and make sure that you have strung the project correctly. This is something that we really encourage. Oh, really encourage owners to use as part of acceptance when they're sort of taking over the project. And this is just one of those kind of analytical workflows that I mentioned. We take the raw data in and that's that measured voltage, we augment it with simulations from our digital twin, and we give a sort of rough sized conclusion to the to the operator using the product.

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We have that final column there length mismatch is basically how far off we think the strings are and from being wired correctly.

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Now the owner can then ask to make a decision on whether or not it is worth fixing, and that's sort of the next step, but it is our goal to put these conclusions within five minutes.

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Don't need a lot of data to run these analytics. We can sort of in a very automated fashion, tell you whether what got installed actually matches the as built drawings that you received, and from here, you fix enough small problems, even at one or 2% so inverters performing at 96% it adds up and across the board, on average, you you can get some pretty material gains. We see for our customer base, for commercial and DG scales about 8% gain for people that start using these, these tools to commission, and then, in an ongoing fashion, maintain their, maintain their sites. Um, let

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me, let me ask you a couple of questions here. So when, if you're, if you're looking at a site, you're, you're, you're sitting in a knock and you've got some flags on sites, and then you're, you.

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Drilling into the site and looking at a more granular level, you I immediately am concerned that the number of of assets involved is is very large, and so is, is Wattch though filtering through this information and helping the human at the knock level know where to drill down. Or is it simply just flagging sites, and then it's up to the human to determine priorities? Yeah,

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so and I'll actually dovetail. I think I can see somebody put in the chat here. I was looking at the questions that I see in the chat, faults versus alerts. Um, so I think we, we think there's kind of two stages to performance management. The one that's hardest is when you're bringing the sites on for the first time. It's that like right sizing of expectations. I'll talk about that in a second. In the steady state you should really be push more than pull the product. Our product will tell you when there's an issue.

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We if you have everything performing well, then we're constantly watching every 10 seconds as data flows in from each site. We're looking at that data and reassessing whether or not it's still healthy, and if it is not, we can create an alert, a notification, for the user, and that says, hey, maybe go take a look at this in terms of prioritization, if we have the financial implication of performance at that site set up, we can actually rank those basically by estimated daily or monthly cost. So how much is how much is this in dollars, going to impact the customer, whether or not you make a resolution step comes down to how much it's going to cost you, and that's where, where it is, where my trucks are, where my boots are.

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And that's sort of outside of the scope of our product, but the cost that it is causing is something. So we go from healthy asset enters an unhealthy state, create an alert, so that the user sends an email, sends a text message, whatever it is, so that the user knows to to go check on it. And that's when we enter one of these workflows where it's like, okay, there's underperformance. We know how much it costs. Let's narrow in on why and But that's after sort of we've already created this notification and and rough prioritization for the for the user. The more painful side is when you onboard a new portfolio, you bring in 50 sites, 100 sites, and lot of time in Wattch. When we do that with especially in CNI, it's all red, it's all F's and D's, and it's that conversation of, you know, some of these are older assets. They have a lot of sub critical issues that you know, anyone can catch when an inverter fails. But if you have an nppt that's coming in and out. Most inverters, most monitoring products, won't flag that kind of problem. But all of that adds up, and across the board, you can have a pretty unhealthy portfolio. And then it's the conversation of, you know, these are 10 year old assets. Does it make sense to go spend the money to bring these?

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They're at 70% you know, it might be two thirds of the you know, you might be able to get to 95 in a third of the cost, and it's another two thirds to get to 100 is that worth it? How do I want to set my expectation?

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And that's, that's the most time consuming part, I think, in and setting, you know, expectation setting little sensitive.

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The 252 275 pairs. Well, with CPS America's exceptional data communication controls and energy storage solutions, go to chintpowersystems.com to find out more. Heller asks a good question, what part do diagnostics, faults and alerts play in the O and M data collection, and which are the best. And I guess I'm curious how Trish, how Omnidian, thinks about these things. Because you're, you're really on the receiving end of this.

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Yeah. So it's also too, it's, it's the nomenclature, right? So it's, it's, to some of our clients, an alert and a fault can be, like the same thing and that and lies where we step in and Wattch steps in is depending on how you you have your your gas set up.

