Comparison of Multi-Technology Routes and Construction of Virtual Simulation Environment for Energy Storage Power Station Using 3D Visualization Technology
Bibliographic record
Abstract
This paper constructs a three-dimensional model of energy storage power station through threedimensional visualization technology, and builds a virtual simulation environment of energy storage power station by inputting realistic environmental parameters.Four different energy storage technology routes, namely lithium-electronic battery energy storage, lead-acid battery energy storage, pumping energy storage and air compression energy storage, are selected, and the energy storage performance of the four technology routes is explored in depth based on the constructed virtual environment.At the same time, the energy storage performance of four different technology routes in the virtual environment of the energy storage power station is compared using the energy storage capacity and energy storage efficiency as the measurement indexes, and the energy storage technology routes suitable for the environment of this paper are highlighted based on the comparison results.In the energy storage simulation, the net energy storage capacities of the four technology routes in the virtual environment of this paper are 728.99MW,724.18MW, 461.50MW and 393.45MW, respectively.Compared with the other three energy storage technology routes, the lead-acid battery energy storage capacity fluctuation is smaller, and the energy storage capacity is higher, with a higher degree of adaptability to the virtual simulation environment in this paper.At the same time, the average energy storage efficiency of lead-acid battery in four quarters is 99.71%, compared with the next highest efficiency of lithium-electronic battery energy storage efficiency increased by 14.29%, which further indicates that the lead-acid battery energy storage technology route in this paper builds the best performance of the virtual simulation environment of the energy storage power station.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".