Aug. 1, 2023

Improving Solar Asset Performance: VDE, Greenbacker & Denowatts | Ep 153

Show Notes
Transcript

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Today on the Clean Power Hour: Best Practices for Improving Solar Asset Performance. In this episode, Tim Montague is joined by Matt Murphy, COO of Greenbacker Capital, Brian Grenko, VP at VDE Americas, and Dan Leary, Founder & CEO of Denowatts.

Brian Grenko focuses on identifying and mitigating technical risks for solar PV and energy storage projects. Matt Murphy excels in optimizing operations, building strong relationships, and driving efficiency. Dan Leary is the CEO and Founder of Denowatts. Denowatts developed the innovative Deno Digital Twin Benchmarking (DTB) technology.

In this episode, our esteemed panel of guests will share their insights on setting realistic performance expectations for solar assets. We'll uncover the key factors that put solar project performance at risk and how to optimize performance while considering real-world conditions.

Furthermore, the panel explores the common challenges faced by solar asset owners and operators in identifying the root causes of underperformance. Our experts will provide valuable tips on troubleshooting and addressing issues promptly.

One of the highlights of this episode is data analytics and its pivotal role in improving solar asset performance. Our guests will explain how data-driven strategies can provide critical intelligence on system health, predictive maintenance, and performance optimization.

KeyTakeaways

The difference between fair market value models and operational models for solar assets
The need for conservative solar resource assessments that account for climate change
Leveraging data analytics and benchmarking to quickly identify and resolve performance issues
The extent and causes of solar underperformance in the market
Operational best practices like regular maintenance and equipment upgrades

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The Clean Power Hour is brought to you by CPS America, maker of North America’s number one 3-phase string inverter, with over 6GW shipped in the US. With a focus on commercial and utility-scale solar and energy storage, the company partners with customers to provide unparalleled performance and service. The CPS America product lineup includes 3-phase string inverters from 25kW to 275kW, exceptional data communication and controls, and energy storage solutions designed for seamless integration with CPS America systems. Learn more at www.chintpowersystems.com

The Clean Power Hour is produced by the Clean Power Consulting Group and created by Tim Montague. Please subscribe on your favorite audio platform and on Youtube: bit.ly/cph-sub | www.CleanPowerHour.com | contact us by email: CleanPowerHour@gmail.com | Speeding the energy transition!

WEBVTT

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I'm Tim Montague, your host with the Clean Power Hour. Welcome to the webinar and panel discussion best practices for improving solar asset performance. We're thrilled to be joined by such an illustrious panel today we have Brian Grenko, who is vice president of VDE. America's a global engineering firm, where he focuses on identifying and mitigating technical risk for solar PV and energy storage projects. We're also joined by Matt Murphy. He is the CEO of green backer capital, a renewable energy IPP with over 1.8 gigawatts of projects under management and another two gigawatts under development. And finally by Dan Leary, he is the CEO and founder of Denowatts, a pioneering solar performance testing and analytics company.

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Are you speeding the energy transition? Here at the Clean Power Hour, our hosts Tim Montague and John Weaver bring you the best in solar batteries and clean technologies every week? I want to go deeper into decarbonisation. We do two, 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.

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Together, we can speed the energy transition.

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Again, I'm Tim Montague with Clean Power Hour, you know, global solar installations in 2023 will likely exceed 350 gigawatts, according to the NEF. And we're so we're closing in on 400 gigawatts a year in the in the world. Just two years ago, that figure was 141 gigawatts, so we're more than doubling in two years. China alone is expected to instal over 100 gigawatts in 2023. Here in the US, we're installing on the order of 25 gigawatts a year now. And we have an instal base of over 150 gigawatts now in 2023. That's enough power to power 26 million homes. Meanwhile, solar asset performance has been declining year over year since the 2010s kWh analytics estimated that solar assets underperformed by 8% on average last year. And that figure has held steady in the five to 9% range for the past decade. So if you're an asset manager or performance engineer, this panel is going to bring you a tremendous amount of value. And with that, I will turn it over to our panellists to make some brief introductory comments. Why don't we start with you, Brian Grenko.

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Yeah, sure. Hey, everybody, thanks for making the time to join this webinar. I'm very pleased to be here. As Tim mentioned, I'm with VDE America's most people know, VDE is a global organisation that's focused on developing product testing standards and testing products against those standards. We also provide technical advisory solutions to multiple industries, including the renewable energy industry.

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We've been here in the US for over 10 years now, celebrating our 10th year anniversary, actually this year. And we focus, especially on solar and energy storage projects. I'm very excited to be here. I'm looking forward to talking about this important subject with this crew that we have here. And I guess I'll just turn it over to my colleague, Matt Green backer.

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Ah, yeah, just echo Brian. Thank you, everyone, for having me. It's a pleasure.

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Pleasure to be here. I did not get the memo that this was a black shirt uniform webinars. So I guess we'll bring the colour for the group. This topic has been sort of central to my life for 12 years. I've watched this industry, you know, and we've seen how models as they got more aggressive. We all saw underperformance in people's fleets as we were learning about how to operate these assets in in the early years, the 2000, eights in the 2000s 10s. And my whole the whole one of the big, big reasons I came to greenback er was just that we believe collectively from our investment side, as well as my perspective that there was a lot of money left on the table in these assets. And that data and smart operation and having operational resources in house was was the key to being a better business.

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So really excited to talk about it today.

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Thank you bet. And last but not least, Dan Leary with Denowatts.

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Thank you, Tim. And Brian and Matt, for for joining.

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On this, this panel today. My company is Denowatts. We set out to deliver better data to the industry. You know how we all have this problem of just a firehose of data coming at us every every second of every day.

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And yet when I feel like we're not able to capture the insight from that data and make better decisions. So my company set out to build a technology platform to deliver that insight. And I think I'm in my 17th year of solar, and I'm learning something new every day about the data and from the data that I think would benefit all so that's what Denowatts is all about. helping folks with better data.

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The Clean Power Hour is brought to you by Denowatts. If you're a solar PV asset manager or performance engineer, you need better data and better business intelligence. With Denowatts digital twin benchmarking technology, you get more accurate, efficient, and faster performance measurement results.

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The fourth generation Deno recently completed a technical review by DNV. You can download the report at denowatts.com, that's D E N O W A T T S.com.

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Now back to the show.

