Dispatchable Distributed Generation Network - A New Concept to Advance DG Technologies
Bibliographic record
Abstract
Renewable energy has been booming globally thanks to its economic, social, and environmental benefits. However, small distributed generation (DG) systems using renewable energy have not yet achieved a significant level of penetration. With deregulation of electricity market, policies have been available to facilitate interconnection of small distributed generators (DGs) with electric grids. However, dispatchability and reliability still present technical barriers for small DGs to play a significant role in the open market, thus limiting their ability to provide value-added services. This paper presents a new concept to enhance the dispatchability of DGs through an aggregated DG network. Dispersed DGs with different energy resources, such as wind turbines, photovoltaics, small hydros, fuel cells, and microturbines, are integrated into a single aggregated generating plant via open power transmission networks. Traditional SCADA dedicated optical fibers, copper and other dedicated wireless physical layers can be replaced by Internet access and low-cost point-to-point wireless communication links to reduce infrastructure costs. The aggregated power generation is balanced between firm DGs and intermittent DGs to allow for the required dispatchability. Day-ahead and hourly generation scheduling can be committed in wholesale electricity trading, thereby achieving the desired added economic benefits. This is usually more favorable than being credited at the avoided cost as seen by utilities with traditional individual DGs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".