It took a while. Thanks everyone. Thanks for joining this little hot-on
Pipesaw. My name is Lucas Tripple. I'm a research software engineer at
Teo Berlin, and they're from Brown School who is leading the development
Pipesaw since now 10 years. Let me give you a very quick introduction
of what is Pipesaw doing and energy system modeling in general before I start with
what is new inversion 1.0. What Pipesaw users are interested in is
basically understanding all the dynamics of the large scale energy system.
Especially if we have tons of renewables in there. They depend on
there's a lot of spatial and temporal dynamics. They're not available all the time.
We have loads and demand which could change. We have, if you're running a sector
company model, we have the transmission and the transport sector,
heat sector, all could be linked to the electricity sector and there are many
many feedback loops. If I want to know what is the technology now I could
invest in and which is cheap. I also have to check what are the other
technologies which I could invest in. That's why you always have to look at the
system and what we are often doing is just dumping all of those various
constraints and various objectives in the single objective function and then
optimize that by operating costs and kpex. Operating costs, what is
cost to one my system and kpex or what can I do to invest in new
technologies which technologies should I use there. This is then
constrained to many things like availability of wind and solar and
stock capacity that could also be like policies or have to meet 30% of
CO2 reduction until specific year. This is what pipsize basically
made to help you with and it stands for pipsons of power system analysis
but these days it's more a power pipson for energy system analysis and
also include sector coupling so also handles industries so you could
even add materials in there. It's fully written in pipson.
Some would argue that Julia, using Julia's faster, we always build this
very large optimization problem and then spend most of the time
solving it actually in the solver. The compute is not too much too important
and also memory peaks and everything is all handled by the solver and
the advantage of pipson is that it's very accessible. All the data
we use in pipson is stored in pandas so most of our users are either
from research but also from industry but are not no software
engineers and pandas is very easy for them to do handle.
It's very highly customizable but this is just the framework but you
need to run pipson models so any energy system model is tons of data.
You just choose the prettiest plots on the left side.
You see Germany and there the installed transmission capacities
transport and installed power plants and also some industry locations.
The transmission capacities are actually data from the OSM talk
if you joined that earlier on the other side we see weather data which
needs to be translated into energy system data so how much power could
I could use at a specific location. Here you also see all those different
dynamics and they also vary across time and space and also wind and
solar and I hand it differently and there the pipson ecosystem
is helping you out because pipson is the framework which helps you to
build this optimization model. We have there also other data libraries
like AdLite which starts the conversion of weather data into energy
system data that's power plant matching which is basically
de-duplication software for power plant data sets and there are many
different pipsa workflows. The pipsa workflow could be something like
pipsa URL or pipsa Earth and what is happening there is basically that
all the data which is used when needed for pipsa is sanded in
specific workflow and then all the retrieving and the process
of the data is then dumped into one of the models and then you
figure out how do you want to cluster you can add your own workflows and
this is just what users came up with to be a table in our
maintaining pipsa URL. Pipsa USA is done by some people on
Stanford. Pipsa Earth is done by the Pipsa Mit Earth initiative. There's
also multiple models for great Britain. There's one for South
America as South Africa. There's a New Zealand version so there are many
many different models and also Pipsa's quite
tested already. There are tons of applications either people
build their own model on their own with the pipsa framework or we
lie on one of those existing workflows and then
made some changes to it. We have not a full list of users. We can't
have a full list of users. There's just some
some truths and publications. If you check the dogs we try to
keep a long list there and if you're interested in what other people
you can get some inspiration there. As I've already said
it was developed since right away. Ten years now actually last week
was the ten years anniversary of our first public release.
And version 1.0 just came out in October last year.
There's not really a reason why we waited so long. We just never did the jump.
But now since it was such a long release we added tons of new features
and did a full refreshment of the two of it. One of the biggest new features
is that we now support the psochastic optimization. The psochastic
optimization allows you to handle uncertainty. And certainly why are we interested
in that? I mean you just need to check the news these days. Nobody knows what
the US government is doing. You had the Russian invasion of Ukraine
where gas prices were raising or all kinds of prices. We know now that gas
pipelines can just blow up. You could also have uncertainties like just
policies. You do not know they plan to reduce emissions under
2050 by that amount but it could change in the future. And this is
all uncertainty you need to handle if I want to do it in investment decision
today. And you could do that quite okay already with pipsa
by just you could use MGA which is a niche feature in pipsa to dive in that
solution space. You could use all those workflows that's easy to define many
different scenarios and then one name and parallel together. You could then
do sensitivity analysis on them. But you will never get a single result
which works across all of different scenarios. Until now because pipsa
are one point zero now and to reduce the source programming out of the
box which basically just means you have to come up with the set of
scenarios and their probabilities. Those probabilities need to be given that
could be for weather. They could be just given for like
the chance of a weather event to happen. It could be if you want to
hatch against very catastrophic but unlikely events they would have a
very low probability or you just use a normal distribution.
