Webinar: Scaling Your IPP With the Right Tech Stack

00:00

Kari Foster Hello everyone. Thank you for joining us today. My name is Kari Foster. I'm the VP of Marketing at Molecule, and today we'll be talking about your tech stack. And what is that exactly? Well, specifically today we're gonna be covering the technology that IPPs need to gain a greater understanding of portfolio risk and return. And those technology needs will be different at each stage of growth. So our experts are here to guide you through that.

Joining us today is Dean Charette, the VP of Partnerships and Alliance at Molecule. And Carley Dolch, Managing Director of Energy Risk Solutions at Ascend Analytics. Thank you both for joining us today.

00:50

Carley Dolch Thank you so much Kari, for the introduction and lovely to have everybody on the webinar today. I just wanted to give a little bit of an overview of myself, Ascend. So I have been working on providing solutions in the energy industry for about 17 years at this point. And I've noticed that companies have a couple of issues trying to keep up with some of the issues in the energy space, especially right now where things are changing very rapidly and there is a need for a first mover's paradigm. Ascend, the entity that I work for, currently has an entire suite of solutions to assist customers to navigate this volatility and rapidly changing ecosystem.

The primary application that we are thinking about today as we walk through this tech stack paradigm with Molecule is an application called PowerSIMM, which does risk analytics to support many different types of decisions in this space, including how do I think about hedging strategy? How do I manage my portfolio? How do I think about new assets they I may want to develop? And the risk profiles around that inform more cohesive decisions.

Something that we know is that most companies tend to think a little bit simplistically around their approach during the initial development cycle. And what we would like to do is to expand the concepts or options that you have in front of you and set you up with the right tech stack in order to make these better capital decisions.

Most companies will look at a PPA for early stage development, but we want to just show you here that providing other insights, especially managing some of that merchant upside that can give you better revenue options along the way is something that you may want to consider with the right tech stack. On the distribution on the left hand side, sorry, the right hand side, you can see that leaving that merchant upside actually gives you lowered risk profiles when you have it matched with the proper hedging strategies as well as really great revenue capabilities. Dean, I think this one's for you.

03:17

Dean Charette Hello everyone, as Kari mentioned, I'm Dean Charette, and welcome everybody to this presentation today. So what is Molecule? Simply put, we're the most modern energy trading and risk management system on the market today. Our platform is built from the ground up to be cloud native and SaaS based. We automate everything, not as a feature, but as a guiding principle.

We have an amazing UI and a powerful, restful API. That means no technical debt-laden solutions and can integrate seamlessly.

We support over 50 commodities, 250,000 products, and generate over a hundred thousand rows of data daily. We serve dozens of companies that you know, including the world's number one airline, national pipelines, and global funds, but our edge is power and renewables. The reason is that renewables in particular are more complex and difficult to model than anything that came previously.

Our angle is that the legacy vendors have been unable to keep up and we have filled the gap. It's for all these reasons that I'm so glad to be here today. I'll now turn it over to Carley to frame out the IPP Tech Stack story.

05:05

Carley Dolch Thanks so much, Dean, and just for everybody's understanding, the reason that Dean and I are partnering today is because we get asked a lot of questions on either side, either from Ascend or the Molecule side, about how to think about marrying risk analytics to trade capabilities. And what Dean and I have noticed is that most companies will only come to us when the problem is very acute and they're trying to make a really big shopping decision at a point in time where they're at critical mass. And what we want to be able to do is help frame, first of all, the shopping journey in general, and at which point you may want to consider being more proactive on understanding when to shop for that particular solution. And then the second dimension that we want to introduce is, aside from timing it correctly, if you do have the correct technical stack up front, you actually make better decisions and you're able to actually accelerate this timeline.

As I mentioned earlier, this is a first movers paradigm, and so being able to move quickly and efficiently is key. We put in a little quote, from a recent news article that was published, I believe last week that mentioned that two thirds of lending entities in the renewable energy space tend to use limited, specialized software, if at all, and tend to heavily rely on basic Excel spreadsheets, Google Sheets, et cetera.

