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Record W2786474566 · doi:10.1109/pesgm.2017.8274229

Available capacity based AGC signal distribution strategy with energy storage system

2017· article· en· W2786474566 on OpenAlexaff
Yuzhong Gong, C. Y. Chung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFrequency Control in Power Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAutomatic Generation ControlElectric power systemComputer scienceDynamic demandFrequency regulationAutomatic frequency controlPower controlDemand responsePower (physics)Energy storageSIGNAL (programming language)Control theory (sociology)Wind powerEngineeringElectronic engineeringControl (management)Electrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

With the increasing penetration of wind power integration, more frequency regulation resources are needed to maintain the power system frequency stability. A distribution strategy of automatic generation control (AGC) signal is proposed to allocate the area control error (ACE) among different generators and energy storage system (ESS). The available AGC capacities (AAC) of generators are evaluated by considering the remained regulation capacity and ramp rate. The AAC of ESS is evaluated with a dynamic power output bound based on rated power and real-time SOC. An AGC signal distribution strategy based on AAC is proposed to decide the change of power references for generators and ESS to fully utilize the long supporting duration of generators and high response rate of ESS. The effectiveness of proposed approach is verified through case studies based on a modified IEEE 30-bus test system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.180
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), 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
Published2017
Admission routes1
Has abstractyes

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