A Reinforcement Learning Controller Based on Double DQN for DC microgrids with Constant Power Loads
Bibliographic record
Abstract
The presence of Constant Power Loads (CPLs) in DC microgrid poses significant challenges due to their nonlinear and time-varying characteristics. Additionally, the unpredictable fluctuations in renewable energy sources further complicate the stability of the system. To address these issues, this paper proposes a robust model-free reinforcement learning control method based on double Deep Q-Network (DQN). The proposed control approach is designed to tackle the dynamic nature of CPLs and adapt to changes in the system environment. Specifically, it focuses on stabilizing the DC-link voltage, which is crucial for maintaining the overall stability of the microgrid. In this study, the performance of the double DQN control method is evaluated under various scenarios, including significant changes in load power, source voltage, and power generation conditions. Through simulation studies, we demonstrate the effectiveness of the proposed approach in controlling and stabilizing the DC-link voltage despite the dynamic nature of the system as well as the uncertainties imposed by CPLs and renewable energy sources.
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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 | 0.000 | 0.000 |
| 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".