Everyone loves to talk about the “broken grid.” But if you look inside a utility’s planning and operations functions, a different picture emerges: infrastructure that is remarkably reliable, built and run by engineers solving impossibly complex problems with often outdated tools.
In this episode, Shayle Kann sits down with Josh Wong, CEO and founder of ThinkLabs AI, a company building physics-informed AI for utility planning and operations. They break down how utility studies get built today, the data cleanup headaches, the silos between teams, and the “worst case” mentality that drives an overbuild of infrastructure.
Shayle and Josh discuss:
- Why utility planning is split into silos across transmission, distribution, and DER planning.
- The gap between studies that identify grid problems and the work of actually solving them.
- What “physics-informed AI” actually means.
- The shift from AI that identifies problems to AI that actually solves them.
- The disconnect between utility planning and real-time operations.
- Should data center microgrids be treated as micro utilities?
Resources:
- Latitude Media: GE spinout ThinkLabs has a new copilot offering for the grid
- Latitude Media: Do microgrids make sense for data centers?
- Latitude Media: The case against relying on behind-the-meter power for data centers
- Latitude Media: Unpacking DOE’s proposal to transform data center interconnection
Credits: Hosted by Shayle Kann. Produced and edited by Max Savage Levenson. Original music and engineering by Sean Marquand. Stephen Lacey is our executive editor.
This episode of Catalyst is brought to you by ENGIE, the smarter energy supplier. ENGIE doesn’t just provide the power to run your business — they supply the energy to move it forward, with reliable, flexible solutions built for what’s next. Learn more at engieresources.com.
Catalyst is brought to you by EnergyHub. Peak season puts every grid to the test — and the utilities that pass are the ones that built flexible capacity before they needed it. EnergyHub works with more than 170 utilities to coordinate 2.5 million devices and 3.4 gigawatts of dispatchable flexibility through a single platform designed to perform when it counts most. See what that looks like at EnergyHub.com.
Catalyst is brought to you by Bloom Energy. Bloom Energy fuel cells deliver affordable, ultra-reliable onsite power for hospitals, utilities, and data centers – at speed and at scale. Learn more by visiting BloomEnergy.com.
Transcript
Shayle Kann: I’m Shayle Kann. I invest in early stage companies at Energy Impact Partners. Welcome to Catalyst.
All right, let me just say that it’s an incredibly exciting time in the energy world and in electricity in particular for many reasons that you already know and I don’t need to explain. And it has for me at least been great to see so many people start paying more attention to energy over the past, I don’t know, 24, 36 months, something like that. But I will admit a little bit of frustration with a particular crowd that is constantly referencing the “broken grid” that needs fixing, knowing full well that most of these people have absolutely no idea how utilities actually work. Even setting that aside, I think very few people outside utilities know what actually goes on inside their planning and operations functions. But once you do start to see inside, I think two conclusions sort of naturally fall out.
First, it’s wildly impressive and we are all fortunate to be afforded the degree of reliability and even affordability that modern society gives us with electricity. And I say that as a Californian who pays over 40 cents a kilowatt hour for power and sometimes experiences wildfire driven proactive power shutoffs. Okay. But the second conclusion is, oh man, what a future AI might deliver here. So I wanted to talk about that second one. I brought on Josh Wong. Josh is the CEO and founder of ThinkLabs where we at EIP are investors. Think Labs is leveraging AI in both utility planning and in operations. We talked both about how things work today and how they’ll change in the future. Josh, welcome.
Josh Wong: Thank you, Shayle. Great to be here.
Shayle Kann: It’s crazy that it has taken me this long to have you on, but I’m excited to finally have you here to help explain how utilities actually work, or at least part of the utility functions. I think we’re going to talk about two parts that, as you’ve pointed out to me separately, do converge, but I want to talk about them somewhat separately. The first being planning, how utilities plan, and the second being operations. What are they doing day to day to maintain the grid? So let’s start with planning. In the modern day, but prior to revolutionization by AI, what does utility resource planning actually look like inside the utility?
