Data-Driven Energy and Reserve Management of Prosumers Under Multi-Uncertainties
Bibliographic record
Abstract
In this paper, we propose a data-driven approach for prosumers to manage energy and reserves under multi-faceted uncertainties in electricity markets. We account for the uncertainties associated with renewable power generation, market prices, and the deployment ratio of regulation reserves. These uncertainties are rarely addressed simultaneously in prior studies, despite the significant impact they can have. Notably, the uncertainty surrounding the deployment ratio of regulation reserves, which represents the call-up ratio of reserve capacity provided by prosumers, has long been overlooked. In this work, we address these interconnected uncertainties within a unified framework, employing a Wasserstein distance-based distributionally robust optimization (WDRO) approach to hedge against them. The proposed model grapples with a substantial computational burden as it tackles multiple uncertainties concurrently. To enhance computational efficiency, we utilize novel approximation techniques to transform the WDRO model into a more tractable form. Furthermore, we analyze the optimality gaps in the WDRO objective function of the approximation approach. Simulation results demonstrate the efficacy of the proposed model and solution methods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".