Relyion Energy Inc: Redefining Energy Management Through AI-Powered Energy Forecasting

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People used to talk about, you
know, five years, seven years,

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10 years of life for for new
batteries.

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And what we were able to
showcase was that actually for

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the batteries that people are
thinking have reached their end

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of life, actually are are
nowhere near their end of life.

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And with the right technology
and the right Business

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Innovation, you can make them
last much longer.

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Welcome to the Environmental
Transformation Podcast, where we

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bring you interviews with
industry leaders, climate

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champions, sustainability
practitioners, EHS and hazmat

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professionals making an impact
in their businesses today.

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Each leader solving complex
challenges and delivering

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solutions within their areas of
expertise.

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I'm your host, Sean Grady, and
thanks for joining us today.

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Before we jump in, make sure to
follow us on your devices and

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Now let's get started.
Welcome to the Environmental

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Transformation podcast.
I'm your host, Sean Grady and

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today's guest is Doctor
Surrender Singh.

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He's the CEO and Co founder of
Rely on Energy.

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Rely on Energy is a redefining
energy management.

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They are redefining energy
management through a proprietary

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AI powered energy forecasting
tool, energy management system

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and battery management system.
So, so that's the EMS and BMS.

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By integrating advanced AI
software and energy, Brain, so

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to speak, is that the company
enables owners, operators, and

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end users to unlock
unprecedented value from power

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generation and utilization
assets.

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So doctors Singh has a
distinguished career focused on

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advancing and incubating
technologies that address

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climate emergencies with a focus
on the fundamentals of science,

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systems engineering, and
business models.

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He's also authored the many
publications.

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He's also has over 50 patents
granted or pending in the in the

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climb tax.
So he's a busy guy.

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He's lots of experience.
He's also worked at GE and a

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couple other companies that he's
also mentoring startups in the

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climate and energy space right
now.

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And so we're really happy to
have Surrender come on the show

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and talk about rely on energy.
Welcome to the show.

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Sean, thank you for the
introduction and thank you for

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having me.
Looking forward to the

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conversation.
Absolutely.

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So, you know, why don't we step
back a bit here and give the

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listeners a little bit of
background on rely on energy and

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you know how you got started in
this space and you know, give us

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a little bad background on that
which you.

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If you could, yeah.
So rely on actually we started

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in 2021.
So we're completing our four

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years and what we pride
ourselves with actually a very

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strong technical foundation that
we've expanded towards business

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fundamentals and so on.
And the three pillars on which

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we've built the technology is on
energy forecasting or AI

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forecasting.
That applies to, you know, many

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different sections in the energy
sector, whether that is demand,

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whether that is lowered, whether
that is power protection that

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whether that is renewable, so
solar and wind and so on,

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battery energy storage or
another other energy storage

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devices.
So forecasting related to all of

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them with the respect to both on
the production side, on the

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utilization side as well as
actually on the pricing side

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also.
So there are things like you

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know, LMP and and so on at the
utility scale level, at the

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ISORTO level that includes the
pricing forecasting also.

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So we do that.
So that's the first pillar,

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which is on the AI based
forecasting.

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The second pillar is on the
energy management system or the

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EMS in in short and there what
we are doing is actually

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optimizing the assets in terms
of how you can maximize the

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revenue that you can generate
from these assets.

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And these assets can be, you
know, whether renewable energy

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assets or even non renewable
energy assets as well.

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So, you know, power production
and utilization devices, that's

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that's what I put them into.
And that again includes, you

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know, solar includes, you know,
conventional power generation

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technologies, including, you
know, fossil fuel power

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generation, including natural
gas power generation as well as

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energy storage.
And the EMS is basically if

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you're sitting on all of these
assets and you want to produce

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power or consume power or store
power at the most optimum

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periods of time, when the
pricing is high or the pricing

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is low, or when you know the
carbon production limit is, is

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high or you want to reduce it
and so on.

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So how do you optimize all of
these assets and optimizing them

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in terms of their value
stacking, in terms of their, you

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know, revenue generation, in
terms of saving on, you know,

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transmission, distribution or,
you know, electricity bills if

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you're a, you know, a commercial
and industrial sector and so on.

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So that's the, the second piece
which it utilizes actually the

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first pillar, which is the
forecasting.

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So the EMS becomes really
exponentially more powerful when

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you're utilizing actually
forecasting or bringing the

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forecasting into the EMS or the
energy management services.

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And then the last piece or the
third pillar, last but not the

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least is in terms of life
maximization or asset life of

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the asset maximization.
And in this particular case

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specifically, it is related to
batteries.

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And that is where the Edge Air
device comes in for the battery

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management system where what we
have already shown with many,

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many patterns that actually
we've applied and from the

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company within a short period of
time, we've published our

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results where we took batteries
that were manufactured by other

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folks and that were close to
their end of life.

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And we took those batteries and
have published our results where

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we took them to 20 plus years of
extended life.

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This is a game changer, if I may
call it actually, because this

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opens up a space where
traditionally batteries were not

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open to.
So what people used to think

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about is lithium ion batteries,
maximum you can utilize them for

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is like, you know, five years,
10 years, right?

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And So what we've done is with,
with these patterns and

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publications that we've already
published out in the public

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domain that have gone through
peer review and other people

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have seen it is that we can take
new batteries or old batteries

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and make them last for, you
know, 10/15/20 plus years of

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life in as a, as a stationary
battery energy storage system.

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And that's where the edge AI
device for the BMS comes in, in

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terms of life extension that
applies to old batteries as well

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as new batteries, independent of
actually the battery chemistry,

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the battery type, the battery
form factor, you know, with

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nickel cobalt containing
batteries or lithium iron

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phosphate or the LFP kind of
batteries that are cheaper to

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make and are growing
significantly right now.

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So we can work with any battery
manufacturer and with this edge

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AI make them last much longer.
So those are the three pillars

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of the technology.
So that's.

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Good.
That's great.

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Well, I mean, you've made a lot
of advancement.

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So I guess what I'll share with
the, the audience here is about

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3 years ago I met Surrender and
we were the company I work for

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was interested in investing a
little bit within with rely on

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as they were growing their,
their tack.

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And it was with the battery
management system with the Edge

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program and and it was really
fascinating to see what they

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were designing and essentially
creating battery backup of Gen.

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sets essentially.
For alternative backup power.

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And they were using, you know,
recycling basically, or reusing

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EV batteries to do that with
this technology.

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And then it was a really
fascinating process and, and the

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design and the technology you
guys have put together to, to

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make those and then produce
those is, is really remarkable.

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And it was fascinating to see
how you were able to, you know,

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maintain and optimize and extend
the life of these batteries that

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like you said, they may not be
able or capable of, of still

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like powering a car.
But when you pull these

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together, stack them together
individually, utilize the cells

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that do have the power through
your tools, you're able to

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maximize these, the life of
these batteries in a way that

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was like, OK, no one's doing
this right now, at least that we

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know of.
And it was really interesting.

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So I really, I'm glad you get
that you brought that up.

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But you know, I think there was
a lot of success early on with

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those that those systems, but
something happened along the way

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that made you pivot a little
bit, didn't it?

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And with these other two pillars
that you've, I mean, not that

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you're probably forgetting or
you're still doing battery

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management, but the EMS and your
forecasting tool is really

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showing to be a bigger game
changer.

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So Sean, that's actually very
interesting.

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So let me put it this way.
I think actually, you know, with

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with the BMS and with the
battery life extension, what we

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have shown is actually
absolutely tremendous with

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respect to where everybody else
was.

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You know, people used to talk
about, you know, five years,

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seven years, 10 years of life
for for new batteries.

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And what we were able to
showcase was that actually for

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the batteries that people are
thinking have reached their end

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of life, actually are are
nowhere near their end of life.

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And with the right technology
and the right Business

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Innovation, you can make them
last much longer.

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And so that is what we, we
started with and as a vehicle to

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demonstrate actually the
capabilities of the BMS, which

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is the brains of the battery
energy storage system using the,

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the human body analogy, right?
So that, that was the key that

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what we built is the, the, the
BMS or the brains of the battery

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energy storage system.
But when you're starting as a

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company, when you go out and,
and, and speak with customers

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and, and, and so on, everybody
would look at you and say that

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show me actually that it works,
right?

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And when you, when you have to
show that it works, what you

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have to do is actually build the
full product.

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And by building the full
product, that is where.

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So even though our tech was on
the brains with the BMS, but we

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have to build the full human
body analogy.

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We had to fill the build the
full battery energy storage

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system so that people can
actually put their hands onto it

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and believe it that yes, it
works.

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And So what we did is actually
as a vehicle to demonstrate the

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the benefits of the BMS or the
brains.

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That is what we did and we built
full battery energy storage

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systems where one of the things
that we recognized in the very

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beginning also was that we were
not going to be manufacturing

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battery energy storage systems
or manufacturing batteries

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ourselves, right.
I, I think there are a lot of

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big companies and, and very
innovative and, and good

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companies all over the world
that have significantly reduced

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the cost, you know, over the
last decades plus within the

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last few years actually the
costs have come down by, you

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know, sixty, 7080%, you know,
and it has become really, really

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cheap with respect to lithium
ion batteries manufacturing.

