Ground Source Heat Pump Modeling, Operation, and Participation in Electricity Markets
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".