Clustering Alternatives in Market-Clearing for Transactive Energy Flexibility Spot Markets
Bibliographic record
Abstract
Flexibility markets establish a win-win game since grid operators can maintain a reliable service provision while flexibility providers obtain revenue for participating. Previous studies have analyzed the effectiveness of flexibility markets in addressing congestion issues. Various clearing mechanisms have been proposed to optimize market operations and mitigate congestion. However, those clearing mechanisms in real-life TE implementations will face scalability problems that could lead to sub-optimal outcomes or have distinct performances when applied to different customer groups tied by physical constraints. This study adapts and evaluates representative clustering strategies, namely clustering by price, clustering by preference, k-means, and spectral clustering, to determine their efficacy in segmenting flexibility market participants. The pertinence of each technique is examined by calculating the Silhouette Score and the Davies-Bouldin Index as clustering quality measures. Social welfare is used as a measure of economic efficiency, and time as an indicator of tractability. A trade-off between market tractability and economic efficiency is discussed. The results show that clustering techniques are unsuitable for fast spot trades as creating the bids clusters can be up to 3 times slower than when considering the individual offers. Therefore, this work proposes to build the clusters in advance or simply group market participants by known and easy-to-aggregate parameters such as bidding price.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".