us it's some permissions thing because you I mean very legitimately they don't want you you know to just share your screen willy-nilly so they ask ask for permissions but then you know probably have not yet given it yeah I think it's fine let me just um do it real fast and then if I go into slideshow mode this should be fine right okay great oh yeah that works all right I and also do you uh one question is in the middle or the end um people can interrupt me as I go along I don't know what the wh
at sort of typical with this group but um I don't mind having you know a conversation especially since I don't know what everybody's interests are like if we hit on something that they really want to know more about it I can spend more time on that and Skip other things so yeah usually what happens to people watch on stream more often than in person and then either I mean usually I'm the one asking questions and either from me or from you know if anybody pings me with a question that's the most
common brout uh more convenient okay so they're watching the live stream yeah that's why also have a live stream because it's also posible and uh you know time zone uh like also sorry also in addition to live stream this recording but they're not asking questions yeah okay Tyler's not let me just pick him real fast check the calendar invite oh guest list is too large oh just I can't actually see if he got invited or not can you weird uh let me just copy it yes I see him oh oh you see him okay ye
ah cuz he's like the named uh I don't I don't know how you see it because I have you two and then the the talk series list which is too large there we go hi Tyler hey oh hey perfect awesome agre all right cool so I think yeah it's about time and I see folks like on the YouTube stream then the other link stream um so then let me just start the recording give you guys a really brief intro um what's see Ken you're Lawrence Berkeley Tyler you're JB we're we're both Lawrence Berkeley lab and and JB a
s well yeah I'll just say law Berkley close enough okay um all right one second all right well yeah hi everybody I'd love to introduce green scone and Tyler Huntington from theis Berkeley National Lab and they've been working on various techn economic models and physical models of the many different de caponization strategies we have available to us and we'll be describing that here welcome awesome thanks um okay so what we're going to do today is I'll start out with some slides and just give yo
u a feel for the kind of modeling that we do at Berkeley lab um and then I'm G to leave time for Tyler to demo some of the cool web tools that he's developed to try and get these um kind of modeling capabilities out to the broader community and so we very much welcome um any kind of like interaction throughout if you have questions or whatever feel free to I don't know what's easiest if you put them in the chat Greg you can just like let me know if I missing that or um you know you can interrupt
me uh but I will just go I'll justly read them out okay perfect so um I think this title is a little bit different than the one that I gave you guys but technoeconomic analysis and life cycle assessment just go together so well they're like peanut butter and jelly so we're going to talk about both of them um and this is kind of how you get a sense for like at high level you know the the costs of doing different things we need to do to decarbonize the economy um and then like how will those thin
gs work at their stated goal right decarbonization and we'll talk about some other environmental impacts as well um so I'm I'm really thrilled to be here with you guys and so happy to have Tyler Huntington he's a brilliant member of my team um we're going to try and give you kind of like a whirlwind tour of the stuff that we do so some of these projects like there's a lot of details I'm not going to cover all the details but if you want to dig into something no problem just stop me so um when I
first got to Berkeley lab like more than 10 years ago I guess it's been like 12 years um a lot of folks in the field were still treating the Technologies itself that they were analyzing as kind of a black box this was especially true in life cycle assessment because life cycle assessment like the people who are doing this research tend to have to be generalists they were it was a small field they were having to analyze all kinds of different Technologies like LCA was their expertise and then the
y didn't really have like a particular like techn class of Technologies or subject matter that they were expert in and so they're just treat this like a black box and you would have to go out and find data somewhere that's like okay what are the inputs to this thing and what are the outputs you know be that a vehicle or a power plant or like some you know manufacturing facility um and then you could answer questions like what is the greenhouse gas footprint of the thing you're making you know if
you're making a battery like how much fuel went into it how how like what do we know about the emissions that came out of the facility and then for costs you know that's really hard to model if you're not GNA model the like details of the process and so sometimes you would just be getting costs like stated from a manufacturer they just say this is what our cost is and you know we're not going to give you any details about like how we got that number um but we far past that now fortunately so no
w we're kind of in this realm where um people have combined expertise in particular types of like Production Technologies or the operation of a complex system and so we can really get into the like nitty-gritty details and what this gives us is like a clearer picture of not just like what the environmental footprint is what the cost is of doing something that we want to do to decarbonize the economy and again that's a broad class of Technologies um but we can also say like well how can we make i
t better like that's one of the more interesting questions is like if we had to focus our efforts on something to make this cheaper or achieve sort of larger emissions reductions what would we do and so um yeah you can ask questions like how high does my yield need to be to be competitive with whatever the next best thing is um Can this process hit some kind of stated goals without breaking the laws of physics right you can get these like basic insights into your technology that you couldn't do
before so we take this kind of multi-disciplinary approach to to technoeconomic analysis and life cycle assessment and one of Berkeley labs like I guess you could say sort of claims to fame is that we do a lot of tea and LCA for emerging Technologies so these are things that have not been commercialized yet and I'd say that's like it's not the only thing we do but it is a niche where we've um been able to kind of establish some amount of leadership so you might start with technology development
you have a concept maybe you have a lab prototype you go on to like do some kind of demonstration at a larger scale um and then at that point like we can take it from there and say all right like you're not going to build a commercial scale facility um not yet but let's like model this out you know there are all kinds of engineering models that are really well set up and have been used by companies for decades um that we can leverage to kind of do this in silicone right like what does this look
