Graph Neural Networks for Learning Real-Time Prices in Electricity Market
This work addresses the problem of integrating low-carbon energy resources into power grids for electricity market operators, but it appears incremental as it builds on existing end-to-end OPF learning solutions.
The paper tackles the scalability and adaptivity issues in learning optimal power flow (OPF) solutions for real-time electricity markets by proposing a graph neural network (GNN) framework that predicts electricity prices, achieving improvements in learning efficiency and adaptivity over existing methods.
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.