Josh Wong: Yeah. There’s many parts of planning. When you say resource, it might be integrated resource on the bulk power system or with centralized generation, there’s transmission planning, there’s distribution planning, then there’s DER planning. Each one of these are on silos. And recently just talking to a very large East Coast IOU, even transmission planning, there are like 12 departments doing their own thing. And each time a study is done, it’s completely separate and siloed. So each use case is like a study. You want to connect a generator, it’s a study. If you want to connect a load, it’s a different type of study, but the underlying model is still the same. So studies today, I would say half, if not more of the time, especially for distribution, is on cleaning up data. So data is in a ton of different places. Data quality is not up to par, and a lot of manual effort is required to pull data together.
Then you run the analysis, which is really tuned for a worst case scenario planning. What’s the five hours of the year? That’s going to be a worst case for the next 10 years. And you plan to that level of standard. Now people are improving, like looking at 96 hours per year to 88 hours per year, 576 hours per year. So each of those are like high best case and low cases on weeks of the season or month over the years. And I would say the gold standard today is probably 8760, which is like 24 hours by 375. So every hour of the year for profile analysis, great for battery storage, which you will need to charge and discharge. And also really necessary if you start looking at things like load flexibility. But people are not there yet. So I think by and large, it’s still worst case scenario planning.
And this process just repeats over and over again. We call these re-studies. When I used to be a planning engineer, when I started my career, we say planning engineers are always right because we can always blame it on the assumptions. But we are always wrong because the assumptions are never correct. And so it becomes this vicious cycle of blaming data, et cetera. And now I think the greatest place where we stuck though is all the studies tells you problems, not solutions. So you run a study through, let’s say a decades old tool for distribution and transmission planning. And it says, “Hey, you have all these congestions and these locations, whether under contingency or not, and you have voltage violations here and there. So red lights or yellow light, you cannot connect to the grid. Restrict access or build more lines.” But the actual solutioning depends on the planner and it’s completely about trial and error or in other words, experience.
And so each time the engineer sees, “Okay, I have a sea of red here, all these problems, how do I solve it?” In the past, it’s fine because it’s all upgrade. Build more substations, add more wires, more transformers. That’s fine, but that’s not going to cut it right now because of affordability and supply chain lags, et cetera. So now we have a ton of other options. Should we flex like curtail, curtail generation or curtail load? Should we add a battery? Where? How big? When do we charge and discharge? That’s an operational issue. Again, hints to operations. Or should we switch the grid around? Well, that’s operations. Planner shouldn’t care about switching, but it is getting more important. And then we do have to upgrade our lines. I think FERC recently just said we do need to reinforce the backbone of our infrastructure, but how much?
Should we do it after giving precedence or preference to flexibility? Or should we bring your own capacity and where? And so there’s all these millions of lines and quadrillions of combinations of lines we can build, but where and how much? And trial and error is not going to cut it. So I think we’re at a time where we are completely overwhelmed by these manual processes, re-studies, and just I would say shot in the dark type of trial and error on what type of solutions we can use to mitigate the grid. And that’s where we’re getting stuck.
Shayle Kann: There’s an extent to which, and when you describe it that way, the studies and re-studies and the judgment required by the planners, your former colleagues and so on, there’s an extent to which my reaction is like, it’s remarkably impressive that we have the reliability that we do have. We actually have quite good reliability. And it’s worked for quite a long time. Do you think that that is because, as you said, basically the planning is built around let’s make sure we solve for the worst case scenario. And so is the net result of that, that we have high reliability, but a lot of overbuild basically?
Josh Wong: I do think the grid as we call it the human’s most complex and largest machine is an act of miracle. It is impressive how much reliability gives us and how much we all depend on it for all livelihoods, social, economic development, security, et cetera. And it’s been keeping us doing well for what we need it to be. And I think redundancy is one thing, but it is an engineering marvel for its time. I just think it sort of failed to keep up with the digitalization and the modernization process, especially for the last two decades or so. But we’re very good at building infrastructure like bare metal, coppers and wires, oil in the ground, et cetera. But running a digital grid as a software company, as a modern company, I think we are just in the beginnings of that phase.