like if you did it at really large commercial scale so you can get the kind of material and energy like flows you in and out of the system you can run different scenarios where you're not just doing one facility but you say like well how many of these we need to build and like maybe the costs vary depending on where you put them you know the the cost of your inputs might vary the distance that they have to be transported might vary um if you have access to like a pipeline maybe you're the cost o
f transporting some like liquid fuel output is going to be different than if you have to truck it um so you get a lot of information from that and then we do this Con integrated analysis where you start looking at not just the life cycle greenhouse gas emissions although that's really important but you can look at energy use you can look at um like the water footprint how much land you're occupying the air quality impacts and you can look at the cost not just of producing something like you know
a fuel or providing a service like energy storage you can go all the way through the use phase and end of life and do kind of a life cycle cost even if different parties sort of bear the burden of those costs it might not be the same entity paying for all of that stuff um and then ultimately like this this last piece is the really important one and that's what was missing I think in kind of earlier you know a decade ago was like this this feedback you know what are what are our takeaways and no
t just you know is this technology good or bad but like how can we improve it you know we kind of figured out where the bottlenecks are and we can tell you what might make sense so um this is obviously not just me and Tyler um there's a whole team that does this work this is the te LCA team at Berkeley lab that's not even inclusive of everybody this is these are like the people who aren't remote so we have a bunch more who are spread out across the country and work with us and then of course we
have like students and postdocs who sort of um come in and out and work with us um either on a short-term basis or sometimes they're with us for you know four five six years um so it's a really A really lovely team to work with um we do work at varying stages of development all the way from like very early stage like research and development um so uh you know that could be stuff where you you're just starting to get lab data you model it you're like these costs are crazy let's talk about like ho
w we might be able to tweak your process and so you're like iterating at like the bench scale experiment level all the way through to you know proof of concept like sometimes we're taking data from a pilot scale operation through to commercialization and maturity we do like a little bit less on the on the side where something is like fully mature unless we think it could change um but yeah we can of work along this Continuum um so I should stop here and say that like although I'm talking about t
ea and LCA is like one single approach and and in some you know in terms of the methods like it is it is a class of of modeling techniques that you can leverage but it you can use it for different stuff right so the first point that I like to make is like it's a research field and sometimes we're just answering cool research questions because they're interesting and even better if it requires us to kind of stretch our our like the methods that we know already um and sometimes that could mean lik
e a novel method for modeling a particular system or sometimes it means taking a couple different off-the-shelf methods that have never been integrated together in a particular way and like finding a way to link them and so that can require some Innovation so it can just be like purely for research and you know writing good papers um we also can provide like technology specific Insight so I talked about like doing this like iterative approach where we're trying to then go back to the folks who a
re developing the technology and say why don't you try tweaking this thing or like here's like the the sort of performance level that you have to get to for this to be viable um that is also a really useful function for tea and LCA kind of integrated into a research program um you might be comparing or evaluating different Technologies so I'll give an example of that later but like carbon dioxide removal is one where we've got different technologies that can effectively take CO2 out of the and p
ut it in some kind of stable form and sometimes you just want to know like which of these is better like given the state of technology today if we don't think it's going to change like that much um tell me how much it costs per like functional unit I care about you know ton of CO2 removed from the atmosphere and then tell me like which of these should I be paying attention to like investing a lot of money in scaling up and those questions might come up for companies who could be looking at a ups
et of technologies that are relevant for them it could come up for government agencies that are like we've got grant money to throw at this thing you know please tell us like where should we be prioritizing you can also do these large scale scenarios so I think you guys have had like Jesse Jenkins talk um so you've seen like these big like National or or you know in some cases global scale scenarios that say like how do we get to like Net Zero greenhouse gas emissions um and sometimes getting ge
tting that picture of what success looks like can be really informative Even If you're sort of working backwards from that like you're developing a policy that might be more specific but just knowing kind of like where you ultimately want to get um Can can be you know extremely instructive so it's not just an academic exercise to do those kinds of scenarios um and then finally policy design and Regulatory um impact assessment so life cycle assessment gets used like directly in um some regulatory
policies so California low carbon fuel standard there are other states that also have low carbon fuel standards um Coria that's used for sustainable aviation fuel so they are directly using life cycle assessment to like assign carbon intensity scores and then tie those things directly to like Monet basically monetary incentives okay so at this point yeah um to what ex I mean you can write reports about this on specific questions but the impact is obviously greater if you can just give tools to
to investment funds and so on or develop companies to say you know I I can answer these questions for myself or compare technologies that that were not you know combined or compared in a given paper to what extent are you do you have that that capability um to what extent can you give out simulations that random people can use to answer their own questions yeah well I mean you you can you can do that the only challeng is like there's there's no one group that's going to do like hyper detailed an
al analysis on every single technology that somebody might be interested in considering and so um this actually gets to a really important point about like harmonization in this field across multiple groups so like there are groups that do the like integrated assessment modeling and do these like really large scale scenarios that Encompass probably most of the technologies that somebody is interested in that are kind of like more on the mature side of things and in that case yeah I mean you can
