Building Demand Response and the Electric Grid: Development and Application of an Operational Cross-Sectoral Model of Building-Side Electrification and Supply-Side Renewable Energy Integration
Bibliographic record
Abstract
Towards the goal of a decarbonized future, electrification of building heating systems provides both a challenge and an opportunity. Meeting additional electricity demand without increasing the associated emissions requires additional renewable capacity, but sources such as wind and solar are weather-reliant and unpredictable, making it difficult to ensure that sufficient generation is available at every instant. However, through demand response, during which buildings are subject to utility control through technology such as smart meters, building thermal mass can be used as a form of energy storage, and building demand curves can be influenced to better match the timing of variable renewable generation. In this thesis, the tension between these two aspects of building electrification is explored through the development of a novel linked model framework in which operational building and electricity system models transfer information back and forth during model setup and parameter specification steps, allowing exploration of how building system electrification impacts electricity system variable renewable expansion, and vice versa. Demand response is represented through two iterations of model development, first by changing building temperatures based on the presence of renewable curtailment (excess generation), and then by quantifying the amount of energy able to be stored in the building system during demand response events, ultimately allowing building demand response to be scheduled within the electricity system model at times that are optimal for the electric grid. This methodology is an important contribution to the literature because of its ability to represent both the supply (electric grid) and demand (building stock) sectors in operational detail, in contrast to many existing models which tend to focus only on a single sector. As well, this thesis’ case studies into residential demand response are particularly insightful given the lack of residential demand response policies in Canada today. Important results of this work indicate that increased efficiency of envelopes and heating systems can effectively limit electricity demand increases associated with increased penetration of electric heating technology, and that building demand response can effectively help cities reach their decarbonization goals.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".