DRL-Based Distributed Coordination of ISO and DSOs in Bi-Level Electricity Markets
Bibliographic record
Abstract
The increasing penetration of distributed energy resources has prompted distribution system operators (DSOs) at the retail electricity market level to coordinate with the independent system operator (ISO) at the wholesale market level, for greater benefits. However, interaction mechanisms between the ISO and DSOs, and impacts of prices and power injections, have not been adequately investigated in literature. This article proposes a distributed coordination framework for the ISO and DSOs across wholesale-retail (bi-level) electricity markets, considering their interactions more fairly. Moreover, to mitigate the challenges arising from the interdependence between the ISO and heterogeneous DSOs, a coupled training mechanism based on the response model is devised. This mechanism iteratively trains the ISO and DSOs by solely exchanging prices and power injections, ensuring the demand–supply balance at both retail and wholesale levels. In addition, a deep reinforcement learning algorithm is introduced for the three-stage iterative training process of heterogeneous agents. Results demonstrate the effectiveness of the proposed method and its advantages in terms of lowering energy prices, clearing of cheaper clean resources and thus, improving overall market efficiency.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".