Jen Szaro, AESP: 0:08

Welcome back to the Energy Beat podcast. Picture it. New Zealand 1863, author Samuel Butler released an article Darwin Among the Machine which is credited by many to be the beginning of the pop culture trope of artificial intelligence we know today to be the beginning of the pop culture trope of artificial intelligence we know today. Fortunately for us, butler's vision of us creating our own successors has not yet come to pass. In fact, today AI shows the promise of helping us solve some of our greatest generational challenges, and without the threat of global annihilation. And today we are joined by Dr Bill Burke, ceo of Virtual Peeker, to talk about how AI will help us tackle some of our greatest contemporary energy challenges. Welcome, bill.

Bill Burke, Virtual Peaker: 0:54

Hello Jen. Thanks so much for having me back on the Energy Beat podcast. I'm excited to be here.

Jen Szaro, AESP: 0:59

We're excited to have you. You're doing such great stuff with your organization. I can't wait to dive in a little deeper. So before we do that, tell us a little bit about yourself and tell the audience all about Virtual Peeker.

Bill Burke, Virtual Peaker: 1:10

Yeah, thanks a lot, Jen. I'm Bill Burke. I'm the founder and CEO of Virtual Peeker. I started the company back in 2015,. Really with the key vision of how do we make demand as controllable as a gas turbine generator, and today we're working to really revolutionize how we manage demand on the electricity grid, making it more efficient, more sustainable and ensuring a prosperous and sustainable legacy for future generations.

Jen Szaro, AESP: 1:39

So tell us. How did you come up with this idea for the company? What drove you to launch such an amazing and innovative approach?

Bill Burke, Virtual Peaker: 1:47

Yeah, thanks for that, jen. So I've been in this space for a very long time. I started back in 2006 working on a project, doing my graduate work at UC Berkeley, working on a project to create the first programmable communicating thermostat project to create the first programmable communicating thermostat and ever since then I've been focused on this area of how do we help utilities get behind the meter and control devices. I focused my dissertation research on it. I worked on that at GE Appliances, really applying this idea of getting behind the meter to help utilities control load across the entire home.

Bill Burke, Virtual Peaker: 2:26

And then this was very early days of Internet of Things, you know, I think, 2010. So Nest Thermostat hadn't even entered the market yet, and so Internet-connected products were very new. Spent some time there at GE and then left to start Virtual Peeker, really with this big idea of making demand as controllable as a gas turbine generator by helping utilities control those internet-connected products. Things like thermostats and water heaters and batteries and EVs and that big vision of demand as controllable as a gas turbine generator has taken some time to really come to fruition, but with our latest technology deployments that include, you know, some innovative AI approaches, some innovative optimization approaches, we think we're getting very close to really bringing this to reality. I'm pretty excited about it. Actually, it's something I've been working on literally since 2006.

Jen Szaro, AESP: 3:21

So I'm not going to lie, you're kind of fulfilling my college dreams by what you're doing. I remember starting out as a physical chemist in this industry and working in the photovoltaic side of things and watching the changes happen in technology and it was never fast enough for me and I always envisioned this whole idea of distributed generation, kind of replacing power plants. And now you're making that happen. Did you have any doubts when you're going through that process of building the company and how did you push past that?

Bill Burke, Virtual Peaker: 3:54

Yeah, yeah, starting a company is not for the faint of heart, for sure. Of course, doubts along the way. So why do people care about this concept of demand is controllable as a gas regenerator. It really comes back to photovoltaics, which you were working on as a physical chemist, right?

Bill Burke, Virtual Peaker: 4:13

The wind doesn't blow all the time, the sun doesn't shine all the time, and so renewable energy is intermittent. It's intermittent in nature, and keeping the grid in balance is something that has to be done in nature, and keeping the grid in balance is something that has to be done, and today that's primarily being done by using gas peaker plants or some sort of fossil fuel energy source. And so if we can turn the demand side of it into a controllable resource that utilities can utilize to help balance the grid, which is what we're working on, we think that we smooth the path toward a totally green grid. That's the reason we're really doing all this, and it's taken some time. Everything has taken some time in this energy transition, but we're pretty excited to be, I think, at the forefront of making this a reality.

