Hello everyone and thank you for coming. Today I will introduce you the tool that we use internally
at RTE and that's also open source. In the back, can you hear me? Can you hear my voice?
louder? Okay, a bit louder. So today I will introduce Antaresimulator.
So yeah, it's a tool that's been open source since 2018. It changed license. So at first it was
under it was license under GPL but then it changed to something like MPL for reasons that I can't
explain but yeah. So first I will introduce hi-tworks. So the idea is very simple. You have a set
of interconnected areas. So a network that can be countries or regions. Whatever you prefer.
And on each of these areas, you have some power consumption like residential, industrial,
cars, electric cars. On some areas, you have some dispatchable production. So production
that you can control. With technical limitations, for example, you cannot start a nuclear
reactor instantly. And you also have some non dispatchable production and more and more of it,
which cannot be controlled or very, very little. We also have links in interconnecting the different
areas. We've also some limitations on the flux, on the important export capacity.
So the modeling is very simple because it's aimed at very long-term simulations. So we keep it
simple. We don't have non-linear stuff and things like that. It's just linear optimization in the back.
So the basic equation is the following. It's the balance constraint. So for each area you have to
produce enough energy to satisfy the load. But you can also use, you can also import some
energy if you need it and you can also export. So if you have too much. And yeah, I have to talk
about the two terms in red. So the unsupply energy and the spill energy. So these are how to say
slack variables. So we give our system a little bit of slack because if we don't introduce these variables,
we get infeasible problems, problems that have no solution. And our users basically they like it
when there's no unsupply energy or as little as possible because it means that the electric system
is correct is adequate. And spill energy is the contrary. So we have to produce too much.
For example, non-dispatible energy and we cannot evacuate that production to the into the network.
This is a problem as well, but not in general, unsupply energy is much worse than spill energy
for our users at least. So the idea is, well, we get a lot of data from climate scenarios,
from load, wind, solar, some of these are connected. And so we establish Monte Carlo scenarios
and we also have some data coming from energy producers like outages, maintenance scenarios
and also inflows for lakes, rivers concerning the hydrogeneration. So we mix all of these together
with some consistency to establish some scenarios, a lot of them. In general, it's 1,000 scenarios,
but sometimes it can be more or less. So I really like this explanation. So we take one scenario
for load, one scenario for the wind generation and so on. And these are linked, one scenario for
hydro and so on. And so we mix them to establish years. So a year is actually a Monte Carlo scenario.
Years are independent. So there is no memory. There is no memory between one year and another.
So it helps us for parallelization. For example, we can parallelize years. So it's really nice for us.
And then we can have some curves, some stochastic results because all of this is probabilistic.
And for example, the expected loss of load or such indicators. Our users, they watch these
peaks very, very carefully. I didn't talk about unit commitment. It's quite technical, but it's
about simulating the how the dispatchable production works and how it is how we start some
thermal clusters in order to satisfy the demand. So we have quite a few users and reference study.
For example, internally at RTE, we have winter and summer outlook that are done with
Ontaris simulator. So these studies are used mainly in order to
assess the adequacy for the next season. So this would be not long term, not so long term, but medium term.
We have the French adequacy report, which is more like a legal obligation on our part of the French
DSO. So we have to publish a report every, I think it's every two years in order to comply with our
legal obligations. We also have some R&D studies about electric vehicles. We continue to read stuff
like that. How do you say? Innovative topics. And then a lot of studies also European studies,
because since Ontaris is open source, it can be used in European studies. There's no need for
to pay for licenses and stuff like that. So anyone can use it, considering they have the ability to
use it. And finally, we RTE published a report on Energy Pathways, 2015. That was in October 2021.
And this report is quite big. Actually, it's a big, a big book, I have it on my desk.
So it's, well, is it all what I said? So it established six scenarios and around that
tries to, how to say. To explain what will happen in each of those scenarios, how much they
will cost to the taxpayer, how much materials are going to be used and so on and so forth.
So during the remaining time, I will talk about next features for Ontaris. So we have,
for a long time, we've had an old legacy GUI, which was written in C++. Surprisingly,
our users like the old one. They are kind of used to it, but we're trying to migrate to a web-based
interface. So we had a project a few years ago. We started a project a few years ago that was called
that is called Ontaris web. And it offers nice features like this variant manager. It looks like
a GUI tree for some of those dollars familiar with GUI, except for Ontaris study. The reason was that
studies are quite large, a few gigabytes of data and they're often deplicated. If you only
just want to change a small subset of data, you have to copy the whole study and make a copy
and then you make 100 copies and then your server is full. So we wanted to avoid this by having
this variant manager where you can actually change what you want and avoid the duplication.
There's also this system map, nice system up, that allows to navigate the whole system.
That's not really a new feature, it already existed, but it looks nicer on the web interface.
And finally, we have a task manager, so it allows us not only to launch simulations remotely
on a central server. Actually, in this case, it's a slurm cluster. If I'm not mistaken.
So we're trying with this Ontaris web tool, we're also trying to migrate from desktop to
more like something more centralized. What tells can I say? We're probably going to be using
the cloud a lot more in the future because it's more efficient. We can run a simulation like
10 times faster because they have super powerful clusters.
Another change is what we call the modular. So up until now, if we want it to add a model, for example,
like I don't know a battery, we had to change the code. It's not so convenient because the code is
a bit old, it's in French, and we need a developer to do that task. However, we're trying to move
to configuration-based models. So we write down the equations, not in a C++ file, but in a
YAMO file, and so it allows the users to write their own models without relying on software developers
in theory. That's something that we're developing right now. We had a Python prototype first,
it works pretty well, and now we're making it porting it into C++.
And one of the difficulties will be to have hybrid simulations with the old model here,
the old models static, a cloud-based. We're going to have both these and these into the same
simulation. So that's one complexity. We have multiple repositories, one for an entire simulator,
one for an entire expansion, which I didn't mention. We have some documentation available.
And also, I would like to mention that our study dairy and RTE international provides trainings,
as well as SAS for an entire S. So yeah, you can contact them at this address, if you would like to.
Any questions?
Who is?
Thank you very much for the presentation. A quick question about the dynamic models.
Yeah.
Do you mean that dynamic models are the same? You're running the simulation.
They have a outcome that happens, so you can re-configure the model of the simulation
to run the simulation. Do the simulation use dynamic addition on the expansion of nodes
during the simulation, or do you have a outcome to change the model and start the new simulation
with the data assembly?
Okay, the quick. The question was about dynamic models. If it's going to
actually reconfigure the simulation, the model during the simulation, or if not.
The answer is no. This only allows us to configure the models beforehand, before running the simulation.
We'll look at the variance on that. You have independent years. Can you have the network changing
over time for you just running different contact all those on the same?
For now, the only thing that changes is the data. The network is fixed.
Sorry, the question was, can the network change the question was, can you have the network change over time?
And the answer is no, you can only have the data associated to the network change.
And I think times up.