A Distributed Optimal Load Control Model for Heterogeneous Homes Responding to Time of Use
Bibliographic record
Abstract
Time-of-Use (TOU) has great potential to reduce electricity payments and improve the stability of the power system with demand response (DR) implementation. This paper presents a load control model for optimal residential DR implementation responding to TOU in a distribution network, in which the main stakeholders are utilities and homes. The load control model is formulated into a linear programming (LP) problem to minimize electricity payment and waiting time. A software home agent (HA) is designed to represent a home owner. The HA can predict and control electricity loads. A heterogeneous load prediction model simulates the benchmark of individual and aggregated load profiles based on statistical information of how people use their appliances including electric vehicles (EV). Each home has a unique load profile depending on its local configurations. Simulation results show that the peak-to-average power ratio (PAPR) and electricity payments are significantly reduced using the proposed models. The proposed optimal control mechanism can be embedded into a home energy management system (EMS) to make intelligent decisions on behalf of homeowners responding to DR policies.
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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) | 0.000 | 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".