论文标题
使用图神经网络降低最佳功率流
Reduced Optimal Power Flow Using Graph Neural Network
论文作者
论文摘要
OPF问题是为电力系统操作而制定和解决的，尤其是用于实时确定生成调度点。对于具有大量变量和约束的大型功率系统网络，以及时找到实时OPF的最佳解决方案需要大量的计算能力。本文提出了一种使用图神经网络（GNN）减少原始OPF问题中约束数量的新方法。 GNN是一种创新的机器学习模型，它利用从节点，边缘和网络拓扑的功能来最大程度地提高其性能。在本文中，我们提出了一个GNN模型，以预测哪种线将大量加载或充满给定的负载曲线和发电能力。仅在OPF问题中监视这些关键行，从而造成降低的OPF（ROPF）问题。预计拟议的ROPF模型可节省计算时间。还对GNN模型的预测进行了全面分析。结论是，GNN在ROPF中的应用能够减少计算时间，同时保留解决方案质量。
OPF problems are formulated and solved for power system operations, especially for determining generation dispatch points in real-time. For large and complex power system networks with large numbers of variables and constraints, finding the optimal solution for real-time OPF in a timely manner requires a massive amount of computing power. This paper presents a new method to reduce the number of constraints in the original OPF problem using a graph neural network (GNN). GNN is an innovative machine learning model that utilizes features from nodes, edges, and network topology to maximize its performance. In this paper, we proposed a GNN model to predict which lines would be heavily loaded or congested with given load profiles and generation capacities. Only these critical lines will be monitored in an OPF problem, creating a reduced OPF (ROPF) problem. Significant saving in computing time is expected from the proposed ROPF model. A comprehensive analysis of predictions from the GNN model was also made. It is concluded that the application of GNN for ROPF is able to reduce computing time while retaining solution quality.
