MétaCan
Menu
Back to cohort
Record W4292259914 · doi:10.3389/fenrg.2022.962361

An evaluation of domestic electric water heaters for frequency control

2022· article· en· W4292259914 on OpenAlexaff
Sheng Xiang, Hongming Yang, Bo Cao

Bibliographic record

VenueFrontiers in Energy Research · 2022
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of New Brunswick
FundersNational Natural Science Foundation of ChinaEducation Department of Hunan Province
KeywordsAutomatic frequency controlElectric power systemAutomotive engineeringDemand responseElectric powerEngineeringPower (physics)Control (management)Computer scienceControl theory (sociology)Electrical engineeringElectricity

Abstract

fetched live from OpenAlex

Maintaining the frequency of a power system close to its nominal value (50 Hz or 60 Hz) is critical, which comes mainly from generators and flexible loads in traditional power systems. Direct load control (DLC) is a method to control controllable loads for power system optimization. In general, it is used to reduce or shave peak demand. Nonetheless, DLC also can be used to provide frequency control services. Domestic electric water heater (DEWH) is an important kind of controllable load, which takes a high percentage of domestic electric power consumption and has a large thermal energy storage capacity. Hence, DEWH can be a prime candidate for DLC. This study proposes a framework to provide frequency control service with DEWHs. A virtual battery pack system (VBPS) is introduced to be equivalent to the capacity of DEWHs, and a series of measurable indicators are proposed to show the capacity of the VBPS when providing frequency control services. An adaptive criterion is applied to classify controllable DEWHs, which helps to maintain end-user comfort. The performances of the proposed frequency control method during normal and contingency conditions are verified through case studies in CYME.

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)0.004
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: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.029
GPT teacher head0.308
Teacher spread0.279 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Energy ResearchSame topicSmart Grid Energy ManagementFrench-language works237,207