00:34:25.672 --> 00:35:26.204
You, you could get 1000 alerts a day, and no, no one human is has correct, and a lot of it is, I don't want to say noise, but most often noise. And so it's the ability to have the analytics, or the AI and the ML that sit in there to really sift through, and as Alex said, having that front end financial information to also really purse through what pieces are matter to the to the ROI right, and analyzing the root cause of the issues and. Having the right data and the right analytics and the right performance people and team to look at it is really, to me, the the cherry on top, so to speak, so that you can determine, all right, we're going to filter out these temporary conditions. Shading is a good is a good example? Oh, it's underperforming, is it? Oh, it went back up. It went down.

00:35:21.820 --> 00:36:29.989
It went up. Why? Well, the one, there's nothing to do about shading, other than if it's depending on why, right? Is it another building coming into play or something, or are they alerts that don't require intervention? So these systems can be very finicky and very noisy. And what the Wattch platform does, and then what the Omnidium platform does is, again, is get rid of the things that don't require to send someone out. What can we do remotely to potentially? That's always our first step is, what can we do remotely to address the situation. Our goal always is to reduce, minimize, eliminate the OPEX costs of of sending a truck out there. And we've, we've, we've proven it on, you know, 500 megawatt portfolio. Tim, an interesting statistic is that the industry benchmark for truck rolls per megawatt is, is six truck rolls per megawatt, your numbers, your your business case is, is going to be upside down pretty quickly, over

00:36:29.989 --> 00:36:34.280
what, over what period of time when you say six truck, six truck rolls per megawatt,

00:36:35.059 --> 00:37:47.646
like, think the data that We used was 12 to 48 months, okay? But we then even truncated it to let's look at just 12 months. If the industry benchmark is for the five year life of an asset is six truck rolls per megawatt. What? What is Omnidians participation in reducing that? And let's even just say, the first year, it's under our our custodianship, and we were able to reduce that by over two thirds. And that's because, well, this is our wheelhouse, right? So, so with the right data and the right analytics and the right performance team looking at it, it is, don't send a truck every time. Send it only when it's going to it when it's necessary to really make, as Alex said, an impact on the financials. And I think we're going to get there later on the conversation. But Alex knows this. Tim, you probably know, I'm sure you know this as well. That number has to come down. It can't be six truck rolls a megawatt. There's no way the financials on these assets are going to work out long term.

00:37:43.074 --> 00:38:11.210
And there's a labor shortage. We haven't even touched on that yet. From a technician standpoint, there's a labor shortage on the qualified expertise that is required to go and do these repairs. Like I said, I said, I think we'll get there a little bit later, but there's lots you can do early on in a project to avoid all of that activity and going to the site. Yeah,

00:38:11.210 --> 00:38:19.309
I guess it really boils down to two things, improving performance and reducing the cost of O and M.

00:38:19.639 --> 00:38:47.150
Those two levers are going to push asset ownership in the right direction, right? This is good for the bottom line of the asset owner. So let's, let's drill down though a little little bit more about let's assume, okay, for a second, that you have good construction that the project got off on the right foot. That's not always the case. Certainly I could live in that world.

00:38:47.840 --> 00:38:48.320
Yes,

00:38:50.960 --> 00:39:26.179
and maybe you can comment on, you know, what percent of projects get off on the right foot, but then there's going to be degradation over time. Things happen, connections come loose, equipment breaks, shading happens. Maybe there's not proper vegetation management. Maybe there's a dust storm and there needs to be eyes on getting a cleaning done. But what does Wattch allow Omnidian to do that moves the ball down the field. I

00:39:26.179 --> 00:39:56.000
think, I think it's about, uh, how should I say, if you're going to make a truck roll, making sure that it is maximally valuable, if you the worst case is diagnostic truck rolls, if you have to roll a truck out the site to figure out what the problem is then you're going back to fix it later, you probably don't have the right replacement part. It's about making sure that the right people come to site depends on what needs to be done. So by the time you roll a truck to site, you should know that the issue cannot be resolved remotely.

