Exergoeconomic Analysis of a Residential Hybrid PV‐Fuel Cell‐Battery System
Bibliographic record
Abstract
Abstract A residential photovoltaic (PV)‐based hydrogen fuel cell (FC) system is analyzed using exergoeconomic methods, and its monthly performance is investigated. Mathematical models for predicting the power outputs of the PV and FC systems are presented. The results reported include the PV output and the shares attributable to the battery and the SOFC in supplying the electrical demand. Moreover, to study the performance of the hybrid system in supplying the daily demand, results are presented for two typical days in summer and winter. An exergoeconomic analysis is performed to determine the electricity unit cost over the system lifetime. The PV‐electrolyzer system is not able to produce a sufficient amount of hydrogen during winter days, so seasonal hydrogen storage is required to feed the FC. Power penetrations of the PV and the battery systems are at maxima during the summer months, while the penetration of the FC system reaches 67% in January and December. Due to its low efficiency (16%), the maximum exergy destruction occurs in the PV modules (86%). The unit cost of electricity varies on a monthly basis, reaching a minimum of 0.26 $ kWh–1 in July and a maximum of 1.8 $ kWh–1 in January and December.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".