Jen Szaro, AESP: 5:01

Yeah, some of your projects are just really outstanding in this reality. Yeah, some of your projects are just really outstanding. So, as we move forward with the latest and greatest in innovation in this area of managing demand and using it to kind of fill in those peaks and valleys, we've got the advent of AI. It's relatively new, fairly well misunderstood, I think, by any non-technical crowd and even those who are in some technical spaces, and the terminology is, you know, very confusing at times. I think so for our listeners, can you quickly spell out what you see as the relationship between AI and machine learning, just for those who might not understand that terminology and what the differences are between the two or how they're technically, how they're related and kind of what that is going to mean for our industry?

Bill Burke, Virtual Peaker: 5:51

Yeah, yeah, yeah, absolutely. I'd be happy to talk about that. So I would first maybe push back a little bit on the concept of AI being new. I think it's a relatively recent entrant into the zeitgeist, but you know, ai as a field has been going strong for many, many years I think I don't know the full history, but dating back at least to the 60s and before and there's been lots of different types of artificial intelligence. So what is what is AI really mean? It means teaching computers how to reason about the world and make decisions sort of on their own is a large part of it, and so that's sort of the broad concept, and it's had many different forms over the years, with tons of subdisciplines. Things like fuzzy logic was a big one at one point. Expert machines is another piece, and AI is composed of all of these different ideas.

Bill Burke, Virtual Peaker: 6:46

I think machine learning is a much newer, maybe newer entrant into that sort of overall bucket of what AI is, and machine learning is really primarily about feeding the computer lots of data and letting the computer learn on its own what that data means, and oftentimes machine learning results in models. It creates models that can then be used to do inference on the world. So prediction, if you will. And those models are typically not human, understandable, meaning that we don't know how the machine necessarily came up with that decision. It's not. It's not reasoned about in the same way that we reason about things necessarily.

Bill Burke, Virtual Peaker: 7:32

But that's the core idea of machine learning and, again, machine learning is a subset of artificial intelligence. Machine learning typically uses things called neural networks. Things called neural networks and the AI chatbots are sort of a massive version of that technology. If you will, implemented on instead of data, like virtual peakers using, like, which are typically numbers, but these AI chatbots are implemented on, obviously, words and language. But it's all stem from the same core technology idea of feeding the machine lots of data and letting the machine come up with, effectively, a model of the world.

Jen Szaro, AESP: 8:12

Right. So I mean, I think today most people think about AI, machine learning, and they think about chat, gpt, right, because that's pervasive. Everyone's kind of seen it and played with it. I've used it myself to craft a few outlines and come up with some snazzy session descriptions, but those are generally different kinds of models than you would probably be using in our industry, right? Because those are more focused on gathering knowledge from what's out there in the internet. So they're what they consider open models. Is that correct?

Bill Burke, Virtual Peaker: 8:42

Yeah. So I think that those models are typically being trained on like I said a couple of minutes ago, being trained on human language, and they're using the words written on websites and books and whatnot in order to create a model for how people talk and how people think. Now that sort of technology is somewhat applicable to our field, but it's generally more applicable in the form of customer engagement sorts of technologies. How do you make it easier for using something like ChatGPT, like you said, creating outlines? How could you use it to help ease the support burden when you're handling a support issue with a customer? Or how can you use or potentially using that sort of technology to write marketing copy that you would then send out to customers in order to build support for whatever you're doing? So that's definitely still used.

Bill Burke, Virtual Peaker: 9:42

What we do is somewhat different in that we use the same sort of general ideas of neural networks and then feed it numbers typically right. We feed it data streams that we've captured or the utilities captured and learn about the world through numbers, and then use those numbers to do prediction, and those predictions usually take the form of some sort of maybe a prediction on the energy consumption tomorrow, maybe a forecast for the total system load over the next several days. Maybe it's forecasting individual device behavior over the next several days, so it's really predictions on what machines are going to do and how they're going to consume energy, and that's the way we're using artificial intelligence or machine learning today.

