Electricity Systems Optimisation with capacity eXpansion and Endogenous technology Learning (ESO-XEL)

The Electricity Systems Optimisation (ESO) framework contains a suite of power system capacity expansion and unit commitment models at different levels of spatial and temporal resolution and modelling complexity. Available for download is the single-node model with long-term capacity expansion from 2015 to 2050 in 5 yearly time steps and at hourly discretisation including endogenous technology cost learning (ESO-XEL). A time compression technique based on k-means clustering is applied to reduce the annual hourly time sets (for onshore wind, offshore wind, solar, power demand, electricity import price) to 11 clusters with 24 hours each. The clustered time series are available within the input data sheet. More information can be found here: https://doi.org/10.1016/j.compchemeng.2017.05.012.

The perfect foresight model with all necessary files is available in folder ESO-XEL_Zenodo.zip. By including/excluding the highlighted constraints in the model formulation one can specify the technologies for which learning is taken into account. The input file provided refers to the power system of Great Britain. The model is written in GAMS 24.8.3 and all instances are solved with CPLEX 12.3. For academics the GAMS software and the IBM CPLEX solver is available free of charge. If you use the ESO-XEL model, please cite the following paper: https://doi.org/10.1016/j.apenergy.2017.07.075

For any questions please contact: c.heuberger14@imperial.ac.uk or niall@imperial.ac.uk