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How utilities can prepare for AI integration

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AI is changing everything, and power utilities are no exception. From optimizing grid operations to enhancing cybersecurity, AI is already making a tangible impact on how energy is generated, distributed, and managed.

But when it comes to grid communications—an area where reliability and security are paramount—what role does AI really play?

In this episode of Power Perspectives, we’re joined by two industry leaders from Nokia: Dominique Verhulst, Global Energy Practice Leader; and Hansen Chan, IP Enterprise Solutions Marketing. Together, we unpack some of the biggest questions utility leaders are asking today:

  • How does AI optimize mission-critical grid communications?
  • What are real-world examples of AI transforming power utilities?
  • How are AI-driven data centers reshaping grid infrastructure needs?
  • What’s on the horizon for AI in the energy sector over the next 5-10 years?

Plus, we briefly dive into one of the latest AI breakthroughs—DeepSeek—and what it means for AI-driven power planning and utility networks. So strap in alongside podcast host Jason Price and producer Matt Chester to learn from the deep expertise that Dominique and Hansen bring to the conversation.

(This episode brought to you by Nokia)

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Want to watch the conversation? Video of Power Perspectives are now being posted to YouTube

 

Thanks to the sponsor of this episode of the Power Perspectives: Nokia

Key Links:

Nokia on Energy Central: energycentral.com/Nokia 

Hansen Chan's profile on Energy Central: energycentral.com/member/profile/hansen-chan/about 

Ask a Question to Our Future Guests: Do you have a burning question for the utility executives and energy industry thought leaders that we feature each week on Power Perspectives? Leave us a message here for your chance to be featured in an upcoming episode: www.speakpipe.com/EnergyCentralPodcast 

 

 

TRANSCRIPT

Jason Price:
Artificial intelligence is rapidly reshaping industries worldwide, and power utilities are no exception. AI is being applied to grid operations, customer engagement, cybersecurity, and infrastructure management, driving efficiency, resilience, and modernization. And with all the AI buzz, many utility leaders are asking, how do we actually apply AI in grid communications? What are the tangible benefits, and how can utilities prepare for the transformation?

To help us better understand these questions, plus understand the critical role of AI in telecommunications. are joined today by two experts from Nokia to break this

Dominik Verhulst is the Global Energy Practice Leader and has been at the forefront of networking and digital transformation for utilities.

And Hansen Chan works in IP enterprise solutions marketing, bringing his specialization in AI driven network automation in digital

Together, we will discuss the real world impact of AI on grid communications, the challenges utilities must prepare for, and the role of mission critical networks in enabling AI powered operations. And as a note to our regular listeners, I'm happy to note again that in 2025, we have video accompanying this podcast.

I recommend you check out the show notes for the link where you can now follow Power Perspectives on YouTube and watch these interviews.

I'm Jason Price, your host coming to you from New York City. And with me as always is Matt Chester, Energy Central producer and community manager from Orlando,

Matt, AI is one of the hottest topics in the Energy Central community right now.

What kind of conversations are utilities having about AI adoption?

 

Matt Chester:
Yeah, Jason, you're right. AI is definitely a major focus in the energy industry right now. within our energy central community, we're seeing a mix of excitement and cautious optimism, I would say. You utilities are exploring AI for everything from grid optimization, predictive maintenance to improving customer engagement, cybersecurity. What's clear when you read between the lines of energy central members discussing AI is that while it has a potential to revolutionize how utilities are operating, its adoption needs to be done strategically. It needs to balance innovation with the critical priorities of reliability, security, regulatory compliance.

 

Jason Price:
So thanks, Matt. Before we dive in, we'd like to thank Nokia for sponsoring today's episode. Now let's get

Dominique and Hansen, welcome to the Energy Central Power Perspectives podcast.

 

Dominique Verhulst:
Hi Jason, Matt, pleasure to be here.

 

Hansen Chan:
Yes, thank you for having me.

 

Jason Price:
Fantastic. Well, we're excited to have you here. So let's start with the conversations and let's get going. So artificial intelligence dominates the headlines. AI brings threats, opportunities, and most of all, the unknown. Everyone seems to be discussing where is AI heading and how do we as a society embody this profound new technology.

So, let's start with the basics. How do you define AI? And what makes this technological movement similar or different? Dominique, why don't we start with you?

