一种考虑风电出力依赖性不确定性的分布鲁棒机会约束优化方法用于含N-1安全约束的最优潮流问题

Abstract The integration of wind power generation introduces uncertainty into transmission line power , potentially increasing N -1 failure risks. This research proposes an N -1 line security-constrained optimal power flow (SCOPF) to mitigate such risks by considering wind power dependent uncertainty. Initially, a modified ambiguity set that integrates copula constraints to capture dependencies among wind farms is established, reducing conservatism. Then, the chance constraints (CC) representing security constraints (SC) are established through distributionally robust optimization , and the tractable forms of the proposed model are derived. Subsequently, dependence sensitivity indexes are proposed to identify components significantly affected by dependent uncertainty, and dependence-sensitivity-based ambiguity sets based on the dependence sensitivity indexes for the CC are established to reduce the solution complexity. Benders decomposition is then utilized to enable parallel processing and reduce computational time. Finally, the efficacy of the proposed strategy is demonstrated using IEEE 24-bus and IEEE 118-bus systems. Experimental results indicate that compared to SCOPF based on stochastic optimization or conventional distributionally robust optimization, the proposed model reduces cost while maintaining robustness, with significant reductions in computational burden attributed to dependence-sensitivity-based ambiguity sets and Benders decomposition.