Shayle Kann: Back to what actually happened inside of the utility, just to make it more tangible, right? I think it’s easy to imagine the bulk power system. Somebody says, I want to connect a new generator or I want to connect a load. I want to connect a data center. Okay. So somebody inside the utility then has to go run a study and say, okay, you’re trying to connect 300 megawatts of load. Do we have capacity for that? What happens if X, Y, and Z happens? Et cetera. So that all makes more sense, I think, intuitively than the distribution side where it’s less obvious to me what would trigger a study. Is it just that utilities are cycling through studies of individual feeders all the time? Or is it like, “Hey, we noticed that there’s a bunch of new load on this feeder, that neighborhood has a bunch of EVs that just suddenly showed up. We should do a study of whether we need to upgrade a substation.” What does the distribution side look like?
Josh Wong: Well, I think it’s actually quite common across both, but the characteristics are different. So transmission system naturally is one big thing. So every single time you study an entire region, I think for a cluster study or large load interconnection study, the grid impact and solution side, I think most utilities are talking about six to nine months to perform one of those studies. And it’s a one time. So risk of re-studies will compound that. And it does cost internal utility, typically a couple of hundred thousand dollars. The typical benchmark is about quarter million dollars per study. So it is a long time and it is very expensive. But you study the whole system at once and it’s completely reactive and ad hoc. So you basically respond to an interconnection request. And maybe now we can cluster it, but I think clustering, it’s sort of a shortcut because the queue changes all the time.
So even if you cluster it, I think it doesn’t add any more certainty to the queue. Distribution, you have simpler studies, but there are places where it is actually more complicated. As I mentioned, data quality suffers so much more because the level of coordination between distribution utilities and transmission and bulk power and the market participants are not there. So you have less checks and balances and audit trails around distribution data. And you have a lot more nodes or customers on distribution than on transmission systems. But the good thing is it can be completely parallelized. So I can study one feed at a time unless you want to study around switching load transfers, et cetera. But the volume is also key. I think when a public study that we did with SCE, I think they are projecting up to 10,000 of these energization requests per month.
And each one of them takes like 30 to 45 days. So if you do it manually and expect different results that do more of the same, it is going to be a massive issue. And so that’s where the scalability, data quality, automation is really key for distribution even more.
Shayle Kann: Okay. So you’ve been alluding to this, but let’s talk about it directly, which is what changes in a world of AI here? You’ve described a bunch of challenges. You’ve got the timeline of all these studies that take a long time. That’s kind of an outcome. The challenges that lead to that are data quality, particularly at the distribution level, all the different parts of the system being siloed and all the studies being siloed and so on. What does it look like in a native AI world? And what’s the fundamental challenge to get to that?
Josh Wong: So AI can be ambiguous. So I think let’s look at general AI and where we have been focusing on. But I think the origins of think labs is to ask a big question. Can we automate the grid, whether it’s planning or operations similar to how we can drive towards autonomous driving? So can AI help drive towards that efficiency and autonomy to handle the complexities with human in the loop? So human-centered AI is what we’re looking at. But AI has been historically used for things like data cleansing. Pattern recognition, really good for data cleansing. Forecasting is probably the dominant use case in AI. And most recently there’s a lot of general AI around like there’s massive amounts of data. Can we retrieve all the records? Can we learn from manuals? Can we learn from previous rate filings? That’s great. For us, we specifically ask, can AI help us plan the grid?
