Design of a Smart Controller Agent for Demand-Side Management with Low Payback Effect
Bibliographic record
Abstract
With high penetration of renewable resources such as wind and solar into conventional electric grid, new challenges are introduced due to the rapid fluctuation on the generation side. Direct load control of thermostatically controlled loads can play a significant role in demand side management (DSM) to cope with the uncertainties and variabilities of the generation. For this purpose, the system operator (SO) requires a reliable forecast of the demand and how much it can be shifted; in order to produce attainable desirable set points to reshape the demand to follow generation side. The focus of this paper is on designing a smart agent that uses a hybrid system of a model-based and a model-free structure to forecast the controllable load and its capacity to be reshaped, and follow the dispatch instructions of the SO, while minimizing the payback effect of the control actions and maintaining customers’ comfort. The main advantages of the proposed system are: 1) real-time model creation; thus, no need for historical data for training, 2) model free controller can automatically adapt to the changes in the system, 3) it can be used as a plug & play component in a DSM program. To evaluate the performance of the proposed controller, a numerical simulator was developed, and the controller was applied over the simulation engine to follow arbitrary desired power profiles. It was observed that the system can follow the dispatch command in less than 5 minutes with a negligible steady state error (less than 5%).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".