  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.

Machine Learning;Systems and Control;Systems and Control;Optimization and Control