 

Dominique Verhulst:
All right, well, Jason, well, AI is basically just simulation of human intelligence in machines or computers to do things that normally would require cognition, right? So this is really based on learning, getting a lot of data in to understand what is kind of normal behavior or not normal behavior and then use that in different ways to either do problem solving or do assistant decision making or predicting certain behaviors. So this is really the core of AI and how it's being used. So maybe Hansen, you want to add a few things to that as well.

 

Hansen Chan:
Yeah, thanks, Dom. I think from the perspective of an engineer, see that AI represents a new approach to implement systems from both software and hardware perspective. Some people call AI actually software 2.0. So in software 1.0, where we use C++ or Python or whatnot, the human programmers will code everything by themselves from the algorithms, from all the value of parameters to everything, you code by the human programmers. But in AI, right? So I think the problem is that, so we let the data speak. We let the data actually even write the software. So in simple terms, right? So of course, you still need programmers to prepare the data set that Dominique was referring to.

You still need programmers to actually write the code about the model, of the AI model. But the essence of the model, are the parameters, billions, even come up to a trillion of parameters for those large language model, AI model. So all this would require a data set to fit in. And data set will actually determine what are the optimal value. So instead of human programmers trying to decide and determine. And from the perspective of hardware, it represents a migration from CPU centric compute platforms to a GPU accelerated compute node. Meaning that GPU now plays a major role of doing all the computation because in AI, in especially in the AI model, like the one that we are talking about most all the time now, transformer model, neural network model, they're all represented by layers of neurons. And they are represented by a matrix. So in AI processing or inference on training, so a lot of matrix modifications are required. So it requires a parallel computing platform, which GPU was designed for in the beginning to process video. So it represents a shift from a CPU-based compute node to a GPU-accelerated compute node. So this represents how AI is actually changing the way we implement technology systems.

 

Dominique Verhulst:
you asked, you know, how is it different or similar? as a technological movement, I would make the comparison to like the mid 90s when we started to see, the internet come up and become popular. And we had, imagine certain things to happen. And we also, there was, a fear that you know, this would eliminate jobs, that, there would be all kinds of problems coming out of it. And for sure, there's things we need to think about ahead of time to, and we'll talk about that a bit later. But I see many similarities, you know, with people thinking about how is this going to affect our daily lives? is it going to take away jobs? Is it going to create more risk or unsafety or whatever. And we're really at a next level of that right now. And I think that what we experienced back in the mid 90s till now, we're gonna see, you know, lot further evolutions of those models, being put into practice.

And it's going to create new opportunities. It's going to create new businesses, new capabilities, and it will enhance ultimately the way we live and also are going to be able to manage grids and networks. At least, that's my take on it.

 

Jason Price:
Yeah, I think that's very good. It's good context. Whenever there's a new technology, there's always the extremes on both ends. The opportunities, but then the potential threats. And over time, as we understand it, industry adapts, people's skills adapt, the marketplace adapts, and then it becomes part of the mainstay. All right, but let's do this. I want to go back to Hansen because you said some things that were interesting, and I feel like it's logical next step. And that is really around AI and its benefits, particularly as it relates to the utility industry. So I'd love for you both to answer. And so I'll start with you, but share with us what are some of the specific ways AI is benefiting power utilities today? Where do you see it eventually moving to in the future? also talk to us about what you feel. And again, both of you, but start with Hansen. What are some of the guardrails necessary to ensure that it's successfully adopted?

 

Hansen Chan:
Very good questions. And I think we have to recognize that AI is not really new in industries or in utilities in particular. I think way back, even go back to 20 or more years ago, there have been a lot of research done using what we call machine learning, which is a branch of AI to benefit, to help with grid operations. So it is not really new. So I think some of the low hanging fruit I think we can see is that in the field of computer vision, where AI can help to identify patterns or some images, like whether this is a drone image, inspecting utility poles and to see whether any poles are in the condition that need to be repaired urgently, or a CDTV camera to prevent intrusion within the substation parameter. So those are technologies, I think, is quite well established. I think those are things that utilities can embrace and evaluate to enhance the physical safety of the electric grid. And moving forward, I think we also see with utilities doing a lot of grid organization and digitalization, meaning that there are more more digital data from the grid, from deploying AMI system to a single-phaser phase measurement unit, PMU systems to power quality meters. So there are more data available, that utilities can use to capture some trend in the grid, so that they can take some early action or preventive action before anything goes wrong, right. And also, I think some of the things also we have to be aware or be cautious when we deploy AI is that, you know, there are quite many things, but I can probably highlight three. So one is to understand how AI come to this prediction, how I come to this decision. So to understand what is the train of thought in the AI model. So instead of letting AI to operate as a black box, we need to understand how be able to explain and interpret what AI says. think secondly, also we need to be able to validate what AI says. Is there a way we can validate that and test that? Perhaps leverage the use of digital training technology in terms of trying this in the real working grid. You do this in the digital training or of the grid, or of a certain transformer, and simulate that. And the third one is also security, which obviously is a wide topic. whether the model should be open and transparent, whether it is developed in-house or some open source model that you can look at and scrutinize, and also scale and optimize for your own domain, right? So then everything is being deployed inside your data center and IT infrastructure, right? Instead of exposing anything towards the public internet. think these are three things that probably need to be considered among

