mosaik-emissions 1.0.0

This simulator implements CO₂-emissions calculations for a given mosaik scenario and power-unit configuration.

Note: This simulator is still a work in progress. Not all desired attributes are implemented yet. If you need support for a specific attribute, please open an issue here.

Installation

pip install mosaik-emissions

Methodology and Usage

CO₂ emissions are dynamically aggregated based on predefined power-unit characteristics. The following attributes are currently implemented:

• P[MW] – power (input/output)
• E[tCO₂eq] – total emissions (output)
• I[tCO₂eq/MWh] – average carbon intensity (output)

The basic calculation converts input power into estimated total carbon emissions by multiplying it with the designated carbon-intensity value (emission factor), typically expressed in gCO₂eq/kWh.

If multiple inputs assigned with different carbon-intensity values, the resulting intensity is computed as a power-weighted average. While the average carbon intensity is always reported in tCO₂eq/MWh (numerically equivalent to gCO₂eq/kWh), total emissions take the simulation step size into account.

The emission factor / carbon intensity is defined as part of the model parameters when simulator entities are created, as commonly done in a mosaik scenario.

The available parameters depend on the selected data source. Several data sources are integrated (see the table below). For each data source, identified by an id, multiple filtering parameters may be used, such as reference year, country, state, fuel type, etc.

Parameters are applied from the most specific to the least specific; the exact priority corresponds to the reversed column order of the respective data file (see the files in the data directory).

Usage example

    emission_config = {
        "id": "CO2MAP",
        "country_code": "DEU",
        "state_code": "BY",
        "scope": "cons",
    }
    emission = world.start("EmissionSim").Emission(**emission_config)
    const = world.start("Const").Const(value=5)
    world.connect(const, emission, ("value", "P[MW]"))
    world.connect(emission, csv_writer, "P[MW]")
    world.connect(emission, csv_writer, "E[tCO₂eq]")
    world.connect(emission, csv_writer, "I[tCO₂eq/MWh]")
    world.run(until=1)

If the selected data source provides suitable (that matches the parameters) historical time series, the simulator will use them. If no appropriate time series are available or if manual configuration is preferred one may directly define the emission factor for a specific entity or provide a callable.

Manual configuration examples

# Manual emission factor
em.create(1, "Emission", **{
    "emission_factor": 8.0,
})

# Manual method
def get_value(**kwargs):
    print("print from a method call:", kwargs)
    return 100.0

em.create(1, "Emission", **{
    "method": get_value,
})

The start parameter sets the starting time for the time series, but the simulator will use the data point nearest to the reference year. If only one data point is available, it will be the sole result. If no reference year is specified, the year is taken from the start parameter. The default value of start is "2020-01-01 00:00:00".

A simple multiplier parameter is also available (default: 1.0) and is applied to the resulting carbon-intensity value.

Custom Data

Data structure

Data files can be extended or replaced, and additional sources may be added directly to the data directory as .csv or zip. To be automatically detected and merged with the internal database, each data file must follow the required column structure:

datetime;carbon_intensity[gCO₂eq/kWh];id;year
2020-01-01 00:00:00+00:00;651.0;CO2MAP;2020
2020-01-01 01:00:00+00:00;679.0;CO2MAP;2020

• datetime – timeseries index
• carbon_intensity[gCO₂eq/kWh] – intensity values (float)
• id – data source identifier (str)
• year – reference year (int)

Any additional columns may follow. As long as the first three columns match this structure, the simulator will automatically read the file and incorporate it into the available data sources.

Other columns, beyond the required first three, are interpreted as filtering parameters. These parameters are applied in reversed column order (i.e., the rightmost column has the highest priority). This allows you to filter data by fields such as fuel type, region, year, or any other metadata you include.

If you introduce parameter columns that do not match the existing ones, you will also need to update the mosaik meta structure in emission_simulator.py accordingly so that the simulator can recognize these new parameters.

Other Usage

If you only want to access carbon-intensity data or test whether a configuration and custom database work correctly, you can use the get_stored_values function. By default, it uses parameters that correspond directly to the data columns.

em = Simulator()
em.init()
values = em.get_stored_values(**{
    "id": "2006IPCC",
    "fuel": "Natural gas",
})
print(values)
assert str(values["intensity"].index[0]) == "2020-01-01 00:00:00+00:00"

Citation

APA-like format

Valko, D., Alsharif, S., & Tolk, D. (2024). An Open-Source Carbon Emissions Simulator for Smart Grid Co-Simulation Scenarios. European Simulation and Modelling Conference 2024 (ESM 2024), San Sebastian, Spain. https://doi.org/10.5281/zenodo.13984401

BibTeX format

@inproceedings{ValkoAlsharifTolk2024,
title={An Open-Source Carbon Emissions Simulator for Smart Grid Co-Simulation Scenarios},
author={Danila Valko and Sharaf Alsharif and Deborah Tolk},
year={2024},
pages={355-360},
publisher={EUROSIS-ETI},
booktitle={European Simulation and Modelling Conference 2024 (ESM 2024)},
doi={10.5281/zenodo.13984401},
}

Sources and References

• Ember - Yearly Electricity Data (2023); Ember - European Electricity Review (2022); Energy Institute - Statistical Review of World Energy (2023) – with major processing by Our World in Data. “Carbon intensity of electricity generation” [dataset]. Ember, “Yearly Electricity Data”; Ember, “European Electricity Review”; Energy Institute, “Statistical Review of World Energy” [original data]. Retrieved February 27, 2024 from https://ourworldindata.org/grapher/carbon-intensity-electricity

• Hourly consumption-based CO2 emissions intensity in Germany / co2map.de, INATECH, University of Freiburg. Retrieved February 27, 2024 from https://api.co2map.de/docs