use kind of off-the-shelf data from those scenarios it might not be super granular you know it's going to be a little bit more hand wavy because that's just how it is when you when you do these these really big scenarios if you want to like use tea and LCA data to run a comparison that nobody's done before and especially if you want to pull those results like from if they came from different groups the only challenge there is that those groups might be using different underlying assumptions like
super basic stuff like um de to equity ratio for like financing a facility internal rate of return um like the underlying cost for the same stuff like one group might model the cost of a boiler differently than another um and so there is some like inera in the field you know we especially in the US try to like align with assumptions that are sort of commonly used by everybody else so that we're not you're not seeing like wild differences in the literature that have nothing to do with technology
itself um but it's not perfect and it's it's definitely not perfect when you start comparing like analyses that happened in different countries so like I was on a master thesis committee um for a student in South Africa recently and like their tea had like really different assumptions and I thought well that would be really crazy if you modeled it that way in the US they're like this is like this is quite standard actually so in fact there is kind of where the question comes from you have if if
I take several papers I cannot compare them because they invariably have different assumptions and I'll point up like hydrogen seems like has many possible de Global rollout scenarios that vastly change whether it's viable or not viable and it's critical so I guess the question is this why I would want to run my own but then start swapping on internal components to make sure they do use the same scenarios yeah I mean well kind of what you're talking about it's like a like people do meta analyse
s and you can just publish that yeah um depending on how the original modeling was done that may be like more or less difficult um you know like if you don't have a like an aspen license and somebody was doing all of their modeling and Aspen and you want to change things about like the engineering behind it like not like the cash flow but like you know some built-in thing where you're gonna have to like fire up Asen I know that may or may not be possible for you like depending on expertise of yo
ur group and what you have licenses to um so yeah but I'm I'm I I think there's a lot of value in being able to leverage the stuff in the literature and do these kinds of meta analyses where you can make sure you're making like an Apples to Apples comparison right that makes sense yeah thank you yeah no problem were there any other questions before we move on Okay cool so I'll try to go kind of fast so we got plenty of time for the demo um so so next I'm going to talk about some of the stuff tha
t we've been doing on sustainable Aviation fuels and again this kind of gives you a flavor of the type of work that we do so like I think kind of the way that people have viewed like the bioeconomy um has evolved over time you know we used to think a lot about like liquid fuels right like biofuels were what everybody was talking about back in like 07 2010 and and on I mean there there were few few years where people were placing pretty big bets on biofuels and and that is still happening it's mo
re targeted and it's focused on sectors where it's difficult to Electrify them um so things like sustainable Aviation fuels but the bioeconomy has also become interesting for a couple of other reasons one is um kind of producing Like A diversity of products that we currently make from like petrochemical manufacturing so the sense is like if you want to become independent of fossil fuels you have to become fully independent so even if any one of these products is not produced at like a particular
ly large volume and if you worked out the greenhouse gas emissions it' be a small fraction of like Global emissions um that we kind of have to have like a full Suite of solution so that we just like don't need like crude oil anymore and ultimately like you know don't need natural gas or don't need as much natural gas um the other thing that's come up is that plants are really good about sucking CO2 out of the atmosphere um without any you know with the sort of minimal energy and effort inputs fr
om us relative to like a direct ear capture facility and so you can also use those to then get carbon into some kind of stable form and and do CO2 removal so um yeah you maximize carbon in the ground here are some examples like integrating into building materials just putting CO2 Underground picture of this like charm they pump bio oil like downhole um and then you could do this this option of like producing a lot of useful fuels and chemicals um that we need as part of the broader sued of decar
bonization strategies um so like what do we need to get there uh one of the things that we' found in our research is that uh we work with a lot of people who are kind of on this like early side like they're at the R&D stage you know you get yields and and tighters if you're like making something microbially that are at first like not particularly impressive um and you really need to do a few things like you need to get some of those Pathways into um microbial strains that are like industrially r
elevant um and can really make like a lot of your product and um you know and high concentrations uh and and then you start like scaling up production and we are a lot of the collaborators that we work with are sort of finding this like early like Valley of Death basically where we don't have resources to do the kind of optimization and scale up that needs to happen right after you're done with your early stage R&D there's like lots of basic science money lots of early stage R&D and then kind of
getting it up over that hump to the point where you can commercialize it is really really challenging um you know one of the things we've noticed that's kind of come come out of our analyses and our experience is working with the folks who are trying to move these products from the R&D stage to commercialization is that like we're probably underinvestigated scale up production of the thing that you're making especially important if the thing you're making is not like a one toone replacement for
something that's already on the market right so like your customers want to like test it um and you know try converting something else or try incorporating into a product so uh yeah there's it's I think through our systems analysis we've definitely run into some of these more practical issues that seem to be coming up over and over again um so yeah that's the typical role for Venture capitals but is the problem that this is not it it's too risky for typical vure Capital to touch because there's
a capital you know Investments that are required yeah yeah like getting funding for some of these hard tech things is really tough and I I honestly feel for the investors because they are forced to vet a whole bunch of different technologies that they may or may not have much experience with like their comfort zone might be like software and you know they've expanded to invest in like hard tech and they're considered the same set of people might be considering like a biom manufacturing process
