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

Seyed Mohammad Shojaei, Reihaneh Aghamolaei () and Mohammad Reza Ghaani ()
Additional contact information
Seyed Mohammad Shojaei: School of Engineering, Trinity College Dublin, The University of Dublin, D02 PN40 Dublin, Ireland
Reihaneh Aghamolaei: Department of Mechanical Engineering, Dublin City University, D09 E432 Dublin, Ireland
Mohammad Reza Ghaani: School of Engineering, Trinity College Dublin, The University of Dublin, D02 PN40 Dublin, Ireland

Sustainability, 2024, vol. 16, issue 21, 1-41

Abstract: 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.

Keywords: power-to-x; machine learning; power-to-gas; power-to-liquid; power-to-heat; data-driven optimization; energy storage; green hydrogen; green ammonia; sustainable aviation fuel (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/21/9555/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/21/9555/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:21:p:9555-:d:1512815

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().