Meaning actually to run a system study. How does generators and loads and battery storage impact the grid? If we need to invest in lines or connections, reinforcements, where do we do it? How do we do it? How much flexibility versus how much storage versus how much wires? That’s a really complex problem. So can AI help us do that? And so for us, what AI is, is physics informed AI. So can we teach the AI how the grid works, how power flows, how voltage traverses through the line? How loads behave in contingencies? Where should we put new lines? Where do we have capacity? So we need to make sure AI is trustworthy. And for AI to be trustworthy, we need AI to learn the physics of the grid. And so can we have physics informed AI? So teaching the AI how the grid works so that the utility, which is the actual entity to authorize the interconnection, can actually believe in the results and drive towards actions of the results.
So that’s where our, I would say, specialized AI work in terms of learning power flow, learning system interconnection impacts, and learning how to actually plan the grid.
Shayle Kann: How analogous is that to an LLM? I think of maybe the analogy would be you train an LLM or let’s say like a coding, an LLM that’s good at coding. You have to train it. It learns how to code. And then when you’re doing inference, you are using that training or using the model that has been trained to execute tasks. Is it a similar thing in the sense that what you have to do is basically train a model on the physics of the grid? And then once it has been trained, then the idea is you can query it like you would query an LLM and get something that is driven by that physics.
Josh Wong: That’s right. I think one, we’re not an LLM, but I think the training inferencing process is similar. An LLM is more meant for generalization. It learns a lot of stuff and you can have natural language type of interfaces, but it is prone to hallucination. So for us, since we’re trying to really bring the world of engineering and AI together, we really lean into machine learning. So deep machine learning models, but we are really playing at the intersection of physics equations and the AI models, the machine learning models themselves. But what that drives towards is a very deterministic model. So our models actually can’t hallucinate. It’s actually deterministic. You ask it the same thing, it will give you the same answer all the time. The other thing is you have that type of efficiency, like the unit economics of AI models. We’re very familiar now with frontier LLMs that will cost billions of dollars to train and months, if not years to train as well.
It learns a lot of stuff, but it is a huge hefty investment. And you have to generalize it towards everybody. So it is a jack of all trades and getting smarter all the way. For us, there’s a few things. One is we need to establish security. So we do models that are exclusive for that utility. We’re not getting one utility’s data and trying to cross-train with another utility’s data because that would violate security of the utilities. So we actually bring all our AI models into a utility and basically train their own quote unquote foundation model. And now you might ask, that’s a lot of work to train a foundation model based on each utility’s own proprietary data. But as a case in point, and we have done that for multiple large IOUs now, a couple of thousand buses, it takes us about 10 minutes per training run for power flow model the size of a state typically.
And that 10 minute training run costs maybe five bucks of compute. And we were able to get power flow models that are over 99.9% accurate across all your system states. So your voltages, congestions, like line flows, voltages, et cetera. And so these are small, deep, deterministic and highly, highly efficient models. But now what you get though is like super speed. So studies that would take previously nine months, as I mentioned, interconnection studies now takes us only a matter of couple of minutes, like 10 minutes or less. And it’s also becomes hyper-parallelizable. I can completely scale up with more GPUs. So I’m not bounded by CPU compute because traditional mathematical iteration, iterative techniques like Newton-Raphson techniques are very much CPU bound. Now we are working with Nvidia to see how we can run GPU native type of calculations, but that’s still evolving. And then the last thing, which is I would say mind blowing for us as well, is that AI can be generative.
It doesn’t just give you the problems. In the past grid simulators only gave you problems. Now we can actually say, “Hey, we’ve learned all these combinations, but now we can generate solutions.” And to your point, this is where the training comes in. We have been so used to doing all these studies, one use case at a time, one study at a time, and there’s no knowledge retention inside the system itself in the process itself. We just keep doing things over and over and over again with different inputs. But here, we can pre-train the AI on the grid where we can say, “Hey, let’s train from net zero to 50% low growth by 2030. That’s pre-trained from half a gig to 20 gigawatts of data centers. Let’s pre-train from zero to 100% renewable penetration by 2040.” So we can bring all these scenarios and prepare the AI for all these complexities.