 

Dominique Verhulst:
Yeah, think utilities will benefit from AI today. And they do already, like Hansen said, because there's so much more data coming from the different assets in the power grids right now due to the digitalization. And also because power utility has been forced to bring in a lot more renewables. We've got a lot of solar panels on rooftops, have a lot more renewable energy coming into the grid. And those assets typically are modern assets that are equipped with newer technologies. They're completely digitalized so we can get a lot more data from them. it is therefore becoming easier to do better demand forecasting and load balancing with all the data that we have the trick to unlock more is to get more data from more assets on a wide scale. So having better distribution automation, having much more tightly integrated digitalized assets across the grid from generation all the way through transmission and distribution is important. And I think that AI will also provide the grids with better tools to assure grid security and to detect potential threats ahead of time or unlawful activity on the grid ahead of time. And that's the benefits that AI will bring. But I agree with Hansen very much so that regulation is going to be essential to have fairness in the system, to have safety embedded with it and you have to find the right balance between innovation and how much you're going to be regulating.

 

Jason Price:
Okay. I want to ask you both about AI and data centers. As we know, data centers are driving massive new energy demand.

talk to us about how is this shift impacting grid infrastructure, but also address it in the context of grid communication infrastructure. Maybe Dom, you want to start and Hansen, you feel free to follow.

 

Dominique Verhulst:
Yeah, well, AI, depending on whose analysis you're looking at, the adoption of AI in the industry or in the world in general, means that our classical data centers, when moved to AI, we see a increase in demand of power that ranges between 50 to 300%. So that's big and the load is more, how should I say, variable. It's not like a constant load. It actually changes a lot depending on how much activity there is. So the focus is going to have to be on strengthening the grid in terms of availability and providing more power to these AI enabled data centers. So peak load management stability is a problem that we need to fix because we can bring renewables into the mix of that, but renewables aren't always available. I mean, they're not always very predictable in the long term. They vary quite a bit.

What we see, probably Hansen can give a few examples of that also, nuclear plants are being looked at as an alternative source. We see this trend both in the US and in Europe where people are thinking about, maybe nuclear can be the kind of stable bulk power generation system we need to enable the system with AI at a large scale. But again, as I said, this is going to have to be balanced with renewables in a way, and we'll have to find better ways to secure frequency stability and availability of the grid.

But nuclear is obviously a good way of doing that. And there's examples of contracts that have been renewed in the US with nuclear facilities to help the likes of Microsoft and Google to do exactly that.

 

Hansen Chan:
Yeah, know, some of those discussions that I have had with utilities, right? I think one of the topics that they brought up is that a lot of those companies, especially the hyperscalers, they are now coming to the state of where they operate, where the utilities operate, and as for 150 megawatts, 300 megawatts. So it's become really a challenge, right, all with this extra loading on the grid. So this is really due to the fact that GPU accelerator node consumes much more power. And the fact that for AI to work in a high performance way that generate excellent outcome to become better and better, to perform better and better, you need a bigger model. And that means you need more GPUs you need a more compute node, and then you need bigger data centers. So this is really, really causing a lot of headaches. So as Dominique said, those small nuclear reactor technologies are being looked at, and then to provide a very stable base load, and then complemented by many renewables. Maybe I can also talk about how does AI affect the grid communications, especially when utilities themselves are emplacing AI and host AI models and AI hardware in their integrated infrastructure. So one thing that's quite unique with AI workload communications is that they are intolerant of any transmission loss compared to other applications. This is due to the fact that AI workload is very distributed, meaning that they operate across a cluster of GPU accelerated nodes. And then all the memories and multiplications and operations of those number, which are actually matrix modifications, are distributed. And then they've done that in a parallel way, meaning that if there is communication problem in the network, it will slow down. So it's like you have a powerful F1 formula race car, and then you drive this on a bumpy muddy road, you totally slow down your Formula 1 race car. So for AI to really perform, and to be able to train your AI efficiently, you need to have a great infrastructure, communication infrastructure that is resilient, that's reliable, and high performance to complement. Otherwise, the effort and investment you put into AI, into IT infrastructure, you cannot really get the best out of it.

