Development of a Chance-Constrained Dual-Objective Fractional Programming for Shandong’s Clean Power Transition
Bibliographic record
Abstract
In this study, an inexact mixed-integer interval stochastic fractional model (IMSFP) is developed for Shandong’s sustainable power system management under uncertainties. Shandong has a high proportion of fossil-fuel power, which has resulted in significant greenhouse gas emissions. Future is an essential period for energy structure transition. Developed IMSFP can effectively tackle dual objective, system efficiency represented as output/input ratios, as well as uncertainties described as interval values and probability distributions in the constraints and objectives. The results indicate that the clean power transition and capacity expansion scheme are sensitive to different constraint-violation risk levels. Obtained interval solutions can provide flexible strategies for resource allocation and expansion capacities under multiple complexities. An economic single objective model (IMCLP) is also developed, which aims at minimizing the system cost. The comparative results illustrate that the IMSFP model can better characterize the real-world power system problems through optimizing a ratio between clean energy utilization and system cost. Biomass and wind power would be major developed electricity forms in the future, and solar energy has great development potential. In short, the proposed IMSFP model is advantageous in balancing conflicting dual objectives and reflecting complicated interactions among system efficiency, economic cost, system reliability, and constraint-violation scenarios.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".