Indirect Load Shaping for CHP Systems Through Real-Time Price Signals
Bibliographic record
Abstract
Direct or centralized loading shaping in smart grid has been heavily investigated. However, it is usually not clear how the users are compensated by providing load shaping services. In this paper, we will discuss indirect load shaping in a distributed manner. On one hand, we aim to reduce the users' energy cost by investigating how to fully utilize the battery pack and the water tank for the combined heat and power (CHP) systems. 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 can be obtained by solving a nonconvex optimization problem. We then discuss when it can be converted into a convex optimization problem. Since the CHP battery pack queue and water tank queue are correlated, the capacity relationship between them is further explored considering different queue weights. On the other hand, based on the users' reaction model, we propose an algorithm with a time complexity of O(log n) to determine the real-time price for the power company to effectively coordinate all the CHP systems and provide distributed load shaping services.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".