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Excellent. Well, let's dive right in. Our first question for the panel is how to set realistic performance expectations throughout the lifecycle, including project closing, and ongoing. And I think we're going to start with Brian on this one, if you would, Brian. And you could talk about performance in general and how we measure performance of solar assets.

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Yeah, thanks, Stan. Well, I think just kind of also piggybacking off the last question, in your comments about general underperformance in the industry. I've been thinking here about underperformance and how that's measured work performance, just in general, how that's measured. It's really about understanding and measuring your actual performance over your expectations. And so when we think about why systems might underperform, you fundamentally should just look at the numerator and denominator. So in other words, like, really did you model the performance accurately. And what we're seeing over the last couple of years are a number of interesting, interesting trends where, you know, go back, even five, certainly 10 years ago, we were building utility scale projects, typically in the desert, Southwest and big, square spaces on flat ground with a similar design. And there were only really a couple of trackers that could kind of scale at that point in time, for instance. And we've seen a lot of interesting technology trends that have enabled the development of solar projects throughout the entire country.

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And so just one of the things that we focus on is catastrophic risk assessment, we have developed some interesting technology around hail risk identification and mitigation that like that's a good example where, you know, in the last five years, we've now deployed solar all over places like Texas, where you have to inch hail events several times a year. And we're also building projects on on very hilly terrain. We're coupling solar energy projects with energy storage systems, both AC coupled dc coupled, when it's the ladder, we're often seeing very, extremely high and Burgard loading ratios. And we're seeing, you know, some people certain certainly take advantage of the ITC credit scheme that's in place. And all of this is kind of coalescing to create a situation where it's, it's becoming more and more difficult to accurately predict how some of these systems will perform.

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And so generally, as I use, well, we, we often take a conservative approach. But you know, the, I have to say that the market has kind of evolved to take quite an aggressive stance on system performance.

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For instance, you know, financial performers are often typically will have a 99% availability assumption, that's just a market standard that's been there for 10 years. And when you're utilising new technologies, sets in new locations with new types of risks. There's going to be industry growing pains, we're feeling them right now. And there are some systems that don't perform it 99% availability. So that's just one example. But I think, overall, the grand scheme is, you know, there's a lot of new technology trends, and we're building plants in a lot of new and different locations. So there's a lot of challenges and setting customers expectations when it comes to you know, realising that this project might not perform how a similar system would have performed in the desert southwest 10 years ago, where it was sunny all the time.

00:09:23.159 --> 00:10:27.210
It interesting concept, Brian to I mean, we see in transactions that people are actually trying to transact at 100% availability now. And we have to, we have to sort of force our way into the making the 99% assumption, which, as you said, is the site sort of start to spread out geographically, technology size types of interconnection. Those are that's a really challenging number to meet. We have a lot a lot of our fleet performs at that number, but it is it is an incredible challenge and involves a lot of people, people every day. And Tim, as you were asking, I think that an important thing to understand In the market, right, so willing, willing buyer willing seller markets, so assets are transacted on at fair market value. A fair market value model is not an operational model. And so a real, a real good thing that every owner and everyone operating a site needs to understand, to set realistic performance expectations is that there is a difference between a fair market value model and an operational model and you need to understand both, you can't have just one.

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Well, let's, let's go a little deeper into that, like, what are some of the routes of underperformance predicted or expected? And can we tease those two apart? And and, Dan, I didn't mean to cut you off. If you wanted to address that first question as well. You're welcome to?

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Can I make a quick suggestion? And for the crowd, can we just set expectations about what we're talking about when we mean predicted and expected because how people refer to forecast and weather adjusted models, this industry just varies widely? Great.

00:11:07.049 --> 00:11:09.299
Tim, I I'd be happy to help out with that one.

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Because this is an important part of a topic that we'll always hounding on every day.

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This is what we do with our weather with our members, energy accounting. And in the financial industry, there's GAAP, generally accepted accounting principles. And in this industry, we still struggle to come up and talk about things in the same way. So as we approach energy accounting, we follow the IEC 617 set of standards. And one of them is just like Matt was saying is, you know, what's the difference between predicted and expected power? Well predicted is your financial, which you're going to use to base your financial performance.

00:11:47.850 --> 00:12:24.330
It's the P 50, the average year of historic weather data that's been collected for a site combined with the electromechanical aspects of the project, it's how much you're going to expect to make in a typical year of energy. Expected energy is something different expected energy is given the resources or the weather adjusted inputs, is the array doing what it's supposed to be doing? So those are two very different things. The weather is out of our control. But the mechanical electrical system is.

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Yeah, and we're always accounting for whether in the in the performance analysis, right. So it's, it's not so much about what's going on in the field, of course, you need to compare? Well, we're levelling the playing field, so to speak with with the model. But talk more, Dan, if you could about, like, what is your technology allow for in terms of benchmarking and your use of a digital twin?

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Oh, sure. So so our, our technology is measuring the weather resources at the site to deliver a digital twin with five second resolution. So every five seconds, we're looking at the two inputs to the expected energy model, which is a radiance in the plane of array, the effect of radiance, and the temperature, the modules, and from that we use the customers or our members, energy models, as they've given up to us to to produce the expected energy output of the site. So this is what we do day in and day out is is using that to perform energy accounting, understand how much energy you made, how much energy you should have made, based on that expected model, and categorising, what was the underperformance if any.

00:13:50.970 --> 00:14:14.610
And power plants are, you know, performing good, bad and ugly for a variety of reasons, Brian and Matt, do you want to address some of the challenges and opportunities that you see overall when it comes to performance and what is causing this plethora of overall underperformance?

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Yeah, Matt, can I can I jump in first, because I think I'll have a different perspective. You're definitely better to speak on the operational side, we're more involved up front, you know, when we're evaluating a project in the design stage through construction, and we're typically as an independent engineer coming up with with that model, performance performance model. And what I mean by that is, what how can we expect the plant to operate and how much electricity it will generate in a typical weather year and I want to go back to that point, on how do we estimate what the weather will be? I wanted to kind of point out that when we one of the most important decisions that we make when in our winter Coming up with an estimate of energy production is the selection of a weather file. And there's publicly available sources of ground data that we look at.

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There's also professional, and also free satellite imagery that we'll review. And it's so critical to do a solar resource assessment to look at multiple data that's available.

