DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
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DeepOPF：一种用于直流最佳潮流的深度神经网络方法，以确保可行性
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发表时间：
2020
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影响因子：
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通讯作者：
S. Low
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文献类型：
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作者：
Tianyu Zhao;Xiang Pan;Minghua Chen;Andreas Venzke;S. Low
最近，深度神经网络方法用于最优潮流（OPF）问题受到了相当大的关注。这些方法的一个关键挑战在于确保物理系统约束的预测解决方案的可行性。由于固有的近似误差，由深度神经网络（DNN）预测的解决方案可能违反操作约束，例如，传输线容量，限制了它们在实践中的适用性。为了应对这一挑战，我们开发了DeepOPF+作为基于所谓的“预防性”框架的DNN方法。具体来说，我们校准DNN训练中使用的发电和输电线路限制，从而预测近似误差，并确保得到的预测解决方案仍然可行。我们从理论上描述了确保普遍可行性所需的校准幅度。我们的DeepOPF+方法比现有的基于DNN的方案有所改进，因为它确保了可行性，并在轻负载和重负载情况下实现了一致的加速性能。一系列测试实例的详细仿真结果表明，所提出的DeepOPF+生成100%可行的解决方案，具有较小的最优性损失。同时，它实现了两个数量级的计算速度比国家的最先进的求解器。
Deep Neural Networks 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 Deep Neural Networks (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.