Okay, my name is Robbie Morrison and I'm here to talk about energy system modelling.
I want to take you right up to the stratosphere.
A couple of things on my background, I won't go through all this, but I started climate
campaigning 33 years ago.
I started high resolution national energy system modelling 23 years ago, and I started
open source energy system modelling 20 years ago, so I was right at the beginning of those
trends pretty much.
I want to talk briefly about the open energy modelling initiative, which started about
eight years ago, and it's an informal collection of modellers.
We now have about 1,000 people involved.
The bulk of them are early stage full time researchers, and that gives you an idea of
how much sort of interest there is in this open side.
There is an entire parallel universe doing clothes modelling that we don't have much
contact with in the power companies, in the World Bank, in the multilateral organisations,
so I'm only going to talk about the open source side.
The final point up here is that this whole field has flipped in the last year radically.
I get contacted by corporations and economists and so forth now, which would never have happened
two years ago, so this is a complete game change.
I'm not going to talk very much about energy system modelling, but if you want an introduction
I recommend this YouTube, which is made with my partner in the car park, and it's descriptive
and it's quite good.
This is a quick schematic showing what these models can capture.
This just happens to be one that I pulled up that's hybrid with agent based modelling
in it, but you see a lot of the entities, if you like, that were being discussed in
the previous talks, but brought together in a collective.
So we have households and we have market operators and we have lines companies and we have markets
and we have AC power flow and we have a lot of kith and the system, hydrosystems, storage,
gas turbine sets and so forth, and a whole lot of external characteristics coming in
through weather conditions, interest rates and so forth, so that's the broad picture.
If you want to look at the models that exist, this Wikipedia page is worthwhile, it's about
half complete and it covers the various models.
Some are directed specifically to the energy sector, but increasingly they're a sector
coupled and they come into the whole energy system.
The basic paradigm is operations research, so the underlying model produces a set of
constraints in a sparse matrix, has a goal function which is normally minimum aggregate
cost and feeds that all into a solver and returns a result.
The way that the analysis proceeds is by so called comparative analysis of scenarios,
so you pick a base scenario, a reference scenario and then you propose different scenarios
that you want to explore with nuclear, without nuclear and so on and so on.
These are the high resolution, they have a lot of detail in them, so they have the plant
and the network and so forth in them.
A lot of external circumstances, weather, demand for energy services and so forth.
They are contiguous time which is really important nowadays because with renewables
and storage you can't kind of do typical periods, you actually have to work your way
through the entire system as it evolves.
The evolution might be out for 30 years, out to 2050.
There's a degree of different types of foresight, sometimes it's perfect foresight so we know
everything about the future, other times it's stepwise so we do recursive dynamics.
What up here, technological progress is included, one factor, multi-factor, for example the
uptake of a particular technology like solar PV will, the model will internally reduce
the costs for that particular technology as it's taken up and it evolves through time.
The optimisation is usually mixed into linear programming, anything else more exotic runs
into performance issues.
Conceptual extensions include embedded decision taking using agency, multi-criteria optimisation,
some assessment of co-benefits such as urban air quality, sensitivity to the framing of
the problem, the role of uncertainty and the exploration of near optimal solutions.
So this is system modelling, all systems have kind of natural systems and problems if you
like together have natural boundaries.
If you want to model Europe or we want to model an energy system in Germany you probably
want to go to the boundaries of Europe for example because that's kind of a natural point.
The methods or naturally seek technical synergies, that's one of the advantages of using these
systems, the least cost approach will pick up the synergies and get them working.
Future climate change is normally included, projected future climate change.
These models may exhibit undue sensitivity to both data quality and to system resolution
so they're not without issues that have to be explored by modellers.
They started off with energy systems, electricity systems coupled into district heating and
into gas and so forth but they're increasingly branching out into land usage, water use,
the industrial sector when you're looking at things like hydrogen, ammonia, thermal
integration and steel production.
Carbon capture is included now outside of the energy system so residual emissions from
cement and from agriculture are now being included in these models.
Comparability also and we've had some talks about vehicle charging but this is to look
at the whole picture and not just the perspective of the householder or even the lines company.
Co-benefits beyond climate change mitigation I mentioned.
What isn't in the models is there is no embedded economy.
If you want to do that then you have to go to process based integrated assessment models
which are widely used by the IPCC and in which case you have a lot more kind of an economic
take on the system.
The model started off being open source but there are good reasons why we want to look
beyond open source and the one, the first reason is to go to open science.
So we want genuinely open data and we want it under communal curation.
We want full transparency and as modelers we want an engaged overarching community so
that we can compare and contribute and support each other.
