Okay, we can start a minute early.
Bobby will talk about the aqueductor, that's home energy management system, that's open
source and you can use that your home by yourself instead of buying that.
Thank you, Bobby for presenting.
Have fun.
Okay.
Hello, everybody.
Thank you for coming to my talk about the energy optimization system, it's not an
automation, but an optimization system called aqueductor US.
My name is Bobby Nelten.
I'm one of the developers of US.
This is my first time to foster them and also my first time to talk about the
US.
So I'm really excited and what to thank the first team and do the audience for
this opportunity.
I'm also a little bit nervous, so I tend to become the loud when I'm nervous.
Yeah, so please stop me if I'm too loud and blowing your ears away.
So what is aqueductor US?
Aqueductor US is a self-hosted energy optimization server.
It creates optimal operating schedules for your batteries, electric vehicles and household
devices.
So it's the little thing from the big thing that we saw before, so it's a lot of stimulation
to be in the household.
Yes, runs entirely on your own hardware.
It's not, if you do not need the cloud connection and it's not required, it's also not
desired.
US builds energy plans based on three key inputs.
Your system configuration measurement data from your devices and forecasts that are
taken from the internet or generated locally.
The workflow is simple, first you describe the configuration, then US runs its optimization
regularly using the current system state, energy prices, demands and forecasts and it then
generates the optimal energy plan.
Finally, your home automation system is executing the plan.
In short, the US is like a strategic brain for your energy system, turning data and forecast
into optimization instructions.
Who made it?
Yes, was founded by Andreas called the aqueductor and Andreas is a researcher in mathematical
optimization and in artificial intelligence and he has also got his own YouTube channel.
He created the first version of aqueductor US.
It's still quite young.
We went public in September 2024, so just one year.
Country, we are a small community, but we have no several active users and more or less constant
flow of commits.
You see, a lot of people helped to improve it and to make it like it is now.
I also want to thank them for this.
What can you do with aqueductor US an example?
Andreas, the founder of US invested in his personal shift from fossil energy to regenerative
for renewable energy as a DIY maker even purchase home storage battery.
We can see it in his videos.
He also installed his own PV system.
This was a big investment and it feels good if you can do something about climate change,
but some of you still have to afford it.
So his goal was clear.
He wanted to create a better future but want to add a minimum of costs.
So, or maybe earn some money with the tools and the assets that he will build it.
To achieve this, he needed to schedule his energy consumption.
The grip exports have to fit to his goal and also the import for sure.
So in the end, or also Andreas uses dynamic electricity tariffs, so these tariffs change
every 15 minutes.
Both for importing power from the grid, also for exporting, for example, excess solar energy.
The prices are published one day in advance.
So in this case, you face a real challenge.
You have been showed, for example, your car.
When showed your car, your discharge, your home battery.
To find an optimal schedule for minimizing costs becomes really complex and maybe time
consuming.
So in the end, maybe it's impossible to do it manually.
In the end, this is a complex optimization task and Andreas is a researcher in mathematical
optimization.
To automate this optimization, Andreas built EOS and uses EOS and also another tool called
Node Rat and Grafana.
In the end, in 2024, by using EOS and all the optimization that is providing the use,
reduce the average electricity price from 36 percent per kilowatt hour to 31 percent per
kilowatt hour.
So a reduction around 30 percent, just by using this tool and scheduling the different
asset at the right time.
In energy management system typically work at least in two different timescays, each
with different types of decisions.
Short-term decisions happen in minutes or seconds.
They are usually reactive and local.
The system directly responds to what is happening right now.
One example is PVCER plus charging.
You charge the battery only with the excess solar energy that is available right now.
The other time horizon relates to long-term decisions, these are strategic and not reactive.
The problems have or these problems have two key features, they are non-linear, energy
doesn't always value the same.
The kilowatt hour store in your battery right now may be cheap, but later on may value
a lot more depending on the future prices of energy on the demand and so on.
The problems are time-coupled.
Today's decision affects the most options.
So if you spend or if you use the battery today, the energy is gone.
But if you keep it, you may be flexible for the future but lose the benefit at the current
moment.
This means we must think about forecast, trends and uncertainty.
The question is always, when should we use stored energy?
Should we act now or maybe later, it may be a benefit to act later.
So this is a typical optimization problem, not a simple control problem.
So we are on another time horizon.
And your ass is definitely a long-term energy planning tool and only long-term energy planning.
It keeps the separate from the short-term control.
Yes, doesn't control devices directly.
Instead it tells the home automation system that device operation modes and the energy
budgets for the current hour.
