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Record W2807436562 · doi:10.1109/tia.2018.2870834

An Interactive Decision-Making Model Based on Energy and Reserve for Electric Vehicles and Power Grid Using Generalized Stackelberg Game

2018· article· en· W2807436562 on OpenAlexaff
Yuanzheng Li, Tianyang Zhao, Chang Liu, Yong Zhao, Ping Wang, Hoay Beng Gooi, Kaicheng Li, Zhaohao Ding

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsYork University
FundersFundamental Research Funds for the Central UniversitiesState Key Laboratory of Synthetical Automation for Process IndustriesNational Natural Science Foundation of China
KeywordsStackelberg competitionGridComputer scienceUniquenessMathematical optimizationGame theoryPower (physics)SimulationOperations researchEngineeringMathematical economicsEconomicsMathematics

Abstract

fetched live from OpenAlex

This paper proposes an interactive decision-making model for the operations of electric vehicles (EVs) and power grid, in the perspective of energy and reserve. In this model, EVs optimize their energy and reserve plans considering prices set by the power grid. Meanwhile, the power grid aims to obtain the optimal energy and reserve prices for EVs, in order to maximize its benefits. Afterward, we use the generalized Stackelberg game to formulate the interactive decision-making model, and prove the existence and uniqueness of the generalized Stackelberg equilibrium. Finally, simulation results verify that the proposed interactive model can well enhance benefits of EVs while guaranteeing their quality of charging services.

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 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: none
Teacher disagreement score0.657
Threshold uncertainty score0.899

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 studies0.0000.000
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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations43
Published2018
Admission routes1
Has abstractyes

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