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And so our innovation, which is
demonstrated now is, is on the

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brains, which is the BMS.
And So what we have now extended

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to is, is showcasing that
because of this brains that

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we've built, we can make the
batteries last much longer and

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we don't have to make the
batteries, we don't have to

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manufacture the batteries
ourselves.

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So whether these are the Teslas
or the LGS or the Panasonics or

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the BYDS or the CATLS, you know,
you name it, any company that is

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manufacturing lithium ion
batteries including you know

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nickel cobalt, you know,
NMCNCALFPLMO, right, Any type of

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battery material with all their
pros and cons, right.

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Each of them have a certain
space to occupy.

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What we are saying is that
actually without BMS technology

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and on top of that, when you add
the EMS and the forecasting,

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what we are doing is actually
maximizing the the value that

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you can extract from these
assets.

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So all of the the commercial and
industrial sector, all of the

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the utility skills sector that
is using more and more

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renewables, using more and more
energy storage and actually

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fossil fuel power generation is
not going away, natural gas

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power generation.
No, it's not going away.

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The base load has to come from
that.

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And So what we are doing is, is
now if you look at the whole

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umbrella, the whole envelope is
what we're saying is we're

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optimizing the whole grid.
We are maximizing the value for

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everybody involved, whether
these are power generators,

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00:12:33,640 --> 00:12:36,800
distributors, you know,
consumers, anybody and

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00:12:36,800 --> 00:12:39,240
everybody.
We're making the grid more

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00:12:39,240 --> 00:12:43,200
efficient, we're making the grid
more reliable, and we're

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00:12:43,200 --> 00:12:46,520
maximizing the value of
everybody who sits in this

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00:12:46,520 --> 00:12:49,960
space, whether you're consuming
power or you're you're, you're

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00:12:49,960 --> 00:12:53,000
making power.
Hello ET Nation, I want to thank

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00:12:53,000 --> 00:12:54,520
you for listening to the
podcast.

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00:12:54,520 --> 00:12:56,400
If you're enjoying the
interviews we bring you,

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00:12:56,760 --> 00:12:59,760
consider supporting the program
by visiting my website at

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seankgrady.com and buy me a cup
of coffee.

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Proceeds will go towards helping
me continue producing timely

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content and offset production
costs.

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I'd also like to take a moment
and recognize a few of our

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sponsors of the show who have
been amazing partners and our ET

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That's cascade-env.com.
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And so this technology of like
the BMS, you know, the brains

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00:15:33,560 --> 00:15:37,040
behind the, the operation, so to
speak, of optimizing the, the

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use of the, the batteries and,
and the cells and the, and the

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power within it.
Is this a technology that, you

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know, maybe one of these big
manufacturers could, could

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purchase to deploy to better
manage their, the batteries

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they're producing now?
Or is this like an add on aspect

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after the fact?
I mean, is this something that

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you know could be deployed
earlier in the in the in the

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process for say some of these
big companies?

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I guess I'm.
Just trying to understand, you

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know, how they could be
deployed.

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Is it, is it after the fact or
you know when the battery's over

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with or could it be used, you
know, during its, you know,

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initial optimization to the
initial use?

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Oh, everywhere, yes.
So there are multiple, you know,

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portions of the technology, some
more invasive and some less

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invasive.
But what we are showcasing is

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that with the Edge AI device
and, and with the EMS and the

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forecasting, it's actually very,
very minimally invasive and it

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doesn't have to wait for the end
of life.

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So it can work with new
batteries, you know, on day one

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that are supplied by
manufacturers and we put our EMS

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software and the if needed, we
can put in our BMS on the

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battery energy storage system.
But the EMS is actually

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00:16:53,920 --> 00:16:58,600
completely independent as well.
So the forecasting is, is very,

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00:16:58,600 --> 00:17:03,040
very, it's 0 invasive, right.
So the energy forecasting can be

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00:17:03,040 --> 00:17:07,720
utilized by you know, existing
power producers and consumers as

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00:17:07,720 --> 00:17:11,720
to if you if you know actually
what the the power demand is

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00:17:11,720 --> 00:17:14,359
going to be tomorrow, what the
power demand is going to be day

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00:17:14,359 --> 00:17:17,359
after tomorrow and when would be
the best time to actually

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00:17:17,680 --> 00:17:19,440
produce.
If you have the flexibility to

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produce at different times of
the day or consumed at different

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times of the day or stored at
different times of the day.

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How would you actually utilize
your asset more efficiently by

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00:17:28,480 --> 00:17:31,960
having this forecast which is
significantly better than

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00:17:31,960 --> 00:17:34,680
everybody else, And let me
actually give an example just on

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00:17:34,680 --> 00:17:36,640
the forecasting side.
I don't know if I'm jumping

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00:17:36,640 --> 00:17:38,600
around, but I'm.
You're you're fine, you're fine.

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00:17:38,720 --> 00:17:41,720
You know about all the.
The different things is, you

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know, we've published many case
studies as well now, which are

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00:17:45,760 --> 00:17:48,280
publicly available.
Actually many, I would direct

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00:17:48,280 --> 00:17:51,040
the, you know, the listeners to,
you know, visit our, our website

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00:17:51,040 --> 00:17:54,200
also where we've given links to,
you know, the different case

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00:17:54,200 --> 00:17:56,840
studies.
And we've done case studies

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00:17:56,840 --> 00:18:00,440
with, you know, many of the, you
know, the Isos and the RT OS.

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00:18:01,080 --> 00:18:05,520
We've published, you know, Kaiso
for case study, PG&E with PGM

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00:18:05,520 --> 00:18:08,960
with Arcot and so on.
And as an example, actually what

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00:18:08,960 --> 00:18:12,880
we've and over multiple years
and each of these you know IS OS

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00:18:12,880 --> 00:18:16,000
and RT OS have their own
forecasting tools as well.

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00:18:16,000 --> 00:18:18,560
And they, they publish, you
know, the day had market and and

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00:18:18,560 --> 00:18:22,120
so on and the LMP forecasting
and things like that and so on.

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00:18:22,760 --> 00:18:25,520
And so with these case studies,
what we've shown is as an

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00:18:25,520 --> 00:18:29,600
example with Kaiso over a one
year period, actually less than

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00:18:29,600 --> 00:18:31,840
a one year period over a couple
of 100 days.

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00:18:32,240 --> 00:18:36,560
What we were able to show is
that within a, A, a small subset

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00:18:36,560 --> 00:18:41,440
of of Kaiso, just looking at
actually PG&E, we were better in

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00:18:41,440 --> 00:18:48,040
forecasting day over day,
cumulatively better by 300 GW

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00:18:48,040 --> 00:18:52,520
hours.
Let me repeat that 300 GW hours,

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00:18:52,520 --> 00:18:54,720
yes?
It was just within.

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00:18:54,720 --> 00:19:00,000
You know, subset of Kaiso, Yeah.
And and when people are talking

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00:19:00,000 --> 00:19:03,200
about, you know, data centres,
you know, coming online and you

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00:19:03,200 --> 00:19:06,680
know, needing, you know,
significant amount of power.

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00:19:07,200 --> 00:19:11,640
And what we're talking about is
just one example, a subset of

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you know Kaiso PG and D where
300 GW hours we were able to

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00:19:16,240 --> 00:19:23,040
demonstrate that we were better
by 3X overall versus versus

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00:19:23,040 --> 00:19:25,760
Kaiso in terms of our
forecasting with respect to you

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00:19:25,760 --> 00:19:27,200
know what the demand is going to
be.

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00:19:27,560 --> 00:19:29,120
Well, OK, so that's.
Very.

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00:19:29,440 --> 00:19:33,640
Small so, but that.
That's great information and

350
00:19:33,640 --> 00:19:38,560
it's interesting case study.
So how did PG&E respond to that?

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00:19:38,560 --> 00:19:41,600
Did they be like, wow, Oh my
gosh, that we've got to have

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00:19:41,600 --> 00:19:44,520
this technology or or you know,
what did they say?

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00:19:44,960 --> 00:19:46,680
Yeah, so.
That's actually a very

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00:19:46,680 --> 00:19:50,120
interesting question.
And so we just recently

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00:19:50,160 --> 00:19:53,200
published this study and I think
it's getting a lot of traction

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00:19:53,200 --> 00:19:56,120
and I'm getting a lot of
inbounds with, with respect to

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00:19:56,120 --> 00:19:58,880
request to find out how this can
be actually utilized even

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further.
And what I was going to say is

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00:20:01,000 --> 00:20:03,880
that when when I paused is that
this was actually one of the

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00:20:03,880 --> 00:20:08,320
small smaller portions of of the
case studies that we've shown or

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00:20:08,320 --> 00:20:11,880
the benefits that we've found.
We did PJM and we did Arcot and

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00:20:12,080 --> 00:20:15,960
when we did it for the all of
Arcot for one of the years, I

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00:20:15,960 --> 00:20:22,560
believe I this was 2024, we were
13 X so with guys, so I

364
00:20:22,560 --> 00:20:25,640
mentioned 3X actually for that
case study with with Arcot we

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00:20:25,640 --> 00:20:32,040
were 13 times better.
This is 1300% plus better versus

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00:20:32,040 --> 00:20:36,080
actually the predictions for the
forecasting coming from our

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00:20:36,080 --> 00:20:38,040
court.
And right now this is very

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00:20:38,040 --> 00:20:41,440
relevant because as you can see,
there are, you know, heat waves

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00:20:41,440 --> 00:20:44,320
going around, you know, all of
North America with significant

370
00:20:44,320 --> 00:20:47,400
portion of the US and, and
Canada where we're talking about

371
00:20:47,400 --> 00:20:50,960
PGM and Urquhart and and so on.
All of them actually going

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00:20:50,960 --> 00:20:55,000
through significant loads and,
and, and load shedding and and

373
00:20:55,000 --> 00:20:59,600
so on.
Now when we are able to forecast

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00:20:59,640 --> 00:21:03,680
and predict what the demand is
going to be day in and day out

375
00:21:03,680 --> 00:21:08,320
and be better by 30 next, the
the the benefit of this is

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00:21:08,320 --> 00:21:12,640
limitless and optimization of
this is limitless.

