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AI-optimized management of a hybrid SOFC-CAES systems with renewable integration for efficient electricity production and peak shaving

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  • Zhong, Jianlan

Abstract

This article presents a cutting-edge smart energy system that combines solid oxide fuel cells (SOFC) with compressed air energy storage (CAES) to improve electricity generation while shaving the peak demand with minimal carbon dioxide emission. The novel technology works with SOFCs under high pressures, substantially enhancing their efficiency compared to what can be achieved under normal atmospheric conditions. In addition to enhancing efficiency, the system intelligently incorporates wind turbines to meet the energy requirements of the CAES compressors. The strategic utilization of renewable energy enhances total power production and aligns with sustainable energy objectives, demonstrating a potential progression in intelligent energy management. An in-depth analysis assesses the feasibility of integrating this smart system, considering several factors such as thermodynamics, exergo-economic, sustainability, and the environment. The non-dominated sorting genetic algorithm is employed to identify an ideal design condition with the aid of an artificial neural network, guaranteeing a balanced attainment across all performance indicators. The proposed integration results in a net power, energy cost, and CO2 index of 480.6 MWh, 215.7 USD/MWh, and 460.8 kg/MWh, highlighting the value of increasing the operating pressure of SOFC through the integration of CAES and adding more wind turbines to increase renewables. The parametric assessment shows that the net power and total cost increase by 250 MWh and 4 USD/h, signifying the importance of multi-objective optimization. The scatter matrix indicates that the compressor pressure ratio is insensitive while keeping wind velocity close to the lower domain achieves the best optimal condition. The optimization achieves a higher net power of 171.8 MWh and a lower energy cost of 137.6 USD/MWh. Also, it enhances the exergy efficiency by 12.9 % and reduces the CO2 index by 57 kg/MWh when prioritizing exergo-environmental facets.

Suggested Citation

  • Zhong, Jianlan, 2025. "AI-optimized management of a hybrid SOFC-CAES systems with renewable integration for efficient electricity production and peak shaving," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225009843
    DOI: 10.1016/j.energy.2025.135342
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