Toy example: Scheduling a battery, from scratch
Let’s walk through an example from scratch! We’ll …
install FlexMeasures
create an account with a battery asset
load hourly prices
optimize a 12h-schedule for a battery that is half full
What do you need? Your own computer, with one of two situations: Either you have Docker or your computer supports Python 3.8+, pip and PostgresDB. The former might be easier, see the installation step below. But you choose.
Below are the flexmeasures CLI commands we’ll run, and which we’ll explain step by step. There are some other crucial steps for installation and setup, so this becomes a complete example from scratch, but this is the meat:
# setup an account with a user, a battery (Id 2) and a market (Id 3)
$ flexmeasures add toy-account --kind battery
# load prices to optimise the schedule against
$ flexmeasures add beliefs --sensor-id 3 --source toy-user prices-tomorrow.csv
# make the schedule
$ flexmeasures add schedule --sensor-id 2 --consumption-price-sensor 3 \
--start ${TOMORROW}T07:00+01:00 --duration PT12H \
--soc-at-start 50% --roundtrip-efficiency 90%
Okay, let’s get started!
Note
You can copy the commands by hovering on the top right corner of code examples. You’ll copy only the commands, not the output!
Install Flexmeasures and the database
If docker is running on your system, you’re good to go. Otherwise, see here.
We start by installing the FlexMeasures platform, and then use Docker to run a postgres database and tell FlexMeasures to create all tables.
$ docker pull lfenergy/flexmeasures:latest
$ docker pull postgres
$ docker network create flexmeasures_network
$ docker run --rm --name flexmeasures-tutorial-db -e POSTGRES_PASSWORD=fm-db-passwd -e POSTGRES_DB=flexmeasures-db -d --network=flexmeasures_network postgres:latest
$ docker run --rm --name flexmeasures-tutorial-fm --env SQLALCHEMY_DATABASE_URI=postgresql://postgres:fm-db-passwd@flexmeasures-tutorial-db:5432/flexmeasures-db --env SECRET_KEY=notsecret --env FLASK_ENV=development --env LOGGING_LEVEL=INFO -d --network=flexmeasures_network -p 5000:5000 lfenergy/flexmeasures
$ docker exec flexmeasures-tutorial-fm bash -c "flexmeasures db upgrade"
Now - what’s very important to remember is this: The rest of this tutorial will happen inside the flexmeasures-tutorial-fm container! This is how you hop inside the container and run a terminal there:
$ docker exec -it flexmeasures-tutorial-fm bash
To leave the container session, hold CTRL-C or type “exit”.
To stop the containers, you can type
$ docker stop flexmeasures-tutorial-db
$ docker stop flexmeasures-tutorial-fm
Note
A tip on Linux/macOS ― You might have the docker command, but need sudo rights to execute it. alias docker='sudo docker' enables you to still run this tutorial.
Note
Got docker-compose? You could run this tutorial with 5 containers :) ― Go to Seeing it work: Running the toy tutorial.
This example is from scratch, so we’ll assume you have nothing prepared but a (Unix) computer with Python (3.8+) and two well-known developer tools, pip and postgres.
We’ll create a database for FlexMeasures:
sudo -i -u postgres
createdb -U postgres flexmeasures-db
createuser --pwprompt -U postgres flexmeasures-user # enter your password, we'll use "fm-db-passwd"
exit
Then, we can install FlexMeasures itself, set some variables and tell FlexMeasures to create all tables:
$ pip install flexmeasures
$ export SQLALCHEMY_DATABASE_URI="postgresql://flexmeasures-user:fm-db-passwd@localhost:5432/flexmeasures-db" SECRET_KEY=notsecret LOGGING_LEVEL="INFO" DEBUG=0
$ flexmeasures db upgrade
Note
When installing with pip, on some platforms problems might come up (e.g. macOS, Windows). One reason is that FlexMeasures requires some libraries with lots of C code support (e.g. Numpy). One way out is to use Docker, which uses a prepared Linux image, so it’ll definitely work.
Add some structural data
The data we need for our example is both structural (e.g. a company account, a user, an asset) and numeric (we want market prices to optimize against).
Let’s create the structural data first.
FlexMeasures offers a command to create a toy account with a battery:
$ flexmeasures add toy-account --kind battery
Toy account Toy Account with user toy-user@flexmeasures.io created successfully. You might want to run `flexmeasures show account --id 1`
The sensor for battery charging is <Sensor 2: charging, unit: MW res.: 0:15:00>.
