Sizing and operation of a pure renewable energy based electric system through hydrogen
Bibliographic record
Abstract
Today, in order to reduce the increase of the carbon dioxide emissions, a large number of renewable energy resources (RES) are already implemented. Considering both the intermittency and uncertainty of the RES, the energy storage system (ESS) is still needed for balancing and stabilizing the power system. Among different existing categories of ESS, the hydrogen storage systems (HSS) have the highest energy density, and are crucial for the RES integration. In addition, RES are located in faraway regions, and are often transmitted to the terminal consumption center through HVDC (high voltage direct current) due to its lower power loss. In this paper, we present a power supply system that achieves low-carbon emissions through combined HSS and HVDC technology. First, the combined HSS and the HVDC model are established. Secondly, the rule-based strategy for operating the HSS microgrid is presented. Then, an operating strategy for a typical network, i.e., the pure RES generation station-HVDC transmission-microgrids, is demonstrated. Finally, the best sizing capacities for all components are found by the genetic algorithm. The results prove the efficiency of the presented sizing approach for a pure RES electric system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".