Integrated community energy and harvesting systems: A climate action strategy for cold climates
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it