The goal in my kind of take is that we should be looking at public policy analysis which
is based on peer production, on commons based peer production and the reason I say that
and I think there was a talk earlier this morning from the European Commission, people
like the European Commission do not have the capacity to explore the solution space and
I will add nor do they have the creativity required.
That's not a criticism, that's just an observation.
So we really want a massive effort in exploring what our future could be out to 2050, the
kind of trajectories and pathways and requirements that are needed.
Some potential for public engagement but very few examples to date when these models
are used for more specific projects.
Our biggest Achilles heel is complete and coherent data for public interest analysis.
We are not data scientists, we are desperate to have data which is complete and coherent.
If it's dirty, it's a problem.
If the semantics behind the data collection is somewhat inconsistent, it's a problem.
If the information is missing, it's a problem.
This may not be an issue for data scientists using statistical techniques or machine learning
but it is for us.
One issue that doesn't get much air play are data standards and quite a lot of the data
standards in this area, especially in the electricity sector are proprietary, they come
under so-called brand, we heard about that, fair, reasonable and non-discriminatory conditions.
The problem is if the data standards are legally encumbered, then the code bases that reflect
that and the data sets that comply with it could become derivative works under intellectual
property law and we are in trouble.
So we want basically CC by 4.0 or something similar on the data standards.
I'll skip the last bit on data sets actually and I'll skip the slide but I just want to
point out that the situation in Europe is pretty awful on a number of levels.
You go to the US and you'll find a much friendlier environment for this kind of public interest
information.
Okay, second part of my talk is about the global south and the question is why is someone
who's white, male and old standing here talking about the global south.
My short answer is I'm from Aotearoa, New Zealand and New Zealand became bicultural
all over my lifetime and I saw that process and contributed to it.
I had radio programmes in the early 90s on sustainability and conservation on tribal
radio, on ewe radio and so forth.
I went to land occupations, I organised joint meetings with tribes, Huey they're called
and they take place on Marae.
So that's kind of my back story about why I can talk about this I think.
This is a map of Africa with the high voltage network present and you will see that there
is very little structure there.
South Africa a little more, David is going to talk a little more about this so I won't.
This is another example of a model called osmosis.
This is in Africa and these are the cumulative trades out for the next 30 years.
So this is the kind of thing that the models are starting to look at.
There are two overarching projects in this area, the osmosis global project.
Osmosis is written in a high level mathematical programming language called Mathprog.
The second one is pipes and meets earth which is written in Python and you'll hear a little
bit more about.
One of the interesting things I thought, I looked up software heritage collects the
forks for a particular code base and this is 135 fork repositories for osmosis and 308
fork repositories for pipes.
So that gives you an idea of how the open source world works when people will fork
the project.
These aren't hostile forks I presume and use them for their own work and hopefully contribute
their contributions back upstream.
This clear activity now in Central America, Costa Rica, South America, countries like
Brazil, India and surrounding regions, South Africa, Sub-Saharan Africa and most of this
is in the context of academic work.
We have no connection or very little connection crossing over with the multilateral development
organisations and so forth.
So this is the parallel universe I mentioned.
How we doing for time?
One of the issues that we face I think is interacting with official agencies because
we are relatively informal and relatively self-directed and we also are a competition
against the agencies like the International Atomic Energy Agency or IRENA or whoever are
doing their own analysis and I quite like this quote from Oliver Getten, every day politics
is therefore dominated not by evidence based policy making but by attempts at policy based
evidence making and that's exactly what we want to avoid.
I talked to the incumbent NGOs about using our kind of analysis and they weren't very
interested but I feel quite encouraged now because there are a new set of foundation
backed think tanks who are actually very keen on this kind of stuff and I'm sorry I can't
mention too many names because I was ill for the two weeks prior to this talk and I didn't
get consent to talk about them but a couple of climate analytics and transition zero.
Some official agencies are starting to talk about open sourcing their stuff but they're
not doing it in a particularly robust way in my opinion and this is a problem.
They will either open wash or they will do what's called throw their code over the wall
which is put it on GitHub but there's no attempt to develop it, there are no issues
listed and whether it even runs is open to question.
In regards working with the global south and as I had about 10 interviews with researchers
in the global south to try and find out what scopes and issues they're unstructured interviews
but it was kind of interesting.
So the clear benefits of open source projects are of course few cost barriers with the caveat
that the commercial solvers can be expensive.
Open license for Garobi might equal three full time researchers in India for example.
There's a soft technology transfer, it's bi-directional, it's lightweight, all the software projects
bundle associated communities and this is I think really a useful part and the work
is transparent, it can be studied and challenged which I think is really important.
