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Multi-Objective Energy Management in a Residential Area with a P2G-Based Storage System

2019· article· en· W3003691962 on OpenAlexaff
Wentao Yang, Weijia Liu, Fushuan Wen, Ivo Palu, C. Y. Chung, Lei Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMathematical optimizationEnergy consumptionComputer scienceInteger programmingPower system simulationEnergy managementElectric power systemLinear programmingScheduling (production processes)Energy storageIterative methodRange (aeronautics)Energy management systemReliability engineeringEnergy (signal processing)Power (physics)EngineeringMathematics

Abstract

fetched live from OpenAlex

The energy management in the residential sector, as a basic unit of energy consumption, has received extensive attention in recent years. To address this issue, a residential energy management system (REMS) is proposed, with two objectives considered, i.e., minimizing the costs and maximizing comfort-awareness of end-users. In the framework of the REMS, the economic role of a power-to-gas based storage system (P2GSS) is examined under different operating scenarios. The problem is first formulated as a mixed integer nonlinear programming (MINLP) model and then solved by an iterative decomposition method. In the proposed method, each objective is regarded as an independent sub-problem and refined iteratively, until the deviation comes into an acceptable range. Simulation results of a sample system demonstrate the positive role of the P2GSS in scheduling REMS economically and the effectiveness of the proposed method.

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 score0.601
Threshold uncertainty score0.833

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 head0.005
GPT teacher head0.173
Teacher spread0.167 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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