Cross-climate analysis of techno-economic metrics in bi-level electrical and hydrogen storage systems for off-grid electrification of buildings
Bibliographic record
Abstract
Hybrid energy storage systems (ESS) are a viable solution for sustainable energy transition and decarbonization within the building sector. Nonetheless, the cost-effective design of hybrid ESSs remains a challenging issue, particularly for stand-alone buildings. In this context, the present study aims to provide insights into the cross-climate performance and techno-economic feasibility of a bi-level ESS driven by PV panels for off-grid power supply to office buildings. The hybrid bi-level ESS strategies incorporate electrical energy storage and two types of hydrogen storage systems, i.e., hydrogen-battery storage (HBS) and metal hydride-battery storage (MHBS) systems. To this end, a comprehensive techno-economic analysis of these systems is carried out under various climatic conditions according to the Köppen classification. A dynamic simulation model is established in TRNSYS coupled with a Fortran code, addressing the transient comportment of bi-level ESS strategies. A typical mid-rise office building is modeled using the OpenStudio-EnergyPlus plugin to simulate hourly energy demand in each climate. Adopting a statistical approach, a multi-objective optimization framework is developed to isolate the office building from the grid while minimizing hybrid system costs. The economic analysis based on the range-bar concept evaluates variations of techno-economic metrics triggered by alterations in the equipment costs within each quartile. The results indicate that the dry climate (zone B) has the lowest median (2 nd quartile cost) levelized cost of electricity (LCOE) for HBS and MHBS systems, at 0.097 and 0.192 $/kWh, respectively, followed by tropical one (zone A). Moreover, the levelized cost of hydrogen (LCOH) values range from 3.87 to 7.71 $/kg. Employing a predictive algorithm, the results imply that by the end of 2050, the LCOH for off-grid electrification of office buildings will be lower than 3.75 $/kg, regardless of the climatic conditions, having on average an annual decrement rate of 0.123 $/kg for all scenarios. • Dry climate shows the lowest median LCOE for both bi-level energy storage systems. • Gaseous hydrogen scenarios show on average 71.4 % lower LCC than metal hydride ones. • The LCOE in cases with metal hydride storage is more sensitive to climatic conditions. • Battery-metal hydride storage in continental zone is the most unfavorable solution. • The LCOH will be reduced by more than half within a 25-year period in all climate zones.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".