Electrifying end-use demands: A rise in capacity and flexibility requirements
Bibliographic record
Abstract
The electrification of end-uses where consumption patterns are linked to behavior, weather, and technology characteristics is expected to impact grid infrastructure in a variety of ways. One way to limit potential negative consequences of end-use electrification on grid infrastructure is through utilization of demand-side management strategies. Previous work has yet to address the simultaneous impact of electrifying building heating and cooling, and road transportation on capacity and the resulting flexibility requirements for the electrical grid. In this paper, two high-resolution models are combined to generate regional demand profiles for building heating, space cooling, passenger vehicles, and commercial road transportation. End-use energy demand profiles are generated for key sub-sectors, with 15-minute resolution, simulating large-scale end-use electrification by 2050 in British Columbia, Canada. Results show that simultaneous electrification in the building and road transportation sectors increases capacity and flexibility requirements by 93% and 320%, respectively. A synergy of demand-side measures limits the increase in capacity and flexibility requirements to 74% and 82%, respectively. Temporal resolution of demand models is critical in the determination of flexibility requirements as maximum positive ramping rates increase by 520% when changing from an hourly to a 15-minute resolution. • Ten individual building and transportation end-use demands are projected for 2050. • Electricity demand profiles are generated in 15-minute resolution. • Impact of EV charging and space heating control on load profiles is analyzed. • The timing and magnitude of peak electricity demands changes significantly. • Grid flexibility requirements are driven by electrification of commercial vehicles.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".