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.

中文翻译：

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最优潮流局部边际价格的图卷积神经网络

最近，可再生能源和电动汽车相结合的实时电力市场受到了广泛关注。到目前为止，大多数文献都通过迭代方法解决了实时电力市场环境中的最优潮流 (OPF) 问题。然而，由于迭代方法的高计算复杂性，实时解决 OPF 问题具有挑战性。受此启发，在本文中，我们提出了一种切比雪夫图卷积神经网络 (ChebGCN)，以提高将低碳能源整合到电网中的效率，并解决端到端现有 OPF 解决方案的可扩展性和适应性问题。所提出的 GCN 方法能够实时预测最优能源市场边际价格。