00:39:56.059 --> 00:40:07.309
I'll touch on that as well, and that you're bringing the right person and the right part the first. Time, and that's, that's super that's super, super important, and in terms of remote capabilities, I think that is getting better Wattch.

00:40:08.210 --> 00:40:23.659
We call ourselves a hybrid SCADA platform over a das platform, because we have our like tagline is, you know, bringing the sophistication of utility scale SCADA to an ease of use and price point that works for commercial and DG scale solar.

00:40:23.719 --> 00:41:45.650
So we want to bring as many of those remote control capabilities that exist in the high end projects down to these lower end projects as well. So whenever possible, if you can complete a task remotely, let's do that before you go out to site. Whether that's clearing a fault, resetting an inverter, re closing a main breaker, reconfiguring an inverter, if you know you you install the new one and forgot to change a setting. There's a lot that can be done remotely these days. IV testing is a new one. We now have. It's like two of the major brands support remote IV testing, and we support that in Wattch. So instead of sending somebody out for a day to IV test each string, let's just click and grab that data from every inverter while I'm sitting in my nice, air conditioned office. And if I figure out that some of my RSDs are broken or my combiner boxes, we can go ahead and get that new that new part ordered before you make the truck roll. So I think bringing it back together step one, do everything we can remotely full control over the project. Wattch enables control for all the assets that support it, and that list is improving. And then the second is authoritatively identifying the problem, so that you don't have to make nuisance truck rolls. You don't have to make diagnostic truck rolls. You can just go out and actually fix the problem, walk directly to the right place on site, and, you know, make the change. But Tim, did that answer? Your answer your question?

00:41:46.550 --> 00:41:49.967
Yeah, that's good.

00:41:46.550 --> 00:42:00.920
So we have a question from the audience, what is the market standard for performance guarantee, and do you see market? See the market catching up to an Omnidians 95% can it go any higher?

00:42:01.639 --> 00:42:05.809
Oh, great question.

00:42:01.639 --> 00:43:16.309
And I'll, I want to put a piece out there, just for some put some context is, is I have a utility scale wind background, and so what I see are lessons learned, same thing in wind, where these, these projects, lots of different parts, no two, you know, lots of different OEMs and different standards, and how are we taking care of them? What data is important? So lessons learned, even though completely different technology, much different scale and size. I want to preface kind of how I'm going to put put forward these next components with that. So we saw in that older industry, which still not old, still growing. It started with availability guarantees, right? I will guarantee we see that in solar as well. I will guarantee your asset is available. 95% sounds really compelling, right? Except for it doesn't matter much in wind, if that asset is available when it's low wind season. Same with solar. It doesn't matter if that asset's available when it's 10pm 9pm 8pm at night. So there we've seen a shift with there are performance guarantees.

00:43:13.730 --> 00:43:58.280
We're not the only one that does a performance guarantee. We are the only one that takes the weather equation out, and I'll say that, I'll package it shortly, is we assume the weather risk. We have over 2 billion data points. You know, think of it as an insurance actuarial. We are had we're managing our risk on how is weather going to impact the performance of that system? We have enough data to support well, we'll take that on. So there might be another provider out there confirming they'll do a 95% performance guarantee challenges. What are the carve outs? Right? What's excluded?

00:43:52.909 --> 00:44:59.000
Not just weather. What is excluded? To you know what we we really are trying to change the industry to focus on the right behaviors to get these assets performing. And so we say we'll take on that weather risk, soiling, shading snow, like we're accounting for all of that. We're going to confirm you're going to get 95% non weather adjusted, and we'll work towards that together. If not, we we have a financial reimbursement. We have to pay back per kilowatt. How much did you Did we miss? We owe you money. To circle that back, I think the industry is, I think is still maturing, as far as guarantees go. But again, if I take my lessons learned from from wind, it's it's just shifting and it's moving and it Tim it all starts with, if you want to guarantee performance, you have to work up the chain.

00:44:59.750 --> 00:45:22.579
The. Right parts, the right construction, the right as builts, the right digital twin, like everything has to start upstream, and that's ultimately our goal, is to focus on, how do we participate in making this really a renewable energy source that can be really financially attractive.