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Oftentimes, when we're in owners engineer, and we're representing the investor that's looking to invest 10s, if not hundreds of millions of dollars into into these projects. We see often that someone just kind of selects one file, and maybe that file might not be representative of the site, maybe it's, it's on the high side. And of course, that's going to benefit certain certain players or certain stakeholders. But that's one of the most important decisions that we can make. But even when we make a decision and say, Okay, well, this weather file, we think that this is representative of all the different data that we looked at, this is a good indication of what we can expect. That's looking backwards, like the last 20 to 25 years. And there's a general question that I think, you know, we've been wrestling with, as a society. And I think there's a lot of us that kind of tend to agree that maybe the last 20 to 25 years have whether or not indicative of what the next 20 to 25, we're going to look like things are getting hotter, the hotter it gets, actually, the generally the worst PV assets perform. You know, there's issues like wildfires that are making significant impacts in places that we hadn't really expected.

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Right, we're seeing this year, we saw fires from Canada affect large parts of the upper Midwest and the Northeast, that's generally not accounted for in these models. And those things add up, you know, piece by piece. We saw the impacts in California when we had wildfires over the last couple years. So I just want I wanted to hone in on on that point. I know, Matt's got a lot to say on the operational side. But again, I think, you know, speaking from the standpoint of how we set expectations, you know, I think that just understanding weather trends and how we identify and utilise weather that we think is representative of how, you know, what the plant can expect to see is, that's one of the areas where it's been, it's been more and more challenging in recent years,

00:17:21.150 --> 00:17:30.059
when when asset managers look back at 2023, you know, two years from now, will they be able to tease apart?

00:17:30.089 --> 00:17:35.940
What was the Canadian smoke versus some other problem with their solar asset?

00:17:36.119 --> 00:17:38.789
Well, absolutely.

00:17:36.119 --> 00:18:06.150
And that speaks to the heart of what Dan Leary was talking about with the difference between like estimated production, and expected production, because the expected production incorporates the actual weather. So like what I was talking about would be like the difference between, like estimated weather an actual weather, but certainly, we understand that how solar generally performed as a function of irradiance temperature, wind speed, and other conditions.

00:18:07.079 --> 00:18:57.029
Yeah, and to that point, with what you just said, Tim, you can tell a lot of things from the data. If you have good data and good tools, like we can, we can find soiling signals in the data, we can see how much wildfires are affecting plants, it's really, it's pretty amazing what you can do with data, especially because the industry is largely not doing it. So there's a huge potential to improve. Just along the same, the same lines that Brian was talking about are important to note or two, as far as getting to the root of underperformance, I think maybe if you're getting like very specific, the databases of weather resource that are free, are also skewed overly aggressive. And so developers have assets, generally, that's what they use.

00:18:53.339 --> 00:19:05.069
So we start from a point of aggressive numbers, which a lot of that is dealt with in diligence process, it comes back, sort of closer to normal.

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But it's not a great place to start right over extended and aggressive when you're trying to transact on assets. And in the actual modelling, I have tremendous sympathy for Brian, because people fight a lot about weather, but they tend to not want even talk about other assumptions. And when you look at a build up of the assumptions of performance of a power plant, it is very complicated. And there are things that change significantly, that if a bunch of them change, it will change the model significantly. So really modelling and understanding assumptions and truing them up your DC wire loss, your AC wire losses, what does that actually look like when you go to build a site? How does it How does it change? Is it super important and it's still underserved? Like people don't want to give it enough headspace they don't want to plan it accurately. I see it slowly changing thanks to people like Dan and Brian, but it's super important. And then the rest of sort If the root of underperformance to me is, is, it's really a lack of operational expertise and just like a failure to plant, right, if you have assets, even in a single geography, you need people who know what they're doing to direct your subcontractors. If you don't have those people, you're relying on a company that is under scope of work to help you that will perform within that scope of work. Every day that I wake up, we need to go outside of our scope of work to operate these power plants. So that's not really a thing that I can deal with. So you need expertise. There's not enough owners that have expertise they are coming along, I'm seeing people sort of building technical teams a little bit more like what we're doing here at Green backer, but slowly, and it starts with the companies that are very large, you know, but you need it, and you need to understand your assets, and you need to understand things like a transformer will fail. And if you don't know how to find one, then your site might be off 30 weeks, and that you will never recover from your model that it's over. Right. So there's just real, a model is a model.

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And we are getting better and modelling every day. And it's really nice to see how good we're getting at making accurate assumptions. But then you get into the real world. And it's a little bit like, you know, the construction guys, they always complain about the engineers because they can drive really well. But when it's got to work, they don't know what they're doing, you know, which is true and not true. But it's the same thing with Operation models, great gives us a good baseline.

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But if you're not actually prepared to be out in the real world, keeping these things online, and you don't have the expertise to do so, then you're gonna have underperformance more than others. And, frankly, over time, we'll get worse.

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I mean, it makes me wonder what percent of solar assets are accurately being monitored for performance.

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There, there's so many factors that go into this. And then there's this whole question of data science, and are you gathering the right data? And then are you crunching the numbers in a relevant way? I don't know. Dan, if you want to comment on the data science, what is the state of the state so to speak? I know that you're a big advocate for better data.

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But how are asset managers and owners processing data today?

00:22:17.730 --> 00:23:43.619
It's the discussions and the training. I'm going to put the the ending statement upfront, is we need more training in the workforce with asset management. And you can go to places like heat spring, Brian Hayes and the team. Excuse me, Brian. I always do this forget the last name. Good for everyone overheats me, thank you, for each spring. We, you know, with a little microscopic example, with weather stations, you know, one of the pieces that's critical to getting good data, did you put the sensor in the right direction? Is it located in the right spot of the array etc? Well, that for years or maybe decades, that wasn't always done consistently. And we just put up a class in a certification, it's a one hour long certification that every installer can take for free. In in its you know, it takes less time than the time it takes to drive out to a site to fix something, which is a pay now or pay later kind of comment. But getting people trained and understanding the right way to do things is so key to that. And it involves, yes, we got to get the better data. But then we have to take the time to discuss what we're learning from the data, the insights, and how do we build up the workforce and train them to know how to use the data to make better decisions?

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That's I think that that's you you said something that that brings up a point that everyone needs to understand, which is this industry is young.

00:23:50.279 --> 00:24:11.369
And you said there's a how to point your weather station in the right direction, certification like that. It's how young this industry is an exponentially growing. So I think people just over assume how sophisticated we are right now. This is the see young industry, there's a lot of learning to be done.

