A toolbox for the simulation and economic evaluation of self-consumption and solar home battery systems.

The code is written in Python (v2.7). The data is saved as pandas dataframes within pickle or hdf files.

The toolbox computes self-consumption indicators for different PV and household demand profiles. Some profiles are historical measured profiles, some others are reconstructed from typical daily profiles. Due to the diversity of consumption patterns, the results can vary significantly from one household to the other.

Sylvain Quoilin Researcher at the European Commission (JRC). E-mail: [email protected] - Twitter: @squoilin

• ipython
• Pandas
• Numpy
• Scipy
• Pickle
• Matplotlib

This is a free software licensed under the “European Union Public Licence" EUPL v1.1. It can be redistributed and/or modified under the terms of this license.

To get familiar with the different function of the toolbox, it is recommended to read the description and run the files with the suffix "Example" in the "SC_ToolBox" folder.

The database of synthetic household profiles is stored in the hdf format in the "Synthetic Household Profiles" folder.

S. Quoilin, K. Kavvadias, A. Mercier, I. Pappone, A. Zucker, Quantifying self-consumption linked to solar home battery systems: statistical analysis and economic assessment, Applied Energy, 2016