The Solid Oxide Fuel Cell (SOFC) will play a crucial role in the future energy sector for green and efficient H2-fueled applications. However, the complex thermal dynamic characteristics and safety performances of SOFC/GT systems introduce significant computational challenges to design systems utilising SOFCs. A wind/P2G/SOFC/GT multi-energy system structure is presented in the paper to demonstrate integrated energy systems that achieve optimal technical and economic performance. To address the design challenge, artificial intelligence technology offers the promise of constructing an accurate SOFC model using a minimal amount of experimental data, thereby alleviating computational demands and accelerating calculation times. In this study, we have developed an ensemble learning model designed to capture the thermodynamic and safety performances of SOFC/GT systems. This approach can accelerate calculations while ensuring the validity of optimisation results.
Keywords renewable energy resources, machine learning, multi energy system, SOFC, power to gas, hydro energy