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• A model using graph neural network is proposed to predict state points and performance metrics. • A configuration representation method based on thermodynamic graph is developed. • GNN can extract structure features from different graphs of three SCO 2 power systems. • GNN achieves excellent accuracy and efficiency and outperforms classical data-driven models. Considering the increasing energy consumption and greenhouse gas emissions, the Supercritical CO 2 (S-CO 2) power system has attracted more and more attention. Due to the expensive computation resource and time cost, data-based solutions for performance prediction are urged. The surrogate model by machine learning is a promising alternative, but it only focuses on the objective functions and ignores the importance of topological structures and physical states of cycles. Aiming at providing a comprehensive model to predict physical states as well as thermodynamic characteristics, a deep learning method based on graph neural network (GNN) are devised in this paper. With the modeling calculation results as training dataset, a well-trained model can accurately reconstruct the physical states consisting of temperature, pressure, enthalpy, entropy (relative error of most samples <5 %) and exergy as well as thermal and exergy efficiency (relative error of most samples <5 %). Moreover, this model shows superior performance compared with traditional machine learning models including Regression Tree, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Gaussian Process Regression (GPR). Finally, the comparison between different training sizes demonstrate the model can help reduce sampling costs for complex systems. Overall, the presented deep learning model can provide a reliable and competitive choice for the digital twin of S-CO 2 power system and other power systems. [ABSTRACT FROM AUTHOR]

By constructing digital twins (DT) of an integrated energy system (IES), one can benefit from DT's predictive capabilities to improve coordinations among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction. This paper is motivated by the fact that practical IESs suffer from multiple uncertainty sources, and complicated surrounding environment. To address this problem, a novel DT-based day-ahead scheduling method is proposed. The physical IES is modelled as a multi-vector energy system in its virtual space that interacts with the physical IES to manipulate its operations. A deep neural network is trained to make statistical cost-saving scheduling by learning from both historical forecasting errors and day-ahead forecasts. Case studies of IESs show that the proposed DT-based method is able to reduce the operating cost of IES by 63.5%, comparing to the existing forecast-based scheduling methods. It is also found that both electric vehicles and thermal energy storages play proactive roles in the proposed method, highlighting their importance in future energy system integration and decarbonisation. • A deep learning and digital twins (DT) based scheduling method is proposed. • Physical IES performance is addressed by its virtual replica's predictive capability. • A machine learning method is proposed to address multiple uncertainty challenges. • Historical U.K. datasets are used for performance evaluations. • Results demonstrate the Digital Twin model reduces the extra operating cost by 63.5%. [ABSTRACT FROM AUTHOR]