Designing & Optimizing Complex Microgrids - Beyond Solar & Storage with Adib Nasle

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So how you drive the system is as important as how you size and the mix of technologies you pick. So it's important to be able to solve the physics, solve the sizing and the mix. And at the same time how these should be orchestrated together to achieve the objective you want. So if it's like, I need carbon emission reduction with profit, how do you do that? The algorithm solves that for you.

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And at the core of it is a science based approach based on decades of research with a proven methodology and platform that is fast and sophisticated.

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Are

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you speeding the energy transition? Here at the Clean Power Hour, our hosts, Tim Montague and John Weaver bring you the best in solar batteries and clean technologies every week, I want to go deeper into decarbonisation. We do too, we're here to help you understand and command the commercial, residential and utility, solar, wind and storage industries. So let's get to together we can speed the energy transition.

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Today on the Clean Power Hour, designing and selling complex energy solutions, my guest today is Adib Nasle. He is the co founder and CEO of a company called Xendee. I'm so excited to bring his MD to the Clean Power Hour, please check out all of our content at clean power hour.com Give us a rating and a review on Apple and Spotify so that others can find this content, check out our YouTube channel, we have all of our interviews on YouTube, and reach out to me on LinkedIn.

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I love connecting with my listeners on LinkedIn. So welcome to the show Adib Nasle.

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And please give our listeners a little background on yourself.

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How did you get interested in the energy transition?

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Hi, Tim, glad to join you today, I got interested in the energy transition just from the fact that I've been in the energy space, specifically software applications or computer applications, in power most of my life, dealing with mission critical systems. And I just was very intrigued with the novel technologies coming on the different ways of being able to generate and store energy. And the opportunity to decarbonize with resiliency, while making profit was a very intriguing set of components that I felt would pave the way for the future of energy. And given my just interest and professional career in that path. I thought it would be an exciting journey to take part in

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it is a exciting time to be alive, the energy transition is happening full bore now, it's not a question of if but when just you know, how quickly can we do this. We want to decarbonize the economy as quickly as we can, which is about 50 Giga tons, globally.

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And so we have to we energy professionals have to remember that that is frankly, just the beginning to creating a safer, healthier future for humanity.

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Because there's a trillion tons of pollution up in the atmosphere. By the time we clean the grid, we might be at 450.

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But that's a conversation for another day. So Adib, you're a technologist, you're an engineer, and you know, you can do many things within the energy transition. But you've honed in on, you know, solar storage, micro grids, Evie, charging infrastructure, and that that suite of use cases is growing as the energy transition grows. But what I guess what is the problem that you were solving when you created Xendee.

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So the fundamental problem that I saw and felt was a lot of uncertainty and certainty around the promised benefits of systems uncertainty around the financial outcomes of the systems and uncertainty around their performance. And I also felt that systems and onsite energy solutions were going to become more complicated, not simpler, which meant that complexity was going to grow. So how do we get people to adopt this technology? How do we get businesses to adopt the technology, and you got to be able to get over the fear factor, whether it's institutional investors, whether it's the buyer and buyer, you have to be able to overcome that and make it real easy. And not only to understand, but also to imagine the possibilities and the benefits of these systems.

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And the core fundamental issue that I felt was out there is whenever we talk about onsite energy systems, whether it's solar, whether it's battery, whether it's Solar with battery with heat pumps and all sorts of other technologies. You know, when when I go and buy something or I want to buy something, let's say I want to go and have ice cream, I always like to get a sample before I commit to the school. When you go buy a car, you'd like to take a test drive before you make the purchase, for sure. But there was no trial ability with onsite energy systems. So if we're truly looking at decarbonizing and truly moving that society to the next generation of energy generation, and the ways we can consume that energy, we have to, you know, have a way to test drive the systems before we make a purchase decision. And with that test drive, you get confidence, and you ask some questions, and those questions are answered, and you build more confidence. And then you adopt that technology. I did not see that in the renewable energy and distributed energy space. And I felt that software was going to provide a practical means of a virtual test dry, so that you can confidently get a good sense of what the systems can do. But the important thing is, is a software has to be reliable, accurate and fast to give you the feedback that you want. So that was a key component that I felt really, if we could solve, it could help the adoption of distributed energy and renewable clean solutions.

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So let's let's drill down on that I love this.