There are some cost cultural considerations I think that are necessary to explore and
I talk about this in Aotearoa and New Zealand becoming bicultural but indigenous languages
bundle different concepts and they're quite noticeably different, sovereignty is an issue,
it's really easy to transgress sovereignty without realising it.
There's a question of representation, the projects are all pretty much white and male
and the global north at the moment and the next question really is also a matter to be
traversed is that the framing of the models and the problems from a global north perspective
may not be very appropriate to the circumstances in the global south.
Global slide challenges, just overarching challenges, most of these won't be very surprising,
code maintenance is always a challenge, support for maintainers.
Building a suitable knowledge commons is going to be a real challenge, for instance the international
energy agency only sells its data under non-disclosure, we don't get hold of that although it's collected
from our national governments, the European Union is focused on data commodification through
its single digital market, the scientific institutions are unnecessarily protective,
I talked about cross cultural issues, we need to find new ways of interacting with official
agencies to get any of this information into the policy process and I'll just conclude
with a quotation from an East German playwright, Heinrich Müller, optimism is just a lack
of information, okay that's it, thank you, yeah any questions, can you speak up a little
too if you ask questions, maybe you said the European Union has some issues with open
data, I know that the European Space Agency has really strong footprint on doing all this
or is it Sentinel data, stuff maximum open to drive a new economy, so has this lot to
spread to the other agencies yet, no the ones I'm going to mention and I will mention some
names, the Meridata for climate, future climate is under bespoke license, the YASA data on
scenarios going forward also under a bespoke license and so on, so a lot of the Horizon
2020 projects are also problematic, the stuff under statute reporting is also legally encumbered,
so I can't for the life of me understand why, but some of it is technically encumbered,
so for example the transparency platform run by ENSOE is legally encumbered, the EEX data
from the European energy exchange also and also technically encumbered, you can't cut
and paste it off the website, it's not very deep protection but and we've complained my
friends to ASA the regulator and they say it's compliant, sorry yeah.
There's the open government license, UK 3.0, I don't know which one they're using,
the other decent experience was with Elexon UK balancing, well I think I'm their only
official licensee but other than that it told me to retract everything, I can use it completely
open which is quite nice, so you know it can sometimes be.
I just want to comment on licensing, the one that, the really the only license that works
is CC by 4.0, if you go to the open government license UK 3.0 you'll find it's not interoperable
with Creative Commons and so you end up with legal data silos, all the licenses are written
by lawyers, I can assure you that and the lawyers all know what they're doing, okay okay okay,
there's a question up there or no, yeah, yeah yeah, Remind went open, that's from Pic, went
to one of the high GPL licenses, I filed a bug report on that because the GPL licenses
have a clause on the, remember when Java was proprietary and you have to have an open
language for a GPL license, they use GAMS which is not an open language and I filed
a bug report and I know that personally the lawyer who responded who said it was okay,
now look I'm not an open source lawyer, I didn't write the textbook but that was where
that discussion went.
Have you seen any new funding come into this particular field to open things up more, because
all I know is that in December I know that the Creative Commons group, they've started,
they've started to hire new roles in this specific role because they landed like a,
you know small millions of Euros grant for this, but beyond that I don't know if there's,
if you know any other groups starting to do stuff in this field.
The overarching, okay, okay, yeah, thank you, oh sorry, the question was funding specifically
for open source and the sort of short answer is, hang on, the short answer is that the
funding, I'm talking about Germany let's say, has been quite good for modelling in general
and it hasn't been specifically directed to open source.
The high level organisation, the Open Energy Modelling Initiative hasn't needed resources
as yet but what will happen going forward I don't know, but the funders are interested
in the kind of open science component of what we do, that's quite clear and I presume that
the next rounds of funding will start looking for real open source projects to be, to be
for support.
Yeah?
So what would you say in all your years of experience has to change and how can we push
for the change so that we get these open data, so what are the levels we have to pull?
In a particular, well, the question was what levers are needed to come to genuinely open
data, it depends on the jurisdiction, in the US it's quite good, federal, work by federal
employees is public domain and there's been enough copyright, legislation around copyright
that most of the stuff isn't actually covered, protected by copyright, they don't have
a database directive.
Working back to Europe, the only solution I can see is CC by 4.0 as a policy, which
is lightweight, doesn't require legislation or change and so forth, but it does require
the European Union to get out of the data commodification and I didn't mention it but
there's a thing called the Data Producers Act which is still live which might come back
into the data act, the proposed data act and that would be a complete travesty for us
because that would mean all this machine generated data would now have its own intellectual
property and I couldn't think of anything worse.
Okay, yep, thank you everyone.