#2 What Actually Makes a Solar Project Bankable? | Logan Boutilier

Most projects look bankable on paper. Fewer hold up when lenders start digging. 

Logan Boutilier, VP of Engineering at Aspen Power and a former DNV Engineer, joins us to break down what actually separates optimistic modeling from lender-grade engineering — why seemingly small modeling choices can quietly impact financing, performance, and long-term reliability. 

If you build, design, or finance solar, this conversation will hit home.

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Beyond the Layout is a long-form solar industry podcast focused on honest, technical conversations with the people building the energy transition.

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Transcript

Defining Bankability And Risk

SPEAKER_01 0:00

Hello, everyone. Welcome to Beyond the Layout. I'm here with my friend and old boss, Logan Boltier. And he is currently VP of engineering at Aspen Power and previously worked at DV and the renowned independent engineering firm. And so we're here to talk about banking solar projects and making projects that are simultaneously bankable, but also will live to the lifetime of the asset class. So, Logan, thanks for hopping on. How's your day going? My day's going great, Rocco. Thanks for having me. I appreciate that we get to do this because we talked about it for so long. Yeah. Yeah. So you taught me everything that I knew about banking projects before I was under you. I was working at Safari Energy and we were running helioscopes. And once in a while I would run a PV Sys, but there are no actual standard procedures and ways of making the assumptions necessary to then go and finance that project and to make sure that that model is really accurate to be used by our asset management team. So could you speak, like starting from soup to nuts, about how to bank projects? Like, what does it mean to what does bankability mean? Let's start with that. It's a good question.

SPEAKER_02 1:09

It's a loaded question, obviously. Um, bankability to me means that a project has the confidence in the within the banking community to invest not only construction debt but also tax equity money into the project, knowing that returns are going to be provided to the banks and all the stakeholders uh involved. That's kind of a very complex answer to say, does the project receive banking money? Yeah. And the banks are very conservative in their investing and they're going to ask a variety of questions to any level of depth that they feel necessary. And I feel that it's it's the independent engineer's duty and the developers and the owners' duty to provide some sort of pushback on the banks in certain commercial environments. Uh ultimately, if the bank is willing to fund projects on favorable terms, that's interest rates, pay back periods, uh, screening projects through their underwriting process, that to me is ultimately the definition of bankability. If a bank is willing to fund whatever debt is needed for the project to move forward, that's what I call bankable.

SPEAKER_01 2:18

So realistically, bankability and quotation marks is kind of like a spectrum where the most favorable terms is the most bankable of a project. And the least favorable terms, maybe that project is still re receiving some of that money or that equity, but you're getting it at such unfavorable terms that that project now maybe not does not pencil. Is that what I'm hearing from that? Yeah, yeah, that's accurate.

Equipment Choices That Survive 30 Years

SPEAKER_01 2:40

Perfect. So how do we make it so that we have the most favorable bankable projects and not the least favorable bankable projects?

SPEAKER_02 2:47

Uh, are you doing what your or are your projects performing to what you say that they're going to perform to? So are you selecting equipment that is going to be reliable, not going to have tracker uptime reliability issues, no snail trails, although I think that we're pretty much past snail trails with the in the module sector. But I've been around the industry long enough to know when I go out, that used to be a problem in the industry. Snail trails lead to module failure that you don't really see unless you're doing an uh a thermo flyover, like a thermography aerial thermography flyover. You have somebody inspecting the project on a consistent enough basis for these things to come up. So equipment selection is very important. You need reliable products that are shown to be tested to known standards uh for IEC, for UL, and that it's that all that equipment is installed in a consistent manner and a reliable manner to where really it can be touch it in, forget it.

SPEAKER_01 3:44

So we'll touch it and forget it.

SPEAKER_03 3:46

Yeah.

SPEAKER_01 3:46

So let's start with the equipment and then we'll go to the energy modeling aspect of this. How do we, as an industry, like this industry is very nascent, right? We've only been around for maybe a couple of decades, three decades. How do we, and when we're financing these projects and trying to say these projects are going to last 25, 30, 35 years, how do we actually get to the point where this equipment vendor or the equipment that you're using is vetted to the point where you're confident that that is going to be around in 30 years because the industry itself hasn't really been this developed

Useful Life Creep And Uncertainty

SPEAKER_01 4:15

for that long? It's such a good question.

SPEAKER_02 4:17

Um, if I could provide a little bit of background story, a little bit of background to what we're talking about in terms of useful life. Um when I started at DNB, this was back in 2014. I started with DNV. All financial models at the time were looking at a 25-year useful life for the project. So, what does that mean? The financial model is taking into account monthly expected revenues for the project that are based off of all of the capex going into the project, the the opex once a project is operational, and the revenue, the revenues expected for the project based off of the predicted energy models that are weather adjusted over time. And those ended at 25 years. So we knew there was a 25-year window that the projects expected to generate revenue. As that number started to increase from 30 to 35 to 40 plus years, you're now going out into the uncertainty of predictability of these projects to be producing the same amount of energy or the same amount of energy with increased downtime, increased degradation over the period of 40 plus years. There was no data in the industry for large-scale solar projects to be operating for 40 plus years. But the banks and the developers wanted to say it's similar to like a car loan. You could take out a 33-year car loan and have a very high monthly payment, or you could go out for a 72-month car loan and have to pay it back over a much smaller monthly payment over a longer period of time.

SPEAKER_01 5:53

And the banks would that make more money because there's higher interest payments. So that's a win-win-win for everybody if the promise of that bears fruit.

SPEAKER_02 6:01

Correct. Yeah.

Satellites vs Stations For Weather Data

SPEAKER_02 6:02

So what we started to come up with in specifically, my experience has been in distributed generation solar. So instead of deploying a single 300 megawatt project, we were representing companies that were building 35 megawatt projects that were scattered around the US, that were scattered around even microclimates, say Pennsylvania, New Jersey, Massachusetts, Maryland at the time. There's some confidence that if the problems or the weather in one location is down in one year, that the weather will not be down across all projects, across all regions. Across all years. Across all years, exactly. So there's there's a little bit more confidence in a geography geographic distribution when you look at a five-year operating period of 30 assets versus a single asset in five years at one location. The ability to predict on a on an annual basis when it comes to energy modeling, if we jump to energy modeling from equipment, um when I started in the industry, we were using NSRDB2, NSR uh TMY2 and TMY3 data sets that were ground-based stations that were well vetted, uh, but had some historically dated data. Uh all the the the data was from the mid-1900s to 1991 and the TMY2 data sets, and then the TMY3 data sets were 91 to 2009-ish for establishing what the P50 or long-term average expected weather would be at a given location at the specific location of the station. So this is, I I feel like I joined in. I'm fortunate that I joined into the energy modeling guru community, which I didn't understand until I joined DV. I thought I was very, very good at doing running PV Sys and understanding energy modeling until I joined DNV and started to hear from the experts. And I learned that I was actually an amateur. So my time at Barrego, I was trained by the historic BEW crew that were the first, they they underwrote as the independent engineer, they underwrote the Sun Edison deals. Um, they started to become a very prominent independent engineer in the industry, set a lot of the PV Sys standards. Some of the experts at DN at BEW at the time consulted with PB Syst to upgrade the software and get it to be the software that it is today. Um, I joked with you all the time in training PV Sys. I'm like, don't go into PV Syst 4 because there's ghosts and the buttons are terrible and you have to do post-processing. And it was just the the the growth of that software has been really to benefit the user and benefit the community as a whole uh over time. So uh as the satellite data providers started to come in. So this, you know, this is uh solar anywhere data, Visala, uh DNB has Solcast. Then there's our solar GIS as well. Solar GIS. There are a bunch of different vendors out there uh that were pushing this is a much better, if you go down to your your 10 kilometer grid, your five, your three, or even your one kilometer resolution, you're gonna get much more accurate, predictable long-term energy models from though from those weather resources than a station that's 50 miles away. Yep.

Picking The Right Resource Tile

SPEAKER_02 9:25

However, what as an independent engineer, our job was to be skeptical of all new information coming in. The incentive is for the satellite providers to sell more of their product. And it took the IE community a number of years. It was actually just two years ago that DNB dropped all of the NSRDB stations and now really only consults with satellite providers. Because it's taken a decade for the satellite providers to understand the regression analysis that they do when they when they run the uh what it's the NREL satellite that reads the uh the expected GHI at a given location, um, that they tracked uncertainties properly through all of the calculations. Uh early on, we discovered at DNV uh a man named Jeff Newmiller who speaks calculus when the rest of us speak arithmetic. He was describing this to me and a bunch of the younger engineers at DNV at the time. Is that the satellite providers were not tracking the uncertainty of the data that that the uh the regression analysis used to kind of fill in the holes. And so you would have reporting of 6% uncertainty in the data, but you got to really add on 4% to that because they forgot they they did not include the uncertainty in the the the pieces of data that they used to fill in the holes or the gaps. Um also things like uh reflectance from large bodies of water or snow cover. Uh now all of that has been updated with the help of the independent engineer community to provide more confidence in the satellite data so that when you read a tile, you now they've overlaid like the thermal images to be able to filter out periods of snow or filter out areas of snow so that you're not getting the GHI fluctuations in the middle of winter that would be affected by reflectance from snow or large bodies of water. Yep. So inputs to your model are very important. One PV Sys report, and I would do this with dozens of different developers doing my time at DV, they would come and say, we run PB Sys, we use the closest NREL station. What else can go wrong? Like, why is this not accurate? Well, if there's a giant mountain range, uh Massachusetts is a great example. Lots of microclimates, lots of uh unreliable NREL stations, but some very, very reliable NREL stations. And you've heard me say this. Anything to do with any project that's outside about a 20-mile radius of Boston moving west, Worcester TMY2 is almost always going to be the selected data source when we were using ground ground-based stations. And it was weird when I joined DB, I heard the experts say this, and I was skeptical. And I'm like,

Why Inflated Yields Break Portfolios

SPEAKER_02 12:20

how do they do that right off the top of their head? Yeah. So then I would go through and I'd do this analysis, and it was painstakingly awful to go through this analysis in Excel where we have to pull down all the NREL data manually from NREL's website, plug it into an Excel tool in different tabs, and then we'd have to actually pull the data down, plug it into PB Sys, output a specific monthly table of GHI, DNI, uh, wind speed and temperature, plug it, it pasted into Excel, populate these charts, filter out the outliers physically by looking at charts. Now this is all done through DNB's solar resource compass, yep. Um, available online. And most developers are starting to use that or or some other quicker means to decide on a long-term weather solution.

