Development and analysis of a novel power-to-gas-to-power system driven by the Allam cycle for simultaneous electricity and water production

Wang, Lina; Alirahmi, Seyed Mojtaba; Yu, Haoshui

doi:10.1016/j.enconman.2024.118934

Energy Conversion and Management

2024

DOI: 10.1016/j.enconman.2024.118934

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Development and analysis of a novel power-to-gas-to-power system driven by the Allam cycle for simultaneous electricity and water production

Lina Wang,

Seyed Mojtaba Alirahmi,

Haoshui Yu

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Cited by 1 publication

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Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review

Shojaei,

Aghamolaei,

Ghaani

2024

Sustainability

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For decades, fossil fuels have been the backbone of reliable energy systems, offering unmatched energy density and flexibility. However, as the world shifts toward renewable energy, overcoming the limitations of intermittent power sources requires a bold reimagining of energy storage and integration. Power-to-X (PtX) technologies, which convert excess renewable electricity into storable energy carriers, offer a promising solution for long-term energy storage and sector coupling. Recent advancements in machine learning (ML) have revolutionized PtX systems by enhancing efficiency, scalability, and sustainability. This review provides a detailed analysis of how ML techniques, such as deep reinforcement learning, data-driven optimization, and predictive diagnostics, are driving innovation in Power-to-Gas (PtG), Power-to-Liquid (PtL), and Power-to-Heat (PtH) systems. For example, deep reinforcement learning has improved real-time decision-making in PtG systems, reducing operational costs and improving grid stability. Additionally, predictive diagnostics powered by ML have increased system reliability by identifying early failures in critical components such as proton exchange membrane fuel cells (PEMFCs). Despite these advancements, challenges such as data quality, real-time processing, and scalability remain, presenting future research opportunities. These advancements are critical to decarbonizing hard-to-electrify sectors, such as heavy industry, transportation, and aviation, aligning with global sustainability goals.

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Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review

Shojaei,

Aghamolaei,

Ghaani

2024

Sustainability

View full text Add to dashboard Cite

show abstract

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Development and analysis of a novel power-to-gas-to-power system driven by the Allam cycle for simultaneous electricity and water production

Cited by 1 publication

References 56 publications

Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review

Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review

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