Awesome

2021-01-11: I archive this personal fork, as this model is being maintained in the upstream repo tum-ens/urbs. I only don't delete it for its (technically outdated, but illustrative) examples 1house and 1node that demonstrate intermediate to advanced use of urbs for performing small-scale case studies.

urbs

urbs is a linear programming optimisation model for capacity expansion planning and unit commitment for distributed energy systems. Its name, latin for city, stems from its origin as a model for optimisation for urban energy systems. Since then, it has been adapted to multiple scales from neighbourhoods to continents.

Features

• urbs is a linear programming model for multi-commodity energy systems with a focus on optimal storage sizing and use.
• It finds the minimum cost energy system to satisfy given demand timeseries for possibly multiple commodities (e.g. electricity).
• By default, operates on hourly-spaced timesteps (configurable).
• Thanks to Pandas, complex data analysis is easy.
• The model itself is quite small thanks to relying on the Pyomo
• The small codebase includes reporting and plotting functionality.

Screenshots

<a href="doc/img/plot.png"><img src="doc/img/plot.png" alt="Timeseries plot of 8 days of electricity generation in vertex 'North' in scenario_all_together in hourly resolution: Hydro and biomass provide flat base load of about 50% to cover the daily fluctuating load, while large share of wind and small part photovoltaic generation cover the rest, supported by a day-night storage." style="width:400px"></a>

<a href="doc/img/comparison.png"><img src="doc/img/comparison.png" alt="Bar chart of cumulated annual electricity generation costs for all 5 scenarios defined in runme.py." style="width:400px"></a>

Continue in tum-ens/urbs for the full and up-to-date README.md.