SPEAKER_01 13:12

Well, they're doing that with multiple different the to the best of my knowledge, and correct me if I'm wrong, the IEs are recommending to compare all those weather resources together for the same parcel, right? For the same exact location, to find the one that's at least the median or the meter, to be able to select that with higher channelings and just saying, here, I'm just using this, insert the name of the weather resource and I'm only using this. I'd rather have you pick and choose which resource you'd like for that specific project to be as representative for that specific microclimate or that location as possible, and then use that instead of just picking one and sticking with one. Right.

SPEAKER_02 13:51

Because the danger of picking one specific source for your data is all data sources have inherent bias in the calculation and how they interpret the results. There's inherent bias in selecting a sole source satellite provider. So the IE community typically is has taken it upon themselves to look at the available resources on a month-by-month basis and decide which resource best represents that specific location. Sometimes it's solar anywhere, sometimes it's Vaisala, sometimes it'll be solar GIS that is the closest to the median. Or uh DMV has a new term. It's the MLE, uh, which is I think the maximum likelihood expected. Uh I don't know, I'm getting the words wrong. Um, but it's way too many acronyms in our industry. Acronyms. I mean, even NSRDB and NREL I didn't miss out without defining those.

SPEAKER_01 14:45

Yeah, it's uh National Renewable Energy Laboratory, which has since been renamed under the Trump administration to I don't even know what because Yeah, because it doesn't exist. Uh or NSRDB, the national solar resource database. Yep. And sometimes even the local weather, like the on-the-ground NSRDB stations will be even more accurate, especially for snow losses, than the satellite interpolation.

Standardizing Energy Modeling Procedures

SPEAKER_01 15:05

So it's really up to the developer to figure out what that what the ideal combination or single source of truth is for that specific site. Exactly. When they're going through it.

SPEAKER_02 15:15

Yeah. And I I'm I feel fortunate that I grew up in and uh grew up in, was raised, grew up in San Diego, and now live in San Diego, so I can understand microclimates. And uh when I was looking at when I was helping provide independent energy reviews and providing energy models for projects in San Diego, we have, I think, uh probably eight NSRDB stations in San Diego, two TMY2 stations and five TMY3 stations. Some of them are right on the coast, some of them are five miles inland. Big difference. We're sitting in Rancho Bernardo right now, and it's beautiful blue skies. If you drive five miles west of here, it could be 10 degrees cooler with uh with mist and overcast all day. Meanwhile, we're sitting here, it's beautiful blue skies. That comes out in the data. And you so you can't just take a project, say a project is gonna be built at my house. If I wanted to fund a project that's being built on my house, uh it would be a little bit misleading if I chose a station that had more reliable data right on the coast. Or vice versa, if I go five miles further inland, the temperatures are gonna be much higher, uh, but they don't get as much cloud cover as we do uh here closer to the coast. Uh so understanding microclimates is also important. And I know we're getting very detailed into resource selection.

SPEAKER_01 16:39

Well, let's like take a let's take a step back here. The biggest you said it could be misleading, right? Let's go into why developers would want to be misleading in the first place about and even the resource providers themselves. I know historically for these satellite providers, they started a little bit high and now, as you said, have gone closer to the mean for what these specific locations should be able to do. Why would we want to say bolster our numbers? What are the downstream effects of that on not only that individual project, but then also the industry as a whole? So, like let's say what not to do, right? If we bolstered our numbers, why would we want to? What's the incentive there? What happens when we do do that? And what happens when an industry does that? It's a good question.

SPEAKER_02 17:22

Um, so depending on where the developer is involved in the process, they are incentivized to increase energy yields. They want higher, higher overall performance with less uncertainty. Some developers and the the incentive is to find the the resource that is the highest justifiable GHI input into PB Sys or whatever energy modeling software is being used because it allows them to borrow more money on the project. So if the long-term 25 to 40 year returns are going with a 1900 uh 1900 GHI value versus a 1700 GHI value, they're going to be able to borrow more money at that rate at the 1900 versus the 1700. And that all plays directly into the yield numbers that come out of the project, which is the energy generation and then the money that gets brought back into the project because you're spinning the meter more.

SPEAKER_01 18:16

Well, and you're talking from the person who's at like or the company or entity

PAN/OND Files And Hidden Assumptions

SPEAKER_01 18:20

that's actually owning the project, right? Upstream of that are developers that are pre-developing that project, right?

SPEAKER_02 18:25

And they're I'm speaking more so about the upstream developers.

SPEAKER_01 18:28

Okay. Okay.

SPEAKER_02 18:29

What's been unique at Aspen is that we're kind of the we play both roles. We're developing projects to crank out as much IR as we possibly can. But then on the flip side, we're also responsible for actually sleeping in the bed that we make.

SPEAKER_01 18:42

Exactly. So some developers don't actually are IPP's independent power producers that own that project for the lifetime of the asset. And for those developers, right, they're almost always incentivized to have a higher number attached to what the energy can produce from that system. Correct. So that when they sell it to an IPP, they get more money right off the bat. They're out of the equation. Now the IPP is stuck footing the bill, proverbially speaking, for a site that they expected to produce, say, 110 and now is actually only producing 100. Correct. Right. Yeah.

SPEAKER_02 19:14

And and one of the things that that I've done at Aspen, with the help of the entire team that was there when you were there and a few other folks is we have a very detailed energy modeling procedures uh document that gets updated occasionally, somewhat depending on which independent engineer we're working with, which banks we're working with. Um, we're all learning, we're all refining things to try to be as precise as we can be when we can be precise. But something you taught me is we still got to make a decision and take an action and actually move this thing forward. We can't spend 20 hours going through all the fine-tooth comb uh details of the project if we're just looking to purchase it from a developer. So we have this fully detailed document. I think it's I think it's 18 to 20 pages showing examples of how to run energy models, giving background to our engineers why we do things the way that we do. I took that document, removed all the background, shortened it down to about a four-page document, and now we hand that to all of our co-developers. So the developers that are incentivized to crank those yields as high as possible to get us into a letter of intent, into exclude exclusivity and reviewing the projects, they get paid their final payment, their developer fee, at construction NTP. So once the construction starts, Aspen is now taking full responsibility for the energy yields for the project. I found it very challenging to continually talk with these co-developers about the same themes about resource selection, how you run LID, sub-hourly modeling. There's so many detailed nuances in running an energy model that developers aren't really paying attention to unless somebody tells them to. At DB, I would can I would find myself continually trying to inform these developers that were selling projects to our customers at the time of the same themes that I was seeing at Aspen. Where is LID coming from? What

Snow, Soiling, And Elevation Pitfalls

SPEAKER_02 21:10

does a qualified LID test report include in terms of the data points and duration and light soaking to be able to plug that into an energy model? So we took it upon ourselves to shorten that energy modeling procedure and share it with our co-developers and say, we want to align on yield as early as possible so that we're not fighting come closing time about a 5% difference in our yield numbers. Let's do it very early. This is how we will run it. You run it the same way that we do, and then we can compare. Yep. That has been the single most liberating and the single most valuable effort in reducing conflict with these co developers that they that we lift the skirt and show them how we do things at Aspen. And it's largely similar, and based on my experience and what I learned at D and V, how to justify. this long term with these co-developers that look we know this hurts your your uh your returns for the project your your developer free fee that we pay you as the project matures but it does no good for the deal to be selling us to to be selling us projects that are not funded properly if we are left holding the bag that's going to be underperforming for the entirety the the life of the project so let's so we dove pretty deep into the solar resource and that being a very big pillar of an input to these models, right?

SPEAKER_01 22:34

And you talked about this procedure that you've made at Aspen and I was a part of that and running the PV Sys back then. What like you said that most of that comes from your experience at D V and as the independent engineers really the buck start stops with them. Right. And so could we go down through what does it look like to have good assumptions? What do you think those good assumptions are? And then when you deviate from those assumptions, what in which cases can you do that? So we really did that I think quite well for solar resource. But what about as you said LID losses module quality factor losses all the losses that go snow and swelling losses all the losses that go in what I like to say for an energy for a quote unquote bankable energy model is there's three different pillars. There's the pillar of the inputs to the model right those are all of your losses that you put onto PV Syst or any other software that you use as well as the weather resource. There's the layout itself with the near shading and then there's the model that you use right and so we've all we've all taken for granted the PV Sys is the model engine, right? You do the near shading in PV Sys or somewhere else and you can import that to PV Sys. And we're really going to focus on the assumptions that are going into this project, right? And so can you go more into those assumptions, those inputs that we're bringing into these models because I think that's where the core of the energy bankability discussion happened.

SPEAKER_02 23:52

Yeah so if I could take one specific example of that is PVSyst comes with a built-in database of pan and O and D files. So PAN files represent essentially the code behind solar modules and how they interact in the software how PBSyst models a solar module in the in uh in the piece of software based off of the environmental conditions how many modules per string you have how many strings per inverter the pan and on the the pan file definition process as you know is very nuanced uh the information that you include it it sucks having to do it however you know day one of DNB when I when I joined DNB um one of my mentors in the industry Colleen O'Brien pulled me in with this other the this other engineer that was starting at the same time and she said this this database is not as good as we'd like it to be so we took it upon ourselves every energy model that DNV makes they make their own pan and od files based off of data sheet values based off of test reports that are available um for specific modules now you've been around long enough in the industry to know that module vendors will produce a data sheet on day one

Near Shading, Terrain, And 3D Limits

SPEAKER_02 25:05

to represent what they expect the project modules to produce but then you get the actual data sheet from them from the vendor when you purchase the modules that have slightly different characteristics.

SPEAKER_01 25:16

And they could also have a report from a test at a facility where the modules they intended to be made but then you bought modules from a different facility and that requires a different report.

