Conservation and demand management in community energy systems
Bibliographic record
Abstract
Community Energy Systems (CESs) are localized systems that can generate, deliver, and/or store energy, which can come in different forms, including electricity, natural gas, and district heating. These can be operated in islanded mode or tied into the main grid, either continually or for backup purposes. Since CESs are by definition small-scale, even small deviations from forecasts can be much more costly to users as those costs of overbuilding or underbuilding are shared among a much smaller group of consumers (rather than the much larger pool across the larger system). Accurate peak load forecasts are very difficult, and they are especially difficult for CESs because inaccuracies cannot be smoothed across a larger base. Conservation and demand management can be efficient tools to smooth over inevitable deviations from forecasts. The conservation model proposed in this chapter would target conservation at the most elastic (price sensitive) consumers only during narrowly defined peak periods in order to increase utilization of fixed assets and drive down unit costs. This would reduce the overall capacity requirements of the system, and these savings would be saved among all users. The three main elements of this model are to (1) lower the peak in order to defer capacity expansions; (2) increase utilization in order to reduce unit costs and rates; and (3) target conservation efforts at the most elastic (price sensitive) consumers so that conservation is procured at the lowest possible cost. Conservation can be achieved through a combination of disincentives for consumption during very narrow peak periods and incentives for consumption during off-peak periods. Together, these have the effect of flattening the demand curve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".