I think the network security is also important as all the data is being transported in the network for AI to consume and for AI model to be trained on. So the confidentiality of the data is required. So I think that's why now with encryption, technology is needed. now, even in threat of quantum computing, cracking existing public key algorithms, it's important to also look at the use of quantum-safe networking technology to encrypt your data for AI influence and also AI training.

So this is another aspect that we can not overlook.

 

Jason Price:
Okay, so Hansen, brought up twice now security, and it's a very important topic. You talked about threats, you called that out. So let's switch gears for a second. I'd love to get your opinion, both of you, on DeepSeek. So this only was announced, I think, two and a half weeks ago. I feel like we've since then learned that, you know, perhaps it was more expensive than what the original quotes were given in terms of how to develop it. The quality of the AI has been now suspected to be less quality than first originally stated. But regardless, DeepSeq is changing the norms on how we're viewing AI, particularly from a computer processing and power planning standpoint. Are you surprised by what we learned? How does this change the strategy at Nokia? just basically, what's your general impressions of that? Dom, you want to

 

Dominique Verhulst:
Yeah, it's a good point and definitely something we looked at and really embraced because I think what it does is it brings, it puts things back into perspective. And what it does, says that the lesson learned here for me is, you know, we're moving to an open source, large language model kind of AI, which can be on-prem rather than in the cloud. And why is this important in this conversation is that, you know, this kind of AI, LLM technology, having that capability on prem for utility is critically important, I think, rather than having something which is completely cloud based, where they have no control of here. If it's an open source model, and Hansen actually referred to it earlier as well. If it's open source, you can actually have a lot of checks and balances on how it makes decisions, how it learns things, how it evolves as an AI model. And having this capability on-prem inside a utility is, I think, very good because it can assure them of better control, you know, enhanced cybersecurity, et cetera. And the good news, of course, is that this is driving more demand in terms of, you know, data center, networking, and CPU. So I think it's all in all, I see this as a positive evolution to put things into a different perspective, which is going to be good for our industry.

 

Jason Price:
I want to now bring this back to the utilities. you know, there's been such a convergence over the past few years where you've got the IT and OT coming together to run the grid in a much more unified way. So I'd love to hear from both of you around, How is AI reshaping how utilities manage optimizing the network? You know to power and run all this requires communications right so underlying you know the systems to talk to each other is the communication component so. Maybe take a moment both of you love to hear from both you just basically how is AI transforming all this what are you seeing out there on the landscape what kind of conversations you having and anything you can share from that perspective.

 

Dominique Verhulst:
Well, I think it's not just AI that is transforming this. I think the grid transformation has already started some time ago where we saw the need to do better distribution automation, to use digital twins in the protection automation and control domain of grids where you can actually model the way your protection automation is working and you can act then, depending on which changes you want to make, you can do that in the model and then try that out before you go and deploy that. And in distribution automation, we also see applications like fault location, isolation and service restoration. We call it FLISSR.

And you can only do that unless you have a lot of data coming from your assets and you also have a strong communications infrastructure fabric across all of these assets in your power utility substations, but also in the wide area context and feeding that into your operational data center.

The technologies that are enabling all that are technologies that we have known in the IT space for quite some time and are maybe were less used in the operational telecom space of utilities, you know, for the last, you know, a couple of decades, I should say. And now the industry is catching up with that. And what we do see is what you mentioned earlier, Jason, is that convergence of OT and IT, not necessarily of the networks, but of the technologies and the talent and the people that we see power utilities needing to manage their grids and their communications networks. They're becoming much more similar to each other. And there's a lot of things that the operational people can learn from the IT team. I think that's a very important change that we see in the utility space as they are preparing to embrace AI and bring AI into their operational domain of the grid.

