Thermal energy storage for smart grid applications
Bibliographic record
Abstract
Energy consumption for commercial building cooling accounts for 15% of all commercial building’s electricity usage [1]. Electric utility companies charge their customers time of use consumption charges ($/kWh) and additionally demand usage charges ($/kW) to limit peak energy consumption and offset their high operating costs. Thus, there is an economic incentive to reduce both the electricity consumption charges and demand charges by developing new energy efficient technologies. Thermal energy storage (TES) systems using a phase change material (PCM) is one such technology that can reduce demand charges and shift the demand from on-peak to off-peak rates. Ice and chilled water have been used in thermal storage systems for many decades, but they have certain limitations, which include a phase change temperature of 0 degrees Celsius and relatively low thermal conductivity in comparison to other materials, which limit their applications as a storage medium. To overcome these limitations, a novel phase change composite (PCC) TES material was developed that has much higher thermal conductivity that significantly improves the charge / discharge rate and a customizable phase change temperature to allow for better integration with HVAC systems. Compared to ice storage, the PCC TES system is capable of very high heat transfer rate and has lower system and operational costs. Economic analysis was performed to compare the PCC TES system with ice system and favorable economics was proven. A 4.5 kWh PCC TES prototype system was also designed for testing and validation purpose.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".