Coordinating maintenance with spares logistics to minimize levelized cost of wind energy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wind power emerges as a sustainable energy resource to meet the increasing electricity needs in the next 20-30 years. Power volatility and maintenance costs are the key challenges in harvesting this type of renewable energy. The levelized cost of energy (LCOE) allows the utility and investors to compare the costs of various generation technologies of unequal lifetimes and capacities. In this study we propose a probabilistic-based LCOE model to assess the investment risks by taking into account four major factors: wind speed, system availability, maintenance policy, and spares stock level. Moment methods are applied to estimate the mean and the variance of the energy yield. The goal of the study is to develop a decision aid methodology guiding the wind farmers to minimize the ownership cost by jointly optimizing the maintenance and the spares inventory. We assume the maintenance and repair service is carried by a third party logistics provider. Genetic algorithm is used to search the optimality of the mixed integer non-linear decision model.
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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 it