Microgrids: An emerging paradigm for meeting building electricity and heat requirements efficiently 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 awayfrom the highly centralised universal service quality model with which weare familiar today towards a more dispersed system with heterogeneousqualities of service. One element of dispersed control is the clusteringof sources and sinks into semi-autonomous mu grids (microgrids).Research, development, demonstration, and deployment (RD3) of mu gridsare advancing rapidly on at least three continents, and significantdemonstrations are currently in progress. This paradigm shift will resultin more electricity generation close to end-uses, often involvingcombined heat and power application for building heating and cooling,increased local integration of renewables, and the possible provision ofheterogeneous qualities of electrical service to match the requirementsof various end-uses. In Europe, mu grid RD3 is entering its third majorround 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 publiclysponsored 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 willbecome much more complex in at least three ways: 1. one cannot simplyassume gas arrives at the gas meter, electricity at its meter, and thetwo systems are virtually independent of one another; rather, energyconversion, heat recovery and use, and renewable energy harvesting mayall be taking place simultaneously within the building energy system; 2.the structure of energy flows in the building must accommodate multipleenergy processes in a manner that permits high overall efficiency; and 3.multiple qualities of electricity may be supplied to various buildingfunctions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".