377
00:21:12,640 --> 00:21:15,400
And so you can have a more
efficient grid and you can

378
00:21:15,400 --> 00:21:18,560
actually make a lot more revenue
and profit when you utilize

379
00:21:18,560 --> 00:21:19,560
these services.
Yeah.

380
00:21:19,560 --> 00:21:21,720
I mean, yeah.
Totally like, you know, the, the

381
00:21:21,720 --> 00:21:24,080
efficiency opportunities are
massive.

382
00:21:24,120 --> 00:21:27,400
And so, you know, the
forecasting tool works and, and

383
00:21:28,080 --> 00:21:31,200
it's actually, you know,
identifying when the peak

384
00:21:31,200 --> 00:21:33,840
demands are or when it's going
to be lower demands.

385
00:21:33,840 --> 00:21:38,960
And how does the utility take
that information and act on it

386
00:21:38,960 --> 00:21:43,080
in, in a proper way so that they
can adjust their, their systems

387
00:21:43,640 --> 00:21:48,080
to, to, to take advantage of
either the savings or, you know,

388
00:21:48,080 --> 00:21:52,160
the increase there, you know, is
there another step here That's

389
00:21:52,160 --> 00:21:55,440
part of this process that yet
you we get that information for

390
00:21:55,440 --> 00:21:57,240
the forecast.
Now we need to adjust.

391
00:21:57,240 --> 00:22:00,280
Now we need to actually do and
make the changes.

392
00:22:00,280 --> 00:22:04,000
How does that work right?
And so maybe the one example

393
00:22:04,000 --> 00:22:07,400
over there is so I'll, I'll pick
on a, a very specific case which

394
00:22:07,400 --> 00:22:09,160
which happened actually not too
long ago.

395
00:22:09,640 --> 00:22:13,960
So just about a month or so ago,
what happened in in Texas was

396
00:22:14,680 --> 00:22:17,880
there was towards the evening,
there was a significant peak

397
00:22:17,880 --> 00:22:21,360
that that happened with respect
to, you know, power spiking.

398
00:22:21,920 --> 00:22:25,880
And what everybody thought is
that, OK, this is this is a huge

399
00:22:25,880 --> 00:22:30,640
peak that has come and everybody
discharged all of their assets

400
00:22:30,640 --> 00:22:32,680
with respect to the batteries
that they were sitting on,

401
00:22:32,680 --> 00:22:35,600
battery energy storage systems.
Thinking.

402
00:22:35,600 --> 00:22:38,720
That this is actually a big peak
and so all of the systems were

403
00:22:38,720 --> 00:22:44,240
discharged, but that was only a
a small peak that nobody knew

404
00:22:44,240 --> 00:22:46,720
that there is going to be a much
bigger peak that is happening

405
00:22:46,720 --> 00:22:51,880
later on.
And later on a much bigger spike

406
00:22:51,880 --> 00:22:54,800
happened and all of the
batteries were already

407
00:22:54,800 --> 00:22:58,760
discharged.
There was no power that actually

408
00:22:58,760 --> 00:23:02,960
they they could have if they
waited and if they recognize

409
00:23:02,960 --> 00:23:05,560
that this is just actually a
pseudo peak and there might be

410
00:23:05,560 --> 00:23:09,240
actually a much bigger peak or
much higher power demand that is

411
00:23:09,240 --> 00:23:12,000
going to happen later, they
would have people would have

412
00:23:12,000 --> 00:23:16,760
waited on it, made more money by
discharging it later and also

413
00:23:16,760 --> 00:23:19,920
actually made the grid more much
more efficient by discharging it

414
00:23:19,920 --> 00:23:22,840
later.
The prices spiked to like $4000

415
00:23:22,840 --> 00:23:25,960
a MW hour where typically the
prices can be as low as a few

416
00:23:25,960 --> 00:23:29,640
dollars a MW hour, right?
So imagine how much efficiency

417
00:23:29,640 --> 00:23:33,680
losses took place and how much
actually what's the right way to

418
00:23:33,680 --> 00:23:39,080
say it is how much revenue or or
profit loss that took place

419
00:23:39,680 --> 00:23:42,440
because you didn't know that
actually there is a, a much

420
00:23:42,440 --> 00:23:44,200
bigger peak that is going to
happen later on.

421
00:23:44,520 --> 00:23:47,600
Now how does it tie to actually
our, our tools, right?

422
00:23:47,600 --> 00:23:50,480
So it's not only the
forecasting, but it is also the

423
00:23:50,640 --> 00:23:53,360
EMS as well.
So the forecasting, you know,

424
00:23:53,360 --> 00:23:56,720
puts the EMS on steroids.
So as to say, you know,

425
00:23:56,720 --> 00:23:59,680
traditionally the EMS or the
energy management systems are

426
00:23:59,680 --> 00:24:02,760
kind of like playing a little
bit on the blind where you know,

427
00:24:02,760 --> 00:24:04,680
what they're doing is actually
finding out.

428
00:24:05,320 --> 00:24:08,240
You know, if they, let's say, if
you take the specific example of

429
00:24:08,240 --> 00:24:13,200
peak shaving, right, a smaller
subset where the EMS says how

430
00:24:13,200 --> 00:24:16,360
they operate is they would, you
know, say that at a certain,

431
00:24:16,360 --> 00:24:19,600
when a demand or the, the load
increases off at a certain

432
00:24:19,720 --> 00:24:22,960
reaches a certain threshold,
then you know, the batteries

433
00:24:22,960 --> 00:24:26,360
should, you know, discharge and
when it reaches a, a threshold

434
00:24:26,360 --> 00:24:28,720
on the low side, then the
battery should charge, right?

435
00:24:29,360 --> 00:24:31,480
But they're, they're kind of
like sitting in the, in the

436
00:24:31,480 --> 00:24:36,520
blind because the, the, your
load can actually be very

437
00:24:36,520 --> 00:24:39,560
different at not only different
times of the day, but different

438
00:24:39,560 --> 00:24:41,920
months of the year and different
seasons of the year.

439
00:24:42,360 --> 00:24:47,200
So if your your EMS is operating
just on a fixed load based peak

440
00:24:47,200 --> 00:24:50,600
shaving or a time based peak
shaving, you're losing out on

441
00:24:50,600 --> 00:24:54,080
all the efficiency and all on
the power savings that you can

442
00:24:54,080 --> 00:24:56,920
do if you operated the EMS more
smartly.

443
00:24:57,240 --> 00:25:00,960
And what we've done is by
joining the forecasting along

444
00:25:00,960 --> 00:25:04,160
with the energy management
system, we're able to maximize

445
00:25:04,160 --> 00:25:07,840
again, the, the, the savings or
the revenue that you can

446
00:25:07,840 --> 00:25:12,360
generate or utilization of your
asset is much more powerful than

447
00:25:12,360 --> 00:25:13,880
what would have been
traditionally.

448
00:25:14,160 --> 00:25:16,720
And so there as an example,
again, as a case study, what we

449
00:25:16,720 --> 00:25:19,000
did is we took an example for a
grocery store.

450
00:25:19,440 --> 00:25:21,360
You know, we've spoken about
utilities.

451
00:25:21,360 --> 00:25:24,000
Now let's talk about actually,
you know, a commercial and

452
00:25:24,000 --> 00:25:25,440
industrial sector as well,
right?

453
00:25:25,760 --> 00:25:29,520
We took a grocery store example
where we, we, we took solar and

454
00:25:29,520 --> 00:25:33,120
storage use case and for the
peak shaving as an example,

455
00:25:33,600 --> 00:25:38,920
when, when the, the load was
high, the, the, the batteries

456
00:25:38,920 --> 00:25:41,440
would, you know, discharge
provide the power so that you

457
00:25:41,440 --> 00:25:44,760
are, you know, demand charges or
the time of use electricity bill

458
00:25:45,120 --> 00:25:47,560
goes down, right?
And you would charge when the

459
00:25:47,560 --> 00:25:50,600
pricing is low and the demand
is, is low so that you don't hit

460
00:25:50,600 --> 00:25:54,800
the high demand charges, right.
We operated the same system

461
00:25:55,240 --> 00:25:58,720
under two different scenarios. 1
is a traditional EMS which is

462
00:25:58,720 --> 00:26:03,240
based on load based peak shaving
and then an EMS which is, you

463
00:26:03,240 --> 00:26:07,080
know, rely on based EMS which
has the forecasting and, and and

464
00:26:07,080 --> 00:26:10,480
smartly at just, you know, at
different times of the day and

465
00:26:10,480 --> 00:26:12,840
different, you know, days of the
month and the different seasons

466
00:26:12,840 --> 00:26:16,040
of the year to find out what is
the right place at which it

467
00:26:16,040 --> 00:26:18,440
should be doing the peak saving
peak shaving.

