Real-time rolling regulation model of integrated energy system based on model predictive control theory
Bibliographic record
Abstract
The integrated energy system in the park faces challenges in producing and consuming renewable energy on a large scale as well as in achieving equilibrium between supply and demand for energy, making it a novel form in the study of integrated energy systems. The study takes the integrated energy system of the park as an example, and constructs a real-time rolling regulation model of two-layer optimal dispatch with multiple time scales. The model includes an upper-layer rolling economic optimization scheduling model and a lower-layer dynamic performance optimization control model, which takes economy and real-time as the objectives and realizes dynamic rolling optimization through model predictive control theory. The electric chillers are producing power to give cold energy during the whole dispatching cycle, while the absorption chillers produce power to supply cold energy only during the peak cold load period. The cold storage tank lowers the system’s operational costs by storing cold energy during low hours and releasing it during portions of the system’s high cold load hours. For the park's integrated energy system's primary energy exchange nodes 1 and 2, the micro gas turbine, and the gas boiler. The dynamic response process of the output power of the equipment takes a long time in model 2, with a value of about 10 min, while the time for the output value to reach the desired value is greatly reduced in model 1, with a value of about 4 min, and at the same time, it can foresee the change of the output power in advance, and make adjustments accordingly. The model constructed in the study has a more rapid calculation process and higher calculation accuracy in a short period of time, which has obvious advantages in online real-time prediction operation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".