A multi-agent system of evaluating residential demand response
Bibliographic record
Abstract
This paper presents a multi-agent system (MAS) to evaluate implementation of the residential demand response (DR), in which the main stakeholders are modeled by a class of software agents including Conventional Home Agents, Smart Home Agents, and a Utility Agent. DR is a recent effort to improve efficiency of the electricity market and the stability of the power system. We develop a Residential Load Model, a Dynamic Price Model, an Energy Management System Model, a Primary Power Plant Model and a Secondary Power Plant Model, which are further incorporated into the MAS. Simulation results show that, without the assistance of the home energy management system (EMS) or other similar technologies, the strategy of dynamic price does not lead to a successful DR application. However, by scheduling controllable load using the home EMS, the peak demand and demand standard deviation is dramatically reduced by 23.8% and 41.6% respectively, and the generation cost also decreases by 30.8%. The scheduling algorithm can be embedded into a home EMS. The proposed agent system can be utilized to evaluate various strategies, emerging techniques and algorithms that enable the DR implementation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".