Electrochemical energy storage power stations decision-making via digital twins and simulation-based data fusion

The digital twin model for power stations utilises a dynamic three-dimensional representation to map the physical system and real-time data, encompassing monitoring control, state evolution, analysis, evaluation and integrating real-time operational laws, power station analysis and inference logic. This enables real-time monitoring, operational management, intelligent analysis, virtual inspection and simulation training. Moreover, the joint Kalman Filter is employed for data fusion to enhance the fusion effect of heterogeneous data from multiple sources within the digital twin-based electrochemical energy storage power station. Simulation results demonstrate promising performance in terms of fusion error and efficiency. By leveraging accurate data fusion, the proposed data-driven digital twin for electrochemical energy storage power stations offers several benefits, including improved accuracy, operational efficiency, proactive maintenance, real-time monitoring, enhanced system reliability and safety. These advantages significantly contribute to optimising the data fusion process in electrochemical energy storage power stations, ultimately leading to enhanced performance and decision-making.