Graph Neural Networks for Learning Real-Time Prices in Electricity Market

TL;DR

This paper introduces a graph neural network framework for real-time electricity market price prediction, enhancing scalability, adaptivity, and efficiency in solving optimal power flow problems in power grids.

Contribution

The paper presents a novel GNN-based approach that exploits local properties and physics-aware regularization to improve OPF learning for electricity markets.

Findings

Enhanced learning efficiency over existing methods

Improved adaptivity to grid topology changes

Reduced model complexity

Abstract

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.