Optimal operation of small, numerous, and disparate DERs via aggregation in transactive distribution systems with universal metering
Bibliographic record
Abstract
Abstract Transactive energy distribution systems (TEDS) have unleashed new economic opportunities in the distribution sector via a new local distribution operator (LDO) that enables transactions between peers and the LDO (P2LDO) and peer‐to‐peer transactions. Aiming to unleash full benefits from existing distributed energy resources (DERs), this paper introduces an optimization algorithm for the operation of small, numerous, and disparate DER aggregations. The proposed algorithm seeks to maximize aggregator profits obtained via P2LDO and peer‐to‐peer transactions for energy and demand response, while being cognizant of capacity obligations acquired during transactive energy distribution system planning phases. In addition, the concept of universal metering is introduced to upgrade the economic opportunity of DERs. Results obtained via case studies show that the proposed approach can help DER owners to increase their revenue. An aggregated case study for 300 m shows that the overall revenue can be increased by more than 100% when operating DERs in an aggregated fashion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".