Using a Supercapacitor to Mitigate Battery Microcycles Due to Wind Shear and Tower Shadow Effects in Wind-Diesel Microgrids
Bibliographic record
Abstract
Wind shear and tower shadow effects generate severe fluctuations on the generated power of wind turbines (WTs). Consequently, in WT-integrated microgrids (MGs) with battery energy storage, these power fluctuations can generate battery microcycles that can significantly reduce the battery's lifetime. In this paper, the impact of battery microcycles on battery lifetime is investigated and a method that uses a hybrid supercapacitor-battery energy storage system to mitigate these microcycles in a wind-diesel microgrid is proposed. The design, power allocation strategy, and control of the power converters are discussed; the supercapacitor size is determined based on the decomposition of frequency components of the WT output power, using discrete Fourier transform to appropriately mitigate the battery microcycles. The components of the MG, wind shear, and tower shadow effects are modeled in detail using MATLAB/Simulink, TurbSim, AeroDyn, and FAST software tools. Finally, the performance of the proposed method is investigated and verified in simulation, considering two case studies where either battery-only or battery-supercapacitor are used. In addition, a cost-benefit analysis of the proposed system is given. The results show that the proposed method can appropriately mitigate the battery microcycles, which can result in increasing battery lifetime and reducing the total system costs.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".