论文标题
使用图神经网络无监督的最佳功率流
Unsupervised Optimal Power Flow Using Graph Neural Networks
论文作者
论文摘要
最佳功率流（OPF）是一个关键的优化问题，它将电源分配给发电机以满足需求，以最低的成本满足需求。在一般情况下，确切解决此问题在计算上是不可行的。在这项工作中，我们建议利用图形信号处理和机器学习。更具体地说，我们使用图形神经网络来学习所需功率与相应分配之间的非线性参数化。我们以无监督的方式学习解决方案，直接最大程度地减少成本。为了考虑到网格的电气约束，我们提出了一种新颖的屏障方法，该方法是可区分的，并且在最初不可行的点上起作用。我们通过模拟表明，在这种无监督的学习上下文中使用GNN会导致与标准求解器相当的解决方案，同时在计算上有效，并且大多数时候避免了限制违规。
Optimal power flow (OPF) is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost. Solving this problem exactly is computationally infeasible in the general case. In this work, we propose to leverage graph signal processing and machine learning. More specifically, we use a graph neural network to learn a nonlinear parametrization between the power demanded and the corresponding allocation. We learn the solution in an unsupervised manner, minimizing the cost directly. In order to take into account the electrical constraints of the grid, we propose a novel barrier method that is differentiable and works on initially infeasible points. We show through simulations that the use of GNNs in this unsupervised learning context leads to solutions comparable to standard solvers while being computationally efficient and avoiding constraint violations most of the time.