00:24:11.430 --> 00:24:37.319
I just came away from a site visit where I saw a weather station built and you know, the anemometer was was properly hoisted higher up in the air than the some of the other equipment but unfortunately, you know, it was done done in a way that it actually cast shade on the installation metre. So you know, that's just to give you an idea, like those sorts of things happen. The kind of gives you it gives us a sense of the sophistication of our industry.

00:24:38.309 --> 00:25:20.549
Yeah, I think, to the to the point that was made about data, right, I think there's there's probably from an owners perspective, I'm an assumption that they have the right tools and are operating correctly, but but are largely not. Like I think people assume they buy a data acquisition system, and they pay a tonne of money for it. They're very expensive. Oh, Ah, and then they have Oh nm providers that like, that's all right. So they've got someone to watch their sites, and they've got this big fancy system that they should be alright. But oh, nm providers, some of them are very good at monitoring, but a lot of them you know, they're they're as good as the alarms that are said they do a good job of responding to alarms, are they gonna find two 3% underperformance? No.

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Should we expect that of them?

00:25:21.059 --> 00:26:56.279
Probably not? Is their due their contract costs reflect finding those kinds of problems? No. So that's not that's not all you need. And the monitoring systems, you know, they they can provide a lot of data, they can collect a lot of data. Do they do a good job of sifting through that data and coming to actionable conclusions? No, they are very poor. That's all sort of driven by people. And a really interesting example is when when Dan started his analytics platform with with Dena mots, we were were we the first first company to have access to and if not, we were close. Yeah, I think you would have been the first. So so we we at that time had a our all of our data sort of going into one platform or data acquisition system. And we're pulling in like 70 80,000 data points in our Dan had his weather stations on some of our sites, and then access to our metre, so not access to inverters not access to any of the other equipment just to our metre and his weather station. And over a period of two weeks, maybe even less than that my whole team started waking up in the morning, and not logging into our data acquisition system, but logging into Dan's analytics platform, because with just the metre data and the solar data, he was coming to conclusions about what was wrong with our power plants faster than we could do it in our data acquisition system, which just like hones in on the point that the usage, you're going to collect quality data, but the usage of the data is the part that not many people are doing that is the real important part.

00:26:53.309 --> 00:27:03.809
Like we eliminated hours and hours of work across a whole team of people every day, just by having somebody sifting through that data in a smarter way. The Clean

00:27:03.809 --> 00:28:21.089
The Power Hour is brought to you by CPS America, the maker of North America's number one three phase string inverter with over six gigawatts shipped in the US. The CPS America product lineup includes three phase string inverters ranging from 25 to 275 kW, their flagship inverter, the CPS 250 So I'm curious when you guys think about performance, you know, you're there's this modelling aspect, there's the to 75 is designed to work with solar plants ranging from two construction and equipment aspect and how a plant is being maintained. And then there's this whole science of, of, of megawatts to two gigawatts, the 250 to 75. pairs well, with CPS data, both capturing it, analysing it, learning from it, perhaps doing machine learning, and maybe Dan can talk about America's exceptional data communication controls and that, but of those kinds of three spheres. Is there one that is a standout as something that asset owners really need to pay energy storage solutions, go to chintpower systems.com to find attention to? Or is it a both and and you've just got to go across the board and, and really dot your i's and cross your t's here.

00:28:19.810 --> 00:28:21.089
out more.

00:28:22.589 --> 00:30:29.549
I mean, I think all three, three are important. You know, I talked about sort of three pillars of performance being modelling operational capabilities, and then data science, I think the first two are naturally evolving. As far as data science is concerned, except for maybe a few outliers in players in the industry, that's a place where we need we need a lot of evolution, a lot of a lot of these platforms, right? You can Export CSV files, but they're not doing the analysis, the real analysis, you need the real sort of predictive analytics that you need in the platform's, you start pulling it out into CSV files, and people working in Excel, people creating other databases to do what essentially the data providers should be doing. And that process is tremendously cumbersome. So where you can, where you can have a lot of really powerful conclusions that will help you to operate your fleet, the process of getting there is extremely difficult right now, and frankly, most companies like mine, do not have don't have the resources to do so. And then it's a it's a process of iterative learning, right? So the first time we run some analytics, it's not going to be right, we're gonna go have to have to go back and change things and tweak things and experiment. But if that whole process of manual and results and CSVs and sort of like this meshing together this data every time you have to go back, it takes so much time. So I think just like generally making strides, feeding the initial data for modelling from birth I in and BDE, taking that in doing capacity testing, getting all that data and then and then working with a platform like Dan's where we don't have to do that sort of Excel wizardry that is just so impossibly cumbersome, prone to error. And it's actually in a platform that is learning and doing these things faster and faster, that the industry is just totally missing the mark. Stone. We have to catch up there, because there's so much power in that data.

00:30:29.789 --> 00:32:21.930
Yeah, I couldn't agree more. I think he hit the nail on the head. You know, Tim, going back to your question, I agree with Matt, it really is a combination of all three. And you know, just working on a recent example, I'm working on a project that was recently constructed. And again, to my earlier point, it's a pretty sophisticated or a complicated project. And so far as it has a mix of bifacial modules and mono facial modules, there's several systems that are that are kind of co located and they have various points of interconnection, I'd like to say it's a very sophisticated asset owner that we're working with that's trying to do the capacity test. But shockingly, and looking at the last, you know, three months of data, there's a consistent issue where inverters are just being directed and they're not. And they're being directed in a way that's inconsistent with how they should be operating. In other words, it's a system that is poi limited, it's pointed, the output of the system is limited at the point of interconnection, but I'm bringing this up as an example. But, you know, interestingly enough, I just don't think, folks, we're looking at the right data. And, you know, I'm being asked to provide an opinion as to whether the site is operating as its intended. It's not off by a percent, it's off by an order of magnitude more than that. And it's maybe it has to do with something as simple as insects, you know, at the end of the day triggering a sensor that's causing adverse to derail. You know, it's all that stuff is lurking in the data. And perhaps the biggest challenge that this industry is having folks that are able to present that data and analyse it, clean it, analyse it, and provide an opinion on it in a manageable way, I just, I can see that there's such a huge opportunity there. And I think it was eloquently mentioned by Matt.

