Virtual power plant formation strategy based on Stackelberg game: A three-step data-driven voltage regulation coordination scheme
Bibliographic record
Abstract
Rising electricity demand and the swift integration of Distributed Energy Resources (DERs) highlight the imperative for effective voltage regulation (VR) strategies to mitigate voltage violations. Conventional VR methods, plagued by significant operational expenses and slow response times, are increasingly focused on harnessing prosumer flexibility. However, this strategy faces challenges, including uncertainties in VR calculations, designing VR coordination signals, and managing and monitoring prosumer actions. This paper introduces a novel three-step VR coordination scheme to tackle these issues. The first step utilizes a Data-driven Distributionally Robust Optimization (DDRO) algorithm with a Wasserstein metric ambiguity set to calculate the required active and reactive power adjustments for VR. The second step involves generating and disseminating price-based coordination signals via a clustering algorithm, reducing signal complexity. The final step proposes using Virtual Power Plants (VPPs) to aggregate smaller prosumers for VR, applying a bi-level Stackelberg game to account for the impact of distributed coordination signals on VPP member selection. Tested on the IEEE 33-bus system, this framework significantly lowers the computational load by approximately 35 % and cuts VR costs by 5.7 % compared to existing methods. • Voltage regulation framework for DSO-VPP collaboration. • Wasserstein-based distributionally robust uncertainty modeling. • VPP aggregation strategy based on bi-level Stackelberg game. • New distributed price-based voltage regulation coordination signal. • Accuracy and fast coordination of distributed energy resources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".