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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 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

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.

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 score0.978
Threshold uncertainty score0.739

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 head0.015
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

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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