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Record W4206155102 · doi:10.1109/tsg.2021.3133377

Ground Source Heat Pump Modeling, Operation, and Participation in Electricity Markets

2021· article· en· W4206155102 on OpenAlexaff
Dario Peralta, Claudio A. Cañizares, Kankar Bhattacharya

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2021
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectricityHeat pumpElectricity retailingElectricity systemEnvironmental economicsElectricity generationEnvironmental scienceElectricity marketElectrical engineeringPower (physics)EconomicsEngineeringMechanical engineeringPhysicsThermodynamicsHeat exchanger

Abstract

fetched live from OpenAlex

Over the last decades, Ground Source Heat Pump (GSHP) systems have grown in popularity and acceptance worldwide, having the potential to bring about significant benefits, especially if these systems participate in electricity markets through a load aggregator to optimize their operations and provide services such as load shifting and demand reduction, as proposed here. In this context, GSHP systems can be considered as an economically viable and attractive alternative to existing Heating Ventilation and Air Conditioning (HVAC) systems for space heating/cooling in buildings and houses from an aggregator point of view. Hence, this paper presents a detailed mathematical model for a GSHP with a vertical U-pipe Ground Heat eXchanger (GHX) configuration to provide residential space heating/cooling, integrating several such GSHPs into a load aggregator model. Then, a two-stage operational strategy for the GSHP price-taker aggregator participating in day-ahead and real-time electricity markets is proposed, to determine the optimal annual heating/cooling load dispatch to control the temperatures for a community of houses that minimizes the aggregator’s cost. Detailed numerical studies and comparisons with HVAC systems are carried out to demonstrate the feasibility and benefits of the proposed aggregated operation of GSHPs in electricity markets.

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 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: Empirical
Teacher disagreement score0.270
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
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.246
Teacher spread0.229 · 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 teacher head, 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

Citations18
Published2021
Admission routes1
Has abstractyes

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