IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p977-d1036695.html
   My bibliography  Save this article

Life Cycle Assessment of Greenhouse Gas (GHG) and NO x Emissions of Power-to-H 2 -to-Power Technology Integrated with Hydrogen-Fueled Gas Turbine

Author

Listed:
  • Guohui Song

    (School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Qi Zhao

    (China State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China)

  • Baohua Shao

    (GreEnerMan Oy, Paraatikuja 6, 90630 Oulu, Finland)

  • Hao Zhao

    (College of Engineering, Peking University, Beijing 100871, China
    Institute of Energy, Peking University, Beijing 100871, China)

  • Hongyan Wang

    (School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Wenyi Tan

    (International Joint Laboratory of Green & Low Carbon Development, Nanjing 211167, China)

Abstract

Hydrogen is expected to play an important role in renewable power storage and the decarbonization of the power sector. In order to clarify the environmental impacts of power regenerated through hydrogen-fueled gas turbines, this work details a life cycle model of the greenhouse gas (GHG) and NO x emissions of the power regenerated by power-to-H 2 -to-power (PHP) technology integrated with a combined cycle gas turbine (CCGT). This work evaluates the influences of several variables on the life cycle of GHG and NO x emissions, including renewable power sources, hydrogen production efficiency, net CCGT efficiency, equivalent operating hours (EOH), and plant scale. The results show that renewable power sources, net CCGT efficiency, and hydrogen production efficiency are the dominant variables, while EOH and plant scale are the minor factors. The results point out the direction for performance improvement in the future. This work also quantifies the life cycle of GHG and NO x emissions of power regenerated under current and future scenarios. For hydro, photovoltaic (PV) and wind power, the life cycle of the GHG emissions of regenerated power varies from 8.8 to 366.1 g CO2e /kWh and that of NO x emissions varies from 0.06 to 2.29 g/kWh. The power regenerated from hydro and wind power always has significant advantages over coal and gas power in terms of GHG and NO x emissions. The power regenerated from PV power has a small advantage over gas power in terms of GHG emissions, but does not have advantages regarding NO x emissions. Preference should be given to storing hydro and wind power, followed by PV power. For biomass power with or without CO 2 capture and storage (CCS), the life cycle of the GHG emissions of regenerated power ranges from 555.2 to 653.5 and from −2385.0 to −1814.4, respectively, in g CO2e /kWh; meanwhile, the life cycle of NOx emissions ranges from 1.61 to 4.65 g/kWh, being greater than that of coal and gas power. Biomass power with CCS is the only power resource that can achieve a negative life cycle for GHG emissions. This work reveals that hydrogen-fueled gas turbines are an important, environmentally friendly technology. It also helps in decision making for grid operation and management.

