electricitymap

A real-time visualisation of the CO2 emissions of electricity generation

2 years after

electricitymap Slack Status

A real-time visualisation of the Greenhouse Gas (in terms of CO2 equivalent) footprint of electricity generation built with d3.js, optimized for Google Chrome. Try it out at http://www.electricitymap.org.

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You can contribute by correcting data sources, translating the map or by writing a parser to add a new country on the map. See the contributing section. You can also submit ideas, feature requests or bugs on the issues page.

Data sources

Carbon intensity calcuation and data source

The carbon intensity of each country is measured from the perspective of a consumer. It represents the greenhouse gas footprint of 1 kWh consumed inside a given country. The footprint is measured in gCO2eq (grams CO2 equivalent), meaning each greenhouse gas is converted to its CO2 equivalent in terms of global warming potential over 100 year (for instance, 1 gram of methane emitted has the same global warming impact during 100 years as ~20 grams of CO2 over the same period).

The carbon intensity of each type of power plant takes into account emissions arising from the whole lifecyle of the plant (construction, fuel production, operational emissions, and decomissioning). Carbon-intensity factors used in the map are detailed in co2eq-parameters.js. These numbers come from the following scientific peer reviewed litterature:

  • IPCC 2014 Assessment Report is used as reference in most instances (see a summary in the wikipedia entry)

Country-specific carbon-intensity factors:

Each country has a CO2 mass flow that depends on neighboring countries. In order to determine the carbon footprint of each country, the set of coupled CO2 mass flow balance equations of each countries must be solved simultaneously. This is done by solving the linear system of equations defining the network of GHG exchanges. Take a look at this notebook for a deeper explanation.

Real-time electricity data sources

Real-time electricity data is obtained using parsers

Production capacity data sources

Production capacities are centralized in the capacities.json file.

Electricity prices (day-ahead) data sources

Real-time weather data sources

We use the US National Weather Service's Global Forecast System (GFS)'s GFS 0.25 Degree Hourly data. Forecasts are made every 6 hours, with a 1 hour time step. The values extracted are wind speed and direction at 10m altitude, and ground solar irradiance (DSWRF - Downward Short-Wave Radiation Flux), which takes into account cloud coverage. In order to obtain an estimate of those values at current time, an interpolation is made between two forecasts (the one at the beginning of the hour, and the one at the end of the hour).

Topology data

We use the Natural Earth Data Cultural Vectors country subdivisions (map admin subunits).

Contribute

Want to help? Join us on slack at http://slack.tmrow.co. In the meantime, here's some things you can do:

  • check out the issues
  • add a new country by writing a parser
  • add a new translation of the map
  • optimise the code, correct inaccuracies...

You can also see a list of missing informations displayed as warnings in the developer console, or question marks in the country panel:

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Getting started

To get started, clone or fork the repository, and install Docker.

The frontend will need compiling. In order to do this, open a terminal and run

docker-compose run --rm web npm run watch

This will watch over source file changes, and recompile if needed.

Now that the frontend is compiled, you can run the application (which will use our existing backend to pull data), by running the following command in a new terminal:

docker-compose up --build

Head over to http://localhost:8000/ and you should see the map!

Once you're done doing your changes, submit a pull request to get them integrated into the production version.

Troubleshooting

  • ERROR: for X Cannot create container for service X: Invalid bind mount spec "<path>": Invalid volume specification: '<volume spec>'. If you get this error after running docker-compose up on Windows, you should tell docker-compose to properly understand Windows paths by setting the environment variable COMPOSE_CONVERT_WINDOWS_PATHS to 0 by running setx COMPOSE_CONVERT_WINDOWS_PATHS 0. You will also need a recent version of docker-compose. We have successfully seen this fix work with v1.13.0-rc4. More info here: https://github.com/docker/compose/issues/4274.

  • No website found at http://localhost:8000: This can happen if you're running Docker in a virtual machine. Find out docker's IP using docker-machine ip default, and replace localhost by your Docker IP when connecting.