00:32:18.779 --> 00:32:57.000
And I think, you know, Dan, I'll pivot to you, because I think this is a big part of the Denobots value proposition, right is, is being able to kind of provide some consistency to asset owners amongst different plants, by measuring, you know, putting more sensors out there, and, you know, providing in many ways, like a redundant system that folks can leverage across a variety of sites, because every project will have its own SCADA system. But you have an interesting product that kind of enables folks to look at all of their assets through like a uniform lens. Yeah, that's, that's,

00:32:58.079 --> 00:34:18.190
that's probably one of my biggest lessons of the last 10 years of working on this. Working with Denowatts, is knowing what we want to measure and why we recently did a project working with DNV, to to do some independent verification and validation of our technology. We work with lots of ies and we said, we really what we do is so complex, it might be helpful to, to the market to have an independent just do an evaluation of what we do. And our My biggest takeaway from that was understanding the history of why we use the sensors the way we do, and well, what is it that we're really trying to do? Because, you know, when we when do you do a capacity test, and you're going to go from, you know, a contractor and EPC to turn over a project to the owner, and you have to hit a target that's got maybe a 2% margin of error to it. You have to be so accurate with the model. And in understanding, okay, well, what is all of the uncertainty that goes into the to the measurement? You know, how precise do we have to be, you know, going through this process, and ISO accreditation and the other things that we've done, to be very accurate, is paying off dividends because we can go and perform these tasks.

00:34:14.226 --> 00:34:34.173
And the energy accounting was such reliability, to get people that information. That that's probably one of the greatest things that we've observed with this is, you know, yeah, we needed a better mousetrap that was simpler and easier to use.

00:34:30.079 --> 00:34:43.920
And we kind of did that with our technology. But really, this is all about getting more accurate data more efficiently in the hands of decision makers to to improve the the industry.

00:34:46.320 --> 00:35:03.360
So let's let's, let's step back here for a second for our audience and in address this question, of what percent of projects are performing as expected and What percent or not? Do we have a read on that?

00:35:04.860 --> 00:35:12.150
Oh, man, I think you're probably the three of us the best person to answer that, given the huge size of your portfolio?

00:35:12.389 --> 00:36:54.659
Well, I mean, all all of our sites have been, you know, performing at like, 140% of the market right at all times? I don't know. I don't know how to answer that question for the industry. And we're a very small slice of the industry, because they the industry, I think, could benefit greatly from a Collaborate the collaboration around sharing data, right? Like this isn't. No one has like real good sort of like, competitive advantage by keeping their data to themselves. Like we we don't share in this industry data well, around performance, like anonymized performance data in the world that I would want to live in is shared amongst everybody. Because that will make us all better. And that's what other industries have have done. But it's not everyone is sort of holding their data close to the cards. I mean, really, you like the I think the main source of published data we have now is from from kilowatt hour analytics, who they put out data every year. And I mean, what that data shows right is the industry is roughly 10%, underperforming models at any given time. And if you look at their data over 10 years, it's actually worse now, because what Brian mentioned early earlier on is that models have been getting more aggressive. I think they're finally starting to even out hopefully you agree with me, Brian. But models have been getting more and more aggressive over the last 20 or so kilowatt hour, or Linux sort of says, this is the underperformance for 10 years, but then you look over time, and the underperformance problems getting worse, because models are getting more aggressive while they're still under performance issues in the industry. So I can only answer with it. I think a significant portion of sites are underperforming.

00:36:55.289 --> 00:37:56.670
Well, yeah. Let me maybe just talk a little bit about availability. I'm glad you met that you brought up the kWh Analytics report that recently came out. And, you know, our friends over at ICF. Were part of that. That process and as part of the publication in the report that was just released, talked about, they had a nice piece on availability. And I think what they said, you know, succinctly put was that, yeah, 99% is, is attainable. But if you just simply look at the data, and they had access to a lot of systems, I think, based on the utility scale, products that they reviewed, the historical data suggested, well, maybe 97 point, I think it was 97.7% was maybe more reasonable, based on actual performance. And so, you know, it's easy for me to talk about availability, because when we do production modelling, we kind of just make make an assumption, and then have to separately look at how availability impacts that because it's basically a hair cut from our production model.

00:37:53.039 --> 00:40:20.940
And so it's easy for me to kind of point the finger at that. But it's also difficult for us when we're upfront in the process. We often don't have visibility, you know, we asked for operational data, as you mentioned, Matt, a lot of people keep that information and close to the vest. And it's been, it's been problematic for the industry. I mean, I think that there'll be hopefully a natural evolution process and more reasonable estimates, we'll we'll we'll eventually kind of, you know, become the standard. But that's just that's certainly Tim, one area where if you look at systems that have been underperforming, I'd say availability is one of the key areas and, and by availability, I'm not just talking about like, hey, are the inverters operating when you want them to, there's been in the press, there's been a lot of discussion about just as we're building out more and more renewable asset, and distributed energy resources, the impact on the grid and interconnection and, and congestion. I mean, those, there's some very real impacts there. And again, there's also assumptions that are made upfront, I liked what Matt said earlier that, you know, you have kind of a fair market value model, and then you have an operational model. And I think that, you know, the the fair market models are informed by third party reports that kind of predict what grid congestion might look like, for example, but I think in reality, a lot of those estimates have also been kind of aggressive. And so we've, we've seen availability manifests not just at the plant level, but also at the grid level. And I think that needs to be acknowledged. And the last point I just want to make quickly is that when you think about this, statistically, this is not a normal distribution. So I think the reality is that because I don't want people to get the wrong point, I do think that there are plenty of systems that are performing as, as expected. And generally, if you look at the average as an industry, we're probably trailing, but I think that you know, it's not a normal distribution, meaning that there's outliers that are having significant effects I just mentioned one product that was underperforming by 10%. So I think that you know, those, those outliers kind of bring the average down. And it makes like for a great soundbite to say that the industry is underperforming by 10%. But unfortunately, I do think that that gives a lot of people a little bit of a different idea of what's actually happening for most folks.

00:40:21.570 --> 00:40:23.340
Yeah, 100% agreed

00:40:23.610 --> 00:41:00.059
the baseline. And that comes back to, you know, appropriate benchmarking and setting expectations. The baseline is also very widely, right, Brian, like you've been, you've been through the diligence process with us many times, you know, that we fight every unrealistic assumption as hard as we can. But I'm assuming that a lot of people don't don't either they lack the sophistication, or they're, they're generally more aggressive than us. But I'm assuming a lot of people are winding up with assumptions a lot worse than ours in the transaction process. So they would naturally be prone to more underperformance. That's not to say there isn't any performance in the industry, but but that the baseline is, is very important.

