Constrained H <sub>2</sub> /H <sub>∞</sub> Control Design of Dynamic Virtual Power Plants via System Level Synthesis and Simple Pole Approximation
Bibliographic record
Abstract
Future power systems are expected to integrate an increasing share of non-synchronous distributed energy resources. This transition introduces significant challenges due to the variability of renewable energy sources and the operational limitations of individual devices. Designing optimal linear feedback controllers that achieve desired aggregate system behavior while respecting both state and input constraints is a critical and challenging task to support this transformation. In this paper, we propose a novel control framework for dynamic virtual power plants. Specifically, we consider a group of heterogeneous distributed energy resources that collectively deliver dynamic ancillary services, such as fast frequency and voltage regulation. Local linear state-feedback ${\mathcal{H}}_{2} / {\mathcal{H}}_{\infty}$ controllers are designed to optimally achieve the desired aggregate system behavior. System level synthesis is a recent technique that reparameterizes the optimal control problem as a convex program and has previously been combined with simple pole approximations to address infinite-dimensional challenges. This work extends the design framework to a multi-controller design that explicitly incorporates the physical and engineering constraints of each DVPP device, including state, input, and output limits, which also provides guaranteed suboptimality bounds and results in a convex and tractable semidefinite program for the control design. Finally, we demonstrate the effectiveness of our control strategy in a case study based on the IEEE nine-bus system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".