Decomposition and distributed predictive control of integrated energy systems
Bibliographic record
Abstract
Integrated energy systems (IESs) play an important role in absorbing renewable energy and improving overall fuel efficiency in distributed energy systems. An IES typically consists of a few energy generation and storage units (e.g., solar panels, wind turbines, battery banks, water tanks) that are closely interconnected. Given the distinct dynamics of the different energy generation and storage units, a centralized control scheme in general does not work well. In this work, we show how an IES can be decomposed into smaller subsystems and how distributed economic model predictive control (EMPC) can be designed based on the decomposed subsystems to optimize the operation of the IES. In the decomposition of the IES, we explore both decomposing the entire system vertically based on the time-scale multiplicity exhibited in the IES dynamics and horizontally based on the closeness of the interconnection between the various operating units. The impact of the order of applying the vertical and horizontal decomposition is also discussed. Based on the decomposed subsystems, a distributed EMPC scheme is designed. We illustrate how the features of the decomposed subsystem models can be used in the design of the local EMPCs to reduce the computational complexity and information exchange between the controllers.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".