Speaker 1: So right now in my desk have got three books.
Speaker 1: The first one is by David Epstein called Range, How
Speaker 1: Generalists Triumph in a specialized world. Fantastic book. Can't recommend
Speaker 1: it enough. The second book, and at three hundred and
Speaker 1: seventy five page is I mean. Book is a copy
Speaker 1: of Banest's new Energy Outlook twenty twenty or NEO, our
Speaker 1: annual long term scenario analysis on the Future of the
Speaker 1: energy economy, just released in November. It's an Outlook all
Speaker 1: the way to twenty fifty, and his Beenest Flagship Report.
Speaker 1: The third book is called Kings of the Yukon, an
Speaker 1: Alaskan River Journey by Adam Weymouth. Here the author records
Speaker 1: his trip down the Yukon by canoe from Teslin Lake
Speaker 1: in Canada all the way through Alaska to where it
Speaker 1: lets out in the Bearing Sea. He's taking the trip
Speaker 1: to see firsthand the run of the Chinook or king salmon,
Speaker 1: and talk to the people that have been marking the
Speaker 1: time of their annual return for generations. But for me,
Speaker 1: the most fascinating parts of the books so far have
Speaker 1: been when he goes into detail about the behaviors of
Speaker 1: the fish, for example, how they know exactly how to
Speaker 1: get to their spawning grounds down to the tiniest tributary
Speaker 1: or creek of the river without ever having been there
Speaker 1: as adults. Well, today on the show, we're gonna keep
Speaker 1: going with the topic of animal behavior. Turns out the
Speaker 1: engine behind the analysis and NEO, the code that does
Speaker 1: the calculations and creates the scenarios in the outlook, was
Speaker 1: built after animal behaviors, specifically those of ants and birds.
Speaker 1: With us, we've got Ian berriman modeling analyst for PNF.
Speaker 1: He will tell us how the model, known as the
Speaker 1: New Energy Forecast Model or NEPHIM works and how he
Speaker 1: took cues from ants and birds and building it. We'll
Speaker 1: also talk quite a bit about Dr Strange from the
Speaker 1: Marvel movies. So Comberbatch fans get set. Okay. Our discussion
Speaker 1: is based on BNS New Energy Outlook. Being a users
Speaker 1: can get this report on BNF dot com, the BENF
Speaker 1: mobile app, and the Bloomberg terminal. As a reminder, BENIF
Speaker 1: to provide investment or strategy advice, and you can hear
Speaker 1: the full dist claimer at the end of the show.
Speaker 1: I'm Mark Taylor, and you're listening to switch on the
Speaker 1: benf podcast Ian Welcome, thanks for having me. So today
Speaker 1: we're going to talk about modeling. I used to be
Speaker 1: an energy analyst, and I'm not a modeler. You know,
Speaker 1: I'll be I'll be very blunt about that one. But
Speaker 1: for everybody that is listening that doesn't even know what
Speaker 1: a model is, can you just take us all the
Speaker 1: way back to the top and tell us what a
Speaker 1: model is and why they're important. It's a good question.
Speaker 1: So I think why models are important is because we
Speaker 1: don't always have time or space to try everything out.
Speaker 1: In reality, we don't have a physical way of testing
Speaker 1: this hypothesis, don't have the materials. When we're talking about
Speaker 1: models that look towards the future, we don't know what
Speaker 1: that is until it happens. So models help us answer
Speaker 1: questions which we wouldn't otherwise be able to answer. Okay,
Speaker 1: So it helps us get through more data and ask
Speaker 1: more stuff of data that we wouldn't otherwise be able
Speaker 1: to do. Exactly Okay, cool. I think it heard like
Speaker 1: could even be considered like like the model that of
Speaker 1: a building that an architect builds before they actually go
Speaker 1: out and build it, they can see what it looks like.
