论文标题
在不确定性下，分配系统的网络认知时间耦合的综合均匀性
Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties
论文作者
论文摘要
在分配馈线中增加分布式能源（DER）的集成在分配传输互连时提供了前所未有的灵活性。为了利用这种灵活性并利用骨料的能力潜力，需要有效地表征可行的变电站功率注入轨迹。本文提供了变电站可行的功率注入轨迹集的椭圆形内部近似，以便在集合中的任何点，都存在DER的可行分解策略，以实现任何负载不确定性实现。该问题被提出为在不确定性下在柔韧性区域内找到可靠的最大体积椭圆形。尽管该问题即使在确定性的情况下也是NP-HARD，但本文基于最佳的第二阶段策略，得出了所得适应性强大优化问题的新近似值。所提出的方法比现有的柔韧性区域近似公式所产生的保守柔韧性表征较低。在现实的分配馈线上证明了所提出方法的功效。
Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.
