Superior Optimization for a Hybrid PEMFC Power System Employing Model Predictions

This research investigated the impacts of model prediction on the optimization of hybrid energy systems using a system consisting of solar panels, batteries, a proton exchange membrane fuel cell (PEMFC), and a chemical hydrogen generation system. A PEMFC has several advantages, such as low operating...

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Bibliographic Details
Published in:International journal of energy research 2023, Vol.2023, p.1-16
Main Authors: Wang, Fu-Cheng, Wang, Jian-Zhi
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alternative energy sources
Batteries
Costs
Design
Electrical loads
Electricity
Energy
Energy consumption
Energy costs
Energy resources
Fuel cells
Fuel technology
Hybrid systems
Hydrogen
Hydrogen production
Machine learning
Neural networks
Operating temperature
Optimization
Power management
Prediction models
Proton exchange membrane fuel cells
Radiation
Renewable resources
Robust control
Solar energy
Solar panels
Solar radiation
Sustainability
System effectiveness
Wind power
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Summary:This research investigated the impacts of model prediction on the optimization of hybrid energy systems using a system consisting of solar panels, batteries, a proton exchange membrane fuel cell (PEMFC), and a chemical hydrogen generation system. A PEMFC has several advantages, such as low operating temperatures, fast response times, high power density, and environmental friendliness, and it can convert hydrogen into electricity. However, because hydrogen costs are an important consideration, the PEMFC is usually integrated with hybrid energy systems to guarantee system sustainability. Therefore, in this study, a whole-year household load and solar radiation data were applied to optimize the system components and power management, thereby reducing the system cost by 42.43% and improving system sustainability by 7.05%. The system responses showed that some hydrogen consumption might be saved if the solar and load profiles could be foreseen. Two prediction models were developed that could accurately forecast the radiation and load profiles. Next, a second-year dataset was employed to verify the effectiveness of the model prediction. The results showed that the system cost was reduced by 40.20% without model prediction and by 44.06% with model prediction compared to the original system settings. Finally, experiments to illustrate the feasibility of the hybrid energy system were conducted using prediction models. Based on the results, the model prediction was deemed effective in improving the performance of hybrid energy systems.
ISSN:0363-907X
1099-114X
DOI:10.1155/2023/9984961