Jen Szaro, AESP: 10:35

Right, because you've got so much data coming in. I can't even imagine a human being trying to make heads or tails of all of that data without the assistance of something like machine learning and AI.

Bill Burke, Virtual Peaker: 10:47

Yeah, I think that machine learning what it doesn't. So humans, you know over you know, all the years of people running grids, humans have gotten pretty decent about knowing when peaks are going to happen or sort of the general load shape tomorrow. You know thinking about the weather and all this stuff. But that's, you know, thinking about the weather and all this stuff, but that's, you know, typically stored in some human's mind and it's more of an intuition about what's going to happen. Of course, people have done some more rudimentary style modeling using regression and things like that, but machine learning really takes it up a notch and allows us to get a lot more accurate and perform predictions that are much longer term than would otherwise be able to be done in an accurate fashion. But data is key.

Jen Szaro, AESP: 11:33

Data is key, absolutely All right. Well, now that we have that understanding, let's start high level and then kind of get more granular into the applications of AI that you're seeing whole promise, especially in the area of demand flexibility and virtual power plants. So, as I alluded to at the top of the podcast, we've been now over 150 years of pop culture painting a captivating tale of gloom and doom If AI takes over. We've all seen the Terminator, at least most of us. If you hear a lot of buzz that AI is going to make this or that job obsolete, what is the future role of AI going to be in day-to-day work for the average energy professional? You know I see it as a tool personally, more so than something that's going to replace me.

Bill Burke, Virtual Peaker: 12:21

Yeah, I see it the same way. Honestly, jen, I think that, having actually never seen Terminator, you know, so maybe I'm not as doom and gloom about AI as a lot of other people in the world.

Bill Burke, Virtual Peaker: 12:34

There's a long backstory about me not being able to watch R-rated movies when I was a kid, so anyway, yeah, it's a real shame, but what I see for our industry is AI helping in, you know, forecasting load and then being able to then control demand in order to meet that load in the future. That's a key component of it. A few other things that I'd like to mention there for sure is providing great customer support. You know, having utility, having utility customer engagement. Technologies that help people in the customer support center provide great customer support to the end-use customers. Those utilities are not particularly well-known for having great customer support, but if we can get good AI technologies to help increase that customer support, it can really help across the board with utility customer engagement and making customers happier. So I see that as one area of interest with AI.

Bill Burke, Virtual Peaker: 13:57

I think that using AI to help predict places in the grid that are going to be constrained is another promising application of AI technology.

Bill Burke, Virtual Peaker: 14:08

So as EVs and as PV grow in the grid, it puts strains on the distribution system, strains that have never been seen before, and if we can use AI to really predict those ahead of time, potentially years in advance.

Bill Burke, Virtual Peaker: 14:24

I've seen some interesting tools for doing that which will help the utilities get ahead of the distribution system build out, help them, you know, deploy technologies to really handle that. And then, of course, you know, what we're doing today is really trying to make that demand as controllable as a gas turbine generator. That's, you know's, what we've been focused on from the beginning, and using AI to predict load, both in aggregate and individual device level load, is critically important to that. So having a good model of the world really smooths that, and we're deploying this new technology that we call top-line demand control. It's a new technology category that marries artificial intelligence, look-ahead optimization. It uses digital twin technology for modeling individual devices and then gives the utility the ability to have dependable and precise control over that total load shape. So marry that with prediction of future load and you now have a great tool to use instead of gas speaker blinds.

Jen Szaro, AESP: 15:31

You know I'm completely sold on this approach. I think it's so long overdue to be able to pick up on these patterns and really understand how customers are going to behave or how loads are going to behave, and all of us have been stuck in the dreaded IVR cycles.

Jen Szaro, AESP: 15:48

You know they're no fun, so I love the idea of being able to apply AI in that way as well, to make the customer experience more enjoyable and more efficient. Right, we've all got our day jobs, we all have things to do, and no one wants to spend time on the phone waiting to get a response. So you know, I think most folks who are in the industry now are growing comfortable with the idea of communications and controls being handled by machine learning. But how do you really get your utilities and customers to trust this technology? You know what kind of pushback are you seeing so far and how have you been able to respond to that?

