Solving the optimal power flow (OPF) problem in real-time electricity market improves the efficiency and reliability in the integration of low-carbon energy resources into the power grids. To address the scalability and adaptivity issues of existing end-to-end OPF learning solutions, we propose a new graph neural network (GNN) framework for predicting the electricity market prices from solving OPFs. The proposed GNN-for-OPF framework innovatively exploits the locality property of prices and introduces physics-aware regularization, while attaining reduced model complexity and fast adaptivity to varying grid topology. Numerical tests have validated the learning efficiency and adaptivity improvements of our proposed method over existing approaches.

中文翻译：

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用于学习电力市场实时价格的图神经网络

解决实时电力市场中的最优潮流（OPF）问题，提高了低碳能源并网的效率和可靠性。为了解决现有端到端 OPF 学习解决方案的可扩展性和适应性问题，我们提出了一种新的图神经网络 (GNN) 框架，用于通过解决 OPF 来预测电力市场价格。所提出的 GNN-for-OPF 框架创新地利用了价格的局部性并引入了物理感知正则化，同时降低了模型复杂性并快速适应不同的网格拓扑。数值测试验证了我们提出的方法相对于现有方法的学习效率和适应性改进。