A k-nearest neighbor space-time simulator with applications to large-scale wind and solar power modeling

被引:4
|
作者
Amonkar, Yash [1 ,2 ]
Farnham, David J. [3 ,4 ]
Lall, Upmanu [1 ,2 ]
机构
[1] Columbia Univ, Columbia Water Ctr, New York, NY 10027 USA
[2] Columbia Univ, Dept Earth & Environm Engn, New York, NY 10027 USA
[3] Carnegie Inst Sci, Dept Global Ecol, Stanford, CA USA
[4] ClimateAi, San Francisco, CA USA
来源
PATTERNS | 2022年 / 3卷 / 03期
关键词
RENEWABLE ENERGY; STORAGE; RELIABILITY; REANALYSIS; SYSTEMS;
D O I
10.1016/j.patter.2022.100454
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We develop and present a k-nearest neighbor space-time simulator that accounts for the spatiotemporal dependence in high-dimensional hydroclimatic fields (e.g., wind and solar) and can simulate synthetic realizations of arbitrary length. We illustrate how this statistical simulation tool can be used in the context of regional power system planning under a scenario of high reliance on wind and solar generation and when long historical records of wind and solar power generation potential are not available. Weshow how our simulation model can be used to assess the probability distribution of the severity and duration of energy "droughts'' at the network scale that need to be managed by long-duration storage or alternate energy sources. We present this estimation of supply-side shortages for the Texas Interconnection.
引用
收藏
页数:12
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