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A platform to model clean energy projects, so that customers, and of course, the developers that are serving those customers can can taste the ice cream before they buy this. And what also popped into my head here is how technology is getting very smart. We now have these large language models, we just call them AI tools. You know, I've started to use one called Claude in my daily workflow. Now I use Claude and and and Claude is amazing at rewriting content, and or taking two pieces of content and smashing them together. Now, cloud makes mistakes. And it's not up to date. It's not it doesn't have access in real time to the internet internet, which is probably a good thing, although, frankly, I wish it did, from a knowledge perspective. But anyway, so tell us a little bit about the nucleus of Xendee.

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Because it's on some level, it sounds fairly complicated, but is it a pure breed you're building a, a software from scratch? Or are you taking tools off the shelf and gluing them together.

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So the core of Xendee is based on decades of research and development, primarily done by our Chief Technology Officer Michael Stadler, where he had the micro grid and grid integration teams at Lawrence Berkeley National Labs, and some of the work that I had done in power systems simulation. So we felt that it would be important to bring those two components in and develop an environment that generates solutions. So it's a generative algorithm. And I know that's like a wordy word. But essentially, you tell the system what you want, and what your goals are, or what your hopes are. And it'll build the optimal system for you. But it's technically sound, because it understands that physics of the system, you know, how, how energy gets generated, and then reliably gets distributed to the end user? Yeah, how we should all operate. Because Tim, you can have, you can have, let's say, a Honda Civic and I can have on the city, and you drive it like a Formula One driver, and I drive it, like, you know, my grandmother used to drive a car, we're gonna get fundamentally different fuel economy break, where treadwear distance, all sorts of efficiencies and performance metrics and costs are going to be fundamentally different. So how you drive the system is as important as how you size and the mix of technologies you pick. So it's important to be able to solve the physics solve the sizing and the mix. And at the same time, how these should be orchestrated together to achieve the objective you want.

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So if it's like I need carbon emission reduction with profit, how do you do that? The algorithm solves that for you.

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And at the core of it is a science based approach based on decades of research. With a proven methodology and platform that is fast and sophisticated.

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Now, whenever I use the word sophisticated, I myself get scared. And I'm like, wow, that that means it's going to be complicated, it's going to be something that's going to be difficult to use. And that's where we looked at video games for inspiration, and build an interface and experience a user journey that is more video game, then engineer.

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Yeah. And so I often think of when you think about decarbonisation, there's many ways to skin the cat, so to speak, for example, behind the meter, wind and solar, you could do very similar things with behind the meter wind as you can do with buying the meter solar.

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But, you know, behind the meter wind, it turns out isn't very practical, unless you're a factory out in a farm field with some acreage around you. And then you could do a two megawatt wind turbine. But you know, pretty much the behind the meter, clean energy industry has been become dominated by solar, and now solar and storage. And then quickly, the value stack, so to speak, that storage brings to the table, add some nuances and some potential revenue streams and new savings. So is is nd so smart, though, that I can say, Okay, here's an address. So it knows what the utility is and what the ISO is and what the incentives are, for example, or is it a combination still of the human pilot, feeding it a bunch of parameters? So yeah, I love this this gaming analogy, but how, how smart is it?

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So it's evolving.

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And it's pretty smart in North America. And it's just because of the availability of data, and the ability to programmatically acquire that information. And in parts of Europe as well. But there are regions of the world where there are blind spots when it comes to data, or at least a resolution isn't what we would like. So to give you an example of how smart the platform is, you give it an address. From that address, it automatically goes and grabbed the marginal carbon emissions from the grid.

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For that zip code. We use this commercial service for that. It goes in grabs the weather information, for the past 30 years at that location, including the solar irradiance and temperature and wind speeds of different altitudes, and so forth. Yep, it also goes and grabs the utilities servicing that area and the tariffs available, it is not smart enough to be able to pick the tariff for you. So the user has to say this is what my contract name is with my utility. Sure.

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It also grabs national averages for fuel prices, whether it's natural gas, biodiesel, diesel itself, all those and then when it comes to incentives, it has some understanding of state level incentives and federal incentives. So those automatically get populated for you. It also does an automatic screen to see whether your address is in a low income community or an energy community where you can get additional tax benefits. So those are all done automatically. The human pilot has to pick the terror. So the other component that the human pilot needs to do is there's also an automation for grabbing meter data to bring in the energy use information.