 

Hansen Chan:
Yeah, I think the term IT OT convergence has been around for many years. I think different utilities are on a different stage in that convergence journey. Some are faster, some may not be as far off. But I think one thing that is common to all utilities, I think, is that all the new digital systems, are deploying, introducing a lot of IT technologies, right? From the way software is being delivered in a software-centric system to a lot of the OT compute technologies, right? Whether this is based on virtual machines or even containers for some less critical system software. So it is, I think, really natural that those systems can evolve.

to embrace AI component to make the software runs better, to be able to unlock the potential of all the data. of just, as an example, instead of just detecting whether certain measurement cross the threshold, you can also allow you to identify any trend, whether this is approaching or this is, you know or decreasing from the threshold. So you have a better insight. Instead of a very rigid cross-oath crossing, you get an alarm. So you can get an alarm to understand when there is something continuously fluctuating in the measurement. So does it indicate any problem that you have to investigate early on? So it gives you a better insight.

And also, with AI component in a system, you can also allow the staff to overcome the learning curve of a new system or allow a newly recruited expert to get on board much quickly, right? Instead of learning with all this intricate, you know, graphic user interface and commands, right? So with AI with GenAI so the staff can actually get to work with the systems right away and employ their expertise, right? Instead of spending all the time trying to learn how to actually navigate the system.

 

Jason Price:
OK, if we can, without naming names, it would be great to hear examples of utilities that are out of the lab, out of the pilot, and are actually integrating some of the things we've been discussing. So are we there yet? Have you seen it? Can you share what's the nature of the types of projects that AI is being integrated into?

 

Dominique Verhulst:
think it's still early days, but I think that there are utilities already out there that have integrated some elements of AI into their demand response and grid automation in terms of predicting what the load is going to be in certain areas at certain moments in time by integrating data from weather systems, etc. So I think that some of it's already been done in quite a few utilities, certainly in North America and in Europe. You know, whether we call that AI or whether we call it, you know, machine learning based kind of algorithms that are being deployed to improve that kind of availability and predictability of the grid. I wouldn't dare to say, but of course there is certainly a push towards digitalize more, having more data available at the source, being capable of doing a lot more with that data in a local context of a power station and between power stations and between power stations and control centers. And that is currently where most of the effort is going, I think, is to really start to digitalize that infrastructure and getting rid of legacy systems, getting a more consistent way of reaching all of the power generation and distribution assets across the grid with telecommunications. That is certainly going on. We've seen examples of that in the US, in Europe, where power utilities are using private wireless networks or they're using fiber networks, very granular fiber networks to the home or to the substation to be able to bring in more intelligence about how they make decisions of managing the grid and predicting the behavior of the grid and put the production against demand at any moment in time to keep the grid stable. But yeah, think it's to answer your question, Jason, it's already happening. Whether we want to...

 

Jason Price:
Yeah.

 

Dominique Verhulst:
put the big AI sticker on that to say this is AI. I don't know. I don't think that people would say that. Maybe you have a different view on that, Hansen.

 

Hansen Chan:
I have seen examples of a use case that utilities are very interested in, which is outage protection. Especially these days with the climate change, lot of weather systems hitting different areas. So it's important for utilities to be able to know the risk of which areas of the service territories are most vulnerable to a certain weather event. So with past data, past weather data and also past outage data and weather forecast, and also geospatial information, such as vegetation and the density of households. So they can use AI to help to map out the risk of different areas for a certain weather event that is coming their way. now they can now plan to deploy their workforce in the right locations and then according to the criticality of different areas. And so that they can really reduce the failure or the outage time affecting the least number of households and protecting critical facilities such as hospitals or whatnot. So that would really help them. So I think this is something that I've seen utilities and also different system vendors, suppliers are working to right to make this more commercially available.

 

Jason Price:

Yeah, I agree. feel like that is one of the bigger opportunities here. I a lot of utilities are moving away from the fix on failure, right? So having that ability to plan and using predictive analytics and using AI to help identify where the soft points are when the storm comes through, they're already able to plan ahead of time and reinforce some of those areas.

So if I may, I want to ask one more question before we get into the lightning round. So the question really is around, what do you feel are some of the biggest trends in AI for the power sector over, say, the next five to 10 years?