Suggested Citation

  • Guohui Song & Qi Zhao & Baohua Shao & Hao Zhao & Hongyan Wang & Wenyi Tan, 2023. "Life Cycle Assessment of Greenhouse Gas (GHG) and NO x Emissions of Power-to-H 2 -to-Power Technology Integrated with Hydrogen-Fueled Gas Turbine," Energies, MDPI, vol. 16(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:977-:d:1036695
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/977/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/2/977/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Taamallah, S. & Vogiatzaki, K. & Alzahrani, F.M. & Mokheimer, E.M.A. & Habib, M.A. & Ghoniem, A.F., 2015. "Fuel flexibility, stability and emissions in premixed hydrogen-rich gas turbine combustion: Technology, fundamentals, and numerical simulations," Applied Energy, Elsevier, vol. 154(C), pages 1020-1047.
    2. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    3. Withey, Patrick & Johnston, Craig & Guo, Jinggang, 2019. "Quantifying the global warming potential of carbon dioxide emissions from bioenergy with carbon capture and storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    4. Ditaranto, Mario & Heggset, Tarjei & Berstad, David, 2020. "Concept of hydrogen fired gas turbine cycle with exhaust gas recirculation: Assessment of process performance," Energy, Elsevier, vol. 192(C).
    5. Song, Guohui & Xiao, Jun & Yan, Chao & Gu, Haiming & Zhao, Hao, 2022. "Quality of gaseous biofuels: Statistical assessment and guidance on production technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    6. Farzaneh-Gord, Mahmood & Deymi-Dashtebayaz, Mahdi, 2009. "A new approach for enhancing performance of a gas turbine (case study: Khangiran refinery)," Applied Energy, Elsevier, vol. 86(12), pages 2750-2759, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Md Sahabuddin & Md Billal Hossain & Maryam Khokhar & Mohamed Sharaf & Sarmad Ejaz & Faisal Ejaz & Csaba Bálint Illés, 2023. "The Effect of Eco-Preneurship and Green Technology Management on Greenhouse Gas Discharge: An Analysis on East Asian Economies," Sustainability, MDPI, vol. 15(8), pages 1-12, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pashchenko, Dmitry, 2023. "Hydrogen-rich gas as a fuel for the gas turbines: A pathway to lower CO2 emission," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    2. Mustafa Alnaeli & Mohammad Alnajideen & Rukshan Navaratne & Hao Shi & Pawel Czyzewski & Ping Wang & Sven Eckart & Ali Alsaegh & Ali Alnasif & Syed Mashruk & Agustin Valera Medina & Philip John Bowen, 2023. "High-Temperature Materials for Complex Components in Ammonia/Hydrogen Gas Turbines: A Critical Review," Energies, MDPI, vol. 16(19), pages 1-46, October.
    3. Wu, Yuwen & Weng, Chunsheng & Zheng, Quan & Wei, Wanli & Bai, Qiaodong, 2021. "Experimental research on the performance of a rotating detonation combustor with a turbine guide vane," Energy, Elsevier, vol. 218(C).
    4. Skabelund, Brent B. & Stechel, Ellen B. & Milcarek, Ryan J., 2023. "Thermodynamic analysis of a gas turbine utilizing ternary CH4/H2/NH3 fuel blends," Energy, Elsevier, vol. 282(C).
    5. Igor Donskoy, 2023. "Techno-Economic Efficiency Estimation of Promising Integrated Oxyfuel Gasification Combined-Cycle Power Plants with Carbon Capture," Clean Technol., MDPI, vol. 5(1), pages 1-18, February.
    6. Shen, Wenkai & Xing, Chang & Liu, Haiqing & Liu, Li & Hu, Qiming & Wu, Guohua & Yang, Yujia & Wu, Shaohua & Qiu, Penghua, 2022. "Exhaust gas recirculation effects on flame heat release rate distribution and dynamic characteristics in a micro gas turbine," Energy, Elsevier, vol. 249(C).
    7. Ma, Chunyan & Wang, Nan & Chen, Yifeng & Khokarale, Santosh Govind & Bui, Thai Q. & Weiland, Fredrik & Lestander, Torbjörn A. & Rudolfsson, Magnus & Mikkola, Jyri-Pekka & Ji, Xiaoyan, 2020. "Towards negative carbon emissions: Carbon capture in bio-syngas from gasification by aqueous pentaethylenehexamine," Applied Energy, Elsevier, vol. 279(C).
    8. Fan, Siyuan & Wang, Yu & Cao, Shengxian & Zhao, Bo & Sun, Tianyi & Liu, Peng, 2022. "A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels," Energy, Elsevier, vol. 239(PD).
    9. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    10. Nair, Purusothmn Nair S Bhasker & Tan, Raymond R. & Foo, Dominic C.Y., 2022. "Extended graphical approach for the implementation of energy-consuming negative emission technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    11. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    12. Marwan Abdullah & Thibault F. Guiberti & Radi A. Alsulami, 2023. "Experimental Assessment on the Coupling Effect of Mixing Length and Methane-Ammonia Blends on Flame Stability and Emissions," Energies, MDPI, vol. 16(7), pages 1-12, March.
    13. Valera-Medina, Agustin & Marsh, Richard & Runyon, Jon & Pugh, Daniel & Beasley, Paul & Hughes, Timothy & Bowen, Phil, 2017. "Ammonia–methane combustion in tangential swirl burners for gas turbine power generation," Applied Energy, Elsevier, vol. 185(P2), pages 1362-1371.
    14. Kotowicz, Janusz & Bartela, Łukasz & Węcel, Daniel & Dubiel, Klaudia, 2017. "Hydrogen generator characteristics for storage of renewably-generated energy," Energy, Elsevier, vol. 118(C), pages 156-171.
    15. Sun, Yuze & Rao, Zhuming & Zhao, Dan & Wang, Bing & Sun, Dakun & Sun, Xiaofeng, 2020. "Characterizing nonlinear dynamic features of self-sustained thermoacoustic oscillations in a premixed swirling combustor," Applied Energy, Elsevier, vol. 264(C).
    16. Yilmaz, Harun & Yilmaz, Ilker, 2019. "Combustion and emission characteristics of premixed CNG/H2/CO/CO2 blending synthetic gas flames in a combustor with variable geometric swirl number," Energy, Elsevier, vol. 172(C), pages 117-133.
    17. Mahdi Deymi-Dashtebayaz & Parisa Kazemiani-Najafabad, 2019. "Energy, Exergy, Economic, and Environmental analysis for various inlet air cooling methods on Shahid Hashemi-Nezhad gas turbines refinery," Energy & Environment, , vol. 30(3), pages 481-498, May.
    18. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    19. Kinsella, L. & Stefaniec, A. & Foley, A. & Caulfield, B., 2023. "Pathways to decarbonising the transport sector: The impacts of electrifying taxi fleets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    20. Sun, Zhao & Hu, Chenfeng & Zhang, Rongjun & Li, Hongwei & Wu, Yu & Sun, Zhiqiang, 2023. "Simulation of the deoxygenated and decarburized biomass cascade utilization system for comprehensive upgrading of green hydrogen generation," Renewable Energy, Elsevier, vol. 219(P2).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:977-:d:1036695. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.