Peak-to-Average Ratio Analysis of A Load Aggregator for Incentive-based Demand Response
Bibliographic record
Abstract
In smart grid, demand response programs have proven to have significant potential in terms of managing distributed systems. Demand response is a strategy to flatten the load profile of the grid by motivating the users based on utility incentives or price signals. As a result, users are inclined to shift their consumption by adjusting their flexible loads to reduce the peak hours and thus the peak-to-average ratio of the grid load profile. The emergence of energy aggregators facilitates the management of financial interactions between the power market and customers. These new players extract the demand flexibility potentials from the grid by employing optimization techniques. This paper studies a multi-agent system that simulate a set of houses under an incentive-based demand response program. Residential agents are capable of performing a model predictive control and forecasting the outside temperature in order to control thermal loads. The peak to average ratio is used to develop the optimization problem of the residential agents and propose a cost function for the aggregator. The results of the simulations allow to analyze the agents response under an incentive-based demand response program and their impact over proposed cost function for the aggregator. The simulated framework enables aggregators to examine the effectiveness of optimization techniques that are aimed for actual implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".