People used to talk about, you know, five years, seven years,
10 years of life for for new batteries.
And what we were able to showcase was that actually for
the batteries that people are thinking have reached their end
of life, actually are are nowhere near their end of life.
And with the right technology and the right Business
Innovation, you can make them last much longer.
Welcome to the Environmental Transformation Podcast, where we
bring you interviews with industry leaders, climate
champions, sustainability practitioners, EHS and hazmat
professionals making an impact in their businesses today.
Each leader solving complex challenges and delivering
solutions within their areas of expertise.
I'm your host, Sean Grady, and thanks for joining us today.
Before we jump in, make sure to follow us on your devices and
visit my website at www.seankgrady.com and sign up
for our newsletter and e-mail announcements.
Now let's get started. Welcome to the Environmental
Transformation podcast. I'm your host, Sean Grady and
today's guest is Doctor Surrender Singh.
He's the CEO and Co founder of Rely on Energy.
Rely on Energy is a redefining energy management.
They are redefining energy management through a proprietary
AI powered energy forecasting tool, energy management system
and battery management system. So, so that's the EMS and BMS.
By integrating advanced AI software and energy, Brain, so
to speak, is that the company enables owners, operators, and
end users to unlock unprecedented value from power
generation and utilization assets.
So doctors Singh has a distinguished career focused on
advancing and incubating technologies that address
climate emergencies with a focus on the fundamentals of science,
systems engineering, and business models.
He's also authored the many publications.
He's also has over 50 patents granted or pending in the in the
climb tax. So he's a busy guy.
He's lots of experience. He's also worked at GE and a
couple other companies that he's also mentoring startups in the
climate and energy space right now.
And so we're really happy to have Surrender come on the show
and talk about rely on energy. Welcome to the show.
Sean, thank you for the introduction and thank you for
having me. Looking forward to the
conversation. Absolutely.
So, you know, why don't we step back a bit here and give the
listeners a little bit of background on rely on energy and
you know how you got started in this space and you know, give us
a little bad background on that which you.
If you could, yeah. So rely on actually we started
in 2021. So we're completing our four
years and what we pride ourselves with actually a very
strong technical foundation that we've expanded towards business
fundamentals and so on. And the three pillars on which
we've built the technology is on energy forecasting or AI
forecasting. That applies to, you know, many
different sections in the energy sector, whether that is demand,
whether that is lowered, whether that is power protection that
whether that is renewable, so solar and wind and so on,
battery energy storage or another other energy storage
devices. So forecasting related to all of
them with the respect to both on the production side, on the
utilization side as well as actually on the pricing side
also. So there are things like you
know, LMP and and so on at the utility scale level, at the
ISORTO level that includes the pricing forecasting also.
So we do that. So that's the first pillar,
which is on the AI based forecasting.
The second pillar is on the energy management system or the
EMS in in short and there what we are doing is actually
optimizing the assets in terms of how you can maximize the
revenue that you can generate from these assets.
And these assets can be, you know, whether renewable energy
assets or even non renewable energy assets as well.
So, you know, power production and utilization devices, that's
that's what I put them into. And that again includes, you
know, solar includes, you know, conventional power generation
technologies, including, you know, fossil fuel power
generation, including natural gas power generation as well as
energy storage. And the EMS is basically if
you're sitting on all of these assets and you want to produce
power or consume power or store power at the most optimum
periods of time, when the pricing is high or the pricing
is low, or when you know the carbon production limit is, is
high or you want to reduce it and so on.
So how do you optimize all of these assets and optimizing them
in terms of their value stacking, in terms of their, you
know, revenue generation, in terms of saving on, you know,
transmission, distribution or, you know, electricity bills if
you're a, you know, a commercial and industrial sector and so on.
So that's the, the second piece which it utilizes actually the
first pillar, which is the forecasting.
So the EMS becomes really exponentially more powerful when
you're utilizing actually forecasting or bringing the
forecasting into the EMS or the energy management services.
And then the last piece or the third pillar, last but not the
least is in terms of life maximization or asset life of
the asset maximization. And in this particular case
specifically, it is related to batteries.
And that is where the Edge Air device comes in for the battery
management system where what we have already shown with many,
many patterns that actually we've applied and from the
company within a short period of time, we've published our
results where we took batteries that were manufactured by other
folks and that were close to their end of life.
And we took those batteries and have published our results where
we took them to 20 plus years of extended life.
This is a game changer, if I may call it actually, because this
opens up a space where traditionally batteries were not
open to. So what people used to think
about is lithium ion batteries, maximum you can utilize them for
is like, you know, five years, 10 years, right?
