Guest Editorial: Special Issue on Power Electronics for Distributed Energy Resources
Bibliographic record
Abstract
Distributed energy resources (DERs) are any energy resources in the electrical distribution systems, which can produce electricity, consume or store energy in a controlled manner, or be utilized to improve energy efficiency. They are typically smaller in scale than the traditional large-generation facilities. DERs include distributed generation units, energy storage facilities including electric vehicles, and controlled loads. Power electronic technologies, as the focus of this Special Issue, are critical to enabling the integration, protection, performance, and interoperability of DERs in power systems. DERs are rapidly growing in the global electricity market and are entering into power systems as an integral part of the system thanks to the increasing penetration of renewable energy and energy storage units. The technologies for DERs have advanced significantly over the past two decades and so have the DER interconnection standards. In addition to meeting the requirements for power system specifications, safety, and protection, DERs are required to support grid operation in recently developed interconnection standards by providing functions of voltage and frequency ride-throughs, voltage and frequency regulations, or inertial responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.027 |
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".