Flexibility through power-to-heat in local integrated energy systems with renewable electricity generation and seasonal thermal energy storage
Bibliographic record
Abstract
In heating dominated regions, the flexibility obtained through coupling heating and power sectors is particularly beneficial for the integration of high shares of variable renewable energy sources. This study concerns the design of an energy system for a new neighborhood in Norway, including a seasonal thermal energy storage storing excess heat from waste incineration, a seawater heat pump, and local power generation. Two supply temperature scenarios are considered for the local heating network: medium-temperature (70 °C), where all heating demands are covered through the network; and low-temperature (45 °C), where booster heat pumps are applied for hot water production. Both scenarios are more cost-effective than if heat demands were to be met through import from the district heating network, however, the difference between the two scenarios is small. The low-temperature scenario has the highest degree of self-sufficiency, and the advantage of additional flexibility gained through the local heat pumps with hot water storage. Cost-optimal charging strategy for the seasonal storage was highly dependent on the pricing of excess heat with respect to the electricity prices. Unlimited sharing of electricity among all users in the neighborhood should be promoted to gain full benefits of local 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".