And So what we've done is with, with these patterns and
publications that we've already published out in the public
domain that have gone through peer review and other people
have seen it is that we can take new batteries or old batteries
and make them last for, you know, 10/15/20 plus years of
life in as a, as a stationary battery energy storage system.
And that's where the edge AI device for the BMS comes in, in
terms of life extension that applies to old batteries as well
as new batteries, independent of actually the battery chemistry,
the battery type, the battery form factor, you know, with
nickel cobalt containing batteries or lithium iron
phosphate or the LFP kind of batteries that are cheaper to
make and are growing significantly right now.
So we can work with any battery manufacturer and with this edge
AI make them last much longer. So those are the three pillars
of the technology. So that's.
Good. That's great.
Well, I mean, you've made a lot of advancement.
So I guess what I'll share with the, the audience here is about
3 years ago I met Surrender and we were the company I work for
was interested in investing a little bit within with rely on
as they were growing their, their tack.
And it was with the battery management system with the Edge
program and and it was really fascinating to see what they
were designing and essentially creating battery backup of Gen.
sets essentially. For alternative backup power.
And they were using, you know, recycling basically, or reusing
EV batteries to do that with this technology.
And then it was a really fascinating process and, and the
design and the technology you guys have put together to, to
make those and then produce those is, is really remarkable.
And it was fascinating to see how you were able to, you know,
maintain and optimize and extend the life of these batteries that
like you said, they may not be able or capable of, of still
like powering a car. But when you pull these
together, stack them together individually, utilize the cells
that do have the power through your tools, you're able to
maximize these, the life of these batteries in a way that
was like, OK, no one's doing this right now, at least that we
know of. And it was really interesting.
So I really, I'm glad you get that you brought that up.
But you know, I think there was a lot of success early on with
those that those systems, but something happened along the way
that made you pivot a little bit, didn't it?
And with these other two pillars that you've, I mean, not that
you're probably forgetting or you're still doing battery
management, but the EMS and your forecasting tool is really
showing to be a bigger game changer.
So Sean, that's actually very interesting.
So let me put it this way. I think actually, you know, with
with the BMS and with the battery life extension, what we
have shown is actually absolutely tremendous with
respect to where everybody else was.
You know, people used to talk about, you know, five years,
seven years, 10 years of life for for new batteries.
And what we were able to showcase was that actually for
the batteries that people are thinking have reached their end
of life, actually are are nowhere near their end of life.
And with the right technology and the right Business
Innovation, you can make them last much longer.
And so that is what we, we started with and as a vehicle to
demonstrate actually the capabilities of the BMS, which
is the brains of the battery energy storage system using the,
the human body analogy, right? So that, that was the key that
what we built is the, the, the BMS or the brains of the battery
energy storage system. But when you're starting as a
company, when you go out and, and, and speak with customers
and, and, and so on, everybody would look at you and say that
show me actually that it works, right?
And when you, when you have to show that it works, what you
have to do is actually build the full product.
And by building the full product, that is where.
So even though our tech was on the brains with the BMS, but we
have to build the full human body analogy.
We had to fill the build the full battery energy storage
system so that people can actually put their hands onto it
and believe it that yes, it works.
And So what we did is actually as a vehicle to demonstrate the
the benefits of the BMS or the brains.
That is what we did and we built full battery energy storage
systems where one of the things that we recognized in the very
beginning also was that we were not going to be manufacturing
battery energy storage systems or manufacturing batteries
ourselves, right. I, I think there are a lot of
big companies and, and very innovative and, and good
companies all over the world that have significantly reduced
the cost, you know, over the last decades plus within the
last few years actually the costs have come down by, you
know, sixty, 7080%, you know, and it has become really, really
cheap with respect to lithium ion batteries manufacturing.
And so our innovation, which is demonstrated now is, is on the
brains, which is the BMS. And So what we have now extended
to is, is showcasing that because of this brains that
we've built, we can make the batteries last much longer and
we don't have to make the batteries, we don't have to
manufacture the batteries ourselves.
So whether these are the Teslas or the LGS or the Panasonics or
the BYDS or the CATLS, you know, you name it, any company that is
manufacturing lithium ion batteries including you know
nickel cobalt, you know, NMCNCALFPLMO, right, Any type of
battery material with all their pros and cons, right.
Each of them have a certain space to occupy.
What we are saying is that actually without BMS technology
and on top of that, when you add the EMS and the forecasting,
what we are doing is actually maximizing the the value that
you can extract from these assets.
So all of the the commercial and industrial sector, all of the
the utility skills sector that is using more and more
renewables, using more and more energy storage and actually
fossil fuel power generation is not going away, natural gas
power generation. No, it's not going away.
The base load has to come from that.