So when we get into inferencing, it’s actually lightning fast. We’re talking about sub-second for an 8760 power flow. We’re talking about in a recent example with a large utility, around 15 minutes to generate about over 10,000 line comp lines that can solve all of the system operator’s region for low growth. And so we can generate the right solutions to the problems. And that’s really where the power of AI is.
Shayle Kann: Okay. So we’ve been talking about the planning side. And there I think we painted a reasonably clear picture of what the past looks like, or really the present in most utilities looks like, which is a lot of planners running a lot of studies manually that take a long time and then need to be restudied and restudied and restudied. And people understand how at least part of this results on the bulk power system, long interconnection queues and so on, but also on the distribution system, why it’s so hard and slow to get new stuff built. And then of course, this new world that you’re describing wherein all this stuff is essentially automated and there’s a human in the loop, but you can generate them very quickly. Let’s switch to the operations side. I’m going to paint for you a picture that I think is what most people imagine when they think of what utility operations looks like to a first order.
And then you can tell me what it really looks like. My guess is that most people think, first of all, it’s mostly automated already. And second of all, to the extent it’s not automated, that there’s a bunch of people in a NOC with a bunch of big screens who are watching the screen light up red when there’s an outage somewhere and then they have to go dispatch somebody to go solve it. But that generally the grid is already fairly automated from an operation standpoint. What is it actually like today?
Josh Wong: I think that’s a fair statement. I think the automation today is there thanks to field automation. So I think traditionally we have been very concerned about automation inside the control room, but the grid itself in a substation at a generator site, it’s pretty much completely autonomous. And that’s why it has been so reliable and so fast and it balances itself so well. And it’s too fast for human in the loop control. So the human in the loop side for operations, it’s really monitoring and responding to the abnormalities. So it’s alarm management. What happens when things go wrong and the operator has to diagnose it? It’s a lot of rules as well.
So there’s an operational planning phase. Let’s traverse in timeline. So operations typically look at maybe a week ahead, two weeks ahead to look at switching procedures. So which lines do I have to take out of service to do plan work? If we can’t do plan work, we cannot upgrade the grid. And now here’s the constraint during that time period. The grid is getting so congested that if we do worst case scenario all the time, the utilities are beginning to struggle to find time to take the grid into contingency for plant work. So if we overload the grid too much, we can’t work on it. That’s a big problem. And the other is the grid is changing so quickly. Our short term forecasts are no longer predictable. And so there’s a huge call for, can we start moving towards probabilistic analysis? So instead of, hey, here’s what I think the single crystal ball future looks like or can we prepare for an ensemble of potential futures?
And how do we risk adjust? So risk inform our decisions. Now, utilities are not there yet, but that’s within today’s operational planning timeline, which is do one time studies, switching, that’s it. Then you go into real time site. Real time, there’s not much that they do, but mostly it’s around dispatch. So which crews go where? Preparing for supplies, lock and tag type of deal. So it’s very manual operation, work procedure, row things. So more rules and work coordination than actually balancing the system or recovering the system. And then when things go wrong, then I think FLISR can kick in. So the self-healing switching can kick in. But the control room more or less is mostly alarm management. Now there’s a flood of alarms and that’s another emerging practice, which is how can we guide the operator through the flood of alarms coming in? I’m jumping forward to the AI world, but we can learn from the patterns of alarms to help root cause it.
But today it’s very much a bit of ADMS rules to help see what’s important and what’s not. And a lot of human intuition that still overwhelms the operator. And then you have the major event side. When the big storm comes, when a wildfire hits, when there’s a winter storm. And so how do we do really emergency type of supplies, dispatch, et cetera, to prepare for those storms?
Shayle Kann: Yeah. I mean, just based on your description of it, it sounds like maybe there is less to be gained from AI in operations versus in planning. In other words, if it’s already more automated today, setting aside alarm management and extreme events, maybe the ceiling is lower for improvement. Is that true in your mind or is it just that it’s different?
