.png){kind=icon}
返回
Offshore wind farm layout optimization using ensemble methods
.png){kind=icon}
OA
AI
DOI：10.1016/j.renene.2023.119061.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
When planning wind farms it is important to optimize the layout to increase production and reduce costs. In this paper we minimize the levelized cost of energy (LCOE) fora floating wind farm using wind data in an area around Porto Santo in Portugal. We use ensemble based optimization (EnOpt), which is frequently applied in the geophysical community to find optimal controls of oil reservoirs. EnOpt is usually used for unconstrained optimization problems or for problems with simple constraints, for example upper and lower bounds on the optimization variables. Here we consider a layout problem with many constraints on the distances between turbines. To handle the constraints, we use an extension of EnOpt called EPF-EnOpt, in which the constrained problem is replaced by a series of unconstrained problems with increasing penalty terms. We compare the performance of this method with EnOpt with a fixed penalty term, and with a deterministic gradient method. All the tested methods reduce the LCOE, but EPF-EnOpt gives better results than both a single run of EnOpt with a fixed penalty term and the deterministic gradient method, and at a lower computational cost than using the gradient method. We also consider the problem of maximizing the annual energy production without taking into account any costs. EPF-EnOpt performs the best also for this problem.
Keyword:
Wind farm layout optimization
Ensemble optimization (EnOpt and EPF-EnOpt)
Constrained optimization
Levelized cost of energy (LCOE)
Floating offshore wind
期刊
IF:
9.1
论文数:
2.6W
被引数:
12.1W
机构
引用论文
New approach on optimization in placement of wind turbines within wind farm by genetic algorithms.png)
RENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏The ERA-Interim reanalysis: configuration and performance of the data assimilation systemERA-过渡再分析: 数据同化系统的配置和性能
OAAI
OAAI分享收藏Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithmRENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
分享收藏Global-Local Heat Demand Development for the Energy Transition Time Frame Up to 2050能源转换时间框架2050年的全球-本地热需求发展EnergiesOAAI
EnergiesIF0
OAAI分享收藏
查看更多内容