468
00:26:18,920 --> 00:26:22,520
The difference was huge.
So what we did was what we found

469
00:26:22,520 --> 00:26:26,720
out that if the the return on
investment for a traditional

470
00:26:26,720 --> 00:26:29,920
scenario was like 50 plus years,
which is meaningless, the

471
00:26:29,920 --> 00:26:31,760
batteries would not last 50
years.

472
00:26:31,760 --> 00:26:35,160
And in in reliance case we were
able to actually have a return

473
00:26:35,160 --> 00:26:36,840
on investment in less than three
years.

474
00:26:36,840 --> 00:26:38,600
So it was about like 2 1/2
years.

475
00:26:38,600 --> 00:26:41,520
And so, So what that means is,
you know, if the batteries were

476
00:26:41,520 --> 00:26:43,960
on the other side when we're
saying actually that we had

477
00:26:44,120 --> 00:26:47,520
shown that we, we can make the
lithium ion batteries last for

478
00:26:47,520 --> 00:26:50,680
20 to 30 years.
What that means is by making an

479
00:26:50,680 --> 00:26:54,160
investment where you did a
return on investment in as short

480
00:26:54,160 --> 00:26:58,960
as 2 1/2 years for the rest
18/20/20 plus years.

481
00:27:00,080 --> 00:27:02,000
You're generating.
Money you're making money,

482
00:27:02,000 --> 00:27:04,440
you're not sitting on a dead
asset.

483
00:27:05,600 --> 00:27:07,320
Traditional.
Scenario.

484
00:27:07,680 --> 00:27:12,000
Whereas here it is making money
for you, You know, by saving

485
00:27:12,000 --> 00:27:14,200
money, you're making money, you
know, right, Right.

486
00:27:14,200 --> 00:27:16,280
Right.
So, so the difference between

487
00:27:16,280 --> 00:27:21,520
say like a traditional EMS and
the rely on EMS is, is really

488
00:27:21,520 --> 00:27:25,120
the brains behind us, the
forecasting tool and this and

489
00:27:25,120 --> 00:27:30,080
and your I guess innovation
around the the the design of it,

490
00:27:30,080 --> 00:27:32,160
right, yes.
Absolutely.

491
00:27:33,560 --> 00:27:34,720
That's great.
Awesome.

492
00:27:35,000 --> 00:27:38,080
You know, when we look at these
these types of tools, you know,

493
00:27:38,080 --> 00:27:42,040
can you talk about how Reliance
EMS supports the applications a

494
00:27:42,040 --> 00:27:44,800
little more?
Let's double click a little bit

495
00:27:44,800 --> 00:27:48,400
more on this peak shaving thing
with EV charging and the micro

496
00:27:48,400 --> 00:27:51,440
grid resiliency Talk a little
bit more about, you know, how

497
00:27:51,440 --> 00:27:55,920
does that work and how can you
know rely on CMS system, you

498
00:27:55,920 --> 00:27:58,640
know, help these other these
these types of micro grids?

499
00:27:59,200 --> 00:28:01,760
Absolutely.
Yes, So there's many, many

500
00:28:01,760 --> 00:28:05,400
different use cases actually,
you know, time of use is is one

501
00:28:05,400 --> 00:28:08,720
example with demand charge
reduction, another example peak

502
00:28:08,720 --> 00:28:11,760
shaving, solar integration or
renewable integration backup

503
00:28:11,760 --> 00:28:15,080
power actually where you know,
it's almost like you know, you

504
00:28:15,080 --> 00:28:18,120
can treat them as huge UPSS so
as to say, right.

505
00:28:18,360 --> 00:28:21,760
So if the power, you know, shuts
down or you know, PSPS events

506
00:28:21,760 --> 00:28:24,120
takes place, you know, power,
public safety power shut off

507
00:28:24,120 --> 00:28:27,120
events takes place, right?
And you need backup services.

508
00:28:27,120 --> 00:28:29,800
So there's the backup power.
These are called, you know,

509
00:28:29,840 --> 00:28:32,680
traditionally these are like the
BTM services or behind of the

510
00:28:32,680 --> 00:28:34,840
meter services, right?
And then there is the front of

511
00:28:34,840 --> 00:28:37,760
the meter as well with respect
to frequency regulation or you

512
00:28:37,760 --> 00:28:39,240
know, voltage regulation and so
on.

513
00:28:39,640 --> 00:28:44,000
So all of those that tie into
these different use cases that

514
00:28:44,000 --> 00:28:48,600
we that we can provide for.
So you talk a little bit about

515
00:28:48,600 --> 00:28:51,840
the, you know, rely on his
hardware light and the battery,

516
00:28:51,840 --> 00:28:54,280
your battery agnostic, you know,
on the BMS.

517
00:28:54,360 --> 00:28:57,560
So why is that?
And, and what's, you know, and

518
00:28:57,560 --> 00:29:01,040
in I guess an intent is that an
intentional design choice?

519
00:29:01,120 --> 00:29:03,000
You know, how does how does that
help customers?

520
00:29:03,000 --> 00:29:10,640
And, and you know, wouldn't you
need to deploy your technology

521
00:29:10,640 --> 00:29:14,960
partners in that space to
really, you know, adopt some of

522
00:29:14,960 --> 00:29:19,600
the technology with you and
their products to, to be a team,

523
00:29:20,480 --> 00:29:22,760
right and making this work?
So there's two.

524
00:29:22,760 --> 00:29:24,800
Portions in, in, in this
question actually.

525
00:29:24,800 --> 00:29:28,680
So 1 is on, on, on being
hardware light and and 2nd is

526
00:29:28,680 --> 00:29:30,880
with respect to being technology
agnostic, right.

527
00:29:31,440 --> 00:29:35,200
So on, on the technology
agnostic side, one thing that I

528
00:29:35,240 --> 00:29:38,680
mentioned earlier, as well as
what we wanted to build since

529
00:29:38,680 --> 00:29:41,720
the beginning itself was, you
know, we, we, we started with

530
00:29:41,720 --> 00:29:46,600
the assumption that the, the
technology evolution is, is, is

531
00:29:46,600 --> 00:29:49,040
going to take place, right.
With lithium ion batteries.

532
00:29:49,040 --> 00:29:51,520
You know, they're called lithium
ion batteries, but they're not

533
00:29:51,520 --> 00:29:53,840
just, you know, one type, right?
So there's, there's many

534
00:29:53,840 --> 00:29:57,000
different, you know,
permutations and combinations

535
00:29:57,000 --> 00:29:59,320
that have taken place within
lithium ion batteries, right?

536
00:29:59,640 --> 00:30:02,040
Whether it, that is because of
the chemistry or the form

537
00:30:02,040 --> 00:30:05,640
factor, you know, the nickel
cobalt containing ones, the NMC,

538
00:30:05,640 --> 00:30:09,160
the NCA, you know, there's the
LMO, there's the LFP, which is

539
00:30:09,160 --> 00:30:10,800
the lithium ion phosphate and so
on.

540
00:30:11,040 --> 00:30:14,360
And different actually within
the same battery materials,

541
00:30:14,360 --> 00:30:16,960
different ratios in which
actually these materials are

542
00:30:16,960 --> 00:30:19,040
also used, right.
So there's many, many different

543
00:30:19,040 --> 00:30:21,320
flavours.
And so that is the current

544
00:30:21,320 --> 00:30:23,840
status of the technology and
that it is not going to stop

545
00:30:23,840 --> 00:30:25,720
over there.
The, the technology evolution

546
00:30:25,720 --> 00:30:29,600
is, is taking place there.
There are, you know, significant

547
00:30:29,600 --> 00:30:32,920
and, and very important
innovators and people and

548
00:30:32,920 --> 00:30:35,600
companies that are there and
technology evolution is going to

549
00:30:35,600 --> 00:30:38,640
take place.
So if we're building ABMS and

550
00:30:38,840 --> 00:30:43,640
EMS technology that has to be
independent of, you know, these

551
00:30:43,680 --> 00:30:46,440
different sources, right,
different companies, different

552
00:30:46,440 --> 00:30:49,280
battery materials and so on.
So what we started with the

553
00:30:49,280 --> 00:30:52,200
assumption was that we have to
build a technology that should

554
00:30:52,200 --> 00:30:55,360
be applicable to all of these
battery chemistry types and and

555
00:30:55,360 --> 00:30:58,280
so on.
And that is why we were, we

556
00:30:58,280 --> 00:31:00,480
started with that assumption and
we were able to build a

557
00:31:00,480 --> 00:31:03,320
technology that can work with
all of these different types of

558
00:31:03,320 --> 00:31:05,480
chemistries and form factors and
different sources.

