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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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visit my website at 
www.seankgrady.com and sign up 

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for our newsletter and e-mail 
announcements. 

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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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distributors, you know, 
consumers, anybody and 

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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, 

236
00:12:56,760 --> 00:12:59,760
consider supporting the program 
by visiting my website at 

237
00:12:59,760 --> 00:13:02,760
seankgrady.com and buy me a cup 
of coffee. 

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00:13:02,800 --> 00:13:05,680
Proceeds will go towards helping
me continue producing timely 

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content and offset production 
costs. 

240
00:13:09,280 --> 00:13:11,560
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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If you're looking for a drilling

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equipment nationwide coverage, 
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To learn more, check out their 
website site@cascade-env.com. 

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That's cascade-env.com. 
Hey, are you looking for an 

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equipment rental company to help
you with your next wastewater 

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remediation or waste management 
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customer by preventing any cross
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The next time you are presented 

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with an environmental challenge,
call E Tank to come up with the 

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solution for your problem. 
I want to thank one of our 

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sponsors, Waste Link. 
Waste Link is the all in one 

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business, visit wastelink.com to
schedule a demo today. 

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00:15:29,680 --> 00:15:33,560
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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00:15:40,680 --> 00:15:43,800
power within it. 
Is this a technology that, you 

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00:15:43,800 --> 00:15:47,600
know, maybe one of these big 
manufacturers could, could 

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00:15:47,600 --> 00:15:53,440
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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00:15:57,200 --> 00:15:59,480
after the fact? 
I mean, is this something that 

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00:15:59,480 --> 00:16:02,400
you know could be deployed 
earlier in the in the in the 

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00:16:02,400 --> 00:16:04,880
process for say some of these 
big companies? 

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00:16:05,760 --> 00:16:07,600
I guess I'm. 
Just trying to understand, you 

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00:16:07,600 --> 00:16:09,000
know, how they could be 
deployed. 

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00:16:09,040 --> 00:16:12,120
Is it, is it after the fact or 
you know when the battery's over

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00:16:12,120 --> 00:16:15,000
with or could it be used, you 
know, during its, you know, 

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00:16:15,000 --> 00:16:17,280
initial optimization to the 
initial use? 

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00:16:17,680 --> 00:16:21,000
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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00:16:24,160 --> 00:16:27,360
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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00:16:31,040 --> 00:16:36,120
forecasting, it's actually very,
very minimally invasive and it 

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00:16:36,120 --> 00:16:38,120
doesn't have to wait for the end
of life. 

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00:16:38,120 --> 00:16:41,960
So it can work with new 
batteries, you know, on day one 

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00:16:42,680 --> 00:16:46,320
that are supplied by 
manufacturers and we put our EMS

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00:16:46,320 --> 00:16:50,640
software and the if needed, we 
can put in our BMS on the 

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00:16:50,640 --> 00:16:53,920
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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00:17:19,440 --> 00:17:22,200
produce at different times of 
the day or consumed at different

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00:17:22,200 --> 00:17:24,520
times of the day or stored at 
different times of the day. 

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00:17:24,839 --> 00:17:28,480
How would you actually utilize 
your asset more efficiently by 

315
00:17:28,480 --> 00:17:31,960
having this forecast which is 
significantly better than 

316
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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00:17:41,720 --> 00:17:45,760
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

323
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 

327
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

331
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 

334
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

337
00:18:41,440 --> 00:18:48,040
forecasting day over day, 
cumulatively better by 300 GW 

338
00:18:48,040 --> 00:18:52,520
hours. 
Let me repeat that 300 GW hours,

339
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 

342
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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00:19:11,800 --> 00:19:16,240
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 

346
00:19:23,040 --> 00:19:25,760
Kaiso in terms of our 
forecasting with respect to you 

347
00:19:25,760 --> 00:19:27,200
know what the demand is going to
be. 

348
00:19:27,560 --> 00:19:29,120
Well, OK, so that's. 
Very. 

349
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?

351
00:19:38,560 --> 00:19:41,600
Did they be like, wow, Oh my 
gosh, that we've got to have 

352
00:19:41,600 --> 00:19:44,520
this technology or or you know, 
what did they say? 

353
00:19:44,960 --> 00:19:46,680
Yeah, so. 
That's actually a very 

354
00:19:46,680 --> 00:19:50,120
interesting question. 
And so we just recently 

355
00:19:50,160 --> 00:19:53,200
published this study and I think
it's getting a lot of traction 

356
00:19:53,200 --> 00:19:56,120
and I'm getting a lot of 
inbounds with, with respect to 

357
00:19:56,120 --> 00:19:58,880
request to find out how this can
be actually utilized even 

358
00:19:58,880 --> 00:20:01,000
further. 
And what I was going to say is 

359
00:20:01,000 --> 00:20:03,880
that when when I paused is that 
this was actually one of the 

360
00:20:03,880 --> 00:20:08,320
small smaller portions of of the
case studies that we've shown or

361
00:20:08,320 --> 00:20:11,880
the benefits that we've found. 
We did PJM and we did Arcot and 

362
00:20:12,080 --> 00:20:15,960
when we did it for the all of 
Arcot for one of the years, I 

363
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 

365
00:20:25,640 --> 00:20:32,040
were 13 times better. 
This is 1300% plus better versus

366
00:20:32,040 --> 00:20:36,080
actually the predictions for the
forecasting coming from our 

367
00:20:36,080 --> 00:20:38,040
court. 
And right now this is very 

368
00:20:38,040 --> 00:20:41,440
relevant because as you can see,
there are, you know, heat waves 

369
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 

372
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

374
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 

376
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.