Impact of Capacity Shortage on The Feasibility of PV-Wind Hybrid Systems in Africa
Bibliographic record
Abstract
Reliable optimization of renewable energy system is the one that balances between electrical sizing of the system components in order to satisfy the load and the cost of that system. This techno-economic optimization can be assured by HOMER software through some sensitivity parameters such as capacity shortage. For Saharan villages in Africa, it is required to install off-grid power systems with low cost. To fulfill this requirement, is it necessary to avoid the over-sizing of system due to high and short peaks of load, so the optimization of PV-wind hybrid system on this article is done with sensitivity analysis of the system for different capacity shortage rates. The only rates that do not exceed the mean values of electrical outage of Algeria are 0 % and 0.5 %, and HOMER had favorited the optimal system with 0.5 % of capacity shortage due to the 18 % gain in total cost of system and the energy cost. The results achieved on this article encourage on techno-economical optimizing PV-wind hybrid systems with acceptable capacity shortage and electrical outage rates for a better economic feasibility in Saharan villages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".