All right, thank you.
All right, so I'm Mark Barnes from Los Alamos National Laboratory.
I guess first off, I apologize as I'm a bit of a cold.
So I'm going to attempt speaking for the people in the back,
but I probably won't succeed.
Anyways, I'm very glad to be here.
Obviously things are in flux right now,
but I was able to get my travel approved.
Yeah, I'm so I'm very excited to be here.
So Los Alamos is a reasonably long history with open source.
We have a lot of university academic collaborations,
and so releasing a research software under a premise of license.
We found it to be very helpful to foster collaborations and bring in students.
So it's been a great value to us and a great value to the academic community as well.
So I'll be talking about here.
I know there's been a good bit of energy analysis software.
This is a little bit more specialized than some of the other packages we've seen.
So this is a add-on package that's specifically focusing on the analysis and mitigation
of geomagnetic events.
So why do we care about them?
As we've probably noticed in kind of more casual reporting,
there's a great variation in terms of predicted severity of events
and it kind of varies so I'm okay not much will happen
to one hemisphere of the world is going to go back
into the pre-industrial age.
So how do we predict this?
It will an electrified transportation network be completely knocked out.
Will your phone explode in your face?
I'll attempt to answer some of this so let's dive in.
Okay so I apologize that half of this presentation is background,
but it was a current in level events.
This was a space weather event named after the British scientist Richard Carrington
back in 1859 and this was the largest observed geomagnetic event
which fortunately occurred before there were continental scale interconnected power systems.
We just had telegraph lines which was enough to observe the effects
but sufficiently simple to not have a major impact.
Anyway so how do these work?
So what we have is charged particles coming off of the sun.
They get trapped in the earth's ionosphere or magnetosphere.
And they form a not current flowing above the surface of the earth through the electric jet.
So this induces a magnetic field
and an electric field on the surface of the earth.
And this will couple into electric transmission lines.
Now what do these fields look like?
So here's a measured waveform of round electric field
from the Quebec March 1989 event which was one of the more severe events of the last century.
And so what we notice is the time scales on the earth about one second to one day in terms of variation.
And the amplitude if you're looking carefully at the y-axis.
So this is on the order of volts per kilometer.
So this is one thing that's good, right?
So even for your largest fablet, a field on the order of volts per kilometer
is not going to make your sound sound galaxy explode in your face.
But it can still have the fact suddenly electric power grid.
So what are those?
So over here we have a large power transformer.
These are very big, very heavy and very expensive.
And for this reason, we operate them right on the edge of saturation.
So that means that even for a not enormous additional current flowing through the transformer,
we can put it into half cycle saturation like we see on the figure here.
So what's this mean?
Well, this means that our transformer's exciting current has these big spikes in it.
And these will introduce harmonics in the power system.
Those can cause trouble.
But the most significant impact is that there's a fundamental component of that current that's out of phase with the voltage.
And this increases your so-called reactive power losses.
And these can impact voltage stability on the grid.
Also we can experience transformer overheating as a result of local hotspots.
Now let's go into how does computing impact power systems?
So we've got a number of different analysis types.
Transient solvers, steady state solvers, dynamic solvers.
But for a mitigation problem where we care about solving this in terms of optimization,
typically we're limited to a steady state solver.
So modeling GIC is in this context.
It's a little bit tricky.
This motivates our workflow that we hear here,
that's suggested by the north national electric reliability corporation.
So we have a number of different steps.
We apply earth connectivity model, put into a coupling model.
Apply DC solve, apply our transformer saturation model.
Apply our AC solve and then perform mitigation measures.
Now what does this look like in terms of circuits?
So in love here we have a positive sequence model,
where we represent a transmission system that's approximately balanced.
As one line, this doesn't work for quasi-DC current.
So what we have is the model on the left here,
which is approximately equal to the real portion of a zero sequence,
not work representation that's commonly used in full studies.
Now in terms of what we do about GICs,
so a common mitigation strategy that suggested is transformer neutral blockers.
These don't impact the power carrying capacity of a power grid.
However, they could impact ground fault reeling,
which means that they're active devices and they're somewhat expensive.
So we can't put them everywhere.
And this motivates analysis and placement strategies
in order to allocate them in a cost-effective way.
And that motivates power models GMD.
So this is a Julia jump package built on top of power models,
infrastructure models,
and includes a number of different analysis and mitigation formulations,
as well as different realizations,
which include our full AC polar equations,
as well as semi-definite programming,
and second order cone realizations.
We also can interface into not optimization solvers
through the jump interface,
or we do have our own custom matrix based solvers
when we're looking at getting a fast result,
which I'm doing here.
As far as what's our data model look like,
so on the right we have our standard AC components,
and then to that we add another set of so-called DC components.
So we represent this as you know,
one single interconnected network.
What do we need in order to solve this problem?
So there's some additional information
that's needed besides our usual AC inputs,
so this includes substation locations,
transfer of winding configurations,
and bus substation mappings.
We originally using a custom file format,
although we've now moved to a semi-standard,
file format that's been developed under PSSC,
and supported under other software packages.
So what's this look like?
Basically let's say CSV if format,
which is relatively easy to parse.
Okay, and now that I'm almost done,
here we have the analysis I promised.
So this is a 500 bus synthetic case,
produced by the Texas A&M University,
and we're applying a pretty hefty,
10 volt per kilometer east-west field.
And we use a screening criteria,
like which of our transformers have a weighted neutral current
over 100 amps?
So this is what we got out of 131 total transformers.
We only see that 13 have very high neutral currents.
So this is actually kind of encouraging,
and it's relatively similar to other studies.
So while there's certainly a degree of exogenous uncertainty in here,
so you don't want to take what I'm saying for grit,
you know, completely trustworthy.
Chances are, you know, the power grid is probably going to be fine,
and if you want to buy a Tesla,
you're probably safe doing that.
Okay, so GitHub repo is here,
and I guess I can answer any questions folks of.
Thank you.
Sure.
Okay, yeah, so there's been a question.
The question was, have transformers been tested
over 100 amps to see what happens,
and yes, there is some hardware testing and modeling.
I believe that's done by Luis Martiel.
Is any other questions?
Cool, thank you.