Speaker 1: You're good to see how it might work exactly. And
Speaker 1: related to that, there will be somewhere on that architect's computer,
Speaker 1: there will be computer model which has all the beams
Speaker 1: and all the weight in the building that is expected
Speaker 1: to carry. And there will be another model which simulates
Speaker 1: winds or earthquakes to make sure the building cap with
Speaker 1: stand them. Okay, cool, Yeah, it sounds complex right, So
Speaker 1: speaking of that, how did you get into modeling? So? Um,
Speaker 1: if you can tell by my accent, I'm originally from Australia,
Speaker 1: many many years ago. I was looking for a PhD opportunity,
Speaker 1: a kind of a vague idea of where I wanted
Speaker 1: to go, something energy related, but no, no great drive,
Speaker 1: sort of drifting for a bit, and then applied to
Speaker 1: a bunch of places and bam, I got this offer
Speaker 1: to do a PhD at Oxford. So I couldn't say no.
Speaker 1: It sounds like a pretty sweet place for to end
Speaker 1: up if you're just drifting. I mean, maybe maybe I'm
Speaker 1: under selling myself, but it wasn't. You don't you don't
Speaker 1: just drift into there like that's there's a whole application process,
Speaker 1: like you can't just rock up and anyway. Okay, So
Speaker 1: you start this PC program at Oxford. Yeah, and the
Speaker 1: top pick which I got into was was solar power.
Speaker 1: And before I before I left Australia, I'm joking with
Speaker 1: my supervisors. I'm like, guys, like, I'm not bringing any
Speaker 1: of the sun with me. You realize you have to
Speaker 1: provide that. It actually was. It was ended up being
Speaker 1: quite a significant part of my PhD because we did
Speaker 1: we didn't have sunlight to test a lot of our equipment,
Speaker 1: so I created computer models which could do that for us.
Speaker 1: These are ray tracing models, so you sort of you
Speaker 1: take rays of sunlight, shoot them out, bounce them off
Speaker 1: objects they will like reflect or concentrate, etcetera. So that
Speaker 1: that was probably about half my PhD was sort of
Speaker 1: working on that. That's cool, so you looked at how
Speaker 1: the sun bounces off solar equipment. Basically, yeah, I mean
Speaker 1: we we're working on a solar powered oven. So basically
Speaker 1: a couple of mirrors which combined would result in like
Speaker 1: a very highly concentrated point of sunlight. So we could
Speaker 1: get past three degrees celsius easily and you could not
Speaker 1: just cooking bake, but you could fry as well, which
Speaker 1: was one of the big selling points. So now you're
Speaker 1: at BNF and you switch your focus from from looking
Speaker 1: at how the sun raise bounce to modeling the future
Speaker 1: of power systems? Is that right? That's right? I mean
Speaker 1: one one naturally leads to the other. I assume can
Speaker 1: you talk a bit about that. So you're modeling the future,
Speaker 1: You're you're looking at different scenarios, you're looking at forecasts,
Speaker 1: you're looking at all kinds of things. You could model anything, right,
Speaker 1: why is the power system a good or bad candidate
Speaker 1: for looking at the future. A way to think about
Speaker 1: this is the sort of how in the modeling. So
Speaker 1: there's there's different types of models out there, and some
Speaker 1: are more complicated than others. The simple ones will often
Speaker 1: sort of take an existing data series and just extrapolate
Speaker 1: forward a bit, and they might do some fancy stuff
Speaker 1: with the data to make sure that that that curve
Speaker 1: is accurate, but that that's sort of like a top
Speaker 1: down approach. They're not understanding the fundamental question. The other
Speaker 1: type of model, and the one I work on is
Speaker 1: part of this category, is sort of what we call
Speaker 1: bottom up. So what we interested in is simulating the
Speaker 1: question we're looking at, which for us is the entire
Speaker 1: power system in fact, so we're not we're not blindly
Speaker 1: extrapolating anything. We were creating an entirely new power system,
Speaker 1: a virtual power system in our computer, and and we're
Speaker 1: using that in the model to look at the future.
Speaker 1: All right, So before we started recording here, you told
Speaker 1: me of this super nerdy analogy that that I want
Speaker 1: you to make here about Doctor Strange, the Marvel character.