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Historically, we refer to that as a load shape. But that's another component that needs to human in the loop, where they can either upload it, they can programmatically pull it, but it needs permission, because of the security around the data. Or they can use a science based research catalog of different energy use patterns based on climate zones and building types.

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So it integrates with like a green button or one of those.

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Yeah, integrates with utility API integrates with what time it integrates with Cheng ability. So all those are built in integrates with PVWatts. And a few other integrations. So there's a lot of integration happening in the background. But as you can imagine, those integrations work in like North America really nicely. But if you're like in sub Sahara Africa, it's not going to necessarily work for you. Yeah. So that's what I meant by it's evolving. I believe the The quality and the resolution of data is improving every day, throughout the world, but it is pretty accessible within North America and parts of Europe currently. And I would say even Australia in some regions. Yeah.

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So let's talk about a specific type of a use case where you have a facility, let's say it's an office building. And it's got a nice open roof. So you could, you could put solar, it's owner occupied, it's a low rise office building. And you want to, you want to decarbonize the footprint of the building. So you want to you want to try to leverage renewable energy, you might want to do some battery storage, if you have battery incentives in your utility territory, let's say you're in California, with SGF. So are you are you then plugging in like a Helia scope design, or you're just saying, Here's the address, here's the square footage, here's the roof space, give me an optimized solar storage and maybe heat pump solution for this building or heat pump. And Evie, you know, there's, it quickly becomes fairly complicated in terms of the options and the menu that you might present to the owner. And that's always a, it's a fine line, right? You want to keep it as simple on some level as possible, but also meaningful?

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Absolutely. So a couple of points. One is we built a lot of flexibility into the platform. So if you've already done a similar study, using a different solution, like alias scope, you can bring that in, if you don't have something like that, the capability is built internally to give you a conservative idea of what that solar system size might look like. As far as the specific use case around an office building, let's say in California, you would put in the address in to the system, we overlay on top of Google Maps. And then you can go in and either visually define the roof space, or like I mentioned, you can upload the previous study that might have been done already for that. And then you can tell the algorithm, how much control you want to give it. So you can say consider solar. If it makes sense, then it will consider, you could say there's already some solar there. And if more makes sense, please design it. And these are my constraints. This is how much roof space I have. This is the spacing that's available. Third is you can force a number in and say I already have a very strong opinion of what it should be.

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And I don't want you to think about it. Yeah. And the final choice that you can give the algorithm is, at a very minimum, I want this amount of solar. And if more than that makes sense, go ahead and design it. So with every technology, the user can hand over as much or as little control as they want to the algorithm and the decision making process. And the algorithm is going to not only decide whether solar makes sense or not, but how much and then how it's going to orchestrate itself as part of the broader energy mix for that building.

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Because chances are that office buildings probably going to have a boiler chances are it's going to have air conditioning loads, sure. Chances are that people are going to need some charging during office times. But do you want to also offer it maybe overnight? And maybe there's opportunities for other technologies, maybe there's combined heat and power? Maybe there's opportunities for fuel cells? Maybe there's opportunities for heat pumps, all these can also be asked of the system? You know, the can can I replace some of the natural gas on the boiler with the CHP system? Or can I bring in a heat pump to address the thermal load? So it doesn't make sense. And when I say it doesn't make sense? Does it make financial sense? And does it meet my decarbonisation, or resilience for some combination of these three, the economic the resilience to the car, or the decarbonisation objectives that I have? And then it tells me what really is going to work and what isn't going to

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and as we scale that up to, let's say community scale micro grid is nd applicable to that larger scale phenomenon. I hearken back to my interview with Craig Lewis of the clean coalition last year, check out episode 160. And I really like what the clean coalition is doing. But that's a much bigger scale micro grid, micro grid that might serve a few 10s of 1000s of people not a few 100, so to speak is is nd useful in that case?

