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Record W4211105564 · doi:10.1109/tpwrd.2022.3150256

Deregulated Distribution System Planning - Incremental Capacity Auction Mechanism With Transactive DERs

2022· article· en· W4211105564 on OpenAlexafffund
Amr A. Mohamed, Carlos Sabillón, Ali Golriz, Marina Lavorato, Marcos J. Rider, Bala Venkatesh

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2022
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsToronto Metropolitan UniversityIndependent Electricity System Operator
FundersIndependent Electricity System Operator
KeywordsDistributed generationElectric power systemEnvironmental economicsDemand responseBusinessElectricityComputer scienceEconomicsEngineeringRenewable energyPower (physics)

Abstract

fetched live from OpenAlex

High penetrations of distributed energy resources (DERs) are driving the transformation of traditional distribution networks into transactive energy distribution systems (TEDS). TEDS depart from conventional distribution system constructs to 1) enable a local distribution operator to maximize social welfare, 2) enable peer-to-peer energy transactions, 3) extract maximum participation and benefits from DERs, 4) usher in competition in the distribution sector to supply electricity via DERs, and 5) hold the potential for a lower asset cost solution, greater customer choice, and higher reliability. The conventional distribution system planning is inadequate for this purpose. Following the new TEDS paradigm, an incremental capacity auction (ICA) is proposed in this paper considering: 1) bids for power capacity from all energy sources (DERs and transmission), 2) bids for network asset upgrades from equipment vendors, and 3) bids from new loads. A holistic auction settlement procedure is developed for the proposed ICA mechanism that can concurrently determine the optimal power capacity to be procured and network asset upgrading, while maximizing social welfare. Further, locational marginal price for power capacity demand is estimated as a by-product of the ICA solution. Benefits are demonstrated via case studies and include increased participation of DERs to supply energy, maximizing the social welfare, and enabling assets deferral considering the available local resources.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.166
Teacher spread0.158 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes2
Has abstractyes

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