Speaker 1: So just for the record, everybody, UM, right now, we're London,
Speaker 1: we're on our third lockdown, and my wife has decided
Speaker 1: that we are actually going to go through and watch
Speaker 1: all the Marvel movies right now. She's listening to a
Speaker 1: podcast that goes into great detail on each and we're
Speaker 1: picking him off one by one. She's never seen any
Speaker 1: of them. She's never seen Doctor Strange. So Ian tell
Speaker 1: us about Doctor Strange and his modeling so that there
Speaker 1: will be a spoiler alert here for your wife. Man,
Speaker 1: she didn't listen to the same rate. So in in
Speaker 1: Avengers Endgame, there's this big bad guy. The Avengers are
Speaker 1: trying to beat him. The odds are stacked against them,
Speaker 1: but they've got Doctor Strange on their side, and Doctor
Speaker 1: Strange has this ability to go backwards and forwards in time,
Speaker 1: but also to look at different alternate realities. And he
Speaker 1: goes away, he visits all these alternate realities, and he
Speaker 1: comes back with this sort of really foreboding message that
Speaker 1: I visited fourteen million sive alternate realities. We only beat
Speaker 1: the bad guy. We only beat Sanos in one of them.
Speaker 1: So super long ards, super long ards. But this is
Speaker 1: a really good example of what our model is doing,
Speaker 1: what Nephem is doing sort of behind the scene. So
Speaker 1: you could imagine that you have a model which is
Speaker 1: just mysterious black box. You feed a data gives you
Speaker 1: an output. That's not what we're doing. We're doing we're
Speaker 1: taking the Doctor Strange approach here. So when when we're modeling,
Speaker 1: we're actually creating these sort of alternate realities. We're creating
Speaker 1: different versions of our power system. Um, they will be
Speaker 1: slightly different, they could be very different, but they're all
Speaker 1: different from each other. And we're doing this because we're
Speaker 1: trying to find the version of our power system which
Speaker 1: is the cheapest. The only way we can do that
Speaker 1: is by actually creating them, looking through them, and then
Speaker 1: trying to find the best one. So Doctor Strange had
Speaker 1: about fourteen million. I did some back of the envelope
Speaker 1: calculations before, and I think we did about semi million
Speaker 1: um of these. Yeah, so those are rookie numbers. Doctor Strange.
Speaker 1: Bumpers up. We're crushing Doctor Strange. That's that's awesome. That's
Speaker 1: all we need to know. So we're done, not even
Speaker 1: in the same league. Yeah, that's really cool. So so
Speaker 1: if I get this straight, you are using you're putting
Speaker 1: a bunch of data in and creating alternate realities, you know,
Speaker 1: for the future power system, and then picking the cheapest
Speaker 1: ones to say those could be most likely. Is that yeah? Well,
Speaker 1: I mean we don't make a judgment call about which
Speaker 1: is most likely. That's yeah, that's that's the policy lessons
Speaker 1: that you draw from from the model. But we do
Speaker 1: identify which which is the cheapest Okay, the cheapest to build? Right, Okay?
Speaker 1: When you say identified, like, that's that's the crux of this,
Speaker 1: because even even with all the interns in the world,
Speaker 1: we can't easily manually look through seventy million different power
Speaker 1: systems to find the best one. Yes, so how do
Speaker 1: you do that? So we we've got a solver, which
Speaker 1: which we use in our model what's a solver. So
Speaker 1: a solver is I guess the best way to describe
Speaker 1: it is it's an optimization engine. And generally think about optimization.
Speaker 1: We're normally trying to either minimize or maximize something. For us,
Speaker 1: we're trying to minimize system cost in the power system.
Speaker 1: And so what a solver does is looks at previous data.
Speaker 1: So we look at we'll have a starting set of
Speaker 1: solutions and we'll look at those and then from the
Speaker 1: system costs its seasoned those from the inputs it knows
Speaker 1: it gave them, it can infer what a better guess
Speaker 1: will be the next time around. We do this, okay,
Speaker 1: so it just gets better and better each time. So
Speaker 1: it tweaks something and and says this is higher or
Speaker 1: lower than the best or or how is that right? Yeah?
