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Record W2228228559 · doi:10.1504/ijpse.2015.071429

Incentives for the reuse of electric vehicle batteries for load-shifting in residences

2015· article· en· W2228228559 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Process Systems Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIncentiveReuseElectricityEnvironmental economicsBattery (electricity)Electric vehicleSmart gridLoad shiftingDemand responseGovernment (linguistics)Load profileAutomotive engineeringEngineeringBusinessComputer scienceElectrical engineeringEconomicsPower (physics)MicroeconomicsWaste management

Abstract

fetched live from OpenAlex

With growing uncertainty in electricity prices and rising concern about climate change, there is increased need to reduce both energy costs and carbon emissions. One way to accomplish these goals is through load-shifting provided by the reuse of the large lithium ion batteries currently found in most electric vehicles (EVs). When used as a load-shifting device, a used EV battery has the potential to help a residential user reduce the cost of their electric bill and contribute to a reduction in emissions while also laying groundwork for the future 'smart grid'. To study this, a MatLAB model is developed and implemented to examine the use of an EV battery that is reused for peak shifting in a typical household. The results of the simulation for number of different incentive types are compared using a multi-criteria decision making approach to arrive at a policy choice that satisfies both government and consumer stakeholders.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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