they might be considering some kind of like energy storage application and um and so they have to figure out like which of these has legs like who you know where where do I put my money and so this actually gets to some of the tools that we've tried to um well tried to that we have deployed and the Tyler is gonna um show you some of but like we we've tried to um particularly for biomanufacturing right so we've looked at like sustainable Aviation fuels and then we've built the capability to model
different like microbially produced products um we've taken the process models that we've worked for many years on and translated those into like a python back end we have these these web tools where you can mess around with the actual engineering parameters and see how the costs change um and we also allow people to sort of put in like policy incentives because we realize especially for technologies that are relevant for decarbonization often the economics don't make sense without the policy i
ncentives yet and the policy incentives are pretty generous and so you really do want to be able to like incorporate those into your analysis so we've started putting this stuff up here and this is available it's um we'll well I mean the link is here but we can also put put it in the chat or something um but one of the interesting like user groups that we didn't I personally didn't expect was that um investors are using this as part of their due diligence to understand like what's the capex goin
g to be when I finally have to put money into like scaling this production up um so building out these kinds of tools can be really useful for actually getting them commercialized because you can give investors enough information that they feel confident in doing the vetting and making informed decisions about like where they want to put their money um from like a larger systemwide perspective we we do kind of like we try to like identify like where the lwh hanging fruit is so you know thanks fo
r de conversation that are like they should be Nob brainers like we're never going to do the harder stuff unless we do this easy stuff so one really obvious one is like biobased processes so things like ethanol production renewable natural gas production um where you have a concentrated CO2 stream and if there were ever going to be something that's a cheap to capture and put underground it would be a nearly pure CO2 stream as opposed to something that's post combustion so you try to kind of do t
hose scenario analyses to just show people like here's how much extra cost it would add like here's how you would theoretically need to get paid per ton of CO2 avoided for it to make economic sense for you and then down the line like when policies come out that actually do pay for this stuff hopefully you can sort of compare them and say this policy pays me enough to do it or this policy doesn't pay me enough to do it um so even if there isn't something like an incentive in place we try to kind
of get this built in in advance so people can make informed decisions when the time is right um the other thing we can do for earlier stage Technologies is just set like performance thresholds so this is a paper we published in pnas about accumulation of like value added chemicals in Plants directly um so I mean this is something that happens commercially for some things like canabo being an example of that um but you can do over all kinds of interesting products that would be useful um replacem
ents for like petrochemicals we use today and so then the question was like how much do you have to accumulate in the plant to make it worth your while to extract it and recover it and so we developed these curves that say like here's how much you need to have in order for this to like break even and you can have sort of different values for the product itself so a really expensive one you can accumulate less and get away with that if it's like a cheaper commodity product then you better be accu
mulating more because you're not going to get paid um that much per kilogram that you produce but um these are the kinds of things that we can do kind of hopefully like looking forward and providing information that you might not use like right now but it's G to be really useful in a few years um I'll skip through this but you know just to say that we we employ a broad Suite of modeling tools including like agroecosystem modeling we collaborate with a really great group at um sanyia National Lab
on that to understand for these bio um manufacturing processes like what's going on with the feed stock as well and how that's impacting things like soil carbon because that's a really important part of the overall greenhouse gas footprint positive or negative um yeah and then I mentioned you know we sort of we like lay out I thought this was a really nice study where we were able to kind of lay out all of the different approaches that were being pursued uh at the Joint bio energy Institute whe
re Tyler and I work um and kind of identify like which ones are kind of like win-wins which ones might have trade-offs where if you pursue one it's going to make some some other part of the process like more difficult for you um but it's it's useful to kind of just take stock of all the different things you might be working on that will ultimately be like linked up um and figure out which ones are going to work well together um and uh oh and lastly I'll just point out that for sustainable Aviati
on field is one of the things that's been super interesting about working on these like biobased Alternatives is that you actually get higher energy density um which matters is way more for Aviation than it does for things like onroad applications because the weight of your aircraft has a lot to that really impacts how efficient your aircraft is you're basically like using most of your energy to get the plane off the ground and so if you can do that with a fuel that takes up less space and doesn
't weigh as much um that's extremely useful and so we've been kind of going after these more energy dense molecules in an effort to just make that work better it also works really well for like kind of specialty application so the military is particularly interested in some of these um molecules um and so again we'll talk about the tools later I'll do um at least one more uh example here so this is a totally shifting gears and maybe I should pause again for questions if there are any yes I a que
stion yeah hi hello Corine sorry I joined a little late but on saf I had a question on is saf ready for prime time where is it in its life cycle oh yeah um well so great question um saf is it it is um there's there are some production routes that are kind of like earlier in their commercialization so the ones that I just showed you are at that stage where they need to move to like they need to move to the point of like being optimized going to toll manufacturers like like that's where some of th
e molecules are that I just talked about um the most mature option um is is hea um so hydropress es fatty acids and that is a like you can make that from like oils from oil crops like soybean oil for example or you can make it from um like waste fats and oils like use cooking oil um Tallow corn oil uh that kind of thing and so hea that's there are facil there are bunch of planned facilities um there are facilities that are making that now um and then kind of in the middle of that would be like t
