Research on the application of BP algorithm incorporating GWO in distributed energy storage scheduling optimization
Bibliographic record
Abstract
Distributed energy storage technology can effectively solve the load peak-to-valley difference and voltage quality problems faced by distribution networks.Reasonable and efficient scheduling of distributed energy storage in distribution networks is an important means to play its role.The study proposes a power prediction-based optimized scheduling strategy for distributed energy storage in distribution grids with hierarchical zoning.Firstly, power prediction is carried out using GWO-EEMD-BP neural network.Then partition optimization is carried out according to distributed power and load distribution, and the energy storage scheduling strategy is formulated based on the energy storage power prediction interval.Finally, experiments and arithmetic examples are analyzed based on the data related to the distribution system of the IEEE-33 distribution node.The predicted SOC values based on GWO-EEMD-BP neural network are basically consistent with the real SOC values.After applying the energy storage scheduling strategy designed in this paper, the system power loss decreased by 260.86 kWh and the load volatility decreased by 67.5%.In addition, this strategy has significant advantages in terms of system operation economic efficiency and voltage quality improvement, and it is capable of scheduling distributed energy storage in the distribution network in a reasonable manner.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".