A Two Stage GA-MILP-based Sizing Optimization Method for Off-grid Integrated Energy System
Bibliographic record
Abstract
Capacity configuration optimization is one of the primary tasks in the construction of the integrated energy system (IES). the uncertainties of renewable energy sources and loads deepen the coupling relationship between the capacity configuration and energy dispatching of IES, which makes optimizing the capacity difficult. This paper proposes a two stage GA-MILP-based capacity sizing allocation method for off- grid IES. Firstly, a double-layer capacity configuration framework based on Leader-Follower is constructed. Secondly, the genetic algorithm (GA) is used to randomly generate the initial configuration in the Leader layer and use it as the input of the Follower layer. In the Follower layer, the system investment cost, operation cost, and maintenance cost are comprehensively considered, the mixed integer linear programming (MILP) method is used to obtain the minimum operating cost and energy dispatching scheme and then return to the Leader layer. The optimal capacity configuration scheme is obtained through multiple iterative optimizations. Finally, the proposed method is applied to an Off-grid island to confirm the rationality and effectiveness of the optimization results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".