Josh Wong: I think it is the complete opposite. I think the potential is even higher when you align. I think today we care so much about connecting data centers and large loads and even generators that we put a lot of emphasis on the business case of planning because you are increasing. It’s the growth engine. But before this whole large big electrification, low growth process, I think the biggest value has been reliability. And there’s a lot more to do with that. And then workforce efficiency, workflow efficiencies. So in the operational side, the role of AI is really to bring a lot of those system studies in the past. That’s one time ad hoc or episodic or periodic or reactive or responsive to customer interconnection request into a continuous real time analysis engine. So what is the true autopilots? What is the true copilot? I would expect that from a car, of course a plane and a spaceship, but can I now expect that from the grid?
Can the grid study, read the roads, analyze scenarios, assess risks all the time? And then that’s one layer. The first layer, the foundational layer would be the grid analysis and simulation intelligence layer. But the layer on top is there’s so much to be gained by having AI learn how the human processes can be improved, how dispatch can be improved, how alarm management can be improved. And that the tail end, which we’re seeing a lot these days is can we learn from each day’s events? Currently, there is no closed loop feedback until you have a postmortem. You have an event that drives an investigation that needs to be reported back up to FERC.
Shayle Kann: Which takes a year to do or something anyway.
Josh Wong: And the records are not good. They’re not reconciled. They have many sources of truths. None of the recordings from SCADA or PMUs or work orders, they never really sort of paint the common picture. You can’t reconcile the history of events from these disparate records, but that’s where AI can really come in as well. To reconstruct the daily events, to replay it and to start putting a closed loop, continuous improvement loop on how can we execute better, dispatch better, and even plan better. How much is that cable actually being utilized historically? Nobody can really tell you that answer today in planning nor operations, especially in distribution. And so I think if we can drive that level of closed loop analytics each day, I think it’s going to be a huge game changer.
Shayle Kann: I think you allude to this, but one thing I’ve always wondered is the degree to which the planning side or the studies that occur on the planning side and the operations side are talking to each other. As an example, let’s say you did a study, a distribution study, let’s just say. And that study, and as you said, that study is planning for, okay, what happens in kind of a worst case scenario? Well, the worst case scenario, let’s say the study says, okay, the worst case scenario is it’s a hot day and X, Y, and Z happens and there’s a possibility we’re going to overload this feeder or something like that. And then we’ll have an outage as a result of that. That occurs in the study phase today. But does that then feed anything into the actual operations phase? Because what you would imagine is that in operations then what should happen is that the grid should basically be looking out for that scenario because somebody else inside the utility studied and determined that that’s the scenario you need to watch out for. Does that happen historically?
Josh Wong: Of course, each utility might be different, but that is a very, very rare scenario that these study assumptions and results actually feed into operations because they’re measured by this different results and KPIs and other kind of stuff. So really the seams, what happens is it seems at best I would say is a set of Excel spreadsheets as always that captures the results of the planning study. And then the operational planning, like a week, two week ahead planning, would use those as fixed criteria to inform their models. So these silos between planning and operations is not just between these two gigantic departments, which I would call the heart of the utility, which is not just connecting people and customer service and all that, but it’s really maintaining the capacity, adequacy, resiliency of the grid, reliability of the grid. But even planning, as I mentioned, there’s like three, four different timeframes that they should look at.
There’s the long-term planning, like 25 to 40 year ahead planning, there’s a medium term time, which is great for generation planning, bulk power integrated planning. Then there is the medium term, which is five to seven, sometimes up to 10 years for transmission expansions, because even buying transformers would take that long. And then there’s short-term planning, which is anywhere from, I would say, months ahead to two years, three years ahead, which has been traditionally what the key for distribution planning and somewhat interconnections as well. So each time you have the seams, they don’t really connect. The forecast don’t connect. The decisions don’t really connect. And I will say the key point is not really just blaming it on people in the process, but you don’t even have an analytical platform that can consume all these different timeframes and trying to piece them together in a coherent timeline.