SPEAKER_02 25:26

So so at DNB and and Aspen largely follows the same procedures that I followed at DNB uh at Aspen we make all of our own PAN and OD files based off of if we have just a data sheet that's sufficient for us to move forward with an energy model. But we make generalized assumptions about LID, low light losses um what else is there? IAM losses there are a number of different test reports that we'll consider there are also some things that we will not consider. So uh if we see a test report come back for IAM losses that shows that at a 90 degree incident angle you have a 102% uh current value coming out something wrong you're saying that the mod the the sunlight's hitting the module and you're getting a boost to the expected energy which is a little bit odd. So uh the independent engineer community brought a lot of skepticism into the use of low light test data or IAM test reports when we started to see these anomalies come through and there's no real way nobody was really tracking the uncertainty in the measurements of those those data points. So the independent engineers that I've worked with have taken things like the IAM test reports and said we just won't rely on that. However we've found that this is reliable these these data points are reliable which is and even during my time at DNV we switched from the ASRA model in pb sys to the Fresnel model in pbsyst depending on an anti-reflective coding on the module or not uh so these are the like these are the details that are needed to produce bankable energy models that the independent engineer community is now confident in proceeding and presenting to the banks. It's very nuanced it's very nuanced and all this gets baked into the pan files. So if you read a PB Syst report you don't really know what exactly is included in that pan file. So when I was at at DNB we would uh we'd produce the pb system reports but we'd ask for whoever our customer was that was producing their own pb system reports if they wanted us to review their pb system reports we would ask for the whole project file yeah so that we could open the PAN files and the O and D files and see how intricate they were or how intricately and detailed they were kind of pulling the levers and changing things behind the scenes. As you know we did when during your time at Aspen we accepted some LID test reports for a particular module but we found that the developer had modified the PAN file in other ways that resulted in an artificially higher uh module

Mismatch, Module Quality, And Sub‑Hourly

SPEAKER_02 28:10

performance in PBSyst and to kind of identify where the rubber hits the road here when we went to fund the project we had not performed that review of the PAN file we kind of just accepted the PAN file with these LID test reports and the results of that verified that in the PAN file but didn't verify every little other detail. Aspen took a one and a half percent hit on those two projects because when the independent engineer looked at it, they identified that oh by the way your low light data was changed and your IAM was changed. You're not following your own procedures and so I had there was some crawling back and saying okay we need to update our procedures now to verify every little detail which is why we make our own PAN files in the first place so that we can control all of that and I want the ability this is this is what I do this is why I produce these energy modeling procedures that are externally facing to our co-developers is my story to them now is if I accept if we are meeting halfway on LID test results I then have to take that half percent boost and I have to justify it to the banks. And if I can't do that with the data that I have Aspen takes the hit but you've been given your developer fee. Yep. So I my my goal and my job is really to communicate that uh that friction back upstream to the earlier stage developers. And it it's tough because they want to crank more megawatt hours out of every project and I'm saying they can't get that when it comes to uh justifying it to the banks.

SPEAKER_01 29:48

And this is a perfect example of an industry where everybody's incentivized to show nicer numbers up front. Yeah. But somebody eventually is going to be holding this asset and is going to have to re-under like the re-baseline what those expected yields are to match real actual yields of the system that is being produced. Right. And so the devil especially in pan owned files are in the details with here. The devils are in the details with pan owned files and you said to create those it's really finding the the data sheet and then any external validated studies for that specific pan owned file for that specific module or inverter, right? Correct. And then embedding that in the pan owned D file that's the gold standard which is what any IE would do and any good developer that is attempting their best to represent the most fair P50 case for these modules and inverters for that system. Yeah. So moving on from pan from modules and inverters pan on D files to the rest of the assumed losses just take like a bird's eye view what are the biggest losses that matter that most developers get wrong especially let's hearken back to your experience at DNV where you were reviewing most developers' reports. You're seeing the inconsistencies between

Haircuts, P50 Integrity, And IE Reviews

SPEAKER_01 31:00

what they were assuming and what an IE would assume. Well the largest is snow and soiling.

SPEAKER_02 31:06

I think that's the largest contributor. I think the industry's kind of come upon come upon um this is a phrase that I joke that I'm going to trademark someday, but sniling losses snow and soiling together. I'm just trying to bring back the snails. Oh yeah keep this keep the snail trails away but I'll take the sniling as long as I can but uh a good example is early on when Aspen was just eight people when I came on to join Aspen um the founders at Aspen said Logan was such a pain in our butt being an IE at D we want him to come in-house and be a pain in the butt to all of our customers and people were buying projects from so bring Logan in he's gonna run what he does on a diligence level and review projects and he's gonna run energy models we did we were doing a project in Copenhagen New York where there were two different snow stations NOAA's uh National Oceanographic administration forgetting the other A there but NOAA NOAA another acronym I think it's atmospheric administrators atmospheric administration ocean atmospheric and snow loss models that the Townsend and the the Kimber the Townsend model for snow losses and the Kimber Mitchell model for uh soiling dust soiling in particular are very sensitive to uh the inputs as we are discussing right now how much rainfall how much snow how long is the snow lasting on the ground the angle all of exactly yeah and the the buildup of the soil soiling and what is what a rainfall event constitutes as a cleaning event versus a a delayed soiling event versus less than an inch is basically no change to what's what the existing soiling conditions are on the module. But the sensitivity of the snow model to elevation or let me to microplankt let me track that back. So it's really the sensitivity of snowfall to elevation is huge. So we're looking at this project in Copenhagen New York where we were building a project our project was being built up on a bluff but the closest NOAA station with fully reliable data that's I think they have three different classifications of fully reliable kind of questionable and some are just not reliable because of data data that's missing they've had to backfill it with nearby stations they've had to interpolate to get the monthly data inputs into uh into the snow loss model. And what we found was Copenhagen New York the town was at a much lower elevation probably five to eight hundred feet or five to eight hundred meters lower than the project site and because of that we went from our very first models used the Copenhagen NOAA station and resulted I think in an annual 8% snow and soiling loss. Yep.

SPEAKER_01 33:59

And remember this is New York so there's a lot of snow regardless of if you're at high elevation versus low elevation.

SPEAKER_02 34:04

Yeah and so

Capacity Testing And Shade Factors

SPEAKER_02 34:05

this was like 12 to 14% in December January February months of snow losses we were using the closest station we were following right doing everything right and when we took it to DNV to look at it they had experts look at the surrounding NOAA stations and said well although although that station is technically the highest quality data you could get the elevation difference is substantial. It causes a substantial difference in the winter time and in New York they get enough rainfall to where soiling losses are going to be no more than two percent per month. I think we modeled at 1% per month because there's enough rainfall to wash them up most of the northeast is going to be at 1% per month for the timber. Yeah yeah so snow losses are the largest driver of of annual uh influence on uh on energy model in this particular location um so then DNB at the time was our independent engineer they said you should use this station so we went from 12 to 14% monthly losses in winter to 35 to 40 monthly losses in winter a substantial right now to like a 13 to 15% annual loss in energy because of the difference. So we we increased five to seven percent on our or we decreased our yield five to seven percent simply because of a five to eight hundred meter elevation difference in two stations in the inputs.

SPEAKER_01 35:31

And what's what I think this gets to is we can as a developer try to do everything right and then there's this one little detail that we forgot big detail which is elevation right and we're talking these numbers which are well we're taking this much more soiling and this much more off of the production but that is actually more accurate and should be done because when you are then owning that asset it's not like in the Catskill Mountains of New York there's going to be less snow. There will be snow there actually will be more snow. So it is it behooves us to do this as early in that process as possible.

SPEAKER_02 36:09

Yep yeah absolutely and and some of the creative things that I being in the industry for almost 20 years now some of the creative ideas that I love hearing because I tend to be more industrious and not as creative um I'm learning to become more creative but aren't we all trying to you know when when I was at D I had the pleasure of working with Tim Townsend who is the Townsend of the Townsend snow lost model. And it it'd be great to to have a a talk with him Rocco uh to have him share the story of how he actually developed the snow lost model I hope you can connect us so that we can have that conversation I I'd love to um because it that would be a hoot having two have a conversation together about this. But um I was talking with him about the potential for snow clearing so a way to reduce those 30% January February March and uh monthly losses to be able to go out and clear all the snow off the modules and get back to zero or one percent um on a daily basis obviously the snow will build up and this was largely before trackers were largely implemented. So the developer though the developer IPP

Availability, Degradation, And Inverter Swaps

SPEAKER_02 37:20

that we were working with every two years would come to us with a plan that here we plan on doing this for snow clearing um it's not just a matter of removing the snow from the modules we have to remove the snow from you have to put the snow somewhere and who's gonna go be removing the snow from the modules are you going to hire highly skilled laborers that are going to understand the nuances of if you nick that wire, if you hit that piece of equipment, if you're it how do you remove the snow from the modules while still protecting the structural integrity and not scratching the module surface. And so we we consistently just brought up we again as an independent engineer we were very skeptical and we tried to find the holes in this creative plan. And this developer every two years would bring us a plan and we would shut it down. They'd bring us a plan and they would shut and we would shut it down. And when I say shut it down I just said it it really means we can't justify adjusting the snow loss model based off of the based off of your plan to keep the snow off the market.

SPEAKER_01 38:18

You need an ample amount of evidence or at least a logical consistency to the plan to be able to modify correct a standard. Right.

SPEAKER_02 38:27

And the whole theme of this conversation is bankability. An independent engineer they would bring us this plan we would then have to justify to the banks why we think that this plan would result in a consistent enough reduction in the snow losses to justify those increased yields and we would also review the OM plan and the qualifications of the laborers going out and clearing the snow and what do you do in a P P50 or a P90 event or even a a 10% likelihood event of snow that you just can't put anywhere.

SPEAKER_01 39:02

Yeah it was like well if you're in the northeast anybody this is 2026 so we just had a gigantic snowfall and that snow was about a foot could have been a foot and a half in other two foot in other areas and that didn't clear for six weeks. Yeah. Right. And that's just where I was if you're on top of or on a hill and you have a lot of snow and you move that somewhere that snow physically has a volume to it. It's not like you can just melt it and it goes away we're talking in the winter where you're not melting the snow you're putting it somewhere else you can only do that so many times before there's nowhere to put the snow yeah where you need to get dump trucks and then is the price of the dump trucks and all the labor worth the squeeze of getting extra production out of the system.

SPEAKER_02 39:43

Absolutely and I you know again being born and raised in San Diego when I learn in big snow events in Boston that they have to put snow on the dump trucks to watch it in the parks it just blew my mind.

SPEAKER_01 39:53

I'm like hey with these things you got to actually find a place to send the snow and ice it's not all white and fluffy it is chunky and icy and brown and it's ugly and it's not as simple as San Diego where oh you didn't have rain

Tracker Reliability And Wind Behavior

SPEAKER_01 40:05

for a few months you can just wash the panels off and get the dust and get the dust off you actually have to physically move the snow.

SPEAKER_02 40:10

Exactly so exactly so following down we we're we're talking the key factors kind of the higher impact losses in a PV Sys model. If you follow down the the waterfall diagram in a in any energy model uh output pv cyst waterfall diagram at the end snow and soiling is a big one um especially in if you take a step up actually in that waterfall diagram you have your your near shading losses yep um that's just making sure you get the obstructions the tree heights the distance from the trees your inter row distances all that has to be right correct for that to to manifest. Yeah and I know um all of these conversations have an impact on uh how you actually test the system when you turn it on so getting the near shading model correct and accurate as as accurate as you can with accurate shit uh tree heights tree line setbacks uh some towns don't let you clear any trees so you have to model 60 foot trees within 30 feet of the array that really they kill production but they may not kill the deal. And that's something that my mentor at Aspen Power is he he's really taught me Scott Delaney has taught me that just because the yields get reduced or you have severe losses associated with trees, it doesn't mean the deal's going away.

