A Multi-Timescale Low Carbon Scheduling Optimization Method for Integrated Energy System Considering Source-load
Why this work is in the frame
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Bibliographic record
Abstract
In order to reduce the instability of integrated energy system caused by wind power, load prediction error, and low-carbon and low-cost operation, a multi-time scale low-carbon scheduling optimization method for integrated energy system is proposed. The fuzzy variables and fitting loads under different time scales are obtained by analyzing the change of prediction error of wind energy, load and user response law under the time-sharing price. To achieve deviation control at different time scales, minimize the cost of daily power purchases, gas purchases, wind discards and carbon emissions. To satisfy load balance, active backup, power purchase constraint and energy storage capacity constraint to construct an optimized scheduling model for integrated energy system. Implementation of low carbon optimization scheduling requirements for integrated energy systems. The experimental results show that this method can realize the optimal dispatch of electric, gas and heat load of integrated energy system. The higher the accuracy of wind power and load prediction, the lower the optimal dispatch cost of integrated energy system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 it