559
00:31:07,360 --> 00:31:12,080
So that is, and how we were able
to do that was because we, we

560
00:31:12,080 --> 00:31:16,840
went away from a traditional
mindset where the technologies

561
00:31:16,840 --> 00:31:21,360
were built on a thing called SoC
or a parameters called SoC and

562
00:31:21,400 --> 00:31:24,640
SOH, which is the state of
charge or state of health, which

563
00:31:24,640 --> 00:31:29,440
are, you know, to simplify it is
it's just ratios of, of, of

564
00:31:29,520 --> 00:31:34,120
parameters to add that take the
ratio of the capacity at

565
00:31:34,120 --> 00:31:37,720
different the times of the, or
the life of the battery in terms

566
00:31:37,720 --> 00:31:39,760
of state of health.
And you know, in terms of the

567
00:31:39,760 --> 00:31:43,200
state of charge, it is at that
particular moment of time, it

568
00:31:43,200 --> 00:31:44,840
doesn't have a physical
significance.

569
00:31:44,840 --> 00:31:47,280
So these are actually built on
empirically drive models.

570
00:31:48,240 --> 00:31:50,600
And so when you're.
Running actually a battery with

571
00:31:50,680 --> 00:31:54,920
ABMS that is dependent on these
empirically Dr. models, then

572
00:31:54,920 --> 00:31:56,960
you're limited.
It doesn't have a physical

573
00:31:56,960 --> 00:31:59,520
significance.
And these batteries, they age at

574
00:31:59,520 --> 00:32:03,560
different rates.
So even if you take like a lab

575
00:32:03,560 --> 00:32:07,600
scenario, right, if you take
batteries that are manufactured

576
00:32:07,600 --> 00:32:10,720
by the same manufacturer, same
chemistry coming from the same

577
00:32:10,720 --> 00:32:14,040
batch, and then you put them
into a lab environment by

578
00:32:14,040 --> 00:32:16,160
running them under same
temperature condition, same

579
00:32:16,160 --> 00:32:18,920
charge and discharge cycles
throughout day in and day out.

580
00:32:19,360 --> 00:32:21,800
Still, if you take, you know,
100 different batteries or

581
00:32:21,800 --> 00:32:24,280
thousand different batteries,
they're going to degrade at

582
00:32:24,280 --> 00:32:27,000
different rates.
There are intrinsic factors that

583
00:32:27,000 --> 00:32:30,240
make the batteries degrade at
different rates and on top of

584
00:32:30,240 --> 00:32:33,440
that then there are extrinsic
factors due to which actually

585
00:32:33,440 --> 00:32:35,160
batteries would degrade at
different rates.

586
00:32:35,680 --> 00:32:39,440
So by that what happens is if
you build Abms technology that

587
00:32:39,440 --> 00:32:43,160
is dependent on the SoC and the
SOH inherently on the ratio of

588
00:32:43,160 --> 00:32:45,560
these these parameters which do
not have a physical

589
00:32:45,560 --> 00:32:49,320
significance.
You end up with, with a, with a,

590
00:32:49,360 --> 00:32:52,160
with a brain that is trying to
control these individual battery

591
00:32:52,160 --> 00:32:55,560
components that are all
degrading at different rates and

592
00:32:55,560 --> 00:32:58,000
you're limited by the worst
performing component and the bad

593
00:32:58,000 --> 00:33:01,960
apple spoils the whole bunch.
So the problem keeps on becoming

594
00:33:01,960 --> 00:33:04,000
worse and worse as the more you
age.

595
00:33:04,720 --> 00:33:07,840
So what we did is actually we
went on to develop this BMS

596
00:33:07,840 --> 00:33:12,320
technology that goes away from
that goes away from the SoC and

597
00:33:12,400 --> 00:33:15,480
SOH kind of, you know, these,
these parameters that do not

598
00:33:15,480 --> 00:33:18,280
have a physical significance.
So we don't, we go down to the

599
00:33:18,280 --> 00:33:20,760
physics of how the batteries
actually degrade, how the

600
00:33:20,760 --> 00:33:24,920
batteries operate, and we make
them actually discharge and

601
00:33:24,920 --> 00:33:28,840
discharge in different manners
so that we are accounting for

602
00:33:28,840 --> 00:33:32,000
the differences in how they're
aging on the fly.

603
00:33:32,360 --> 00:33:36,280
Rather than actually looking or
or or being dependent on just

604
00:33:36,280 --> 00:33:39,640
the history or the past on
empirically drive models, we

605
00:33:39,640 --> 00:33:42,520
look at actually the current
status of the battery and the

606
00:33:42,520 --> 00:33:46,120
system all together, individual
components and the whole system

607
00:33:46,120 --> 00:33:50,080
all together and operate them
differently on the fly.

608
00:33:50,080 --> 00:33:53,480
Rather than looking at the past,
we look at the present and how

609
00:33:53,480 --> 00:33:55,760
they would evolve in the future.
Well, how long does?

610
00:33:55,760 --> 00:33:59,160
That assessment take, you know,
take to kind of evaluate the

611
00:33:59,160 --> 00:34:03,880
different batteries that you
are, you know, essentially going

612
00:34:03,880 --> 00:34:06,440
to manage in that space.
Like, you know, how long does it

613
00:34:06,440 --> 00:34:09,320
take for the system to kind of
evaluate each of the batteries

614
00:34:09,600 --> 00:34:12,000
in that way based on its
characteristic and everything

615
00:34:12,000 --> 00:34:13,199
else?
What's what's that look like?

616
00:34:13,400 --> 00:34:16,239
Very important.
Question and actually what we do

617
00:34:16,239 --> 00:34:19,360
is actually we do it on the fly.
So it's not that actually you

618
00:34:19,360 --> 00:34:23,880
need a lot of data to run these
batteries and then find out

619
00:34:23,880 --> 00:34:26,159
actually how they're degrading
and then actually operate them

620
00:34:26,159 --> 00:34:29,040
in a different manner.
What we do it is we do it on the

621
00:34:29,040 --> 00:34:31,560
fly.
So we start with the stock

622
00:34:31,560 --> 00:34:33,360
model.
You know, when, when you're

623
00:34:33,360 --> 00:34:35,960
starting with actually a new
battery of energy storage

624
00:34:35,960 --> 00:34:38,520
system, you start with actually
a stock model.

625
00:34:38,520 --> 00:34:41,040
So when you, when you start all
batteries, they start with

626
00:34:41,040 --> 00:34:43,600
actually a very flat line and
then they start crashing.

627
00:34:44,120 --> 00:34:46,080
So the big thing is actually
very uniform.

628
00:34:46,080 --> 00:34:49,360
So you start with a stock model,
but then it keeps on adjusting

629
00:34:49,360 --> 00:34:50,920
on the fly.
And that's how we're able to

630
00:34:50,920 --> 00:34:56,120
actually, you know, not wait for
huge amounts of data and, and

631
00:34:56,120 --> 00:34:59,080
correspondingly not wait for a
huge amount of time before we

632
00:34:59,080 --> 00:35:01,720
can actually start operating.
We can start on day one.

633
00:35:03,160 --> 00:35:06,800
OK, All right.
What does you know, what does it

634
00:35:06,800 --> 00:35:11,720
look like for a customer who
says hey, surrender, I want rely

635
00:35:11,720 --> 00:35:18,480
on energies, you know EMSBMS and
forecasting technology to come

636
00:35:18,480 --> 00:35:22,960
and evaluate my operations to
see what you guys can do to help

637
00:35:23,360 --> 00:35:27,560
us, you know, optimize our
energy usage, save money and

638
00:35:27,560 --> 00:35:29,640
generate revenue, whatever it
depends on if I've got a bunch

639
00:35:29,640 --> 00:35:32,720
of batteries I can generate
revenue with right I mean tell

640
00:35:32,720 --> 00:35:36,040
me how that looks What does that
look like for a customer right

641
00:35:36,040 --> 00:35:37,360
there's.
Multiple ways on this.

642
00:35:37,400 --> 00:35:40,800
So one of the the easiest ways
is that actually we can operate

643
00:35:40,800 --> 00:35:44,440
in a in a shadow mode, right?
So and by by what?

644
00:35:44,800 --> 00:35:49,000
By that what I mean is if you,
if you have an existing set up

645
00:35:49,040 --> 00:35:52,160
an existing operation and you're
utilizing actually a certain

646
00:35:52,160 --> 00:35:55,120
technology, you don't need to
modify anything at all.

647
00:35:55,120 --> 00:35:57,960
What we are going to do is we
can operate in a in a shadow

648
00:35:57,960 --> 00:36:02,320
mode where you don't have to
change anything that is on the

649
00:36:02,320 --> 00:36:05,920
system itself, but you can
actually recognize if we were

650
00:36:05,920 --> 00:36:09,000
maintaining and operating the
whole energy management system

651
00:36:09,000 --> 00:36:12,160
and the forecasting, then what
it would look like, right.

652
00:36:12,400 --> 00:36:15,440
So you can, the customer can
recognize or start seeing the

653
00:36:15,440 --> 00:36:18,640
benefits directly in parallel
without even modifying their

654
00:36:18,640 --> 00:36:20,600
system.
That is one way to do it.

655
00:36:20,960 --> 00:36:24,000
Second way is that we can
actually get the data from the

656
00:36:24,000 --> 00:36:25,920
customer and they can anonymize
it.

657
00:36:25,920 --> 00:36:29,240
You know, they can redact it in,
in whatever form and so on.