SPEAKER_01 41:29

Well you're so because you're putting panels that close to the trees you're actually getting an uptick in gross power of the system. You're just getting a downtick in normalized power of the system.

SPEAKER_02 41:38

Right. So if I say it in a different way you're making a choice the project is making a choice to increase capacity into shaded areas at the expense of yield but you're getting a benefit of increased megawatt hours throughout the year. Yep because you are investing more by putting modules in in poor producing locations so that you can actually increase the overall energy output of the project. Yields go down but megawatt hours go up because your system capacity increases. And this is something I've had to learn in being an IPP and and working with developers so closely is engineers aren't the only decision makers when it comes to moving a project. As much as we wish as much as we want to it's it's follow the money.

SPEAKER_01 42:31

If we if we take a 5% hit on yield but our IRR is only only reduced by a half percent yeah then well because you already have the labors there you already have the boots on the ground you're hiring the EPC it might not be that much cost to add an extra few hundred panels and a racking there to get an increase in gross megawatt hours because that's only a little bit extra on the on the estimating side or the actual spend of the system side even and then you get that gain for the lifetime of the asset where you're getting more megawatts for the lifetime.

SPEAKER_02 43:07

Yeah. Yeah yeah totally agree. So again that step up in the the PB sys loss diagram to your near field shading model um something that is that that I'm particularly sensitive to given that I've always worked in distributive generation um meaning smaller parcels more wetlands less civil work we're not we're not going out and mass grading sites to try to maximize or minimize terrain losses which we'll get to uh we're trying to squeeze all the modules that we can into tight areas in the uh to connect on the distribution grid large scale 100 megawatt plus solar plants don't have the same sensitivity to shading as the smaller scale ones do. Because if you think about it, if you take a thousand acres and you install solar solar modules all That thousand acres, it's really just the edges of the arrays that are affected by tall trees on the sides. However, if you have the same the same trees that are that as close to the array for a five megawatt facility, you have a much higher impact. Your window

Bankability Via Spares, Opex, And Story

SPEAKER_02 44:15

of uh clear sky, no shading, gets minimized when when the the trees are a hundred yards apart from each other, and you only have a hundred-yard uh corridor to install all of your modules in. Um so modeling for the the trees, nearby objects, uh row to row shading and modeling terrain. That's one of the more complex things that we have to try to model is the terrain. So um, yeah, there are a few different software tools out there to model terrain losses. Uh, it's it's becoming, I think there's some tools in the work, in the works to take out to be able to map modules to terrain accurately and then do a 3D model representation of it.

SPEAKER_01 45:00

You and I've talked about this. The best way right now that most developers are using or that are using PV case to model with the terrain, right? And then they're taking that and they're importing that into PB Syst to do the actual energy estimates, right? But in PV Sys, correct me if I'm wrong, but I believe they don't actually simulate it in full 3D. They actually take an estimate of what those terrain losses are going to be and then flatten that array and simulate it in 2D. Correct. Could you go into that, please? So that like to me, when I learned that, I was very confused because we spend all this time trying to model the terrain just to stick a 1.9% extra loss. Right.

SPEAKER_02 45:34

So our understanding of this, and and you can you probably know more about this than I do from your your recent research, but uh the way you described it is is accurate to my understanding is you take PV case provides a 3D mapping of the terrain. And PV case does an amazing job where you have the option of you can either take this terrain that they pull in from USGIS data or from Google data, and they they map this 3D space, this this terrain net, if for lack of a better term. Um a mesh. That's the term that I was looking for. A 3D mesh. And in PV case, you have the option of choosing to map to that mesh. So fixed tilt tables can map to that mesh uh for fixed tilt systems, or you can establish a maximum height of your modules off of the ground, and PV case will adjust the tables at a it, they will adjust the elevation of the tables to meet your minimum and maximum height requirements off of that that mesh. Uh for trackers, they will try to match the tracker took two slopes to that mesh. Uh again, based on the constraints that you're putting in. Again, and I as I mean, as I start to build this in my head, you can see all the little tiny inputs that have that have a dramatic difference and a dramatic effect on the overall yields. And the row to row shading is a big problem if you don't map it correctly.

SPEAKER_01 47:01

Yep. It's very easy if there's no terrain that that there's no hills, but if you have a hilly site, right? It could be you actually get more production. Say you're on a south-facing slope of this

DG Procurement And Factory QA Reality

SPEAKER_01 47:10

hill, and you can actually squeeze them closer together and have less row to row spacing. But because they're at different elevations, say for a fixed tilt system, you might actually have more yield than if it was flat. But then now, say for a north-facing slope, you might have significantly less yield if it goes flat, right?

SPEAKER_02 47:26

Yeah, so so PV case outputs a file, a PBC file that we that uh is imported into PB Syst. And what Pb Sys does is it kind of pulls out that shade model and runs an hourly simulation and calculates the percentage base. It's so when you pull in, you can define it in a pb case, you can define it in a pb sys the stringing ends with uh with the split cell modules, we had to make a change about 10 years ago to where one module is really two modules.

SPEAKER_01 48:01

Yes, because of the half-cut module.

SPEAKER_02 48:02

Correct split, yeah. Yeah, so you have two circuits in each solar module now.

SPEAKER_01 48:06

Instead of three with the diodes, you have six. Yeah, well two circuits and the third. Correct.

SPEAKER_02 48:12

So essentially six circuits inside of each module, whereas before you'd only have three with the diodes, uh, or three in the the full cut cell modules. Correct. Uh, but what PV Syst is doing is it's estimating just the shading percentage and then it uh it basically maps that shading percentage at every hour of the year, and then does a 2D representation of that in hitting the energy model or the available resource hitting the solar cells. What's in the works and where where the industry is going next is the ability to do a 3D model of that without the uh the lack of without the simplified 2D version, it's going to a 3D version.

SPEAKER_01 48:55

Yeah, I'm pretty sure uh plant predict and solar farmer are the only two models on the market that do an actual full 3D simulation right now. So I wonder if PV Sys is actually working on it by all that.

SPEAKER_02 49:07

I imagine they are. Um I hope that they are. I hope they are too. Yeah. So that's kind of where we're at with terrain modeling and ensuring that you're getting um your rotor row losses accurate. Now, a caveat to rotor losses, as the solar industry has grown, and we're gonna go back to snow losses here. Rotor row losses typically hit you the most in the middle of winter. Now, if you're at a 35 to 40 percent monthly loss due to snow, it's not gonna impact you because the system I shouldn't say the the rotor row losses are gonna be minimized because you're gonna have snow cover on the modules. Uh whereas if so a developer can look at that and say, I'll take that rotor row hit because we're going to have the soil, the snow losses at the same time. And the lowest uh your your your GHI is much lower anyway. We want to maximize

Future: Repowers, Thermography, Hybridization

SPEAKER_02 50:00

production in the summer. So we may take a rotor row hit in the winter, everything's covered in in snow. Uh, plus you have a lot more ambient light, diffuse light in the winter, typically, because you have gray skies. Yeah, it's to maximize production with there are no hard shade lines in the in a fully cloudy day where all of the light is diffused. You don't have any direct beam hitting the module. So a developer really has to play with the the ground coverage ratio to figure out what's best for them and best for project returns.

SPEAKER_01 50:28

And that's what's beautiful about solar development is that there are so many little things you can tweak to try to fit the project to what you want it to be, that there is no optimal project, right? For any one person, what is optimal may be different for the next person or correct or developer.

SPEAKER_03 50:44

Yeah.

SPEAKER_01 50:44

So we went through we went through snow and soiling losses, very big, obviously modules and inverters, terrain losses. What would be the next? And obviously, all near shading, which includes trees, obstructions on roofs, nearby roofs, right? What would be the next biggest one, would you say after those four? So uh those are the big ones. Those are the big ones. If you get those right, you're like, how much percent of the way there? You're 98% of the way there. All right. All right. So now we get into the fun parts of module and string mismatch and module quality. And when Logan says fun, we mean like fun for nerds and engineers and not probably fun for other people that want to bottle this. Yeah. So it's quite technical.

SPEAKER_02 51:27

Ridiculously technical. Um and when we say technical, it requires quite a bit of time to investigate and back up and provide the justification for the values that go into these that the module quality factor and and mismatch and how it's run.

SPEAKER_01 51:41

And L ID as well.

SPEAKER_02 51:42

And L I D. And sub-hourly. Well, sub-hourly is in that module quality. Depending on how it's run and which software you're using. Uh so as some background, DNB would run independent energy models, meaning from ground up, DNB would build the energy model based off of the plans that are available for the project. At the time, we would also review a developer's PD system. See the difference. Well, we would not we would produce our own as the bankable model that the that the IE is standing by as their own independent model. But as you know, if if one of our customers at DNB, and we do this at Aspen as well, is we say, look, we have 20 projects coming through. We want you to do an independent model for five, and we want you, and then we want you to review what we do for 15. So there's a there's the that like would set off alarm and alarms in my head if I didn't know at when I was at DNB, if I didn't know what their procedures were. The biggest question I always got, or that I always had back to my the the developer customers that we were working with at DNB, and that I give now upstream to the developers that we purchase projects from, now that I'm at Aspen, tell me what goes into your module quality factor, and tell me what goes into your mismatch losses. Those two things can be interchanged, and different people, different companies use those two, those two terms or inputs

Takeaways And Closing Reflections

SPEAKER_02 53:10

uh slightly differently. They're kind of a catch-all for all of your ambiguous losses in the system. Yeah. So this is where you capture things like nameplate losses, where you deal with the tolerance, the zero to plus five watt binning class, uh, where you deal with the uncertainty in the measurements of the power output of the modules, um, where you deal with sub-hourly losses that come in because of the limitations of PV Syst being hourly only, although I think that they're already making a change to be able to do sub-hourly modeling in PV Syst as well.

SPEAKER_01 53:44

I'm pretty sure solar farmer and plant predict also do sub-hourly losses. Like, say fit when we say sub-hourly, an 8760 is one hour for every hour of the year, 8,760 hours per year. Then you also have 15-minute models. You could even do five-minute models. Some providers are even giving one minute models, though I am dubious of the certainty of a P50 case with a one-minute model. So that's what we're talking about when we talk about sub-hourly losses, is capturing the losses in between that single hour that we're running that simulation. Right.

SPEAKER_02 54:15

So in that module quality factor, you're it the way that we model things is you're accounting for the nameplate bidding of the modules. Now it's all zero to plus five watts. But when I started, it was some of it was plus or minus three percent, some of it's negative five to positive five.

