Considering the separate management of transmission networks (TNs) and distribution networks (DNs), this article proposes a chance-constrained optimal power flow (CCOPF) formulation for integrated transmission and distribution (I-T&D) networks and its solution algorithm with limited information interaction. The uncertainties of loads and renewable generations are considered in the formulation, which guarantees that generations, power flows, and voltage magnitudes in both TNs and DNs remain within their bounds with a predefined probability. A double-iterative solution algorithm is proposed to solve CCOPF, of which the inner-iteration is applied to solve a deterministic OPF of I-T&D networks while the outer-iteration is to repeatedly update uncertain margins around a forecasted solution to converge finally. Particularly, the heterogeneous decomposition algorithm is applied to solve the deterministic OPF, and an I-T&D-power-flow-based two-point estimation method is proposed to calculate uncertainty margins. The overall solution algorithm is realized based on boundary information exchange between TNs and DNs, where the data and model privacy of TNs and DNs are well-preserved. Numerical experiments demonstrate the accuracy and efficiency of the proposed solution algorithm.

中文翻译：

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信息交互作用受限的输配电网机会约束最优潮流

考虑到输电网络（TNs）和配电网络（DNs）的单独管理，本文提出了机会约束的最优输电（IT＆D）网络的最优潮流（CCOPF）公式及其在信息交互受限的情况下的求解算法。公式中考虑了负载和可再生能源发电的不确定性，这确保了TN和DN中的发电，潮流和电压幅值均保持在预定范围之内。提出了一种双重迭代求解算法来求解CCOPF，其中内部迭代法用于求解IT＆D网络的确定性OPF，而外部迭代法则是在预测的解决方案周围反复更新不确定性余量以最终收敛。尤其，应用异构分解算法求解确定性OPF，提出了一种基于IT＆D潮流的两点估计方法来计算不确定性裕度。整体解决方案算法是基于TN和DN之间的边界信息交换实现的，其中TN和DN的数据和模型保密性得到了很好的保留。数值实验证明了该算法的准确性和有效性。