论文标题
DEEPOPF+：DC最佳功率流的深神经网络方法，以确保可行性
DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
论文作者
论文摘要
最佳功率流（OPF）问题的深度神经网络（DNNS）最近受到了很大的关注。这些方法的关键挑战在于确保预测解决方案对物理系统约束的可行性。由于固有的近似错误，DNN预测的解决方案可能违反了操作约束，例如传输线容量，从而限制了其在实践中的适用性。为了应对这一挑战，我们将DEEPOPF+作为DNN方法基于所谓的“预防”框架。具体而言，我们校准了DNN训练中使用的生成和传输线极限，从而预测近似误差并确保所得的预测溶液仍然可行。从理论上讲，我们表征了确保通用可行性所需的校准幅度。我们的DEEPOPF+方法改进了现有的基于DNN的方案，因为它可以确保可行性并在轻载和重载方案中达到一致的速度性能。一系列测试实例的详细仿真结果表明，所提出的DEEPOPF+产生了100％可行的解决方案，并且较小的最佳损失。同时，与最先进的求解器相比，它达到了两个数量级的计算加速。
Deep Neural Networks (DNNs) approaches for the Optimal Power Flow (OPF) problem received considerable attention recently. A key challenge of these approaches lies in ensuring the feasibility of the predicted solutions to physical system constraints. Due to the inherent approximation errors, the solutions predicted by DNNs may violate the operating constraints, e.g., the transmission line capacities, limiting their applicability in practice. To address this challenge, we develop DeepOPF+ as a DNN approach based on the so-called "preventive" framework. Specifically, we calibrate the generation and transmission line limits used in the DNN training, thereby anticipating approximation errors and ensuring that the resulting predicted solutions remain feasible. We theoretically characterize the calibration magnitude necessary for ensuring universal feasibility. Our DeepOPF+ approach improves over existing DNN-based schemes in that it ensures feasibility and achieves a consistent speed up performance in both light-load and heavy-load regimes. Detailed simulation results on a range of test instances show that the proposed DeepOPF+ generates 100% feasible solutions with minor optimality loss. Meanwhile, it achieves a computational speedup of two orders of magnitude compared to state-of-the-art solvers.
