Demand Side Management using Model-Free Fuzzy Controller in a Direct Load Control Program
Bibliographic record
Abstract
Integrating renewable resources such as wind and solar into the electric power systems introduces new challenges to the grid due to fast fluctuations which reduces the reliability of the system. Demand side management (DSM) is one method to cope with the uncertainty and variability of the generation. Direct load control of thermostatically controlled appliances can play a significant role for this purpose; however, the system operator requires a reliable estimation about the magnitude of the load and how much it can be shifted, in order to produce attainable desired aggregated load. The focus of this paper is on designing a model-free controller to follow the system operator's dispatch instructions. The main advantage of such controller is to eliminate the requirement for training or identifying the controllable loads' parameters; thus, it can be used as a plug & play component. The other advantage is that this system can dynamically cope with system changes. In this research, the controller changes the thermostat set points of the individual loads in a systematically manner so that the aggregated power consumptions of the loads would follow the desired aggregated load. 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 10 minutes with a negligible steady state error (less than 5%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".