Bill Burke, Virtual Peaker: 16:30

Yeah, so I think for customers like, if you think about the utilities customers this sort of technology should be relatively transparent to them. The customer interactions should seem natural. The way their devices are behaving if they're in an AI-controlled load management program, the way their devices are behaving should seem natural and really that customer experience should be preserved. That should be the goal of using AI in any of these technologies is to effectively make it transparent for the customer and just make them delighted by what the AI is doing for them. And we're used to that with our phones, we're used to that with lots of different recommendation engines from Netflix or whoever. And providing a great customer experience should be the key thing. And if we can do that, then I think the utilities customers are going to be happy to adopt these sorts of technologies. I think that the utilities customers are going to be happy to adopt these sorts of technologies. I think that getting the utility behind the concept of using AI is something that is going to be challenging in some respects, but at the same time, there's a real desire to adopt this sort of technology at certain points in the organization, a real desire to adopt this sort of technology at certain points in the organization. So you know, the innovation centers at utilities are really excited about AI and we get reached out to quite a lot to just talk to utilities, about our use of AI. So that's fantastic.

Bill Burke, Virtual Peaker: 18:08

Where it gets harder to push in AI tools is when you talk about, or when you get to, the really operational components of utilities. So when you think about distribution operations or you think about power purchasing, those sorts of areas, are they're so critical to literally keeping the lights on that? There's a heart. There's a higher barrier of entry into that for AI, because the AI someone, has to prove itself. The AI has to operate similarly to the way they're used to operating and it has to be transparent so that they can understand what's happening. And so that's really where the barrier is, is really getting down to the nuts and bolts of how do we keep the lights on, and we think frankly, we think our top line demand control technology creates the metaphors between, or the analogies, if you will, between, generation and demand. That will help all those folks really understand and then be able to operate it. Of course, they'll have to prove it to themselves that it works, but we're pretty excited about that step in and of itself.

Jen Szaro, AESP: 19:20

I think you're spot on with that. I think it is the operations folks. They're the ones responsible for the reliability scores of the utility right and get the blowback if things don't go well. So you know, I guess it's understandable that they would be a little reticent to jump into something new and change management. I think within the utility sector has always been a challenge, at least from when I worked there, and it's hard, especially when you don't, I think at this point, have necessarily the right regulatory constructs in place to help mold utilities and allow them to change and be a little bit more flexible. Are there areas of the country that you see are really ready for this, more so than others?

Bill Burke, Virtual Peaker: 20:01

Yeah, definitely.

Bill Burke, Virtual Peaker: 20:02

You know there's places with much higher cost electricity than others, and California, new England, places like that are, I think, typically more apt to take on these technologies because they see the price going up and they want to do something about that price and so they're willing to look at these efficiencies and look at technologies to improve the sort of overall system efficiency in order to keep costs down.

Bill Burke, Virtual Peaker: 20:33

But even utilities throughout the United States, even places that have relatively low cost of energy, are recognizing that we're moving into a demand-constrained paradigm, how power ramps because of demand over the course of the day is going to create a situation where you don't have enough instantaneous power, if you will, you don't have enough capacity in order to meet the needs, and that's going to be the cost driver over the energy, the base energy need, and even places with, like I said, even places with low energy costs are starting to recognize that. So we see interest basically all across North America today, which is where we're operating, in these sorts of tools. Now some of that capacity constraint, if you will, for some utilities is much farther off than it is for places like California and Texas and New England, but even in spite of that they recognize that it's coming.

Jen Szaro, AESP: 21:41

And I know that there are a lot of places in the US that are really heavily investing in renewables and wind and solar we know have intermittency issues, and so I would think that that would also be a huge driver for in making these investments to help sort of fill in those gaps. Are you seeing that where I know I've heard here in Florida there's a lot of investment happening in utility scale solar. They just haven't started. Really, I don't think thinking about filling in the gaps yet. So are you seeing other places, other regions where that's happening, where they're like yep, we've got days where we have 60% plus penetration and we need to fill the gaps in?