he ethanol to Jet where I think that is going to be on the market this year okay that's my understanding and that's like there's a company called Lanza jet that's doing that l a jet okay I'll take a look yeah the reason I ask is because some of these Airlines have stopped buying credits to offset their emissions because it looks like they see saf as in a very viable scalable technology in the near term they're focusing on efforts in in that direction versus buying offsets yeah well um the I mean
I won't get too deep into the policy but there's this whole like the um Coria applies internationally you can Google as like C SAA if you're not yeah and and I think um there like voluntary participation but basically you you either need to source saff and you know they keep track of the like greenhouse gas footprint of the staff that you're that you're sourcing um or you can buy some sort of carbon offsets and unfortunately I'm not an expert on like exactly what the restrictions are on like wh
at sorts of offsets you can buy as an alternative to sourcing staff but yeah yeah I mean it's it's it's definitely getting scaled up the downside is like some of the lowest greenhouse gas intensity staff is made from these waste feed stocks which like Wast feed Stocks by definition are like the supply is finite right you don't have any more waste yeah no great thank you yeah no problem um okay any other questions before I move on all right great um so let's totally switch gears we'll talk about
bad iies for a little bit so there's a there's a couple of folks in my group who've been working on um energy storage and applying a lot of the same kinds of like systems analysis techniques that we use um but we're interested in like where it makes sense to use lithium ion batteries and in this case in particular we we wanted to dig into this question of um stationary battery storage for the grid and we know that there are um particularly High emitting power plants that serve Peak loads so they
don't operate very often um but when they do they emit a lot um we call these peer plants and um you know depending on how you kind of project out the like way the grid is going to evolve you know these might end up operating more often or for like longer periods of time when they do have to get turned on if we end up in situations where we have like extreme heat and people are running their air conditioners and stuff uh so we wanted to see if it was possible to replace some of these in Califor
nia it's largely natural gas fired peer plants with batteries which would be zero emitting at the um at the point of use so um in this case we had GRE data because we could just go to like California independent system operator K kaiso we could get data on exactly how these power plants operate then we could figure out how you would design batteries to replace them and um and then kind of cost those out and see like does it make sense and if we start putting different monetary values on co2 emis
sions if we put different monetary values on like human health impacts associated with the air pollution um how does that change the economics for the system so uh this shows you um kind of how we model the hypothetical behavior of a battery um that's installed to replace a peer plant like the first takeaway is that if you put a battery there you definitely should not only use it to do exactly what the peer plant did so you can kind of open it up and say all right battery you can also do Arbitra
ge um oh and by the way you can also um sell other grid services like frequency regulation um and that will make a lot more sense so it it's not g to be a one one replacement you kind of have to like relax your constraints and let the battery do some other things to make extra money otherwise it's like really the costs really aren't going to work out um and and then we looked at all of these different facilities and kind of figured out like which ones would make the most sense now the C at here
so we have this like Net Present Value so anything higher than zero should be good right but you will notice that a bunch of these are less than zero meaning like oh no you shouldn't replace these plants with um with batteries the caveat here is that each facility will negotiate um resource adequacy payments which are basically like power generators getting paid to like exist um and you can stack those with other things like frequency regulation and so depending on whether the battery gets paid
more than the peer power plant was getting paid um these these dots could all shift up like it could make more economic sense and in fact we we have come to appreciate that it probably does look more favorable for the batteries um after you start incorporating resource adequacy payments um and we've got an Outreach from investors um utilities basically indicating that some of these things that we analyze like they're doing them they're really interested in the data because they're going to do it
in real life um so this is kind of a kind of interesting that one because that one's really neat uh yeah comparing peer like diesel or something or or natural gas convert to batteries our current batteries don't last for more than a month whereas peer can like deliver fuel in the summer and use it the winter is that part of the analysis you yeah I we we model optimal charging Behavior so we have like you know hourly like prices for electricity and the batteries have to charge we also integrate
the fact that like you get capacity fade over time as you cycle these batteries um and so like yeah this kind of this the battery lifetime thing is interesting so there's viously like yes the battery has to like charge itself up and and we model that um over time the battery's capacity Fades anybody who like has a phone knows that you know or a laptop knows that this happens right and so one of the questions was like that doesn't that's not really the same thing that happens with powerful like h
ow do you deal with this when you put a battery on the grid um and there's at least some folks who think you would basically oversize your battery so that over the lifetime of that battery actually being in service for its intended like stationary storage use that um you would only commit to a level of service that's commensurate with the Capa the expected capacity at the very end of its life so basically you you're paying to make your battery bigger than it really needs to be because the way th
e contracts work like don't really allow for like reduced capacity and reduced level of service over time um so this has not been integrated into any other studies um at least as of like when this one came out um but it was you know we heard the same thing for multiple people so I think that this does actually happen that you may have to kind of oversize your battery right makes sense andish are you're muted M sorry sorry I was unmuted took me so no this is a very interesting slide Karine a coup
le questions on this uh understanding this value chain yeah have you have you also factored in I I see upfront materials in assembly have you factored in how Upstream do you go in the value chain analysis do you go up to the mining ele when you're mining the material and second question is are you looking at how the batter is being charged what is the source power is it green or not in this in this analysis yeah yeah so um we okay uh I'll I'll is one by one so um in for the for the economics yea