And So what we are doing is, is now if you look at the whole
umbrella, the whole envelope is what we're saying is we're
optimizing the whole grid. We are maximizing the value for
everybody involved, whether these are power generators,
distributors, you know, consumers, anybody and
everybody. We're making the grid more
efficient, we're making the grid more reliable, and we're
maximizing the value of everybody who sits in this
space, whether you're consuming power or you're you're, you're
making power. Hello ET Nation, I want to thank
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And so this technology of like the BMS, you know, the brains
behind the, the operation, so to speak, of optimizing the, the
use of the, the batteries and, and the cells and the, and the
power within it. Is this a technology that, you
know, maybe one of these big manufacturers could, could
purchase to deploy to better manage their, the batteries
they're producing now? Or is this like an add on aspect
after the fact? I mean, is this something that
you know could be deployed earlier in the in the in the
process for say some of these big companies?
I guess I'm. Just trying to understand, you
know, how they could be deployed.
Is it, is it after the fact or you know when the battery's over
with or could it be used, you know, during its, you know,
initial optimization to the initial use?
Oh, everywhere, yes. So there are multiple, you know,
portions of the technology, some more invasive and some less
invasive. But what we are showcasing is
that with the Edge AI device and, and with the EMS and the
forecasting, it's actually very, very minimally invasive and it
doesn't have to wait for the end of life.
So it can work with new batteries, you know, on day one
that are supplied by manufacturers and we put our EMS
software and the if needed, we can put in our BMS on the
battery energy storage system. But the EMS is actually
completely independent as well. So the forecasting is, is very,
very, it's 0 invasive, right. So the energy forecasting can be
utilized by you know, existing power producers and consumers as
to if you if you know actually what the the power demand is
going to be tomorrow, what the power demand is going to be day
after tomorrow and when would be the best time to actually
produce. If you have the flexibility to
produce at different times of the day or consumed at different
times of the day or stored at different times of the day.
How would you actually utilize your asset more efficiently by
having this forecast which is significantly better than
everybody else, And let me actually give an example just on
the forecasting side. I don't know if I'm jumping
around, but I'm. You're you're fine, you're fine.
You know about all the. The different things is, you
know, we've published many case studies as well now, which are
publicly available. Actually many, I would direct
the, you know, the listeners to, you know, visit our, our website
also where we've given links to, you know, the different case
studies. And we've done case studies
with, you know, many of the, you know, the Isos and the RT OS.
We've published, you know, Kaiso for case study, PG&E with PGM
with Arcot and so on. And as an example, actually what
we've and over multiple years and each of these you know IS OS
and RT OS have their own forecasting tools as well.
And they, they publish, you know, the day had market and and
so on and the LMP forecasting and things like that and so on.
And so with these case studies, what we've shown is as an
example with Kaiso over a one year period, actually less than
a one year period over a couple of 100 days.
What we were able to show is that within a, A, a small subset
of of Kaiso, just looking at actually PG&E, we were better in
forecasting day over day, cumulatively better by 300 GW
hours. Let me repeat that 300 GW hours,
yes? It was just within.
You know, subset of Kaiso, Yeah. And and when people are talking
about, you know, data centres, you know, coming online and you
know, needing, you know, significant amount of power.
And what we're talking about is just one example, a subset of
you know Kaiso PG and D where 300 GW hours we were able to
demonstrate that we were better by 3X overall versus versus
Kaiso in terms of our forecasting with respect to you
know what the demand is going to be.
Well, OK, so that's. Very.
Small so, but that. That's great information and
it's interesting case study. So how did PG&E respond to that?
Did they be like, wow, Oh my gosh, that we've got to have
this technology or or you know, what did they say?
Yeah, so. That's actually a very
interesting question. And so we just recently
published this study and I think it's getting a lot of traction
and I'm getting a lot of inbounds with, with respect to
request to find out how this can be actually utilized even
further. And what I was going to say is
that when when I paused is that this was actually one of the
small smaller portions of of the case studies that we've shown or
the benefits that we've found. We did PJM and we did Arcot and
when we did it for the all of Arcot for one of the years, I
believe I this was 2024, we were 13 X so with guys, so I
mentioned 3X actually for that case study with with Arcot we
were 13 times better. This is 1300% plus better versus
actually the predictions for the forecasting coming from our
court. And right now this is very
relevant because as you can see, there are, you know, heat waves
going around, you know, all of North America with significant
portion of the US and, and Canada where we're talking about
PGM and Urquhart and and so on. All of them actually going
through significant loads and, and, and load shedding and and
so on. Now when we are able to forecast
and predict what the demand is going to be day in and day out
and be better by 30 next, the the the benefit of this is
limitless and optimization of this is limitless.
And so you can have a more efficient grid and you can
actually make a lot more revenue and profit when you utilize
these services. Yeah.
I mean, yeah. Totally like, you know, the, the
efficiency opportunities are massive.
