Distributed Energy Resources Electrical Systems: Current status and Future prospective
Bibliographic record
Abstract
Distributed generation (DG) is an approach that utilizes small scale technologies to generate electricity close to the consumer side. Generally, DG can provide high reliability, high security, low cost electricity, and less environment impact. This paper gives an overview of some of the most significant issues related to the distributed generation (DG). It discusses different aspects of DG, such as definitions, technologies, motivation for moving to DG, some drawbacks associated with the centralized systems which have led to DG. DGs challenges, standards and polices are also presented. In addition, the economic impact and a price comparison between central power plants and DGs are discussed. Also a case study was conducted in order to study the impact of using distributed generations in Edmonton downtown. Three distributed generations, combustion turbine types with 25 MW capacities each have been implemented in Edmonton power system. The total cost estimations have been studied in this case, and the results have revealed that this type of distributed generation is inexpensive and more economic compared with price from the utility. It was estimated from the calculation that the price for the energy is about ¢6.27/kWh while the current electricity price from the utility is ¢8.561/kWh, for long term estimation it is found that the proposed CTs in this project has 11 years for a payback period, after that the project start earning money which is relatively good and wroth investment. The second part of the study analyses the impact of DGs on the system losses using Power-World software, and the result have proven that the loss is significantly decreases when the DG systems are in operation, hence DGs help reducing the costs that associated with the system’s losses.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".