Comparison of the future residual load in fifteen countries and requirements to grid-supportive building operation
Bibliographic record
Abstract
Many countries in the world plan to increase their share of wind and solar power. In order to efficiently utilize large amounts intermittent renewable power, flexible consumers such as buildings with heat pumps and chillers may play a crucial role. However, it is not clear how heat pumps and chillers should be operated in order to make the best use of the volatile renewable energy. For this purpose, the residual loads of 13 European countries, Great Britain, and Alberta in the year 2030 were simulated and analyzed. The term " residual load " refers to the electricity demand that is not covered with intermittent renewable systems and that, therefore, must be met by dispatchable electricity generation units. It was calculated as the difference of the wind and PV generation simulated as part of this study, and the electric load of 2011. The results show a high relative variability in the residual load in almost all analyzed countries. In winter, the lowest residual loads (i.e. the most favorable times for electricity consumption) occur either around noon (particularly in the countries with the highest amount of wind and solar power), or at night. In summer, the residual loads are usually lowest around noon, which coincides well with the typical cooling load profile of a building. PV-dominated countries show stronger daily variations in the residual load, which can be managed even with relatively small storage capacities as typically found in buildings. In contrast, in wind-dominated countries, the residual load fluctuates on longer time scales, which requires larger storages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".