Scenario selection of wind forecast errors for stochastic unit commitment: A UK case study

(accepted) Integration of renewable generation is crucial in future generation mix of power systems as many are the benefits that it offers. At certain penetration levels, latent risks could materialize as a result of not adequately managing the additional variability, therefore this situation prompts the necessity of enhancing traditional tools to operate power systems. In this paper a sensitivity analysis is conducted to assess key variables that improve the performance of Stochastic Unit Commitment (SUC). Focus is given to the right number of scenarios that sufficiently capture the variability of wind power forecast errors, in which a trade-off between quality of solution and computation time is considered. The proposed methodology is tested with real data from the Balancing Mechanism Reporting System aiming to represent the UK Power System.