Optimal Sizing and Assessment of Standalone Photovoltaic Systems for Community Health Centers in Mali
Bibliographic record
Abstract
Despite abundant solar resources, Mali has remained one of the least electrified countries in the world. Besides daily life activities and the economy, the shortage of electricity has severely affected the quality of healthcare services in the country. In the absence of electrical grids, standalone photovoltaic (PV) systems could be an alternative option in Mali for the electrification of isolated community health centers. However, because standalone PV systems are highly weather-dependent, they must be properly sized according to the local weather conditions. This paper presents the optimal sizing of standalone PV systems for the electrification of community health centers in Mali. The optimization for PV systems was performed for five different locations through simulation and modeling using PVsyst, considering the autonomy of 1 to 3 days and the probability of loss of load for 1 to 5%. Furthermore, for the economic analysis, the levelized cost of electricity (LCOE), payback period and return on investment for the standalone PV systems were calculated. Through the optimization, it was found that the standalone PV systems with PV array sizes ranging from 1650 to 2400 watts, along with 606 Ah battery storage, would be suitable to supply the daily energy demand for community health centers anywhere in the country. Moreover, by only replacing the 606 Ah battery storage with 1212 Ah and 1818 Ah sizes, the PV systems would be able to help and keep the energy reserves for 2 and 3 autonomous days, respectively. Furthermore, the results show that in comparison to a LCOE of 0.94–0.98 USD/kWh for a diesel generator, the LCOE for the standalone PV system would range from 0.23 to 0.46 USD/kWh without discounted rates and from 0.33 to 0.60 USD/kWh if discounted at 6%. In addition to a lower LCOE, the saving of 46–76 tons of CO2 during the project’s lifespan, the short payback periods and high return of investment (ROI) values make standalone PV systems a suitable electrification option for Mali. Considering the total expenses, LCOE, payback period, and ROI, standalone PV systems for community health centers were found to be economically viable in all cases for Mali.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".