A Machine Learning-Aided DERMS to Manage Large Numbers of Small-Scale Distributed Energy Resources
Bibliographic record
Abstract
The rapid rise in the penetration of distributed energy resources (DERs) has introduced significant challenges in modern power systems. The integration of numerous small-scale DERs further complicates the landscape, leading to increased complexity and making these challenges more difficult to address. This paper seeks to address the limitations of existing approaches by introducing a three-level Distributed Energy Resources Management System (DERMS). The proposed DERMS aggregates numerous small-scale DERs at the bottom level and manages them effectively alongside individual DERs at the top level, offering a more coordinated and efficient solution. Additionally, the Random Forest Regression (RFR) model has been selected as the machine learning model to simplify the optimal power flow (OPF) problem using a Sequential Adjustment Method (SAM), significantly enhancing the computational efficiency of the process. The effectiveness and robustness of the proposed DERMS framework have been demonstrated through several complex case studies simulated on the modified IEEE 123-bus test system, highlighting its potential to improve the reliability and stability of power systems in the face of increasing DER penetration. Furthermore, this approach provides a scalable solution that can adapt to the evolving demands of modern power grids, ensuring sustainable and reliable energy management in the future.
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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 | 0.001 |
| 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".