Optimal dispatch of clean resources in networked microgrids: A rank transactive energy framework
Bibliographic record
Abstract
In this paper, a rank-based transactive energy framework is proposed to optimize the coordinated dispatch of clean local resources in networked microgrids. To enable effective coordination, the framework employs a ranking system to manage energy exchanges among microgrids. This approach minimizes load shedding while preserving the end-user privacy. The proposed framework leverages a novel learning-based probabilistic method to optimize the operation of networked microgrids using a distributionally robust, quantile-based chance-constrained approach. This eliminates the need for perfect knowledge of probability density functions by introducing a stochastic quantization technique for nonparametric probabilistic analyses. Quantiles are generated using a modified extreme learning machine autoencoder, trained through a two-phase process with historical data. To mitigate the computational burden associated with stochastic quantiles for numerous uncertainties, a step-by-step mixture process is employed, with the Lebesgue measure serving as the convergence criterion. The efficacy of the framework and the optimization approach is validated through several case studies and comprehensive comparisons.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".