If this is what you need to define a set of scenarios and
when you do that pipsa will cast the full network or
parameters you have and use scenario dimension to it.
And that allows you then that you for that all this patch decisions you have
you can adjust in for each scenario for some
will come to an example in seconds but all decisions
which are used for the operation of the network are then
scenarios specific. While investment decisions so what
in which technology do I even want to invest is
optimized across all of the possible scenarios.
It works out of the box with pipsa in the API now. Let's look at
the simple small example where we have just a low
medium and high gas price and the probabilities are kind of
similar. If we solve all of those scenarios
individually and deterministic we see if the gas prices low
we just go full on on gas because it's the cheapest and nothing else can
compete with that and we have the lowest system costs but if you
go to the medium and the high gas price we also mix in wind and solar
and for the high one even ignite. But all of those work for
the specific scenarios and optimize for that specific scenario but all of
those do not are not optimized for each other. If we want the same
model now statistically we include all of those three scenarios
and we see that the statistics solution is of course not the
best one in any of those scenarios but it's the best one
across all of them together. This is then just risk neutral
optimization and I hope I'm not losing everyone but
with neutral optimization is I do not care about which scenario
actually happens. I just want to have the minimal cost
across all of them. This is not how most people would actually behave.
If I plan like battery storage which costs half a billion in Sweden
I want to be sure that or even for birth case scenarios I don't go bankrupt.
So I want to hedge against those worst case scenarios which could
be catastrophic to me. This is also possible in pipes
are now with the risk adverse optimization and this is included with
the CVA so you can define or you want to penalize the
worst scenarios of your optimization problem and you don't have to
define what are the worst scenarios. This is all handled by pipes
itself and yeah one also just in the API where we easy to
access and if we see here the same optimization problem
done with the CVA attached we see we pay a little of premium
because the system costs are better but we are hatched against
the high cost scenario better and we have a better system which
in this worst case scenario. This was a speedy one off of only
the stress optimization functionality we have now in pipes
there is much much more with version 1.0 first of all if you ever looked
pipes up before October last year already it's worth to look on it again because
we have an all new documentation everything is fresh up and clean and
yeah the quality of the documentation is very high now
we have other new features a couple of plotting functionality
we don't have a GUI what we saw earlier GUI is still not the
thing there are developments on that as well but it's very hard to find
a single GUI for all those different ideas how pipes are can be used
there is the plotting module there is a new interactive maps there are
the new components classes there is a network collection which
possible to compare different networks doesn't matter if they have
multi-wise and if they are still has to have just
a single temporal dimension yeah those are all new features on
on pipes are it's worth to check out the new documentation we
have a large community on the discord channel already
this link is missing but you can reach it a bit just going on pipes
or arc and then the link you will find there if you have any questions you can reach out
now or just join one of those channels is it either discord
the documentation or get up yeah and that's pipes are
the speed version
you can use it for medium loadages as well we don't do that
the question is can you use it for medium and high medium and low
voltage levels as well yes you can do that we are not doing that too much you may
look on those very large capacity expansion
problems and there you are not too much interested in that because then the
models get too complex but you could also run pipes for low voltage or even
at home to run energy management that they come with the battery so
what's coming next version 1.1 next week probably but after that we have plans
on doing more of the GUI stuff where it's also easier to run pipes or models and then
easier to run all those different existing workflows we have already but no announcement
for
if it depends on what you want to do with the flooding map flooding map
and one example is the volcano where you have a volcano eruption
and then the solar capacity is lower that's easy to integrate
flooding map the modeler then has to decide what is actually the impact
on the system do power plants fell out or do so what it's possible
but it's up to the modeler basically
yeah yeah yeah yeah yeah yeah so
sorry again
there it's no the question is are there after with the question the question is are their
pre-existing components how to model like heat pumps for example
There are not, you have to come up with the version on how you embed them in pipes yourself right now.
There are also plans on allowing you to have a components library where you can pre-select list of components.
You can then easily attach to a network.
Currently, it's not possible because currently there are also many different ways on how you could add them to the network.
But it's possible to add, it's just not trivial.
Yeah?
Yeah, it's the package that's divided by profile.
The mix is quite wide, so I'm software developer and working only as a software developer now for the package.
But I'm the first one before it was basically mainly post dog and PhD driven.
But I wouldn't say they don't have software developer knowledge, but it's the research driven and they came from energy system modeling,
not from software engineering.
We have brought in this app and we also get many, many more contributions.
We are not just our university working on that.
There are other institutions, which sometimes have more software engineering knowledge.
Otherwise, that wouldn't be possible.
So far it works.
It's quite well.
The question is, you are always really relying on a couple of core people who have the big brain and know everything.
And it's hard to get new people on board.
And that takes a lot of time and then resources and then you're defending as well.
So community contributions we have a lot.
But it's hard to get community contributions, which are like complex topics like stochastic optimization.
Thank you.