SPEAKER_01 54:33

I haven't seen any of that. That's wild.

SPEAKER_02 54:35

It's it I mean, you remember looking at at pan files that I would bring into Aspen of older modules. You say, I remember you bringing this up, like this this one's way different than anything I've seen. I said, Yeah, because the data sheet says minus five to plus five watts. That was wild. Yes. And and and I'm glad that the industry at least has at least standardized on a zero to plus five watt bidding class. Uh so the way that the way that the independent engineer community looks at that is uh, and we can get into again bankable. How do we bank our modules as well with equipment? But uh the way that the independent engineer community looks at that is typically utility scale projects are having a you know, 100 plus megawatt project is hiring a company like CEA, Intertech now, to go and perform a factory site visit of their actual modules being produced over in Asia.

SPEAKER_01 55:32

Somebody's verifying that the modules themselves are being produced to the standard by which the mod the module manufacturer is saying. Correct, right? And you can afford that when you are making a utility scale project display.

SPEAKER_02 55:43

When you're purchasing of a gigawatt versus a gigawatt of modules over five years, you have the purchasing power to hire somebody to go and inspect the actual modules that are going to your project.

SPEAKER_01 55:54

But our DG developers typically don't have that purchasing power.

SPEAKER_02 55:58

Yeah. So if we could take a slight tangent here to talk about module purchases in the DG community. Um the way I always prided myself on trying to be as commercially as commercially minded as possible in closing DG deals when I was at D. Understanding things like frost heave ramifications for projects that are already in the ground. To tell a developer to go or a contractor to go rip a project out because they didn't account for an additional two feet of depth needed to resist frost heave forces is not a commercially a commercially viable opinion to go say go rip it all out. You've already built this project, now rebuild it. Right. So while the industry started to learn about frost heave and how to properly account for it, how to mitigate it with like frost sleeves or backfilling with non-frost susceptible materials or foam, uh, we still had projects that were in the ground that needed to have funding close. So when I was at DB, we developed a a monitoring and uh a monitoring and mitigation plan that we would put into our IE reports that included surveying the project at its baseline, which is its installed condition at mechanical completion or substantial completion, and including in the OM agreement to update that on an annual basis. Um I won't get into too much detail, but that seems that that works to get it through financing. And, you know, we had developers, we had vendors that swore that there was no frost heave on any of their projects, hundreds of megawatts installed in the Northeast. And on the other, on the flip side, DNB was also hired by a dozen different IPPs to investigate frost heave from those same developers and those same vendors. So we knew it was a risk, we knew it was a problem. What what is Frost Heave? You said you went right into Frost Heave. Let's let's define it just for a moment before solar five. So so when when when there are frost susceptible soils, which include fatty clays similar to expansive soils that get saturated with the the spring melt and freeze thaw cycles that happen in in uh it anywhere in the northeast where it snows, you have this estimated frost depth and you have what's called an ad-freeze bond strength. So as the soil around the metal, and we all know this people who park their cars outside, the ground will be perfectly dry, but your car is wet in the morning. Yep. Because water tends to condense onto a metal surface, not onto a stone surface. So all the so all the moisture that's in the soil condenses onto the metal piles that are in the ground, and then that and then that water forms ice lenses that causes the water to expand.

SPEAKER_01 58:38

And then that pops those ice, those metal things out like a pimple. Correct.

SPEAKER_02 58:42

So if you have frost susceptible soils, there's pressure that builds up inside of all the voids that are filled with water. That now you have these ice, these ice lenses that are expanding, and there's only one place to go, and that's up. Yep. So we saw occurrences when I was at DV, we saw occurrences of 18 to 24 inches of frost jacking occurring on projects that I'm not joking, we saw pictures at mechanical completion because we were the independent engineer. At mechanical completion, the table perfectly flat. We go out two years later and we see we see foundations jacked up through the back of modules, and it just looks like an undulating terrain. It it just blew my mind that the difference between the same picture taken at mechanical completion versus two years of terrible winters. Yep. Um, and I say that because it's all building into the bankability conversation.

SPEAKER_01 59:32

This is an append selection part of the bankability conversation.

SPEAKER_02 59:35

Well, and and the specific design. So you have to take the equipment that's all listed for its purpose, assemblies, individual components, but then you have to have to put that somewhere and understand what the environmental loads are going to be on that particular system through the 25 to 40 year useful life of the project.

SPEAKER_01 59:54

Um and that's really going back to our first topic is you could have great manufacturers, great equipment, but then you have something like Frosty've come in and you did not put something to a mitigation strategy in place. And even three years into that, two years into that project, it looks nothing like what you expected it to, right?

SPEAKER_02 1:00:16

Right. So to tie it back, I think I I started to talk about my goal in providing good customer service was to find commercially reasonable solutions rather than just putting up a brick wall as an independent engineer and saying, no, you must do it this way or else.

SPEAKER_01 1:00:31

If only all developers had someone as reasonable as that as their independent engineer representative of choice.

SPEAKER_02 1:00:38

Well, I mean, the proof's in the pudding, my customers kept coming back and wanting me to do the work. So I think that that is really the only way that I could judge. And I did not get access to any of those projects operating data after the fact, um, in terms of how the project was performing relative to my energy models. And it they weren't just mine, it would, they were DNBs, they were uh the people that were reviewing and approving my energy models at DNV. Um, and that's something that we do at Aspen Power. We have a peer review process, we have an approval process as well that only highly qualified, experienced energy modelers perform. Uh so anytime an energy model goes to our project finance team, it's already been peer reviewed and very scrutinized down to the all the details that we're talking about. Yep. And for a time, I was providing all the approvals. And you know this 50% of the projects that would come from me to me for approval, I would reject and say, do this.

SPEAKER_01 1:01:35

And you this mimics exactly what DNV would do, right? Because you would have that, the IE would have somebody that makes the model, then you validate the model trust, but verify, you're verifying it, and then you then make that iteratively better, right? And Logan's point at me because most of those rejections were my models at the time. So it's a humbling process.

SPEAKER_02 1:01:54

I guess it's a very humbling process. Going from Barrago at the time, learning how to do PV Syst through BW's training. And then I joined DNV and I thought I was an expert. No, I wasn't child. No, I wasn't child. Exactly. So kind of drawing back to this module quality factor in this match. Uh, at DNB, when I would review a developer's energy model, just their PB Syst report, I would ask for the PV Sys files so I could verify the contents of the Pananone D files. But the largest question I would ask was always tell me what goes into your module quality factor, what goes into the mismatch. Uh, because you have things, if you have undulating terrain that is modeled as a 2D flat surface, you're gonna need additional loss of mismatch. Yeah, if you have a module pointing at five, just a five degree difference in in uh in its uh its its azimuth or its incident angle, you there's some inherent mismatch there. And PV Syst has the ability to do mismatch among electrically connected components, but PV Sys does not have the ability to calculate mismatch. Say if you have a string of 24 modules across three tables of eight each, and the middle one is pointed straight up, and the two on the side are pointed slightly east and slightly west. You have a mismatch across how much what the what the incident angle is for the incoming irradiance. So um PBS does not have the ability to model that.

SPEAKER_01 1:03:23

And that's something that's really cool about Solar Farmer is that they have every module they take like 36 points, and then they actually string it all together in their 3D sh scene, and then they do all those calculations in real 3D, which is why it takes forever for that simulation to run. But it's really cool that you can actually model that now.

SPEAKER_02 1:03:40

Yeah, so so this bankable energy theme that we're talking about, that's kind of where the industry is going is being able to calculate and and quantify what that mismatch loss is without taking an overly uh punitive approach.

SPEAKER_01 1:03:53

Well, and to be clear, what is currently considered bankable is the PB sys approach, correct? Which is not even doing that additional 3D validation. Right. Right.

SPEAKER_02 1:04:02

And and we're all trying to become more accurate because I think you know it it benefits the industry to have systems producing what they're expected to produce. Yeah, it benefits no one. It actually hurts the industry when you have overly aggressive developers, overly aggressive vendors like module suppliers that or resource weather resources, satellite resources. Correct. Uh yeah, the the the resource providers, it it it really hurts the industry if we have projects that are not performing as they should. There are there are companies that are that have operating fleets that are performing very well, but across the industry, we we have a shortfall. We're the the the systems are not producing uh what the developers and the IPPs have said they were when the project originally went to financing.

SPEAKER_01 1:04:47

Now, and the worst part is that the incentives typically are all there for people to make this as optimistic as possible, right? Instead of taking a probability 50 case or saying it's a probability 50 case, but really it's a P25, P10 case where it's very optimistic. And the incentives are there for that part of the reason for this conversation is so that if people wanted to design a truly P50 case, hopefully they can listen to this and have better insight into how to do that than before.

SPEAKER_00 1:05:14

Yeah.

SPEAKER_01 1:05:14

Right. And so we're talking about putting all of the extraneous losses that may not be captured in a perfect third pillar, the model itself, into that module quality loss factor or into the the mismatch losses, and then having that baked into that energy model as that write down, right? So you can add it there, and you're heavily scrutinizing that as an IE and at aspen, uh putting your IE hat on at aspen, right? And then after that, right, because that is very we could talk about that for hours, right? What goes into that, those specific just details? I think can you give a generally held wisdom approach? Like, say, if somebody was going at the system and they wanted to do this reasonably well. With minimal time, what would you ask them to do? That's a loaded question because I'm an engineer.

SPEAKER_02 1:06:06

I want all the details.

SPEAKER_01 1:06:07

All right. Then we'll say we'll plead the fifth here and not not give a very go. I mean, they go as deep as you have time for.

SPEAKER_02 1:06:14

They could go to PV watts and run a very simple calculation based off of DC capacity, AC capacity, and generalized DC and AC losses. They can go to solar resource compass and get the sub-hourly losses. They can go to solar resource compass and get the if they pay for the the well to get the sub hourly losses, you have to pay for solar resource compass. But solar resource compass will also give you a yield number with all of your monthly output for the inputs and the outputs needed for a financial model. Um it depends on which stage you're at, because uh you you asked for a general overview kind of when you're getting the first touch on a project. And you and I would go back and forth when we would I'm always first touch and you're always last touch. So correct, yeah, and it's a good balance. And my my response was always where we can be precise, we should be. If we have the information available to do it precisely now, let's do it now. Yep, it'll save us effort in the future, it'll help close that gap. That's typically a five to eight percent difference in energy yield between what the developer's initial model was and what the IPP's final number was, or what the the final bankable IE model is. It's typically a five to eight percent difference, is what we've seen.

SPEAKER_01 1:07:26

And to put it in perspective, the final bankable I like the bankable number is the IE's best representation of what the site is actually going to produce. Correct. So without any further assessment, we could try to say that is as close to a perfect P50 as possible. Correct. That's the aim, at least.

