Bio-inspired computational intelligence metaheuristic-based optimization and sensitivity analysis approach to determine techno-economic feasibility of hydrogen refueling stations for fuel cell vehicles
Bibliographic record
Abstract
This study presents a comprehensive economic and technological evaluation of renewable hybrid power systems for hydrogen refueling stations (HRS) in Nizwa, Oman, leveraging cutting-edge optimization algorithms to determine the most cost-effective and efficient hybrid energy system configurations. Three hybrid energy systems of photovoltaic-wind turbine-battery (PV-WT-B), photovoltaic-wind-fuel cell-battery (PV-WT-FC-B), and wind turbine-battery (WT-B) were evaluated based on net present cost (NPC), levelized cost of energy (LCOE), and levelized cost of hydrogen (LCOH). The study employs advanced optimization techniques, including the Mayfly Algorithm, Genetic Algorithm, CUKO Search, Gray Wolf Optimizer (GWO), Constrained Particle Swarm Optimization (CPSO), Harmony Search (HS), and Flower Pollination Algorithm to determine the most viable hybrid energy system for the HRS in Nizwa. The results indicate that CPSO consistently achieves the lowest NPC, LCOE, and LCOH, whereas HS and GWO yield higher costs due to convergence inefficiencies. Sensitivity analysis reveals a strong inverse correlation between PV capacity and cost metrics, highlighting the economic advantage of increased solar generation. Additionally, hybrid configurations integrating PV and wind turbine (PV-WT-B, PV-WT-FC-B) significantly reduce NPC compared to WT-B, reinforcing the role of solar energy in optimizing economic costs. Furthermore, fuel cell integration (PV-WT-FC-B) imposes additional economic burdens, making PV-WT-B the most viable solution for HRS deployment in Oman. More so, the annual worth and return-on-investment analysis demonstrated that the PV-WT-B is the preferred energy system to meet the needs of the HRS in terms of investment. The findings underscore the importance of renewable energy fraction and capacity factor in energy economics, demonstrating that higher PV integration enhances sustainability and cost-efficiency. This study provides a transformative framework for decarbonizing Oman's transportation sector, offering insights into optimal hydrogen production strategies to advance the global clean energy transition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".