h I mean everything's Incorporated but we don't have to like go and like model the operations of the mine right like you can just look at like how much it costs to like buy those inputs and that's and that's good enough so like yes it's all in the economic model in the greenhouse life cycle greenhouse gas model that is also included so you'll see um stuff like let's see module materials and assembly operations maintenance battery replacement um like all oh yeah upfront upfront materials and St s
o see I'm looking at the emissions category like how we broke this down um yeah module materials and assembly that includes all of the Upstream emissions associated with producing everything that goes into those batteries okay great thank you this is very interesting thank you yeah yeah I'd be had the paper's um open source uh or open access I'd be happy to share it it's got like all of the details you possibly there's tons of stuff and supporting information too would love to read the paper yea
h yeah great um I can't you had a you have oh yeah like how how do we model the actual um like the emissions from charging um yeah so there's there's different ways that you can do that in this case we used a model that was developed by my co-author and and wonderful colleague Max Alf Hammer they had taken data for the California grid um this kaiso data and developed a regression model yeah so he's an economist like this is what they do develop really fancy regression models um so for like every
hour of the year you know you could model like what is based on like historical like real data what power plants are responding when you like increase load on the grid and so that was what we used to estimate the emissions was like specific power plants ramping up or down like and actually it wasn't you didn't even have to like make that jump like we just had like emissions and you could say like any hour of the day like you add load what like how do the emissions change gotcha yeah thank you y
eah super fancy model I do not know how to do those crazy fancy regression model Tyler probably does um okay so uh this was another study kind of along the same lines but instead of stationary uh energy storage we were interested in trucking and basically like when and where does it make sense to Electrify probably you know one of the hardest things to Electrify and Freight which is heavy duty Long Haul Freight um we did include drage corridors as well so um it's kind of the like 154 Interstate
um Long Haul like corridors and then um 46 drage corridors drage meaning like the trucks that this the sort of shorter Hall like they'll go from like the port to you know like a rail terminal or something um okay so we integrate a lot of stuff to make this work um and this was actually the culmination of like several different studies so we've been kind of doing this for a while and then building on our model each time to kind of make it better fix things that we thought didn't work that well um
so we model diesel trucks and we um model the like emissions controls associated with those so um you know controlling like nitrous oxide emissions nox and particulate matter so diesel particulate filter um is that's what that means SC selective catalytic reduction uh reduction um and then we model the electric trucks we assume lithium ion batteries um we have some like assumptions baked in about this specific chemistry um but I won't get into that uh we model the electricity grid we use data f
rom the national renewable energy laboratory they've done like scenarios out to 2050 um at the balancing area level which you can see this is like an enr invention this doesn't map to like balancing authorities which I know is confusing but um and then uh we look at hourly marginal generation so if you plug your truck in anywhere in the US at a particular hour of the year on any given year in the future like what kind of power plants are ramping up to meet that demand um we have uh sort of diffe
rent model years for the diesel trucks as well and then depending on like where those trucks like what time a day they start driving and where they're driving and we do have like origin destination points from the freight analysis framework which is a public data set um we can develop these different like charging load scenarios and translate those into Power Plant EMS from there we can figure out what um air pollutant emissions are getting emitted and where and then is estimate the human health
damages so we use um an integrated assessment model called inmap this is like the gold standard for air pollution folks and you can monetize those things with a social cost of carbon for greenhouse gas emissions and then you can use a the value of a statistical life for human health impacts and that largely covers it if you're just you just look at like mortality as a result of air pollution impacts and then put the value on a human life um okay and so ultimately you can get Corridor level impa
cts like in what year does a particular Corridor switch from being like not favorable to Electrify to favorable to Electrify this was so fun to do um and it can be different depending on climate change in human health so in general like from a climate change perspective it's better sooner like most corridors are good now from a human health perspective it's like slightly less fre takes longer um but we'll get into the results right now oh yeah grid scenarios matter like huge shout out to enr the
y really helped us out like understanding their data kind of yeah like modeling this was not trivial um and a wonderful grad student named will mcneel developed a model based on um some of enrolls data okay impacts of freight electrification over time so the way to read this now we did every single year out to 2050 but those would be very large figures so we're only going to show you like sort of points at different decades um so renewable energy costs there's two scenarios that enroll modeled a
ctually they did multiple scenarios but we show the high and the low renewable energy cost so low renewable energy cost means like a lot of cheap like wind and solar High renewable energy cost means it doesn't it's not that cheap um and then there this is like through the current year this is 2050 and this paper was published in like late 2023 so just a few months ago and you can see like from a climate change perspective um like it's just it's good to Electrify most corridors now you just like
full steam ahead there is this like weird result where under a low renewable energy cost scenario the marginal generator is actually dirtier and the funny thing about that is like this this does show up in real data if you like aggressively deploy a bunch of wind and solar now because it's cheap it starts to eat into like Coal Fire generators that are still online which is you're think like that's awesome but then if you go and add extra load to the grid um those Coal Fire generators end up bein
g on the margin like they are the next like cheapest thing that's not generating and they ramp back up so I it's just it's a funky result but like we found this multiple times this seems to Bear out in the actual data but it's like a shortterm phenomenon um yeah for renewable energy costs and that oh go ahead point is that a a function of not having batteries in this model or just something else that you say the total capacity with batteries is so large that whatever's left this coal yeah so in
