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Record W2796115129 · doi:10.1109/lcomm.2018.2822798

Dynamic Pricing Mechanism in Smart Grid Communications Is Shaping Up

2018· article· en· W2796115129 on OpenAlexaff
Saba Al–Rubaye, Anwer Al‐Dulaimi, Shahid Mumtaz, Jonathan Rodrı́guez

Bibliographic record

VenueIEEE Communications Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsExfo Electro-Optical Engineering (Canada)
Fundersnot available
KeywordsDynamic pricingSmart gridComputer scienceDemand responseDynamic demandElectricityLoad managementSupply and demandEnvironmental economicsKey (lock)Electricity pricingElectricity marketMicroeconomicsPower (physics)Computer securityEconomics

Abstract

fetched live from OpenAlex

An efficient smart grid communication system is a key enabling technology for modernized utility that can adjust electricity usage to balance generation and demand in real time. However, the utility industry is not able to meet the power demand for the wholesale market during overloading or emergency situations. To mitigate power shortage pressures, utility providers need to employ a dynamic pricing policy to enforce higher pricing than pre-estimated statistic pricing to motivate consumers to reduce their power consumption. In this letter, the time of use (TOU) approach is proposed to regulate price variances considering desired power demand. In this mechanism, the consumers deliver their TOU electricity demands and subsequent control signals to utility providers through communication network infrastructure. The utility provider creates an hourly demand profile for each consumer by predicating individual requests over short- and long-time frames. The control signaling exchanged and prior arrangement of services enable utility providers to evaluate the status of wholesale market demand and assign prices considering dynamic changes in electricity demand. Numerical analysis study was carried out to validate the advantages of the proposed mechanism.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.037
GPT teacher head0.266
Teacher spread0.229 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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