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Record W3162746084 · doi:10.1109/tec.2021.3079400

Small Modular Reactor (SMR) Based Hybrid Energy System for Electricity & District Heating

2021· article· en· W3162746084 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 Energy Conversion · 2021
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsModular designNuclear engineeringElectricityEnvironmental scienceWaste managementAutomotive engineeringElectrical engineeringEngineeringComputer scienceOperating system

Abstract

fetched live from OpenAlex

Hybrid energy systems with small modular reactors (SMRs)—a fast-emerging nuclear power plant technology—and renewables hold significant promise for the development of clean energy systems. This paper proposes a simulation model of SMR-based hybrid energy system for electricity and district heating (DH) with a detailed dynamic model of the reactor and a quasi-static model of the DH system in Siemens PTI PSS/E and PSS/Sincal. A multi-timescale approach, separating the load following and frequency regulation operation, is proposed to assess the flexible operation in the presence of highly intermittent renewable energy sources (RESs). A portion of a modified IEEE 30-bus system network is used as a test system for an isolated community to simulate the proposed hybrid energy system, and comparative results demonstrate the potential benefits of the DH system, thermal energy storage, and electrical energy storage to the SMR’s flexible operation.

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 categoriesMeta-epidemiology (narrow)
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 score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.182
Teacher spread0.170 · 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