.png){kind=icon}
返回
A Computational Framework for Uncertainty Quantification and Stochastic Optimization in Unit Commitment With Wind Power Generation
.png){kind=icon}
PRE
AI
DOI：10.1109/TPWRS.2010.2048133.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a computational framework for integrating a state-of-the-art numerical weather prediction (NWP) model in stochastic unit commitment/economic dispatch formulations that account for wind power uncertainty. We first enhance the NWP model with an ensemble-based uncertainty quantification strategy implemented in a distributed-memory parallel computing architecture. We discuss computational issues arising in the implementation of the framework and validate the model using real wind-speed data obtained from a set of meteorological stations. We build a simulated power system to demonstrate the developments.
Keyword:
Closed-loop
economic dispatch
unit commitment
weather forecasting
wind
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem用于热单位承诺问题的计算有效的混合整数线性公式
PREAI
分享收藏
查看更多内容