SPEAKER_02 1:07:43

Yes, and what I think the developers in the IPPs are trying to avoid is what they call a haircut.

unknown 1:07:50

Yeah.

SPEAKER_02 1:07:51

IEs are always accused. Oh, I the IEs just take a haircut.

SPEAKER_01 1:07:55

No, I haircut means I'm at 100 and the IE is saying 95, which is a 5% haircut. Yeah, a five percent hair.

SPEAKER_02 1:08:00

So DMB just takes a haircut across uh the developer's production number. No, it's it is a it's a scientific and an engineering process to go through, and it it may be five percent difference. It doesn't mean the IE is taking a haircut, or it doesn't mean that the banks are taking a haircut. It just means that there's a difference between what the develop how the developer modeled it and how the IE modeled it. And it's not IEs are not out there just saying, oh, take 1% hit no matter what.

SPEAKER_01 1:08:27

No, they're doing their own models and then saying, hey, based off of our models, our models are different than your models by one, five, however many percent. Correct. And then they're saying, well, we believe this and the banks believe them. So the cap the accessible capital is based off of the IE's number and not the developer's number. Now the developer is well within the rights to keep trying to use that number for their asset management team and to see if that actually hits. And if the developer's right, great, they have more profit from the project, right? But you still don't have access to that accessible capital, which is really the whole purpose. Well, one of the two purposes of bankability, right? The first is to get the accessible capital, but then the second is to have your asset management team judged and put projections in accord with what is actually real and reasonable for those projects. Right? Those are the two real a lot, correct me if I'm wrong, but that's what I interpreted.

SPEAKER_02 1:09:20

I'd say there's like a I hate to say there's a four fourth leg to this stool, because I don't like four-legged stools which is unstable in in mesh environments, 3D mesh environments. Your engineering brain. But it it also, and I alluded to this when we talked about the near shading scene. The importance of getting that near shading scene accurate in in your simulation is that when you go to run a capacity test, you the the the uh uh front shade beam losses. Yeah, so your upshade beam losses are whenever that is what is it?

SPEAKER_01 1:09:53

When one when when the factor is less than one. Yeah. One means no shading. There is no shading on the entire system. There's no shading from the sun, from say you have a tree next to your system, and the sun hits that tree and then only hits 50% of the panels, F shade beam will be fifty 0.5 or 50.

SPEAKER_02 1:10:12

So if it hits 30% of the modules, it would be 0.7. Yes, correct. So the capacity test procedures that are accepted by the banks and included in EPC contracts as a performance test, you have to filter out those periods where F-shade beam is less than 0.99, really. There's there's 1% buffer in there. And so this is where timelines and EPC contract payments and everything comes into this bankable model of looking at a project that's bankable. Are you going to be able to close this project in March? Yep.

SPEAKER_01 1:10:44

Well, the F J Beam during winter could be for these systems in the Northeast, never F1.

SPEAKER_02 1:10:50

And and we did this. Um, I was working on a project in Massachusetts. Uh gosh, this was I hate to say it, but 12 years ago. 12 years ago. I do it doesn't feel like I've been in the industry for that long. But uh, I looked at this site, and uh so before before PV Syst had the ability to do PV Syst 4. I've already said there were ghosts. Yeah, one of the more greatly frustrating aspects of it was their near field shading model uh interface was atrocious. It was no I mean I I people think it's bad now. They made fast improvements, and I love it. And we I use PB Syst every day at work, and I'm so thankful for all user interface improvements that they've had over the past decade. But they started out where you would define a table and then you would have to, you could not grab the table and move it with your mouse to a particular location. You had to enter in coordinates of a table for each table that you made, and then you had to do the same thing for trees. So at DNV, we would do a stopgap measure in Google SketchUp, where we build everything in Google SketchUp. We would then go to the equinoxes and the solstice, the the summer, the winter solstice, and and one of the equinoxes, and we would click hour by hour throughout the day and count how many module strings were affected by this shading. We'd plug those numbers into three different columns for the two solstices and the equinox. Then we would extrapolate. We'd extrapolate an annual shade loss, a percentage shade loss on an annual basis. So this is not accurate. This is on an annual basis. We would say eight percent shade losses, and then we'd go into PB Syst and manually adjust the fins on the horizon losses to match that eight percent shade loss. Oh my god.

SPEAKER_01 1:12:42

So totally inaccurate, but it somewhat worked. And that was considered bankable at the time because there was a dearth of better ways to do it. And as the industry progressed, we've the goalpost has reasonably moved for what could is considered bankability with better and more realistic estimates and better models, right?

SPEAKER_02 1:13:01

Yeah. So to tie that back to the performance model or the uh the the capacity test, yep. When we went to test this same system, when the EPC went to test the same system, uh there was never a day where uh where the F shade beam factor was one because of the like we had south side shading, we had north side shading, we had east and west side shading. It just had to do with the particular nuances again. In the DG market, we're trying to smash as many modules as we can into super tight spaces, which is and when you have wetlands where you can't can't clear trees, you have property lines that are in very close and tight. The developers in the IPPs are making a choice to increase capacity in favor of megawatt hours, but the yield drops, so the efficiency of the system reduces. And this is an example. So what is amazing about PB Syst is that once you have that 3D model built, you can then simulate simulate every hour of the day and see the gray hard shade lines, and you can see the yellow affected strings inside of PB Syst. So again, to be commercially reasonable in trying to close projects and help make them bankable. Uh, I asked for drone footage at 9 a.m., noon, and 3 p.m. To see the shape on a particular date. And what I did was I said, okay, let's take these real-world drone photos on a clear sky day and compare them to how the PV Syst model shows the shading should be. I wrote that up in the report, put the pictures in the report, and the bank accepted it. We're having difficulty passing the capacity test or the performance test, because I think we ran a 24-hour performance test for this project. Yep. Uh much more qualitative, much like a PR test or a not a five-day or a real no regression analysis, very qualitative. And my language in the report was although we don't have a qualified performance test passing result, what we have confirmed is that the shade model characteristics on this date are consistent with what the inputs to the model are. Therefore, we're confident that long term the energy model is accurate to the installed conditions. They closed. Yeah, so they're commercially reasonable, commercially reasonable ways of moving beyond just this brick wall closed-in environment that IE sometimes get criticized for. Uh, but there has to be a thought-out, there has to be a procedure, a methodology.

SPEAKER_01 1:15:35

You have to be a science as a scientist. You have to put on your science hat and like actually do that.

SPEAKER_02 1:15:40

Correct. So to kind of close out this big contributors to the PB Sys loss tree, um, where there are differences, uh, I mean, AC and DC omic losses are are calced based off of wire size and distances and equipment selection and locations.

SPEAKER_01 1:15:55

Those are as trivial as it gets, even though they're difficult to produce. Correct, correct. Or time time intensive, not difficult to produce.

SPEAKER_02 1:16:01

Transformer losses, both uh both resistive and constant, or load or no load. Load or no load. Uh, and then what's challenging sometimes in that model of reviewing a developer's PB sys model uh is where does availability come in? Yeah, well, that's where did they where does degradation come in?

SPEAKER_01 1:16:20

Availability and degradation. Big, big ones.

SPEAKER_02 1:16:23

Yeah. So recently, when I first started at Aspen, it we took a 99% availability for fixed tilt systems and a 98.5% availability for tracker systems on annual energy. Period. Period. That's three to four days of downtime every year is expected to be built in for the model. Uh, the IEs have now done much more research to make it a more robust model to where it accounts for teething issues. So you take an additional one to one and a half percent hit in the first two years, I believe it is, but then you're rewarded at year three of operation. With only a half a percent hit or with like, yeah, so it's a 99.5% availability for fixed tilt, 99% for trackers. But then as the inverter warranties start to expire, and most DG projects are built with string inverter or central and small central inverters these days. What everybody calls string inverters. Well, you're not connecting string inverters to uh or strings to an SMA or a uh a chin or some chin models. Anyways, it gets complicated, but what colloquially is held as string inverters or small central inverters. Correct. So as the warranties expire, those inverters start to fail based off of the the bathtub curve essentially for any uh any electronic device that's out operating in in the wild.

SPEAKER_01 1:17:42

Especially being constantly exposed to sun and heat and winter and snow and all the elements.

SPEAKER_02 1:17:48

Yeah, so you know, from years eight to fifteen, as those inverters expire, uh they will be replaced. The entire the expectation is that all of the inverters will be replaced by year 15. The the the shorter age ones will be replaced starting in year eight. So you take an availability hit in years eight through fifteen to replace those inverters. To replace those inverters, but then you get it. Once those are back, you get it back from years 15 to 26. And then from year 26 onward, happens again. Yeah, but then as we discussed uncertainties get this this 25 to 40 year life, like the uncertainty of well, how many inverters are going to start to fail at year 26, you now have the issue of um, you know, mismatched module connectors or even matched module connectors not being mated properly that build into the unseen degradation of systems that asset managers kind of throw up their hands.

SPEAKER_01 1:18:39

And they're well, that's why the degradation is a consistent loss. And even though most module degradations follow a logarithmic curve to a quasi-asymptote around 80% or 75%, we assume a consistent loss throughout all that year. A literal per year, yeah, based off of DNB studies. Yeah, yep. And so you have that baked into the model. That is something that is typically outside the 8760 because you're looking at that from year two onward, right? Not year one. In year one, you have almost no unavailability, especially for the capacity test. And then you add that unavailability on for the actual numbers. And so that's part of the bankability of the lifetime of the energy simulation, not just the first year's 8760 P50 numbers.

SPEAKER_03 1:19:28

Correct.

SPEAKER_02 1:19:28

So we talked a lot about a little bit about equipment selection, a lot about energy models. Yep. Um what I there are a couple other aspects I just want to touch on. Please, that's why we're asking more questions. Um equipment selection as it specifically relates to trackers.

SPEAKER_01 1:19:46

Logan has many opinions on trackers.

SPEAKER_02 1:19:49

Yeah, I'm I'm somewhat pri proud that my uh the AVL that I manage at Aspen is very short when it comes to trackers. Uh, I've seen too many trackers not operating as expected and not in a reliable way.

SPEAKER_01 1:20:04

Yeah. So I I remember there's many, I grew up in OM, and many a time there would be truck rolls rolled to fix trackers. You'd have one not working, you'd have many not working. There's a litany of problems. I remember one week I would just go out and I would actually grease up the tracker gears. And I was listening to a book that I was listening to the account of Monte Cristo at the time. I was just listening to that for two or three days straight as I had this grease gun lubing up the tracker bushings, right? Yep. And so there's a lot that has to do with trackers. Yeah.

