green hydrogen thesis

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 21 June 2024

Worldwide greenhouse gas emissions of green hydrogen production and transport

• Kiane de Kleijne   ORCID: orcid.org/0000-0002-5150-9902 1 , 2 ,
• Mark A. J. Huijbregts   ORCID: orcid.org/0000-0002-7037-680X 1 , 3 ,
• Florian Knobloch   ORCID: orcid.org/0000-0003-3428-768X 4 ,
• Rosalie van Zelm 1 ,
• Jelle P. Hilbers   ORCID: orcid.org/0000-0002-9401-589X 1 ,
• Heleen de Coninck 1 , 2 &
• Steef V. Hanssen   ORCID: orcid.org/0000-0002-7673-8509 1  

Nature Energy ( 2024 ) Cite this article

1050 Accesses

111 Altmetric

Metrics details

• Climate-change mitigation
• Environmental impact
• Hydrogen energy

Large-scale introduction of green hydrogen is envisioned to play an important role in reaching net-zero greenhouse gas emissions. The production and transport of green hydrogen itself is, however, not free from emissions. Here we assess the life-cycle greenhouse gas emissions for 1,025 planned green hydrogen facilities, covering different electrolyser technologies and renewable electricity sources in 72 countries. We demonstrate that the current exclusion of life-cycle emissions of renewables, component manufacturing and hydrogen leakage in regulations gives a false impression that green hydrogen can easily meet emission thresholds. Evaluating different hydrogen production configurations, we find median production emissions in the most optimistic configuration of 2.9 kg CO 2 equivalents (CO 2 e) kg H 2 −1 (0.8–4.6 kgCO 2 e kg H 2 −1 , 95% confidence interval). Including 1,000 km transport via pipeline or liquid hydrogen shipping adds another 1.5 or 1.8 kgCO 2 e kg H 2 −1 , respectively. We conclude that achieving low-emission green hydrogen at scale requires well-chosen production configurations with substantial emission reductions along the supply chain.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 12 digital issues and online access to articles

111,21 € per year

only 9,27 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Optimal supply chains and power sector benefits of green hydrogen

A cost comparison of various hourly-reliable and net-zero hydrogen production pathways in the United States

Probabilistic feasibility space of scaling up green hydrogen supply

Data availability.

All data used to produce the outputs presented in this paper can be accessed via Zenodo ( https://doi.org/10.5281/zenodo.11203454 ) 85 . We used publicly available data from the IEA Hydrogen Projects Database (version of October 2022) for hydrogen facility-specific information on electricity source, electrolyser technology, location and project size (accessible via https://www.iea.org/data-andstatistics/data-product/hydrogen-projects-database ). For the calculation of location-specific emissions of solar electricity, we used the solar irradiance map available from Global Solar Atlas 2.0, a free, web-based application developed and operated by the company Solargis s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding provided by the Energy Sector Management Assistance Program (ESMAP). For additional information, see https://globalsolaratlas.info . For the calculation of location-specific emissions of wind electricity, we used wind speed maps available from the Global Wind Atlas 3.0, a free, web-based application developed, owned and operated by the Technical University of Denmark (DTU). The Global Wind Atlas 3.0 is released in partnership with the World Bank Group, utilizing data provided by Vortex, using funding provided by the Energy Sector Management Assistance Program (ESMAP). For additional information, see https://globalwindatlas.info . To calculate the emissions of wind electricity based on wind speed and the onshore and offshore location, we created a generalized linear model based on wind turbine data from https://doi.org/10.1111/jiec.13325 (ref. 24 ). We used the GHG intensities of national 2030 grid mixes modelled for a 2 °C policy scenario published by Knobloch et al. 26 at https://doi.org/10.1038/s41893-020-0488-7 . For calculating sea water desalination requirements, we used publicly available data on country-level water stress scores from the World Resources Institute ( https://doi.org/10.46830/writn.18.00146 ) 27 .

Code availability

All code used to produce the outputs presented in this paper can be accessed via Zenodo ( https://doi.org/10.5281/zenodo.11203454 ) 85 .

Global Hydrogen Review 2022. International Energy Agency https://doi.org/10.1787/39351842-en (2022).

Global Hydrogen Trade to Meet the 1.5 °C Climate Goal: Part II — Technology Review of Hydrogen Carriers (International Renewable Energy Agency, 2022).

Inflation Reduction Act of 2022, H.R.5376, 117th Cong. (US House of Representatives, 2022).

Net Zero Ambition Progress Update (BP, 2023).

Proposal for a Regulation of the European Parliament and of the Council on Establishing a Framework of Measures for Strengthening Europe’s Net-Zero Technology Products Manufacturing Ecosystem (Net Zero Industry Act). COM(2023) 161 Final (European Commission, 2023).

G20 Energy Transitions Ministers. G20 Energy Transitions Ministers’ Meeting Outcome Document and Chair’s Summary, 22 July 2023, Goa, India (The Government of India, 2023).

Babiker, M. et al. Cross-sectoral perspectives. In Climate Change 2022: Mitigation of Climate change. Working Group III contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Shukla, P. R. et al.) Ch. 12 (Cambridge Univ. Press, 2022).

Geopolitics of the Energy Transformation: The Hydrogen Factor (International Renewable Energy Agency, 2022).

Van de Graaf, T., Overland, I., Scholten, D. & Westphal, K. The new oil? The geopolitics and international governance of hydrogen. Energy Res. Soc. Sci. 70 , 101667 (2020).

Article   Google Scholar  

Towards Hydrogen Definitions Based on Their Emissions Intensity (International Energy Agency, 2023).

Bareiß, K., de la Rua, C., Möckl, M. & Hamacher, T. Life cycle assessment of hydrogen from proton exchange membrane water electrolysis in future energy systems. Appl. Energy 237 , 862–872 (2019).

Mac Dowell, N. et al. The hydrogen economy: a pragmatic path forward. Joule 5 , 2524–2529 (2021).

Gerloff, N. Comparative life-cycle-assessment analysis of three major water electrolysis technologies while applying various energy scenarios for a greener hydrogen production. J. Energy Storage 43 , 102759 (2021).

Terlouw, T., Bauer, C., McKenna, R. & Mazzotti, M. Large-scale hydrogen production via water electrolysis: a techno-economic and environmental assessment. Energy Environ. Sci. 15 , 3583–3602 (2022).

Tsiklios, C., Hermesmann, M. & Müller, T. E. Hydrogen transport in large-scale transmission pipeline networks: Thermodynamic and environmental assessment of repurposed and new pipeline configurations. Appl. Energy 327 , 120097 (2022).

Al-Breiki, M. & Bicer, Y. Investigating the technical feasibility of various energy carriers for alternative and sustainable overseas energy transport scenarios. Energy Convers. Manag. 209 , 112652 (2020).

Vilbergsson, K. V. et al. Can remote green hydrogen production play a key role in decarbonizing Europe in the future? A cradle-to-gate LCA of hydrogen production in Austria, Belgium, and Iceland. Int. J. Hydrogen Energy https://doi.org/10.1016/j.ijhydene.2023.01.081 (2023).

Ishimoto, Y. et al. Large-scale production and transport of hydrogen from Norway to Europe and Japan: value chain analysis and comparison of liquid hydrogen and ammonia as energy carriers. Int. J. Hydrog. Energy 45 , 32865–32883 (2020).

Velazquez Abad, A. & Dodds, P. E. Green hydrogen characterisation initiatives: definitions, standards, guarantees of origin, and challenges. Energy Policy 138 , 111300 (2020).

Lebrouhi, B. E., Djoupo, J. J., Lamrani, B., Benabdelaziz, K. & Kousksou, T. Global hydrogen development—a technological and geopolitical overview. Int. J. Hydrog. Energy 47 , 7016–7048 (2022).

Odenweller, A., Ueckerdt, F., Nemet, G. F., Jensterle, M. & Luderer, G. Probabilistic feasibility space of scaling up green hydrogen supply. Nat. Energy 7 , 854–865 (2022).

Hydrogen Projects Database. International Energy Agency https://www.iea.org/data-and-statistics/data-product/hydrogen-projects-database (2022).

Bosmans, J. H. C. C., Dammeier, L. C. & Huijbregts, M. A. J. J. Greenhouse gas footprints of utility-scale photovoltaic facilities at the global scale. Environ. Res. Lett. 16 , 094056 (2021).

Dammeier, L. C., Bosmans, J. H. C. & Huijbregts, M. A. J. Variability in greenhouse gas footprints of the global wind farm fleet. J. Ind. Ecol. 27 , 272–282 (2023).

Kadiyala, A., Kommalapati, R. & Huque, Z. Evaluation of the life cycle greenhouse gas emissions from hydroelectricity generation systems. Sustainability 8 , 1–14 (2016).

Google Scholar  

Knobloch, F. et al. Net emission reductions from electric cars and heat pumps in 59 world regions over time. Nat. Sustain. 3 , 437–447 (2020).

Hofste, R. et al. Aqueduct 3.0: updated decision-relevant global water risk indicators. World Resources Institute https://doi.org/10.46830/writn.18.00146 . (2019)

Palmer, G., Roberts, A., Hoadley, A., Dargaville, R. & Honnery, D. Life-cycle greenhouse gas emissions and net energy assessment of large-scale hydrogen production via electrolysis and solar PV. Energy Environ. Sci. 14 , 5113–5131 (2021).

Kolb, S., Müller, J., Luna-Jaspe, N. & Karl, J. Renewable hydrogen imports for the German energy transition—a comparative life cycle assessment. J. Clean. Prod . 373 , 133289 (2022).

Schill, W. P. Residual load, renewable surplus generation and storage requirements in Germany. Energy Policy 73 , 65–79 (2014).

Daggash, H. A. et al. Closing the carbon cycle to maximise climate change mitigation: power-to-methanol vs. power-to-direct air capture. Sustain. Energy Fuels 2 , 1153–1169 (2018).

Sternberg, A. & Bardow, A. Power-to-what?—Environmental assessment of energy storage systems. Energy Environ. Sci. 8 , 389–400 (2015).

Reuß, M., Grube, T., Robinius, M. & Stolten, D. A hydrogen supply chain with spatial resolution: comparative analysis of infrastructure technologies in Germany. Appl. Energy 247 , 438–453 (2019).

Bauer, C. et al. On the climate impacts of blue hydrogen production. Sustain. Energy Fuels 6 , 66–75 (2022).

Tahan, M. R. Recent advances in hydrogen compressors for use in large-scale renewable energy integration. Int. J. Hydrog. Energy 47 , 35275–35292 (2022).

Ocko, I. B. & Hamburg, S. P. Climate consequences of hydrogen emissions. Atmos. Chem. Phys. 22 , 9349–9368 (2022).

Warwick, N. et al. Atmospheric implications of increased hydrogen use. Department of Business, Energy and Industrial Strategy https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1067144/atmospheric-implications-of-increased-hydrogen-use.pdf#:~:text=%60Atmospheric implications of increased hydrogen use An increase,stratospheric %28%3E40 (2022).

Pressure drop in pipelines transporting compressed hydrogen gas. Fraunhofer IFF https://doi.org/10.13140/RG.2.2.17431.96168 (2023).

Wijayanta, A. T., Oda, T., Purnomo, C. W., Kashiwagi, T. & Aziz, M. Liquid hydrogen, methylcyclohexane, and ammonia as potential hydrogen storage: comparison review. Int. J. Hydrog. Energy 44 , 15026–15044 (2019).

Al-Breiki, M. & Bicer, Y. Comparative life cycle assessment of sustainable energy carriers including production, storage, overseas transport and utilization. J. Clean. Prod. 279 , 123481 (2021).

Global Hydrogen Review 2021. International Energy Agency https://doi.org/10.1787/39351842-en (2021).

Tracking Clean Energy Progress 2023. International Energy Agency https://www.iea.org/reports/tracking-clean-energy-progress-2023 (2023).

Forster, P. M. et al. Indicators of Global Climate Change 2022: annual update of large-scale indicators of the state of the climate system and human influence. Earth Syst. Sci. Data 15 , 2295–2327 (2023).

Rogelj, J. et al. Credibility gap in net-zero climate targets leaves world at high risk. Science 380 , 1014–1016 (2023).

European Commission. Commission Delegated Regulation (EU) 2023/1185 of 10 February 2023 supplementing Directive (EU) 2018/2001 of the European Parliament and of the Council by Establishing a Minimum Threshold for Greenhouse Gas Emissions Savings of Recycled Ccarbon Fuels and by Specifying a Methodology for Assessing Greenhouse Gas Emissions Savings from Renewable Liquid and Gaseous Transport Fuels of Non-Biological Origin and from Recycled Carbon Fuels (2023).

U.S. Department of Energy Clean Hydrogen Production Standard (CHPS) Draft Guidance. US Department of Energy https://www.hydrogen.energy.gov/clean-hydrogen-production-standard.html (2023).

de Kleijne, K., de Coninck, H., van Zelm, R., Huijbregts, M. A. J. & Hanssen, S. V. The many greenhouse gas footprints of green hydrogen. Sustain. Energy Fuels 6 , 4383–4387 (2022).

Dillman, K. & Heinonen, J. Towards a safe hydrogen economy: an absolute climate sustainability assessment of hydrogen production. Climate 11 , 1–18 (2023).

Cheng, W. & Lee, S. How green are the national hydrogen strategies? Sustainability 14 , 1–33 (2022).

Dillman, K. J. & Heinonen, J. A. ‘Just’ hydrogen economy: a normative energy justice assessment of the hydrogen economy. Renew. Sustain. Energy Rev. 167 , 112648 (2022).

Scita, R., Raimondi, P. P. & Noussan, M. Green hydrogen: the holy grail of decarbonisation? An analysis of the technical and geopolitical implications of the future hydrogen economy. FEEM Working Paper no. 13.2020 (2020).

Vogl, V., Åhman, M. & Nilsson, L. J. Assessment of hydrogen direct reduction for fossil-free steelmaking. J. Clean. Prod. 203 , 736–745 (2018).

Osman, A. I. et al. Hydrogen production, storage, utilisation and environmental impacts: a review. Environ. Chem. Lett. 20 , 153–188 (2022).

Vartiainen, E. et al. True cost of solar hydrogen. Sol. RRL 6 , 2100487 (2022).

Devlin, A., Kossen, J., Goldie-Jones, H. & Yang, A. Global green hydrogen-based steel opportunities surrounding high quality renewable energy and iron ore deposits. Nat. Commun. 14 , 2578 (2023).

Smolinka, T. et al. Studie IndWEDe: Industrialisierung der Wasserelektrolyse in Deutschland: Chancen und Herausforderungen für nachhaltigen Wasserstoff für Verkehr, Strom und Wärme. Fraunhofer https://www.ipa.fraunhofer.de/de/Publikationen/studien/studie-indWEDe.html (2018).

Mehmeti, A., Angelis-Dimakis, A., Arampatzis, G., McPhail, S. J. & Ulgiati, S. Life cycle assessment and water footprint of hydrogen production methods: from conventional to emerging technologies. Environments 5 , 24 (2018).

Brigljević, B. et al. When bigger is not greener: ensuring the sustainability of power-to-gas hydrogen on a national scale. Environ. Sci. Technol. 56 , 12828–12837 (2022).

Integration of Hydrohub gigawatt electrolysis facilities in five industrial clusters in the Netherlands. Hydrohub https://ispt.eu/media/ISPT-samenvattend-rapport-GigaWatt-online-def.pdf (2020).

Schmidt, T. S. et al. Additional emissions and cost from storing electricity in stationary battery systems. Environ. Sci. Technol. 53 , 3379–3390 (2019).

Bauer, C. et al. Electricity storage and hydrogen—technologies, costs and impacts on climate change. Paul Scherrer Institut https://www.psi.ch/de/media/72878/download?attachment (2022).

Lenzen, M. Life cycle energy and greenhouse gas emissions of nuclear energy: a review. Energy Convers. Manag. 49 , 2178–2199 (2008).

IPCC. Technology-specific cost and performance parameters. In Climate Change 2014: Mitigation of Climate Change: Working Group III Contribution to the IPCC Fifth Assessment Report (ed. Intergovernmental Panel on Climate Change) 1329–1356 (Cambridge Univ. Press, 2015).

Pomponi, F. & Hart, J. The greenhouse gas emissions of nuclear energy—life cycle assessment of a European pressurised reactor. Appl. Energy 290 , 116743 (2021).

Davis, N. et al. Global Wind Atlas v3. DTU https://doi.org/10.11583/DTU.9420803.v1 (2019).

Bosmans, J. H. C., Dammeier, L. C. & Huijbregts, M. A. J. Corrigendum: greenhouse gas footprints of utility-scale photovoltaic facilities at the global scale (2021 Environ. Res. Lett. 16 094056). Environ. Res. Lett. 18 , 059501 (2023).

Global photovoltaic power potential by country. ESMAP http://documents.worldbank.org/curated/en/466331592817725242/Global-Photovoltaic-Power-Potential-by-Country (2020).

Documentation: methodology. Solargis https://solargis.com/docs/methodology (2023).

Bosmans, J. et al. Determinants of the distribution of utility-scale photovoltaic power facilities across the globe. Environ. Res. Lett. 17 , 114006 (2022).

Uusitalo, V., Väisänen, S., Inkeri, E. & Soukka, R. Potential for greenhouse gas emission reductions using surplus electricity in hydrogen, methane and methanol production via electrolysis. Energy Convers. Manag. 134 , 125–134 (2017).

Koj, J. C., Wulf, C., Linssen, J., Schreiber, A. & Zapp, P. Utilisation of excess electricity in different power-to-transport chains and their environmental assessment. Transp. Res. Part D 64 , 23–35 (2018).

Rumayor, M., Dominguez-Ramos, A. & Irabien, A. Formic acid manufacture: carbon dioxide utilization alternatives. Appl. Sci. 8 , 914 (2018).

