A Poisson model for anisotropic solar ramp rate correlations

被引:59
作者
Arias-Castro, Ery [1 ]
Kleissl, Jan [2 ]
Lave, Matthew [2 ]
机构
[1] Univ Calif San Diego, Dept Math, San Diego, CA 92103 USA
[2] Univ Calif San Diego, Dept Mech & Aerosp Engn, Ctr Renewable Resources & Integrat, San Diego, CA 92103 USA
基金
美国国家科学基金会;
关键词
Solar ramp rate; Spatial correlations; Variability; Geographic dispersion; IRRADIANCE VARIABILITY; POWER PRODUCTION; PV SYSTEMS; NETWORK; SENSORS; SCALE; PLANT;
D O I
10.1016/j.solener.2013.12.028
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Spatial correlations between ramp rates are important determinants for output variability of solar power plants, since correlations determine the amount of geographic smoothing of solar irradiance across the plant footprint. Previous works have modeled correlations empirically as a decreasing function of the distance between sites, resulting in isotropic models. Field measurements show that correlations are anisotropic correlations are different for along-wind site pairs than for cross-wind site pairs. Here, cloud fields are modeled using a spatial Poisson process. By advecting the cloud field using a constant cloud velocity, spatial correlations for ramp rates are obtained. Spatial correlations were shown to be a function of along-wind and cross-wind distance, ramp timescale, cloud speed, cloud cover fraction, and cloud radius. The resulting anisotropic correlation model explains the anisotropic effects well at timescales less than 60 s but performs worse than existing empirical isotropic models at longer time scales. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:192 / 202
页数:11
相关论文
共 17 条
[1]   Cloud motion vectors from a network of ground sensors in a solar power plant [J].
Bosch, J. L. ;
Kleissl, J. .
SOLAR ENERGY, 2013, 95 :13-20
[2]   Deriving cloud velocity from an array of solar radiation measurements [J].
Bosch, J. L. ;
Zheng, Y. ;
Kleissl, J. .
SOLAR ENERGY, 2013, 87 :196-203
[3]   The character of power output from utility-scale photovoltaic systems [J].
Curtright, Aimee E. ;
Apt, Jay .
PROGRESS IN PHOTOVOLTAICS, 2008, 16 (03) :241-247
[4]  
Fung V., 2013, ATMOS MEAS TECH
[5]   Differences between along-wind and cross-wind solar irradiance variability on small spatial scales [J].
Hinkelman, Laura M. .
SOLAR ENERGY, 2013, 88 :192-203
[6]   Quantifying PV power Output Variability [J].
Hoff, Thomas E. ;
Perez, Richard .
SOLAR ENERGY, 2010, 84 (10) :1782-1793
[7]  
Lave M., 2012, SUST ENERGY IEEE T, VPP, P1
[8]   Cloud speed impact on solar variability scaling - Application to the wavelet variability model [J].
Lave, Matthew ;
Kleissl, Jan .
SOLAR ENERGY, 2013, 91 :11-21
[9]   High-frequency irradiance fluctuations and geographic smoothing [J].
Lave, Matthew ;
Kleissl, Jan ;
Arias-Castro, Ery .
SOLAR ENERGY, 2012, 86 (08) :2190-2199
[10]   Intra-hour forecasts of solar power production using measurements from a network of irradiance sensors [J].
Lonij, Vincent P. A. ;
Brooks, Adria E. ;
Cronin, Alexander D. ;
Leuthold, Michael ;
Koch, Kevin .
SOLAR ENERGY, 2013, 97 :58-66