Aggregation and Control of DERs in a Distribution System for the Provision of Grid Services
Bibliographic record
Abstract
Distributed energy resources (DERs) can be integrated into a distribution system to provide grid services and enhance the reliability of the power system. For the provision of grid services, DERs, such as photovoltaic (PV) systems and battery energy storage systems (BESSs), need to be aggregated and properly controlled. Particularly, renewable energy resources are intermittent in nature and may require real-time monitoring and control strategies. Uncontrolled power injections from DERs in the DER-integrated distribution system can cause overvoltage and thermal issues, damaging the system components. This paper explores several existing DER aggregation and control strategies in the literature. Moreover, a framework for designing aggregation and control schemes for DERs in the distribution system to provide grid services is presented. In the framework, the distribution system is divided into multiple sections, and rule-based algorithms are used to manage the DERs. Furthermore, the effectiveness of DER aggregation in providing peak shaving and energy services is demonstrated in this paper.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".