Bill Burke, Virtual Peaker: 22:20

Yeah, definitely.

Bill Burke, Virtual Peaker: 22:22

We see that a lot of utilities in places with a lot high renewable penetration are beginning to think about both reducing the peak and consuming more energy when renewables are producing.

Bill Burke, Virtual Peaker: 22:37

So it really goes both directions, because they both create ramp rate issues, and when I say ramp rate I mean how quickly the apparent system load changes really defines how they turn on generators and how quickly they turn on generators and how quickly those generators need to increase their power output. And so if you think about the somewhat nightmare scenario of everybody driving EVs coming home from work around five o'clock and plugging in this is about the same time that the sun is starting to go down you see a massive increase in demand at the same time, a massive decrease in power output from the solar panels. This creates the scary scenario. So how utilities are absolutely thinking about, how do we charge cars in the middle of the day when the solar is going, and how do we then defer when they turn on or when they start charging in the afternoon when people come home. So these are all things that AI and the sort of technologies that we're working on can help utilities with.

Jen Szaro, AESP: 23:43

Yeah, absolutely. As an EV driver myself, I'm hypersensitive to it being in the industry, but all my neighbors are starting to get EVs now too, on the same transformer. So it's going to be interesting, with that sort of clustering effect, to see what happens if they don't start to embrace things like demand flexibility.

Bill Burke, Virtual Peaker: 24:05

So you know, that's the sort of scenario where utilities are particularly nervous. A single service transformer with multiple EVs. It takes a long time to get new transformers. Overloading them reduces their life, causes them to blow up. Blow up probably not the right word, but to fail more quickly and using demand flexibility, you can really shift that around and save those transformers if you can control when the EVs charge, and that's the sort of technology we work on Absolutely.

Jen Szaro, AESP: 24:38

So tell me, once we get past the trust issues, what are the other areas of promise that you see for AI in our industry and what are some things you think AI probably would not be able to solve for us in our industry.

Bill Burke, Virtual Peaker: 24:52

Yeah, that's a great question. So you know we've talked a little bit about the customer engagement side of it. You mentioned the long IVR queues, which is painful. Ai can help with those sorts of things. They can help with the customer interaction, they can help write and copy, they can help with just knowledge right, they, they? There's a lot of complex questions that customers ask and if the AI can source the correct answer, that's super helpful.

Bill Burke, Virtual Peaker: 25:18

Energy efficiency is another one Understanding how consumers use energy, recommending energy saving measures. These can be applicable for both consumers and utilities. I think energy efficiency and AI have a lot of promise, for sure. And then, of course, grid management, which is what we're doing. Ai can optimize the balance between supply and demand, enhance the efficiency of the grid operation centers again, if we get past the trust issues and then some challenges that AI might not be able to solve, Obviously, at least today and in the near future, AI can't build infrastructure right. It can't help us modernize the infrastructure by actually physically building these things. It can help predict where we need to make those infrastructure modernizations. So it's a great promise there, but the actual building that's all humans today and for probably quite a long time.

Jen Szaro, AESP: 26:25

Yeah, I think so. I also see my line people fixing the lines as they go down as well. So maybe, to your point, we can get there faster and find the faults more quickly, but I think we do still need people in this industry for sure.

Bill Burke, Virtual Peaker: 26:41

Absolutely. It can't solve the regulatory and policy issues because it can't really today navigate the complex regulatory environment. It can't really create policies. What AI can do is help people understand the policies more quickly. I recently heard a utility executive talking about feeding AI a policy document and getting a quick summary of it in order to understand the high points.

Bill Burke, Virtual Peaker: 27:10

So AI has promise in helping us understand these complex issues, but it doesn't have the ability to actually create them yet. That's still going to require humans and there's a lot of human decisions that have to be made because there's tradeoffs between cost and people and equity and all this other stuff that AI is not there for yet.

