Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method

Advanced stochastic programming-based power system operations planning requires wind power forecast in the form of scenarios. Generating wind power scenarios reflecting the intertemporal dependence over the forecast horizon is paramount for multi-period operations planning routines. Yet, less attention has been given to such time-coupled (temporal) wind power scenario generation (SG). Recent literature shows that copula-based SG methods are suitable for typical operations planning routines like economic dispatch and unit commitment. This work proposes a new and efficient data-driven temporal wind power SG framework using regular vine copula with variance reduction. The proposed SG puts forth two contributions to improve the quality of the temporal scenarios. The first contribution is to introduce the regular vine copula to model the temporal dependence structure of the wind power forecast error, which is shown to fit the real-world data better than the existing copula models. The second contribution is to propose a uniform design-based vine copula sampling algorithm, which benefits the downstream operations planning applications with improved convergence and accuracy of the solutions. A detailed multivariate scenario evaluation using multiple metrics shows that the proposed SG improves the quality of the temporal scenarios compared to the existing benchmarks. The Diebold-Mariano statistical test also verifies the significant improvement in the quality of the wind power scenarios.