Bareschino, P. et al. Life cycle assessment and feasibility analysis of a combined chemical looping combustion and power-to-methane system for CO 2 capture and utilization. Renew. Sustain. Energy Rev. 130 , 109962 (2020).

Sternberg, A. & Bardow, A. Life cycle assessment of power-to-gas: syngas vs methane. ACS Sustain. Chem. Eng. 4 , 4156–4165 (2016).

Meylan, F. D., Piguet, F. P. & Erkman, S. Power-to-gas through CO 2 methanation: assessment of the carbon balance regarding EU directives. J. Energy Storage 11 , 16–24 (2017).

Biernacki, P., Röther, T., Paul, W., Werner, P. & Steinigeweg, S. Environmental impact of the excess electricity conversion into methanol. J. Clean. Prod. 191 , 87–98 (2018).

Jens, C. M., Müller, L., Leonhard, K. & Bardow, A. To Integrate or not to integrate—techno-economic and life cycle assessment of CO 2 capture and conversion to methyl formate using methanol. ACS Sustain. Chem. Eng. 7 , 12270–12280 (2019).

d’Amore-Domenech, R., Meca, V. L., Pollet, B. G. & Leo, T. J. On the bulk transport of green hydrogen at sea: comparison between submarine pipeline and compressed and liquefied transport by ship. Energy 267 , 126621 (2023).

Stolzenburg, K. & Mubbala, R. Integrated design for demonstration of efficient liquefaction of hydrogen (IDEALHY). Fuel Cells and Hydrogen Joint Undertaking (FCH JU). IDEALHY https://www.idealhy.eu/uploads/documents/IDEALHY_D3-16_Liquefaction_Report_web.pdf (2013).

Egerer, J., Grimm, V., Niazmand, K. & Runge, P. The economics of global green ammonia trade—‘Shipping Australian wind and sunshine to Germany’. SSRN Electron. J. 334 , 120662 (2022).

Armijo, J. & Philibert, C. Flexible production of green hydrogen and ammonia from variable solar and wind energy: case study of Chile and Argentina. Int. J. Hydrog. Energy 45 , 1541–1558 (2020).

McKinlay, C. J., Turnock, S. R. & Hudson, D. A. Route to zero emission shipping: hydrogen, ammonia or methanol? Int. J. Hydrog. Energy 46 , 28282–28297 (2021).

European Commission Commission Delegated Regulation (EU) 2021/2139 of 4 June 2021. Off. J. Eur. Union 442 , 1–349 (2021).

Wang, M. et al. Can sustainable ammonia synthesis pathways compete with fossil-fuel based Haber–Bosch processes? Energy Environ. Sci. 14 , 2535–2548 (2021).

de Kleijne, K. et al. Data and code for ‘Worldwide greenhouse gas emissions of green hydrogen production and transport’. Zenodo https://doi.org/10.5281/zenodo.11203454 (2024).

Download references

Acknowledgements

M.A.J.H. was financed by Grant 016.Vici.170.190 from the Netherlands Organisation for Scientific Research (NWO).

Author information

Authors and affiliations.

Department of Environmental Science, Radboud Institute for Biological and Environmental Sciences, Radboud University, Nijmegen, The Netherlands

Kiane de Kleijne, Mark A. J. Huijbregts, Rosalie van Zelm, Jelle P. Hilbers, Heleen de Coninck & Steef V. Hanssen

Technology, Innovation and Society Group, Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands

Kiane de Kleijne & Heleen de Coninck

Department of Circularity & Sustainability Impacts, TNO, Utrecht, The Netherlands

Mark A. J. Huijbregts

Cambridge Centre for Environment, Energy and Natural Resource Governance, University of Cambridge, Cambridge, UK

Florian Knobloch

You can also search for this author in PubMed   Google Scholar

Contributions

K.d.K., S.V.H., M.A.J.H. and H.d.C. conceived and designed the study; K.d.K. performed the research; K.d.K. analysed the data with contributions from S.V.H., M.A.J.H. and J.P.H.; K.d.K., S.V.H. and F.K. wrote the manuscript; K.d.K., M.A.J.H., F.K., R.v.Z., J.P.H., H.d.C. and S.V.H. provided revisions to the manuscript.

Corresponding authors

Correspondence to Kiane de Kleijne or Steef V. Hanssen .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Energy thanks Kevin Dillman, Petra Zapp and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary methods, Figs. 1–6 and Tables 1–12.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

de Kleijne, K., Huijbregts, M.A.J., Knobloch, F. et al. Worldwide greenhouse gas emissions of green hydrogen production and transport. Nat Energy (2024). https://doi.org/10.1038/s41560-024-01563-1

Download citation

Received : 18 August 2023

Accepted : 27 May 2024

Published : 21 June 2024

DOI : https://doi.org/10.1038/s41560-024-01563-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

• Search Menu
• Sign in through your institution
• Advance articles
• Special issues
• Research articles
• Review articles
• Editor's Choice
• Author Guidelines
• Guidelines for Special Issue
• Submission Site
• Open Access and APCs
• Self-Archiving Policy
• Reasons to submit
• About Journal of Computational Design and Engineering
• Editorial Board
• Advertising & Corporate Services
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

1. introduction, 2. leveraging metaheuristic algorithms for real-world optimization problems: h2, 3. production of green hydrogen, 4. storage of hydrogen, 5. transportation of hydrogen, 6. consumption of hydrogen, 7. multi-objective metaheuristic optimization for green hydrogen, 8. conclusions and future trends, conflict of interest statement.

• < Previous

Challenges and opportunities in green hydrogen supply chain through metaheuristic optimization

• Article contents
• Figures & tables
• Supplementary Data

Saman A Gorji, Challenges and opportunities in green hydrogen supply chain through metaheuristic optimization, Journal of Computational Design and Engineering , Volume 10, Issue 3, June 2023, Pages 1143–1157, https://doi.org/10.1093/jcde/qwad043

• Permissions Icon Permissions

A comprehensive analysis of the green hydrogen supply chain is presented in this paper, encompassing production, storage, transportation, and consumption, with a focus on the application of metaheuristic optimization. The challenges associated with each stage are highlighted, and the potential of metaheuristic optimization methods to address these challenges is discussed. The primary method of green hydrogen production, water electrolysis through renewable energy, is outlined along with the importance of its optimization. Various storage methods, such as compressed gas, liquid hydrogen, and material-based storage, are covered with an emphasis on the need for optimization to improve safety, capacity, and performance. Different transportation options, including pipelines, trucks, and ships, are explored, and factors influencing the choice of transportation methods in different regions are identified. Various hydrogen consumption methods and their associated challenges, such as fuel cell performance optimization, hydrogen-based heating systems design, and energy conversion technology choice, are also discussed. The paper further investigates multi-objective approaches for the optimization of problems in this domain. The significant potential of metaheuristic optimization techniques is highlighted as a key to addressing these challenges and improving overall efficiency and sustainability with respect to future trends in this rapidly advancing area.

Analyses metaheuristic optimization in green hydrogen supply chain.

Examines production, storage, transportation, and consumption.

Tackles complex, multi-objective problems for green hydrogen optimization.

Suggests future research with room for innovative optimization techniques.

The Earth’s climate system is rapidly changing, with a global mean temperature increase of 0.85 ○ C from 1880 to 2012. The Intergovernmental Panel on Climate Change attributes this warming to the unprecedented levels of greenhouse gases, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), primarily caused by human activities (Change et al ., 2007 ), expected to worsen in the future. To address this issue, countries are focusing on sustainable development and low-carbon technologies. However, current mitigation strategies are insufficient, and a fundamental transformation is needed, with more research on the practical implementation of sustainable energy sources.

Green hydrogen, generated through electrolysis using renewable energy sources, offers a cleaner alternative to grey and blue hydrogen, with the potential to significantly impact the global energy landscape due to its reduced emissions, versatility, and storability (Carmo et al ., 2013 ; Hermesmann & Müller, 2022 ; Howarth & Jacobson, 2021 ; Jacobson et al ., 2019 ; Sharaf & Orhan, 2014 ). As shown in Table  1 , green hydrogen provides key benefits such as reduced greenhouse gas emissions (Gorji, 2022 ), energy carrier capabilities, and extended storage and transportation. However, challenges remain, including higher production costs, intermittent renewable energy sources, and the need for efficient storage and transportation solutions (Ball & Wietschel, 2009 ; Bertuccioli et al ., 2014 ; Sherif et al ., 2005 ).

Benefits and challenges of green hydrogen.

In this context, the supply chain plays a vital role in ensuring the efficient transformation of renewable energy into hydrogen, its safe storage and transportation, and its utilization (Gondal, 2019 ; Li et al ., 2023 ). However, challenges associated with production, storage, transportation, and consumption must be addressed to unlock its full potential, with optimization being key. At present, no single optimization technique is universally agreed upon as the most appropriate for solving all problems across various domains and industries. Each of the existing methodologies has its specific challenges. For example, exact methods can be time-consuming and challenging to apply when resolving optimal solutions for issues related to green hydrogen production in the supply chain. Moreover, the dynamic nature of energy production and distribution systems adds complexity to the implementation of these algorithms. As a result, the use of exact methods does not guarantee that the obtained solutions will remain optimal. This complexity highlights a need for innovative solutions and ongoing research.

Among the available methodologies, metaheuristic methods have demonstrated promising performance in enhancing the efficiency and cost-effectiveness of green hydrogen’s supply chain (Riera et al ., 2023 ). Applying metaheuristic algorithms to the green hydrogen supply chain presents various advantages. These flexible algorithms effectively solve optimization problems, addressing challenges in production, transportation, and storage of green hydrogen. They efficiently identify global optimal solutions, crucial for sustainability and efficiency in the supply chain. Additionally, metaheuristic algorithms are faster and more robust to noise, errors, and data uncertainty, making them suitable for real-world applications like the green hydrogen supply chain. Their adaptability allows customization to meet specific problem requirements, optimizing performance and efficiency. Metaheuristic optimization methods, such as genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and ant colony optimization (ACO), have been widely employed in addressing the challenges associated with the production, storage, transportation, and consumption of green hydrogen (Al-Rashed et al ., 2022 ; Safari et al ., 2013 ; Zhang et al ., 2018 ).

Given the interdisciplinary nature of green hydrogen, optimization techniques from various fields such as power electronics, control systems, materials science, and computational intelligence can be combined to develop innovative solutions for the challenges faced by the green hydrogen supply chain. For example, GAs have been employed to optimize the design and sizing of green hydrogen production systems, while PSO and ACO have been used to optimize design and sizing as well as energy management strategies in hydrogen-based microgrids (Abo-Elyousr et al ., 2021 ; Ding et al ., 2020 ; Mehrjerdi et al ., 2022 ; Mohammadshahi et al ., 2022 ; Monforti Ferrario et al ., 2021 ).

As the green hydrogen sector continues to evolve, there is an increasing need for advanced optimization techniques that can address the complex and multi-objective nature of the problems encountered in this field. The aim of this paper is to offer insight into the contribution of metaheuristic optimization methods towards the green hydrogen supply chain. The paper intends to provide a comprehensive overview of green hydrogen production, storage, transportation, and consumption, thereby providing a valuable avenue for further research. By underscoring the importance of these optimization techniques, the study underscores their capacity to tackle the obstacles that arise in green hydrogen systems. The main contributions of this paper are as follows:

An investigation into how to leverage metaheuristic algorithms for real-world optimization problems and how they can contribute to the green hydrogen supply chain.

A discussion of the challenges and opportunities in green hydrogen production, storage, transportation, and consumption, considering the interdisciplinary nature of the field.

An exploration of the potential for advanced optimization techniques to address complex and multi-objective problems, particularly in the context of hydrogen-based microgrids.

Suggestions for future research directions in the green hydrogen field, emphasizing the development and application of innovative optimization techniques.

The paper is organized as follows: Section  2 explores the role of metaheuristic algorithms, including their application to the green hydrogen supply chain. Sections  3 – 6 discuss the production, storage, transportation, and consumption of green hydrogen, highlighting the role and impact of metaheuristic optimization in each stage. Section  7 presents multi-objective optimization approaches. Section  8 concludes the paper and suggests directions for future research.

Metaheuristic algorithms are a class of computational techniques that have gained significant attention due to their ability to solve complex optimization problems (Cheng et al ., 2021 ; El-Shorbagy & El-Refaey, 2022 ; Tavasoli et al ., 2021 ). Unlike traditional optimization methods, which rely on mathematical models and exact solutions, metaheuristic algorithms search a large solution space intelligently to find optimal or near-optimal solutions (Abbaspour et al ., 2022 ). These algorithms are inspired by natural phenomena such as evolution, swarm behaviour, and physical processes, and can handle complex and non-linear optimization problems that are difficult to solve using traditional optimization methods (Hu et al ., 2022 ). Metaheuristics provide several advantages, including flexibility, generality, scalability, and robustness, allowing them to be applied to a wide range of problems in various fields (Yazdani et al ., 2020 ). They can also be customized and tailored to specific problem domains, providing problem-specific solutions (Gharib et al ., 2022 ). Metaheuristic algorithms are increasingly recognized as a valuable tool for solving optimization problems, especially in emerging research areas like the green hydrogen supply chain. Understanding metaheuristic concepts is crucial for utilizing metaheuristic techniques, and following (Wang, 2010 ) definitions offer insightful guidance:

Definition 1 : A heuristic is a problem-solving method that utilizes trial and error to drive the development of a solution.

Definition 2 : A metaheuristic is a more general, higher level type of heuristic that is applied to a wider range of problems.

Definition 3 : Metaheuristic computing is a type of adaptive computing that utilizes general heuristic rules to solve a particular category of computational problems.

Building upon these definitions, a generic metaheuristic framework for solving optimization problems is defined as follows, adopted from Crainic and Toulouse ( 2003 ):

A comprehensive framework, depicted in Fig.  1 and adapted from Osaba et al . ( 2021 ), has been employed to tackle the complex optimization challenges associated with the green hydrogen supply chain. This adapted framework comprises two distinct environments, the lab environment and the application environment, each encompassing specific stages that facilitate the systematic and effective resolution of the optimization problem.

A general framework for solving optimization problems with metaheuristic algorithms in green hydrogen supply chain. Adapted from Osaba et al . ( 2021 ).

Within the lab environment, the process is initiated with the identification of a pertinent problem in the green hydrogen supply chain. Following this, stage 1, i.e., the problem statement stage is conducted, in which functional requirements are meticulously defined and non-functional requirements are thoroughly analysed. Additionally, in this environment, stage 3 or the modelling and assessment stage ensues, which encompasses the development of a mathematical model, the meticulous design of algorithmic techniques, and the comprehensive assessment of the solution’s performance. In the application environment, stage 2, or the algorithmic verification and testing stage is undertaken, focusing on real-world verification and necessary parameter adjustments.

A Generic Metaheuristic Fr ame work

As demonstrated in Fig.  1 , the interconnectivity of the stages and the iterative nature of the optimization process are crucial elements of the framework. Decision diamonds have been incorporated to evaluate the compliance of the proposed solution, the emergence of new constraints, the fulfilment of objectives, and the requirement for supplementary testing. Based on the outcomes of these assessments, the process reverts to the pertinent stages, ultimately striving to devise a solution that satisfies the established criteria and can be efficiently implemented in real-world scenarios.

Green hydrogen production is crucial for a sustainable energy future, and various methods have been developed to enhance efficiency and reduce costs. This section explores the challenges and opportunities in green hydrogen production, focusing on the role of metaheuristic optimization and advanced power electronics and microgrid technologies. The discussion covers different production methods, the impact of optimization techniques, and the advancements and applications of these techniques in the context of green hydrogen production.

3.1. Overview of green hydrogen production: challenges and comparison of different methods

Green hydrogen production primarily involves the electrolysis of water using renewable energy sources, such as solar, wind, and hydropower. This process breaks down water into its constituent elements, hydrogen and oxygen, through the use of an electric current. There are several electrolysis technologies available for green hydrogen production, including alkaline electrolysis, proton exchange membrane (PEM) electrolysis, solid oxide electrolysis, and anion exchange membrane (AEM) electrolysis. Each of these methods has its advantages and limitations, as summarized in Table  2 . Despite the promise of green hydrogen, there are several challenges associated with its production:

Efficiency : Different electrolysers have varying efficiency levels, which impact the overall energy consumption and cost of hydrogen production.

Operating conditions : Electrolysis technologies have different operating temperature ranges, pressure requirements, and response times, which can influence the choice of technology for specific applications and energy systems.

Cost and investment : Capital costs, maintenance costs, and required investments for different electrolysis technologies vary, affecting the overall cost of green hydrogen production.

Durability : The durability of different electrolysis technologies impacts the long-term viability and cost-effectiveness of green hydrogen production.

Gas purity : The purity of hydrogen produced by different electrolysis methods may impact its suitability for specific applications and the need for additional purification steps.

Infrastructure development : The deployment of green hydrogen production facilities requires significant investments in renewable energy infrastructure and electrolysis equipment, as well as the integration of these systems with existing energy infrastructure.

Technology maturity : Different electrolysers have varying degrees of maturity, with alkaline and PEM [e.g., NEL (nel, 2023 ) and ITM (itm, 2023 )] being more established than solid oxide [e.g., Bloom (blo, 2023 )] and AEM [e.g., Enapter (ena, 2023 )]. Maturity affects factors such as cost, performance, and availability.

Comparison of green hydrogen production methods.

To address these challenges and enable the widespread adoption of green hydrogen, it is essential to develop innovative solutions that improve the efficiency and cost-effectiveness.

3.2. The need, role, and impact of metaheuristic optimization in improving green hydrogen production

As discussed in the introduction, metaheuristic optimization techniques play a crucial role in improving the efficiency and performance of green hydrogen production systems. Figure  2 illustrates a green hydrogen production microgrid, highlighting the key aspects where metaheuristic optimization techniques can be applied to address the challenges:

System design and sizing : Optimal sizing and design of electrolyser systems, renewable energy sources, and energy storage systems can significantly enhance the overall efficiency and cost-effectiveness of green hydrogen production.