 

Dominique Verhulst:
Well, I mean there's a long list but if you want me to to say what I think is going to be the biggest trend with AI in utilities, it's automated grid operations where AI is going to be used for you know, voltage control, frequency regulation, fault detection, reducing the needs for manual interventions, really automating a lot more and

the use more of drones and robots to run inspections that can be, you know, to take pictures of power lines, poles, et cetera, to feed that into the model, as you said earlier, based on that information. When a bad weather event comes in, you may have a better idea of where a certain failure may occur ahead of time. I think, know, automated grid operations would be my, you know, my number one for what's going to be the biggest trend in AI for power utilities.

 

Hansen Chan:
I agree, but in addition, I think the outage would be another area that would really help utilities to provide more reliable quality power to their customers.

 

Jason Price:
Yeah, I totally agree on that. think there's a lot of game there. All right, so gentlemen, we're now approached what we call the lightning round, which gives us an opportunity to learn a little bit more about you, the person, rather than you, just the professional. So we're gonna throw a bunch of questions at you. Don, why don't we start with you, then over to Hansen, and we'll follow that pattern. So let's start. So you both work for Nokia. What kind of mobile phone do you use if you're allowed to reveal?

 

Dominique Verhulst:
I'm happy to reveal I'm using an iPhone.

 

Hansen Chan:
Yeah, me too.

 

Jason Price:
Okay, back to you, Dom. What is your favorite way to spend the day off?

 

Dominique Verhulst:
Well, a day or a week. If it's just a day, then my favorite time to spend the day off would probably be on a motorcycle. But yeah, that would be the day off. If I have more time, would be probably spend the time in Italy. yeah, that's my favorite pastime.

 

Hansen Chan:
Ha ha!

 

Jason Price:

Hansen?

 

Hansen Chan:
For me, for one day off, I will be more a homebody, listening to classical music and reading on books to spend the day.

 

Jason Price:
sounds nice. Alright, so talking about AI, what's your favorite movie that has dealt with artificial intelligence? Dom?

 

Dominique Verhulst:
Well, actually, I don't have to think long about that one. The favorite movie that still sits in my brain is like the 2001 Space Odyssey of Stanley Kubrick. I thought that was a mind blowing movie when I think back at that movie, which actually was shot in 1968, believe it or not. It's a very long time ago. It's amazing, right?

 

Jason Price:
Right. You've given this topic, you're making me want to go back and watch it again.

 

Hansen Chan:
For me, I would say Terminator 2 in particular.

 

Jason Price:
All right. Who are your role models growing up?

 

Dominique Verhulst:
Wow. You know, I would have to say my dad as a kid when I was growing up because I really looked up to him as a person and how he was able to, you know, fluently converse in three or four different languages. I thought that was very inspiring and I wanted to be at least as good as he was.

And then later on when I grew up and started to get interested in electronics and computer science and so on, then Jobs and Wozniak were my heroes back then. yeah, that would be my role models.

 

Hansen Chan:
For me, my high school hero would be Richard Feynman, who is the founder of quantum electrodynamics. So I was introduced to Richard Feynman's books by my high school teachers when I was in grade 10. And then from that time, I started to be mesmerized by quantum physics.

 

Jason Price:
Okay, nice. All right, so we also collect lightning round questions from past podcasts guests. So I'm gonna ask you both a question that came from Adam Rayfeld of Burns McDonald, you're both going to be at DistribuTech in a few weeks. What are you most looking forward to learning while you're there?

 

Dominique Verhulst:
Well, I would love to see how the industry has advanced in terms of adopting new technologies. In particular, of course, the telecom part of it, how IP and Ethernet and protection automation and control is evolving, how the VPAC Alliance is doing, how the adoption of 61 850 as a way to define substations and power utility assets, but also protocols is moving forward in the world. So I'm really excited about seeing how all that is evolving and where we are from a year ago now.

 

Hansen Chan:
I think for me, that event is always an exciting place where all the people, whether they are utility operators, system vendors, suppliers, consultants, all meet to share ideas. I think what I would be looking forward to is to learn any updates or advancements in the deployment of grid automation, particularly IEC 61850 and also the use of more cutting edge IT technologies from virtual machines to cloud containers into those systems to see what is the experience when they try it, when they deploy it, and what we can learn from.

 

Jason Price:
All right, so last question. It's really now your opportunity to ask a question to a future guest.

 

Dominique Verhulst:
My question to the next guest would be, what would you see as the best way to assure the power to the AI enabled world moving forward?

 

Hansen Chan:
I think for me, what are the most rewarding and enjoyable aspects of your career in the energy industries?

