论文标题
拓扑感知的图形神经网络，用于学习可行和自适应AC-OPF解决方案
Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions
论文作者
论文摘要
解决最佳功率流（OPF）问题是确保实时电网操作中系统效率和可靠性的基本任务。我们开发了一种新的拓扑信息图神经网络（GNN）方法，用于预测实时AC-OPF问题的最佳解决方案。为了将网格拓扑结合到NN模型中，拟议的GNN-FOPF框架创新地利用了位置边际价格和电压幅度的当地属性。此外，我们为一般OPF学习开发了一种物理感知（AC-）流动可行性正则化方法。我们提出的设计的优点包括降低模型的复杂性，提高的通用性和可行性保证。通过在网格拓扑意义上提供对图形子空间稳定性的分析理解，我们表明拟议的GNN可以通过有效的重新训练策略迅速适应不同的网格拓扑。对不同大小的各种测试系统的数值测试已验证了我们提出的基于GNN的学习框架的预测准确性，提高流量可行性以及拓扑适应性能力。
Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topology-informed graph neural network (GNN) approach for predicting the optimal solutions of real-time ac-OPF problem. To incorporate grid topology to the NN model, the proposed GNN-for-OPF framework innovatively exploits the locality property of locational marginal prices and voltage magnitude. Furthermore, we develop a physics-aware (ac-)flow feasibility regularization approach for general OPF learning. The advantages of our proposed designs include reduced model complexity, improved generalizability and feasibility guarantees. By providing the analytical understanding on the graph subspace stability under grid topology contingency, we show the proposed GNN can quickly adapt to varying grid topology by an efficient re-training strategy. Numerical tests on various test systems of different sizes have validated the prediction accuracy, improved flow feasibility, and topology adaptivity capability of our proposed GNN-based learning framework.
