Deployment of renewable energy resources for energy justice and poverty mitigation
Bibliographic record
Abstract
Rural settlements are key to the global food supply due to arable lands. Rural electricity access in developing countries is threatened by low power demands and poverty. Agriculture and agro-industries can mitigate poverty and boost power demands, ensuring economic sustainability. Most rural grids cannot support industrial loads due to design constraints. This study presents a methodology that evaluates the grid’s hosting capacity for industrial loads, and defines a sustainable solution based on the integration of distributed renewable energy resources to adapt the grid to industrial demand. The methodology was applied to a case study in Nigeria using the IEEE 34 bus system. Solar PV and storage were integrated at proposed sites, restoring system voltages. Solar PV and pumped hydro storage were found to be the most cost-effective solutions. Three potential pumped hydro sites were identified along the Cross River in the Nigerian case study. There was a mismatch between daily and yearly ratings of energy devices due to seasonal variation in renewable generation. A storage dispatch model is needed to harness excess generation for sustainable energy, food, and water access.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".