the very near term like now I mean you're kind of dealing with like what's on what's online now plus like kind of very aggressive like near-term deployment of wind and solar like batteries are still I mean they're they're costly to deploy at a really large scale right so yeah that's why part of this is kind of a near-term thing also I mean I will just point out like batteries are not great for like seasonal storage like you can't really do that so so we should be thinking of like what what makes
up for the intermittency of the Renewables is dirty power yeah yep pretty much in the near term and then it gets cleaner and the longer long term okay makes sense um yeah from a human health standpoint it does take a little bit longer for all of these corridors to start looking good and in the long run like low renewable energy cost is better you do still have this kind of area in the Northeast that just doesn't have like great renewable resources and they also have a lot of like Pretty Dirty p
ower plants and not all of those are offline by 2050 right some of those hang around like this is like I I went to school in Pennsylvania there's like coal plants and so there are kind of these like holdouts even in 2050 now the interesting thing is if you start factoring in the impacts of policy so this factors in like all of the the credits and stuff that are part of the inflation reduction act um the the results are really striking like basically from a human I mean it was already favorable f
rom a climate change standpoint right we even without the IRA but when you factor in the ira suddenly like in 2030 your map looks so much better than it did in 2030 before all of these extra credits are are factored in from the IRA so you b basically like it buys you like a decade you kind of get to accelerate to the point where it's favorable to Electrify Freight on most corridors so that was a really interesting result um and it kind of like takes long like even those holdouts in the Northeast
that still looked not that great by 2050 like those are those are gone in this other scenario they start to look favorable to Electrify from a human health standpoint um so I won't spend too much time on the rest of the slides because I want to have time for Tyler to do a demo of the web tools but selecting battery chemistries is really important um We are continuing this work and starting to incorporate more detailed modeling of the actual battery Lifetime and the capacity fade uh because like
batteries unfortunately don't degrade just like really really slowly and steadily they basically hit an inflection point where it really like drops off a cliff and there's uncertainty as to like when exactly that inflection point happens and so that is why I mean what I'm showing you is like some old results from like a study we did in California on like second life batteries but like the point remains that you need to know where that inflection point is in terms of the storage capacity of the
battery in order to say with confidence like how the economics work out espe especially when you're incorporating these into something like a truck where you need to be able to sell that truck used and you have to know like when am I going to have to replace my battery um that has that has a huge impact so I'm gonna skip the CDR part because I think you guys are gonna separately invite um Jennifer petrid to speak and she will talk about that um so I guess what I will um finish with I just want t
o leave you with while Tyler is getting his demo set up is that like doing these analyses is is complicated it requires integration of a bunch of different models but we are really trying to figure out creative ways to get these things in a form where we can share them more broadly anybody can use them and web tools are a nice way to do that um and so Tyler will talk a little bit about some of the like geospatial modeling um that we've put online as well as the tea and LCA all right Tyler take i
t away and don't forget to unmute thanks Ken thanks for reminder I probably wouldn't have unmuted everyone see the tools screen up now yes awesome yeah so as Karen alluded to we have developed this ecosystem of web-based tools to share out our modeling work which is obviously tied up in and published papers um but we want to obviously make it as interactive as possible for both the research community and Industry as K mentioned there's been increasing interest from investors um in using these to
ols to estimate things like scale up costs capex Opex or some of these emerging Technologies so the homepage for this this ecosystem of tools is lead. jay.org and each of the tools here operates independently really nicely and there is also some interoperability between them um which we can demo today um and I'll start by showing you the bio sighting tool and this tool is um a interactive mapping platform for showing you what the bioeconomy looks like from a bird's eyee view across uh the US and
a little backstory on the origins of this tool initially it just Covered California it was funded by California energy commission Grant and looked at all of the bioeconomy resources and INF structure in California and we realized that scaling this out to the National level could really um provide a lot more value so over time it evolved into this national uh tool and what we've done here is aggregated data from a variety of sources both empirically collected survey data from like the USDA um Na
tional agricultural surveys as well as some model data from um the billion ton study which was last released in 2016 and hopefully there should be a new uh release coming out relatively soon um so some of this data does seem a little outdated and in fact the nas um data from 2017 will soon be replaced by the latest release of that survey data which just came out um and so we show a lot of resources that tie into the bioeconomy as speed stocks for bioproduct and biof fuel production um and as wel
l as existing infrastructure such as facilities that are are already processing those feed stocks and producing things like biofuels um renewable diesel staff um so that's what a lot of these points are representing in the map um and we also show some other things of interest when thinking about the bioeconomy so one of the things that has become um especially in the Forefront recently is thinking about how scaling up the bioeconomy will impact different socioeconomic groups differently and obvi
ously we don't want to perpetuate historical patterns of disproportionate impact on groups that have been historically marginalized so one of the things we've recently added is this layer of environmental justice indicators which shows you at a census trct level um whether or not sensor tracks are identified as a disadvantage from a variety of criteria that relate to climate change impacts um socioeconomic indicators and other environmental indicators that play into human health hazards um so wh
en thinking about where we are going to site future facilities for producing biofuels bioproducts um we want to be conscious of where um historically the energy economy has had negative impacts on different folks and we also got these transmission regions um showing you just uh they're kind of simplified from the actual grid but um the general transmission regions that have implications for the cost of electricity depending on where you're thinking about citing a plant and also have tax policy i