Yes, they've watched what should happen over time and the home automation system decides
how to make it happen in real time.
So there's a real difference and work.
The separation is by design.
Each layer focuses on what it can do best, keeping both systems in our view simple and
effective, and in our view this makes a perfect fit to bring these two together.
U.S. uses a genetic algorithm to create an optimized energy plan.
The genetic algorithm creates many candidate energy plans in the 100 or 1000.
Each plan represents a different strategy.
When to charge, this charge went to export import, both prices and so on.
The plans are not executed immediately, but they're handed over to simulations.
The simulation also needs to see it here, some forecasts, some weather forecasts,
devils, production forecasts, and so on.
And then they simulate what will happen.
The simulation really answers the simple questions if you follow this plan, what will happen?
In the end, we know the fitness, the costs that will displan produce.
U.S. then compares to the results of all these different plans, select the optimal energy plan
and only this one is handed over to the home automation system to be executed.
So in short, U.S. optimizes energy management by simulating futures, not by reacting
to the current situation.
And the simulation is also one of the, and our, one of the biggest benefits for this genetic
algorithm, because you can, with a simulation, you can really simulate nearly everything.
You do not have to have linear problems, for example, you might be non-linear, there might
be a time-capital and so on, simulation works for all of that.
So how can you access for U.S. for you to use?
U.S. provides several interfaces to exchange data and to receive commands.
The primary interface is the rest interface by HTP request and by HTP responses.
And by this interface, you can control everything of U.S.
and you can also monitor everything.
Here on the left side, you see the speaker front of the rest API.
It is, for example, testing of the rest API and this automatically provided by the underlying
library, we're using in faith, fast API server for this.
And the window on the right side shows the response to an energy management plan request.
The plan is a JSON structure to be further processed by the request.
The JSON structure is oriented to the S2 standard, which I think is maybe a topic in the next
talk.
U.S. can also be directly integrated into homo-system, I think one of the most popular
open source homo-domations systems, it is integrated as an add-on.
And U.S. can provide the actual optimization instruction and the whole optimization solution
to the homo-system so-called entity status sets that are used by homo-system.
Here, for example, the actual instruction for better one is to switch to fast charge, where
the home application one shall be off.
The rest interface is still workable also when using it in homo-system.
And we have another special adapter that uses the data exchange with Node-Ret, though there
is a popular slow-based programming tool also used for homo-domation.
Yeah, that's it for the technique.
If you're interested, my pledges please get involved.
You have to make complex energy optimization freely and available and easily available
in open source.
There are a lot of means you can help.
Use Airs, report to us, provide your own ideas.
We already have some for us to go for.
It's we want to switch to 50 minutes of automation time slots because the prices are created
in 50 minutes time slots.
I want to integrate my heat pump, so it's also can be simulated and controlled.
On the slide you see your online resources, we are on GitHub for sure, there are the sources,
the tests and the documentation is also the documentation sources are on GitHub, also there's
an issues page where you can provide new ideas and hand commands and also bugs, they're
always from bugs and our documentation is also on read the docs where you can read it.
And we have a forum on aqueduct.net.
It is mostly German but any languages welcome.
Thank you.
We have time for questions.
It's not implemented.
We're using the format.
The question is whether we have implemented S2 product or sorry.
Now we did not implement the full S2 product or but we use it for example for this JSON
structure to structure the data that we provide in the plan.
Yeah sure there's most of it possible.
The question is whether there are any method or additional method to close the gap between
the predictions to be more precise.
I think?
Yeah, we have mostly using predictions from the internet so you can ask for PV prediction
for example.
Aqueduct is running its own server where you can ask for your specific configuration
of PV system to get predictions from.
Or we have load predictions from load profiles that are calculated by a certain set of households
that they're using in an adapting.
But because we're also getting the measurement from the system so we can go back and to
some adjustments.
And in future I think there will also some machine learning models to adjust this to your
personal situation because what you get is more over a bigger area and so you can adjust
your personal situation.
That is planned or that can happen if somebody is coding it but it's not in at the moment.
Okay thank you.
So you have a hander like if I want to load my dishwasher now and want to run it now
or not by charge my liquor cart, how is the handle?
You just tell a certain time slot time window when you want to charge or your dishwasher
to be run.
Is that anticipated that the prediction that I might from my dishwasher cost usually I might?
No you get an optimization instruction you say okay this is my time window or these are my
time windows and then you get an optimal time window that will be towed for example to
home assistant that has to switch on your dishwasher for example.
So if you tell what you want you do the optimization and you get something that's hopefully
fulfilling your wishes.
Okay thank you.