And what we know is that, in the complexity of today's energy markets, that's not going to cut it. And so being able to understand what these tech stack solutions can do for you and when to buy them we believe can help you actually unlock way more opportunities for your entity.

There are two primary strategies that the entities tend to frame depending on where they are in their life cycle. So when you are an earlier stage developer, you tend to think about how do I get the funding for the renewables that I want to get online, right? Which makes a lot of sense. You need that capital in order to do it.

And so most entities will build origination teams. And they are very risk averse, which makes a lot of sense. You don't have a lot to work with there. And their position on their portfolio is to target the investors but not necessarily framing up what analytics are required for the second piece, which is operational strategy, where you need your portfolio to not just meet the requirements for funding, but you need to understand the flexibility in your portfolio. You need to be able to measure, monitor, and track risks, and also making sure that your cashflow objectives are hit and that the investor confidence that you have by creating that portfolio in the first place is maintained and you can redeploy capital or seek additional investments to scale your IPP.

What we know is that if you solve for operational strategy ahead of time during the fundability phase, effectively you can get better internal rates of return. You can truly manage better risk profiles, and also you can increase investor confidence and get you better terms upfront. And so again, having that correct tech stack to support those data points is critical. I'm gonna pass it back over to Dean.

08:43

Dean Charette So what's an ETRM? It's your system of record for all things energy trading: a single source of truth for positions, valuations, and reporting. This slide illustrates how portfolio analytics integrates directly into the trade lifecycle. Forecasts, risk insights and hedge strategies flow naturally into contract entry and hedge matching.

From there, positions are tracked, valuations are calculated, ideally automatically, and settlements are executed. The big idea? That analytics isn't just an add-on, it's embedded throughout the process, enabling transparency, efficiency, and ultimately, informed decision making.

This next slide is offered primarily as a reference. It's a schematic, showing how analytics platform and the ETRM integrate technically. The arrows in the middle represent key data flows. With market information and asset specs flow in to the analytics platform, forecasted price and volumes flow in to the ETRM, and hedge positions flow in either direction, depending on the context.

However, this diagram also underscores the complexity and volume of data involved, highlighting the need for a seamless integration. One ideally orchestrated through a robust and well-designed API.

So, here we come to the idea of the power combined: namely, how a closed loop, end-to-end alignment helps IPPs act with agility and confidence, providing the ability to react immediately to any disruption, whether driven by market events, unanticipated reg regulations, or new policies. But instead of dwelling on the abstract, let's explore some concrete examples.

This one starts with a commercial transition that many in ERCOT know well: rolling blackouts and load shedding prompted ERCOT to establish the ORDC, revising scarcity pricing, and shifting market dynamics. This prompted portfolio managers to ask, how do we capture these new revenue opportunities? Well, that's where analytics comes in.

11:52

Carley Dolch Yeah, so a lot of customers will come to us and ask, how can I predict when the next big weather event is going to happen, and how do I measure that tail risk impact? And our answer tends to be, you don't know necessarily when something will happen, but understanding the risk profiles in front of you and being able to put in different strategies in order to mitigate them when they do happen and costing those out is critical.

So in the case of a severe weather event, you may wanna be able to consider some more elaborative hedging strategies. Here we mention that you may wanna consider a revenue swap, a revenue put, or call can also help in some ways to provide that confidence that if you are out during a critical time, you won't incur losses.

12:44

Dean Charette And to make that actionable, you need a system that can incorporate these custom structures: not off platform, not on spreadsheets. But in the closed loop, where everything from insights through execution are in a single coordinated workflow.

Let's take another real world example, this time on the regulatory front. FERC Order 841 was a major turning point. It required grids to grant storage assets equal market access, but unlocking that value isn't automatic. Analytics is required to inform your business strategy. Carley, do you wanna take us through that?

13:38

Carley Dolch Yeah, sure. So when it comes particularly to storage, storage is harder to value than some other assets that you may include in your portfolio because you obviously have to intake the energy off of the grid during low price times, and ideally dispatch during high price times. And there's additional revenue stacks that are included in storage that are difficult to measure, such as ancillary services.