Jen Szaro, AESP: 27:30

Yeah, I mean I think that idea of kind of poring over regulatory filings and historical filings to try to understand what a great application for sure. I mean, that's such a pain point I think our regulatory bodies have to face.

Bill Burke, Virtual Peaker: 27:45

Yeah, it's a massive challenge, even for us as a company selling to utilities, understanding where the policies are coming out that benefit us or don't benefit us. Just understanding that in a quick way, because there's a lot of different ways utilities are regulated across North America. Without a doubt, At least 50 different ways probably.

Jen Szaro, AESP: 28:06

At least, and then some right.

Bill Burke, Virtual Peaker: 28:08

Yeah for sure. And then it can't, today, solve the cybersecurity threats. It can help us with threat detection. It can help us, in certain scenarios, understand what's happening and maybe even predict what might happen in the future. But cybersecurity threats are a little bit like war. Right, there's a human waging it on one side and you really need another human to match wits with that person. So it's not quite there yet, but it's definitely still helpful.

Jen Szaro, AESP: 28:41

I've got a son who's working in that space and he's like yeah, mom, I'm still very much needed. So that made me comforted. Considered to be paid for his college education.

Bill Burke, Virtual Peaker: 28:51

Yeah, absolutely. I think that cybersecurity issue is going to be powered by humans for quite some time to come.

Jen Szaro, AESP: 29:00

So it's been such a blur. So much has happened in the last three to five years in our industry. What do you think the state of adoption for AI is going to look like over the next five years?

Bill Burke, Virtual Peaker: 29:13

It's really hard to know exactly what's going to happen in the future. Right, there's no AI crystal ball just yet. If there were, I would probably not be here, yeah.

Bill Burke, Virtual Peaker: 29:52

So in the next five years, the adoption of AI to facilitate key objectives across many different spheres. Right In the utility sector specifically, we're thinking about how to manage transportation. Electrification this is a big concern and, like I've said a few times already, predicting where distribution system constraints are is going to be a big part of it. We're going to enhance supply and demand predictions as renewable energy adoption increases. Ai has a huge capability there and it's already begun taking hold at utilities today. That prediction is going to accelerate.

Bill Burke, Virtual Peaker: 30:29

We're really going to be focused on shifting peak consumptions using AI in order to delay and even avoid infrastructure investments. This technology that will allow us to control demand and predict demand very accurately, you know hosts a lot of different benefits and you know some of it is reducing that peak sort of globally. But really getting down into the distribution system and adjusting demand at very granular levels allows utilities to save a lot of money on infrastructure upgrades. Delay those infrastructure upgrades. They'll eventually have to do them, but hopefully they can do them at a time that's most cost effective for them and most beneficial to the customers.

Jen Szaro, AESP: 31:19

Yeah, just allowing for the time to plan for those in a meaningful way, I think, rather than having to be reactive, is where I think it's going to really help us. I mean, we all know how long it takes to build the average natural gas plant. We know what it's like to try to build a transmission and get that cited and permitted. These are things that take a really long time. So if we can delay those in a meaningful way with demand flexibility and virtual power plants, I think we've got a chance to kind of ensure that we're getting it right and making the best use of our rate payer dollars too.

Bill Burke, Virtual Peaker: 31:56

I agree entirely.

Jen Szaro, AESP: 31:59

So what should utilities and energy users be doing now to sort of be ready for AI entering the landscape in a meaningful way? Is there anything like I know? I was just out with the folks from the Flex Lab over at Lawrence Berkeley Labs. Wow, that's an impressive group out there. I see all this work happening with grid interactive efficient buildings. That's really exciting. I see so much happening in the interactive efficient buildings. That's really exciting. I see so much happening in the home automation space. How do we tap into this? How do we support that in a meaningful way?

Bill Burke, Virtual Peaker: 32:34

Yeah, that's a great question. So I mentioned data is really key and utilities upgrading their systems to collect and be able to analyze that data, I think is the key first step. So if the utility doesn't have smart meters, they need them yesterday. They need good telemetry off of all their infrastructure. They need to start storing that data. They need to work on accelerating getting that data into a computer right, reducing the time lag between when the data is collected and when it gets in, because AI doesn't work without data in most situations. So the more data they can get, the better. And that goes across the board, across their infrastructure in every way, across the board, across their infrastructure in every way.

