Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value
Bibliographic record
Abstract
Abstract Demand response (DR) plays an essential role in power system management. To facilitate the implementation of these techniques, many aggregators have appeared in response as new mediating entities in the electricity market. These actors exploit the technologies to engage customers in DR programs, offering grid services like load scheduling. However, the growing number of aggregators has become a new challenge, making it difficult for utilities to manage the load scheduling problem. This paper presents a multi-agent reinforcement Learning (MARL) approach to a price-based DR program for multiple aggregators. A dynamic pricing scheme based on discounts is proposed to encourage residential customers to change their consumption patterns. This strategy is based on a cooperative framework for a set of DR Aggregators (DRAs). The DRAs take advantage of a reward offered by a Distribution System Operator (DSO) for performing a peak-shaving over the total system aggregated demand. Furthermore, a Shapley-Value-based reward sharing mechanism is implemented to fairly determine the individual contribution and calculate the individual reward for each DRA. Simulation results verify the merits of the proposed model for a multi-aggregator system, improving DRAs’ pricing strategies considering the overall objectives of the system. Consumption peaks were managed by reducing the Peak-to-Average Ratio (PAR) by 15%, and the MARL mechanism's performance was improved in terms of reward function maximization and convergence time, the latter being reduced by 29%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".