Electrolyser dynamic response : By optimizing the dynamic response of electrolysers to variable renewable energy input, metaheuristic techniques can improve system reliability and stability.

Energy resource allocation : Effective allocation of renewable energy resources and energy storage in microgrids can maximize green hydrogen production while minimizing costs and environmental impact.

Waste heat recovery : Optimizing waste heat recovery processes can enhance overall system efficiency and reduce energy demand. During the electrolysis process, a considerable amount of heat is generated as a by-product. This waste heat, if not managed properly, can lead to energy losses. By implementing waste heat recovery techniques, the captured thermal energy can be utilized for various purposes, such as pre-heating the feedwater, space heating, or even generating electricity through thermoelectric generators.

Water consumption optimization : Metaheuristic techniques can help minimize water consumption in green hydrogen production, reducing its environmental footprint and resource requirements.

Schematic representation of a green hydrogen production microgrid, highlighting key aspects for metaheuristic optimization.

3.3. Applications of metaheuristic optimization techniques for green hydrogen production

Metaheuristic optimization techniques have gained significant attention in recent years due to their ability to solve complex, large-scale, and highly constrained problems. Green hydrogen production systems, especially hydrogen microgrids, present a series of challenges that require efficient, scalable, and flexible optimization methods. As discussed in the introduction, the complexity and non-linearity of these systems demand advanced optimization techniques to achieve optimal performance.

In a metaheuristic optimization problem, there are three main components: an objective function, constraints, and a search space. The objective function represents the goal of the optimization, which is typically to minimize or maximize a certain value. Constraints are the limitations or requirements that must be satisfied by a feasible solution, while the search space is the set of all possible solutions to the problem.

Several well-known metaheuristic methods, such as GA, PSO, SA, ACO, and differential evolution, have been widely used to tackle various optimization problems. These methods share a general structure but differ in specific variation operators and selection processes. General pseudo-code for metaheuristic optimization is presented, where steps 1, 2, 3, 4(9), and 10 are common to most metaheuristic methods, while steps 5, 6, 7, and 8 vary depending on the specific technique used. For instance, GA employs crossover and mutation operators in step 6 to generate new solutions, evaluates their fitness in step 7, and uses a selection mechanism like tournament or roulette wheel selection in step 5 to update the population in step 8. On the other hand, PSO updates particle positions based on their velocities and the best-known positions in step 6, evaluates the fitness of the new positions in step 7, and updates the population in step 8 by replacing the previous positions with the new ones. PSO uses the personal and global best positions as guidance for the search in step 5, influencing the particles’ movement towards promising regions of the search space.

Hence, the objective function, constraints, and other components in the pseudo-code can be tailored to address each green hydrogen production challenge. By doing so, metaheuristic techniques can be applied to various aspects of green hydrogen production:

System design and sizing : The objective function can be formulated to minimize the total cost of the system, including the capital, operating, and maintenance costs, while satisfying constraints on energy production, storage capacity, and system reliability.

Electrolyser dynamic response : The objective function can aim to minimize the deviation between the desired and actual output of the electrolyser, subject to constraints on the input power and electrolyser operating limits.

Energy resource allocation : The objective function can be designed to maximize green hydrogen production or minimize energy costs while adhering to constraints on renewable energy availability, storage capacity, and load demand.

Waste heat recovery : The objective function may target maximizing the waste heat recovery efficiency, constrained by the heat exchanger limits and the available waste heat.

Water consumption optimization : The objective function can focus on minimizing water consumption in the green hydrogen production process, considering constraints on water availability, water quality requirements, and electrolyser limits.

By applying metaheuristic optimization techniques to these green hydrogen production challenges, it is possible to improve the overall efficiency, cost-effectiveness, and sustainability of hydrogen microgrids. Further research and practical applications can continue to advance the state of the art in this area.

In this section, the challenges and methods associated with hydrogen storage will be discussed, emphasizing the role of metaheuristic optimization techniques in addressing these challenges and improving storage efficiency.

4.1. Overview of hydrogen storage: challenges and comparison of different methods

Hydrogen storage is a critical aspect of the green hydrogen supply chain, as it ensures the availability of hydrogen for end-use applications while maintaining safety and efficiency. There are various methods for storing hydrogen, each with its own set of challenges and optimization opportunities. In this subsection, an overview of the main hydrogen storage methods will be provided which discusses their associated challenges and presents a comparison of their key characteristics in Table  3 . Additionally, a schematic representation of a hydrogen storage system (see Fig.  3 ) is presented, which highlights the key aspects of metaheuristic optimization.

Schematic representation of hydrogen storage system highlighting key aspects for metaheuristic optimization.

Comparison of hydrogen storage methods.

Note . For optimization opportunities A, B, C, and D, see Fig.  3 .

The main challenges in hydrogen storage include storage facility layout, hydrogen compression and liquefaction, temperature and pressure control, and optimal storage material selection. In the next subsection, we will explore the need, role, and impact of metaheuristic optimization techniques in addressing these challenges and improving hydrogen storage.

4.2. The need, role, and impact of metaheuristic optimization in improving hydrogen storage

As the demand for green hydrogen increases, it becomes crucial to address the challenges associated with its storage to ensure its efficient use in various applications. Metaheuristic optimization techniques have emerged as powerful tools for tackling complex optimization problems, particularly when traditional methods fail to provide satisfactory solutions. In the context of hydrogen storage, metaheuristic optimization can play a vital role in overcoming challenges and maximizing system performance in several ways:

Storage facility layout optimization: Metaheuristic techniques can be employed to design efficient storage facility layouts that maximize the utilization of available space while ensuring safety and operational efficiency. This can help minimize capital and operational costs and reduce energy consumption during storage and retrieval processes.

Hydrogen compression and liquefaction optimization: The compression and liquefaction processes for hydrogen storage are energy-intensive and can benefit from optimization. Metaheuristic algorithms can be used to optimize compression and liquefaction processes by minimizing energy consumption, maximizing throughput, and ensuring optimal operating conditions.

Temperature and pressure control optimization: Effective control of temperature and pressure is essential for safe and efficient hydrogen storage. Metaheuristic techniques can optimize temperature and pressure control strategies, ensuring the stability of stored hydrogen and minimizing energy losses due to leakage or evaporation.

Optimal storage material selection: Choosing the right storage material is crucial for maximizing storage capacity and minimizing costs. Metaheuristic optimization can be employed to identify optimal storage materials based on various criteria such as storage capacity, cost, weight, and safety considerations.

4.3. Applications of metaheuristic optimization techniques for hydrogen storage

In this section, we focus on the applications of metaheuristic optimization techniques for hydrogen storage, specifically for the identified challenges. We refer back to the general pseudo-code and metaheuristic optimization techniques discussed in the previous section and concentrate on the objective functions and constraints for each challenge.

Storage facility layout optimization : The objective function can be formulated to minimize capital and operational costs, maximize space utilization, and minimize energy consumption during storage and retrieval processes, while satisfying constraints on safety regulations, storage capacity requirements, and facility dimensions.

Hydrogen compression and liquefaction optimization : The objective function can aim to minimize energy consumption, maximize throughput, and optimize operating conditions, subject to constraints on pressure and temperature limits, equipment specifications, and safety requirements.

Temperature and pressure control optimization : The objective function can be designed to ensure the stability of stored hydrogen, minimize energy losses due to leakage or evaporation, and minimize energy consumption for temperature and pressure control, while adhering to constraints on temperature and pressure ranges, equipment specifications, and safety requirements.

Optimal storage material selection : The objective function can focus on maximizing storage capacity, minimizing costs, minimizing weight, and ensuring safety, considering constraints on material availability, material compatibility with hydrogen, and performance requirements.

In conclusion, applying metaheuristic optimization techniques for hydrogen storage can lead to significant improvements in system performance, cost-effectiveness, and sustainability. As the demand for green hydrogen continues to grow, researchers and practitioners can explore new ways to leverage these techniques to develop more advanced and efficient hydrogen storage solutions. Potential future research directions include investigating the applicability of emerging metaheuristic algorithms, developing hybrid approaches that combine the strengths of different techniques, and exploring the integration of machine learning methods to further enhance optimization performance.

Hydrogen transportation plays a crucial role in the green hydrogen supply chain, connecting production facilities to end-users. Ensuring the efficient and safe transportation of hydrogen is essential to enable the widespread adoption of green hydrogen as an energy carrier. This section will discuss the challenges associated with hydrogen transportation, compare different transportation methods, and illustrate its potential in addressing the unique challenges associated with transporting hydrogen.

5.1. Overview of hydrogen transportation: challenges and comparison of different methods

Safe, efficient, and cost-effective transportation methods are essential for the large-scale deployment of hydrogen as an energy carrier. The main challenges associated with hydrogen transportation include high costs, safety concerns, energy losses, and the need for specialized infrastructure. Additionally, the physical properties of hydrogen, such as its low density, high flammability, and potential for embrittlement, further complicate transportation. The common hydrogen transportation methods include:

Pipeline : Hydrogen can be transported through pipelines similar to natural gas. However, due to its low density and high flammability, hydrogen pipelines require specific materials and safety measures. Pipeline transportation is generally suited for long distances and large volumes but has high initial infrastructure costs.

Compressed gas trucks : Compressed hydrogen gas can be transported in high-pressure tanks mounted on trucks. This method is flexible and suitable for short to medium distances but has high compression losses and costs.

Liquid hydrogen trucks : Liquid hydrogen can be transported in cryogenic tanks on trucks. This method is more energy-dense than compressed gas trucks but requires additional energy for liquefaction and has higher costs.

Liquefied hydrogen ships : Liquefied hydrogen can be transported in large quantities over long distances using specialized ships with cryogenic storage tanks. This method is suitable for intercontinental transportation but has high initial costs and infrastructure requirements.

Chemical carriers : Hydrogen can be transported in the form of chemical carriers, such as ammonia or methanol. These carriers can be converted back to hydrogen at the destination. This method offers flexibility and high energy density but may have higher costs and additional complexity.

Material carriers : Hydrogen can be stored and transported in porous materials, such as metal-organic frameworks, which adsorb hydrogen at high pressure and release it at lower pressure. This method can offer high storage capacity and safety but may require further costs and research.

Table  4 presents a comparison of the different hydrogen transportation methods in terms of cost, safety, energy loss, infrastructure requirements, flexibility, and transport distance. Each method has its advantages and disadvantages, and the choice of the most appropriate transportation method depends on various factors, such as the scale of the hydrogen supply chain, the distance between production and consumption sites, and the availability of existing infrastructure.

Comparison of hydrogen transportation methods.

Factors influencing the choice of transportation method for different regions include distance, infrastructure, geographical and political constraints, and the available transportation methods. For example, regions with an extensive pipeline network may prefer using pipelines, while regions with limited infrastructure may rely on trucks or ships. Geographical constraints, such as the presence of mountains or water bodies, may also dictate the choice of transportation method. Political considerations, such as cross-border agreements and regulations, may further influence the selection of hydrogen transportation methods. As a result, it is crucial for decision-makers to evaluate these factors when choosing the most suitable transportation method for their specific region. The regional suitability of hydrogen transportation methods is presented in Table  5 .

Regional suitability of hydrogen transportation methods.

As the hydrogen economy grows, it is essential to develop efficient and cost-effective transportation methods that can cater to different regions and their specific requirements. Future developments may include the expansion of pipeline networks, advancements in truck and ship transportation technologies, and the exploration of novel transportation methods like chemical carriers or porous material carriers.

5.2. The need, role, and impact of metaheuristic optimization in improving hydrogen transportation

Hydrogen transportation is a critical aspect of the hydrogen economy, and it requires efficient solutions to overcome various challenges associated with different transportation methods. Metaheuristic optimization techniques can play a significant role in addressing these challenges and enhancing the overall efficiency and cost-effectiveness of hydrogen transportation. Some of the key challenges in hydrogen transportation that can benefit from the application of metaheuristic optimization techniques include:

Refuelling station location planning : Choosing optimal locations for hydrogen refuelling stations is crucial to ensure adequate coverage, minimize transportation costs, and maximize the utilization of hydrogen infrastructure. Metaheuristic optimization techniques can help in identifying the most appropriate locations for refuelling stations based on factors such as demand, accessibility, and existing infrastructure.

Multi-modal transportation optimization : Integrating different transportation methods, such as pipelines, trucks, and ships, can provide a more efficient and cost-effective solution for hydrogen transportation. Metaheuristic optimization techniques can be employed to find the best combination of transportation modes while considering factors like cost, distance, and infrastructure availability.

Hydrogen supply scheduling : Efficiently scheduling the supply of hydrogen to different end-users is essential to ensure reliable service and minimize operational costs. Metaheuristic optimization techniques can be utilized to optimize supply schedules by taking into account factors like demand, transportation constraints, and storage capacities.

Hydrogen pipeline network design : Designing an optimal pipeline network for hydrogen transportation involves considering multiple factors such as pipeline routes, materials, safety, and cost. Metaheuristic optimization techniques can aid in the design of efficient pipeline networks by optimizing these factors and identifying the most suitable solutions.

Addressing these challenges through the application of metaheuristic optimization techniques can lead to significant improvements in the efficiency, reliability, and cost-effectiveness of hydrogen transportation systems. This, in turn, can contribute to the overall growth and success of the hydrogen economy.

5.3. Applications of metaheuristic optimization techniques for hydrogen transportation

Metaheuristic optimization techniques have been successfully applied to various aspects of hydrogen transportation, helping to address the challenges mentioned in the previous subsection. In this section, we discuss the application of metaheuristic optimization techniques to the following aspects of hydrogen transportation:

Multi-modal transportation optimization : The objective function for multi-modal transportation optimization focuses on minimizing the total cost of hydrogen transportation, including the costs associated with different transportation modes and infrastructure. Constraints may include transportation capacities, distances, and the availability of different transportation modes. By applying metaheuristic optimization techniques, an optimal combination of transportation methods can be identified that meets the required demand while minimizing costs.

Hydrogen supply scheduling : The objective function for hydrogen supply scheduling aims to minimize the total operational costs, which may include transportation, storage, and production costs, while meeting the hydrogen demand of end-users. Constraints in this context can involve transportation and storage capacities, as well as demand and supply variations over time. Metaheuristic optimization techniques can be used to find efficient supply schedules that satisfy the constraints and minimize operational costs.

Hydrogen pipeline network design : In the case of hydrogen pipeline network design, the objective function focuses on minimizing the total cost of the network, including capital, operational, and maintenance costs. Constraints may involve pipeline route restrictions, safety regulations, and material requirements. Metaheuristic optimization techniques can help identify optimal pipeline network designs that meet the constraints and minimize overall costs.

Refuelling station location planning : The objective function for refuelling station location planning seeks to minimize the total cost of establishing and maintaining refuelling stations while ensuring adequate coverage and accessibility. Constraints can include the number of stations, their capacities, and geographical restrictions. By applying metaheuristic optimization techniques, optimal locations for refuelling stations can be determined that satisfy the constraints and minimize costs.

Figure  4 illustrates the transportation of green hydrogen across the world, highlighting the key aspects where metaheuristic optimization techniques can be applied to address the challenges. In summary, metaheuristic optimization techniques can play a crucial role in improving the efficiency and cost-effectiveness of hydrogen transportation systems. By addressing various challenges in transportation planning, scheduling, and design, these techniques contribute to the development of robust and reliable hydrogen transportation networks.

Hydrogen consumption is a critical aspect of the hydrogen economy, as it involves using hydrogen as an energy carrier in various applications such as transportation, heating, and industrial processes. In this section, we will discuss the different methods of hydrogen consumption, the challenges associated with them, and the role of metaheuristic optimization in addressing these challenges.

6.1. Overview of hydrogen consumption: challenges and comparison of different methods

Hydrogen consumption methods encompass a variety of technologies that convert hydrogen into useful forms of energy. These methods include fuel cells, hydrogen combustion turbines, hydrogen internal combustion engines, H2-based heating systems, and chemical processes such as ammonia and methanol production.

The challenges associated with hydrogen consumption are diverse, ranging from technological limitations to integration issues with existing energy systems. These challenges have been listed in Table  6 and are summarized as follows:

Fuel cell performance optimization : Fuel cells are devices that convert the chemical energy stored in hydrogen directly into electricity through an electrochemical reaction. Optimizing fuel cell performance involves improving their efficiency, durability, and cost, as well as addressing issues related to hydrogen purity and operating conditions.

H2-based heating systems design : Hydrogen can be used for heating purposes in residential, commercial, and industrial settings. Designing H2-based heating systems involves optimizing the system’s efficiency, safety, and reliability while minimizing costs and environmental impacts.

Energy conversion technology choice (including combustion process optimization) : The choice of energy conversion technology, such as hydrogen combustion turbines or internal combustion engines, depends on factors such as efficiency, cost, and environmental impact. Combustion process optimization involves improving the performance, efficiency, and emissions of hydrogen combustion technologies.

H2 Integration in multi-energy systems (including chemical process optimization) : Integrating hydrogen into multi-energy systems requires careful planning and coordination, taking into account the interactions between different energy carriers and infrastructures. Chemical process optimization involves improving the efficiency, cost, and environmental performance of hydrogen-based chemical processes, such as ammonia and methanol production.

Comparison of hydrogen consumption methods.

6.2. The need, role, and impact of metaheuristic optimization in improving hydrogen consumption

After investigating the challenges associated with hydrogen consumption, it is essential to investigate the comparison of different hydrogen consumption methods further:

Fuel cells : Fuel cells convert hydrogen into electricity directly through an electrochemical reaction. They are highly efficient, have low emissions, and are suitable for a wide range of applications, including transportation, stationary power generation, and portable power devices. Fuel cells come in various types, such as proton exchange membrane fuel cells (PEMFCs), solid oxide fuel cells (SOFCs), alkaline fuel cells, and molten carbonate fuel cells, each with its own advantages and limitations. PEMFCs, e.g., have a low operating temperature and quick start-up, making them suitable for transportation applications. In contrast, SOFCs operate at high temperatures and are more suitable for stationary power generation with high efficiency and fuel flexibility. However, fuel cells can be expensive and sensitive to impurities in hydrogen fuel, which may require additional purification steps or more robust fuel cell designs to ensure long-term performance and durability.

