Optimization of multi-element energy storage layout and zoning in oilfield power grid under high ratio distributed power source access
Bibliographic record
Abstract
Abstract Distributed power generation is a small-scale power generation system, which is distributed in an oil field power grid. In order to improve the poor effect of weak voltage nodes, a new partition optimization method of multi-energy storage layout in the oilfield power grid is proposed by introducing a high proportion of distributed power supply access. The investment and construction cost, the cumulative life cycle discharge loss cost, the operating income and the market income of electric energy are mined, and the model is constructed based on the mining results. The output results are input into the economic benefit maximization function to obtain the constraint conditions of the basic optimization objective function. Based on the constraints, the objective function of multi-energy storage system optimization is constructed, and the function is solved by a differential evolution algorithm to realize the partition optimization of multi-energy storage layout in an oil field power grid. Through experiments, it can be seen that the operation performance of the oil field power grid has been significantly improved through partition optimization of multi-energy storage layout under the condition of a high proportion of distributed power supply access. The experimental results show that the voltage fluctuation of the distributed power supply is small and that the self-absorption rate of photovoltaic power is high.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".