It will sort of overwhelm any traditional software engine. And so in some ways I would say the utilities are actually adapting their people and processes around the limitations of the analytics intelligence and decision-making frameworks that they have. So I think that has to be addressed.
Shayle Kann: All right, Josh, the last question I want to ask you is a little different. And it’s like a hot topic at the moment, hotly debated, on which I think you’ll have an opinion, even if it’s not exactly in your wheelhouse or what you’re doing at ThinkLabs, which is there are tens of gigawatts of data centers now that are being planned with behind the meter generation. And increasingly because of challenges in grid connection and all the moratoria and stuff like that, that behind the meter generation is acting as a bridge to a long-term interconnect. And so these are essentially, they’re operating these massive microgrids or they’re planning to operate these massive microgrids, right? And it’s comprised usually of some kind of gas generation or a fuel cell, maybe. maybe there’s a battery, but that’s basically it. But really, they’re operating a micro grid. And we’ve been talking about all of the complexities of operating the macro grid.
And I wonder, the debate that I think is raging right now is like, is that harder than it seems basically? Is it going to be operationally very difficult to run one of these very large loads that probably has a spiky load profile off of this gigantic microgrid? From what you know about operations of a macro grid, do you think it’s going to be easy or hard? I mean, or what’s the nuance here?
Josh Wong: Well, it’s a bit of both, but I think we were building microgrids on the distribution side since the ’90s and 2000s. We understand that they can add a bit of local resiliency, but let’s remember why we have a grid in the first place, which is so that we don’t become individual islands. And individual islands are always more expensive than sharing the resources that we have. And so yeah, I think they can use it to protect themselves and to advance their time, speed to power, their own capacity requirements, et cetera. But it’s not going to sort of serve the greater good by sharing, having centralized generators plan appropriately and serving a variety. So it is really being an island. So technically, I think good thing is yes, you can mitigate some of the immediate capacity constraints, but I think there are existing latent capacities. So let me just make a statement here, which is I believe the grid has enough existing latent capacity to connect the majority, if not all of the data centers today.
We just need to find where they are and improve the overall processes of planning and operations for the utility. So if we are to build all these generators behind the meter, why not just use those to really upgrade the infrastructure itself to plan better to operate better? But in the meantime, while the utility’s modernizing, yes, generators can connect faster if it’s a hard capacity that they don’t have on that location, that the grid doesn’t have at the location. But bringing these additional generators and batteries and fuel cells, et cetera, will complex the other side of the equation, which are the non-steady states. All your transients, all your electromagnetic transients will get way more complicated with all these behind the meter resources. So is it really good or bad? Yeah, you might alleviate some constraints, but you might create new ones. So it’s not that easy of an answer.
And those studies I would say would bog down the utility even more. Maybe one last opinion on the whole data center and microgrid side is I really believe that, of course, data centers are driving economic growth, et cetera, for both the data center and the utility and hopefully the community. But that scale, you’re basically building new cities and towns. And so in that case, I really think these data centers should be seen as, I would say, rather than micro grids, I would rather say micro utilities. They have a responsibility and they have a capability to reinforce the grid and benefit all those people around them. So can we have similar updated, modern planning and operational processes around these data centers as a micro utility and not just as a singular customer?
Shayle Kann: All right, Josh, it took too long to have you on, but glad we did it. Thank you for the time.
Josh Wong: No, thank you. Thank you.
Shayle Kann: Josh Wong is the CEO and founder of ThinkLabs. This show is a production of Latitude Media. You can head over to latitudemedia.com for links to today’s topics. This episode is produced by Max Savage Levinson, mixing and theme song by Sean Marquand. Anne Bailey edits the video version of the show. Stephen Lacey is our executive editor. All of our episodes are now on YouTube, subscribe to Latitude Media for episodes of this show and open circuit. You can also find the audio version of this show anywhere you get your podcasts. I’m Shayle Kann and this is Catalyst.