Hydrogen combustion turbines : Hydrogen combustion turbines burn hydrogen to generate electricity, offering a cleaner alternative for large-scale power generation. They have the potential for high efficiency and low emissions due to their ability to operate at higher temperatures and pressures. However, they may require modifications to existing natural gas turbines and face challenges related to hydrogen’s low energy density and high flame speed. Additionally, the scalability of hydrogen combustion turbines can be limited by their complexity and higher capital costs compared to internal combustion engines.

Hydrogen internal combustion engines : Hydrogen can be used as a fuel in internal combustion engines, either by replacing or blending with conventional fuels like gasoline. These engines have the advantage of leveraging existing infrastructure and technology, making them more easily scalable and cost-effective. However, they generally have lower efficiency and higher emissions compared to fuel cells and hydrogen combustion turbines, mainly due to incomplete combustion and lower thermodynamic efficiency.

H2-based heating systems : Hydrogen can be used for heating purposes in residential, commercial, and industrial settings. These systems can provide low-emission heating solutions but may require modifications to existing natural gas infrastructure and the development of hydrogen-specific appliances.

Chemical processes (e.g., ammonia production and methanol production) : Hydrogen is a crucial component in the production of chemicals like ammonia and methanol, which can be used as fuels or feedstocks for other industrial processes. These processes can help create value-added products from hydrogen and contribute to the overall hydrogen economy. However, they may involve energy-intensive processes and environmental concerns related to their production and use.

As explained, each method has its advantages and limitations, and the choice of a suitable method depends on various factors, including efficiency, cost, environmental impact, and compatibility with existing infrastructure. As the hydrogen economy continues to develop, it is likely that a combination of these methods will be employed, and advances in technology and optimization techniques will help address their respective challenges and limitations.

6.3. Applications of metaheuristic optimization techniques in hydrogen consumption

In this subsection, the applications of metaheuristic optimization techniques in hydrogen consumption are discussed, focusing on the four main areas for optimization identified in Fig.  5 : fuel cell performance optimization, H2-based heating systems design, energy conversion technology choice (including combustion process optimization), and H2 integration in multi-energy systems (including chemical process optimization).

Fuel cell performance optimization: The objective here is to maximize the efficiency and lifetime of fuel cells while minimizing their cost. Metaheuristic optimization techniques can be used to optimize parameters such as operating temperature, pressure, and flow rates, as well as the design of individual components like the membrane, catalyst, and electrodes. Constraints include material properties, durability, and safety requirements.

H2-based heating systems design: The goal is to design efficient, cost-effective, and environmentally friendly hydrogen-based heating systems. Optimization techniques can be employed to determine the optimal size, configuration, and control strategies for these systems, considering factors such as heat demand, energy prices, and greenhouse gas emissions. Constraints may include available space, budget, and regulatory requirements.

Energy conversion technology choice (including combustion process optimization): Selecting the most suitable energy conversion technology for a specific application is critical for maximizing efficiency and minimizing costs and emissions. Metaheuristic optimization methods can be used to compare and select the best technology based on various criteria such as efficiency, capital cost, operating cost, and environmental impact. Constraints include technical feasibility, available infrastructure, and regulatory restrictions.

H2 Integration in multi-energy systems (including chemical process optimization): In this area, the objective is to optimally integrate hydrogen into multi-energy systems, such as microgrids and industrial processes, to maximize system efficiency and flexibility while minimizing costs and environmental impacts. Metaheuristic optimization techniques can help in determining the optimal scheduling, control, and design of hydrogen production, storage, and consumption components, as well as the optimal use of hydrogen in chemical processes like ammonia and methanol production. Constraints may include energy demand, resource availability, and environmental regulations.

Schematic representation of hydrogen consumption methods, highlighting key aspects for metaheuristic optimization.

In summary, metaheuristic optimization techniques offer significant potential for improving the performance, efficiency, and environmental impact of hydrogen consumption technologies. As these technologies continue to develop and mature, the application of optimization methods will play a crucial role in realizing the full potential of hydrogen as a clean and sustainable energy carrier.

Multi-objective optimization plays a crucial role in solving complex problems that involve multiple conflicting objectives. Dominant solutions, also known as Pareto-optimal solutions, represent the optimal trade-offs among these objectives. In the context of green hydrogen supply chains, multi-objective optimization can help address the challenges and improve efficiency, cost, performance, and safety (Alirahmi et al ., 2021 ; Sharafi & ELMekkawy, 2014 ; Xu et al ., 2020 ). In the following subsections, two detailed examples of multi-objective optimization problems are presented, focusing on various stages of the supply chain, emphasizing an algorithm-agnostic approach.

7.1. Example 1: production and transportation

Here, the focus is optimizing the green hydrogen production and transportation stages using a multi-objective approach. The objectives for this optimization problem are:

minimizing the total cost of hydrogen production (C1);

minimizing the total cost of hydrogen transportation (C2);

maximizing the efficiency of hydrogen production (E1); and

maximizing the safety of hydrogen transportation (E2).

The optimization problem can be formulated as follows:

Subject to constraints related to green hydrogen production (such as electrolyser capacity, renewable energy availability, and water consumption) and transportation (such as pipeline capacity, refuelling station location, and transportation mode availability), a Pareto-based multi-objective optimization technique can be used to tackle this problem, according to algorithm 2. In this example, dominant solution optimization aims to identify the best trade-off between the conflicting objectives. By finding the Pareto-optimal solutions, decision-makers can efficiently allocate resources and make informed decisions on the production and transportation of green hydrogen while balancing the economic, environmental, and safety aspects.

7.2. Example 2: storage and consumption

In this example, the focus is on optimizing the green hydrogen storage and consumption stages using a multi-objective approach. The objectives for this optimization problem are:

minimizing the total cost of hydrogen storage (C3);

minimizing the total cost of hydrogen consumption (C4);

maximizing the storage system efficiency (E3); and

maximizing the energy conversion efficiency (E4).

Subject to constraints related to green hydrogen storage (such as storage capacity, pressure and temperature control, and storage materials) and consumption (such as fuel cell performance, H2-based heating systems design, and energy conversion technology choice), a Pareto-based multi-objective optimization technique can be used to tackle this problem, according to algorithm  2 . In this example, dominant solution optimization focuses on finding the Pareto-optimal solutions that represent the best trade-offs between minimizing costs (C3 and C4) and maximizing efficiencies (E3 and E4). Identifying these solutions allows decision-makers to make informed choices on hydrogen storage and consumption strategies while optimizing economic and efficiency aspects.

Generic Metaheuristic Algorithm for Examples 1-2

This section presents a recap of the main points covered in the paper, followed by state-of-the-art research and development in the optimization of the hydrogen supply chain.

8.1. Summary

In this paper, a comprehensive overview of the hydrogen supply chain was provided, highlighting the challenges associated with each stage and discussing the opportunities for metaheuristic optimization techniques.

For green hydrogen production, the primary method, i.e., water electrolysis through renewable energy, was outlined, and the importance of optimizing these methods to maximize efficiency and minimize costs and environmental impacts was discussed. In terms of storage, the primary methods such as compressed gas, liquid hydrogen, and material-based storage were covered, emphasizing the need for optimization to improve safety, capacity, and performance. Regarding hydrogen transportation, different options were explored, including pipelines, trucks, and ships, and factors that influence the choice of transportation methods in different regions were identified. Additionally, various hydrogen consumption methods were discussed along with their associated challenges, such as fuel cell performance optimization, H2-based heating systems design, and energy conversion technology choice.

Throughout the paper, the significant potential of metaheuristic optimization techniques was highlighted as a key to addressing these challenges and improving the overall efficiency and sustainability of hydrogen-based energy systems.

8.2. Future trends and research recommendations

This subsection presents an overview of the upcoming trends in the hydrogen supply chain, with a focus on the key aspects of production, storage, transportation, and consumption. As illustrated in Fig.  6a , the diagram demonstrates the potential losses associated with each stage, emphasizing the importance of optimization and innovative approaches in the green hydrogen supply chain. Figure  6b also highlights the future trends and opportunities, which are investigated in more detail as follows:

Green hydrogen supply chain and metaheuristic optimization: (a) Indicative losses in each stage, and (b) future research directions.

8.2.1. Production

For green hydrogen production, advanced water electrolysis technologies, such as waste heat recovery and solid-state electrolysers, are being developed to improve efficiency and lower costs. Additionally, the design and optimization of the electrolyser systems, including the development of more efficient and durable catalysts and membranes, as well as the thermal management of the system. Integrating these production methods with Direct Current (DC) microgrids can further enhance efficiency, as Photo-Voltaic (PV) panels, batteries, and electrolysers are all DC sources and loads from an electrical perspective. In terms of power conversion, utilizing more efficient materials such as gallium nitride (GaN) and superconductive materials can significantly increase the efficiency of power conversion processes, further contributing to the reduction of energy losses and improving overall system performance. These trends are summarized as the following items:

Increased focus on green hydrogen;

Advanced water electrolysis technologies (waste heat recovery and solid-state electrolysers; Saufi Sulaiman et al ., 2019 );

Renewable energy sources (solar and wind; Patel et al ., 2021 );

DC microgrid (efficient interconnection of PV, battery, and electrolyser; Moradi et al ., 2022 ); and

Advanced materials for power conversion (GaN or superconductors; Shahbazi et al ., 2021 ).

8.2.2. Storage

For hydrogen storage, research is focusing on the development of advanced storage materials such as metal-organic frameworks, nano-structured materials, and solid-state materials, which can offer improved storage capacities, safety, and efficiency. Cryogenic storage improvements and high-pressure vessels will contribute to enhanced safety and storage capacities. Additionally, as these advanced materials and technologies are accepted more widely, economies of scale and technology advancements will lead to lower costs associated with hydrogen storage. These trends are summarized as below:

Development of advanced storage materials (metal-organic frameworks, nano-structured materials, and solid oxide research; Srivastava et al ., 2022 );

Improved safety and storage capacities (cryogenic storage improvements and high-pressure vessels); and

Lower costs (economies of scale and technology advancements).

8.2.3. Transportation

An expansion of hydrogen infrastructure, including pipelines, trucks, and ships, is expected to facilitate the distribution of hydrogen more effectively. The number of hydrogen refuelling stations will also increase, with modular designs and faster refuelling technologies improving the accessibility of hydrogen fuel for end-users. Advanced transportation systems, such as autonomous vehicles and digital logistics platforms, will further optimize the transportation process, as listed below:

Expansion of hydrogen infrastructure (pipelines, trucks, and ships);

Increased hydrogen refuelling stations (modular designs and faster refuelling); and

Advanced transportation systems (autonomous vehicles and digital logistics platforms).

8.2.4. Consumption

As for consumption, integration into existing energy systems, such as hybrid systems and smart energy management, will be crucial to optimize energy utilization. Advances in fuel cell technology, including improvements in efficiency, durability, and cost reduction. Additionally, the development of more efficient and scalable hydrogen-based power generation technologies, such as advanced hydrogen turbines and internal combustion engines, is essential for the broader adoption and integration of hydrogen into various industrial and transportation sectors. These trends are summarized below:

Integration of hydrogen into existing energy systems (hybrid systems and smart energy management);

Advances in fuel cell technology (efficiency, durability, cost reduction, PEMFCs, and SOFCs; Suresh et al ., 2022 ); and

Growing demand for hydrogen in industrial processes (ammonia, methanol, refining, steelmaking, and green chemicals).

8.2. Optimization trends

In light of mentioned future trends, the role of metaheuristic optimization techniques in the hydrogen economy becomes even more crucial. Metaheuristic optimization techniques can be applied to a wide range of problems in the hydrogen supply chain, from optimizing the production process and ensuring efficient storage and transportation, to maximizing the performance of hydrogen consumption systems. By intelligently exploring the search space and finding near-optimal solutions, these techniques can help identify the best system configurations, material choices, and operational strategies that maximize performance while minimizing costs, energy consumption, and environmental impacts. Moreover, as the field continues to advance, novel metaheuristic algorithms will be developed, allowing for even more effective optimization of complex hydrogen systems.

None declared.

Abbaspour S. , Aghsami A. , Jolai F. , Yazdani M. ( 2022 ). An integrated queueing-inventory-routing problem in a green dual-channel supply chain considering pricing and delivery period: A case study of construction material supplier . Journal of Computational Design and Engineering , 9 ( 5 ), 1917 – 1951 .. https://doi.org/10.1093/jcde/qwac089 .

Google Scholar

Abo-Elyousr F. K. , Guerrero J. M. , Ramadan H. S. ( 2021 ). Prospective hydrogen-based microgrid systems for optimal leverage via metaheuristic approaches . Applied Energy , 300 , 117384 . https://doi.org/10.1016/j.apenergy.2021.117384 .

Al-Rashed A. A. , Alsarraf J. , Alnaqi A. A. ( 2022 ). Exergy optimization of a novel hydrogen production plant with fuel cell, heat recovery, and MED using NSGAII genetic algorithm . International Journal of Hydrogen Energy , 47 ( 62 ), 26673 – 26686 .. https://doi.org/10.1016/j.ijhydene.2021.12.215 .

Alirahmi S. M. , Razmi A. R. , Arabkoohsar A. ( 2021 ). Comprehensive assessment and multi-objective optimization of a green concept based on a combination of hydrogen and compressed air energy storage (CAES) systems . Renewable and Sustainable Energy Reviews , 142 , 110850 . https://doi.org/10.1016/j.rser.2021.110850 .

Ball M. , Wietschel M. ( 2009 ). The future of hydrogen – Opportunities and challenges . International Journal of Hydrogen Energy , 34 ( 2 ), 615 – 627 .. https://doi.org/10.1016/j.ijhydene.2008.11.014 .

Bertuccioli L. , Chan A. , Hart D. , Lehner F. , Madden B. , Standen E. ( 2014 ). Development of water electrolysis in the European Union . In Fuel cells and hydrogen joint undertakings . E4tech Sàrl with Element Energy Ltd Available at : https://www.e4tech.com/resources/107-development-of-water-electrolysis-in-the-european-union.php?filter=year%3A2014 .

Google Preview

Bloom Energy . ( 2023 ). https://www.bloomenergy.com/bloomelectrolyzer/ . Accessed on 16 March 2023 .

Carmo M. , Fritz D. L. , Mergel J. , Stolten D. ( 2013 ). A comprehensive review on PEM water electrolysis . International Journal of Hydrogen Energy , 38 ( 12 ), 4901 – 4934 .. https://doi.org/10.1016/j.ijhydene.2013.01.151 .

Cheng M.-Y. , Cao M.-T. , Tsai P.-K. ( 2021 ). Predicting load on ground anchor using a metaheuristic optimized least squares support vector regression model: A Taiwan case study . Journal of Computational Design and Engineering , 8 ( 1 ), 268 – 282 .. https://doi.org/10.1093/jcde/qwaa077 .

Climate change . ( 2007 ). Intergovernmental Panel on Climate Change. World Meteorological Organization .

Crainic T. G. , Toulouse M. ( 2003 ). Parallel strategies for meta-heuristics . In Glover F. , Kochenberger G. A. (Eds.), Handbook of metaheuristics (pp. 475 – 513 .). Springer . https://doi.org/10.1007/0-306-48056-5_17 .

Ding X. , Sun W. , Harrison G. P. , Lv X. , Weng Y. ( 2020 ). Multi-objective optimization for an integrated renewable, power-to-gas and solid oxide fuel cell/gas turbine hybrid system in microgrid . Energy , 213 , 118804 . https://doi.org/10.1016/j.energy.2020.118804 .

El-Shorbagy M. A. , El-Refaey A. M. ( 2022 ). A hybrid genetic–firefly algorithm for engineering design problems . Journal of Computational Design and Engineering , 9 ( 2 ), 706 – 730 .. https://doi.org/10.1093/jcde/qwac013 .

Enapter . ( 2023 ). https://www.enapter.com/ . Accessed on 16 March 2023 .

Gharib Z. , Yazdani M. , Bozorgi-Amiri A. , Tavakkoli-Moghaddam R. , Taghipourian M. J. ( 2022 ). Developing an integrated model for planning the delivery of construction materials to post-disaster reconstruction projects . Journal of Computational Design and Engineering , 9 ( 3 ), 1135 – 1156 .. https://doi.org/10.1093/jcde/qwac042 .

Gondal I. A. ( 2019 ). Offshore renewable energy resources and their potential in a green hydrogen supply chain through power-to-gas . Sustainable Energy & Fuels , 3 ( 6 ), 1468 – 1489 .. https://doi.org/10.1039/C8SE00544C .

Gorji S. A. ( 2022 ). Reconfigurable quadratic converters for electrolyzers utilized in DC microgrids . IEEE Access , 10 , 109677 – 109687 .. https://doi.org/10.1109/ACCESS.2022.3214581 .

Hermesmann M. , Müller T. ( 2022 ). Green, turquoise, blue, or grey? Environmentally friendly hydrogen production in transforming energy systems . Progress in Energy and Combustion Science , 90 , 100996 . https://doi.org/10.1016/j.pecs.2022.100996 .

Howarth R. W. , Jacobson M. Z. ( 2021 ). How green is blue hydrogen? . Energy Science & Engineering , 9 ( 10 ), 1676 – 1687 .. https://doi.org/10.1002/ese3.956 .

Hu H. , Shan W. , Tang Y. , Heidari A. A. , Chen H. , Liu H. , Wang M. , Escorcia-Gutierrez J. , Mansour R. F. , Chen J. ( 2022 ). Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection . Journal of Computational Design and Engineering , 9 ( 6 ), 2524 – 2555 .. https://doi.org/10.1093/jcde/qwac119 .

