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Record W4323767274 · doi:10.1109/tsg.2023.3254655

Aggregate Modeling of Thermostatically Controlled Loads for Microgrid Energy Management Systems

2023· article· en· W4323767274 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaAgencia Nacional de Investigación y DesarrolloInstituto de Sistemas Complejos de Ingeniería
KeywordsMicrogridFlexibility (engineering)Aggregate (composite)Renewable energyContext (archaeology)Computer scienceTransient (computer programming)EngineeringControl engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Second-to-second renewable power fluctuations can severely hinder the frequency regulation performance of modern isolated microgrids, as these typically have a low inertia and significant renewable energy integration. In this context, the present paper studies the coordinated control of Thermostatically Controlled Loads (TCLs) for managing short-term power imbalances, and their integration in microgrid operations through the use of aggregate TCL models. In particular, two computationally efficient and accurate aggregate TCL models are developed: a virtual battery model representing the aggregate flexibility of TCLs considering solar irradiance heat gains and wall/floor heat transfers, and a frequency transient model representing the aggregate dynamics of a TCL collection considering communication delays and the presence of model uncertainty and time-variability. The proposed aggregate TCL models are then used to design a practical Energy Management System (EMS) integrating TCL flexibility, and study the impact of TCL integration on microgrid operation and frequency control. Computational experiments using detailed frequency transient and thermal dynamic models are presented, demonstrating the accuracy of the proposed aggregate TCL models, as well as the economic and reliability benefits resulting from using these aggregate models to integrate TCLs in microgrid operations.

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 score0.990
Threshold uncertainty score0.728

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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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