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

Abstract

Abstract-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.

Reliability constraint stochastic UC by considering the correlation of random variables with Copula theory

“…These scenarios contain generation and transmission elements outage, wind speed variations and possible system demand fluctuations. Components outage, load variation and intermittent wind feature modelled as operational scenarios with different approaches [15]. In this paper, for obtaining random numbers to generate scenario tree, we used Copula theory that evaluates historical data and brings new kind of data for stochastic UC problem.…”

A new methodology for solving the unit commitment in insular grids including uncertainty of renewable energies

“…In [8], a research effort was conducted to determine the appropriate number of scenarios using as a case study the UK power system, concluding that not necessarily a high amount of scenarios is required, specifically in those cases where the quality of wind power prediction was reasonably, producing a reduction in the amount of generation required for compensating the inaccuracy at the end. With the main goal to increase the number of scenarios in order to improve the model of wind power uncertainty used on the stochastic UC problem, in this paper a methodology able to manage a large amount of scenarios in a reduced computational time is proposed, obtaining a solution with a reasonable quality in terms of generation cost.…”