Robust Hierarchical Control Mechanism for Aggregated Thermostatically Controlled Loads
Bibliographic record
Abstract
Thermostatically controlled loads (TCLs) are good candidates for direct load control (DLC). They can be aggregated to join the electricity market through a centralized management performed by virtual power plant (VPP). However, there are two main concerns that arise when TCLs participate in DLC. The first is related to the dispatchability of VPP against normal energy demand of individual TCLs in uncertain time-variant environments. The second refers to the tremendous increase of communication and computational requirements needed to perform DLC on a large population of TCLs. This paper introduces aggregators between the DLC controller and TCLs with a novel robust control mechanism to reconcile these concerns. The control mechanism is implemented with two layers: the upper layer suppresses the control payback effect with a quadratic optimization model, and the lower layer addresses the power trajectory tracking with a novel payback tracking error model (PTEM). The control method needs minimum sensing infrastructure since it requires power data only at the aggregation level. Our simulations resulted in a robust reference power tracking by the aggregator with a percentage root mean squared error between 3.33% - 5.69% under uncertain time-variant environments. The continuous responsiveness indicates that the aggregators manage to convert the aggregated TCLs into “manageable resources” that ensure the dispatchability of VPP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".