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Definitely. In fact, you don't want to at the beginning when I was mentioning some sophistication. Part of that. And part of what is needed that we felt was missing in the market when it comes to designing community microgrids campuses is the need to capture the underlying infrastructure the distribution system. So cables and transformers are critical components, because they're going to control not only the safety, the efficiency, but also the reliability of these systems as well. And they have to be sized properly, the voltages have to be understood and considered, then there's losses through systems, you know, you run cables, you're on track that you're gonna have losses, and you have to account for those losses. So Xendee is unique, in that we have the concept of cables and transformers and layouts and all the engineering specifications that are needed to understand the size and mix of these components as well to ensure that energy can get from where it's being produced, to where it's being consumed at these different buildings in a safe and reliable way. So it's not a single building solution, you can have a single building solution, like we talked about with the office building example. But we are unique in our capability to actually support clean communities, community micro grids, Community Energy Systems, both from the electrical and thermal side, and to be able to support not only the understanding, but the proper sizing of the underlying distribution systems. And this is where the competence component comes in, you know, the systems are now becoming complex, now you're talking about an all or community. So there's a level of complexity here. So if you want to take that test drive, it's got to be a fairly close simulation of of what the reality will be if there's going to be any level of trust and confidence to make the investment decision to go ahead and build out the project. And that's what we bring to the table that no one else has been able to do is to bring in that combination of that engineering rigor with the control strategy, and the sizing and mix of the technologies and speak to two worlds that typically have been disconnected, where it has required some sort of a translator in between the finance world and the engineering. And we put the engineer and the financier inside the software itself. So the software communicates in financial terms to the financial decision maker, and in engineering terms to the technical decision maker. But when you have both of them together, the engineering gives confidence to the financier and the and the financiers needs and objectives or constraints into what the engineering is also looking at whether they can deliver or not. And that's a really, we found that to be a really powerful combination to overcome that fear factor. And to really enable that trial ability with a high level of competence and reliability.

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I remember this. This was what got me so excited about Xendee in our in our first conversation about a month ago, this kind of integration of first principles and electrical engineering principles. So that you're you're basically getting a, a bomb, a bill of materials out of Xendee that really will hold water or muster when the engineers really dig in and start to fine tune a project and scope out. This is the type of pipe and wire you're going to use on these specific runs. And that is that is very cool.

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And Tim, you had touched on on a point as well around like the core of Xendee in the earlier question. And one thing I wanted to point out is another component that we thought about, and we wanted to enable was collaboration. So we talked about distributed energy, but we also felt that the future workforce was going to be a distributed workforce. So a distributed workforce with extra Throughout the world, would need to come together and collaborate to solve these difficult problems to solve carbon to address the energy transition.

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So that's why we went not only with the browser, web based environment, it's CyberSecure.

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But also, every output is an open standards. So that data portability extensibility, the ability for commercial industry to collaborate with research and universities would be as seamless as possible. So that there are no artificial barriers for data export, and for the ability for information exchange between different platforms and different stakeholders, so that the story can be told, the benefits can be expressed in a variety of ways to engage as broad of an audience as possible, to make the transition happen as successfully in the aggressive timelines that we need. And we thought that collaboration and information exchange was going to be an important part. There'll be cheating, that that is a underlying built in component within the software itself as well.

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I love it. And you said something, though, about the platform needs to produce a result that is true to reality, as close as possible. So how long have EPCs and developers been using Xendee and have projects come to commercial operation that the team can look back a year post construction, and compare results to what the model said?

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So there's a few pieces to that question. The first I would say is, as far as like the math and the science, behind the software, we, the engineering team has published over 200 peer reviewed journal articles around the math, the methodology, and the accuracy of the approach, which is an optimization approach. Our first customers came on board in 2018, when we formed the company, and they started the planning process with some very sophisticated microgrid plans.

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The challenge that we also saw, now, we initially decided to focus on the design, because you really have to have a way to plan the systems, reduce those upfront soft costs and deliver a high level of confidence. But then your other question is, how do these systems perform. And remember, I said, we can build the same thing, but you can drive your civic very differently than I can. So we can plan a route together. But if your driving behavior is fundamentally aggressive, or inefficient, or maybe more efficient than how I drive it, we're gonna get very different numbers, although we have the same exact car, same exact fuel tank, same exact tires, brakes, steering wheel, everything, right. But how you drive the system is going to be an important component. So what we started on a few years ago, was to take that same underlying algorithm that solves all of this within a planning world, right? So we're imagining this test drive. And we don't have any real data because it hasn't been built yet. So we have to work on historical data, right?

