Microgrids: An emerging paradigm for meeting building electricity and heat requirements \nefficiently and with appropriate energy quality
Bibliographic record
Abstract
The first major paradigm shift in electricity generation, delivery, and control is emerging in the developed world, notably Europe, North America, and Japan. This shift will move electricity supply away from the highly centralised universal service quality model with which we are familiar today towards a more dispersed system with heterogeneous qualities of service. One element of dispersed control is the clustering of sources and sinks into semi-autonomous mu grids (microgrids). Research, development, demonstration, and deployment (RD3) of mu grids are advancing rapidly on at least three continents, and significant demonstrations are currently in progress. This paradigm shift will result in more electricity generation close to end-uses, often involving combined heat and power application for building heating and cooling, increased local integration of renewables, and the possible provision of heterogeneous qualities of electrical service to match the requirements of various end-uses. In Europe, mu grid RD3 is entering its third major round under the 7th European Commission Framework Programme; in the U.S., one specific mu grid concept is undergoing rigorous laboratory testing, and in Japan, where the most activity exists, four major publicly sponsored and two privately sponsored demonstrations are in progress. This evolution poses new challenges to the way buildings are designed, built, and operated. Traditional building energy supply systems will become much more complex in at least three ways: 1. one cannot simply assume gas arrives at the gas meter, electricity at its meter, and the two systems are virtually independent of one another; rather, energy conversion, heat recovery and use, and renewable energy harvesting may all be taking place simultaneously within the building energy system; 2. the structure of energy flows in the building must accommodate multiple energy processes in a manner that permits high overall efficiency; and 3. multiple qualities of electricity may be supplied to various building functions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".