
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Solar photovoltaic potential and diffusion assessment for Pakistan

AbstractIn Pakistan, around 58% of current electricity is generated from fossil fuels and only 2.4% is generated using renewable energy (RE) resources even though country is blessed with enormous RE potential. Among other RE resources, Pakistan's geographical location offers high solar energy potential, which implies that actual potential assessment should be undertaken. This study, as such, undertakes a comprehensive assessment of solar energy potential and prospects of solar photovoltaic (PV) systems for both off‐grid and grid‐connected systems. This study also estimates the future available capacity of rooftop and rural off‐grid solar PV capacity. Three different types of solar PV modules of the same size, that is, thin‐film, premium, and standard were modeled to compare energy outputs. NREL's System Advisor Model (SAM) is used to estimate the geographical and technical potential of solar PV considering updated data and geographical information. SAM results suggest that an average of 4.5 kWh/kWp/day is obtained from an installed capacity of 1 KWp. The logistic modeling equations are further used to forecast the solar PV penetration over a period until 2090. The research investigation concludes that 2.8 × 106 GWh of electricity can be generated annually in Pakistan. The estimated results prove that solar PV has the potential to meet the present as well as future energy needs of Pakistan.
Technology, Renewable energy, Environmental economics, Economics, Science, Energy Engineering and Power Technology, solar PV, Environmental science, Meteorology, Engineering, Solar energy, Electricity, Artificial Intelligence, Indoor Air Pollution in Developing Countries, Solar Energy, Pakistan, Machine Learning Methods for Solar Radiation Forecasting, Grid, Waste management, Photovoltaic system, Energy, Geography, T, diffusion, Q, Fossil fuel, prospects, Pollution, Hydrogen Energy Systems and Technologies, Grid parity, Photovoltaics, SAM, Electrical engineering, potential assessment, Environmental Science, Physical Sciences, Computer Science, Photovoltaic Power, Geodesy
Technology, Renewable energy, Environmental economics, Economics, Science, Energy Engineering and Power Technology, solar PV, Environmental science, Meteorology, Engineering, Solar energy, Electricity, Artificial Intelligence, Indoor Air Pollution in Developing Countries, Solar Energy, Pakistan, Machine Learning Methods for Solar Radiation Forecasting, Grid, Waste management, Photovoltaic system, Energy, Geography, T, diffusion, Q, Fossil fuel, prospects, Pollution, Hydrogen Energy Systems and Technologies, Grid parity, Photovoltaics, SAM, Electrical engineering, potential assessment, Environmental Science, Physical Sciences, Computer Science, Photovoltaic Power, Geodesy
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).24 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
