Optimal Combined Heat and Power system scheduling in smart grid
Bibliographic record
Abstract
Combined Heat and Power (CHP) systems are well known for their high efficiency and relatively low emissions. Existing CHP economic dispatch schemes do not use the energy buffer to minimize the average cost in the long term. Motivated by the queueing analysis and buffer management solutions in data communication systems, in this paper, we investigate how to use a battery pack and a water tank to optimize the average cost for the CHP systems by jointly considering the real-time electricity price, renewable energy generation, energy buffer states, etc. We first formulate the queueing models for the CHP systems, and then propose an algorithm based on the Lyapunov optimization technique which does not need any statistical information about the system dynamics. The optimal control actions are obtained by solving a non-convex optimization problem. We then discuss when it can be converted into a convex optimization problem. Since the battery pack queue and water tank queue are correlated by the CHP, the capacity relationship between them is further explored. Through the theoretical performance analysis, we also show the tradeoff between the cost saving and the energy buffer capacity. Finally, the effectiveness of the proposed algorithms is evaluated with practical data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".