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Record W3162290144 · doi:10.1109/tec.2021.3080698

Optimal Operation of SMR-RES Hybrid Energy System for Electricity & District Heating

2021· article· en· W3162290144 on OpenAlexaff
Bikash Poudel, Ramakrishna Gokaraju

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2021
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCogenerationRenewable energyEngineeringModular designAutomotive engineeringElectricityHybrid systemElectric power systemWind powerElectricity generationElectrical engineeringComputer sciencePower (physics)

Abstract

fetched live from OpenAlex

Cogeneration coupling of small modular reactor (SMR)-based nuclear power plants (NPPs) with district heating (DH) systems enhances an SMR’s ability to provide flexible operation. Coordinated reactor control and DH steam extraction can fulfill the load following requirements, while a battery energy storage system (BESS) and steam bypass system can absorb short-term disturbances, allowing the system to host renewable energy sources (RESs) such as wind and photovoltaics (PV). This paper develops an optimal operation framework for a SMR-RES hybrid energy system for electricity and DH and demonstrates the proposed scheme using a portion of the IEEE-30 bus system. Simulation results from a set of studies are provided to demonstrate load following (LF) and frequency regulation (FR) operation for a month-long period. The operational results for different RES hosting levels are analyzed, evaluating the optimum size of wind and PV plants for the proposed system. The results show the proposed hybrid energy system is feasible as a stand-alone system and can optimally host as high as 50% renewables.

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)0.000
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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

Citations35
Published2021
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Energy ConversionSame topicIntegrated Energy Systems OptimizationFrench-language works237,207