A Dynamic Internal Trading Price Strategy for Networked Microgrids: A Deep Reinforcement Learning-Based Game-Theoretic Approach
Bibliographic record
Abstract
In this study, a novel two-step optimization model is developed for maximizing the amount of internal power trading in a distribution network comprising several networked microgrids. In the first step, a soft actor-critic-based optimization model is developed to help the retailer agent in determining dynamic internal trading prices for its local microgrid network. A better internal price encourages microgrids to increase the amount of internal power trading, and thus the retailer’s profit is also increased. Unlike deep Q learning-based methods, the proposed method is able to handle large state and action spaces. In addition, using entropy-regularized reinforcement learning helps to accelerate and stabilize the learning process and also prevents trapping in local optima. In the second step, an optimization model is developed to facilitate internal trading among various networked microgrids using a cooperative strategy. Since the policy network plays the role of an approximator, the learning model can handle uncertainties in the distribution network. Finally, results of the proposed model show the superiority of the proposed model over the direct power trading schemes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".