658
00:36:29,720 --> 00:36:34,040
We can take the data and run
studies on our side and and give

659
00:36:34,040 --> 00:36:36,520
the results back to them.
So for example, in terms of the

660
00:36:36,520 --> 00:36:40,440
forecasting study, right, So we
can give the results back and

661
00:36:40,440 --> 00:36:43,480
they can compare the results,
you know, day in and day out,

662
00:36:43,880 --> 00:36:47,400
you know, they can do it for a
week, you know, 15 days a month

663
00:36:47,400 --> 00:36:50,120
and so on.
Then you can continuously see

664
00:36:50,120 --> 00:36:52,640
that there is, you know, we're
significantly better than what

665
00:36:52,640 --> 00:36:55,640
they already have or, you know,
versus what exists in the

666
00:36:55,640 --> 00:36:58,240
market, they're automatically
going to get converted.

667
00:36:58,520 --> 00:37:01,320
So these are the, the, the least
invasive ones.

668
00:37:01,320 --> 00:37:04,880
And then the, the ultimate goal
is that obviously we start

669
00:37:04,880 --> 00:37:07,720
operating or managing the assets
ourselves.

670
00:37:08,040 --> 00:37:09,520
We provide the forecasting
tools.

671
00:37:09,520 --> 00:37:12,640
We, we, we manage the assets in
terms of the energy management

672
00:37:12,640 --> 00:37:17,320
system and we, we maximize the
value and the life of these

673
00:37:17,320 --> 00:37:18,920
assets.
Moving on, one for the

674
00:37:18,920 --> 00:37:22,120
customers, so in that.
Scenario, the last scenario

675
00:37:22,120 --> 00:37:25,360
there are you guys is this like
a cloud service?

676
00:37:25,360 --> 00:37:28,640
Is this on premise?
How is this tied into their

677
00:37:28,640 --> 00:37:31,880
system so that you can actually
start managing this right so?

678
00:37:32,200 --> 00:37:36,200
The AI forecasting and the EMS
is completely cloud based.

679
00:37:36,760 --> 00:37:40,440
The, the BMS side is an edge AI
device and that's where actually

680
00:37:40,440 --> 00:37:43,040
the hardware light component
comes in where you, you had

681
00:37:43,040 --> 00:37:45,920
asked earlier, which I, I, I, I
think we started discussing

682
00:37:45,920 --> 00:37:47,920
other things.
But the hardware light portion

683
00:37:47,920 --> 00:37:52,200
is the edge AI device, which is
actually a small controller that

684
00:37:52,440 --> 00:37:56,160
can snap on to or sit on the
outside of the, the battery

685
00:37:56,160 --> 00:37:59,560
energy storage system.
And that edge AI device or the

686
00:37:59,560 --> 00:38:03,160
BMS portion is the only portion
that is specifically related to

687
00:38:03,160 --> 00:38:05,760
the batteries.
But the EMS and the forecasting

688
00:38:05,760 --> 00:38:10,240
is applies to the batteries, but
many other types of assets in

689
00:38:10,240 --> 00:38:11,600
addition to the batteries as
well.

690
00:38:13,240 --> 00:38:14,280
OK.
All right.

691
00:38:14,320 --> 00:38:17,640
Wow.
You know, I say here, I see here

692
00:38:17,640 --> 00:38:20,400
you've generated about
$1,000,000 in revenue from early

693
00:38:20,400 --> 00:38:22,320
adopters.
What's the next commercial

694
00:38:22,320 --> 00:38:27,040
growth and market expansion for
you guys, right, so.

695
00:38:27,240 --> 00:38:32,760
And the what we did as as an
example is we showcased our our

696
00:38:32,760 --> 00:38:34,840
benefits on, on the BMS
technology.

697
00:38:35,200 --> 00:38:37,920
So we use that at the battery
energy storage systems as a

698
00:38:37,920 --> 00:38:41,080
weaker to demonstrate the
benefits of our BMS and the EMS.

699
00:38:41,640 --> 00:38:45,360
Now the next stage the growth of
the company is with respect to

700
00:38:45,360 --> 00:38:48,360
all of the huge waves that are
actually simultaneously

701
00:38:48,360 --> 00:38:52,160
happening right now with the
data center growth, with the the

702
00:38:52,160 --> 00:38:55,080
power demand growth, with
electrification of everything,

703
00:38:55,080 --> 00:38:57,680
with more renewables, with more
decarbonization, more

704
00:38:57,680 --> 00:39:00,200
sustainability.
So there's so many multiple

705
00:39:00,200 --> 00:39:02,840
waves that are happening
actually right now and we're

706
00:39:02,840 --> 00:39:06,400
sitting in the middle of it
where are cloud based AI

707
00:39:06,400 --> 00:39:10,280
forecasting and the EMS and the
edge AI device.

708
00:39:10,280 --> 00:39:15,360
BMS can significantly increase
the value at commercial and

709
00:39:15,360 --> 00:39:18,360
industrial scale and actually
going into the utility scale.

710
00:39:18,640 --> 00:39:21,880
The utilities have traditionally
and rightfully so have been very

711
00:39:21,880 --> 00:39:25,760
risk and worse, they want to see
the technology out in the field.

712
00:39:25,800 --> 00:39:27,840
You know, they won't be the
first ones to actually adopt A

713
00:39:27,840 --> 00:39:30,200
new technology.
What we've done now with

714
00:39:30,200 --> 00:39:32,960
significant patterns actually
that have been applied for.

715
00:39:33,040 --> 00:39:36,200
We have significant data that is
already published that have gone

716
00:39:36,200 --> 00:39:38,760
through peer review.
We have systems that have

717
00:39:38,760 --> 00:39:40,520
already been sold to different
customers.

718
00:39:41,000 --> 00:39:43,840
So with all of that actually
already achieved, now the next

719
00:39:43,920 --> 00:39:46,160
exponential growth is is just
about to happen.

720
00:39:46,680 --> 00:39:48,440
Wow.
Yeah, that that's really good.

721
00:39:48,440 --> 00:39:52,600
I mean, when you when you when
you think about it, I mean is it

722
00:39:52,600 --> 00:39:56,520
the utility scale the biggest,
you know, opportunity for you

723
00:39:56,520 --> 00:40:00,800
guys in a sense because of the
the large volume of energy that

724
00:40:00,800 --> 00:40:03,760
will constantly being, you know,
either delivered just, you know,

725
00:40:03,960 --> 00:40:06,560
through distribution
transmission, I mean.

726
00:40:07,680 --> 00:40:11,200
It seems like that's probably
the most lucrative or the most,

727
00:40:11,240 --> 00:40:14,880
you know, impactful type target
for you guys in a way, right?

728
00:40:14,880 --> 00:40:17,240
I mean, I'm wrong here, but it
seems like that would be it.

729
00:40:17,280 --> 00:40:19,720
What I would say is I.
Think actually there are kind of

730
00:40:19,760 --> 00:40:24,520
like 3 sectors that are really
important and are all going to

731
00:40:24,520 --> 00:40:26,200
grow.
You know there's the utility

732
00:40:26,200 --> 00:40:29,400
scale which you as rightfully
mentioned there is the data

733
00:40:29,400 --> 00:40:31,920
center kind of like the sub
sector, you know that is

734
00:40:31,920 --> 00:40:35,440
significant consumers.
Of the of the energy, those

735
00:40:35,880 --> 00:40:39,680
guys, right, yes.
Absolutely, and then I wouldn't

736
00:40:39,680 --> 00:40:41,800
disregard actually the C and I
sector.

737
00:40:41,880 --> 00:40:44,800
This is more like actually, you
know, the distributed energy

738
00:40:44,800 --> 00:40:48,160
kind of analogy that we can
utilize from, you know what

739
00:40:48,160 --> 00:40:50,800
happened with the Internet as
well in in in the past.

740
00:40:51,120 --> 00:40:54,720
And so the C and I sector with
the distributed kind of grid is,

741
00:40:54,720 --> 00:40:58,320
is going to also significantly
grow and and see huge benefits.

742
00:40:58,720 --> 00:41:02,080
What we have to do is actually
to have an efficient and an

743
00:41:02,080 --> 00:41:06,040
optimized grid.
We need to make sure that these

744
00:41:06,040 --> 00:41:08,640
are there are not only we're
looking at the system

745
00:41:08,640 --> 00:41:11,560
holistically at utility scale
level, but also these kind of

746
00:41:11,560 --> 00:41:14,680
like small, you know if you can
call it the nodes, right or the

747
00:41:14,680 --> 00:41:18,960
neurons at at different places.
So these can be optimized and

748
00:41:18,960 --> 00:41:20,920
make the the grid even more
efficient.

749
00:41:22,200 --> 00:41:25,760
Well, how is Relion's approach
to forecasting different from

750
00:41:25,760 --> 00:41:30,200
traditional grid or ISO
forecasting methods like Casio?

751
00:41:30,400 --> 00:41:32,600
Yeah, so.
What, what you've done is

752
00:41:32,600 --> 00:41:36,000
actually we're, we're looking at
it actually again, the physics

753
00:41:36,000 --> 00:41:39,480
of, of every system.
So rather than looking at it

754
00:41:39,480 --> 00:41:44,600
just as a, you know, just
blindly finding out what the the

755
00:41:44,600 --> 00:41:48,040
next number can be, you know, in
terms of just looking at it from

756
00:41:48,040 --> 00:41:51,600
a maths, A mathematics
standpoint, what we look at it

757
00:41:51,640 --> 00:41:54,480
is, is from a physical and a
physics standpoint.