ITM Power . ( 2023 ). https://itm-power.com/ . Accessed on 16 March 2023 .

Jacobson M. Z. , Delucchi M. A. , Bazouin G. , Bauer Z. A. F. , Heavey C. C. , Fisher E. , Morris S. B. , Piekutowski D. J. Y. , Vencill T. A. , Yeskoo T. W. ( 2019 ). 100% Clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for the 50 United States . Energy and Environmental Science , 8 ( 7 ), 2093 – 2117 .. https://doi.org/10.1039/C5EE01283J .

Li X. , Raorane C. J. , Xia C. , Wu Y. , Tran T. K. N. , Khademi T. ( 2023 ). Latest approaches on green hydrogen as a potential source of renewable energy towards sustainable energy: Spotlighting of recent innovations, challenges, and future insights . Fuel , 334 , 126684 . https://doi.org/10.1016/j.fuel.2022.126684 .

Mehrjerdi H. , Saboori H. , Jadid S. ( 2022 ). Power-to-gas utilization in optimal sizing of hybrid power, water, and hydrogen microgrids with energy and gas storage . Journal of Energy Storage , 45 , 103745 . https://doi.org/10.1016/j.est.2021.103745 .

Mohammadshahi S. S. , Boulaire F. A. , Love J. , Gorji S. A. , Mackinnon I. D. ( 2022 ). A flexible analytical model for operational investigation of solar hydrogen plants . International Journal of Hydrogen Energy , 47 ( 2 ), 782 – 808 .. https://doi.org/10.1016/j.ijhydene.2021.10.072 .

Monforti Ferrario A. , Bartolini A. , Segura Manzano F. , Vivas F. J. , Comodi G. , McPhail S. J. , Andujar J. M. ( 2021 ). A model-based parametric and optimal sizing of a battery/hydrogen storage of a real hybrid microgrid supplying a residential load: Towards island operation . Advances in Applied Energy , 3 , 100048 . https://doi.org/10.1016/j.adapen.2021.100048 .

Moradi A. , Gorji S. A. , Hakemi A. , Ganjavi A. , Sera D. ( 2022 ). Study of a DC micro-gird configuration to produce hydrogen (DCMG-H2) . In Proceedings of the 2022 IEEE 7th Southern Power Electronics Conference (SPEC) (pp. 1 – 5 .). IEEE . https://doi.org/10.1109/SPEC55080.2022.10058435 .

Nel Hydrogen . ( 2023 ). https://nelhydrogen.com/ .  Accessed on 16 March 2023 .

Osaba E. , Villar-Rodriguez E. , Del Ser J. , Nebro A. J. , Molina D. , LaTorre A. , Suganthan P. N. , Coello Coello C. A. , Herrera F. ( 2021 ). A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems . Swarm and Evolutionary Computation , 64 , 100888 . https://doi.org/10.1016/j.swevo.2021.100888 .

Patel H. V. , Gorji S. A. , Shahi S. S. M. , Love J. G. ( 2021 ). Implementation of a lab-scale green hydrogen production system with solar PV emulator and energy storage system . In Proceedings of the 2021 11th International Conference on Power and Energy Systems (ICPES) (pp. 201 – 208 .). IEEE . https://doi.org/10.1109/ICPES53652.2021.9683797 .

Riera J. A. , Lima R. M. , Knio O. M. ( 2023 ). A review of hydrogen production and supply chain modeling and optimization . International Journal of Hydrogen Energy , 48 , 13731 – 13755 .. https://doi.org/10.1016/j.ijhydene.2022.12.242 .

Safari S. , Ardehali M. , Sirizi M. ( 2013 ). Particle swarm optimization based fuzzy logic controller for autonomous green power energy system with hydrogen storage . Energy Conversion and Management , 65 , 41 – 49 .. https://doi.org/10.1016/j.enconman.2012.08.012 .

Saufi Sulaiman M. , Singh B. , Mohamed W. ( 2019 ). Experimental and theoretical study of thermoelectric generator waste heat recovery model for an ultra-low temperature pem fuel cell powered vehicle . Energy , 179 , 628 – 646 .. https://doi.org/10.1016/j.energy.2019.05.022 .

Shahbazi M. , Dehghan-Manshadi A. , Hossain M. , Ravishankar R. , Mackinnon I. ( 2021 ). Microstructure and superconducting properties of copper sheathed MGB2 wires with ti barrier fabricated by in situ powder-in-tube method . IEEE Transactions on Applied Superconductivity , 31 ( 5 ), 1 – 5 .. https://doi.org/10.1109/TASC.2021.3065634 .

Sharaf O. Z. , Orhan M. F. ( 2014 ). An overview of fuel cell technology: Fundamentals and applications . Renewable and Sustainable Energy Reviews , 32 , 810 – 853 .. https://doi.org/10.1016/j.rser.2014.01.012 .

Sharafi M. , ELMekkawy T. Y. ( 2014 ). Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach . Renewable Energy , 68 , 67 – 79 .. https://doi.org/10.1016/j.renene.2014.01.011 .

Sherif S. , Barbir F. , Veziroglu T. ( 2005 ). Wind energy and the hydrogen economy–Review of the technology . Solar Energy , 78 ( 5 ), 647 – 660 . . https://doi.org/10.1016/j.solener.2005.01.002 .

Srivastava S. , Shet S. P. , Shanmuga Priya S. , Sudhakar K. , Tahir M. ( 2022 ). Molecular simulation of copper based metal-organic framework (Cu-MOF) for hydrogen adsorption . International Journal of Hydrogen Energy , 47 ( 35 ), 15820 – 15831 .. https://doi.org/10.1016/j.ijhydene.2022.03.089 .

Suresh R. , Rajendran S. , Kumar P. S. , Dutta K. , Vo D.-V. N. ( 2022 ). Current advances in microbial fuel cell technology toward removal of organic contaminants – A review . Chemosphere , 287 , 132186 . https://doi.org/10.1016/j.chemosphere.2021.132186 .

Tavasoli N. , Rezaee K. , Momenzadeh M. , Sehhati M. ( 2021 ). An ensemble soft weighted gene selection-based approach and cancer classification using modified metaheuristic learning . Journal of Computational Design and Engineering , 8 ( 4 ), 1172 – 1189 .. https://doi.org/10.1093/jcde/qwab039 .

Wang Y. ( 2010 ). A sociopsychological perspective on collective intelligence in metaheuristic computing . International Journal of Applied Metaheuristic Computing , 1 ( 1 ), 110 – 128 .. https://doi.org/10.4018/jamc.2010102606 .

Xu C. , Ke Y. , Li Y. , Chu H. , Wu Y. ( 2020 ). Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-II and CRITIC-TOPSIS . Energy Conversion and Management , 215 , 112892 . https://doi.org/10.1016/j.enconman.2020.112892 .

Yazdani M. , Mojtahedi M. , Loosemore M. ( 2020 ). Enhancing evacuation response to extreme weather disasters using public transportation systems: A novel simheuristic approach . Journal of Computational Design and Engineering , 7 ( 2 ), 195 – 210 .. https://doi.org/10.1093/jcde/qwaa017 .

Zhang W. , Maleki A. , Rosen M. A. , Liu J. ( 2018 ). Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage . Energy , 163 , 191 – 207 .. https://doi.org/10.1016/j.energy.2018.08.112 .

Email alerts

Citing articles via.

• Advertising and Corporate Services
• Journals Career Network

Affiliations

• Online ISSN 2288-5048
• Copyright © 2024 Society for Computational Design and Engineering
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Author Biographies
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

An overview of the efficiency and long-term viability of powered hydrogen production.

1. Introduction

2. energy production, hydrogen technologies, and environmental sustainability, 2.1. energy production methods, 2.2. hydrogen production methods, 2.3. global greenhouse gas (ghg) emissions.

Click here to enlarge figure

3. Methodology

• Data collection: Data were extracted from specialized scientific study reports in the literature, as cited in the bibliography: [ 1 , 2 , 3 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 30 , 31 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 42 , 47 , 50 , 51 , 53 , 55 , 60 , 62 , 63 , 64 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 ].
• Information authentication: The objective was to provide greater clarity on the advances made in the field of green hydrogen based on electrolysis. In this context, the rise of a relatively new technology promoted research and the development of articles in reputable journals, books, and academic institutions, allowing the identification of reports that added value to the field.
• Organization and distribution of topics: A close relationship was found among different authors, confirming the findings discussed in the literature. Table 2 , Table 3 and Table 4 summarize the relevant information collected, showing the most accurate results in each statement.
• Analysis of selected information: The most relevant and accurate information was chosen, making it possible to carry out the analysis and discussion presented later in this work. It was very useful to make a table of reference sources through the years and map the trend of the energy sector and the evolution of renewable technology.

4. Hydrogen as a Sustainable Solution for Electricity Production

5. The Principal Characteristics of Energy Depend on the Production Process of Hydrogen

6. efficiency trends in hydrogen electrolysis and fuel cells advances and challenges.

6.1. Fuel Cells

6.2. current challenges in fuel cell technology, 6.3. improvements in fuel cell efficiency, 7. recent advancements in hydrogen production and storage technologies, 7.1. hydrogen production, 7.2. hydrogen storage technologies, 8. discussion, 9. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest, abbreviations.