 

Jason Price:
Okay, very good. All right, so thanks for indulging us with the lightning round. with given that, and knowing you have an audience of utility professionals listening to the show, why don't I give you both the final word, what would be something that you'd want the audience to take away from today's discussion? Hansen, you wanna go first this time and then over to Dominique?

 

Hansen Chan:
Sure, I think we have all these efforts put into modernization, from the grid system itself to communications and also the IP infrastructure and also with more and more data becoming available and also the compute is becoming also more affordable. So I think we will see GPU becoming more affordable and efficient and deployable in different locations. I think it's really the time to actually evaluate and understand AI. I think there's some learning curve to do, but I think it's important for us, for all utility engineers to understand how AI works, the power of AI and how to use it responsibly. So I think it is really on us to make this happen.

 

Dominique Verhulst:
it's a matter of embracing it, not trying to push it out. think, as I said earlier, digitalization is going to help us a long way. We're going to start to see the occurrence of more edge compute happening in power grids in their substations. There's going to be a lot more IT like technologies, virtualization that are gonna start to happen all across the grid operations. my prediction is that in the next five to 10 years from now, we're gonna see a shift of more taking intelligence out of specific hardware devices that people are using in grids for great automation and control today. And that's going to be much more of a software based function. And we're going to be able to do things more in an automated way because we will have so much more data coming from many more assets, potentially hundreds of thousands of places. So I think we're heading to a very exciting future where we unlock a lot of capabilities. And again, as Hansen said, we're to have to make sure we put the guardrails in place. But I truly believe that the open source models, the on-prem models of AI are going to be extremely useful for power utilities. And again, in terms of managing the telecommunications networks,

We're already seeing AI introduced in many of the things we do see for network management, for automating certain tasks in the provisioning of services in the telecom network, for specific power utility applications, et cetera. So I'd say embrace it, don't be afraid of it, and it's all gonna be good.

 

Hansen Chan:
I cannot agree more.

 

Jason Price:
thank you for joining us today. Thank you for your thought leadership and sharing the insight with our audience. And I'm fully confident you're going to get a lot of questions and keep the conversation going on the Energy Central platform. Well, people will post their questions and we'll prompt you when the questions come in so you can respond back to them and stay engaged with the community.

 

Jason Price:
And thank you for today's sponsor, Nokia. Nokia creates technology that helps the world act together. Nokia is a B2B technology innovation leader pioneering the future where networks meet cloud. And once again, I'm your host, Jason Price. Plug in and stay fully charged in the discussion by hopping into the community at energycentral.com and we'll see you next time at the Energy Central's Power Perspectives podcast.

 

About Energy Central Podcasts

Power Perspectives features conversations with thought leaders in the utility sector. At least twice monthly, we connect with an Energy Central Power Industry Network community member to discuss compelling topics that impact professionals who work in the power industry. Some podcasts may be a continuation of thought-provoking posts or discussions started in the community or with an industry leader that is interested in sharing their expertise and doing a deeper dive into hot topics or issues relevant to the industry.

Power Perspectives is the premiere podcast series from Energy Central, a Power Industry Network of Communities built specifically for professionals in the electric power industry and a place where professionals can share, learn, and connect in a collaborative environment. Supported by leading industry organizations, our mission is to help global power industry professionals work better. Since 1995, we’ve been a trusted news and information source for professionals working in the power industry, and today our managed communities are a place for lively discussions, debates, and analysis to take place. If you’re not yet a member, visit www.EnergyCentral.com to register for free and join over 200,000 of your peers working in the power industry.

Power Perspectives is hosted by Jason Price, Community Ambassador of Energy Central. Jason is a Business Development Executive at West Monroe, working in the East Coast Energy and Utilities Group. Jason is joined in the podcast booth by the producer of the podcast, Matt Chester, who is also the Community Manager of Energy Central and energy analyst/independent consultant in energy policy, markets, and technology.  

If you want to be a guest on a future episode of Power Perspectives, let us know! We’ll be pulling guests from our community members who submit engaging content that gets our community talking, and perhaps that next guest will be you! Likewise, if you see an article submitted by a fellow Energy Central community member that you’d like to see broken down in more detail in a conversation, feel free to send us a note to nominate them.  For more information, contact us at [email protected]. Podcast interviews are free for Expert Members and professionals who work for a utility.  We have package offers available for solution providers and vendors. 

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