Speaker 1: And I mean to put it in terms that makes
Speaker 1: sense for for what we're doing, Like the solver might
Speaker 1: try a solution which has a bit more wind and
Speaker 1: a bit more solo. And if you're a faithful subscribe
Speaker 1: a b NF, you you might realize that, hey, those
Speaker 1: technologies are pretty cheap. And if we added that to
Speaker 1: the system and the system costs came down, then the
Speaker 1: solver is going to recognize that, and it's going on, Hey,
Speaker 1: that that that wind and solar solution was pretty good.
Speaker 1: Maybe I'll try some more that is similar to that
Speaker 1: and see if I can't get an even better solution.
Speaker 1: And I imagine there's cases where will go too far
Speaker 1: and say that that too far and made the more
Speaker 1: expensive again, and they'll go back exactly. So we might
Speaker 1: start adding too much, and then we'll start getting curtailment.
Speaker 1: Like some curtailment's fine, but you get to a limit
Speaker 1: where it's just too much, it doesn't make sense, and
Speaker 1: the model just backs off. What's curtailment in case anybody
Speaker 1: isn't now, So, curtailment is when the energy system producing
Speaker 1: more energy than we actually need. So this tends to
Speaker 1: be a problem when we've got things we can't easily
Speaker 1: turn off, like wind or solar. Before we started recording,
Speaker 1: you also talked about an analogy about ants in how
Speaker 1: you build these scenarios. Really, can you tell us a
Speaker 1: bit more about that? Well, this is this is better
Speaker 1: than analogy, even better than an awkward analogy, which is
Speaker 1: my favorite type of analogy. This is this is more
Speaker 1: or less actually what's happening. So so under the hood
Speaker 1: of this solver, the solver is actually based off ant
Speaker 1: behavior in the real world, so it's an ant colony optimizer.
Speaker 1: Is the sort of type of solver the way using
Speaker 1: is that the name we've given it, or is that
Speaker 1: the name that is out in the in the wild,
Speaker 1: in the industry, in the wild. So I mean this
Speaker 1: is this is not our software. This is like commercial
Speaker 1: software that we've bought. But it's very good. It was
Speaker 1: I think it was developed in partnership with the European
Speaker 1: Space Agency and it's holdsome world records for optimal orbital
Speaker 1: flight paths for different Shuttle launches, and I don't I
Speaker 1: don't know exactly. I mean, it's it's it's good stuff though, Okay, amazing.
Speaker 1: I'm convinced. Yeah. The way the way to think of
Speaker 1: what what is happening? If we've got these these these
Speaker 1: alternate realities. The thing is we don't have all seventy
Speaker 1: million of them at once, like we have to fit
Speaker 1: these inside our computers. So normally we're doing, depending on
Speaker 1: the computer, somewhere between four to eighty of these simultaneously
Speaker 1: and what the answer doing, and they're carrying information about
Speaker 1: these alternate realities to and from the solver. So we'll
Speaker 1: create a generation of ants. They'll go out, each ant,
Speaker 1: we'll go to a different reality, report back the system cost,
Speaker 1: and then our solver will create a new generation ants
Speaker 1: based on the information that the previous one provided. And
Speaker 1: at a very high level that that's essentially what's happening.
Speaker 1: And it's the mathematics the underpinn all this are based
Speaker 1: on like path optimization and and the way the ants
Speaker 1: will do that in the real world. And in the
Speaker 1: end you find the least cost futures is that right?
Speaker 1: There will be there will be one lucky ant that
Speaker 1: finds it, one lucky ant, and so let's get into that.
Speaker 1: So you you've found that in this year's edition of
Speaker 1: well Napham the New Energy Forecast Model, but which was
Speaker 1: the basis for the New Energy Outlook, the or NEO,
Speaker 1: which is bens Flagskap report. Is that right? That's right? Okay?