Being able to capture valuation on storage is one component, but also understanding how it behaves within the context of your portfolio. Whether you're co-locating an asset or you have a standalone and storage asset, it may act as a literal physical hedge as well and give you additional confidence for your revenue.

14:27

Dean Charette And to operationalize that strategy, you need a platform that can keep up, a system that supports multiple technologies so that nothing is managed in a silo. This is where integration becomes a real strategic edge. The result? Confident decision making based on a complete portfolio view.

Here's one more example, this time focused on policy. The Inflation Reduction Act of 2022 was a game changer for project developers. By enabling direct transfer of ITC and PTC credits, the door was opened for more flexible commercial strategies. But how do you negotiate between the trade-offs? I'll hand it to Carley to unpack the analytics.

15:29

Carley Dolch Yeah. Thanks so much, Dean. This is actually one of my favorite ones because it demonstrates how legacy thinking will lead to subpar results in a dynamic industry. So most entities, again, will bake in long-term, low risk PPAs to get that financing. And they do that because in the past that probably would've been an appropriate strategy for renewables.

But when you have the uplift in revenue confidence from, if, for example, production tax credits, you have that additional revenue confidence, right? And so what you may want to do is marry that to a lower price competitive PPA, and have the same tenor that you're receiving the tax credits, typically about 10 years, matched to that PPA tenor.

And then you may want to stop the PPA and then enjoy the merchant upside that you can capture in particularly volatile markets, such as ERCOT. And so being able to manage those risk profiles, understand and ID the what-if scenarios on production, the volume that your assets can produce to capture the tax credits, match it to a PPA, maybe it's production-contingent, a unit-contingent PPA, and playing with that tenor to make sure that it's appropriate for how you long-term want to manage your revenues, I think is critical. And Dean, how do you operationalize that?

16:58

Dean Charette Well, an integrated PPA module allows for quick execution, no matter which strategy you choose. Having this flexibility under the hood allows you to focus on the business and certainly not on back-office complexity.

17:18

Carley Dolch Yeah. And I wanted to add here, Dean, if we could just pause here just for one more moment. This is probably the most common use case for the marriage of a risk analytic platform, along with a trade management system: managing your PPAs in the context of your portfolio, understanding how to make decisions around designing the PPAs, or when to let them go, and ID those alternative structures, such as merchant upside, which needs to be occasionally managed with hedges, right? This all like circles around this particular decision. So just wanted to highlight that here. Thanks Dean.

17:55

Dean Charette No, certainly. And it's also not, the analogy is in markets where you know, you can make a directional bet or you can bet on volatility.

And from our perspective, both the analytics platform and the ETRM, we're kind of the volatility play. You know, if nothing ever, if you imagine a case where, you know, 2016 and nothing changes with the respected technologies or renewables outlook or renewables policy, then it could be the case that you're, you know, spreadsheet or your legacy system is just fine. But as, when you get to a stage where things do change, you want a system that could change, you know, with you. And what happens is the analytics platform helps to inform what, you know, what strategies you should do. But you also need that back office, the ETRM, a system to be able to reflect all those changes quickly so that you could, you know, if it's case of changing a PPA structure, you know, you don't have to it's not a, it's not a long-term process to update your system. You just, it's right there and you can just act with confidence.

19:15

Carley Dolch Very true.

19:18

Dean Charette So here's a different perspective, I'll say, showing how everyone on a floor from scheduler through to the CFO really takes advantage of the tech stack. So let's consider a summer on the floor of an IPP.

So a trader might be asking whether a particular hedge product will be able to protect efficiently for price spikes in the next week. Now, you know, they'll certainly be doing some short-term forecasting, but you can imagine them also employing some scenario analysis as well. But on the back end, they want to have confidence that both their data feeds and trade entries are happening automatically and accurately, and as we say to six nines.

Next we have a risk analyst. And the risk analyst has multiple views, and one of them is to look at your, their, you know, P 95, P 99 downside. But they often also have a view of their risk limits. So in order to manage the one side, they're gonna wanna have a strong, robust, well-tested CFaR/GMaR engine, and also a very flexible stress testing model to be able to, you know, capture all the dynamics in their particular market. And as mentioned, at the same time, they'll also want to have an eye towards their position reports, and especially the limit reporting. A scheduler might be looking at their physical deliveries and how they should be managed across assets.