SPEAKER_02 1:20:38

So um hiring an independent engineer and reviewing a bankability study is very important when it comes to operating these assets long term. Now, some IPPs are incentivized, much like developers, incentivized to maximize profits. We're all incentivized to maximize IRR.

SPEAKER_01 1:20:57

Um I mean, it's it's a business. If we weren't doing that, the whole industry would not work. Absolutely. You need to make profit from these systems to actually build these systems, or else no one will be building them.

SPEAKER_02 1:21:08

Correct. Um, and you don't the challenge I have sometimes is making the balance between uh gold plating a project and doing uh installing a project to a sufficient enough quality to support all of the assumptions in the financial models, the OM costs, the downtime, all of that. And it it's not unique to me or to Aspen or any particular company. It's industry-wide, is how do we make that balance? You could gold plate something and install it for six dollars a lot, which used to be the install cost back in like 15 years ago. Yep. Back when silicon. But that's not gonna be profitable.

SPEAKER_01 1:21:45

And no, you're not gonna build a lot of those projects, right?

SPEAKER_02 1:21:48

Correct. Uh, so how do you do things like wire management? Uh, and now we're talking about the actual implementation and construction of the project. So uh having high construction standards and inspections by qualified third parties really helps in supporting an argument with the banks that the project was built in a bankable manner.

SPEAKER_01 1:22:10

And it also helps make sure that when you hand this off to asset management, that you're not giving them a crap sandwich, that you're giving them something that is going to be hopefully as easy as possible to maintain for the lifetime of that asset.

SPEAKER_03 1:22:22

Yeah.

SPEAKER_01 1:22:23

And so that's where the sleeves for the frost saving come into play, right? That's where having a tracker brand that is cost effective, but then also isn't going to break down and will be in business. I know there are many tracker brands that were in business 10 years ago that are not now. And then when there is a warranty, there's not not even a warranty, but a part that breaks, how do you even fix it when the brand's not in existence?

SPEAKER_02 1:22:46

Aspen owns a couple of projects, actually owns about a dozen projects with tracker vendors that are no longer in business. We better go out on the the third-party market to try to find replacement parts. And where we can't do that, we actually have some projects that are at risk um of not meeting its their minimum production guarantees. So we're actually looking at modifying the system to be very expensive fixed tilt systems just to keep them operational. Yep.

SPEAKER_01 1:23:11

Um instead of having a tracker be flat at stowed at zero, right? Getting the sound, it might be even better to turn change this to a fixed tilt system if the trackers aren't working.

SPEAKER_02 1:23:22

Yeah, and and stowing at zero is a very sensitive subject.

SPEAKER_01 1:23:27

Yeah, well, wind reflecting. Yeah, yeah, because it will, especially with the wind issues with that.

SPEAKER_02 1:23:31

Yeah, so and and just to provide background for for people that are listening is uh solar racking systems had gone from robust Unistrut, square stock steel, or even wood frame uh on rooftops to so these are thick steel materials, you know how they behave, you know what their moduli are for all of how they're going to behave under stressful conditions. And as the industry has progressed, we've had much more creative solutions come on board that resulted in lighter materials, more engineered solutions, much more elegant on paper solutions. Yeah. But as we started to lighten all of the steel components or aluminum components that support the modules, you're changing the way that these structures behave dynamically. So when I started in the industry, almost all solar structures were considered to be rigid structures. You did not have to account for dynamic excitation from uh oscillating vortices or uh trailing, uh trailing rows being affected by the vortices that come off of the upwind side of the wind modeling.

SPEAKER_01 1:24:38

What Logan is saying in engineering terms is when wind passes over structures, it creates waves. And that waves can incite, especially tracker structures which are movable, to bend and contort in ways that are not desired.

SPEAKER_02 1:24:54

Correct. And and again, when I started in the industry, in addition to considering them to be rigid structures, the only real ASCE, American Society of Civil Engineers, which is a code book that prescribes environmental loads onto building structures, the only way to really analyze PV systems was with a static wind load of a single row. Uh, as we discovered, and which it worked at the time because we had rigid structures that were built with rigid materials that would somewhat overestimate, so you had factors of safety at every calculation along the way, somewhat overestimate the wind loads on a structure. As the structural components started to get lighter, we started to see that these structures are not rigid. They actually move in the wind and they are they are affected almost to a so if you have the static wind load, which is you have a design wind load with certain characteristics of wind hitting a tilted surface like a wing, you're gonna have uplift, you're gonna have overturning moment. That all has to be calculated by a licensed engineer. What we found though is that if you put another row in front of that, you not only have the static wind load, but now you have these oscillating vortices that cause what are called dynamic loads that can be upwards of 20% of the static load. So we used to design it to 100% of the static load when we discovered we actually need to do 120% of the static load based off of wind tunnel testing. Uh, and then we we kind of locked that in for fixed tilt. So I was really fortunate to be invited by um a plan reviewer at the Division of the State Architect in California to join a uh a small group of industry experts to help write the new wind load code and seismic code. It was the COC so Structural Engineers Association of California, uh put together this panel of folks that that got together and said, we ASCE7 can't accurately interpret the wind loads on PV systems that have like the the sawtooth type structure for rooftop and fixed tilt systems. We need to we need to come up with a prescriptive method to describe the wind loads on these systems so that the vendors don't have to have a wind tunnel report to support the wind loads. Number one. And then number two, the seismic loads of ballasted systems. These are all bankability concerns for projects that ballasted systems move in the event of an earthquake. They scrape at the roofing material. Uh, you have to have certain offsets from existing other structures on the roof so that if this ballasted system is sliding around on the roof, it's not gonna hop and then hit another something else, another piece of equipment. Um, I was really fortunate to be invited to do that. That's how I met uh to partake in that panel. That's how I met the folks that I eventually hired me at DNV, uh, was through that that panel discussions and helping write the code. So I was really fortunate to be invited to go do that. Uh and then to we we discussed fixed tilt, but now trackers, as they have gotten lighter and They have gotten much more elegant in their design. The modules now are a much lighter component and they're easier to rotate. So now you get these. They're like big fins on an airplane that are able to be susceptible. It's scary as hell, right? If you're sitting at the window seat on your wing and you see the wings flat up and down, like that's what they're supposed to do. Yep. And it's designed to do that, but it's scary because you think that they should be rigid. But if they were rigid, it wouldn't be able to take off. No. It would be too heavy. Um, but that's where, you know, as we as the industry progressed and the IE started to investigate failures of these trackers, the wind tunnel studies like or the wind tunnel labs like uh RWDI and uh University of Ontario and uh uh CPP, they started to develop testing standards for trackers that allowed us to investigate things like torsional divergence, torsional galloping, um, oscillating vortices. There were four or five different failure mechanisms that they were seeing out in the field that they then developed a test standard for. And it's important that if you're considering a tracker for your project, that you are reviewing a bankability uh report from a qualified engineer based on a wind tunnel laboratory test result from a qualified wind tunnel laboratory.

SPEAKER_01 1:29:17

And this is not just for single access trackers, even though it's very pertinent for single access, it's also for fixed tilt on ground mounts and on rooftops. It's just have good wind tunnel testing. And if you're fortunate enough to be able to have a vendor that does that, prioritize that at all costs. Correct. Would be your recommendation. That's my recommendation. Yeah. Yeah. And then so that happens for racking, that's a perfect example of something moving beyond just the simple PV sys and getting the energy model, but how to make sure that what you've built actually stands the test of time and by proxy is bankable and you can get financing for it.

SPEAKER_02 1:29:53

Correct. And and just to bolt on this last piece, and I'm uh certainly not an expert in this field, is do the the questions part of the bankability review or a project level review by the banks is a review of the operations and maintenance agreement.

SPEAKER_01 1:30:10

Yeah, I'll I'll have somebody on that's for asset management that can talk to all about that.

SPEAKER_02 1:30:14

Yeah, so to put it in a very high-level nutshell and summarize it, the IE is going to opine on whether the budget and scope in the OM agreement are reasonable to support the the predictions in the energy, in the energy model and the financial model. Um, I think they most oper uh most OM agreements assume one truck roll for preventative maintenance, a specific set of things, uh, of tasks that need to be done during that truck roll. Yeah. And then they authorize a corrective maintenance budget uh in in the OM agreement, but it only is used in the events that corrective maintenance is needed. So there's preventative maintenance that's done every year, and then corrective maintenance is if something is found to be out of whack, the corrective maintenance is a budget to go fix it. You're assuming you don't have to fix anything in the preventative maintenance budget, but the corrective maintenance budget is there to fix things.

SPEAKER_01 1:31:08

And if there's not enough budget there that the IE assumes it would be necessary for a project with this specific build material, right? So the racking components, the modules, the inverters, and the specific build configuration, then they will opine and say, hey, maybe you actually need more budget for this. Or there is an additional quote unquote haircut to the system.

SPEAKER_02 1:31:31

So we talked about the projects where we received that weird pan file that cost us uh uh percent and a half at a deal close time with the banks. Those same projects were using an unproven, quote, unproven unbankable tracker model uh that we we really uh these projects were being built. So we had to use that. We had to use that with that vendor. We were not gonna change the change our own. Yeah. So at Aspen, we were looking for the narrative to tell for the store. The the narrative to tell about the project of how we're accounting for a non-bankable product. I I shouldn't say non-bankable, a product that that I had considered to be unproven in the industry. Um one that is not a surefire six dollars per watt product. Right. So well, yeah, cor correct. I'm not sure about the six dollars per watt. I meant insofar as like it is the gold plate, gotcha standard. It's not a gold plate product, largely because because they didn't have a full bankability report from an IE. It the scope that they did have an IE look at wasn't robust enough to talk about uh torsional diversions, some of the uh some of the the minor tracker failures that we've seen across the industry. Uh so we took a creative approach and said, well, let's take if this tracker is not in if the performance of this tracker cannot justify our availability assumptions, number one, let's just take an avail and another uh an availability hit in our energy model. So we took an additional one and a half percent availability hit. The number still works for the project. Okay, well, let's triple like which spare which parts of the tracker are we have the most? Let's triple the amount of spare parts, keep them at the site. So we're gonna have to pay for that out of CapEx, and then we are likely gonna have more truck rolls. So let's let's increase our OpEx budget by 20%. Right. That all made the project work financially, and it was bankable. So the project became bankable because we used we we we used money, we used capital in other areas. We reduced the energy model, which is the future revenue predictions for the project. Uh, and a a caveat about tracker availability, it's not just downtime and uptime. It's also is the tracker pointing where it's supposed to be every row, every hour of every day. Yeah. If half the rows are pointed east and the sun's in the west, that's unavailable. That's unavailability. Yep. Um, so we justified to the banks that we are reasonably accounting for this risk and mitigating it through increased unavailable or increased unavailability or decreased availability, increased spare parts, and increased truck rolls. And the banks said that's acceptable. We will fund this.