00:45:23.480 --> 00:45:58.039
So I love this question, what, what kinds of things can you actually see and fix remotely? And one of the contexts here is, I think it was Trish's comment that, you know, there aren't enough technicians in the world to service our now growing portfolio of solar assets. So this remote monitoring and diagnosis is extremely important, but what kinds of problems can we effectively identify remotely?

00:45:58.789 --> 00:46:01.849
And either one or both of you can answer that?

00:46:02.119 --> 00:46:19.130
Yeah, I'll take the first pass. I tried to dovetail that question a little bit in my last answer, but I think just to rattle off, it changes a bit by component. For inverters, they're getting better, but just resetting them still clears a lot of issues with with modern string inverters like that.

00:46:19.159 --> 00:46:46.219
Shouldn't have to happen, but it does so. Just being able to reset an inverter can clear a lot of a lot of issues. So depending on your sort of, generally in the industry, we hear das to mean renal read only, and SCADA to mean sort of read write to the site. If you have that SCADA capability, you can reset inverters. You can, you say like main breakers and reclosers, most sites at that size are going to have SCADA.

00:46:46.219 --> 00:47:08.510
Might be very expensive, but you can reset that depending on the allowed operating parameters from the utility. Same goes for arc faults on inverters, reset those, and then where things get really interesting is running sort of mutative diagnostic. So an IV curve test is a great example. You can't gain that information from an inverter while it's operating normally.

00:47:08.510 --> 00:48:18.980
You have to put it in a special operating mode, have it sweep over the input voltage range and capture that data. And that's a task that normally would need to be completed on site. You de energize the inverter, plug in a piece of test equipment, but if you can control that inverter remotely. You can just coach it through running that same test, all remotely, and that helps you have one additional piece of information before you go out to site. So the answer is, you know, depending on the if something fails, it can't be resolved remotely, obviously, but any problem that can be resolved without a physical action on site. We let's, let's do that. And then if we do have to go in and change something physically on site, then let's know exactly what it is by, you know, introspecting, tweaking as much as possible before you go out there. Another good example is like waveform captures, if you have a power quality meter, a lot of these revenue grade meters these days are actually very smart. You can poke at them and say, Hey, capture me a waveform so I can see exactly what's going on in sight. Pull that remotely one less step to do in person. So I think, I think that's the sort of the high end is solve some problems and diagnose almost all others.

00:48:15.619 --> 00:48:23.090
Really want to know what's wrong before you out there. Trish, do you have anything you want to add on that? No,

00:48:23.090 --> 00:49:15.110
it's kind of what I'll put forward is really a culmination of the conversation we've already had. Is it starts upstream. First of all, like, are you getting? We could talk about, we remote diagnose 75% of the problems. Well, Tim, and the reason is, is because we've gotten they're not really problems. You have to sift through that first layer first, right? So that's the opportunity where a Wattch comes in and partner with dominion, with our AI and ML is to those six truck rolls per megawatt are because the intelligence, the data, the analytics, the performance engineer isn't those eyes aren't on that asset. You're set. Every time you see something, you're sending it. As Alex said, components are continuously improving. Things you know, are getting better. We can see more.

00:49:15.679 --> 00:49:39.349
That's the piece is, you know, not operating in the dark and then understanding, well, I think it's for me, and I think it aligns with with Alex as well. It's less about remote diagnostics, I think again, as nomenclature, and it's more about, well, which problems really warrant a truck to roll?

00:49:40.219 --> 00:49:59.719
Yeah, it doesn't, I think we're moving away from, I think historically, there are two types of projects. There are projects that got a truck roll for every single fault code that any inverter through, and those were all underwater, and there were the projects that got no O and M whatsoever. And you know, those may have actually been more profitable, even though they were finding out their issues three months later. So.

00:49:59.989 --> 00:50:16.849
Yeah, and the right answer is, like, somewhere in the middle to just, we're using the words fault and alert. When, when Wattch. We say fault. We mean a piece of hardware, usually an inverter, but, but also any kind of device, sort of self reporting an issue, fan, fault, arc, fault, things like that.

