Electricity Systems Optimisation with capacity eXpansion and Endogenous technology Learning (ESO-XEL): Myopic Foresight Option

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. A time compression technique based on k-means clustering is applied to reduce the annual hourly time sets for onshore wind, offshore wind, solar (from https://www.renewables.ninja/), power demand, and 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 and here https://doi.org/10.1016/j.apenergy.2017.07.075.

Available for download is:

1. the single-node model with perfect foresight long-term capacity expansion from 2015 to 2050 in 5 yearly time steps and at hourly discretisation including endogenous technology cost learning (ESO-XEL) under DOI 10.5281/zenodo.1048943.
2. the single-node model with myopic long-term capacity expansion from 2015 to 2050 in 5 yearly time steps and at hourly discretisation including endogenous technology cost learning (ESO-XEL myopic) in this upload.

The myopic foresight model with all necessary files is contained in folder ESO-XEL_myopic_Zenodo.zip. The myopic model is solved via a rolling-horizon approach and allows for iterative update of input parameters enabling, e.g., the representation of disruptive events. The input file provided refers to the power system of Great Britain. By including/excluding the highlighted constraints in the model formulation one can specify the technologies for which learning is taken into account. 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 and niall@imperial.ac.uk