And so, you know, the forecasting tool works and, and
it's actually, you know, identifying when the peak
demands are or when it's going to be lower demands.
And how does the utility take that information and act on it
in, in a proper way so that they can adjust their, their systems
to, to, to take advantage of either the savings or, you know,
the increase there, you know, is there another step here That's
part of this process that yet you we get that information for
the forecast. Now we need to adjust.
Now we need to actually do and make the changes.
How does that work right? And so maybe the one example
over there is so I'll, I'll pick on a, a very specific case which
which happened actually not too long ago.
So just about a month or so ago, what happened in in Texas was
there was towards the evening, there was a significant peak
that that happened with respect to, you know, power spiking.
And what everybody thought is that, OK, this is this is a huge
peak that has come and everybody discharged all of their assets
with respect to the batteries that they were sitting on,
battery energy storage systems. Thinking.
That this is actually a big peak and so all of the systems were
discharged, but that was only a a small peak that nobody knew
that there is going to be a much bigger peak that is happening
later on. And later on a much bigger spike
happened and all of the batteries were already
discharged. There was no power that actually
they they could have if they waited and if they recognize
that this is just actually a pseudo peak and there might be
actually a much bigger peak or much higher power demand that is
going to happen later, they would have people would have
waited on it, made more money by discharging it later and also
actually made the grid more much more efficient by discharging it
later. The prices spiked to like $4000
a MW hour where typically the prices can be as low as a few
dollars a MW hour, right? So imagine how much efficiency
losses took place and how much actually what's the right way to
say it is how much revenue or or profit loss that took place
because you didn't know that actually there is a, a much
bigger peak that is going to happen later on.
Now how does it tie to actually our, our tools, right?
So it's not only the forecasting, but it is also the
EMS as well. So the forecasting, you know,
puts the EMS on steroids. So as to say, you know,
traditionally the EMS or the energy management systems are
kind of like playing a little bit on the blind where you know,
what they're doing is actually finding out.
You know, if they, let's say, if you take the specific example of
peak shaving, right, a smaller subset where the EMS says how
they operate is they would, you know, say that at a certain,
when a demand or the, the load increases off at a certain
reaches a certain threshold, then you know, the batteries
should, you know, discharge and when it reaches a, a threshold
on the low side, then the battery should charge, right?
But they're, they're kind of like sitting in the, in the
blind because the, the, your load can actually be very
different at not only different times of the day, but different
months of the year and different seasons of the year.
So if your your EMS is operating just on a fixed load based peak
shaving or a time based peak shaving, you're losing out on
all the efficiency and all on the power savings that you can
do if you operated the EMS more smartly.
And what we've done is by joining the forecasting along
with the energy management system, we're able to maximize
again, the, the, the savings or the revenue that you can
generate or utilization of your asset is much more powerful than
what would have been traditionally.
And so there as an example, again, as a case study, what we
did is we took an example for a grocery store.
You know, we've spoken about utilities.
Now let's talk about actually, you know, a commercial and
industrial sector as well, right?
We took a grocery store example where we, we, we took solar and
storage use case and for the peak shaving as an example,
when, when the, the load was high, the, the, the batteries
would, you know, discharge provide the power so that you
are, you know, demand charges or the time of use electricity bill
goes down, right? And you would charge when the
pricing is low and the demand is, is low so that you don't hit
the high demand charges, right. We operated the same system
under two different scenarios. 1 is a traditional EMS which is
based on load based peak shaving and then an EMS which is, you
know, rely on based EMS which has the forecasting and, and and
smartly at just, you know, at different times of the day and
different, you know, days of the month and the different seasons
of the year to find out what is the right place at which it
should be doing the peak saving peak shaving.
The difference was huge. So what we did was what we found
out that if the the return on investment for a traditional
scenario was like 50 plus years, which is meaningless, the
batteries would not last 50 years.
And in in reliance case we were able to actually have a return
on investment in less than three years.
So it was about like 2 1/2 years.
And so, So what that means is, you know, if the batteries were
on the other side when we're saying actually that we had
shown that we, we can make the lithium ion batteries last for
20 to 30 years. What that means is by making an
investment where you did a return on investment in as short
as 2 1/2 years for the rest 18/20/20 plus years.
You're generating. Money you're making money,
you're not sitting on a dead asset.
Traditional. Scenario.
Whereas here it is making money for you, You know, by saving
money, you're making money, you know, right, Right.
Right. So, so the difference between
say like a traditional EMS and the rely on EMS is, is really
the brains behind us, the forecasting tool and this and
and your I guess innovation around the the the design of it,
right, yes. Absolutely.
That's great. Awesome.