• Dunn, S. Hydrogen Futures: Toward a Sustainable Energy System. Int. J. Hydrogen Energy 2002 , 27 , 235–264. [ Google Scholar ] [ CrossRef ]
• Žarković, M.; Lakić, S.; Ćetković, J.; Pejović, B.; Redzepagic, S.; Vodenska, I.; Vujadinović, R. Effects of Renewable and Non-Renewable Energy Consumption, GHG, ICT on Sustainable Economic Growth: Evidence from Old and New EU Countries. Sustainability 2022 , 14 , 9662. [ Google Scholar ] [ CrossRef ]
• de León, C.M.; Ríos, C.; Brey, J.J. Cost of Green Hydrogen: Limitations of Production from a Stand-Alone Photovoltaic System. Int. J. Hydrogen Energy 2023 , 48 , 11885–11898. [ Google Scholar ] [ CrossRef ]
• Gabric, A.J. The Climate Change Crisis: A Review of Its Causes and Possible Responses. Atmosphere 2023 , 14 , 1081. [ Google Scholar ] [ CrossRef ]
• Short, J.R.; Farmer, A. Cities and Climate Change. Earth 2021 , 2 , 1038–1045. [ Google Scholar ] [ CrossRef ]
• Sun, C.; Negro, E.; Vezzù, K.; Pagot, G.; Cavinato, G.; Nale, A.; Herve Bang, Y.; Di Noto, V. Hybrid Inorganic-Organic Proton-Conducting Membranes Based on SPEEK Doped with WO3 Nanoparticles for Application in Vanadium Redox Flow Batteries. Electrochim. Acta 2019 , 309 , 311–325. [ Google Scholar ] [ CrossRef ]
• He, Y. Renewable and Non-Renewable Energy Consumption and Trade Policy: Do They Matter for Environmental Sustainability? Energies 2022 , 15 , 3559. [ Google Scholar ] [ CrossRef ]
• Geopolitics of the Energy Transformation: The Hydrogen Factor ; IRENA: New York, NY, USA, 2022.
• Hassan, Q.; Azzawi, I.D.J.; Sameen, A.Z.; Salman, H.M. Hydrogen Fuel Cell Vehicles: Opportunities and Challenges. Sustainability 2023 , 15 , 11501. [ Google Scholar ] [ CrossRef ]
• Liao, C.H.; Huang, C.W.; Wu, J.C.S. Hydrogen Production from Semiconductor-Based Photocatalysis via Water Splitting. Catalysts 2012 , 2 , 490–516. [ Google Scholar ] [ CrossRef ]
• Yousaf, M.; Mahmood, A.; Elkamel, A.; Rizwan, M.; Zaman, M. Techno-Economic Analysis of Integrated Hydrogen and Methanol Production Process by CO 2 Hydrogenation. Int. J. Greenh. Gas Control. 2022 , 115 , 103615. [ Google Scholar ] [ CrossRef ]
• Ball, M.; Weeda, M. The Hydrogen Economy—Vision or Reality? In Compendium of Hydrogen Energy: Hydrogen Use, Safety and the Hydrogen Economy ; Woodhead Publishing: Cambridge, UK, 2016; Volume 4, pp. 237–266. [ Google Scholar ] [ CrossRef ]
• Levin, D.B.; Chahine, R. Challenges for Renewable Hydrogen Production from Biomass. Int. J. Hydrogen Energy 2010 , 35 , 4962–4969. [ Google Scholar ] [ CrossRef ]
• Santos, D.M.F.; Sequeira, C.A.C.; Figueiredo, J.L. Hydrogen Production by Alkaline Water Electrolysis. Quim. Nova 2013 , 36 , 1176–1193. [ Google Scholar ] [ CrossRef ]
• Kyung, D.; Yu, C.-H.; Lin, Y.-P.; Yap, J.; Mclellan, B. A Historical Analysis of Hydrogen Economy Research, Development, and Expectations, 1972 to 2020. Environments 2023 , 10 , 11. [ Google Scholar ] [ CrossRef ]
• Brandon, N.P.; Kurban, Z. Clean Energy and the Hydrogen Economy. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2017 , 375 , 20160400. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vakulchuk, R.; Overland, I.; Scholten, D. Renewable Energy and Geopolitics: A Review. Renew. Sustain. Energy Rev. 2020 , 122 , 109547. [ Google Scholar ] [ CrossRef ]
• Scita, R.; Raimondi, P.P.; Noussan, M. Green Hydrogen: The Holy Grail of Decarbonisation? An Analysis of the Technical and Geopolitical Implications of the Future Hydrogen Economy. SSRN Electron. J. 2020 . [ Google Scholar ] [ CrossRef ]
• Clark, W.W.; Rifkin, J. A Green Hydrogen Economy. Energy Policy 2006 , 34 , 2630–2639. [ Google Scholar ] [ CrossRef ]
• Sarker, A.K.; Azad, A.K.; Rasul, M.G.; Doppalapudi, A.T. Prospect of Green Hydrogen Generation from Hybrid Renewable Energy Sources: A Review. Energies 2023 , 16 , 1556. [ Google Scholar ] [ CrossRef ]
• Marnellos, E.; Zun, M.T.; Mclellan, B.C. Cost Projection of Global Green Hydrogen Production Scenarios. Hydrogen 2023 , 4 , 932–960. [ Google Scholar ] [ CrossRef ]
• Liu, Z.; Han, B.; Lu, Z.; Guan, W.; Li, Y.; Song, C.; Chen, L.; Singhal, S.C. Efficiency and Stability of Hydrogen Production from Seawater Using Solid Oxide Electrolysis Cells. Appl. Energy 2021 , 300 , 117439. [ Google Scholar ] [ CrossRef ]
• Zhang, K.; Liang, X.; Wang, L.; Sun, K.; Wang, Y.; Xie, Z.; Wu, Q.; Bai, X.; Hamdy, M.S.; Chen, H.; et al. Status and Perspectives of Key Materials for PEM Electrolyzer. Nano Res. Energy 2022 , 1 , e9120032. [ Google Scholar ] [ CrossRef ]
• Pandian, M.S.; Anwari, M.; Husodo, B.Y.; Hiendro, A. Efficiency and Economics Analysis of Proton Exchange Membrane Fuel Cell. In Proceedings of the 2010 9th International Power and Energy Conference, IPEC 2010, Singapore, 27–29 October 2010; pp. 875–880. [ Google Scholar ] [ CrossRef ]
• Vidas, L.; Castro, R. Recent Developments on Hydrogen Production Technologies: State-of-the-Art Review with a Focus on Green-Electrolysis. Appl. Sci. 2021 , 11 , 11363. [ Google Scholar ] [ CrossRef ]
• Kumar, S.S.; Himabindu, V. Hydrogen Production by PEM Water Electrolysis—A Review. Mater. Sci. Energy Technol. 2019 , 2 , 442–454. [ Google Scholar ] [ CrossRef ]
• James, B.D.; Nrel, J.M.M.; Saur, G.; Ramsden, T. Techno-Economic Analysis of PEM Electrolysis for Hydrogen Production ; U.S. Department of Energy: Golden, CO, USA, 2014.
• Zhang, H.; Su, S.; Lin, G.; Chen, J. Efficiency Calculation and Configuration Design of a PEM Electrolyzer System for Hydrogen Production. Int. J. Electrochem. Sci 2012 , 7 , 4143–4157. [ Google Scholar ] [ CrossRef ]
• Dobó, Z.; Palotás, Á.B. Impact of the Voltage Fluctuation of the Power Supply on the Efficiency of Alkaline Water Electrolysis. Int. J. Hydrogen Energy 2016 , 41 , 11849–11856. [ Google Scholar ] [ CrossRef ]
• Wang, Z.; Mori, M.; Araki, T. Steam Electrolysis Performance of Intermediate-Temperature Solid Oxide Electrolysis Cell and Efficiency of Hydrogen Production System at 300 Nm3 H−1. Int. J. Hydrogen Energy 2010 , 35 , 4451–4458. [ Google Scholar ] [ CrossRef ]
• Scheepers, F.; Stähler, M.; Stähler, A.; Rauls, E.; Müller, M.; Carmo, M.; Lehnert, W. Improving the Efficiency of PEM Electrolyzers through Membrane-Specific Pressure Optimization. Energies 2020 , 13 , 612. [ Google Scholar ] [ CrossRef ]
• Ang, T.-Z.; Salem, M.; Kamarol, M.; Das, H.S.; Nazari, M.A.; Prabaharan, N. A Comprehensive Study of Renewable Energy Sources: Classifications, Challenges and Suggestions. Energy Strategy Rev. 2022 , 43 , 2211–2467. [ Google Scholar ] [ CrossRef ]
• Nehrir, M.H.; Wang, C. Modeling and Control of Fuel Cells: Distributed Generation Applications ; Wiley: New York, NY, USA, 2009; p. 296. [ Google Scholar ]
• Rosen, M.A.; Koohi-Fayegh, S. The Prospects for Hydrogen as an Energy Carrier: An Overview of Hydrogen Energy and Hydrogen Energy Systems. Energy Ecol. Environ. 2016 , 1 , 10–29. [ Google Scholar ] [ CrossRef ]
• Nnabuife, S.G.; Darko, C.K.; Obiako, P.C.; Kuang, B.; Sun, X.; Jenkins, K. A Comparative Analysis of Different Hydrogen Production Methods and Their Environmental Impact. Clean Technol. 2023 , 5 , 1344–1380. [ Google Scholar ] [ CrossRef ]
• da Rosa, A.V.; Ordóñez, J.C. Hydrogen Production. In Fundamentals of Renewable Energy Processes ; Academic Press: Cambridge, MA, USA, 2022; pp. 419–470. [ Google Scholar ] [ CrossRef ]
• Kayfeci, M.; Keçebaş, A.; Bayat, M. Hydrogen Production. In Solar Hydrogen Production: Processes, Systems and Technologies ; Academic Press: Cambridge, MA, USA, 2019; pp. 45–83. [ Google Scholar ] [ CrossRef ]
• Ayers, K.E.; Anderson, E.B.; Capuano, C.; Carter, B.; Dalton, L.; Hanlon, G.; Manco, J.; Niedzwiecki, M. Research Advances towards Low Cost, High Efficiency PEM Electrolysis. ECS Trans. 2010 , 33 , 3–15. [ Google Scholar ] [ CrossRef ]
• Hinkley, J.; Hayward, J.; Mcnaughton, R.; Gillespie, R.; Watt, M.; Lovegrove, K. Cost Assessment of Hydrogen Production from PV and Electrolysis Ayako Matsumoto (Mitsui Global Strategic Studies Institute) ; CSIRO: Perth, Australia, 2016. [ Google Scholar ]
• Rashid, M.; Khaloofah, M.; Mesfer, A.; Naseem, H.; Danish, M.; Mesfer, M.K. Al Hydrogen Production by Water Electrolysis: A Review of Alkaline Water Electrolysis, PEM Water Electrolysis and High Temperature Water Electrolysis. Int. J. Eng. Adv. Technol. 2015 , 4 , 2249–8958. [ Google Scholar ]
• Wang, A.; Shao, P.; Lan, F.; Jin, H. Organic Chemicals in Coal Available to Microbes to Produce Biogenic Coalbed Methane: A Review of Current Knowledge. J. Nat. Gas Sci. Eng. 2018 , 60 , 40–48. [ Google Scholar ] [ CrossRef ]
• Das, A.; Peu, S.D. A Comprehensive Review on Recent Advancements in Thermochemical Processes for Clean Hydrogen Production to Decarbonize the Energy Sector. Sustainability 2022 , 14 , 11206. [ Google Scholar ] [ CrossRef ]
• Acar, C.; Dincer, I. 3.1 Hydrogen Production. In Comprehensive Energy Systems ; Elsevier: Amsterdam, The Netherlands, 2018; Volume 3, pp. 1–40. [ Google Scholar ] [ CrossRef ]
• Office of Fossil Energy United States Department of Energy. Hydrogen Strategy Enabling A Low-Carbon Economy ; United States Department of Energy: Washington, DC, USA, 2020.
• Kumar, A.; Jones, D.D.; Hanna, M.A. Thermochemical Biomass Gasification: A Review of the Current Status of the Technology. Energies 2009 , 2 , 556–581. [ Google Scholar ] [ CrossRef ]
• Weiland, N. Systems Analysis on Biomass Gasification to Carbon-Negative Hydrogen ; National Renewable Energy Laboratory (NREL): Golden, CO, USA, 2023. [ Google Scholar ]
• Norton, B.; Eames, P.C.; Lo, S.N.G. Full-Energy-Chain Analysis of Greenhouse Gas Emissions for Solar Thermal Electric Power Generation Systems. Renew Energy 1998 , 15 , 131–136. [ Google Scholar ] [ CrossRef ]
• Falter, C.; Sizmann, A. Solar Thermochemical Hydrogen Production in the USA. Sustainability 2021 , 13 , 7804. [ Google Scholar ] [ CrossRef ]
• Acevedo, Y.; Prosser, J.; Huya-Kouadio, J.; McNamara, K.; James, B. Hydrogen Production Cost with Alkaline Electrolysis ; National Renewable Energy Laboratory (NREL): Golden, CO, USA, 2023. [ Google Scholar ]
• Kotisaari, M.; Thomann, O.; Montinaro, D.; Kiviaho, J. Evaluation of a SOE Stack for Hydrogen and Syngas Production: A Performance and Durability Analysis. Fuel Cells 2017 , 17 , 571–580. [ Google Scholar ] [ CrossRef ]
• Yu, J.; Ju, F.; Wahab, M.; Agyekum, E.B.; Matasane, C.; Uhunamure, S.E. Estimating the Effects of Economic Complexity and Technological Innovations on CO 2 Emissions: Policy Instruments for N-11 Countries. Sustainability 2022 , 14 , 16856. [ Google Scholar ] [ CrossRef ]
• Liu, H.; Clausen, L.R.; Wang, L.; Chen, M. Pathway toward Cost-Effective Green Hydrogen Production by Solid Oxide Electrolyzer. Energy Env. Sci 2023 , 16 , 2090–2111. [ Google Scholar ] [ CrossRef ]
• World Nuclear Association. Carbon Dioxide Emissions from Electricity. Available online: https://world-nuclear.org/information-library/energy-and-the-environment/carbon-dioxide-emissions-from-electricity (accessed on 17 September 2023).
• Greenhouse Gas Emissions from Energy Data Explorer—Data Tools—IEA. Available online: https://www.iea.org/data-and-statistics/data-tools/greenhouse-gas-emissions-from-energy-data-explorer (accessed on 17 September 2023).
• Methodology for Determining the Greenhouse Gas Emissions Associated with the Production of Hydrogen. Available online: https://unece.org/sites/default/files/2023-09/2.%20Laurent%20Antoni%20-2023_09_12_H2_Antoni_IPHE.pdf (accessed on 17 September 2023).
• Our World in Data. Greenhouse Gas Emissions by Sector, World. 2023. Available online: https://ourworldindata.org/grapher/ghg-emissions-by-sector (accessed on 17 September 2023).
• World Energy Balances—Data Product—IEA. Available online: https://www.iea.org/data-and-statistics/data-product/world-energy-balances (accessed on 17 September 2023).
• GHG Protocol. Scope 2 Guidance. Available online: https://ghgprotocol.org/sites/default/files/2023-03/Scope%202%20Guidance.pdf (accessed on 12 November 2023).
• United Nations Sustainable Development Goals. 2020. Available online: https://unstats.un.org/sdgs/report/2020/The-Sustainable-Development-Goals-Report-2020.pdf (accessed on 17 September 2023).
• Executive Summary—Energy Efficiency 2021—Analysis—IEA. Available online: https://www.iea.org/reports/energy-efficiency-2021/executive-summary (accessed on 17 September 2023).
• Ritchie, H.; Roser, M.; Rosado, P. Energy. 2022. Available online: https://ourworldindata.org/energy (accessed on 17 September 2023).
• GHG Protocol. IPCC Emissions Factor Database. Available online: https://ghgprotocol.org/Third-Party-Databases/IPCC-Emissions-Factor-Database (accessed on 4 May 2023).
• Zhou, Y.; Li, R.; Lv, Z.; Liu, J.; Zhou, H.; Xu, C. Green Hydrogen: A Promising Way to the Carbon-Free Society. Chin. J. Chem. Eng. 2022 , 43 , 2–13. [ Google Scholar ] [ CrossRef ]
• Thanapalan, K.K.T.; Williams, J.G.; Liu, G.P.; Rees, D. Modelling of a pem fuel cell system. IFAC Proc. Vol. 2008 , 41 , 4636–4641. [ Google Scholar ] [ CrossRef ]
• Montilla-DJesus, M.; Santos-Martin, D.; Arnaltes, S.; Castronuovo, E.D. Optimal Reactive Power Allocation in an Offshore Wind Farms with LCC-HVdc Link Connection. Renew Energy 2012 , 40 , 157–166. [ Google Scholar ] [ CrossRef ]
• Welcome to the International Association for Hydrogen Energy. Available online: https://www.iahe-hyes.org/ (accessed on 26 June 2023).
• Iliev, I.K.; Beloev, H.I.; Ilieva, D.I.; Badur, J. A Novel Method for Calculating Greenhouse Gas Emissions from the Combustion of Energy Fuels. Arch. Thermodyn. 2023 , 43 , 3–20. [ Google Scholar ] [ CrossRef ]
• Harrison, K.W.; Remick, R.; Martin, G.D. Hydrogen Production: Fundamentals and Case Study Summaries. In Proceedings of the 18th World Hydrogen Energy Conference, Essen, Germany, 16–20 May 2010. [ Google Scholar ]
• Barelli, L.; Bidini, G.; Cinti, G. Airflow Management in Solid Oxide Electrolyzer (SOE) Operation: Performance Analysis. ChemEngineering 2017 , 1 , 13. [ Google Scholar ] [ CrossRef ]
• Keçebaş, A.; Kayfeci, M. Hydrogen Properties. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 3–29. [ Google Scholar ]
• Nastasi, B. Hydrogen Policy, Market, and R&D Projects. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 31–44. [ Google Scholar ]
• Karellas, S.; Roumpedakis, T.C. Solar Thermal Power Plants. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 179–235. [ Google Scholar ]
• Keçebaş, A.; Kayfeci, M.; Bayat, M. Electrochemical Hydrogen Generation. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 299–317. [ Google Scholar ]
• Touloupakis, E.; Torzillo, G. Photobiological Hydrogen Production. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 511–525. [ Google Scholar ]
• Villafán-Vidales, H.I.; Arancibia-Bulnes, C.A.; Valades-Pelayo, P.J.; Romero-Paredes, H.; Cuentas-Gallegos, A.K.; Arreola-Ramos, C.E. Hydrogen from Solar Thermal Energy. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 319–363. [ Google Scholar ]
• Guzmán, H.; Farkhondehfal, M.A.; Tolod, K.R.; Hernández, S.; Russo, N. Photo/Electrocatalytic Hydrogen Exploitation for CO2 Reduction toward Solar Fuels Production. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 365–418. [ Google Scholar ]
• Narayanan, H.; Viswanathan, B.; Krishnamurthy, K.R.; Nair, H. Hydrogen from Photo-Electrocatalytic Water Splitting. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 419–486. [ Google Scholar ]
• Harrison, K.W.; Remick, R.; Martin, G.D.; Hoskin, A. Hydrogen Production: Fundamentals and Cas Study Summaries ; National Renewable Energy Laboratory: Golden, CO, USA, 2010; preprint . [ Google Scholar ]
• Möller, M.C.; Krauter, S. Hybrid Energy System Model in Matlab/Simulink Based on Solar Energy, Lithium-Ion Battery and Hydrogen. Energies 2022 , 15 , 2201. [ Google Scholar ] [ CrossRef ]
• Fan, L.; Tu, Z.; Chan, S.H. Recent Development of Hydrogen and Fuel Cell Technologies: A Review. Energy Rep. 2021 , 7 , 8421–8446. [ Google Scholar ] [ CrossRef ]
• Ammendola, P.; Yan, X.; Zheng, W.; Wei, Y.; Yan, Z. Current Status and Economic Analysis of Green Hydrogen Energy Industry Chain. Processes 2024 , 12 , 315. [ Google Scholar ] [ CrossRef ]
• Report on the Status of the Solid Oxide Fuel Cell Program ; United States Department of Energy: Washington, DC, USA, 2019.
• Williams, M.C.; Vora, S.D.; Jesionowski, G. Worldwide Status of Solid Oxide Fuel Cell Technology. ECS Trans 2020 , 96 , 1–10. [ Google Scholar ] [ CrossRef ]
• Next Generation Solid Oxide Fuel Cell and Electrolysis Technology|NewSOC|Project|Fact Sheet|H2020|CORDIS|European Commission. Available online: https://www.clean-hydrogen.europa.eu/projects-dashboard/projects-repository/newsoc_en (accessed on 18 March 2023).
• Rathore, S.S.; Biswas, S.; Fini, D.; Kulkarni, A.P.; Giddey, S. Direct Ammonia Solid-Oxide Fuel Cells: A Review of Progress and Prospects. Int. J. Hydrogen Energy 2021 , 46 , 35365–35384. [ Google Scholar ] [ CrossRef ]
• Smolinka, T. Fuels—Hydrogen Production|Water Electrolysis. In Encyclopedia of Electrochemical Power Sources ; Elsevier: Amsterdam, The Netherlands, 2009; pp. 394–413. [ Google Scholar ] [ CrossRef ]
• United States Department of Energy. DOE Hydrogen Program Record 23003: Electrolyzer Installations in the United States ; United States Department of Energy: Washington, DC, USA, 2023.
• Innovation in Alkaline Electrolysis—Stargate Hydrogen. Available online: https://stargatehydrogen.com/why-us/innovation-in-alkaline-electrolysis/ (accessed on 18 March 2023).
• 100MW Green Hydrogen Plant. Available online: https://www.sinohyenergy.com/100mw-green-hydrogen-plant/ (accessed on 17 March 2023).
• Laurent For More Information on HyDeal Ambition and Its Ecosystem of Production-Supply of Green Hydrogen at Competitive Prices, Please Contact. Available online: https://mcphy.com/en/news/hydeal-ambition/ (accessed on 17 March 2023).
• Wappler, M.; Unguder, D.; Lu, X.; Ohlmeyer, H.; Teschke, H.; Lueke, W. Building the Green Hydrogen Market e Current State and Outlook on Green Hydrogen Demand and Electrolyzer Manufacturing. Int. J. Hydrogen Energy 2022 , 47 , 33551–33570. [ Google Scholar ] [ CrossRef ]
• UNFCCC. Greenhouse Gas Emissions Calculator. Available online: https://unfccc.int/documents/271269 (accessed on 17 March 2023).
• Padro, C.E.G.; Putsche, V. Survey of the Economics of Hydrogen Technologies ; National Renewable Energy Laboratory: Golden, CO, USA, 1999. [ Google Scholar ] [ CrossRef ]
• Hill, N.; Bramwell, R.; Karagianni, E.; Jones, L.; Maccarthy, J.; Hinton, S.; Walker, C. 2020 Government Greenhouse Gas Conversion Factors for Company Reporting Methodology Paper for Conversion Factors Final Report 2 ; Department for Business, Energy & Industrial Strategy (BEIS): London, UK, 2020.
• Global Average Levelised Cost of Hydrogen Production by Energy Source and Technology, 2019 and 2050—Charts—Data & Statistics—IEA. Available online: https://www.iea.org/data-and-statistics/charts/global-average-levelised-cost-of-hydrogen-production-by-energy-source-and-technology-2019-and-2050 (accessed on 10 March 2024).
• GHG Protocol. Calculation Tools and Guidance. Available online: https://ghgprotocol.org/calculation-tools-and-guidance (accessed on 7 March 2023).