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What the weather has been for the past 30 years, what the energy consumption has looked like for the past few years, right? We have to always look into the history to build out our trial, or our test drive for you. But then when you build it, how can you make sure that those benefits those promises, all those dollar numbers that were promised or you begin to get delivered. And what we saw was a break in the process. People were building systems and designing them using one set of tools. And then a different control was coming in with a different set of logic to drive the system. And the numbers would fundamentally not match.

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Because the logic that's being used to run the system for the next 1015 years, is completely decoupled probably doesn't even know what the planning assumptions were based on. So what we have done, which is again, unique, is we've taken that same underlying algorithm that is able to come up with how the system should be orchestrated every 15 minutes for the next 20 years using historical data, and we bought it into the real world. So Now it's using real time information. And it's using machine learning with artificial intelligence to forecast what the load is going to look like over the next 48 hours and what the weather is going to look like and what the renewable generation production is going to look like. And it updates itself every five minutes, like a rolling update, and says for the next five minutes, this is what I foresee. And it uses because it's using the same map the same algorithm, it's going to ensure that those benefits that design promise, because it's the same brain that made those promises and design, that same brain is coming in and sitting in the pilot's seat, and making sure that that system is going to be driven the way that it was intended to be driven to achieve the outcomes, so that we have done pilots on currently, they've been very successful, to come to your question around how it's been performing. So obviously, folks wanted to benchmark it against what they're using currently, you know, is it actually is this approach of bringing that same brain that did the design for me and sticking in the pilot seat and making sure you know, it's flying the airplane or driving the car, the way it was supposed to better than what we already have. And what they quickly found out was they were able to capture 30% more profit, and 65% better savings, by bringing that same brain in versus their own current best practice best, best logic and rules. So that's the next capability that we are putting out in the market. But we first have to solve the selling and designing problem to have a addressable market, that then these things can go in and operate.

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So what you're getting at it sounds to me like is performance monitoring? Like in the solar industry, this is a big thing, right? Is the asset performing as anticipated, and you build a digital twin, embed that in a computer in, you know, and then have a weather station collecting real time data. And and that's fairly simple. But then when you're when you're adding in batteries and generators, and Evie chargers and heat pumps, that gets very complicated. Are you able to do some kind of performance monitoring of a micro grid?

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Yeah,

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absolutely. So you nailed it. So Tim, there's the monitoring component of it. But to your point, when it's a single technology, like a solar array, you're beholden to the web, right, the sun's going to come up, the sun's not going to come up, and what the temperature is going to be outside as far as what your numbers are going to look like.

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And you people try their best to mitigate around some of the uncertainty around that. But to your point, when you bring in other technologies, like batteries, and generators, and chargers and heat pumps, it becomes complicated becomes complicated on multiple dimensions. One is, there's also grid interaction opportunities, right, there's demand response sales back to the grid, there is the ability to Island and protect the system now that you normally wouldn't have. So there's a resiliency value that you can deliver as well, along with these additional financial levers that you can pull.

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There's other incentives from SGF that you mentioned, to renewable energy credits, to low carbon fuel credits for charging stations. So all of these are now in the brain that's sitting in the pilot's seat of that system, because it was in the design, it knows it. But now it's getting real time data, it's getting real time signals for sales and market participation. And it's looking into the future of what the forecast is saying, and adjusting itself and coming with new information. But as soon as you go beyond one or two pieces of technology, the math becomes very complicated, which is where we shine. So the we built the system for complexity. Like I said, at the beginning, we envisioned a future where the consumer was going to become a prosumer of energy. And the systems were going to become more complicated because as people see what's possible, they want more. First there was solar, then it's like, gosh, if I had a battery that means I can now you know arbitrage this energy, store it, use it, sell it Island, all sorts of things become possible then it's oh, I want heat pumps. What does that look like? Now as part of that, oh, I want to back a generator with combined heat and power and I need some Evie chargers. But should they be level one, level two, level three, how many what your algorithm takes care of all of that and it does it very elec Getting in very quickly. But with a high level of reliability, and then that brain knows all that information, all the promises that were made all the constraints, and it knows what system is built. So it's doing not just a monitoring, but it's also giving the signals for the control. So we refer to it as a model predictive controller, it's a software control that just has a better set of logic that it can give to the hardware, whether it's a sight controller or the battery itself, to operate itself in the way that delivers the benefits that were promised to the customer, as efficiently and as reliably as possible in a dynamic environment.