You know, when we look at these these types of tools, you know,
can you talk about how Reliance EMS supports the applications a
little more? Let's double click a little bit
more on this peak shaving thing with EV charging and the micro
grid resiliency Talk a little bit more about, you know, how
does that work and how can you know rely on CMS system, you
know, help these other these these types of micro grids?
Absolutely. Yes, So there's many, many
different use cases actually, you know, time of use is is one
example with demand charge reduction, another example peak
shaving, solar integration or renewable integration backup
power actually where you know, it's almost like you know, you
can treat them as huge UPSS so as to say, right.
So if the power, you know, shuts down or you know, PSPS events
takes place, you know, power, public safety power shut off
events takes place, right? And you need backup services.
So there's the backup power. These are called, you know,
traditionally these are like the BTM services or behind of the
meter services, right? And then there is the front of
the meter as well with respect to frequency regulation or you
know, voltage regulation and so on.
So all of those that tie into these different use cases that
we that we can provide for. So you talk a little bit about
the, you know, rely on his hardware light and the battery,
your battery agnostic, you know, on the BMS.
So why is that? And, and what's, you know, and
in I guess an intent is that an intentional design choice?
You know, how does how does that help customers?
And, and you know, wouldn't you need to deploy your technology
partners in that space to really, you know, adopt some of
the technology with you and their products to, to be a team,
right and making this work? So there's two.
Portions in, in, in this question actually.
So 1 is on, on, on being hardware light and and 2nd is
with respect to being technology agnostic, right.
So on, on the technology agnostic side, one thing that I
mentioned earlier, as well as what we wanted to build since
the beginning itself was, you know, we, we, we started with
the assumption that the, the technology evolution is, is, is
going to take place, right. With lithium ion batteries.
You know, they're called lithium ion batteries, but they're not
just, you know, one type, right? So there's, there's many
different, you know, permutations and combinations
that have taken place within lithium ion batteries, right?
Whether it, that is because of the chemistry or the form
factor, you know, the nickel cobalt containing ones, the NMC,
the NCA, you know, there's the LMO, there's the LFP, which is
the lithium ion phosphate and so on.
And different actually within the same battery materials,
different ratios in which actually these materials are
also used, right. So there's many, many different
flavours. And so that is the current
status of the technology and that it is not going to stop
over there. The, the technology evolution
is, is taking place there. There are, you know, significant
and, and very important innovators and people and
companies that are there and technology evolution is going to
take place. So if we're building ABMS and
EMS technology that has to be independent of, you know, these
different sources, right, different companies, different
battery materials and so on. So what we started with the
assumption was that we have to build a technology that should
be applicable to all of these battery chemistry types and and
so on. And that is why we were, we
started with that assumption and we were able to build a
technology that can work with all of these different types of
chemistries and form factors and different sources.
So that is, and how we were able to do that was because we, we
went away from a traditional mindset where the technologies
were built on a thing called SoC or a parameters called SoC and
SOH, which is the state of charge or state of health, which
are, you know, to simplify it is it's just ratios of, of, of
parameters to add that take the ratio of the capacity at
different the times of the, or the life of the battery in terms
of state of health. And you know, in terms of the
state of charge, it is at that particular moment of time, it
doesn't have a physical significance.
So these are actually built on empirically drive models.
And so when you're. Running actually a battery with
ABMS that is dependent on these empirically Dr. models, then
you're limited. It doesn't have a physical
significance. And these batteries, they age at
different rates. So even if you take like a lab
scenario, right, if you take batteries that are manufactured
by the same manufacturer, same chemistry coming from the same
batch, and then you put them into a lab environment by
running them under same temperature condition, same
charge and discharge cycles throughout day in and day out.
Still, if you take, you know, 100 different batteries or
thousand different batteries, they're going to degrade at
different rates. There are intrinsic factors that
make the batteries degrade at different rates and on top of
that then there are extrinsic factors due to which actually
batteries would degrade at different rates.
So by that what happens is if you build Abms technology that
is dependent on the SoC and the SOH inherently on the ratio of
these these parameters which do not have a physical
significance. You end up with, with a, with a,
with a brain that is trying to control these individual battery
components that are all degrading at different rates and
you're limited by the worst performing component and the bad
apple spoils the whole bunch. So the problem keeps on becoming
worse and worse as the more you age.
So what we did is actually we went on to develop this BMS
technology that goes away from that goes away from the SoC and
SOH kind of, you know, these, these parameters that do not
have a physical significance. So we don't, we go down to the
physics of how the batteries actually degrade, how the
batteries operate, and we make them actually discharge and
discharge in different manners so that we are accounting for
the differences in how they're aging on the fly.
Rather than actually looking or or or being dependent on just
the history or the past on empirically drive models, we
look at actually the current status of the battery and the
system all together, individual components and the whole system
all together and operate them differently on the fly.
