Low-carbon Expansion Generation Optimization (LEGO) model

LEGO is a mixed integer quadratically constrained optimization problem and has been designed to be a multi-purpose tool, like a Swiss army knife, that can be employed to study many different aspects of the energy sector. Ranging from short-term unit commitment to long-term generation and transmission expansion planning. The underlying modeling philosophies are: modularity and flexibility. LEGO is also composed of thematic modules that can be added or removed from the model easily via data options depending on the scope of the study. You can use this code freely according to the LICENSE file, but please cite our paper [1] if you do.

Setup

1. Install MPI implementation according to your OS (e.g., MPICH, OpenMPI or Microsoft-MPI)
2. Create environment from the 'environment.yml' file
   1. For Pros: Use whatever environment manager you like, e.g., for conda:

      conda env create -f environment.yml
      conda activate LEGO-Pyomo_env

   2. For all others: Use the Conda-Activation-Scripts provided in this repository:
      1. Download Anaconda to manage Python packages: https://www.anaconda.com/download
      2. Choose "Just Me (recommended)" when asked for the installation type
      3. Leave all other options on default
      4. Execute the activation script:
         • Windows: Execute the activate_environment_windows.bat file
         • Unix: Execute the activate_environment_unix.sh file
           • When executing it from terminal, use source activate_environment_unix.sh
      5. In the now opened command line, you can use the activated environment. The working directory will be the current folder of the script
3. Test if it works
   • Run the following command in the now opened terminal:

     python LEGO.py data/example