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Towards energy freedom: Exploring sustainable solutions for energy independence and self-sufficiency using integrated renewable energy-driven hydrogen system

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  • Laimon, M.
  • Yusaf, T.

Abstract

In the pursuit of sustainable energy solutions, the integration of renewable energy sources and hydrogen technologies has emerged as a promising avenue. This paper introduces the Integrated Renewable Energy-Driven Hydrogen System as a holistic approach to achieve energy independence and self-sufficiency. Seamlessly integrating renewable energy sources, hydrogen production, storage, and utilization, this system enables diverse applications across various sectors. By harnessing solar and/or wind energy, the Integrated Renewable Energy-Driven Hydrogen System optimizes energy generation, distribution, and storage. Employing a systematic methodology, the paper thoroughly examines the advantages of this integrated system over other alternatives, emphasizing its zero greenhouse gas emissions, versatility, energy resilience, and potential for large-scale hydrogen production. Thus, the proposed system sets our study apart, offering a distinct and efficient alternative compared to conventional approaches. Recent advancements and challenges in hydrogen energy are also discussed, highlighting increasing public awareness and technological progress. Findings reveal a payback period ranging from 2.8 to 6.7 years, depending on the renewable energy configuration, emphasizing the economic attractiveness and potential return on investment. This research significantly contributes to the ongoing discourse on renewable energy integration and underscores the viability of the Integrated Renewable Energy-Driven Hydrogen System as a transformative solution for achieving energy independence. The employed model is innovative and transferable to other contexts.

Suggested Citation

  • Laimon, M. & Yusaf, T., 2024. "Towards energy freedom: Exploring sustainable solutions for energy independence and self-sufficiency using integrated renewable energy-driven hydrogen system," Renewable Energy, Elsevier, vol. 222(C).
  • Handle: RePEc:eee:renene:v:222:y:2024:i:c:s0960148124000132
    DOI: 10.1016/j.renene.2024.119948
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    1. Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003. "Underlying trends and seasonality in UK energy demand: a sectoral analysis," Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
    2. Kumar Narayan, Paresh & Narayan, Seema & Popp, Stephan, 2010. "Energy consumption at the state level: The unit root null hypothesis from Australia," Applied Energy, Elsevier, vol. 87(6), pages 1953-1962, June.
    3. Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
    4. Yi-Ming Wei & Gang Wu & Ying Fan & Lan-Cui Liu, 2006. "Progress in energy complex system modelling and analysis," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 25(1/2), pages 109-128.
    5. Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
    6. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    7. Narayan, Paresh Kumar & Smyth, Russell, 2005. "Electricity consumption, employment and real income in Australia evidence from multivariate Granger causality tests," Energy Policy, Elsevier, vol. 33(9), pages 1109-1116, June.
    8. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    9. Zhang, Ziyu & Ding, Tao & Zhou, Quan & Sun, Yuge & Qu, Ming & Zeng, Ziyu & Ju, Yuntao & Li, Li & Wang, Kang & Chi, Fangde, 2021. "A review of technologies and applications on versatile energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    10. Li, Yanfei & Kimura, Shigeru, 2021. "Economic competitiveness and environmental implications of hydrogen energy and fuel cell electric vehicles in ASEAN countries: The current and future scenarios," Energy Policy, Elsevier, vol. 148(PB).
    11. Yi Zuo & Ying-ling Shi & Yu-zhuo Zhang, 2017. "Research on the Sustainable Development of an Economic-Energy-Environment (3E) System Based on System Dynamics (SD): A Case Study of the Beijing-Tianjin-Hebei Region in China," Sustainability, MDPI, vol. 9(10), pages 1-23, September.
    12. Yue, Meiling & Lambert, Hugo & Pahon, Elodie & Roche, Robin & Jemei, Samir & Hissel, Daniel, 2021. "Hydrogen energy systems: A critical review of technologies, applications, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    13. ChungHyuk Lee & Wilton J. M. Kort-Kamp & Haoran Yu & David A. Cullen & Brian M. Patterson & Tanvir Alam Arman & Siddharth Komini Babu & Rangachary Mukundan & Rod L. Borup & Jacob S. Spendelow, 2023. "Grooved electrodes for high-power-density fuel cells," Nature Energy, Nature, vol. 8(7), pages 685-694, July.
    14. Fedorczak-Cisak, Małgorzata & Radziszewska-Zielina, Elżbieta & Nowak-Ocłoń, Marzena & Biskupski, Jacek & Jastrzębski, Paweł & Kotowicz, Anna & Varbanov, Petar Sabev & Klemeš, Jiří Jaromír, 2023. "A concept to maximise energy self-sufficiency of the housing stock in central Europe based on renewable resources and efficiency improvement," Energy, Elsevier, vol. 278(C).
    15. Talal Yusaf & Louis Fernandes & Abd Rahim Abu Talib & Yazan S. M. Altarazi & Waleed Alrefae & Kumaran Kadirgama & Devarajan Ramasamy & Aruna Jayasuriya & Gordon Brown & Rizalman Mamat & Hayder Al Dhah, 2022. "Sustainable Aviation—Hydrogen Is the Future," Sustainability, MDPI, vol. 14(1), pages 1-17, January.
    16. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    17. Sözen, Adnan & Arcaklioglu, Erol & Özkaymak, Mehmet, 2005. "Turkey's net energy consumption," Applied Energy, Elsevier, vol. 81(2), pages 209-221, June.
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    1. Yang, Yuyan & Xu, Xiao & Luo, Yichen & Liu, Junyong & Hu, Weihao, 2024. "Distributionally robust planning method for expressway hydrogen refueling station powered by a wind-PV system," Renewable Energy, Elsevier, vol. 225(C).

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