Co‐designed Incentives for an Aimed Renewable Energy Contribution and Volunteer Load Shedding
Bibliographic record
Abstract
Changing mainstream electricity supply from a thermal to a renewable‐supplied grid has important benefits to sustainable electricity generation. However, challenges, especially at high levels of renewable energy sources (RESs) penetration, need to be addressed. In particular, for economic and stability reasons, RESs are underdogs in competition with fossil‐fuel generation unless proper incentives are provided and incorporated into electricity bills of consumers. Also, the intermittent output of RESs can compromise grid efficiency and increase the cost of electricity. These issues can be resolved, using demand response, if load flexibility was not limited. Volunteer load shedding could help with this problem if consumers are willing to voluntarily shed their non‐essential loads (NLs). This study investigates the impact of NLs planned outage rates on the required incentive, in order to reach different levels of RES penetration. To illustrate the effectiveness of the contributions of consumer load shedding on the integration of RESs and utility grids, their collaborative impact is explored against a numerical system based on real historical data. The results demonstrate the positive impact of consumers' contribution by substantial reduction in the incentive. The margin of savings will then be used to evaluate the value of volunteer load shedding of consumers.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 0.001 |
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".