758
00:41:54,480 --> 00:41:58,120
I call, you know, our system and
there are actually this

759
00:41:58,120 --> 00:42:00,520
terminology has been used or
have been started.

760
00:42:01,000 --> 00:42:04,920
It's starting to get used more
and more right now is physical

761
00:42:04,920 --> 00:42:06,960
AI.
So what we're doing is actually

762
00:42:06,960 --> 00:42:11,600
we've brought in the physical AI
with the physics also along with

763
00:42:11,600 --> 00:42:13,280
it.
So what we're doing is we're

764
00:42:13,280 --> 00:42:18,280
looking at it the whole energy
sector from the standpoint that

765
00:42:18,280 --> 00:42:22,920
there are these individual
parameters that all are really

766
00:42:22,920 --> 00:42:25,320
important to look at.
So whether these are, you know,

767
00:42:25,440 --> 00:42:28,440
the the temperature, you know,
whether it is the weather, you

768
00:42:28,440 --> 00:42:32,080
know, weather or how the
batteries operate, how you know

769
00:42:32,080 --> 00:42:34,800
the solar power, you know,
generation takes place and so

770
00:42:34,800 --> 00:42:36,720
on.
So we're looking at it from a

771
00:42:36,720 --> 00:42:40,240
physical standpoint with the
physics involved rather than

772
00:42:40,240 --> 00:42:42,960
just predicting actually a
number from a mathematics

773
00:42:42,960 --> 00:42:46,720
standpoint.
So there is AML and AI portion

774
00:42:46,720 --> 00:42:49,240
that looks at the physics of the
system and that's how we're able

775
00:42:49,240 --> 00:42:53,000
to forecast it better.
It seems to me like, you know,

776
00:42:53,200 --> 00:42:58,280
some investors, like utility
scale type investors should be

777
00:42:58,280 --> 00:43:01,640
really thinking about reaching
out to talk to you guys about

778
00:43:01,640 --> 00:43:06,360
the technology or some of these
solar power battery backup type.

779
00:43:06,400 --> 00:43:09,600
You know, companies should be
reaching out to to, you know,

780
00:43:09,600 --> 00:43:12,520
find out how they can, you know,
work with you to utilize your

781
00:43:12,520 --> 00:43:15,160
technology.
I mean, what am I missing here?

782
00:43:15,160 --> 00:43:18,200
Who else is, who should be a
customer, You know, if you're an

783
00:43:18,200 --> 00:43:22,160
investor, you know, or partner
who's listening?

784
00:43:23,240 --> 00:43:26,240
What makes now the right time to
work with you guys?

785
00:43:27,360 --> 00:43:29,480
I think.
This this is actually a really

786
00:43:29,480 --> 00:43:33,800
good time with respect to, you
know, all of the the changes

787
00:43:33,800 --> 00:43:37,160
that are taking place in in the
grid right now with the data

788
00:43:37,160 --> 00:43:40,120
center growth, with the Gen.
AI growth, with more renewables

789
00:43:40,120 --> 00:43:44,400
growth as well and with climate
changes also taking place.

790
00:43:44,840 --> 00:43:47,400
So we're at the right place at
the right time.

791
00:43:47,400 --> 00:43:51,160
So we're, we're enjoying the
benefits of, you know, being

792
00:43:51,560 --> 00:43:54,320
sitting on these these right
waves that are taking place at

793
00:43:54,320 --> 00:43:56,480
the moment.
And and just the future is

794
00:43:56,480 --> 00:44:00,080
amazing.
Well, I mean, if you were to

795
00:44:00,080 --> 00:44:04,080
Fast forward 10 years from now,
how will the platform like rely

796
00:44:04,080 --> 00:44:07,760
on reshape the relationship
between energy producers,

797
00:44:07,760 --> 00:44:10,040
consumers and the grid?
I I think.

798
00:44:10,280 --> 00:44:14,880
The, the way the the sector is
growing is I don't think anybody

799
00:44:14,880 --> 00:44:18,680
would doubt the fact that this
is going to be big from multiple

800
00:44:18,680 --> 00:44:21,920
fronts, right.
What is important to recognize

801
00:44:21,920 --> 00:44:25,480
is that there are going to be
multiple winners in in in this

802
00:44:25,480 --> 00:44:29,400
space.
This whole pie is so big that

803
00:44:29,400 --> 00:44:32,320
actually each of those winners
is going to be big themselves

804
00:44:32,320 --> 00:44:35,680
and rely on is what we are
making sure is that rely on is

805
00:44:35,680 --> 00:44:37,160
one of them.
You're getting a piece of the.

806
00:44:37,160 --> 00:44:38,480
Pie, right?
Yes, yes.

807
00:44:39,360 --> 00:44:40,080
Yeah.
I.

808
00:44:40,120 --> 00:44:42,480
Think that the feature is
amazing and we're we're we're

809
00:44:42,520 --> 00:44:44,920
just you know making sure we're
doing the right things at the

810
00:44:44,920 --> 00:44:47,160
right time well, I was just.
Curious because one of the

811
00:44:47,160 --> 00:44:51,240
questions I didn't ask, which is
like, you know, I'm assuming you

812
00:44:51,240 --> 00:44:53,080
get competition in this space
too.

813
00:44:53,080 --> 00:44:55,240
There's other, you know,
companies that are doing

814
00:44:55,240 --> 00:44:59,400
something similar to or maybe
it's exactly the same type of

815
00:44:59,400 --> 00:45:02,000
similar type thing.
I mean, So what does that look

816
00:45:02,000 --> 00:45:04,440
like from the landscape from
your perspective as well?

817
00:45:04,440 --> 00:45:08,840
Is it does anybody compare to
you guys or do you have such a,

818
00:45:08,840 --> 00:45:13,400
you know, kind of edge on
everybody else right now that

819
00:45:13,400 --> 00:45:16,760
you know, you kind of are in the
driver's seat here, right?

820
00:45:16,760 --> 00:45:18,560
So I, I think competition is
good.

821
00:45:18,640 --> 00:45:21,960
You know, it makes, you know,
everybody better and, and, and

822
00:45:22,040 --> 00:45:25,440
you know, you always stay on
your feet and become more

823
00:45:25,440 --> 00:45:27,480
innovative and, and you run
faster, right.

824
00:45:27,680 --> 00:45:31,080
So competition is is good.
At the same time though, what we

825
00:45:31,080 --> 00:45:33,880
saw is I think there are a lot
of companies that are kind of

826
00:45:33,920 --> 00:45:36,800
like operating in these silos.
You know, there are companies

827
00:45:36,800 --> 00:45:39,800
that are just working on, you
know, the battery analytics

828
00:45:39,800 --> 00:45:41,160
side.
You know, there are companies

829
00:45:41,160 --> 00:45:44,320
that are working on the battery,
you know, Second Life or, or

830
00:45:44,320 --> 00:45:46,760
repurposing side.
There are recycling companies,

831
00:45:46,760 --> 00:45:49,400
right.
There are companies that are on

832
00:45:49,680 --> 00:45:52,960
the forecasting side and so on.
But they're and there are, you

833
00:45:52,960 --> 00:45:55,800
know, energy management services
companies as well as well.

834
00:45:56,280 --> 00:45:59,120
So, but all of them are
operating in silos.

835
00:45:59,680 --> 00:46:04,560
There was a huge need that we
saw that there needs to be a

836
00:46:04,560 --> 00:46:08,360
holistic solution that makes all
of these things actually become

837
00:46:08,360 --> 00:46:10,400
better.
So instead of being, you know,

838
00:46:10,400 --> 00:46:15,360
111, you can become elevens or
100 elevens and so on by

839
00:46:15,360 --> 00:46:18,320
combining all of these things
together and be, you know, the

840
00:46:18,320 --> 00:46:20,240
best at actually each of those
things as well.

841
00:46:20,640 --> 00:46:23,680
So you know, there is a lot of
competition and that is a good

842
00:46:23,680 --> 00:46:24,640
thing.
You know it.

843
00:46:24,640 --> 00:46:27,560
It makes us better and hopefully
makes others better as well.

844
00:46:28,120 --> 00:46:31,720
And what, what we are doing is
actually doing this more

845
00:46:31,720 --> 00:46:34,440
holistically by looking at the
whole system problem.

846
00:46:34,760 --> 00:46:36,560
That is where my background
comes in.

847
00:46:36,560 --> 00:46:39,800
Actually, I, I'm more like a
systems engineer and, and a

848
00:46:39,800 --> 00:46:41,640
techno economics background and
so on.

849
00:46:42,160 --> 00:46:44,640
And so we're looking at the
whole system together and and

850
00:46:44,680 --> 00:46:46,960
and doing this holistically,
which nobody else is doing.

851
00:46:47,960 --> 00:46:50,880
So if you're like a utility
design engineer helping

852
00:46:50,880 --> 00:46:54,160
utilities build their networks
and their grids and everything

853
00:46:54,160 --> 00:46:58,840
is, is that that type of a firm
somebody that should be reaching

854
00:46:58,840 --> 00:47:01,840
out to you as well in an early
stage to help, you know,

855
00:47:02,480 --> 00:47:05,440
implement and deploy this
technology in the design?