• Vickers, J.; Peterson, D.; Randolph, K.; Irwin, L.; Desantis, D.; Hamdan, M.; Stetson, N.; Miller, E.; Satyapal, S. DOE Hydrogen and Fuel Cells Program Record Title: Cost of Electrolytic Hydrogen Production with Existing Technology. 2000. Available online: https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/20004-cost-electrolytic-hydrogen-production.pdf?Status=Master (accessed on 7 March 2023).
• Bossel, U.; Eliasson, B. Energy Hydrogen Economy. Available online: https://afdc.energy.gov/files/pdfs/hyd_economy_bossel_eliasson.pdf (accessed on 17 September 2023).
• Home International Partnership for Hydrogen&Fuel Cells in the Economy. Available online: https://www.energy.gov/eere/fuelcells/international-partnership-hydrogen-and-fuel-cells-economy (accessed on 1 December 2023).
• Hydrogen Fuel Cell Engines Module 4: Fuel Cell Engine Technology Contents. 2022. Available online: https://www.energy.gov/sites/default/files/2014/03/f12/fcm04r0.pdf (accessed on 17 September 2023).
• Janssen, J.L.L.C.C.; Weeda, M.; Detz, R.J.; van der Zwaan, B. Country-Specific Cost Projections for Renewable Hydrogen Production through off-Grid Electricity Systems. Appl. Energy 2022 , 309 , 118398. [ Google Scholar ] [ CrossRef ]
• Global Hydrogen Review 2021 ; OECD: Paris, France, 2021. [ CrossRef ]
• Nasser, M.; Megahed, T.F.; Ookawara, S.; Hassan, H. A Review of Water Electrolysis–Based Systems for Hydrogen Production Using Hybrid/Solar/Wind Energy Systems. Environ. Sci. Pollut. Res. 2022 , 29 , 86994–87018. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ji, G.; Yao, J.G.; Clough, P.T.; Da Costa, J.C.D.; Anthony, E.J.; Fennell, P.S.; Wang, W.; Zhao, M. Enhanced Hydrogen Production from Thermochemical Processes. Energy Environ. Sci 2018 , 11 , 2647–2672. [ Google Scholar ] [ CrossRef ]
• Wang, F.; Harindintwali, J.D.; Yuan, Z.; Wang, M.; Wang, F.; Li, S.; Yin, Z.; Huang, L.; Fu, Y.; Li, L.; et al. Technologies and perspectives for Achieving Carbon Neutrality. Innovation 2021 , 35 , 48. [ Google Scholar ] [ CrossRef ]
• Felseghi, R.A.; Carcadea, E.; Raboaca, M.S.; Trufin, C.N.; Filote, C. Hydrogen Fuel Cell Technology for the Sustainable Future of Stationary Applications. Energies 2019 , 12 , 4593. [ Google Scholar ] [ CrossRef ]
• Ganci, F.; Baguet, T.; Aiello, G.; Cusumano, V.; Mandin, P.; Sunseri, C.; Inguanta, R. Nanostructured Ni Based Anode and Cathode for Alkaline Water Electrolyzers. Energies 2019 , 12 , 3669. [ Google Scholar ] [ CrossRef ]
• Karaca, A.E.; Qureshy, A.M.M.I.; Dincer, I. An Overview and Critical Assessment of Thermochemical Hydrogen Production Methods. J. Clean Prod. 2023 , 385 , 135706. [ Google Scholar ] [ CrossRef ]
• Rajalakshmi, N.; Balaji, R.; Ramakrishnan, S. Recent Developments in Hydrogen Fuel Cells: Strengths and Weaknesses. In Sustainable Fuel Technologies Handbook ; Academic Press: Cambridge, MA, USA, 2021; pp. 431–456. [ Google Scholar ] [ CrossRef ]
• Zhang, H.; Wang, L.; van Herle, J.; Maréchal, F.; Desideri, U. Techno-Economic Optimization of CO 2 -to-Methanol with Solid-Oxide Electrolyzer. Energies 2019 , 12 , 3742. [ Google Scholar ] [ CrossRef ]
• Vostakola, M.F.; Ozcan, H.; El-Emam, R.S.; Horri, B.A. Recent Advances in High-Temperature Steam Electrolysis with Solid Oxide Electrolysers for Green Hydrogen Production. Energies 2023 , 16 , 3327. [ Google Scholar ] [ CrossRef ]
• Yu, M.; Wang, K.; Vredenburg, H. Insights into Low-Carbon Hydrogen Production Methods: Green, Blue and Aqua Hydrogen. Int. J. Hydrogen Energy 2021 , 46 , 21261–21273. [ Google Scholar ] [ CrossRef ]
• Shapiro, D.S. PEM Electrolyzer and Gas Storage for a Regenerative Fuel Cell System. 2002. Available online: https://www.proquest.com/openview/4268d79e22093d975884c7d7a430714c/1?pq-origsite=gscholar&cbl=18750&diss=y (accessed on 17 September 2023).
• de Jesús Yaritza, M.L. Characterization of Proton Exchange Membrane Fuel Cell and PEM Electrolyzer Using Non-Steady State Electrochemical Techniques. 2004. Available online: https://scholar.uprm.edu/entities/publication/9b4c67f7-6117-474c-a84e-c73937062c8b (accessed on 17 September 2023).
• Knobbe, M.W. Analysis of PEM Electrolyzers ; Los Alamos National Lab. (LANL): Los Alamos, NM, USA, 1998. [ Google Scholar ]
• Brauns, J.; Turek, T. Alkaline Water Electrolysis Powered by Renewable Energy: A Review. Processes 2020 , 8 , 248. [ Google Scholar ] [ CrossRef ]
• Peters, R.; Deja, R.; Blum, L.; Nguyen, V.N.; Fang, Q.; Stolten, D. Influence of Operating Parameters on Overall System Efficiencies Using Solid Oxide Electrolysis Technology. Int. J. Hydrogen Energy 2015 , 40 , 7103–7113. [ Google Scholar ] [ CrossRef ]
• Zeng, K.; Zhang, D. Recent Progress in Alkaline Water Electrolysis for Hydrogen Production and Applications. Prog. Energy Combust. Sci. 2010 , 36 , 307–326. [ Google Scholar ] [ CrossRef ]
• Jun, A.; Kim, J.; Shin, J.; Kim, G. Supporting Information Achieving High Efficiency and Eliminating Degradation in Solid Oxide Electrochemical Cells Using High Oxygen-Capacity Perovskite. 2016. Available online: https://www.researchgate.net/publication/307949925_Achieving_High_Efficiency_and_Eliminating_Degradation_in_Solid_Oxide_Electrochemical_Cells_Using_High_Oxygen-Capacity_Perovskite (accessed on 17 September 2023).
• Laguna-Bercero, M.A. Recent Advances in High Temperature Electrolysis Using Solid Oxide Fuel Cells: A Review. J. Power Sources 2012 , 203 , 4–16. [ Google Scholar ] [ CrossRef ]
• Marini, S.; Salvi, P.; Nelli, P.; Pesenti, R.; Villa, M.; Berrettoni, M.; Zangari, G.; Kiros, Y. Advanced Alkaline Water Electrolysis. Electrochim. Acta 2012 , 82 , 384–391. [ Google Scholar ] [ CrossRef ]
• Badgett, A.; Ruth, M.; Pivovar, B. Economic Considerations for Hydrogen Production with a Focus on Polymer Electrolyte Membrane Electrolysis. In Electrochemical Power Sources: Fundamentals, Systems, and Applications Hydrogen Production by Water Electrolysis ; Elsevier: Amsterdam, The Netherlands, 2022; pp. 327–364. [ Google Scholar ] [ CrossRef ]
• Chanthakett, A.; Arif, M.T.; Khan, M.M.K.; Oo, A.M.T. Hydrogen Production from Municipal Solid Waste (MSW) for Cleaner Environment ; Elsevier: Amsterdam, The Netherlands, 2021. [ Google Scholar ]
• Arcos, J.M.M.; Santos, D.M.F. The Hydrogen Color Spectrum: Techno-Economic Analysis of the Available Technologies for Hydrogen Production. Gases 2023 , 3 , 25–46. [ Google Scholar ] [ CrossRef ]
• Cho, H.H.; Strezov, V.; Evans, T.J. Environmental Impact Assessment of Hydrogen Production via Steam Methane Reforming Based on Emissions Data. Energy Rep. 2022 , 8 , 13585–13595. [ Google Scholar ] [ CrossRef ]
• Uusitalo, V.; Väisänen, S.; Inkeri, E.; Soukka, R. Potential for Greenhouse Gas Emission Reductions Using Surplus Electricity in Hydrogen, Methane and Methanol Production via Electrolysis. Energy Convers. Manag. 2017 , 134 , 125–134. [ Google Scholar ] [ CrossRef ]
• Palmer, G.; Roberts, A.; Hoadley, A.; Dargaville, R.; Honnery, D. Life-Cycle Greenhouse Gas Emissions and Net Energy Assessment of Large-Scale Hydrogen Production via Electrolysis and Solar PV. Energy Environ. Sci 2021 , 14 , 5113–5131. [ Google Scholar ] [ CrossRef ]
• Dufour, J.; Serrano, D.P.; Gálvez, J.L.; Moreno, J.; González, A. Hydrogen Production from Fossil Fuels: Life Cycle Assessment of Technologies with Low Greenhouse Gas Emissions. Energy Fuels 2011 , 25 , 2194–2202. [ Google Scholar ] [ CrossRef ]
• Granovskii, M.; Dincer, I.; Rosen, M.A. Economic Aspects of Greenhouse Gas Emissions Reduction by Utilisation of Wind and Solar Energies to Produce Electricity and Hydrogen. In Proceedings of the 2006 IEEE EIC Climate Change Technology Conference, EICCCC 2006, Ottawa, ON, Canada, 10–12 May 2006. [ Google Scholar ] [ CrossRef ]
• Gemechu, E.D.; Kumar, A. The Environmental Performance of Hydrogen Production Pathways Based on Renewable Sources. In Renewable-Energy-Driven Future: Technologies, Modelling, Applications, Sustainability and Policies ; Academic Press: Cambridge, MA, USA, 2021; pp. 375–406. [ Google Scholar ] [ CrossRef ]
• Busch, P.; Kendall, A.; Lipman, T. A Systematic Review of Life Cycle Greenhouse Gas Intensity Values for Hydrogen Production Pathways. Renew. Sustain. Energy Rev. 2023 , 184 , 113588. [ Google Scholar ] [ CrossRef ]
• Fereidooni, M.; Mostafaeipour, A.; Kalantar, V.; Goudarzi, H. A Comprehensive Evaluation of Hydrogen Production from Photovoltaic Power Station. Renew. Sustain. Energy Rev. 2018 , 82 , 415–423. [ Google Scholar ] [ CrossRef ]
• Muradov, N.Z.; Veziroǧlu, T.N. From Hydrocarbon to Hydrogen–Carbon to Hydrogen Economy. Int. J. Hydrogen Energy 2005 , 30 , 225–237. [ Google Scholar ] [ CrossRef ]
• Dincer, I.; Acar, C. Review and Evaluation of Hydrogen Production Methods for Better Sustainability. Int. J. Hydrogen Energy 2015 , 40 , 11094–11111. [ Google Scholar ] [ CrossRef ]
• Gallardo, F.I.; Ferrario, A.M.; Lamagna, M.; Bocci, E.; Garcia, D.A.; Baeza-Jeria, T.E. A Techno-Economic Analysis of Solar Hydrogen Production by Electrolysis in the North of Chile and the Case of Exportation from Atacama Desert to Japan. Int. J. Hydrogen Energy 2021 , 46 , 13709–13728. [ Google Scholar ] [ CrossRef ]
• Ayers, K.E.; Capuano, C.; Anderson, E.B. Recent Advances in Cell Cost and Efficiency for PEM-Based Water Electrolysis. ECS Trans. 2012 , 41 , 15–22. [ Google Scholar ] [ CrossRef ]
• Babic, U.; Suermann, M.; Büchi, F.N.; Gubler, L.; Schmidt, T.J. Critical Review—Identifying Critical Gaps for Polymer Electrolyte Water Electrolysis Development. J. Electrochem. Soc. 2017 , 164 , F387–F399. [ Google Scholar ] [ CrossRef ]
• Intergovernmental Panel on Climate Change (IPCC). 2006 IPCC Guidelines for National Greenhouse Gas Inventories ; IPCC: Paris, France, 2006.
• Wang, Y.; Pang, Y.; Xu, H.; Martinez, A.; Chen, K.S. PEM Fuel Cell and Electrolysis Cell Technologies and Hydrogen Infrastructure Development—A Review. Energy Environ. Sci. 2022 , 15 , 2288–2328. [ Google Scholar ] [ CrossRef ]
• Barone, G.; Buonomano, A.; Forzano, C.; Palombo, A. Solar Thermal Collectors. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 151–178. [ Google Scholar ]
• Verma, A. Life Cycle Assessment and Greenhouse Gas Abatement Costs of Hydrogen Production from Underground Coal Gasification. Master’s Thesis, University of Alberta, Edmonton, AB, Canada, 2015. [ Google Scholar ]
• Buffo, G.; Marocco, P.; Ferrero, D.; Lanzini, A.; Santarelli, M. Power-to-X and Power-to-Power Routes. In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 529–557. [ Google Scholar ]
• Bayod-Rújula, A.A. Solar Photovoltaics (PV). In Solar Hydrogen Production ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 237–295. [ Google Scholar ]
• Beijing SinoHy Energy Co., Ltd. Available online: https://www.sinohyenergy.com/ (accessed on 13 March 2024).
• Fakhreddine, O.; Gharbia, Y.; Derakhshandeh, J.F.; Amer, A.M. Challenges and Solutions of Hydrogen Fuel Cells in Transportation Systems: A Review and Prospects. World Electr. Veh. J. 2023 , 14 , 156. [ Google Scholar ] [ CrossRef ]
• Kovtunenko, V.A. The Holby–Morgan Model of Platinum Catalyst Degradation in PEM Fuel Cells: Range of Feasible Parameters Achieved Using Voltage Cycling. Technologies 2023 , 11 , 184. [ Google Scholar ] [ CrossRef ]
• Tellez-Cruz, M.M.; Escorihuela, J.; Solorza-Feria, O.; Compañ, V. Proton Exchange Membrane Fuel Cells (PEMFCs): Advances and Challenges. Polymers 2021 , 13 , 3064. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Le, T.H.; Kim, M.P.; Park, C.H.; Tran, Q.N. Recent Developments in Materials for Physical Hydrogen Storage: A Review. Materials 2024 , 17 , 666. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Khzouz, M.; Gkanas, E.I. Hydrogen Technologies for Mobility and Stationary Applications: Hydrogen Production, Storage and Infrastructure Development. In Renewable Energy—Resources, Challenges and Applications ; IntechOpen: Rijeka, Croatia, 2020. [ Google Scholar ]
• Borja, N.K.; Fabros, C.J.E.; Doma, B.T. Prediction of Hydrogen Adsorption and Moduli of Metal–Organic Frameworks (MOFs) Using Machine Learning Strategies. Energies 2024 , 17 , 927. [ Google Scholar ] [ CrossRef ]
• MOFs for Advanced Applications ; MDPI: Basel, Switzerland, 2022; ISBN 978-3-0365-3542-5.
• Zeleňák, V.; Saldan, I. Factors Affecting Hydrogen Adsorption in Metal–Organic Frameworks: A Short Review. Nanomaterials 2021 , 11 , 1638. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dall’Armi, C.; Pivetta, D.; Taccani, R. Hybrid PEM Fuel Cell Power Plants Fuelled by Hydrogen for Improving Sustainability in Shipping: State of the Art and Review on Active Projects. Energies 2023 , 16 , 2022. [ Google Scholar ] [ CrossRef ]
• Olabi, A.G.; Wilberforce, T.; Sayed, E.T.; Elsaid, K.; Abdelkareem, M.A. Prospects of Fuel Cell Combined Heat and Power Systems. Energies 2020 , 13 , 4104. [ Google Scholar ] [ CrossRef ]
• Cigolotti, V.; Genovese, M.; Fragiacomo, P. Comprehensive Review on Fuel Cell Technology for Stationary Applications as Sustainable and Efficient Poly-Generation Energy Systems. Energies 2021 , 14 , 4963. [ Google Scholar ] [ CrossRef ]
• Renau, J.; García, V.; Domenech, L.; Verdejo, P.; Real, A.; Giménez, A.; Sánchez, F.; Lozano, A.; Barreras, F. Novel Use of Green Hydrogen Fuel Cell-Based Combined Heat and Power Systems to Reduce Primary Energy Intake and Greenhouse Emissions in the Building Sector. Sustainability 2021 , 13 , 1776. [ Google Scholar ] [ CrossRef ]
• Luo, Y.; Meng, Q.; Chi, Y.; Wang, Q.; Zeng, Y.; Deng, Z.; Zou, Y. A Low-Carbon Optimal Operation Method for an Industrial Park Multi-Energy Coupling System Utilizing By-Product Hydrogen. Sustainability 2024 , 16 , 2354. [ Google Scholar ] [ CrossRef ]
• Costa, P.; Pinto, F.; André, R.N.; Marques, P. Integration of Gasification and Solid Oxide Fuel Cells (SOFCs) for Combined Heat and Power (CHP). Processes 2021 , 9 , 254. [ Google Scholar ] [ CrossRef ]
• Chakraborty, S.; Elangovan, D.; Palaniswamy, K.; Fly, A.; Ravi, D.; Seelan, D.A.S.; Rajagopal, T.K.R. A Review on the Numerical Studies on the Performance of Proton Exchange Membrane Fuel Cell (PEMFC) Flow Channel Designs for Automotive Applications. Energies 2022 , 15 , 9520. [ Google Scholar ] [ CrossRef ]
• Yan, S.; Yang, M.; Sun, C.; Xu, S. Liquid Water Characteristics in the Compressed Gradient Porosity Gas Diffusion Layer of Proton Exchange Membrane Fuel Cells Using the Lattice Boltzmann Method. Energies 2023 , 16 , 6010. [ Google Scholar ] [ CrossRef ]
• Tang, X.; Yang, M.; Shi, L.; Hou, Z.; Xu, S.; Sun, C. Adaptive State-of-Health Temperature Sensitivity Characteristics for Durability Improvement of PEM Fuel Cells. Chem. Eng. J. 2024 , 491 , 151951. [ Google Scholar ] [ CrossRef ]
• Corda, G.; Breda, S.; D’Adamo, A. A MATLAB/Simulink Model of a PEM Fuel Cell System Including Ageing Phenomenon ; SAE: Warrendale, PA, USA, 2023. [ Google Scholar ]
• Safarishaal, M.; Sarvi, M. New Hybrid Maximum Power Point Tracking Methods for Fuel Cell Using Artificial Intelligent. AIP Adv. 2023 , 13 , 045207. [ Google Scholar ] [ CrossRef ]
• Rubio, A.; Agila, W.; González, L.; Aviles-Cedeno, J. Distributed Intelligence in Autonomous PEM Fuel Cell Control. Energies 2023 , 16 , 4830. [ Google Scholar ] [ CrossRef ]
• Shuai, Q.; Wang, Y.; Jiang, Z.; Hua, Q. Reinforcement Learning-Based Energy Management for Fuel Cell Electrical Vehicles Considering Fuel Cell Degradation. Energies 2024 , 17 , 1586. [ Google Scholar ] [ CrossRef ]
• Chen, Z.; Huang, K.; Zhang, T.; Xia, J.; Wu, J.; Zhang, Z.; Zhang, B. Surface Modified CoCrFeNiMo High Entropy Alloys for Oxygen Evolution Reaction in Alkaline Seawater. Processes 2023 , 11 , 245. [ Google Scholar ] [ CrossRef ]
• Mohammed-Ibrahim, J.; Moussab, H. Recent Advances on Hydrogen Production through Seawater Electrolysis. Mater. Sci. Energy Technol. 2020 , 3 , 780–807. [ Google Scholar ] [ CrossRef ]
• Sun, F.; Qin, J.; Wang, Z.; Yu, M.; Wu, X.; Sun, X.; Qiu, J. Energy-Saving Hydrogen Production by Chlorine-Free Hybrid Seawater Splitting Coupling Hydrazine Degradation. Nat. Commun. 2021 , 12 , 4182. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Khaodee, W.; Jiwanuruk, T.; Ountaksinkul, K.; Charojrochkul, S.; Charoensuk, J.; Wongsakulphasatch, S.; Assabumrungrat, S. Compact Heat Integrated Reactor System of Steam Reformer, Shift Reactor and Combustor for Hydrogen Production from Ethanol. Processes 2020 , 8 , 708. [ Google Scholar ] [ CrossRef ]
• Slobodkin, I.; Davydova, E.; Sananis, M.; Breytus, A.; Rothschild, A. Electrochemical and Chemical Cycle for High-Efficiency Decoupled Water Splitting in a near-Neutral Electrolyte. Nat. Mater. 2024 , 23 , 398–405. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Saleh, M.; Abdelhamid, H.N.; Fouad, D.M.; El-Bery, H.M. Enhancing Photocatalytic Water Splitting: Comparative Study of TiO 2 Decorated Nanocrystals (Pt and Cu) Using Different Synthesis Methods. Fuel 2023 , 354 , 129248. [ Google Scholar ] [ CrossRef ]
• IEA. It Is Time for CCUS to Deliver ; IEA: Paris, France, 2024.
• IEA. CCUS in Clean Energy Transitions ; IEA: Paris, France, 2020.
• Wang, S.; Wang, J.; Zhang, Y.; Dong, L.; Dong, H.; Du, Q.; Gao, J. Effect of External Mineral Addition on PM Generated from Zhundong Coal Combustion. Energies 2023 , 16 , 730. [ Google Scholar ] [ CrossRef ]
• Edem, D.E.; Abba, M.K.; Nourian, A.; Babaie, M.; Naeem, Z. Experimental Study on the Interplay between Different Brine Types/Concentrations and CO 2 Injectivity for Effective CO 2 Storage in Deep Saline Aquifers. Sustainability 2022 , 14 , 986. [ Google Scholar ] [ CrossRef ]
• Hasan, M.M.F.; Zantye, M.S.; Kazi, M.-K. Challenges and Opportunities in Carbon Capture, Utilization and Storage: A Process Systems Engineering Perspective. Comput. Chem. Eng. 2022 , 166 , 107925. [ Google Scholar ] [ CrossRef ]
• Nath, F.; Mahmood, M.N.; Yousuf, N. Recent Advances in CCUS: A Critical Review on Technologies, Regulatory Aspects and Economics. Geoenergy Sci. Eng. 2024 , 238 , 212726. [ Google Scholar ] [ CrossRef ]
• Zhao, D.; Wang, X.; Yue, L.; He, Y.; Chen, B. Porous Metal–Organic Frameworks for Hydrogen Storage. Chem. Commun. 2022 , 58 , 11059–11078. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dutt, S.; Kumar, A.; Singh, S. Synthesis of Metal Organic Frameworks (MOFs) and Their Derived Materials for Energy Storage Applications. Clean Technol. 2023 , 5 , 140–166. [ Google Scholar ] [ CrossRef ]
• Li, H.; Zhang, X.; Zhang, C.; Ding, Z.; Jin, X. Application and Analysis of Liquid Organic Hydrogen Carrier (LOHC) Technology in Practical Projects. Energies 2024 , 17 , 1940. [ Google Scholar ] [ CrossRef ]
• Le, T.-H.; Tran, N.; Lee, H.-J. Development of Liquid Organic Hydrogen Carriers for Hydrogen Storage and Transport. Int. J. Mol. Sci. 2024 , 25 , 1359. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Niermann, M.; Drünert, S.; Kaltschmitt, M.; Bonhoff, K. Liquid Organic Hydrogen Carriers (LOHCs)—Techno-Economic Analysis of LOHCs in a Defined Process Chain. Energy Environ. Sci 2019 , 12 , 290–307. [ Google Scholar ] [ CrossRef ]
• Hartani, M.A.; Hamouda, M.; Abdelkhalek, O.; Benhamou, A.; Ali, B.; Mekhilef, S. Robust Frequency-Decoupling-Based Power Split of Battery/Supercapacitor Hybrid Energy Storage Systems in DC Microgrids. In Proceedings of the 1st International Conference on Physics of Semiconductor Devices, Renewable Energies and Environment, Bechar, Algeria, 14–16 November 2022; MDPI: Basel, Switzerland, 2023; p. 6. [ Google Scholar ]

