Optimal control of microgrids - algorithms and field implementation
Bibliographic record
Abstract
A microgrid is a collection of distributed generation assets, storage devices and electrical and/or thermal loads connected to each other. In this paper, a generic model-predictive control algorithm for microgrids is presented. The algorithm has been implemented at Bella Coola, a remote community in British Columbia, Canada. The approach comprises two parts: unit commitment to decide the optimal set of distributed generators that must be switched on to meet predicted load requirements, and convex optimal control to minimize operational costs once the commitment is known. The unit commitment problem is recast as a 0–1 Knapsack problem and is solved via dynamic programming, while the optimal dispatch problem is posed as a sparse linear programming problem and solved via off-the-shelf software. Worst-case complexity and scalability considerations, and not optimality, often drive algorithm choice in industrial control settings; therefore, the solution proposed in this paper is efficient and can be rigorously bounded in terms of memory and run-time. Simulation results using real field data, practical considerations, and details of the implementation at Bella Coola are provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".