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So when you look into your crystal ball, thinking about how we as a society and how we as energy professionals, this includes EPCs, developers, asset owners, IPPs. How is Xendee going to be embraced and or alter the way people are doing their work?

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So the way we envision addressing how people do their work is through a single platform approach. So in the past, folks have done the work. But using, I would say, cobbled together set of processes, and that cobbling together. And when I say cobble together like one tool to size, something, one tool to do something else, one complete different tool to figure out controls those steps, and that approach worked in the past, with grant based one off systems, we see a future where impact comes through standardization. The standardization approach requires, in my opinion, a platform strategy. And with the ability to have a single, integrated solution. We enable the ability for standardization.

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And with standardization comes scalability. And I would hope that every developer out there every project, energy as a service, or charging as a service, project developer, owner operator is looking to decarbonize and deliver resilience profitably at scale.

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So not just a one off demonstration project, but really something that drives impact for them from a business perspective and impact for the public good. That requires scalability. And this disconnected approach of doing things one z one z onesies isn't the solution to that. And we felt that our approach to a single platform to deliver indispensable value at the form was critical stages, including operations of a project from initial discovery through feasibility design, and the lifecycle operation of it was how we could deliver scale, reliability, and unlocking the opportunity and delivering a lot of confidence because once you have disability to streamline, then you have the ability to template. And that means you now have commercial off the shelf solutions that you can deploy with tailoring for specific on site needs, but you don't have to re engineer everything every single time.

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I love it. And I'm reminded that I need to give credit to Ryan Mayfield for introducing me to Xendee check out episode 197 unlocking the potential of solar and storage with industry veteran Ryan Mayfield of Mayfield energy well known solar and storage consulting engineering firm, so

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they've been a great partner may feel has been a great partner. And they bring a diversity of capabilities and their ability to leverage our technology to really deliver impact for their customers. They just got this massive contract to provide Island and micro grids for First Nation communities and Alaska to totally take them off of diesel.

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And it was it's very impressive how they were able to deliver a high level of reliable financial and decarbonisation objectives using our platform. And we really look forward to working with them. The opportunity in Alaska to be just one. That's

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so cool. Well, I am just tickled to bring Xendee to the Clean Power Hour. Thank you, Adib Nasle, co founder and CEO, check out all of our content at Cleanpowerhour.com.

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Please give us a rating and review. Tell a friend about the show. And reach out to me on LinkedIn. I love hearing from my listeners, I mean that in all sincerity, reach out to me on LinkedIn, Adib, how can our listeners find you,

00:40:34.019 --> 00:41:04.199
they can find me on LinkedIn, they can go to our website and contact information is there. And they can also probably find me at different conferences around clean energy and renewable technologies. So and we're in San Diego. So if anyone's in Southern California, they should feel free to drop a line, I look forward to engaging with the broader community. And I'm grateful to him for this opportunity to have this conversation with you and your audience.

00:41:05.050 --> 00:41:43.179
Well, thank you so much. I'm Tim Montague, let's grow solar and storage. Take care. Hey, listeners. This is Tim, I want to give a shout out to all of you. I do this for you twice a week. Thank you for being here. Thank you for giving us your time. I really appreciate you and what you're all about. You are part and parcel of the energy transition, whether you're an energy professional today, or an aspiring energy professional. So thank you, I want to let you know that the Clean Power Hour has launched a listener survey.

00:41:43.599 --> 00:41:47.739
And it would mean so much to me.

00:41:43.599 --> 00:42:21.699
If you would go to cleanpowerhour.com. Click on the About Us link right there on the main navigation that takes you to the about page. And you'll see a big graphic listener survey, just click on that graphic and it takes just a couple of minutes. If you fill out the survey, I will send you a lovely baseball cap with our logo on it. The other thing I want our listeners to know is that this podcast is made possible by corporate sponsors.

00:42:22.059 --> 00:42:40.090
We have chin power systems, the leading three phase string inverter manufacturer in North America. So check out CPS America. But we are very actively looking for additional support to make this show work.

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And you see here our media kit.

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With all the sponsor benefits and statistics about the show.

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

00:43:19.900 --> 00:43:34.360
So check out cleanpowerhour.com both our listener survey on the about us and our media kit and become a sponsor today. Thank you so much. Let's go solar and storage

Designing & Optimizing Complex Microgrids - Beyond Solar & Storage with Adib Nasle | EP209

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