Techno-Economic Design and Sensitivity Analysis of a DC Microgrid for a Remote Community: A Case Study in Ghana
Bibliographic record
Abstract
Access to electricity is crucial to human development. Many villages in Ghana remain undeveloped due to a lack of electricity. One way to increase energy access is to electrify these communities using renewable resources. Resource availability, reliability, sustainability, and cost-benefit analysis are vital in the design process. To that end, this paper presents a techno-economic design of a renewable-energy-based microgrid for an island community in Ghana using HOMER Pro. To ensure an efficient design, factors such as maximum annual capacity shortage and minimum renewable fraction are utilized as design constraints. The proposed system combines a hybrid solar-wind-diesel generator-battery-converter as the optimal system architecture. From the simulation results, the optimal component sizes are solar-102 kW, wind-24.3 kW, diesel generator-30 kW, converter-50 kW, and battery-289 kWh. The system's total annual energy production is 207,827 kWh, with a 98.6% renewable fraction and an estimated yearly CO2 emission of 1,488 kg, making the system environmentally friendly. The reported capital, net present, and operating costs are $133,275.00, $250,689.00, and $4,696.54, respectively, at an LCOE of $0.09812. The system has a simple payback period of approximately 7 years with a 35.4% return on investment. Additionally, detailed sensitivity analyses are carried out on key variables, including fuel price, inflation, wind speed, solar radiation, and battery minimum state of charge to assess their impact on system performance. Finally, the study analyzes demand increases and load efficiency improvements to the existing system performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".