Solar Photovoltaics and Battery Energy Storage systems potential for grid flexibility and to address electrical demand of air-source heat pump systems in canadian homes
Bibliographic record
Abstract
Air-source (air-to-air) heat pumps (ASHPs) provide an efficient electrification of space heating, but can increase electrical demand in homes, especially when replacing fossil fuel heating systems. While there is potential for solar photovoltaics (PV) and battery energy storage systems (BESSs) to meet ASHP system electrical demand in cold climates, it is not well documented in the literature. This paper aims at uncovering this potential in terms of grid flexibility using high temporal resolution (2.5-minute) simulations to better reflect electrical demand and component interactions. A detailed data-driven ASHP model is used to represent heat pump performance at different temperatures and compressor speeds, and key transient characteristics (i.e., cycling, defrost and start-up). Solar PV and BESSs, with an energy management control strategy (EMCS), showed notable potential to meet the electrical demand of ASHP systems during grid peak electrical demand hours, reducing the burden on the grid by 100% in many cases. Investigation across different Canadian cities, in different climates, demonstrated the necessity for region-suitable EMCSs to consistently satisfy ASHP system electrical demand during grid peak electrical demand hours and improve grid flexibility.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".