Well, if they're looking across assets, they're certainly gonna want to do some portfolio analysis, and that's where the analytics platform will be key. But at the same time, they're gonna want to have a strong inventory management module, and in particular one that'll help and ensure to mitigate against operational risks.

When it comes to the CFO, if it doesn't jingle, it doesn't matter. But these come into two timeframes: one short-term and one long-term. Looking towards the long term, yeah, they're gonna wanna look at the long-term forecasts and their cash flows positions under those different forecasts. And at the same time, they're gonna wanna have their finger on the pulse of current P\&L reporting and their mark-to-market dashboard.

Calling back to my old days as a quant, you know, your team might have a load forecasting model that's an edge for your company. If that's the case, you're gonna want to have an analytics platform with the flexibility to allow you to incorporate those bespoke models. At the same time, you are gonna wanna have some customized reports so that you can communicate those ideas to your firm.

So with that, I'll call on to Carley to help close the loop on this end-to-end discussion.

22:47

Carley Dolch Yeah, of course. So, as we have seen, the tech stack that we are thinking through touches many different parts of the business. It accelerates decisions, it reduces reporting friction, right? Because if you have one consistent platform that you're making those decisions from and you have high confidence in it, everything syncs up and flows appropriately.

You eliminate process inefficiencies through having that type of functionality. And I'm gonna skip to the bottom and I'm gonna come back to four. You want everything to be fast, efficient, and high confidence because this is a first mover's paradigm. The quicker you can get into the interconnection queue, the better. The more that you can scale that volume to get more into the interconnection queue, the better. And what that means is when we go back to number four, which is maximize resource utilization, you can operate with lean teams by having the software do a lot of the thoughtfulness, efficiency, and operation workflows so that your teams not only can run more lean, but actually can run better and at a faster scale.

And so as you grow stacking up the tech stack and investing in that unleashes all of these other capabilities for you to realize really growing your business and I think that is critical. So just to wrap up, we feel very strongly that having the right tech stack at the right time and not waiting until it's an emergency to try to figure out the shopping process, really does support your business overall.

And these are the reasons that we try to help customers realize along the way whenever we work with them. And so, Kari, I'm gonna pass it back to you.

24:41

Kari Foster Excellent. Thank you Carley and Dean. Very helpful, very useful presentation today. And so just a quick reminder, we are going to have a Q and A now. If you have any questions, then go ahead and put those into the chat panel now and we will cover those. We do have a few that came in before the webinar. This is a very good one, very timely as well, you know, having to do with renewables. So why are renewables more complex than anything that came before? Who would like to answer that? Maybe a mix of both of you?

25:16

Carley Dolch Yeah, I'm happy to give that an initial shot. So, my background is with utilities trying to manage reliability on large thermal assets. And those are kind of easy because they're very predictable. You control your fuel supply and in the past, weather was a little bit more balanced and predictable, and when renewables come online, you introduce the problem where weather is the fuel, so it's intermittent, unpredictable, and highly intermittent, right? Like the wind can blow now and then not in five minutes. And so that's really difficult to manage and capture. But the other thing that renewables are affected by is not just the fuel component, but weather being the problem. So if you have a significant weather event, there may be reliability issues on the grid because of that renewable penetration.

And so balancing, how do I literally, operate those assets to ensure reliability and my commitments is really difficult. Storage introduces a really significant level of complexity because it's more dynamic. In some ways it acts like a gas peaker, right? Like you control when it can be deployed and not.

But understanding the cycling and revenue capabilities is very difficult to monitor. And the other piece that's really tricky is that, if you don't have reliability and you tend to be operating when the sun is shining and you got solar, right, the sun is shining. It tends to be that everybody else is enjoying that too, and so prices are depressed.

The opposite is true: if the sun isn't shining in a har in an area with high solar penetration, revenues are low. So being able to use just simplistic price forecasting and expected generation in its paradigm that thermals were able to live in is not gonna do you justice. And so being able to realize return on renewables is much more complex.

Dean, I'd love to hear what you can add to that.

