期刊論文
| 學年 | 113 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2024-09-28 |
| 作品名稱 | Multi-objective mathematical model for optimal wind turbine placement in wind farm under uncertainty |
| 作品名稱（其他語言） | |
| 著者 | Guanting Li; Tzu-Chia Chen |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Journal of Engineering Research, Available online 28 September 2024 |
| 摘要 | The main objective of this research is to introduce three energy risk management models grounded in optimization techniques for the strategic placement of wind turbines, considering wake effects and uncertainties in wind speed and direction. For this purpose, wind speed and direction data are gathered, and Monte Carlo simulation is employed to model the uncertainties. Subsequently, the risk management models undergo optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Pareto envelope-based selection algorithm II (PESA-II), and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Findings reveal that the wind farm's maximum power output reaches approximately 5.8 megawatts across all three algorithms and optimal turbine placements. A risk assessment was conducted using a tenth percentile criterion, revealing a significant production risk within the study area, with production falling below 1.8 megawatts in 90 % of cases. Regarding the performance evaluation of the algorithms across all three models, superior performance in terms of solution proximity to the ideal solution is exhibited by PESA-II, while enhanced diversity and solution spread compared to the other algorithms are demonstrated by NSGA-II. |
| 關鍵字 | Wind turbine; Monte Carlo Simulation; Multi-objective optimization; Wake effect; Meta-heuristic aglrotithms |
| 語言 | zh_TW |
| ISSN | 2307-1877 |
| 期刊性質 | 國內 |
| 收錄於 | SCI Scopus |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126616 ) |