  The real-time electricity market with the integration of renewable energies
and electric vehicles have been receiving significant attention recently. So
far most of the literature addresses the optimal power flow (OPF) problem in
the real-time electricity market context by iterative methods. However, solving
OPF problems in real-time is challenging due to the high computational
complexity by the iterative methods. Motivated by this fact, in this paper, we
propose a Chebyshev Graph Convolutional Neural Networks (ChebGCN) to improve
the efficiency of integrating low-carbon energy sources into power grids and to
address scalability and adaptivity of end-to-end existing OPF solutions. The
proposed GCN method is capable to predict the optimal energy market marginal
prices in real time. Numerical analysis is used to benchmark the results and
validate the improvement.

Systems and Control;Systems and Control