mplications for tax incentives so um just bringing this all together in a way that you can really visualize how the country looks um and how it might look if we were to um scale things up so one of the cool features of this tool is this sighting mode um where you can go to any place on the map and run a hypothetical scenario of placing a new facility in a location so for example you might go somewhere that looks relatively resourcer indicated by these uh these green Shades and put a point down t
here um and say you might want to put a saff plant up here or an ethanol facility um you could see whether that site has good fusibility based on the surrounding biomass um that shows up within a defined buffer region of your location and you can change that buffer region over here to whatever sort of Supply radius you want to consider for a given scenario um and then each of these bubbles is representative of a type of feed stock that you could expect within that buffer radius um based on the d
ata that you've selected in these filters to the left and depending on whether or not you've um selected different types of feed stocks that you could accept at this facility that will impact what shows up in your buffer buffer zone um and then taking this sort of scenario analysis one step further um you can run a technoeconomic analysis for um this facility and then following up on that and a life cycle assessment to see um what those capex Opex and environmental impacts of that site um might
look like so um we have a series of builtin examples of products that you might select from for producing at a given location and then going to our tea LCA tool which can also be accessed from that homepage I started from um will run uh it's a little slow but it will run um a model that we've um developed based on simulation software and essentially proxy models score in Python um to give you a system view of producing that product at that particular location specific to the feed stocks that wer
e available within the buffer and we show you sort of a process flow diagram of those major stages involved in producing your product just jump to the other page in the interest of time oh did it not jum in the screen share I'm sorry um how do I tabs oh yeah I probably open a new tab it open a new tab let me try well you share the whole screen so you will see all the tabs you know how to share the entire screen yeah you stop sharing this and then you reshare but now whole screen scen Google Chro
me oh Google Chrome doesn't R to that's weird oh that might be security thing so maybe just share the the tat by itself all right let me show the the tab all right sorry about that um so this is what our um te LCA tool looks like once the model has been run um B had a few screenshots of this in her slides but we have all these process parameters in these sections to the left which relate to specific process stages in your system and a lot of the data in the SED Stock Supply sector is ported over
directly from that bio sighting map um and it's specific to the biomass that you allowed in your buffer region and um cost all these different compositional elements to your feed stock are um computed based on what's available to you and then a lot of the other parameters we've set as sort of reasonable defaults based on um of a baseline scenario which you can change up to um these different built-in scenarios uh for state of technology and sort of an optimal case or you can go in and adjust an
y of these process parameters yourself and the model will update correspondingly um you'll just have to obviously rerun it so in this um minimum selling price Model which is what MSP stands for we're showing you what the production cost of your product in this case ethanol looks like um broken down by these major process stages um for those unfamiliar with the term minimum cell selling price that's just the price at which you would need to sell the product on the market in order for the entire p
roject of the facility to be sort of great even over the lifespan of um it projected lifespan and so uh we give you some visualizations of what those cost breakdowns look like we include the ren credits in this case which are policy incentives that would offset um the cost and the electricity credits that you would get from selling electricity produced in the facility back to the grid um and then based on those offsets we give you a net minimum selling price um and show you what the capex and Op
ex uh breaks down into as well all this data um can be downloaded and along with documentation of all the assumptions that go into the model here um these other two tabs show you what the water footprint of this um system would incur based on producing you know a certain amount of ethanol and the greenhouse gas emissions involved from Cradle to grave of um that system so everything from the tea to the LCA is kind of built in here and uh we have these examples for a variety of products that have
been really well studied in our research group um and you can run them um based on sort of the data that we've developed uh through kind of rigorous system simulations um but we've recently added this custom option where you can go in and if you know a lot of details about a certain product that you want to model um which isn't off the shelf um you can enter those in here and the data given is is purely um for demonstration purposes this isn't for a real product but you can ostensibly run a mode
l for a custom product and get similar outputs um same types of outputs uh and it's not as um necessarily is going to be as accurate or granular because um we know a lot more about these wellestablished Pathways but it's a good starting point um for getting some sort of back of the envelope calculations on what a tea or LCA might look like um um and then I think that's a pretty good summary of this tool anything to add on this curtain oh this is great thank you Tyler I know some of the numbers l
ooked a little crazy because I think the glucose and xylos yield got like pushed to zero but like yeah it's it's years and years of work have gone into this and um this is incredible actually let me ask you just one final question about this thing uh this is excellent as a forward analysis do you have you run it in a iners design mode where it's like just where would I make an ethanol plan and if I if I had to oh sort of like an optimization case yeah yeah it's cited for me tell me what what par
ameters you know variants I should use that kind of thing what tax credits I must demand we haven't really explored that use case um I mean I think it's a really valid one for sure and um you know given finite number of resources to build new facilities you would want to answer that question of where is where our resources best spent um but we haven't really dug into that question with existing tools cool practical matter is this stuff available as like a python library in in addition to the lik
e uh guey version like because then you can shove it into optimization tools right at the moment it is not um it's in a private repo um I think we have intentions of open sourcing it as a python library that folks could utilize for those kinds of purposes um and also building out an API for folks to use um in a more sort of programmatic way um but at the moment it's just the gooey on the web all right that makes sense well thank you very much just fantastic yeah yeah thank you for having us have
a great rest of your day all right bye bye
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