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Record W4407572722 · doi:10.1002/9781394191529.ch22

Transactive Energy Management and Distribution System Reform Using Market Concepts

2025· other· en· W4407572722 on OpenAlexaff
Amr A. Mohamed, Bala Venkatesh, Carlos Sabillón, Ali Golriz

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsIndependent Electricity System OperatorOntario Power Generation
Fundersnot available
KeywordsTransactive memoryBusinessDistribution (mathematics)Environmental economicsComputer scienceProcess managementKnowledge managementEconomicsMathematics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.004
GPT teacher head0.191
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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