Share and Cite

Guerrero-Rodríguez, N.F.; De La Rosa-Leonardo, D.A.; Tapia-Marte, R.; Ramírez-Rivera, F.A.; Faxas-Guzmán, J.; Rey-Boué, A.B.; Reyes-Archundia, E. An Overview of the Efficiency and Long-Term Viability of Powered Hydrogen Production. Sustainability 2024 , 16 , 5569. https://doi.org/10.3390/su16135569

Guerrero-Rodríguez NF, De La Rosa-Leonardo DA, Tapia-Marte R, Ramírez-Rivera FA, Faxas-Guzmán J, Rey-Boué AB, Reyes-Archundia E. An Overview of the Efficiency and Long-Term Viability of Powered Hydrogen Production. Sustainability . 2024; 16(13):5569. https://doi.org/10.3390/su16135569

Guerrero-Rodríguez, Nestor F., Daniel A. De La Rosa-Leonardo, Ricardo Tapia-Marte, Francisco A. Ramírez-Rivera, Juan Faxas-Guzmán, Alexis B. Rey-Boué, and Enrique Reyes-Archundia. 2024. "An Overview of the Efficiency and Long-Term Viability of Powered Hydrogen Production" Sustainability 16, no. 13: 5569. https://doi.org/10.3390/su16135569

Article Metrics

Further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Google Custom Search

Wir verwenden Google für unsere Suche. Mit Klick auf „Suche aktivieren“ aktivieren Sie das Suchfeld und akzeptieren die Nutzungsbedingungen.

Hinweise zum Einsatz der Google Suche

• Professorship of Economics of Energy Markets
• TUM School of Management
• Technische Universität München

New master's thesis topic: Can green hydrogen enable the sustainable growth in Europe? An industry perspective

Research, Aktuelles | 22.11.2022

The project consists in developing an automated mathematical/statistical model that takes a country overall energy consumption database (e.g., Germany) and analyze how the substitution of one energy input with another (e.g., methane with renewables and/or green hydrogen) works. This will enable further academic research and give insights on how the energy mix of a country or macrogeographic zone (i.e., Europe) can be of low or no emission level

• CNRS - National Center for Scientific Research
• Posted on: 27 June 2024

PhD(M/F): Surface analysis of ceramic electrodes for green hydrogen production using solid oxide electrolysis cells

Job Information

Offer description.

This PhD thesis is part of the CELCER-EHT project in the frame of the National plan to develop green hydrogen production technologies. The project gathers 13 French academic partners. Part of the spectroscopic and electrochemical characterization of the materials will be performed at the Surface Analysis Laboratory of the Institute of Chemistry and Processes for Energy, Environment and Health ICPEES,which is a joint institute (UMR7515) between CNRS and University of Strasbourg. The PhD student will join the Energy and Fuels for a Sustainable Environment (ECED) team in the Catalysis and Materials department at ICPEES. In addition, the PhD student will participate in several synchrotron experiment campaigns in France and Germany. In addition, the PhD student will have access to several characterization platforms available in Strasbourg and if necessary to partners' Institutions.

The solid oxide electrolysis cell (SOEC) technology has a huge potential for future mass production of green hydrogen, mainly due to its high electrical-to-chemical energy conversion efficiency. However, the durability and the performance of SOEC devices are inferior to that of other competitive electrolysis technologies inhibiting the commercialization of SOECs. The objective of the PhD thesis is to investigate the surface of cathode and anode electrodes used in SOEC. To this end the candidate will use ex situ, in situ and operando state-of-the art characterization techniques. Experiments will be performed in Strasbourg as well as in several European synchrotron facilities. The aim is to describe the dynamic evolution of the electrodes under high-temperature water electrolysis conditions. The ultimate goal of the thesis is to optimize the efficiency and the durability of SOEC by promoting the electrode performance via incorporation of oxide nanoparticles directly on their surfaces.

Where to apply

Requirements, additional information.

The candidate should: - have a background in one of the following topics: material science, physical chemistry, chemistry, electrochemistry, catalysis. - have strong interest in experimental research and data analysis, and for interdisciplinary and collaborative work. - have oral and written English proficiency and basic knowledge of French. - have a level of English and French between B2 and C1 (Common European Framework of Reference for Languages). - be highly motivated and autonomous

The following skills would be a plus - Knowledge of electrochemistry and fuel cell/water electrolysis technology. - Experience in characterization techniques such as XPS, APXPS, FTIR, NEXAFS, EXAFS etc. - Experience in solid oxide cells manufacturing processes via ink formulation, screen printing etc. - Experience in electrochemical characterization methods such as voltage-current density measurements and impedance spectroscopy. - Experience in ultra-high vacuum technology, maintenance, sample handling etc.

Work Location(s)

Share this page.

• Bibliography
• More Referencing guides Blog Automated transliteration Relevant bibliographies by topics
• Automated transliteration
• Relevant bibliographies by topics
• Referencing guides

Chemical Society Reviews

Reactive capture and electrochemical conversion of co 2 with ionic liquids and deep eutectic solvents.

* Corresponding authors

a Chemical and Biomolecular Engineering, Case Western Reserve University, Cleveland, OH, USA E-mail: [email protected]

b Department of Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey

c Koç University TÜPRAŞ Energy Center (KUTEM), Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey

d Department of Materials Science and Technology, Faculty of Science, Turkish-German University, Sahinkaya Cad., Beykoz, 34820 Istanbul, Turkey

e Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA

f Conn Center for Renewable Energy Research, University of Louisville, Louisville, KY 40292, USA

g Materials Science Division, Lawrence Livermore National Laboratory, Livermore, CA, USA

h Department of Chemistry, University of California, Irvine, Irvine, CA 92697, USA

i Department of Chemistry, University of California, Davis, Davis, CA 95616, USA

j Department of Mathematics, Computer Science, & Engineering Technology, Elizabeth City State University, 1704 Weeksville Road, Elizabeth City, NC 27909, USA

k Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA

l Koç University Surface Science and Technology Center (KUYTAM), Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey

Ionic liquids (ILs) and deep eutectic solvents (DESs) have tremendous potential for reactive capture and conversion (RCC) of CO 2 due to their wide electrochemical stability window, low volatility, and high CO 2 solubility. There is environmental and economic interest in the direct utilization of the captured CO 2 using electrified and modular processes that forgo the thermal- or pressure-swing regeneration steps to concentrate CO 2 , eliminating the need to compress, transport, or store the gas. The conventional electrochemical conversion of CO 2 with aqueous electrolytes presents limited CO 2 solubility and high energy requirement to achieve industrially relevant products. Additionally, aqueous systems have competitive hydrogen evolution. In the past decade, there has been significant progress toward the design of ILs and DESs, and their composites to separate CO 2 from dilute streams. In parallel, but not necessarily in synergy, there have been studies focused on a few select ILs and DESs for electrochemical reduction of CO 2 , often diluting them with aqueous or non-aqueous solvents. The resulting electrode–electrolyte interfaces present a complex speciation for RCC. In this review, we describe how the ILs and DESs are tuned for RCC and specifically address the CO 2 chemisorption and electroreduction mechanisms. Critical bulk and interfacial properties of ILs and DESs are discussed in the context of RCC, and the potential of these electrolytes are presented through a techno-economic evaluation.

Article information

Download Citation

Permissions.

S. Dongare, M. Zeeshan, A. S. Aydogdu, R. Dikki, S. F. Kurtoğlu-Öztulum, O. K. Coskun, M. Muñoz, A. Banerjee, M. Gautam, R. D. Ross, J. S. Stanley, R. S. Brower, B. Muchharla, R. L. Sacci, J. M. Velázquez, B. Kumar, J. Y. Yang, C. Hahn, S. Keskin, C. G. Morales-Guio, A. Uzun, J. M. Spurgeon and B. Gurkan, Chem. Soc. Rev. , 2024, Advance Article , DOI: 10.1039/D4CS00390J

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence . You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication , please go to the Copyright Clearance Center request page .

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page .

Read more about how to correctly acknowledge RSC content .

Social activity

Search articles by author.

This article has not yet been cited.

Advertisements

Plug Power: Still Big Holes To Plug

• Plug Power constantly fails to meet financial expectations, despite potential in green hydrogen.
• The conditional DoE loan amounting to $1.66 billion won't solve the operational problems with the business.
• Investors should avoid PLUG stock due to dilution risk from the massive cash burn levels.
• Looking for more investing ideas like this one? Get them exclusively at Out Fox The Street. Learn More »

audioundwerbung

Plug Power Inc. ( NASDAQ: PLUG ) continues to fail majestically despite the big opportunity ahead in green hydrogen. The financial prospects of the company won't be helped by a large government loan, and the initial excitement has quickly disappeared. My investment thesis remains Bearish on the stock, despite the potential for strong support at the current price.

Source: Finviz

Ignored Loan

Plug Power soared to $5 on the conditional loan guarantee of as much as $1.66 billion from the U.S. Department of Energy. While the green hydrogen company can sure use cheap debt financing from the government, the financials aren't altered by borrowing more money.

The loan guarantee is already being questioned by a U.S. Senator, and the loan is conditional. The deal apparently covers the construction of up to six green hydrogen production plants.

The recent Q1'24 results highlight why the U.S. Senator might have some concerns about lending the big money loser $1.7 billion. Plug Power took an extended period to finally launch the commercial-scale hydrogen plant in Georgia, and sales slumped to only $120 million in the quarter.

Plug Power reported revenues that collapsed nearly 43% from the prior Q1 and missed analyst estimates by over $37 million. The company had recently promoted the progress of commercial-scale green hydrogen plants in Georgia and Tennessee to 25 tons-per-day (TPD), yet sales collapsed.

Despite all the excitement on reaching nameplate capacity on those plants, fuel delivery revenue was only $18 million with costs at $59 million. These quarterly results were relatively in line with the quarterly results in 2023.

Source: Plug Power Q1'24 earnings release

In total, revenues of $120 million were swamped by costs of $279 million. Despite the collapse in revenues YoY, total costs of revenues were the same.

Plug Power did successfully cut operating expenses to just $100 million in the quarter, down from $140 million in the prior year. Adjusted operating expenses of $103 million were down from $131 million in a slightly positive sign as the company implemented plans to cut expenses by $75 million.

Again, the issue is that Plug Power is nowhere close to even producing positive gross margins to cover the reduced operating expenses. The company is working on the Louisiana green hydrogen plant to boost capacity to 40 TPD, but Plug Power still hasn't shown any improving financials, despite the Georgia plant starting liquid green hydrogen production in late January.

The general frustration with the financials is this constant willingness to subsidize the business when customers should be willing to overpay to move to clean energy. The company forecasts ending the year with a green hydrogen fueling business at 65 TPD, up from 50 TPD now. The problem is that Plug Power is only now producing 25 TPD, and the Louisiana project brings the total to just 40 TPD. The company will only supply up to 60% to 65% of demand, leaving Plug Power in a position of still losing money on 35% or more of production. The business shouldn't have negative margins

The big Texas project adds a massive 45 TPD in green hydrogen production. The latest forecast is for a start date in late 2025, suggesting Plug Power will have a substantial amount of fuel supplied via the vastly higher repurchases from third parties where the costs are $12 to $14 per kg versus sales at only $6 to $7 per kg.

Even the future revenue amounts questions the ability of Plug Power to ever hit financial targets. The backlog is only $1.1 billion, with substantial amounts assigned to electrolyzers and power purchase agreements. The green hydrogen fuel business is a minor part of the story.

Source: Plug Power Q1'24 10-Q

Cash Problems

Plug Power ended the quarter with a cash balance of nearly $400 million, with $220 million restricted. The company has $210 million in convertible debt, plus several other finance and lease obligations.

The issue is cash flow, with Plug Power burning $168 million during the quarter from operations. CapEx was another $93 million, leading to a total quarterly burn of $261 million.

The company always sees low sales in the March quarter and a ton of equipment/electrolyzer projects are at the site inspection process leading to additional sales in near term. Plug Power promotes only 10% of sales in Q1, but the amount was much closer to 25% last year, with relatively flat quarterly revenues over the year.

Plug Power has no history of predicting quarterly and annual sales while expenses are always at elevated levels, even for a company reaching $1+ billion in annual sales as projected. The financials become horrible after the company misses targets dramatically and those fuel and power purchase losses add up.

Analysts forecast smaller loses going forward, but the forecasts are still for losses the rest of the year to nearly double the Q1 loss. The balance sheet doesn't support this level of losses and cash burn.

Even worse, analysts haven't been accurate on Plug Power losses since Q2'20.

Source: Seeking Alpha

The DoE loan just helps the company cover the capex to build new facilities totaling $1.7 billion. The loan will lead to additional interest expenses and the management team hasn't shown any discipline to turn the green hydrogen plants into a profitable business with the need to cut off new supply contracts that aren't economical.

The key investor takeaway is that Plug Power investors continue to face more dilution ahead, while the stock trades at the lows below $2.50. The green hydrogen company continues to have highly volatile sales, while expenses are always elevated.

Plug Power should report higher sales due to equipment sales ramping throughout the year, but the key green hydrogen fuel business isn't amounting to much. Investors should avoid the stock due to the high cash burn rates and likely need to raise additional capital.

If you'd like to learn more about how to best position yourself in under valued stocks mispriced by the market as Q2 comes to an end, consider joining Out Fox The Street . 

The service offers a model portfolio, daily updates, trade alerts and real-time chat. Sign up now for a risk-free 2-week trial. 

This article was written by

Stone Fox Capital (aka Mark Holder) is a CPA with degrees in Accounting and Finance. He is also Series 65 licensed and has 30 years of investing experience, including 10 years as a portfolio manager.

Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. The information contained herein is for informational purposes only. Nothing in this article should be taken as a solicitation to purchase or sell securities. Before buying or selling any stock, you should do your own research and reach your own conclusion or consult a financial advisor. Investing includes risks, including loss of principal.

Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.

Recommended For You

About plug stock.

More on PLUG

Related stocks.

Trending Analysis

Trending news.