Rather than looking at the past, we look at the present and how
they would evolve in the future. Well, how long does?
That assessment take, you know, take to kind of evaluate the
different batteries that you are, you know, essentially going
to manage in that space. Like, you know, how long does it
take for the system to kind of evaluate each of the batteries
in that way based on its characteristic and everything
else? What's what's that look like?
Very important. Question and actually what we do
is actually we do it on the fly. So it's not that actually you
need a lot of data to run these batteries and then find out
actually how they're degrading and then actually operate them
in a different manner. What we do it is we do it on the
fly. So we start with the stock
model. You know, when, when you're
starting with actually a new battery of energy storage
system, you start with actually a stock model.
So when you, when you start all batteries, they start with
actually a very flat line and then they start crashing.
So the big thing is actually very uniform.
So you start with a stock model, but then it keeps on adjusting
on the fly. And that's how we're able to
actually, you know, not wait for huge amounts of data and, and
correspondingly not wait for a huge amount of time before we
can actually start operating. We can start on day one.
OK, All right. What does you know, what does it
look like for a customer who says hey, surrender, I want rely
on energies, you know EMSBMS and forecasting technology to come
and evaluate my operations to see what you guys can do to help
us, you know, optimize our energy usage, save money and
generate revenue, whatever it depends on if I've got a bunch
of batteries I can generate revenue with right I mean tell
me how that looks What does that look like for a customer right
there's. Multiple ways on this.
So one of the the easiest ways is that actually we can operate
in a in a shadow mode, right? So and by by what?
By that what I mean is if you, if you have an existing set up
an existing operation and you're utilizing actually a certain
technology, you don't need to modify anything at all.
What we are going to do is we can operate in a in a shadow
mode where you don't have to change anything that is on the
system itself, but you can actually recognize if we were
maintaining and operating the whole energy management system
and the forecasting, then what it would look like, right.
So you can, the customer can recognize or start seeing the
benefits directly in parallel without even modifying their
system. That is one way to do it.
Second way is that we can actually get the data from the
customer and they can anonymize it.
You know, they can redact it in, in whatever form and so on.
We can take the data and run studies on our side and and give
the results back to them. So for example, in terms of the
forecasting study, right, So we can give the results back and
they can compare the results, you know, day in and day out,
you know, they can do it for a week, you know, 15 days a month
and so on. Then you can continuously see
that there is, you know, we're significantly better than what
they already have or, you know, versus what exists in the
market, they're automatically going to get converted.
So these are the, the, the least invasive ones.
And then the, the ultimate goal is that obviously we start
operating or managing the assets ourselves.
We provide the forecasting tools.
We, we, we manage the assets in terms of the energy management
system and we, we maximize the value and the life of these
assets. Moving on, one for the
customers, so in that. Scenario, the last scenario
there are you guys is this like a cloud service?
Is this on premise? How is this tied into their
system so that you can actually start managing this right so?
The AI forecasting and the EMS is completely cloud based.
The, the BMS side is an edge AI device and that's where actually
the hardware light component comes in where you, you had
asked earlier, which I, I, I, I think we started discussing
other things. But the hardware light portion
is the edge AI device, which is actually a small controller that
can snap on to or sit on the outside of the, the battery
energy storage system. And that edge AI device or the
BMS portion is the only portion that is specifically related to
the batteries. But the EMS and the forecasting
is applies to the batteries, but many other types of assets in
addition to the batteries as well.
OK. All right.
Wow. You know, I say here, I see here
you've generated about $1,000,000 in revenue from early
adopters. What's the next commercial
growth and market expansion for you guys, right, so.
And the what we did as as an example is we showcased our our
benefits on, on the BMS technology.
So we use that at the battery energy storage systems as a
weaker to demonstrate the benefits of our BMS and the EMS.
Now the next stage the growth of the company is with respect to
all of the huge waves that are actually simultaneously
happening right now with the data center growth, with the the
power demand growth, with electrification of everything,
with more renewables, with more decarbonization, more
sustainability. So there's so many multiple
waves that are happening actually right now and we're
sitting in the middle of it where are cloud based AI
forecasting and the EMS and the edge AI device.
BMS can significantly increase the value at commercial and
industrial scale and actually going into the utility scale.
The utilities have traditionally and rightfully so have been very
risk and worse, they want to see the technology out in the field.
You know, they won't be the first ones to actually adopt A
new technology. What we've done now with
significant patterns actually that have been applied for.
We have significant data that is already published that have gone
through peer review. We have systems that have
already been sold to different customers.
So with all of that actually already achieved, now the next
exponential growth is is just about to happen.
Wow. Yeah, that that's really good.
