Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System

Zhimin Guo, Zhiyuan Ye, Pengcheng Ni, Can Cao, Xiaozhao Wei, Jian Zhao and Xing He ()
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Zhimin Guo: State Grid Henan Electric Power Research Institute, Zhengzhou 450000, China
Zhiyuan Ye: Anhui Jiyuan Software Co., Ltd., Hefei 230000, China
Pengcheng Ni: Anhui Jiyuan Software Co., Ltd., Hefei 230000, China
Can Cao: Anhui Jiyuan Software Co., Ltd., Hefei 230000, China
Xiaozhao Wei: State Grid Henan Electric Power Research Institute, Zhengzhou 450000, China
Jian Zhao: State Grid Henan Electric Power Research Institute, Zhengzhou 450000, China
Xing He: Faculty of Electric Power Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2023, vol. 16, issue 6, 1-21

Abstract: Hydrogen (H 2 ) energy is an ideal non-polluting renewable energy and can achieve long-term energy storage, which can effectively regulate the intermittence and seasonal fluctuation of solar energy. Solid oxide fuel cells (SOFC) can generate electricity from H 2 with only outputs of water, waste heat, and almost no pollution. To solve the power generation instability and discontinuity of solar photovoltaic (PV) systems, a hybrid PV-SOFC power generation system has become one feasible solution. The “digital twin”, which integrates physical systems and information technology, offers a new view to deal with the current problems encountered during smart energy development. In particular, an accurate and reliable system model is the basis for achieving this vision. As core components, the reliable modelling of the PV cells and fuel cells (FCs) is crucial to the whole hybrid PV-SOFC power generation system’s optimal and reliable operation, which is based on the reliable identification of unknown model parameters. Hence, in this study, an artificial rabbits optimization (ARO)-based parameter identification strategy was proposed for the accurate modelling of PV cells and SOFCs, which was then validated on the PV double diode model (DDM) and SOFC electrochemical model under various operation scenarios. The simulation results demonstrated that ARO shows a more desirable performance in optimization accuracy and stability compared to other algorithms. For instance, the root mean square error (RMSE) obtained by ARO are 1.81% and 13.11% smaller than that obtained by ABC and WOA algorithms under the DDM of a PV cell. Meanwhile, for SOFC electrochemical model parameter identification under the 5 kW cell stack dataset, the RMSE obtained by ARO was only 2.72% and 4.88% to that of PSO for the (1 atm, 1173 K) and (3 atm, 1273 K) conditions, respectively. By establishing a digital twin model for PV cells and SOFCs, intelligent operation and management of both can be further achieved.

Keywords: parameter identification; photovoltaic (PV) cell; solid oxide fuel cell (SOFC); hybrid PV-SOFC system; artificial rabbits optimization; digital twin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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