Speaker 1: How does an ant, an individual aunt know when it's
Speaker 1: found the cheapest system cost? So that's a that's a
Speaker 1: good question, because an ant doesn't know how to calculate
Speaker 1: a system cost. So for all the good work that
Speaker 1: the soul is doing, that's that's really just the top
Speaker 1: layer of what's going on in this model. So most
Speaker 1: of the code what's happening is the actual calculation of
Speaker 1: the system cost, and that fundamentally is what NEPHEM is
Speaker 1: the New Energy Forecasting model. It's creating from a given
Speaker 1: set of input data, simulation of the entire power system
Speaker 1: so that we can actually perform that calculation. In the
Speaker 1: methodology document, it says the model solved for a capacity
Speaker 1: mix that minimizes system cost while ensuring I really demand
Speaker 1: is met for an entire year. You Mike, sure all
Speaker 1: the demand is met, putting all that data in, and
Speaker 1: then it says, okay, this is the this is the
Speaker 1: mix that minimizes system costs. Is that right? Yeah? So
Speaker 1: I think maybe the best way to think about it
Speaker 1: is is each and is a different capacity mix. And
Speaker 1: what I mean by capacity mixes we look at all
Speaker 1: the different technologies we have available to us. When solar, coal, gas, nuclear,
Speaker 1: that exactly exactly, and we'll have a different mixture of
Speaker 1: them per ant, So some will have more wind, some
Speaker 1: will have more call, but most of the ants are
Speaker 1: different from each other. So what's happening with this AUNT
Speaker 1: carries the mix, and then Nephem is saying, from this
Speaker 1: given mix of technologies, this is how much the power
Speaker 1: system costs. That's cool, and so then one lucky ant
Speaker 1: is the winner. Well, yeah, I said one lucky ant.
Speaker 1: There's there's a few ants because although those are global analysis,
Speaker 1: we break things down regionally. We don't model every country
Speaker 1: in the world simultaneously, so there's there's quite a few
Speaker 1: ants that win. What regions do you do? How do
Speaker 1: you put it out? So Europe is nine regions, So
Speaker 1: the larger countries we all model individually, so UK, France, Italy, Germany,
Speaker 1: and then the smaller countries we lump together and they'll
Speaker 1: there's a north, south, east, West, and then we do
Speaker 1: iber Area, which is Spain and Portugal together. The US,
Speaker 1: there's thirteen different regions we do that by ISO. China
Speaker 1: is six different regions. India is actually just one region,
Speaker 1: which is our largest region ends up being our largest region. Okay,
Speaker 1: we're going to take a short break and when we
Speaker 1: come back, the new Energy Forecast model with the environment,
Speaker 1: can you tell us a bit more about the data
Speaker 1: that you need in order to run the model and
Speaker 1: how you got it. Yeah, So there's a huge amount
Speaker 1: of data that goes in into this process. And I
Speaker 1: guess the question you're asking is how many data points
Speaker 1: do I need to like synthesize a power system for
Speaker 1: a given region. That's one question, But I'm also just
Speaker 1: really curious about how you went and guard the data, Like,
Speaker 1: I mean, some some of it will come from the terminal,
Speaker 1: some of it's our own data. A lot of the
Speaker 1: cost data, particularly all the renewable cost data, is all internal.
Speaker 1: In fact, I think every cost cost data point is internal.
Speaker 1: There will be commodity prices which come from other teams
Speaker 1: inside ben f and Bloomberg. There will be demand data
Speaker 1: which will come from Bloomberg Economics. There's also demand data
Speaker 1: which comes from the real world, So we'll feed in
Speaker 1: real world demand series for electricity demand from the countries
Speaker 1: where we have that data. How do you go about
Speaker 1: assimilating all this data and planning out the operation of
Speaker 1: the model. So, like, to me a non modeler, that
Speaker 1: just sounds this sounds like a nightmare logistical task or
Speaker 1: planning task. How do you go about planning this model
Speaker 1: for someone who has to do it? It is also
Speaker 1: a nightmare logistical task? Okay, I mean we we so
Speaker 1: we have a database. There's a separate like well defined
Speaker 1: data BRASE which when everything's filled out, that contains every
Speaker 1: single piece of data you need to run the model
Speaker 1: and the processes to get the data in there. There's
Speaker 1: actually a couple of different ones. We use a lot
Speaker 1: of Python scripts to shunt data around, scrape data from website.