I mean, when you when you when you think about it, I mean is it
the utility scale the biggest, you know, opportunity for you
guys in a sense because of the the large volume of energy that
will constantly being, you know, either delivered just, you know,
through distribution transmission, I mean.
It seems like that's probably the most lucrative or the most,
you know, impactful type target for you guys in a way, right?
I mean, I'm wrong here, but it seems like that would be it.
What I would say is I. Think actually there are kind of
like 3 sectors that are really important and are all going to
grow. You know there's the utility
scale which you as rightfully mentioned there is the data
center kind of like the sub sector, you know that is
significant consumers. Of the of the energy, those
guys, right, yes. Absolutely, and then I wouldn't
disregard actually the C and I sector.
This is more like actually, you know, the distributed energy
kind of analogy that we can utilize from, you know what
happened with the Internet as well in in in the past.
And so the C and I sector with the distributed kind of grid is,
is going to also significantly grow and and see huge benefits.
What we have to do is actually to have an efficient and an
optimized grid. We need to make sure that these
are there are not only we're looking at the system
holistically at utility scale level, but also these kind of
like small, you know if you can call it the nodes, right or the
neurons at at different places. So these can be optimized and
make the the grid even more efficient.
Well, how is Relion's approach to forecasting different from
traditional grid or ISO forecasting methods like Casio?
Yeah, so. What, what you've done is
actually we're, we're looking at it actually again, the physics
of, of every system. So rather than looking at it
just as a, you know, just blindly finding out what the the
next number can be, you know, in terms of just looking at it from
a maths, A mathematics standpoint, what we look at it
is, is from a physical and a physics standpoint.
I call, you know, our system and there are actually this
terminology has been used or have been started.
It's starting to get used more and more right now is physical
AI. So what we're doing is actually
we've brought in the physical AI with the physics also along with
it. So what we're doing is we're
looking at it the whole energy sector from the standpoint that
there are these individual parameters that all are really
important to look at. So whether these are, you know,
the the temperature, you know, whether it is the weather, you
know, weather or how the batteries operate, how you know
the solar power, you know, generation takes place and so
on. So we're looking at it from a
physical standpoint with the physics involved rather than
just predicting actually a number from a mathematics
standpoint. So there is AML and AI portion
that looks at the physics of the system and that's how we're able
to forecast it better. It seems to me like, you know,
some investors, like utility scale type investors should be
really thinking about reaching out to talk to you guys about
the technology or some of these solar power battery backup type.
You know, companies should be reaching out to to, you know,
find out how they can, you know, work with you to utilize your
technology. I mean, what am I missing here?
Who else is, who should be a customer, You know, if you're an
investor, you know, or partner who's listening?
What makes now the right time to work with you guys?
I think. This this is actually a really
good time with respect to, you know, all of the the changes
that are taking place in in the grid right now with the data
center growth, with the Gen. AI growth, with more renewables
growth as well and with climate changes also taking place.
So we're at the right place at the right time.
So we're, we're enjoying the benefits of, you know, being
sitting on these these right waves that are taking place at
the moment. And and just the future is
amazing. Well, I mean, if you were to
Fast forward 10 years from now, how will the platform like rely
on reshape the relationship between energy producers,
consumers and the grid? I I think.
The, the way the the sector is growing is I don't think anybody
would doubt the fact that this is going to be big from multiple
fronts, right. What is important to recognize
is that there are going to be multiple winners in in in this
space. This whole pie is so big that
actually each of those winners is going to be big themselves
and rely on is what we are making sure is that rely on is
one of them. You're getting a piece of the.
Pie, right? Yes, yes.
Yeah. I.
Think that the feature is amazing and we're we're we're
just you know making sure we're doing the right things at the
right time well, I was just. Curious because one of the
questions I didn't ask, which is like, you know, I'm assuming you
get competition in this space too.
There's other, you know, companies that are doing
something similar to or maybe it's exactly the same type of
similar type thing. I mean, So what does that look
like from the landscape from your perspective as well?
Is it does anybody compare to you guys or do you have such a,
you know, kind of edge on everybody else right now that
you know, you kind of are in the driver's seat here, right?
So I, I think competition is good.
You know, it makes, you know, everybody better and, and, and
you know, you always stay on your feet and become more
innovative and, and you run faster, right.
So competition is is good. At the same time though, what we
saw is I think there are a lot of companies that are kind of
like operating in these silos. You know, there are companies
that are just working on, you know, the battery analytics
side. You know, there are companies
that are working on the battery, you know, Second Life or, or
repurposing side. There are recycling companies,
right. There are companies that are on
the forecasting side and so on. But they're and there are, you
know, energy management services companies as well as well.
So, but all of them are operating in silos.
