Modelling and Transient Simulation of Solar-powered Hydrogen Energy Storage Systems
Bibliographic record
Abstract
Practical energy storage systems are essential for matching the intermittent production of renewable power sources with the time dependent power demand of residences and businesses. This study assesses the technical viability of battery and hydrogen energy storage systems, scaled for a Canadian residential dwelling with low annual electric power consumption. The simulated consumption is an electric load, encompassing all major electricity demands within the house. The simulated load does not include space heating or cooling requirements. The conceptual dwelling uses a PV array to provide power to the load and storage system. System architectures were evaluated based on their ability to capture the maximum amount of energy from the PV array over the course of the simulated year. \n \nTransient modelling was performed to thoroughly study two system architectures; an all-battery energy storage system, and a hybrid energy storage system, combining battery and hydrogen gas storage. The simulated systems were sized to meet a minimum load reliability of no greater than 15 load faults (no-power events) for the simulated year. Simulation of the all-battery storage system indicated that for a 10kWp PV array to meet the load demand with the required reliability, a 172.8kWh lead-acid battery bank was required. The battery system was capable of capturing 25% of every kWh of PV production, and had an overall electrical efficiency of 78%. \n \nSimulation of the hybrid energy storage system indicated that the 10kWp PV array, combined with an 11kW PEM electrolyser, a 6 kW PEM fuel cell, a 20kWh lead acid battery bank, and a 10m3 hydrogen storage tank exceeded the reliability requirement, with only 6 load faults in the simulated year. The hybrid storage system was able to capture 58% of every kWh of PV production, and had an overall electrical efficiency of 48% (weighted average of battery and hydrogen efficiency).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".