00:41:00.599 --> 00:41:16.260
So the three of you have worked together on a fair number of projects, is there a success story here, of you know, before and after, you really sink your teeth into the performance of the of the portfolio,

00:41:17.429 --> 00:41:39.179
almost everything we work on. And I'm not saying that I'm not saying that sort of in a sly way, I'm serious, like, if you take all data is imperfect, but if you just like, make an assumption, that kilowatt hour and all the status is pretty good, which they're, they're great company, so I'm sure it's good. We're like seven and a half percent over their average number. So,

00:41:39.659 --> 00:41:50.039
you know, Tim, to expand on that. When I, when I first started reading those numbers some years ago, I said, Wow, this, this can't be right.

00:41:47.039 --> 00:41:52.710
Because when I'm looking at in our data is not anywhere in yet.

00:41:50.039 --> 00:42:02.670
But you know, something, as I start to see trends now, I think I'm going to answer your question about industry wide underperformance this way. Yeah, there. There's probably some industry wide underperformance.

00:42:02.670 --> 00:42:43.739
And you have to separate out and we talked about predicted, are we talking about expected for one thing, but it's not with everybody. There are portfolios that do absolutely fine. And I can see it in the amount of time and how they invest in their teams, and how they invest in you know, taking the time every day to understand the data. And I think it's it's known across multiple industries, like if you're, if you're paying attention, and you're, you're you're managing, you're looking at metrics, you're gonna start, you're gonna improve better, you're gonna improve, you're gonna perform better. When you're when you're diligent, when you're diligent when you when you invest in your teams, and asset management processes.

00:42:44.579 --> 00:43:17.039
So that we have a question from bossu in the audience, and please do type your questions into the q&a, we'll do our best to get to those. But Vasu, from Nashville, from Nashville is is asking about this modelling. Expected versus actual. And he found that there was a 70% MOV, I'm not exactly sure what Moe is, some of our systems are severely underperforming. I want to catch on to performance in real time, so we can fix issues right away.

00:43:14.039 --> 00:43:21.329
I'd love to hear what you all think, could be viable options.

00:43:17.039 --> 00:43:21.840
Do you have any input on that question?

00:43:22.829 --> 00:44:20.789
I mean, I thought if I don't understand this Moe, acronym, as well as anyone else, you're understand it. But if you have systems that are drastically underperforming, you're potentially not talking as much about what we're talking about today, you're talking about your use of equipment offline, or you have trackers pointed the wrong way. And the first step to sort of data is a very simple step, which just means you have to have the correct alarms. And you need to make sure that you're not getting alarms that are nuisances, and that you're getting the real alarms, and then you're responding to what they tell you. So most monitoring systems, being unaware of what you're using to monitor will, will help with that basic alarm methods. But the but it sounds to me like when you have drastic underperformance like this, it's just it's it's, it's more elementary, it's you got to respond when equipments offline and get it back online as fast as possible. That goes into the operational expertise portion of this that we touched on just a little bit.

00:44:21.360 --> 00:44:30.300
Part of being an industry that is maturing, is that there's also a lot of technology players that are major equipment manufacturers that are no longer in business.

00:44:30.300 --> 00:44:57.269
And so there's, you know, sites with equipment that's been in operation for 510 years, where if it goes offline, it's it's difficult. In some cases, you need, you know, proprietary technology to replace spare parts. So as Matt said, yeah, when you have 70% That's a perfect example of like one of the outliers that I was talking about, which you know, those plants all correlated together, kind of pull them down the average. I agree with that.

00:44:53.730 --> 00:44:59.429
There's very, very likely just some fundamental like equipment issues.

00:44:59.969 --> 00:45:31.380
it the the place, I would advise Vashi to start. So expected power is measured in a very specific way. It's done with an energy model with the you doesn't have to be a sophisticated energy model, it depends on the site. But with this is an I guess, as this is now an advertisement, if you want to try out a denowatt system, we can send one out. And what it'll do is you put it on the array, we plug in what the what the size of the system, we build a very basic model for it.

00:45:31.710 --> 00:45:44.670
And it'll start telling you in real time, if, what it what the array should be producing, because if it's not producing that, then we'll start to look at, you know, what's the underlying reasons why it's not.

00:45:40.260 --> 00:46:21.300
It could be, it could range from things that are obvious, that we can see through alerts and the other equipment that's at the site, or it might require some, some follow on services on site commissioning, aerial inspections and other things to determine what's the root cause of the underperformance. But then, you know, once once you have the right system set up, and it's called, it's called a benchmark, once you have the benchmark set up, and you know what your target is every day, in fact, every minute of every day, and you can monitor that, and then get your alerts. I think that was another follow up question is how do we understand if its performance is changing in real time? You do that through very high level alerts?

00:46:22.409 --> 00:47:53.190
It Tim, I want to go back to your previous question about success stories, because I think I want to highlight this for the group because I think it's important for folks to understand that the right tool for the right job, meaning that like we talk about different types of solar projects, you know, at VDE, we work on utility scale projects that you know, might be 500 megawatts in size, and include like, you know, a monstrous battery energy storage system, we also work on distributed energy systems like carport structures, and fixed tilt systems on rooftops and grounds at school campuses where we're talking about like maybe a three to five megawatt project. And I want to say like those types of projects are really, in my opinion, we're a product like Deno watch shines. For, as an independent engineer, I can speak independently, I don't have to do a sales pitch. But I will say that like for utility scale systems, you know, there, there's different types of metrology equipment. And when you're building an asset, that's hundreds of millions of dollars, you want to get the best, most precise and accurate equipment that's possible, for sure. And then you can also get a lot of other redundant equipment. And that also provides you with with with meaningful perspective, and data that you can review. But for smaller sites, like what we've seen, the trend in recent years is a lot of folks are just getting rid of med stations altogether, and they're relying on satellite data. And satellite data over a long period of time is pretty accurate and reliable.

00:47:53.190 --> 00:48:20.070
But if you're looking at it on an hour to hour basis, it's absolutely not. And so I really think that, you know, the Denowatts platform, for example, is perfect for projects that are kind of in that that size category, we're going to be seeing a lot more of those projects with the passage of the IRA, right, because there's a lot of boat, there's a bonus metric, or bonus incentive for projects, I think they're like three megawatts or less in size.