There was a huge need that we saw that there needs to be a
holistic solution that makes all of these things actually become
better. So instead of being, you know,
111, you can become elevens or 100 elevens and so on by
combining all of these things together and be, you know, the
best at actually each of those things as well.
So you know, there is a lot of competition and that is a good
thing. You know it.
It makes us better and hopefully makes others better as well.
And what, what we are doing is actually doing this more
holistically by looking at the whole system problem.
That is where my background comes in.
Actually, I, I'm more like a systems engineer and, and a
techno economics background and so on.
And so we're looking at the whole system together and and
and doing this holistically, which nobody else is doing.
So if you're like a utility design engineer helping
utilities build their networks and their grids and everything
is, is that that type of a firm somebody that should be reaching
out to you as well in an early stage to help, you know,
implement and deploy this technology in the design?
Absolutely. I you know, what we can do is we
can help with respect to, you know, how much load is expected
to increase at, you know, various places of the grid.
Where would be the right place to put, you know, more power
generation or more. You know, if a data center needs
to be put at a certain location, we can find out whether that is
the right place or are there other alternative places?
What are other technologies that should be put in and what
they're right sizing should be right In terms of design before
even going to, you know, operation, we can help on the
design side. And then going into the
operation side as well. How do you optimize all of the
different, you know, consumption and generation devices that that
you have and how do you make them last longer?
How do you minimize your, your degradation?
How do you maximize your, you know, power savings and so on.
So all of that actually at various stages, you know,
design, operation and, you know, life, all of that, actually we
apply at multiple places. Yeah, you know.
This the, the, the utility energy boom is what I would call
it, that's going on right now across the US with, you know,
the demand of power from all these data centers and the, you
know, for AI, it's, it's remarkable right now.
What the, what, what we're hearing from the utility sector
is like there's just isn't enough engineering firms, you
know, production firms design, you know, firms, whatever to
help them meet the demand that's already out there for all the
data centers that's being required or requested to be
built across the country. It's, it's amazing and it's
going to have a big and there's not enough power.
That's the other thing is there is not enough power to meet the
demand as well right now. So anything in these guys could
do to optimize and will become more efficient would be, you
know, in my mind a good move, right?
I mean, that way you're taking advantage of the existing power
they do have and using it more efficiently to meet the demand,
right? And and.
There are differences with, with respect to what the data centers
of the past were versus what the new data centers are and how
they're actually being utilized, right.
So, you know, traditionally, you know, the Internet, the data
center load used to be, you know, very kind of uniform, but
with now the Gen. AI, with the AI training and and
so on, there are these, you know, data centers that have
these, you know, power spikes that happen, you know, so
frequently and at at, you know, very high, you know, loads that
you need to have solutions that are more innovative with respect
to how do you get all of these power spikes that are happening
so frequently and you know, big spikes.
How do you utilize assets or how do you bring assets that are
most efficient in terms of providing that kind of load
which is changing at at the least amount of cost.
And that is where again our innovation comes in with the
with the with the forecasting, the EMS and the BMS as to how
you can utilize these, you know, assets, you know most
efficiently. Well, it sounds.
Like, you know, I know for the listeners who've hung on for
this interview and and, and I appreciate you all.
This was a very technical conversation around around that,
you know, some some technology and solutions and software
that's really helping energy systems become more efficient.
And a lot of times, you know, if you're not a real techie or a
real engineer in this in this space, might get hard to really
conceptualize it all. But I know that Doctor surrender
seeing here with the rely on energy is doing an amazing job
of creating tools that I think will help not only, you know,
companies across the country become more efficient, but, you
know, also help deliver power in a more efficient way for in a
cost effective way for us. And so how would be the best way
surrender for someone to get ahold of you?
I'm thinking of, you know, you know, some company that needs
your help. What's the best way for them to?
I would. Say you know anybody who would
like to speak with us. Our e-mail to reach out is
contact us at relyonenergy.com and we would be glad to speak
with with everyone looking forward to it.
Yeah, that's great. Well, you will make sure we
promote this, you know, on when, when we release the podcast.
This will be a great episode for people really, you know,
challenged with how do we deliver more efficient power
with the existing assets we have.
This seems to be a solution that could really help not only
through battery backup batteries, you know, management,
but the CMS and the your, your forecasting tool.
I think it's a game changer. So really appreciate you coming
on the surrender and and explaining a little more in
depth about this. And you know, I'm sure for the
listeners, there's a lot more detail that I know he could get
into because I've actually had other conversations with me and
he can be very specific about the engineering design behind
this stuff, but it's more than we can cover today.
But thank you for coming on the show and we really appreciate
you and and and, you know, best of luck with the rely on energy
and and where you guys are going.
Thank you, Sean. Thanks for.
Listening and watching the show. If you enjoyed the show then
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