00:50:17.300 --> 00:50:36.320
For a long time, that was how people looked at the health of a site. Is by tracking down faults. A lot of tools create an alert, which is a notification for every fault That's crazy. A lot of these sites generate 1000s of faults a month, weaving of it sort of completely backwards, which is, let's actually analyze the performance from a first principles perspective. And this is something we share with Omni.

00:50:36.320 --> 00:50:39.829
And then we can go look at fault codes to help understand why.

00:50:40.010 --> 00:50:53.960
But if you know you have a fault code that isn't going to you know, there's also this idea of, like preventative maintenance, if, if a fault code indicates that something is going to die, that's also important to know.

00:50:49.280 --> 00:51:03.260
But we should not be spending human time, human capital, on every, every single little fault code. It's it's about creating the right alerting rule set so that you only get alerts for high signal events.

00:51:04.219 --> 00:51:18.500
So as asset owners and developers are adding more storage into their solar projects, either initially or retrofitting in storage. Does that change the game at all?

00:51:18.500 --> 00:51:26.179
From a data collection point of view and or the the way that data is collected from these sites.

00:51:26.780 --> 00:51:30.230
Yeah, batteries.

00:51:26.780 --> 00:51:37.309
Love batteries. I will, I will throw one from ninium First, which is that this, this idea of availability versus performance.

00:51:33.260 --> 00:52:12.469
It gets much worse with batteries, because it's, it's a battery can be available for 95% of the year and only make 5% of the revenue. Like revenue, like some of these batteries, the leverage on that peak demand event. Or, you know, you know, if you miss one sort of demand spike for a behind the meter battery, you can be underwater for the whole month of the whole year. So this, this like separation between availability and actual sort of financial performance just gets wider. So you have to, you have to take the same lessons and then do even better in the storage world. Would you agree with that? Trish, my characterization there 1,000%

00:52:12.679 --> 00:52:32.210
Yeah, that if that battery isn't performing when you need it on, on, whether you're, you know, following a curve demand or whatnot, the entire project is is upside down. Yes, you need it to be available performing. You need to be performing ready to roll when those demand spikes go up.

00:52:32.360 --> 00:52:41.284
So, yeah, I think storage from a we talk about solar plus storage there, they're getting married together. But the when we talk about the health of a battery.

00:52:41.340 --> 00:53:21.900
It's very different than the health of a of a solar farm. In some senses, it's actually easier to measure if you charge the thing up all the way and empty it and it charges and discharges at the right rate to the right total capacity. That's a pretty good assessment of health. The weather doesn't really play into it in terms of, sort of the electrical health of the system. What makes it so difficult in solar is divorcing that the weather piece and understanding sort of the weather adjusted. Has it just been a cloudy month, or is my do I have problems? I think to answer your actual question about data collection, yes, I think right now, most solar monitoring tools will pull in a data point or two from storage.

00:53:21.956 --> 00:54:05.340
They'll give you a meter or they'll give you a top level state of charge, but they're just not designed for a truly sort of multi asset to be a multi asset solution. Wattch had the advantage of being a relatively new business in the monitoring space. And we, from day one, said, this is a multi asset product, and sort of batteries have been baked into our data model from from the very beginning. And being able to pull there's also way more data from an average energy storage system than there is from a solar system. So being ready to capture all of that and present it in a unified and intelligent way, incredibly important. And it's something that I think is going to be a big wake up call in the industry as people start wanting higher storage attachment rates.

00:54:05.396 --> 00:54:46.972
Residential does not have this problem, whatever it's worth, because it's all these like walled garden ecosystems, but the larger IPPs won't want to be trapped in any one OEMs sort of software ecosystem, so they'll have to be open data standards, and there are, but getting all that information into one place today is a huge gap, and I think it's something that we're able to solve well for our customers, and the demand for that is is really, really only increasing, and it's one of the things I like talking to the Omnidian folks the most about is like, where do you see this? Because think solar is still growing up compared to wind. I mean, storage, I feel like, is so nascent in terms of, terms of these, these kinds of questions.

00:54:47.028 --> 00:54:55.219
So it's exciting to work with other people that are thinking hard about what the future one year, five years, 10 years out is going to look like.

