Optimal Residential Demand Response for Multiple Heterogeneous Homes With Real-Time Price Prediction in a Multiagent Framework
Why this work is in the frame
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Bibliographic record
Abstract
Demand response (DR) is a recent effort to improve the efficiency of the electricity market and the stability of the power system. A successful implementation relies on both appropriate policy design and enabling technology. This paper presents a multiagent system to evaluate optimal residential DR implementation in a distribution network, in which the main stakeholders are modeled by heterogeneous home agents (HAs) and a retailer agent (RA). The HA is able to predict and control electricity load demand. A real-time price prediction model is developed for the HA and the RA. The optimal control of electricity consumption is formulated into a convex programming problem to minimize electricity payment and waiting time under real-time pricing. Simulation results show that the peak-to-average power ratio and electricity payments are significantly reduced using the proposed algorithms. The HA, with the proposed optimal control algorithms, can be embedded into a home energy management system to make intelligent decisions on behalf of homeowners responding to DR policies. The proposed agent system can be utilized to evaluate various strategies and emerging technologies that enable the implementation of DR.
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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 it