27:21

Dean Charette Carley, it's so coincidental. 'Cause I had a conversation with somebody just yesterday about the complexities of renewables and the analogy that the gentleman came up, presented, was specifically related to, to storage and saying, well, when you have a natural gas storage asset, and I know this, they're actually quite complex to be able to model and manage, but if you imagine when it comes to terms of credits, it isn't just one commodity that comes in and one commodity that comes out according to some particular schedule. You have these multiple kind, kind of alphabet soup of the different credits that go into the storage, and then all these agencies that you deposit them on the way out. And, as you can appreciate, the complexity comes into, you know, some of those on the, out, on the output side. Some of those can you know, take the place of others in some circumstances and not in others. And as you mentioned, you have the stochastic input for how many credits. You know, how many credits you get and the, and finally, you know, as you can notice, that things absolutely change, that the rules change often in different jurisdictions.

28:41

Carley Dolch Yeah, and something else I think to consider too, speaking of policy changes and the rapid changing of policy is that our markets and governmental entities are trying to keep up with, how do I manage this intermittent type of asset. And I know that reliability is really critical to my constituency. And so they try to bake in all of these other additive elements to the markets or to policy or to tax credits in order to mitigate those. And so keeping up with it has become increasingly difficult for entities who are under the gun to develop or meet reliability commitments.

29:24

Dean Charette Yeah, and I can speak just from, you know, having you know, 20 years experience as a quant to moving here, that one thing I've appreciated over the short time at Molecule is, you know, stochastic differential equations are relatively easy compared to a lot of the inventory management that's done on the ETRM side.

You know, you look at each individual decision. You have a, you know, a particular credit that comes in and then, you know, you have a schedule that's assigned to it. But when you have, you know, a handful of these credits and they go to all these different repositories and there's all these different schedules, it's very complex.

And this is where, you know, Molecule is in that, I would say, in that, partially in that class of software where it's almost as though if you don't notice it, it's working properly, right? It's working at its best case. And when all of that is happening seamlessly in the background and you don't have to worry about it, well, that's when you could focus on strategy, commercial issues, and you know how to, you know, look to the future.

30:36

Carley Dolch Yeah, that's right. Something else to consider too is that if you have a lot of intermittency and price volatility and exposure, you need to get more creative and advance with hedging strategy and being able to stack up the hedges that you have in your inventory and actually execute trades on merchant position becomes much more complex. And so having an automated way to do that. Where you are compliant, you have the auditability, and you understanding your positions is critical.

31:06

Dean Charette No, it's true. And I think one of the reasons why, you know, Ascend is just so forward thinking and we're so happy to work with them, but they do, they're on the forefront. So when you have, you know, new structures or new hedge strategies, you don't, you're gonna want it. It's not already baked in, and you're gonna wanna have an agile system to be able to accommodate that as quickly as possible.

31:32

Carley Dolch Yeah, I'm totally with you, Dean.

31:35

Kari Foster Well, thank you. What, wow, what a great answer to the question. Great discussion to both from both of you. Let's do a couple of more questions. So let me see here. Data centers and other CNI off takers with sustainability targets changing the PPA market. Carley, do you wanna take that one?

31:57

Carley Dolch Yeah, let's go for it. We at Ascend are seeing the emergence of a new class of clients in CNI and data centers who are trying to understand how to build out an energy procurement platform and their portfolios alongside of it, to cover all of their positions. They come online and demand more power, it becomes an issue where they need the power. They need it at high volume.

I was at a conference actually recently where I heard somebody say that to generate one image on ChatGPT is the equivalent of a day's worth of energy on an entire refrigerator. And so the energy demand is quite acute. And so it changes PP's because the, in the old paradigm, you would just choose the lowest price PPA, but now entities are really looking at the value of that PPA: what am I getting? Is it meeting my sustainability goals? How is the price? And also what is my coverage around the clock, and do I have reliability baked into it? And so the shoppers are becoming more sophisticated, demand is increasing, and as they come online, they also increase volatility. We're seeing emerging volatility in markets like PJM, which is so stable, but there's growth in data centers there. And so it's really complicating the market for , and advanced analytics is required both on the design side from the IPPs or developers as well as the CNI off-takers to understand and mutually align on where the value lies.