856
00:47:06,120 --> 00:47:08,200
Absolutely.
I you know, what we can do is we

857
00:47:08,200 --> 00:47:12,080
can help with respect to, you
know, how much load is expected

858
00:47:12,080 --> 00:47:14,640
to increase at, you know,
various places of the grid.

859
00:47:14,640 --> 00:47:18,120
Where would be the right place
to put, you know, more power

860
00:47:18,120 --> 00:47:21,280
generation or more.
You know, if a data center needs

861
00:47:21,280 --> 00:47:23,960
to be put at a certain location,
we can find out whether that is

862
00:47:23,960 --> 00:47:26,520
the right place or are there
other alternative places?

863
00:47:27,040 --> 00:47:29,720
What are other technologies that
should be put in and what

864
00:47:29,720 --> 00:47:33,360
they're right sizing should be
right In terms of design before

865
00:47:33,360 --> 00:47:36,040
even going to, you know,
operation, we can help on the

866
00:47:36,040 --> 00:47:38,200
design side.
And then going into the

867
00:47:38,200 --> 00:47:40,880
operation side as well.
How do you optimize all of the

868
00:47:40,880 --> 00:47:44,440
different, you know, consumption
and generation devices that that

869
00:47:44,440 --> 00:47:46,960
you have and how do you make
them last longer?

870
00:47:47,240 --> 00:47:49,760
How do you minimize your, your
degradation?

871
00:47:49,760 --> 00:47:52,400
How do you maximize your, you
know, power savings and so on.

872
00:47:52,400 --> 00:47:55,640
So all of that actually at
various stages, you know,

873
00:47:55,680 --> 00:48:00,120
design, operation and, you know,
life, all of that, actually we

874
00:48:00,120 --> 00:48:02,800
apply at multiple places.
Yeah, you know.

875
00:48:02,800 --> 00:48:07,360
This the, the, the utility
energy boom is what I would call

876
00:48:07,360 --> 00:48:10,480
it, that's going on right now
across the US with, you know,

877
00:48:10,480 --> 00:48:14,600
the demand of power from all
these data centers and the, you

878
00:48:14,600 --> 00:48:18,040
know, for AI, it's, it's
remarkable right now.

879
00:48:18,040 --> 00:48:21,160
What the, what, what we're
hearing from the utility sector

880
00:48:21,160 --> 00:48:25,240
is like there's just isn't
enough engineering firms, you

881
00:48:25,240 --> 00:48:28,760
know, production firms design,
you know, firms, whatever to

882
00:48:28,760 --> 00:48:33,080
help them meet the demand that's
already out there for all the

883
00:48:33,240 --> 00:48:36,600
data centers that's being
required or requested to be

884
00:48:36,600 --> 00:48:39,480
built across the country.
It's, it's amazing and it's

885
00:48:39,480 --> 00:48:41,840
going to have a big and there's
not enough power.

886
00:48:41,840 --> 00:48:44,280
That's the other thing is there
is not enough power to meet the

887
00:48:44,280 --> 00:48:47,520
demand as well right now.
So anything in these guys could

888
00:48:47,520 --> 00:48:52,040
do to optimize and will become
more efficient would be, you

889
00:48:52,040 --> 00:48:54,800
know, in my mind a good move,
right?

890
00:48:54,800 --> 00:48:58,640
I mean, that way you're taking
advantage of the existing power

891
00:48:58,640 --> 00:49:02,200
they do have and using it more
efficiently to meet the demand,

892
00:49:02,680 --> 00:49:03,960
right?
And and.

893
00:49:03,960 --> 00:49:07,520
There are differences with, with
respect to what the data centers

894
00:49:07,520 --> 00:49:10,760
of the past were versus what the
new data centers are and how

895
00:49:10,760 --> 00:49:12,400
they're actually being utilized,
right.

896
00:49:12,760 --> 00:49:15,520
So, you know, traditionally, you
know, the Internet, the data

897
00:49:15,520 --> 00:49:19,760
center load used to be, you
know, very kind of uniform, but

898
00:49:19,760 --> 00:49:22,520
with now the Gen.
AI, with the AI training and and

899
00:49:22,520 --> 00:49:26,800
so on, there are these, you
know, data centers that have

900
00:49:26,800 --> 00:49:30,080
these, you know, power spikes
that happen, you know, so

901
00:49:30,080 --> 00:49:34,720
frequently and at at, you know,
very high, you know, loads that

902
00:49:34,720 --> 00:49:38,440
you need to have solutions that
are more innovative with respect

903
00:49:38,440 --> 00:49:42,840
to how do you get all of these
power spikes that are happening

904
00:49:42,920 --> 00:49:45,360
so frequently and you know, big
spikes.

905
00:49:45,680 --> 00:49:50,400
How do you utilize assets or how
do you bring assets that are

906
00:49:50,400 --> 00:49:53,680
most efficient in terms of
providing that kind of load

907
00:49:53,680 --> 00:49:56,680
which is changing at at the
least amount of cost.

908
00:49:56,680 --> 00:49:58,920
And that is where again our
innovation comes in with the

909
00:49:58,960 --> 00:50:03,160
with the with the forecasting,
the EMS and the BMS as to how

910
00:50:03,160 --> 00:50:06,200
you can utilize these, you know,
assets, you know most

911
00:50:06,200 --> 00:50:07,920
efficiently.
Well, it sounds.

912
00:50:07,920 --> 00:50:10,720
Like, you know, I know for the
listeners who've hung on for

913
00:50:10,720 --> 00:50:13,640
this interview and and, and I
appreciate you all.

914
00:50:14,000 --> 00:50:17,560
This was a very technical
conversation around around that,

915
00:50:17,560 --> 00:50:22,200
you know, some some technology
and solutions and software

916
00:50:22,200 --> 00:50:25,640
that's really helping energy
systems become more efficient.

917
00:50:25,640 --> 00:50:28,200
And a lot of times, you know, if
you're not a real techie or a

918
00:50:28,200 --> 00:50:31,840
real engineer in this in this
space, might get hard to really

919
00:50:31,840 --> 00:50:35,960
conceptualize it all.
But I know that Doctor surrender

920
00:50:35,960 --> 00:50:39,440
seeing here with the rely on
energy is doing an amazing job

921
00:50:40,000 --> 00:50:43,800
of creating tools that I think
will help not only, you know,

922
00:50:44,880 --> 00:50:47,880
companies across the country
become more efficient, but, you

923
00:50:47,880 --> 00:50:51,400
know, also help deliver power in
a more efficient way for in a

924
00:50:51,400 --> 00:50:56,400
cost effective way for us.
And so how would be the best way

925
00:50:56,520 --> 00:50:58,760
surrender for someone to get
ahold of you?

926
00:50:59,040 --> 00:51:02,880
I'm thinking of, you know, you
know, some company that needs

927
00:51:02,880 --> 00:51:04,680
your help.
What's the best way for them to?

928
00:51:04,800 --> 00:51:07,280
I would.
Say you know anybody who would

929
00:51:07,280 --> 00:51:10,040
like to speak with us.
Our e-mail to reach out is

930
00:51:10,040 --> 00:51:15,000
contact us at relyonenergy.com
and we would be glad to speak

931
00:51:15,000 --> 00:51:17,120
with with everyone looking
forward to it.

932
00:51:17,760 --> 00:51:19,920
Yeah, that's great.
Well, you will make sure we

933
00:51:19,920 --> 00:51:22,960
promote this, you know, on when,
when we release the podcast.

934
00:51:22,960 --> 00:51:25,920
This will be a great episode for
people really, you know,

935
00:51:26,120 --> 00:51:29,560
challenged with how do we
deliver more efficient power

936
00:51:29,680 --> 00:51:31,520
with the existing assets we
have.

937
00:51:31,760 --> 00:51:34,880
This seems to be a solution that
could really help not only

938
00:51:34,880 --> 00:51:37,600
through battery backup
batteries, you know, management,

939
00:51:37,600 --> 00:51:40,240
but the CMS and the your, your
forecasting tool.

940
00:51:40,240 --> 00:51:43,400
I think it's a game changer.
So really appreciate you coming

941
00:51:43,400 --> 00:51:45,760
on the surrender and and
explaining a little more in

942
00:51:45,760 --> 00:51:48,920
depth about this.
And you know, I'm sure for the

943
00:51:48,920 --> 00:51:51,920
listeners, there's a lot more
detail that I know he could get

944
00:51:51,920 --> 00:51:54,720
into because I've actually had
other conversations with me and

945
00:51:54,720 --> 00:51:57,800
he can be very specific about
the engineering design behind

946
00:51:57,800 --> 00:52:00,680
this stuff, but it's more than
we can cover today.

947
00:52:00,680 --> 00:52:03,200
But thank you for coming on the
show and we really appreciate

948
00:52:03,200 --> 00:52:06,160
you and and and, you know, best
of luck with the rely on energy

949
00:52:06,160 --> 00:52:07,520
and and where you guys are
going.

950
00:52:08,240 --> 00:52:10,040
Thank you, Sean.
Thanks for.

951
00:52:10,040 --> 00:52:12,920
Listening and watching the show.
If you enjoyed the show then

952
00:52:12,920 --> 00:52:15,640
please share it with your
friends and Co workers on social

953
00:52:15,640 --> 00:52:19,120
media and tell somebody in
person thanks for being with.

954
00:52:19,120 --> 00:52:20,120
US ET nation.