Performance analysis based on probabilistic modelling of Quaid-e-Azam Solar Park (QASP) Pakistan

Khosa, Azhar Abbas; Rashid, Tanzeel-ur; Shah, Najam-Ul-Hassan; Usman, Muhammad; Khalil, Muhammad

doi:10.1016/j.esr.2020.100479

Cited by 16 publications

(7 citation statements)

References 19 publications

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“…Individually, miscellaneous elements have little impact; however, when combined with primary elements, they exert a much greater influence. The findings are consistent with prior studies [32,33]. Sub-factors related to various factors such as the non-availability of a grid (graded 9), lack of investment (graded 10), data uncertainties (graded 12), and efficiency degradation (graded 22) have an unfavorable influence on embracing solar technology [3,17,20].…”

Section: Impact Of Other (Miscellaneous) Factorssupporting

confidence: 89%

Barriers to Solar PV Adoption in Developing Countries: Multiple Regression and Analytical Hierarchy Process Approach

Mustafa,

Malik,

Maqsoom

2024

Sustainability

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The globe is transitioning from traditional methods of electricity generation to renewable resources in order to achieve sustainable goals. Solar energy is a promising and abundant renewable resource that shows great potential as a viable alternative to traditional energy. Furthermore, the production of electricity from solar energy is the most cost-effective compared to other kinds of renewable energy. Nevertheless, the execution of solar initiatives in underdeveloped nations is encountering several obstacles. Identifying the most significant obstacles in the execution of solar projects is of utmost importance. This study uses a linear regression model (LRM) and an analytical hierarchical process (AHP) to determine the main barriers to the implementation of renewable energy projects in a developing economy, i.e., Pakistan. By conducting an extensive review of the relevant literature and consulting with experts, the most significant categories of obstacles were determined. A survey based on 429 responses was collected from the participants working at solar projects. Subsequently, the responses were subjected to processing and analysis using the relative importance index (RII), AHP, and linear regression modeling techniques. The linear regression analysis revealed several significant variables that hinder progress, including financial conditions, policies, technological awareness, institutional support, social and environmental awareness, market stability, and other miscellaneous factors. The AHP analysis revealed the key factors that have the greatest impact, which include effective policies, financial stability, technological expertise, institutional support, market stability, various aspects, and social and environmental awareness. The study’s conclusions are beneficial for all stakeholders and project managers in enhancing the project management of solar initiatives. It would also facilitate prompt decision-making regarding policy formulation and implementation.

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Section: Impact Of Other (Miscellaneous) Factorssupporting

confidence: 89%

Barriers to Solar PV Adoption in Developing Countries: Multiple Regression and Analytical Hierarchy Process Approach

Mustafa,

Malik,

Maqsoom

2024

Sustainability

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“…Bahawalpur region shows a peak under the 'low' vulnerability zone, with 'low' vulnerability covering 91.27% of its total area. The reason for this result is that the Bahawalpur region has the largest desert in Pakistan, most of the area being barren and uncultivated [111]; therefore, all the chosen indicators collectively did not contribute significantly, and the region yields low values.…”

Section: District Wise Comparison Of Eva Resultsmentioning

confidence: 95%

Investigating Eco-Environmental Vulnerability for China–Pakistan Economic Corridor Key Sector Punjab Using Multi-Sources Geo-Information

Kamran

Bian

et al. 2021

IJGI

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China-Pakistan economic corridor (CPEC), a critical part of the Belt and Road initiative (BRI), is subjected to rapid infrastructure development, which may lead to potential eco-environmental vulnerability. This study uses multi-source geo-information, and the multi-criteria decision-making (MCDM)-based best–worst method (BWM) to quantify the baseline eco-environmental vulnerability of one key CPEC sector—the Punjab province. The Punjab province is an important connection between northern and southern CPEC routes in Pakistan. In this study, we have established an indicator system consisting of twenty-two influential factors in a geospatial database to conduct eco-environmental vulnerability analysis. The overall setup is supported by a geographic information system (GIS) to perform spatial analysis. The resulting map was categorized into five vulnerability levels: very low, low, medium, high, and very high. The results revealed that the overall eco-environmental health of the Punjab province is reasonably good as 4.64% and 59.45% area of the key sector lies in ‘very low’ and ‘low’ vulnerability categories; however, there also exist highly vulnerable areas, particularly in the proximity of CPEC projects. Although high vulnerability areas constitute a very small percentage, only 0.08% of the Punjab province, still, decision-makers need to be aware of those regions and make corresponding protection strategies. Our study demonstrated that the MCDM-BWM-based EVA model could be effectively used to quantify vulnerability in other areas of CPEC. The findings of the study emphasize that management policies should be aligned with research-based recommendations for ecological protection, natural resource utilization, and sustainable development in regions participating in Belt and Road Initiative (BRI).

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“…When determining where to install the solar PV system, a number of factors must be taken into account. According to, [ 39 ] the plant's location satisfies many requirements for power generation by using this PV technology, including a sufficiently sunny environment, a sufficient unoccupied area, and excellent insulation. These parameter profiles are typically investigated and analyzed using the PV domain as a model.…”

Section: Case Studymentioning

confidence: 99%

Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

Abubakar,

Che,

Faheem

et al. 2024

Adv Energy and Sustain Res

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To address the rising energy demands in industrial and public sectors, integrating zero‐carbon emission energy sources into the power grid is crucial. Smart grids, equipped with advanced sensing, computing, and communication technologies, offer an efficient way to incorporate renewable energy resources and manage power systems effectively. However, improving solar energy efficiency, which currently contributes around 3.6% to global electricity, is a challenge in smart grid infrastructures. This research tackles this issue by deploying machine learning models, specifically recurrent neural network (RNN), long short‐term memory (LSTM), and gate recurrent unit (GRU), to predict measurements that could enhance solar power generation in smart grids. The objective is to boost both performance and accuracy of solar power generation in the smart grid. The study conducts experimental analyses and performance evaluations of these models in smart grid environments, considering factors like power output, irradiance, and performance ratio. The results, presented through graphical visualizations, show notable improvements, particularly with the LSTM model, which achieves a 97% accuracy, outperforming the RNN and GRU models. This outcome highlights the LSTM model's effectiveness in accurately predicting measurements, thereby advancing solar power generation efficiency in the smart grid framework.

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Performance analysis based on probabilistic modelling of Quaid-e-Azam Solar Park (QASP) Pakistan

Cited by 16 publications

References 19 publications

Barriers to Solar PV Adoption in Developing Countries: Multiple Regression and Analytical Hierarchy Process Approach

Barriers to Solar PV Adoption in Developing Countries: Multiple Regression and Analytical Hierarchy Process Approach

Investigating Eco-Environmental Vulnerability for China–Pakistan Economic Corridor Key Sector Punjab Using Multi-Sources Geo-Information

Intelligent Modeling and Optimization of Solar Plant Production Integration in the Smart Grid Using Machine Learning Models

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