Speaker 1: It's there will be some manual points like not the
Speaker 1: big series, but some of the smaller ones will will addit.
Speaker 1: Those the cost data. Again, this is this is all
Speaker 1: a huge data sets that are all coming in because
Speaker 1: when when I say something like the cost of solar,
Speaker 1: it's not just one data point. It will differ by
Speaker 1: country and it differs by year. So we need to
Speaker 1: know that what the cost of solar is in a
Speaker 1: bunch of different countries from now until. Okay, that's no
Speaker 1: small task in it itself, right, No, Yeah, seems pretty intense. Okay,
Speaker 1: I was curious, would you say that is the hardest
Speaker 1: part or what is the hardest part of putting this
Speaker 1: particular model together. This has been so many hard parts.
Speaker 1: I think the storage algorithm was quite quite difficult. Obviously,
Speaker 1: when you're modeling an entire power system, there's different types
Speaker 1: of plants, and they behave differently. A wind or solar
Speaker 1: plant is relatively easy to model from the real world.
Speaker 1: We have this data what window solar generation looks like
Speaker 1: across a given representative weather year, and we can put
Speaker 1: different weather years in the model if we want. But
Speaker 1: for something like storage, like a battery, you don't have
Speaker 1: a well defined data series which is what the output
Speaker 1: is meant to look like. For a battery, the outputs
Speaker 1: the function of what the rest of the system is doing,
Speaker 1: because if there's a lot of cheap energy, or even
Speaker 1: excess energy, then the battery wants to charge during those hours,
Speaker 1: and if there's a shortfall and supply or want to
Speaker 1: discharge in those hours. So that's a completely different type
Speaker 1: of power plant and takes a lot of extra modeling
Speaker 1: effort inside the model to actually calculate. What was the
Speaker 1: most surprising result from the exercise. I think there's a
Speaker 1: lot of cool results that come out of this. I
Speaker 1: think some of the more surprising ones are to do
Speaker 1: with some of the weird scenarios we've done. I'll talk
Speaker 1: about a scenario we we did where we had very
Speaker 1: cheap batteries, and I think most of us would assume
Speaker 1: if you have a power system and you add a
Speaker 1: bunch of cheap our ties, that the winners are probably
Speaker 1: more likely to be renewables. And that's I think it's
Speaker 1: an intuitive thing that that we have, but it's it's
Speaker 1: not necessarily correct, and not at least not in the
Speaker 1: strictest sense, because if you think about a battery, what
Speaker 1: batteries are trying to do is to smooth out to
Speaker 1: supply and demand imbalance. If I had a battery to
Speaker 1: this picture, what happens is that battery will see the
Speaker 1: supplied demand imbalance over the course of the day, and
Speaker 1: the battery says, hey, I want to charge at midday
Speaker 1: when there's like a bunch of extra solar, and then
Speaker 1: what I want to do in the evening is discharge
Speaker 1: because that's where all the demand does. And if you
Speaker 1: do that with a battery, you'll end up raising demand
Speaker 1: from the rest of the system at midday and lowering
Speaker 1: demand in the evening. And hey, like that helps the
Speaker 1: solar guy. It helps the battery, but that's not the
Speaker 1: end of the story, because it also helps the gas plant.
Speaker 1: Without the battery, there's going to be a gas plant
Speaker 1: somewhere in California, which is watching demand fall at midday,
Speaker 1: they're like, do we turn off? We aren't turn off.
Speaker 1: There's like thermodynamic properties inherent to that gas plant that
Speaker 1: means it's very difficult to switch off and on instantly.
