Risk‐adjustable stochastic schedule based on Sobol augmented Latin hypercube sampling considering correlation of wind power uncertainties

The risks associated with wind power forecast (WF) deviations are of paramount importance to many system participants (PSPs). However, traditional sampling approaches computationally prohibitive model these deviations. Additionally, setting a risk level for satisfying different PSPs receives little attention. This paper constructs risk-adjustable stochastic day-ahead scheduling (RSDS) balance the requirements PSPs, and proposes Sobol-augmented Latin Hypercube Sampling (SaLHS) approach improve efficiency scenario generation process in RSDS. At first, SaLHS D-vine copula combined generate WF error scenarios RSDS considering correlations farms. Specifically, improves uniformity removes correlation random samples. Then, Glue-VaR-based adequacy index (GVGAI) is proposed measure operational risk. By adjusting parameters GVGAI, desirable can be obtained PSPs. Furthermore, multi-objective constructed cost GVGAI. last, an entropy-Weighted Aggregated Sum Product Assessment method find best compromise solution based on Pareto front by ε-constraint method. A modified IEEE-RTS used validate effectiveness via numerical simulations.