Solid oxide fuel cell (SOFC) control strategy enhancement by adaptive neuro-fuzzy inference system (ANFIS).

Global warming is a vital problem that many researchers tried to solve with so many solutions such as: reducing electricity production with conventional generators by using renewable energy resources and using hydrogen as an alternative to fossil fuels. The universal Economic crisis came across to cut off certain quantities at scheduled times. This, directly, affects renewable energy sources connected to the grid due to voltage and frequency variations. To solve this dilemma, in this paper, the grid-connected solid oxide fuel cell (SOFC) model which is fed by green hydrogen to produce AC power, is developed by an Adaptive neuro-fuzzy inference system (ANFIS). It is one of the artificial intelligence applications to improve grid-connected SOFC dynamic response. ANFIS is mathematically presented and simulated using MATLAB/SIMULINK. The results show that the ANFIS controller has succeeded in enhancing most of the desired control and operation signals.

Hybrid off-grid energy systems optimal sizing with integrated hydrogen storage based on deterministic balance approach.

The transition to sustainable power infrastructure necessitates integrating various renewable energy sources efficiently. Our study introduces the deterministic balanced method (DBM) for optimizing hybrid energy systems, with a particular focus on using hydrogen for energy balance. The DBM translates the sizing optimization problem into a deterministic one, significantly reducing the number of iterations compared to state-of-the-art methods. Comparative analysis with HOMER Pro demonstrates a strong alignment of results, with deviations limited to a 5% margin, confirming the precision of our method in sizing determinations. Utilizing solar and wind data, our research includes a case study of Cairo International Airport, applying the DBM to actual energy demands.