Speaker 1: They can't really do it at all. So they're they're
Speaker 1: in a lot of pain at mid day. So they're
Speaker 1: turning their plant right down to the like minimal technical
Speaker 1: feasible limits they can, hoping that like demand doesn't go
Speaker 1: any lower and they have to turn off, and then
Speaker 1: they breathe this huge sigh of relief when demand comes
Speaker 1: back in the evening and they can turn their plant
Speaker 1: back up and and make their money. And when I
Speaker 1: had batteries to the system, they don't have to turn
Speaker 1: down during the midday, or if I had enough batteries
Speaker 1: and they don't, they get to sit there and operate
Speaker 1: the day through because the batteries are taking the power
Speaker 1: from produced both solar and thereby decreasing overall demand. Is
Speaker 1: that right during the midday, Well, it's it's it's all
Speaker 1: like if you if you're a gas plant, what you
Speaker 1: get paid over the course of that day is very
Speaker 1: very low at midday because demand or net demand so
Speaker 1: low and high in the evening, and if I increased
Speaker 1: demand at midday, then the price goes up and I
Speaker 1: get paid more in midday. I might also get paid
Speaker 1: less in the evening. But like the gas plants will
Speaker 1: have extra additional costs if they're constantly turning their plan
Speaker 1: up and down all the time to sort of make
Speaker 1: room for the rest of the grid which is going crazy.
Speaker 1: So what is optimal? Is it optimal to have a
Speaker 1: bit more expensive batteries or what it's It's a difficult
Speaker 1: question to ask, and that the approach we've taken is
Speaker 1: perhaps a little bit different. So you can write out
Speaker 1: all the equations for this behavior in a specific set
Speaker 1: of instructions and feed it to a computer and get
Speaker 1: a result. It takes an incredible amount of time, but
Speaker 1: you can fully optimize that problem. But we've got slightly
Speaker 1: different priorities when we're producing NEO, because we're not interested
Speaker 1: in a fully optimized, guaranteed perfect dispatch for a system.
Speaker 1: We're interested in in the larger picture. We're interested in
Speaker 1: comparing systems. To make sense to this, I think it's
Speaker 1: maybe maybe useful to borrow one of the the strangers
Speaker 1: alternate realities. For a moment, there's an alternate reality where
Speaker 1: where we've already solved the energy transition, things have gone great.
Speaker 1: Being if still a company that's good. And in this
Speaker 1: sort of zero carbon utopia, people have all these new
Speaker 1: and interesting hobbies, have got all the spare time, and
Speaker 1: bird watching is huge. So imagine that in this alternate reality,
Speaker 1: the B and b n F stands for birds. Birds.
Speaker 1: Birds are big business. Everyone loves birds. And the CEO
Speaker 1: of BENF comes down and he's like, man, people just
Speaker 1: love this bird. This bird content we're producing. We need more.
Speaker 1: And he goes to the best analyst and he's like,
Speaker 1: you have you seen those swarms of starlings. That's sort
Speaker 1: of clouds that undulate and pulse pulsate across the sort
Speaker 1: of evening sky, like I think most of us have
Speaker 1: seen them, and that it says like, yes, sir, it's
Speaker 1: it's actually a murmuration of starlings. That's the collective now.
Speaker 1: And the CEO is like, you're the man for the job,
Speaker 1: or you're the woman for the job. So this analyst
Speaker 1: goes up north. It's very smart, smart person, and they're
Speaker 1: trying to quantify with a set of equations what this
Speaker 1: flock of birds is doing, and they're looking at the
Speaker 1: whole flock, they're looking at the macro and they're trying
Speaker 1: to figure it out. They've got a PhD in maths,
Speaker 1: but they just can't do it. It's just too complex.
Speaker 1: They come up with sort of rough equations, but it
Speaker 1: doesn't hold in all circumstances and things are just falling
Speaker 1: apart and they're pulling their hair out. And the reason
Speaker 1: is because they've they've taken the wrong approach. So if
Speaker 1: you if you want to model this flock of birds,
Speaker 1: a better way to do it is to zoom in
Speaker 1: on the individual bird. So birds aren't particularly intelligent, like
Speaker 1: the expression like bird bird brain is there is there
Speaker 1: for a reason, like they bump into windows all the time.
