A Multistage Distribution-Generation Planning Model for Clean Power Generation under Multiple Uncertainties—A Case Study of Urumqi, China
Bibliographic record
Abstract
In this research, a multistagedistribution-generation planning (MDGP) model is developed for clean power generation in the regional distributed generation (DG) power system under multiple uncertainties. The developed model has been applied for sustainable energy system management at Urumqi, China. Various scenarios are designed to reflect variations indemand modes of districts, seasonal limits, potentials of energy replacement, and clean power generation. The model can provide an effective linkage between economic cost and stability of DG power systems. Different power generation schemes would be obtained under different seasonal scenarios and system-failure risk levels. On the other hand, net system costs would be obtained and analyzed. The results indicate that the traditional power generation can be replaced by renewable energy power in DG power systems to satisfy the environmental requestsofthe city of Urumqi. The obtained solutions can help decision-makers get feasible decision alternatives to improve clean power planning in the Urumqi area under various uncertainties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".