Exploring Solar and Wind Energy as a Power Generation Source for Solving the Electricity Crisis in Libya
Bibliographic record
Abstract
The current study is focused on the economic and financial assessments of solar and wind power potential for nine selected regions in Libya for the first time. As the existing meteorological data, including wind speed and global solar radiation, are extremely limited due to the civil war in the country, it was therefore decided to use the NASA (National Aeronautics and Space Administration) database as a source of meteorological information to assess the wind and solar potential. The results showed that the country has huge solar energy potential compared to wind energy potential. Additionally, it is found that Al Kufrah is a suitable region for the future installation of the Photovoltaic (PV) power plant due to high annual solar radiation. Based on the actual wind speed analysis, Benghazi and Dernah are the best regions for large-scale wind farm installation in the future taking into account existing meteorological data limitations. The values of the wind power density in all regions are considerable and small-scale wind turbines can be used to generate electricity based on NASA average monthly wind data for 37 years (1982–2019). Moreover, this work aimed to evaluate the wind/PV systems technical and economically through RETScreen Expert (Version 6.0, CanmetENERGY Varennes Research Centre of Natural Resources Canada, Varennes, Canada). Focusing on the power supply crisis in the country, the potential of electricity production by 5 kW grid-connected residential/household rooftop PV in all regions is proposed and presented. Additionally, this paper evaluated a techno-economic analysis of the 50MW wind/PV system in suitable places. The performance of a 5 kW and 50 MW PV solar system with three PV technologies, namely mono-crystalline silicon, poly-crystalline silicon, and thin-film (CdTe), was also analyzed. The results demonstrated that the development of the wind/PV system in the selected regions is both technically and economically feasible. The outcomes of this study can help decision-makers in designing and installing PV power plants as an alternative source for the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".