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.

中文翻译：

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不确定条件下配电系统的网络认知时间耦合聚合灵活性

配电馈线内分布式能源（DER）的日益集成为配电与输电互连提供了前所未有的灵活性。为了利用这种灵活性并利用总DER的容量潜力，需要有效地描述可行的变电站电力注入轨迹。本文提供了变电站内可行功率注入轨迹集合的椭圆内部近似，以便对于该集合中的任何点，对于任何负载不确定性都存在可行的DER分解策略。该问题被公式化为在不确定性下在柔性区域内找到鲁棒的最大体积椭球的问题之一。尽管即使在确定性情况下，问题也是NP难题，本文基于最优的第二阶段策略，得出了所得的自适应鲁棒优化问题的新颖近似值。与现有的柔韧性区域近似公式相比，所提出的方法产生的保守柔韧性特征较少。所提出的方法的有效性已在实际的分配器上得到了证明。