Weighted scalarization versus compromise solution in multi-objective economic dispatch for microgrids
Bibliographic record
Abstract
In this thesis, we evaluate a multi-objective-moving-horizon-optimization (MO-MHO) approach as an instrument for improvement of economic dispatch in microgrids. In particular, we investigate the effect of adaptation of the multi-objective optimization strategy used in a moving horizon framework on the end result of the economic dispatch. Power dispatch in microgrids is inherently a high-dimensional problem often cast as a mixed-integer stochastic program with conflicting objectives. Implied is the fact that exhaustive exploration of the whole Pareto front in a related multi-objective approach is not a computationally tractable decision tool. It is thus proposed to represent the problem as a bi-objective optimization problem. The two objective functions are formulated by carefully grouping together the least conflicting components of the microgrid dispatch problem. The optimization is performed in a moving horizon i.e. over a window "looking into the future". The solution method for the optimization problem can then be selected or adjusted in real time by employing an independent set of assessment functions evaluated along the trajectories of optimal solutions already implemented over a window in the past. The proposed strategy is applied to a case study of a remote microgrid. Its performance is evaluated based on simulation results that suggest that choosing the compromise solution method for the MO-MHO problem may be superior to the usual scalarization 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".