00:48:15.690 --> 00:49:57.869
You know, there's, you tend to see more domestic content in the smaller projects, because the price points associated with the with, with the energy that's being sold. So and we're working on one of those projects right now, where there's like, no less than 10 combinations of tilt NASM. That's, so how are you going to do a capacity test on that project? It's, it's really, really difficult to do it and to do it well. And the most important thing that we can do, as an industry is to make sure that the systems are operating as they're intended and modelled on day one. If we want if we're concerned about the financial return on investment, and the viability of the project over its 35 to 40 year lifetime or whatnot, most important thing we can do is make sure it's functioning as intended on day one. So you know, like the denowatts platform for for instance. It's it's a it's a very good value proposition for developers, just from a cost standpoint. You know, to be clear, like you're not using Dennett, correct me if I'm wrong on the terminology, but I don't think you're using Class A pirate nominators, you're using the thermopile. But you're using a photodiode. But you know, the difference in accuracy, there's is so small, for especially when you think about projects of that size, that it's it's from a value perspective, it's a it's a great option, and I think that, you know, no one's going to instal a radiometer on a on a on a school site, you know, in order to capture like, DNI and irradiance. So, you know, a big part of this is like finding the right tool for the right job.

00:49:59.730 --> 00:50:29.369
Man I'm curious, you know, when you acquire assets that have been operating for some time, for example, and then you arrive at some assessment that they're not performing up to snuff? Could you just walk us through the the logic that you would go through for, oh, you know, for an asset that you don't necessarily know a lot about, historically, because you didn't develop it and build it yourself?

00:50:31.679 --> 00:50:35.250
You asking for me to go through the process of how you acquire it.

00:50:35.489 --> 00:50:43.440
Now, how do you determine what are the root causes of underperformance in an asset that you don't know the full history of.

00:50:44.250 --> 00:50:55.409
So there's usually some and for operational portfolios, like paperwork, actual people recording data, it's been it's been very poor in my experience at Green Becker.

00:50:55.829 --> 00:51:11.489
But we're usually able to, to sort of get some insights from the paperwork. So like, maintenance reports, there'll be sporadic years will be missing, they won't be great. There'll be some ticketing, but it's probably like 20% of the total times people went out to site.

00:51:08.550 --> 00:51:18.090
So we use that we use that data to try to get an understanding we we know how much it produced, versus how much they expected.

00:51:14.610 --> 00:52:14.940
It would produce. And then when we're buying operational assets, we send either our own personnel or like a very small subset of Oh, nm providers that that we really trust and, and no offence here, Brian, but I think the industry in its history usually sends I ease out but like you, you don't, you don't want that you want. You want actual people that operate sites to go look and open equipment, and really take a look. So we'll do that sometimes in combination with it, because it's really helpful that both but we'll go and pick the sites apart, we'll open every cabinet we can quickly to do like, oh, yeah, this transformer is clearly offline, a bunch of these inverters are a problem. We also know historically, right? What products operate well, and which ones don't, based on our own experience, so there's a whole bunch of things and sort of how we put it all together is we we come up with a punch list of this is what needs to be fixed.

00:52:14.940 --> 00:52:40.679
This is what we think it'll cost this is how long we think it'll take this is where we know we can get performance to if we do it, we run that through a company like VDE. They say yeah, like we would, if you are going to do this, we'll when it's done, we'll sign off on performance and X level. And then we just work that all into the model. And as long as it works out, well, we're we're actually pretty excited to acquire distressed assets, because we feel like we're really prepared. And it's not rocket science.

00:52:41.400 --> 00:52:42.780
So Thomas, that's

00:52:42.780 --> 00:52:50.280
a best practice, Tim, you know, the the idea of sending both engineers and the people that actually like know how plants perform, because they're the boots on the ground?

00:52:50.670 --> 00:53:17.849
That's definitely true. I think the combination is super helpful in evaluating assets, because you're just coming at it from different perspectives. And I think that's a big part of what we miss, you know, we don't get that. And I'm really grateful for the opportunity to kind of be part of that process, where you actually are talking to the folks that are owning and operating these these assets and going out there and seeing the the blown diodes in the field, for instance.

00:53:18.750 --> 00:53:28.500
So Thomas asked, though, you know, what is your recommendation for honing in on the three to 5% outlying losses that Oh, nm methods will not pick up?

00:53:29.219 --> 00:53:36.360
That's a great question. I think there's a lot of different approaches, I think these guys might actually be, Dan, you might be the best person to answer this question.

00:53:36.360 --> 00:54:12.420
But one thing that, that I like about Dena watts, and we do it ourselves, as well as is comparison modelling. Like if you can tell what other plants are doing in the area? Or what other types of equipment or even on your own sites like, right, are these? Do you understand the difference between these strings and these inverters and these inverters over here on the other side of the field? Do you? Are they operating differently? are they performing differently? Do you understand why that's those are really good things that can be done, you know, in the painful way in Excel, that can give you some understanding of what's going on at a sub level.

00:54:07.949 --> 00:54:42.630
Some of the, it's also like a lot of the a lot of the operational performance begins and ends at inverters, which can be a lot more granular if you have string inverters, but it's central inverters. That's, that's way, way too sort of high level. So understanding subcomponent performance is critical. That's where you get into, you know, 234 percent of a plant range. But it's just it's sort of you got to you got to attack it from, from an angle of believing that on off is not enough knowledge.

00:54:42.869 --> 00:54:54.510
Well, and data science is really so critical, you know, also just doing an analysis like a temporal analysis, what's the behaviour in the morning hours versus the afternoon versus you know, solar noon versus the afternoon?

00:54:54.510 --> 00:55:35.789
That's, that's a big one. I mean, a lot of times, you know, the sophisticated folks that are doing the day analysis will tell you that there are traits in the data that you can that will reveal common issues whether it be like shading issues, whether it be site stating shade site shading, or rotor rose shading, you can tease out, potentially some hourly clipping effects, certainly things like PID or Le t ID, they all have like characteristics signatures in the data. So we talked a lot about data science. And that being an opportunity, I think that, you know, there's, I'll continue to say, again, I think there's a huge opportunity there for folks to work with the data that they have, especially like when you're talking about that three to 5% range.

00:55:36.989 --> 00:55:56.789
I'll say that I read a really great report this morning. Just put out by raptor wraps on on that, you know, what is that three to 5% made up of and you think you can find that on the internet, but they did a really good job of, of getting into the details that you can't always get into with traditional data sources.

