It took a while. Thanks everyone. Thanks for joining us, little talk on Pipesa. My name is Lucas
Triple. I'm research software engineer at Teo Berlin, and they're from Brown School who
is leading the development of Pipesa since now 10 years. Let me give you a very quick introduction
in what is Pipesa doing and energy system modeling in general before I start with what
is new inversion 1.0. What Pipesa uses 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 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 and 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 full system. 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 K-Pakes. So operating costs, what does it cost to one my system and K-Pakes
or what can I do to invest in new technologies which technologies should I use there?
And this is then constrained to many, many things like availability of wind and solar
capacity that could also be like policies or have to meet 30% of CO2 reduction until
specific year. This is what Pipe size basically made to help you with and it stands for Pipe
from Power System Analysis but these days it's more a part of Pipe for energy system analysis
and also include sector coupling so also handles industry so you could even
add materials in there. It's fully written in Pipe. Some would argue that Julia is faster.
We always build this very large optimization problem and then spend most of the time solving it
actually in the solver. So the compute is not too important and also memory peaks and everything
is all handled by the solver and the advantage of Pipe is that it's very accessible. All the data
we use in Pipe size 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.
Yeah and yeah it's very highly customizable but this is just the framework but you need to run
Pipe size model so any energy system model is tons of data. You just choose the prettiest plots
on the left side we see Germany and there the installed transmission capacities,
installed transport and installed power plants and also some industry locations.
The transmission capacities are actually data from the OSM Torch 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 produce 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 Pipe size ecosystem is helping you out because Pipe size the framework
which helps you to build this optimization model we have there are also other data libraries
like AdLite which starts the conversion of weather data into energy system data there's
power plant matching which is basically a de-duplication software for power plant data sets
and there are many different Pipe size workflows the Pipe size workflow is could be something
like Pipe size URL or Pipe size Earth and what is happening there is basically that all the
data which is used or needed for Pipe size is sanded in a specific workflow and then already
retrieving and the process of the data is then dumped into one of the models and then you
can configure how do you want a cluster you can add your own workflows and this is just what
users came up with to be a table in our maintaining Pipe size URL. Pipe size USA is done by
some people on the stand for Pipe size Earth is done by the Pipe size mid-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 Pipe size credits
better tested already there's tons of applications either people build their own model on their
own with the Pipe size framework or rely on one of those existing workflows and then
made some change to it. We have not a full list of users we can't have a full list of users
this is just a mix some some some some true 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 did with
Pipe size you can get some inspiration there and as already said it was developed since
write a while 10 years now actually last week was the 10 years anniversary of our first public release
and version 1.0 just came out in yeah 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 yeah of the tool one of the biggest new features is
that that we now supports the psochastic optimization the psochastic optimization allows you to
handle uncertainty uncertainty 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 very raising or all kind of prices we need no 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 an investment decision today and you could do that quite okay
already with pipsa by just you could use NGA 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 run them and parallel together you could then send this sensitivity analysis on them
but you will never get a single result which works across all of different scenarios until now
because pipsa 1.0 now includes introducing psochastic 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 all parameters
you have and add a new scenario dimension to it and that allows you then that you for that all
dispatch decisions you have you can adjust in for each scenario for some will come to an
example in a second 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 and yeah 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 a 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 I optimize for that specific
scenario but all of those do not are not optimized for each other if we want the same
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 against all 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 burst 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 and pipes are now
with the risk adverse optimization and this is included with the sea bar so you can define
oh 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 are 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
yeah because the sea bar attached we see we pay a little of premium because the system costs are
bit higher but we are hatched against the high the high cost scenario better and we have a better
system which operates in this worst case scenario this was a speedy one off of only this to have
the optimization functionality we have now in pipes are there is much much more with version 1.0 first of
all if you ever looked pipes into pipes that 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's a new interactive maps there
are from the new components classes there's a network collection which makes it possible to compare
different networks doesn't matter if they have multi-wise and if they're still has to
graph there just have a single temporal dimension yeah those are all new features on on on pipes
are it's worth to check out the new documentation we have a large community on the discord channel already
we're just the link is missing but you can reach it a bit just going on pipes are 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 this quote the documentation or get up yeah and that's pipes are
even the speed version
you can use it for medium law judge as well we don't do that yeah 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 need to look on those very large capacity expansion
problems and bear 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 at back home with the battery.
So that's possible.
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.
So.
About a certain piece, like let's say I would have a flooding map
about Germany and the risk of flooding scenarios
with this directly you work with high stuff.
In the current way, or how would I get such a map
into this organization necessary for it?
Or.
It depends on what you want to do with the flooding map.
Floating map, every one example is the volcano where you have
a volcano eruption, and then the solar capacity is lower.
That's easy to integrate.
A flooding map, the modeler then has to decide what is actually
the impact on the system.
Do power plans fail out or do?
So what it's possible about it's up to the model, basically.
Yeah.
How does it from a low given property that's
the way it needs to be?
Sorry, again.
For my university, I want to figure out
that the property of the city or the country,
or that people already bought them to come to the city.
And then there is no.
And the question is, are there, I have to repeat the question.
The question is, are there pre-existing components,
how to model like heat pumps, for example?
There are not.
You have to come up with a version on how you embed them in pipes
are yourselves 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, is it the package that's divided by low credit numbers
with a packet empty?
And how is that collaboration with you, the packet?
The mix is quite wide.
So I am 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-doc
and PhD driven.
But I wouldn't say they don't have software developer knowledge,
but it's research driven and they came from energy system modeling
and not from software engineering.
We have brought in this up 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 now.
Otherwise, that wouldn't be possible.
So far it works, it works 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
do funding 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.
Yeah.
And how do you do that?
Awesome.
OK.
Thank you.