The sensor for Day ahead prices is <Sensor 3: Day ahead prices, unit: EUR/MWh res.: 1:00:00>.
And with that, we’re done with the structural data for this tutorial!
If you want, you can inspect what you created:
$ flexmeasures show account --id 1
=============================
Account Toy Account (ID:1):
=============================
Account has no roles.
All users:
Id Name Email Last Login Roles
---- -------- ------------------------ ------------ -------------
1 toy-user toy-user@flexmeasures.io account-admin
All assets:
Id Name Type Location
---- ------------ -------- -----------------
3 toy-battery battery (52.374, 4.88969)
2 toy-building building (52.374, 4.88969)
1 toy-solar solar (52.374, 4.88969)
$ flexmeasures show asset --id 3
===========================
Asset toy-battery (ID:3):
===========================
Type Location Attributes
------- ----------------- ---------------------
battery (52.374, 4.88969) capacity_in_mw:0.5
min_soc_in_mwh:0.05
max_soc_in_mwh:0.45
All sensors in asset:
Id Name Unit Resolution Timezone Attributes
---- -------- ------ ------------ ---------------- ------------
2 charging MW 15 minutes Europe/Amsterdam
Yes, that is quite a large battery :)
Note
Obviously, you can use the flexmeasures command to create your own, custom account and assets. See CLI Commands. And to create, edit or read asset data via the API, see Version 3.0.
We can also look at the battery asset in the UI of FlexMeasures (in Docker, the FlexMeasures web server already runs, on your PC you can start it with flexmeasures run).
Visit http://localhost:5000/assets (username is “toy-user@flexmeasures.io”, password is “toy-password”) and select “toy-battery”:
Note
You won’t see the map tiles, as we have not configured the MAPBOX_ACCESS_TOKEN. If you have one, you can configure it via flexmeasures.cfg (for Docker, see Configuration and customization).
Add some price data
Now to add price data. First, we’ll create the csv file with prices (EUR/MWh, see the setup for sensor 3 above) for tomorrow.
$ TOMORROW=$(date --date="next day" '+%Y-%m-%d')
$ echo "Hour,Price
$ ${TOMORROW}T00:00:00,10
$ ${TOMORROW}T01:00:00,11
$ ${TOMORROW}T02:00:00,12
$ ${TOMORROW}T03:00:00,15
$ ${TOMORROW}T04:00:00,18
$ ${TOMORROW}T05:00:00,17
$ ${TOMORROW}T06:00:00,10.5
$ ${TOMORROW}T07:00:00,9
$ ${TOMORROW}T08:00:00,9.5
$ ${TOMORROW}T09:00:00,9
$ ${TOMORROW}T10:00:00,8.5
$ ${TOMORROW}T11:00:00,10
$ ${TOMORROW}T12:00:00,8
$ ${TOMORROW}T13:00:00,5
$ ${TOMORROW}T14:00:00,4
$ ${TOMORROW}T15:00:00,4
$ ${TOMORROW}T16:00:00,5.5
$ ${TOMORROW}T17:00:00,8
$ ${TOMORROW}T18:00:00,12
$ ${TOMORROW}T19:00:00,13
$ ${TOMORROW}T20:00:00,14
$ ${TOMORROW}T21:00:00,12.5
$ ${TOMORROW}T22:00:00,10
$ ${TOMORROW}T23:00:00,7" > prices-tomorrow.csv
This is time series data, in FlexMeasures we call “beliefs”. Beliefs can also be sent to FlexMeasures via API or imported from open data hubs like ENTSO-E or OpenWeatherMap. However, in this tutorial we’ll show how you can read data in from a CSV file. Sometimes that’s just what you need :)
$ flexmeasures add beliefs --sensor-id 3 --source toy-user prices-tomorrow.csv
Successfully created beliefs
In FlexMeasures, all beliefs have a data source. Here, we use the username of the user we created earlier. We could also pass a user ID, or the name of a new data source we want to use for CLI scripts.
Note
Attention: We created and imported prices where the times have no time zone component! That happens a lot. FlexMeasures will then interpret them as UTC time. So if you are in Amsterdam time, the start time for the first price, when expressed in your time zone, is actually 2022-03-03 01:00:00+01:00.
Let’s look at the price data we just loaded:
$ flexmeasures show beliefs --sensor-id 3 --start ${TOMORROW}T01:00:00+01:00 --duration PT24H
Beliefs for Sensor 'Day ahead prices' (Id 3).
Data spans a day and starts at 2022-03-03 01:00:00+01:00.