SPEAKER_01 1:34:14

I think that's an excellent point to bring up, which is you don't need this gold-plated product to reach bankability, right? Every project, especially in the GG space, has its story to it. And if you properly account for the risks associated with that project in that story and build a plan to mitigate those risks, you can take something that would ordinarily be a crap sandwich and make it into a very, very bankable product product because you've mitigated those risks effectively. Correct. And you can do that on any case-by-case basis for any project, whether it's a gold-labeled product or the newest vendor on the market. Yeah. Right. And so anything could be bankable if you've packaged it and accounted for the risks properly.

SPEAKER_02 1:35:03

I mean, to circle back to what we talked about at the top of the PV sys loss diagram, you could fund snow clearing for every project. You could have and make it work.

SPEAKER_01 1:35:11

But is that in your asset management costs?

SPEAKER_02 1:35:13

Well, that's exactly that's that that's what an I a qu a competent IE will ask that question, make sure it's funded in the financial model and included in the ONM scope. Yep. Um, a good a good note on trackers in snowy regions, because as the solar market has grown, we've grown into places like New York and Wisconsin and Minnesota, where there are frosty risks, there's snow loss risk, um, because it's so variable. Uh is a it's important to review the warranty terms of trackers because some tracker vendors require the owner to keep a maximum, a maximum snow cover at the site. And again, that's impossible. It's almost impossible in Copenhagen, New York. It's almost impossible to keep an 18-inch maximum snow cover when you get three feet in 24 hours there. Yeah, you're not going to do that. So it's important to review the warranty provisions and what is going to violate the warranty. And if you think about it from a mechanical perspective, it makes sense that these the tracker motors are designed as efficiently as they possibly can to rotate under environmental loads. If you're throwing snow on the ground that are good, that's going to resist that that motor movement, the tracker vendor is like, we can't control that. Yeah. We're going to protect ourselves. And they're, they're in their right to do that. Uh, but it also you have to have a competent asset management team to say, how where do we stow these trackers to where we're not going to get wind damage from the storm, where it's going to not allow snow buildup.

SPEAKER_01 1:36:46

Uh stowing flat, again, sensitive subject. That's because it stowing flat increases the vortices of the wind. And so it's like you don't want to stow flat. You actually want to stow off flat. Right.

SPEAKER_02 1:36:57

That's the thing.

SPEAKER_01 1:36:57

The most wind issues.

SPEAKER_02 1:36:59

Uh torsional divergence, much like the Tacoma Narrows bridge. Yeah, normal wind gusts, but you have uh aeroelastic flutter. That was the fourth mechanism.

SPEAKER_01 1:37:07

The aeroelastic flutter. I don't I always forget that.

SPEAKER_02 1:37:12

It's rather nuanced, but it's a fun, fun one. So yeah.

SPEAKER_01 1:37:16

And so we have basically, I think we've laid out from soup to nuts, right? We have the energy model, we have the equipment, we have the plan, we have the way we're going to install it, we have mitigating risks for that specific location, right? We have the asset management plan. That is all wrapped up into one gigantic hundred-page bow of a document that you're going to have validated by the I independent engineers, and then also have the banks review and eventually approve. Is there anything there that we missed that you think would be beneficial for developers to know to for you to opine on that you have experience in? That would that would help.

SPEAKER_02 1:37:52

I think uh the last piece that I would add in there uh is just in an add-on to what we discussed. So uh large-scale utility module purchasers, developers and builders, uh they hire companies like CEA, Intertech. Um, there's another company based out of Arizona that will go visit these factories in Malaysia, Indonesia, China sometimes, India, wherever they're producing modules. Uh again, my expertise and experience has been in the DG field where we're buying 10 to 30 megawatts at a time, not 300 megawatts. So we're we don't really have the justification to spend the or to expel the costs of hiring an independent party to go inspect our particular modules. Usually we're buying modules on the secondhand market, so from a distributor. And this really helped in us closing deals with banks. Uh, banks that had, and part of part of what's happened in the finance community is you have these experts in-house at the banks that are engineers that are used to doing 300 megawatt deals now coming down and doing smaller and smaller portfolio deals again.

SPEAKER_01 1:39:00

But expecting the same diligence, diligence, not diligence as a utility scale project, which DG companies just simply don't have the capital for.

SPEAKER_02 1:39:09

Right. We don't and don't do. And so what really I found beneficial in describing the way that DG IPPs and developers procure modules is uh, you know, an AES building a 300 megawatt facility will say, Ginko, here's my construction schedule. I will need 30 megawatts of projects or 30 megawatts of modules per month for 18 months. Oh, guess what? We're three months into construction, now we're delayed. What does Jinko do with those 30 megawatts of modules? Do they shut down their factory? No, they keep it churning, they keep it going. And there are second, there are like second tier, uh, I shouldn't say tier, secondary market suppliers that come in and say, Jinko, I will buy those 30 megawatts of modules and I'll sell them on the secondary market. That's where we really jeopardize or jeopardize. We capitalize on uh that excess. At that as uh capitalize on that excess. And when I get asked the question, well, when are when are your modules being inspected? I say never. We don't pay CEA to do it. However, CEA was there a month before and they'll be there a month after. And with Chinko's permission and AES's permission, we've been we've been very thankful that we can rely on those two CEA reports as saying, although your particular modules weren't inspected, the ones before and after before and the ones after were all inspected and they were consistent and found to be green flags, some amber, but no red flags. And so we've we've been thankful that the industry has opened up enough to where we can share these quality inspection reports as part of our bankability for the equipment that we're purchasing. And that's uh and that worked. And it I I'm not it it provided a narrative as to how we made the decisions that we we did when it comes to module procurement. And I think most IPPs in the DG space operate under the same, the same guidelines. We're not gonna pay a third-party inspector to inspect our particular modules and find 12 of them to ship to a P Bell or a Kiwa, I think is what they're called now, uh to do all the robust testing for specific LID and low light and all the all that information.

SPEAKER_01 1:41:29

That you're well, yeah, the product that you're using. Yeah. Perfect. So that covers bankability, at least as it stands right now. Thanks for going over all that. I'm really curious, based off of your experience, you've been in the industry for 20 years, where do you see this going? Right? We just took a snapshot and actually like a journey to the past to see where were we, how did we get here, what is industry best practices right now? Where would you like to see the industry going in the future? What would you think better practices in the future would entail? This is an ability to opine without expertise. This is a prediction into our crystal ball. So that is not meant to lock you into this box. It's just meant to try to say, hey, what are we thinking in terms of innovation?

SPEAKER_02 1:42:14

Wait, are you trying to get me out of an industrious box here?

SPEAKER_01 1:42:16

Because uh, attempting to with foresight, uh, with nice language, attempting to to uh what would you say, cajole into into insight.

SPEAKER_02 1:42:28

Correct. Yeah. So um I think as you see as you see the industry changing, and it it the solar coaster is a reality. We all don't really know where the industry is going to go, what the federal regulations and state regulations are gonna have for the industry as a whole. Uh, I've tried to stay away from really getting into that because as an engineer that's very industrious, and I just want to build good projects. I've had the the luxury of being able to kind of put my blinders on and say, just give me a project to design and build and operate correctly. Yep. Um, I think what what we'll see is uh an increased reliance on uh extrapolating or extracting capital from operating fleets. So Aspen is very incentivized to keep our operational fleet at as close to 100% availability as possible. That's not possible, but as close to it as possible, uh in retrofitting our existing projects that have issues and continuing to build good quality projects up front.

SPEAKER_01 1:43:32

And potentially repowering projects that already have an interconnection project, uh, interconnection agreement that are nearing end of life to then have even more energy being produced from them.

SPEAKER_02 1:43:42

Yep. Um, and you and I have talked about innovations in not only the testing and commissioning activities that happen at between mechanical and substantial completion, uh, integrating it, replacing full IV curve tracing every year with aerial thermography that allows targeted IV curve tracing. I know some uh asset managers are already deploying that and they're finding some success in doing so. Uh again, you you talked about repowering. So you have old products on the out in operating prior uh operating projects that no longer have warranty coverage, that no longer have replacement parts. Um part of part of my duties and my team's duties at Aspen is to support our asset management team in redesigning these projects and finding, hey, this inverter, these inverters are failing. That vendor's out of uh out of business. They provide no support. Help us find another inverter that we can plug in and give us a new set of drawings to retrofit. You know, have an engineer uh come in and and provide the electrical engineering.

SPEAKER_01 1:44:44

Sometimes you work with the utility to make sure that they're okay with that inverter and whatever settings are. Or in compliance with interconnection agreements.

SPEAKER_02 1:44:52

Um and I also see a future that's longer term of uh taking existing interconnection agreements and supplementing them with non-solar. So battery storage, um, combined uh combined uh LNG, liquid natural gas, compression expansion cycles, even micronuclear, supplementing the solar during the day because it's the cheapest value, the cheapest energy source when the sun is shining in a predictable manner. But what do you do at night? You can still use that interconnection agreement to inject energy into the grid.

SPEAKER_01 1:45:27

Yeah, because it's already assuming that energy coming. Oh, that's brilliant. That's brilliant. All right. Uh I love the the ideas there. Now we got to put it into action. It's that's me being creative.

SPEAKER_02 1:45:40

Now I love both step.

SPEAKER_01 1:45:42

This is why engineers don't like being creative because now we're like, oh, those are so many problems to solve. How do we actually do that?

SPEAKER_02 1:45:47

Logan didn't do that five. Look, it's been five years and that didn't get implemented.

SPEAKER_01 1:45:51

But it's not about that. It's about hopefully somebody can do that. Yes, right? Yes. All right. Well, thank you. Is there anything else you'd like to say before we close out? I think this this has been pleasurable. Longer than I had expected. It's uh it's been great. We got into a lot of details. Yeah, thanks for providing as many details in your experience. I think one of the goals here is to have it so that people who have actually done the work, right, who actually know this intimately can help other people who don't and haven't had that experience to get to glean that wisdom. Yes. And I think you did a really good job today. So thank you for for everything.

SPEAKER_02 1:46:25

Yeah, we're I mean, we're here to make sure that the solar industry is representing the best quality that we can have going forward. It benefits everyone to have these conversations. And you know, I don't think there was any secret Sask shared here. It's just very we know what bankable looks like, and there are efficient ways to make commercial decisions and uh work with the project stakeholders to find a project that's viable in a in a manner that's not going to kill projects. Sweet. Well said.

SPEAKER_01 1:46:53

I think we'll sum it up there. Thanks.