A stand-alone hybrid renewable energy system assessment using cost optimization method
Bibliographic record
Abstract
In this paper, a stand-alone hybrid renewable energy system is proposed, which consists of solar PV, wind turbine, and energy storage with the combination of battery and hydrogen. This energy storage system partly relies on energy conversion through two components, electrolysis and fuel cell. When the hybrid system generates more electricity than load demand, hydrogen is produced via electrolysis and stored in a tank; when the hybrid system needs more electricity, the fuel cell can convert hydrogen back into electricity. Cost optimization of the proposed hybrid system is essential for finding optimal sizing of individual components. In this paper, the net present cost (NPC) method is used to minimize the total cost of the system through the software tool HOMER (Hybrid Optimization Model for Electric Renewables) developed by National Renewable Energy Laboratory. The NPC method is best tailored for such a purpose because it considers all revenues and costs occurring during the life cycle of the project. A case study is conducted for economic assessment of the proposed hybrid system implemented in Ardabil, north-western Iran. Two additional energy storage scenarios, with only battery and with only hydrogen, are also investigated in the case study. It is found that using the combination of battery and hydrogen as energy storage is a more efficient option. The proposed stand-alone hybrid renewable energy system is suitable to supply power for rural homes and farms, and marine electric systems.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".