The time resolution (x-axis) is an hour.
┌────────────────────────────────────────────────────────────┐
│ ▗▀▚▖ │ 18EUR/MWh
│ ▞ ▝▌ │
│ ▐ ▚ │
│ ▗▘ ▐ │
│ ▌ ▌ ▖ │
│ ▞ ▚ ▗▄▀▝▄ │
│ ▗▘ ▐ ▗▞▀ ▚ │ 13EUR/MWh
│ ▗▄▘ ▌ ▐▘ ▚ │
│ ▗▞▘ ▚ ▌ ▚ │
│▞▘ ▝▄ ▗ ▐ ▝▖ │
│ ▚▄▄▀▚▄▄ ▞▘▚ ▌ ▝▖ │
│ ▀▀▛ ▚ ▐ ▚ │
│ ▚ ▗▘ ▚│ 8EUR/MWh
│ ▌ ▗▘ ▝│
│ ▝▖ ▞ │
│ ▐▖ ▗▀ │
│ ▝▚▄▄▄▄▘ │
└────────────────────────────────────────────────────────────┘
5 10 15 20
██ Day ahead prices
Again, we can also view these prices in the FlexMeasures UI:
Note
Technically, these prices for tomorrow may be forecasts (depending on whether you are running through this tutorial before or after the day-ahead market’s gate closure). You can also use FlexMeasures to compute forecasts yourself. See Forecasting & scheduling.
Make a schedule
Finally, we can create the schedule, which is the main benefit of FlexMeasures (smart real-time control).
We’ll ask FlexMeasures for a schedule for our charging sensor (Id 2). We also need to specify what to optimise against. Here we pass the Id of our market price sensor (3). To keep it short, we’ll only ask for a 12-hour window starting at 7am. Finally, the scheduler should know what the state of charge of the battery is when the schedule starts (50%) and what its roundtrip efficiency is (90%).
$ flexmeasures add schedule --sensor-id 2 --consumption-price-sensor 3 \
--start ${TOMORROW}T07:00+01:00 --duration PT12H \
--soc-at-start 50% --roundtrip-efficiency 90%
New schedule is stored.
Great. Let’s see what we made:
$ flexmeasures show beliefs --sensor-id 2 --start ${TOMORROW}T07:00:00+01:00 --duration PT12H
Beliefs for Sensor 'charging' (Id 2).
Data spans 12 hours and starts at 2022-03-04 07:00:00+01:00.
The time resolution (x-axis) is 15 minutes.
┌────────────────────────────────────────────────────────────┐
│ ▐ ▐▀▀▌ ▛▀▀│
│ ▞▌ ▞ ▐ ▌ │ 0.4MW
│ ▌▌ ▌ ▐ ▐ │
│ ▗▘▌ ▌ ▐ ▐ │
│ ▐ ▐ ▗▘ ▝▖ ▐ │
│ ▞ ▐ ▐ ▌ ▌ │ 0.2MW
│ ▗▘ ▐ ▐ ▌ ▌ │
│ ▐ ▝▖ ▌ ▚ ▞ │
│▀▘───▀▀▀▀▀▀▀▀▀▀▀▀▀▀▌────▐─────▝▀▀▀▀▀▀▀▀▜─────▐▀▀▀▀▀▀▀▀▀─────│ 0MW
│ ▌ ▞ ▐ ▗▘ │
│ ▚ ▌ ▐ ▐ │
│ ▐ ▗▘ ▝▖ ▌ │ -0.2MW
│ ▐ ▐ ▌ ▌ │
│ ▐ ▐ ▌ ▗▘ │
│ ▌ ▞ ▌ ▐ │
│ ▌ ▌ ▐ ▐ │ -0.4MW
│ ▙▄▄▌ ▐▄▄▞ │
└────────────────────────────────────────────────────────────┘
10 20 30 40
██ charging
Here, negative values denote output from the grid, so that’s when the battery gets charged.
We can also look at the charging schedule in the FlexMeasures UI (reachable via the asset page for the battery):
Recall that we only asked for a 12 hour schedule here. We started our schedule after the high price peak (at 5am) and it also had to end before the second price peak fully realised (at 9pm). Our scheduler didn’t have many opportunities to optimize, but it found some. For instance, it does buy at the lowest price (around 3pm) and sells it off when prices start rising again (around 6pm).
Note
The flexmeasures add schedule command also accepts state-of-charge targets, so the schedule can be more sophisticated. But that is not the point of this tutorial. See flexmeasures add schedule --help.