Applications of Renewable Energies in Low-Temperature Regions: A Scientometric Analysis of Recent Advancements and Future Research Directions

被引:0
|
作者
Rodriguez-Aburto, Cesar [1 ]
Poma-Garcia, Jose [1 ]
Montano-Pisfil, Jorge [1 ]
Morcillo-Valdivia, Pablo [1 ]
Solis-Farfan, Roberto [1 ]
Curay-Tribeno, Jose [1 ]
Pilco-Nunez, Alex [2 ]
Flores-Salinas, Jose [2 ]
Tineo-Cordova, Freddy [2 ]
Viru-Vasquez, Paul [3 ]
Bravo-Toledo, Luigi [4 ]
机构
[1] Univ Nacl Callao, Fac Elect & Elect Engn, Callao 07011, Peru
[2] Univ Nacl Ingn, Fac Chem & Text Engn, Tupac Amaru 210 Ave, Lima 15024, Peru
[3] Pontificia Univ Catolica Peru, Inst Calidad, Lima 15024, Peru
[4] Univ Nacl Callao, Fac Environm Engn & Nat Resources, Callao 07011, Peru
关键词
low-temperature regions; renewable energies; scientometric analysis; green hydrogen; LCA; EXTREME-WEATHER; CLIMATE-CHANGE; COLD SPELL; HEAT WAVES; HYDROGEN; INTEGRATION; SECURITY; IMPACTS; SCIENCE; STORAGE;
D O I
10.3390/en18040904
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This study presents a scientometric analysis of renewable energy applications in low-temperature regions, focusing on green hydrogen production, carbon storage, and emerging trends. Using bibliometric tools such as RStudio and VOSviewer, the research evaluates publication trends from 1988 to 2024, revealing an exponential growth in renewable energy studies post-2021, driven by global policies promoting carbon neutrality. Life cycle assessment (LCA) plays a crucial role in evaluating the environmental impact of energy systems, underscoring the need to integrate renewable sources for emission reduction. Hydrogen production via electrolysis has emerged as a key solution in decarbonizing hard-to-abate sectors, while carbon storage technologies, such as bioenergy with carbon capture and storage (BECCS), are gaining traction. Government policies, including carbon taxes, fossil fuel phase-out strategies, and renewable energy subsidies, significantly shape the energy transition in cold regions by incentivizing low-carbon alternatives. Multi-objective optimization techniques, leveraging artificial intelligence (AI) and machine learning, are expected to enhance decision-making processes, optimizing energy efficiency, reliability, and economic feasibility in renewable energy systems. Future research must address three critical challenges: (1) strengthening policy frameworks and financial incentives for large-scale renewable energy deployment, (2) advancing energy storage, hydrogen production, and hybrid energy systems, and (3) integrating multi-objective optimization approaches to enhance cost-effectiveness and resilience in extreme climates. It is expected that the research will contribute to the field of knowledge regarding renewable energy applications in low-temperature regions.
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页数:21
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