33:35

Dean Charette Yeah, and the one thing I'll point out, I'll just make a couple of quick points, is that the, the modeling on the ETM side for PPAs is actually formidable. And it's not only just the different structures that are presented and how they link to generation and all the credits, it's just the massive of data that's required. So there are customers that, there are clients that are looking at, you know, 30-year PPAs and they do want to be able to look at that, you know, perhaps hourly over that time period. And it's truly non-trivial to be able to manage all of that data. And the second piece, and it's kind of this larger sustainability piece, is that, and I'll answer, I'll address this kind of in reverse, where, you know, if you have a system that has, you know, makes mistakes sometimes, or every time you want to look at a different structure, you have to do a lot of a lot of development. That doesn't really, it's more difficult for you to, you know, propose more panels, propose to enlarge your portfolio. On the other hand, if you don't have to worry about that, if you have none of those operational incidents, if you have, you know, every time that is a new structure that you're considering, you can present it to management. Well, that's what allows you to scale quickly.

35:00

Carley Dolch Yeah. Also, the volume of contracts may be so voluminous that they become also harder to manage as well. Like that is something that's outside of the scope of doing in a spreadsheet and understanding your exposures, positions, and actual realized results on those agreements is critical.

35:19

Dean Charette Yeah, absolutely. And as soon as you start linking that to generation and to the credits, it becomes, you know, many dimensions. And then every single time that Carley's software comes out and says, you gotta change things because there's a new policy, you need to be able to accommodate that quickly or her team gets upset at you, so.

35:41

Kari Foster We have time for one more question. Let's talk a little bit about AI. How do you envision AI factoring into the tech stack at each stage?

35:53

Carley Dolch Interesting. So AI is something that I think can inform pieces of primarily bid strategy. It might be, there's two dimensions to AI. One is machine learning, and the other is language processing, like literally ChatGPT cut type of stuff, versus algorithms that help to make the math more robust. If you are doing short term trading, you absolutely need machine learning capabilities to keep up with some of these evolving market dynamics because your last year's behavior, irrespective of just weather, like policy change, supply stack changes, the whole kit and caboodle makes it so that it's, you can't do a simple regression unless year's behavior to inform the following year. And so I think that for me, that's probably the most acute place, but Dean, I'd love to learn if you have other perspectives I.

36:56

Dean Charette No, the, I mean, the first note I made I'll make is that, you know, don't overestimate how easily software companies adapt. You know, a lot of these, a lot of coders have been doing this for, you know, 30 years. It's hard to think about that, but just a lot of people have been doing, you know, these roles for a long time.

Currently we look at natural language for the on the trade entry side, and some intelligent assistance for data extraction. And I mean, these are the, one of the elements that I really underappreciated before coming to an ETRM, which is just all the intelligence that can go around just data cleaning and, you know, looking at you know, operational, all the operational risk issues about we have an eye towards, you know, limits. And what, you know, gaps in portfolios and giving you information about that.

37:53

Carley Dolch Yeah. Maybe also on the flip side, something to consider too is that if you're an entity who's using spreadsheets, it's very difficult to like ChatGPT your way into a more cohesive solution overall. So, something to think about is Ascend has invested over $25 million into our risk analytic platform on every level over the past three years alone. And so being able to just ChatGPT, like how do I think about this trade is almost unreplicatable just through native or easy to access, easy to access applications, free applications if you will.

38:32

Dean Charette And I will mention that none of the images behind me were created by AI.

38:40

Carley Dolch We'd love to joke in a sense that maybe we should use it more to accelerate the problem that we solve. I don't know.

38:48

Kari Foster Well, that's great to hear because so many images these days are created using AI. Well, thank you both. Really great discussion today and I really appreciate all of your insights. That brings us to the end, and I hope that today's webinar has been enlightening and helpful for all of you. I'd like to thank our presenters today, Carley Dolch and Dean Charette, and especially big thanks to all of you for joining us.

Have a great rest of your day. Thank you all again.

39:18

Carley Dolch Thank you so much.