Integrated community energy and harvesting systems: A climate action strategy for cold climates
Bibliographic record
Abstract
In this article, the Integrated Community Energy and Harvesting System is introduced, a grid modernization solution for cold climates that incites a paradigm shift in virtual power plant design and operation. The focus is on system-wide GHG reductions by using variable temperature micro-thermal networks and prioritizing the harvesting of existing residual (waste) energy resources in communities, such as high-grade heat from decentralized fossil-fuel marginal generators, low-grade heat from cooling processes and curtailed carbon-free electricity. The novel strategy enables rapid fuel switching between residual energy resources, changing the micro-thermal network temperature between 20 and 70 °C on the scale of an hour, which provides valuable electrical demand response while maximizing the use of existing underutilized energy resources. Thermal storage is shown to have a critical role in both storing residual energy for later use, daily and seasonally, and enabling electrical demand response by rapidly changing the micro-thermal network temperature. The quantity of residual energy sources identified highlights that, as much as, 50% of all building heating loads could be met by energy currently rejected to the atmosphere. To illustrate the ICE-Harvest system’s effectiveness, a detailed case study is conducted on a typical integrated community and then applied to 1,000 prospective sites across a cold climate jurisdiction with a relatively low-carbon grid. It is shown that cooling process heat recovery, an energy source which is already located at buildings, can provide 24% of the prospective sites’ heating load when powered by carbon free, otherwise curtailed electricity. The results demonstrate that mass deployment of ICE-Harvest systems has the potential to provide 72% of the heating demand of these building clusters from residual energy sources, corresponding to an over 58% reduction in GHG emissions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".