Optimized Solution for Increasing Electricity Access with Mini-Grid Technology in Nigeria
Bibliographic record
Abstract
Electricity challenge in Nigeria is such that only a dramatic increase in both generation and distribution capacity could leapfrog it out of the situation. Reports show that about 61% of the population have access to electricity, and those that do have are currently grappling with epileptic supply. The power network has installed capacity of about 12.5GW whereas the distribution network has capacity for about 6 GW, and often only about 4.5 GW is available to the consumers. Shortfalls are often met with private generations using diesel generators, resulting in noise and environmental pollution with the attendant health consequences. Frightfully so, the challenge is further exacerbated by the burgeoning population. Nevertheless, the government have set an ambitious target for increasing electricity access in VISION 2030 national development plan. A major milestone of this policy is the audacious ambition to increase the existing grid capacity to 30 GW, with 30% of the mix coming from low carbon technology sources. This study explores the use of available renewable energy resources at eleven locations in different regions of the country for optimal generation of energy in contribution to the Nigerian energy matrix. The study was conducted using HOMER™ and Power BI models. Four energy sources were investigated comprising of solar, wind, hydro and diesel. Our findings show that most places in the southern and middle belt parts of the country support mini-grid systems but are more receptive to grid extension, while a majority of the locations in the north are more environmentally friendly to the implementation of mini-grids. In addition, most places investigated in the south, solar technology contributes more than 80% to the energy mix, meanwhile, most northern locations despite having higher solar irradiation are better suited for wind technology with above 60% contribution to the fold.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".