2019
DOI: 10.3390/pr7040196
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Assessment of Wind Energy Potential for the Production of Renewable Hydrogen in Sindh Province of Pakistan

Abstract: In this study, we developed a new hybrid mathematical model that combines wind-speed range with the log law to derive the wind energy potential for wind-generated hydrogen production in Pakistan. In addition, we electrolyzed wind-generated power in order to assess the generation capacity of wind-generated renewable hydrogen. The advantage of the Weibull model is that it more accurately reflects power generation potential (i.e., the capacity factor). When applied to selected sites, we have found commercially vi… Show more

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Cited by 173 publications
(65 citation statements)
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“…The factors have been grouped into policy, technical, socioeconomic, and geographic. Ghana has a technical potential for wind energy at 82.8 TWh yearly and total final electricity consumption of 6.9 TWh a year (Mohsin et al 2018a andIqbal et al 2019). There is a need to build grid infrastructure and modernize the existing ones to take on VRE.…”
Section: Introductionmentioning
confidence: 99%
“…The factors have been grouped into policy, technical, socioeconomic, and geographic. Ghana has a technical potential for wind energy at 82.8 TWh yearly and total final electricity consumption of 6.9 TWh a year (Mohsin et al 2018a andIqbal et al 2019). There is a need to build grid infrastructure and modernize the existing ones to take on VRE.…”
Section: Introductionmentioning
confidence: 99%
“…Their experimental results indicate that the hybrid method has higher forecasting accuracy for short-term wind power forecasting. Siyal et al [9] develop a new hybrid mathematical model that combines a wind-speed range with the log law to derive the wind energy potential for wind-generated hydrogen production in Pakistan, and electrolyze wind-generated power in order to assess the generation capacity of wind-generated renewable hydrogen. They offer a strategy for reducing the energy shortfall, and thus contribute to the fight against climate change and global warming.…”
Section: New Energy Issuesmentioning
confidence: 99%
“…The EEE is the dependent variable and the explanatory variables used in this study are GDP per capita, carbon intensity, energy intensity, and the openness index. The explanatory variable GDP per capita is repeatedly used in previous studies [40,41]. The second variable, energy intensity affects a country's carbon dioxide emissions.…”
Section: Regression Analysismentioning
confidence: 99%