Techno-economic feasibility study of hydrogen storage in enhancing the reliability of a renewable-based microgrid for residential applications
Bibliographic record
Abstract
The rapid transition toward cleaner energy requires microgrid models that are not only technically feasible but also economically and environmentally compelling for local contexts. This work develops and evaluates a hybrid renewable microgrid tailored for a residential building in Nazipur, Patnitala Thana, Naogaon District, Bangladesh. Using HOMER Pro (v3.14.2), the system integrates solar photovoltaic (PV), wind turbines (WT), an electrolyzer-hydrogen tank-fuel cell chain ("power-to-gas-to-power"), and a grid connection. The optimized design achieves a remarkably low cost of energy (COE) $0.0396/kWh and a net present cost (NPC) of $145,664 with minimal annual operating expenses ($1,100). The total carbon footprint is limited to 11,158 kg/yr, reflecting a 95.8% reduction compared with conventional supply, while hydrogen is generated at $3.32/kg, reinforcing its role as a viable long-term storage medium. Beyond techno-economics, the study examines system stability through dynamic voltage and frequency response modelling in MATLAB, and explores resilience under uncertainty via sensitivity analysis of solar radiation, wind speed, hub height, temperature, and financial variables. The findings highlight that integrating hydrogen into renewable-based microgrids offers a scalable pathway for decarbonizing residential sectors in Bangladesh and similar developing regions. This research thus advances the discourse on hydrogen-augmented microgrids, underscoring their potential to bridge the gap between sustainability targets and local energy security. This study uniquely integrates a correlation-based sensitivity analysis with MATLAB dynamic validation to establish the reliability and feasibility of a hydrogen-augmented hybrid microgrid for residential applications.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".