Speaker 1: And birds are only actually following a few simple set
Speaker 1: of rules when they're flying in flocks. So the first
Speaker 1: rule is basically like don't bump into my neighbors. That's
Speaker 1: pretty simple. The second rule is sort of go roughly
Speaker 1: in the direction that my neighbors are going in, and
Speaker 1: the third is kind of like gravitate towards the center
Speaker 1: of mass of the flock. And it sounds crazy, but
Speaker 1: if you model those individual birds and you program those
Speaker 1: three rules into them, that amazing flocking behavior which we've
Speaker 1: all seen, sort of springs up out of those rules.
Speaker 1: So it's called emergent behavior. And that's a much better
Speaker 1: way of taking this problem and making it something digestible
Speaker 1: that we can get answers from. And so in our reality,
Speaker 1: we we don't have birds, but all we do is
Speaker 1: we have power plants, and we can sort of program
Speaker 1: these simple set of rules into the power plants and
Speaker 1: then sort of set them free, if you will, to
Speaker 1: to follow their own behavior. And that's how we stitch
Speaker 1: the whole system together, which is different like if we
Speaker 1: top down, if we tried to ask the question like
Speaker 1: what's the optimal mix at twelve o'clock on this given
Speaker 1: day for this given demand condition in California, Like that's
Speaker 1: a very difficult question, Like you can't solve it. It
Speaker 1: just takes a very long time. Our approaches say, well, look,
Speaker 1: if you will, like let the power plants decide for themselves.
Speaker 1: We've given them all the cost and operational data they need.
Speaker 1: They can make that decision for themselves. So if I'm
Speaker 1: just random gas plant, you know in Nevada, or whatever
Speaker 1: I can based on certain conditions that you give me,
Speaker 1: I will make a choice on what to do at
Speaker 1: that given hour. Yeah, like if if the price goes
Speaker 1: too low, demand is too low, like I'll switch off
Speaker 1: if you switch the whole thing together. Essentially, what you
Speaker 1: have a bunch of ants scarring alternate realities, trying to
Speaker 1: choose the cheapest flock of power plants? Simple? What can
Speaker 1: NEHEM do that you haven't yet investigated? So what Nehem
Speaker 1: can do that we haven't fully explored yet is a
Speaker 1: lot more work on on zero carbon. So we've done
Speaker 1: some emissions scenarios and they've been very instructive. But what
Speaker 1: we haven't done yet is eached up everything together into
Speaker 1: a cross sectoral optimization. What I mean by that is
Speaker 1: at the moment we've got a view on decarbonization pathways
Speaker 1: for steel for example, and for other sectors. There will
Speaker 1: be electrification as a decarbonization pathway. So transport is a
Speaker 1: good example of that, where we get more electric vehicles
Speaker 1: as long as our electricity is zero carbon, that's fine,
Speaker 1: but it makes the electricity system bigger, so it makes
Speaker 1: that problem more difficult to solve. So we've done quite
Speaker 1: a way into sort of stitching those together at the moment,
Speaker 1: but not everything and not fully optimized. So you have
Speaker 1: all these countries that are making net zero targets and
Speaker 1: you want to model out what that could actually look
Speaker 1: like in practice exactly. And I mean there's a there's
Speaker 1: a difference here as well, because we can take the
Speaker 1: sort of self nominated targets, sort of particular countries done
Speaker 1: and model lat That's a very different question from what's
Speaker 1: the global optimized least cost pathway to zero carbon system?
Speaker 1: Because it might mean some other countries we haven't thought
Speaker 1: of do more heavy lifting than others. It's a it's
Speaker 1: a different story as a whole, rather than the individual
Speaker 1: country story stitched together. One final question I guess is
Speaker 1: what would you like to change on the model for
Speaker 1: next year? Definitely the name. I nominated a bunch of them,
Speaker 1: but they were all shot down. Most of them weren't
Speaker 1: serious conversations Global Renewable Energy Transition Analyzer. So the acronyms GRETTA,
Speaker 1: which I think is very appropriate. Oh man, that's gold.
Speaker 1: I love it. Why would you not do that? That's
Speaker 1: that's great, that's so good. Ian, Thanks for joining us,
Speaker 1: Thanks for having me Today's episode of Switched On was
Speaker 1: edited by Rex Warner of Great Stoke Media. Bloomberg Guny
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