Bill Burke, Virtual Peaker: 33:28

And then the second. So that's sort of primary. The second step is really starting to think about that change management inside the utility. How do we get everybody on board with being very analytically focused and thinking about adoption of AI technologies where it makes the most sense? Change management is hard. It's hard for most organizations and having the leadership in place and the leadership helping with that change management, I think is the second massive step that needs to be taken.

Jen Szaro, AESP: 33:57

Yeah, I could not agree more. I think that level of leadership to take the fear out of change management and help people see the benefits can really make or break the utilities. Adoption right now of grid modernization technology specifically.

Bill Burke, Virtual Peaker: 34:13

Absolutely.

Jen Szaro, AESP: 34:15

And going back to the customer, the user, end user. For me, this is always one of the biggest challenges and the hardest pieces of the puzzle to make fit, because it has to be a win-win for them as well.

Jen Szaro, AESP: 34:29

Right, they have to understand what's in it for them to participate in these kinds of programs, and so what sort of communication do you see utilities really needing to do with their partners, to ensure that you know this isn't just something being driven by the utility, but also something that's really going to benefit the customer partners in all of this?

Bill Burke, Virtual Peaker: 34:50

Yeah, that's a great point, Absolutely. I think in many places we have to recognize that the customers, the end-to-use customers, aren't adopting distributed energy resources, things like smart thermostats and whatnot for utility benefit. They're adopting them for themselves. They want this technology, right. They want to use a heat pump, water heater because it lowers their electricity bill or lowers their overall energy bill. They want to use EVs because they're fond of drive and they love the cars that they're buying. They're not buying them for utility benefit, right. They're buying them for their own transportation.

Bill Burke, Virtual Peaker: 35:30

And so making sure that customers understand the benefit of adopting these sorts of programs is critical, and that is through good customer engagement technologies, good customer engagement mechanisms. I should say not technologies Technology definitely has a role to play in that, but helping those customers understand we're able to reduce overall energy costs because you're adopting these technologies. How much are we able to reduce it? I think Fremont Power has been a fantastic example of that. I think almost annually they state publicly how much money their virtual power plant has saved their rate payers, which is huge for customers to understand what the benefit is and really adopt this sort of technology.

Bill Burke, Virtual Peaker: 36:24

But a lot of customers are adopting the technologies because they want it and they're actually going to their utility. We see this in a lot of co-ops and we hear this from a lot of our cooperative customers. They say we want to stand up a program because our customers are asking us to. So a lot of customers understand that by doing these sorts of things they're going to help save the world effectively. But that doesn't get you out of telling them how much going to help save the world effectively, but that doesn't get you out of telling them how much they're helping save the world as well.

Jen Szaro, AESP: 36:50

Absolutely. I think you need to remind people of the impact that they could have themselves and then, if we can, as an industry, empower that behavior. I mean I think that's the win-win that I see for us. Absolutely, Me too.

Bill Burke, Virtual Peaker: 37:03

Me too.

Jen Szaro, AESP: 37:04

Well, thank you, bill. This has been a really incredible conversation. I'm just so intrigued by all the work that you and your team are doing right now at Virtual Peeker, and I cannot wait to see what comes next with AI over the next years, and so thanks for spending time with us today on the Energy Beat podcast.

Bill Burke, Virtual Peaker: 37:24

Jen, this has been fantastic. I really enjoyed it as well. Wide-ranging discussion, we were a bit all over the place, but hopefully that's the good part. Yeah, I think so too. I think hopefully we helped everybody who's listening understand AI a little bit more how it could help utilities control demand better, help utilities engage customers better. There's a lot of excitement in the world. Thanks a lot for having me.

Jen Szaro, AESP: 37:47

Thank you, and if you'd like to learn more about Bill and his company, just head over to virtualpeakercom and check them out. Thank you so much for being with us today. We'll see you next time.