00:54:55.670 --> 00:55:09.297
We've got one more question from the audience that we should get to, and we're down to the wire. Here, but so thank you all for being here. Really appreciate your participation.

00:55:09.373 --> 00:55:23.539
How do asset owners measure the costs and benefits of third party O and M? How do they make that decision whether or not to hire an Omnidian or to conduct all that activity themselves,

00:55:24.079 --> 00:56:26.750
to me are, it's, it's, it's not a complex formula. It really depends we have, we Alex knows, and I know we know several IPPs that self perform. But what, what makes that business case work is they have clusters of DG portfolios near their utility scale portfolios. So they've been really strategic. If they're going to self perform, they've been really strategic about the geography. We can use our team that's at this 150 megawatt site to go also service these other 50 megawatts within 100 mile radius. A lot of it comes down to, I mean, it's head count. So one is the technicians, depending on your portfolio. If, to me, it's pretty simple, it's going to should be geography driven, because you, as we talked about very early on, if you have an in house team of, say, 1020, even 30 technicians.

00:56:22.039 --> 00:57:21.530
Are you going to set up regional hubs? You could, you might, right. You might have a regional hub in the, you know, northeast, a regional hub in the in the southwest, to take care of those assets. What we're seeing with distributed generation, what we have seen, what we continue to see, is scattered portfolios. It typically doesn't pencil out to have your your technicians in house. You may have technical asset managers in house, and they are, they are true partners of ours as well that you you have folks in in house that are responsible for the performance of those assets. But a very I might be over, overshooting here, but the only time I have seen a an asset owner self perform is when they have had a large enough portfolio in a very condensed region to justify having their own technicians.

00:57:22.010 --> 00:57:50.510
I mean, this is a little outside of our direct scope, but I completely agree with everything Trish said. I think it's an opportunity cost of where you want to develop, like I think it's, it's, you know, maybe self performing. You can make a case that it's less expensive, but you are trading this opportunity cost of sort of being constrained geographically. And I think different business strategies, and we'll see in the long run, I think they'll probably be successful people doing both.

00:57:47.030 --> 00:57:50.989
There are, we are, well, hopefully

00:57:50.989 --> 00:58:01.699
we've left, we've left our audience with with some additional questions, and I would like the panelists to please let our audience know.

00:57:58.190 --> 00:58:07.070
How can the audience and listeners to this recording find you online or in the real world?

00:58:07.760 --> 00:58:08.929
Trish you want to go first

00:58:09.170 --> 00:58:23.300
sure Trish Graf at Omnidian, and my email is T Graf, G, R, A, f@omnidian.com probably leave it somewhere in the chat, so I will put that in there. Just Nussey was quicker to it.

00:58:24.050 --> 00:58:25.579
I just dropped you in there. Did I get that right?

00:58:26.630 --> 00:59:04.460
That's my email address. I will make the same plug I did at the end of podcast, Tim, which is that, you know, we say we're connecting tomorrow's grid. I also love connecting with the people that make tomorrow's grids work. We are a very sort of feedback driven product organization. If you have ideas, thoughts, criticism, whatever it is, I would love to hear it and have a conversation with you about it. I think everyone talking about how to build a better renewables industry is the way that we move the ball forward. So albeit, SPI, come find me or sorry. Re plus, and that's my email. Shoot us. Reach out. Love to have the conversation and hear what you're thinking about the space.

00:59:04.760 --> 00:59:09.800
Likewise, the entire Omnidian team will be at re plus, please look out and find us

00:59:10.519 --> 00:59:37.190
great. And you can check out all of our content at Cleanpowerhour.com Give us a rating and a review on Apple or Spotify, and reach out to me on LinkedIn. I love hearing from my listeners. Or you can contact me via the website. I want to thank Trish Graf with Omnidian and Alex Nussey With Wattch for joining us for this webinar today. I'm Tim Montague, let's grow solar and storage. Take care, everybody. Thanks, Tim.

00:59:37.369 --> 00:59:38.570
Cheers. You.

Data Driven O&M Strategies with Wattch and Omnidian | EP232

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