Two‐stage stochastic demand response in smart grid considering random appliance usage patterns
Bibliographic record
Abstract
By effectively adjusting the appliance usage patterns of customers, demand response (DR) is expected to bring significant economic and environmental benefits to the future smart grid. Two kinds of appliances should be considered for DR, i.e. shiftable appliances such as dishwashers and laundry machines, and non‐shiftable appliances such as lights and stoves. Although the shiftable appliances can be well controlled by energy management systems, the random usage patterns of non‐shiftable appliances will result in uncertainties to electrical demands and thus, affect the efficiency and reliability of smart grid operation. A two‐stage stochastic programming problem is formulated, for which the distribution system operation cost is minimised in the first stage, by considering various distribution system operation constraints. The scheduling of shiftable appliances is optimised in the second stage, by considering the random usage patterns of non‐shiftable appliances. To reduce the computational complexity caused by a large number of home appliances in distribution systems, scenario reduction technique is applied to reduce the number of possible scenarios while still retaining the essential features of the original scenario set. Extensive simulations are performed to evaluate the proposed DR scheme in IEEE 33‐bus and 119‐bus test distribution systems based on real appliance usage pattern data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".