Identification of appropriate sites for solar-based green hydrogen production using a combination of density-based clustering, Best-Worst Method, and Spatial GIS
被引:8
作者:
Amjad, Fahd
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机构:
Sultan Qaboos Univ, Coll Econ & Polit Sci, Dept Operat Management & Business Stat, Muscat, OmanSultan Qaboos Univ, Coll Econ & Polit Sci, Dept Operat Management & Business Stat, Muscat, Oman
Hydrogen production;
Spatial multi criteria analysis;
Density -based clustering;
Location decision;
Best Worst Method;
MULTICRITERIA DECISION-MAKING;
RENEWABLE ENERGY;
WIND FARM;
SELECTION METHODOLOGY;
GEOTHERMAL-ENERGY;
RANKING LOCATIONS;
POWER-GENERATION;
SYSTEM;
PV;
TECHNOLOGIES;
D O I:
10.1016/j.ijhydene.2024.04.310
中图分类号:
O64 [物理化学(理论化学)、化学物理学];
学科分类号:
070304 ;
081704 ;
摘要:
Hydrogen is a relatively new source of sustainable energy resources; presently, its usage ranges from direct energy generation in mixed -cycle thermal power plants to its usage in the internal combustion engine. If the generation of hydrogen is coupled with renewable energy resources, the use of hydrocarbons can be substantially reduced in both energy generation and transportation. In this paper, we conducted a national -level assessment to identify the hydrogen generation capacity of Pakistan. The methodology used in this study is a combination of spatial multi -criteria analysis and density -based clustering in a geographical information systems environment. Based on the analysis, the national clusters capable of producing green hydrogen were identified. The results shows that the hydrogen generation capacity for Pakistan is approximately 7 million tons per year using solar photovoltaic energy. The Surab-Gwadar alignment, N-25 national highway, and Quetta -to -D -I -Khan alignment offer potential hydrogen generation sites with water resources. Most of the identified sites for the production of hydrogen in the country were found to be near the national electricity transmission networks. The Best -Worst Method was further employed to provide policymakers with a road map for prioritizing the development of these capacities based on their proximity to the national networks, their terrain suitability, and their generation capacities.
机构:
Ural Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, RussiaUral Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, Russia
Agyekum, Ephraim Bonah
Kumar, Nallapaneni Manoj
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City Univ Hong Kong, Sch Energy & Environm, Kowloon, Hong Kong, Peoples R ChinaUral Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, Russia
Kumar, Nallapaneni Manoj
Mehmood, Usman
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机构:
Univ Punjab, Natl Ctr GIS & Space Applicat, Dept Space Sci, Remote Sensing GIS & Climat Res Lab, Lahore, PakistanUral Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, Russia
Mehmood, Usman
Panjwani, Manoj Kumar
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机构:
Sukkur IBA Univ, Dept Energy Syst Engn, Sukkur 65200, PakistanUral Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, Russia
机构:
Ural Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, RussiaUral Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, Russia
Agyekum, Ephraim Bonah
Kumar, Nallapaneni Manoj
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Sch Energy & Environm, Kowloon, Hong Kong, Peoples R ChinaUral Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, Russia
Kumar, Nallapaneni Manoj
Mehmood, Usman
论文数: 0引用数: 0
h-index: 0
机构:
Univ Punjab, Natl Ctr GIS & Space Applicat, Dept Space Sci, Remote Sensing GIS & Climat Res Lab, Lahore, PakistanUral Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, Russia
Mehmood, Usman
Panjwani, Manoj Kumar
论文数: 0引用数: 0
h-index: 0
机构:
Sukkur IBA Univ, Dept Energy Syst Engn, Sukkur 65200, PakistanUral Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, Russia