Enhancing Integrated Gas and Electricity Networks Operation with Coupling Attention-Graph Convolutional Network under Renewable Energy Variability

The growing integration of renewable energy sources into the power grid necessitates innovative approaches to energy system management. Integrated gas and electricity networks offer a promising solution to this challenge, enabling the efficient, reliable, and sustainable operation of energy systems. This paper presents a novel approach to the optimal scheduling of integrated gas and electricity networks, addressing the challenges posed by high penetration of renewable energy sources. First, a learning-assisted methodology is proposed to leverage Graph Convolutional Networks (GCNs) and Bayesian-based uncertainty models to enhance the accuracy and efficiency of scheduling integrated energy systems. The proposed GCN model effectively captures the complex interactions within the integrated network, facilitating accurate power and gas flow predictions. Meanwhile, the Bayesian-based model adeptly manages the inherent uncertainties associated with renewable energy generation, employing a chance-constrained approach to ensure system reliability. The effectiveness of the proposed methodology is demonstrated through extensive simulations on an IEEE 39-bus electricity network coupled with a 22-node hydrogen network. Results indicate significant improvements in computational efficiency and predictive accuracy compared to traditional model-based methods and existing data-driven techniques.