Transactive Energy Management and Distribution System Reform Using Market Concepts
Bibliographic record
Abstract
The increasing integration of distributed energy resources (DERs), encompassing renewables, storage, electric vehicles, and smart loads, into distribution systems is primarily propelled by endeavors to reduce costs. To fully harness the benefits offered by DERs, it becomes imperative to unlock distribution systems through the facilitation of three key components. First and foremost is implementing a transactive energy management (TEM) market mechanism overseen by a distribution system operator (DSO). Secondly, there is a need to facilitate various transaction types, including peer-to-peer (P2P) interactions and collaboration between transmission and distribution networks. Lastly, the integration should accommodate a diverse array of technologies, ranging from privately owned DERs to utility-owned energy storage batteries, as well as prosumers and aggregator entities. The introduction of a TEM brings forth numerous advantages, including the creation of more lucrative economic opportunities for DERs and an overall increase in social welfare, benefiting both end-user customers and generation entities. In pursuit of this objective, a comprehensive three-phase TEM market platform is introduced, optimizing economic prospects for DERs while maximizing social welfare for all market participants. This TEM market mechanism considers various transaction types for both energy and ancillary services. The model also elucidates the interaction between the bulk electricity market, governed by an independent system operator (ISO), and the TEM under a DSO control model. The proposed TEM market mechanism is pragmatically implemented as a mixed-integer linear programming formulation, featuring a network reconfiguration capability. To validate its effectiveness, the TEM market model has been tested on 34-bus systems, demonstrating its prowess in settling energy and ancillary service transactions while furnishing distribution locational marginal prices.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".