A robust reliability evaluation model with sequential acceleration method for power systems considering renewable energy temporal-spatial correlation

As the penetration of renewable energy increases, it becomes critical to evaluate the power system reliability, while considering the correlations among renewable energy sources (RESs) and their uncertainties. In this paper, the robust reliability evaluation model based on the minimum volume enclosing ellipsoid (MVEE) algorithm is established, and the sequential acceleration method is proposed to improve the convergence. First, the robust reliability evaluation model is built, where the robust state analysis is performed under each sampling. Second, the sequential Cross-Entropy-Latin Hypercube Sampling (CE-LHS) acceleration method is presented, which first obtains the optimal probability distribution of system parameters, and then forces the sampling values to be evenly distributed to overcome the truncated tail effect. Correspondingly, the reliability indices are improved. Third, for state analysis, the robust multi-period optimal load shedding (RMPOLS) model is established, where the dynamic performance of energy storage and system ramping rate constraints are described in detail. Therefore, the periods are strongly coupled, and the impact of RES uncertainties is carefully considered. Besides, to address the temporal-spatial correlation of RESs, the MVEE algorithm is introduced to generate the convex hull of RESs scenarios. Therefore, the RMPOLS model can be transformed into a second-order cone form. Finally, to further reduce the computational complexity, the branch flow constraints scanning strategy is proposed, which can quickly remove inactive constraints in advance. Numerical results prove that the MVEE-based robust reliability evaluation model considers the temporal-spatial correlation of RES and thus improves the system reliability by almost 30 %. Besides, branch flow constraint scanning can eliminate more than 65 % of the SOC constraints. Moreover, CE-LHS can improve the convergence by about 8 times compared with Monte Carlo Sampling (MCS) while the error is less than 1 %.