Energy management of hydrogen-based stand-alone renewable energy system by using boost and buck converters
Bibliographic record
Abstract
Electrolytic hydrogen offers a promising alternative for long-term energy storage of renewable energy (RE). A stand-alone RE system based on energy storage in the form of hydrogen has been developed at the Hydrogen Research Institute and successfully tested for automatic operation with an in-house designed control system and power conditioning devices. In this system the excess energy with respect to load demand is sent to the electrolyzer to produce hydrogen which, when properly stored, is available to the fuel cell to produce electricity when there is insufficient wind and solar energy with respect to load requirement. The RE system components have substantially different voltage-current characteristics, and they are integrated through the designed and developed power conditioning devices on a DC bus, which allows power to be managed between input power, energy storage and load. The DC-DC buck and boost converters are connected for power conditioning between the DC bus and the electrolyzer, and between the fuel cell and the DC bus, respectively. With respect to the energy level at the DC bus and the control strategy, the controller sends the conditioned signal (duty ratio) to the digitally controlled DC-DC converters for on/off operation, thus controlling the power flow to the electrolyzer and from the fuel cell. The converters' control is based on a chopper multiphase switching technique, which reduces the harmonics levels and ripples with a simple additional filter. The simulated and experimental results of the boost and buck converter operations are compared. Also the effect of duty ratio with respect to the number of cells in the converters has been studied for harmonic reduction. It has been found that the designed and developed DC-DC converters and the control system give effective power flow control with automatic operation of the system under different load conditions and with intermittent inputs from wind turbine generator and photovoltaic array.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".