A Peak-Shaving-Oriented Incentive Mechanism for Smart Grids
Bibliographic record
Abstract
Prosumers play a crucial role in smart grids, especially within local energy communities (LECs), since they can both consume and produce energy.When peer-to-peer (P2P) energy trading is available, prosumers can exchange their produced energy with each other: if done properly, this may lead to better energy self-consumption throughout the grid, resulting in reduced transmission losses, lower energy costs, and decreased wear and tear to the grid.Previous work on this topic led to a mechanism capable of obtaining several such goals, like preventing intentional energy production curtailment, disincentivizing simultaneous energy consumption that may lead to congestions, encouraging users to consume their own produced energy as much as possible, and ensuring that even if users initially create schedules with a selfish approach, they will ultimately converge upon a configuration that garners mutual agreement.However, this mechanism has not yet been analyzed from the perspective of peak shaving.Therefore, this paper aims to cover this shortcoming.Our objective in this work is to create a new mechanism that, under certain conditions, guarantees the achievement of optimal peak shaving.We will use it as a baseline to compare the existing mechanisms and understand under which conditions it leads to peak shaving.We performed simulations on a dataset from a grid in Cardiff, UK, and the results show that the existing mechanisms achieve optimal peak shaving both if the users act selfishly, and if they are allowed to form coalitions among themselves.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".