Optimal Configuration and Techno-Economic Analysis of Hybrid Photovoltaic/PEM Fuel Cell Power System
Bibliographic record
Abstract
In this study, a renewable energy-based hybrid system was designed capable of meeting known electrical load requirements, as the system includes a combination of photovoltaic cells (PV), a fuel cell, batteries, an electrolyzer, and a hydrogen tank. This hybrid system supplies the cell tower located in the village of Ouanougha, country of Algeria with the annual electrical energy demand of 47 kWh/day. A Hybrid optimization model for electric renewable (HOMER) simulation software is utilized for modeling, optimize sizing, simulation as well as performing the techno-economic analysis of this hybrid system. HOMER software gives several optimum system configurations, which are compared among themselves for identifying the optimum system configuration. The comparison is based on the total net present cost (TNPC) and levelized cost of energy (LCOE). Other cost parameters can be provided such as initial capital cost, operation, and maintenance cost (O&M). The simulation result shows that, the proposed hybrid system has the lowest TNPC, LCOE and Initial capital, which are 64,384 $, 0.259 \$/kWh and 35,850 \$, respectively. On the other hand, it proved that the hybrid system is environmentally friendly and without producing any polluting gas. This paper also focuses on the operational strategy for feeding the load, as the results show that the hybrid system generally fulfills the requirements of the load.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".