00:55:57.869 --> 00:56:08.219
In general, Dan, what can data tell us about an array and what can it not be?

00:56:03.989 --> 00:56:24.809
You know, obviously, there's really no substitute for boots on the ground and visually inspecting things. But in the absence of that day in and day out, we're reliant on data. What exactly can you determine about the the system? For example, we had a question about shading.

00:56:25.889 --> 00:56:35.730
But you know, shading soiling is is the equipment underperforming all these potential things talk tell us some of the nuances that you can tease apart with data?

00:56:36.360 --> 00:56:49.619
Certainly, well, when you have a good benchmark, and again, you gotta be using the right benchmark for the job. For energy accounting, it's our determination that we get the best consistent results using is Brian described earlier.

00:56:50.610 --> 00:57:50.849
photodiode based pyranometers, which are spectrally, similar to the panels where we're measuring and stuff, you know, when the, when the fire when the smokes blow over, and everybody's data goes haywire, we're still getting great, great benchmark on those sites. So by having that, that great certainty about the data, then we can tell on a daily basis, well, this is how much money you're leaving on the table by not dispatching a van to go investigate or fix something. So that's the first thing you see is how to how to, you know, produce the most you can optimise what you have. But the second piece of it is really the long play, which comes back to the data sciences, and the pillar of how do we use data science to get us better predictions, I think we're doing a pretty good job with expected energy modelling in the models that are available, but predictive data sets really have to be a lot better. So pulling all that back into the predictive, I think those are the two ways that we'll all benefit the most from the data.

00:57:50.849 --> 00:57:51.030
The Clean

00:57:51.420 --> 00:58:12.038
The Clean Power Hour is brought to you by Denowatts. If you're a solar PV asset manager or performance engineer, you need better data and better business intelligence. With Denowatts, digital twin benchmarking technology, you get more accurate, efficient, and faster performance measurement results.

00:58:08.306 --> 00:58:19.753
The fourth generation Deno recently completed a technical review by DNV. You can download the report at denowatts.com.

00:58:16.085 --> 00:58:19.753
That's D E N O W A T T S.com.

00:58:19.817 --> 00:58:23.927
Now back to the show. Very well.

00:58:19.817 --> 00:58:39.929
Well, in our last few minutes, I think it would be helpful for the audience. If the three of you would let the audience know what is the best way that they can reach you or your teams? And I'll let you just individually answer that for the audience.

00:58:35.818 --> 00:58:39.929
And for the recording. Matt Murphy,

00:58:39.989 --> 00:58:41.190
I'll be happy to go first.

00:58:41.219 --> 00:58:43.440
Okay, I mean, Brian Grenko, your

00:58:44.789 --> 00:58:59.550
feel, feel free to hit me up on LinkedIn. You can also reach me at VDE my first name dot last name@vde.com If you're interested and happy to talk with anyone about the important issues that we talked about today?

00:59:00.389 --> 00:59:10.157
Yes, same same here. LinkedIn works well. First Name matthewmurphy@greenbackercapital.com with a period between the names.

00:59:06.289 --> 01:00:02.010
I'm the only one here that doesn't have anything to sell anybody on buying assets. But this is a topic I'm extremely, extremely passionate about. I just like to quickly say before I let down to like I don't this isn't it. This isn't sort of trying to say the industry is underperforming like we this is possible to have strong performance. And we're showing it here. I mean, my fleet thanks to our hard work and Brian and Dan. We're at over 102% of expected weather adjusted numbers going on seven years now. But I mentioned seven points above the kilowatt hour analytics numbers. I was actually messing it up in my head that's against that's against forecasts. So it's possible and I think the industry can improve but but we are we are headed to a place of far greater intelligence and It's a fun fun ride to be on.

01:00:03.269 --> 01:00:13.500
And Dan Larry, you can reach me at dleary@denowatts.com. I would love to answer more of the questions that were asked. And maybe with Tim's help, we can find a way to do that.

01:00:14.369 --> 01:00:35.579
You can always email me tim@cleanpowerhour.com Check out all of our content at cleanpowerhour.com Give us a rating and review on Apple and Spotify, and subscribe to our YouTube channel. I want to thank Matt Murphy with green backer, Brian Grenko with VDE Americas and Dan Leary with Denawatts for being here today.

01:00:36.510 --> 01:00:38.099
Thanks, everyone.

01:00:36.510 --> 01:00:38.099
Pleasure. Thank you.

01:00:38.460 --> 01:00:42.019
Hey, listeners.

01:00:38.460 --> 01:01:15.882
This is Tim, I want to give a shout out to all of you. I do this for you twice a week. Thank you for being here. Thank you for giving us your time. I really appreciate you and what you're all about. You are part and parcel of the energy transition, whether you're an energy professional today, or an aspiring energy professional. So thank you, I want to let you know that the Clean Power Hour has launched a listener survey.

01:01:10.818 --> 01:01:15.882
And it would mean so much to me.

01:01:15.960 --> 01:01:53.224
If you would go to cleanpowerhour.com. Click on the About Us link right there on the main navigation that takes you to the about page. And you'll see a big graphic listener survey, just click on that graphic, and it takes just a couple of minutes. If you fill out the survey, I will send you a lovely baseball cap with our logo on it. The other thing I want our listeners to know is that this podcast is made possible by corporate sponsors.

01:01:48.320 --> 01:02:16.407
We have two wonderful sponsors today, chin power systems, the leading three phase string inverter manufacturer in North America, and Denowatts, a performance monitoring platform for utility scale solar. So check out VDE America, and Denowatts. But we are very actively looking for additional support to make this show work.

01:02:16.485 --> 01:02:21.311
And you see here our media kit.

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With all the sponsor benefits and statistics about the show.

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You know we're dropping two episodes a week. We have now over 320,000 downloads on YouTube. And we're getting about 45,000 downloads per month. So this is a great way to bring your brand to our listeners and our listeners our decision makers in clean energy. This includes projects executives, engineers, finance, project management, and many other professionals who are making decisions about and developing, designing, installing and making possible clean energy projects.

01:02:58.893 --> 01:03:14.400
So check out cleanpowerhour.com both our listener survey on the about us and our media kit and become a sponsor today. Thank you so much. Let's go solar and storage

Improving Solar Asset Performance: VDE, Greenbacker & Denowatts | Ep 153

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