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Getting started¤

Prerequisites¤

  1. Python 3.11 or newer installed on your system.
  2. Access to a microgrid system supported by the frequenz.sdk or you can use the sandbox.
  3. Basic knowledge of microgrid concepts.
  4. Familiarity with Channels.
  5. Install the Frequenz SDK

Create a project¤

Create a Python file¤

You can start by simply creating a Python script (e.g., pv_optimization.py) using your favorite text editor.

Use Frequenz Repository Configuration¤

As an alternative and specially for larger projects, it's recommended to set up the project using the Frequenz Repository Configuration.

Import necessary modules¤

You can now open the app's main file and start adding content. Begin by importing the necessary libraries.

import asyncio

from datetime import timedelta
from frequenz.sdk import microgrid
from frequenz.sdk.actor import ResamplerConfig

Create the application skeleton¤

The main logic of your application will run within an async function. Let's create a skeleton that contains all the necessary code to initialize a microgrid.

async def run() -> None:
    # This points to the default Frequenz microgrid sandbox
    microgrid_host = "microgrid.sandbox.api.frequenz.io"
    microgrid_port = 62060

    # Initialize the microgrid
    await microgrid.initialize(
        microgrid_host,
        microgrid_port,
        ResamplerConfig(resampling_period=timedelta(seconds=1)),
    )

    # Define your application logic here
    # ...

Define the main() function¤

Create a main() function that will set up and run the run() function using asyncio.

def main() -> None:
    asyncio.run(run())

if __name__ == "__main__":
    main()

Implement the application logic¤

Inside the run() function, implement the core logic of your application. This will include creating receivers for data streams, processing the data, making decisions, and eventually sending control messages to the microgrid system. We will cover more details in the following tutorials. For now, let's simply read the power measurements from the microgrid's grid meter and print them on the screen. The grid meter is a meter that is directly connected to the grid connection point.

async def run() -> None:
    # This points to the default Frequenz microgrid sandbox
    ...

    # Define your application logic here
    grid_meter = microgrid.grid().power.new_receiver()

    async for power in grid_meter:
        print(power.value)

Putting it all together¤

Here is the full version of your first Frequenz SDK application.

import asyncio

from datetime import timedelta
from frequenz.sdk import microgrid
from frequenz.sdk.actor import ResamplerConfig

async def run() -> None:
    # This points to the default Frequenz microgrid sandbox
    microgrid_host = "microgrid.sandbox.api.frequenz.io"
    microgrid_port = 62060

    # Initialize the microgrid
    await microgrid.initialize(
        microgrid_host,
        microgrid_port,
        ResamplerConfig(resampling_period=timedelta(seconds=1)),
    )

    # Define your application logic here
    grid_meter = microgrid.grid().power.new_receiver()

    async for power in grid_meter:
        print(power.value)

def main() -> None:
    asyncio.run(run())

if __name__ == "__main__":
    main()

Run your application¤

You're now ready to run your application. When working on an existing microgrid, make sure to update the microgrid_host and microgrid_port variables before running the script.

# Example usage
python pv_optimization.py