Solar forecasting methods for renewable energy integration

被引:719
作者
Inman, Rich H.
Pedro, Hugo T. C.
Coimbra, Carlos F. M. [1 ]
机构
[1] Univ Calif San Diego, Dept Mech & Aerosp Engn, Jacobs Sch Engn, Ctr Excellence Renewable Energy Integrat, La Jolla, CA 92093 USA
基金
美国国家科学基金会;
关键词
Weather-dependent renewable energy; Solar forecasting; Solar meteorology; Solar variability; Solar energy integration; Evolutionary forecasting methods; ARTIFICIAL-INTELLIGENCE TECHNIQUES; SATELLITE-DERIVED IRRADIANCES; RECURRENT NEURAL-NETWORKS; TIME-SERIES; POWER PREDICTION; RADIATION DATA; ANALOG METHOD; CLOUD COVER; RAYLEIGH ATMOSPHERE; STATISTICAL-MODEL;
D O I
10.1016/j.pecs.2013.06.002
中图分类号
O414.1 [热力学];
学科分类号
摘要
The higher penetration of renewable resources in the energy portfolios of several communities accentuates the need for accurate forecasting of variable resources (solar, wind, tidal) at several different temporal scales in order to achieve power grid balance. Solar generation technologies have experienced strong energy market growth in the past few years, with corresponding increase in local grid penetration rates. As is the case with wind, the solar resource at the ground level is highly variable mostly due to cloud cover variability, atmospheric aerosol levels, and indirectly and to a lesser extent, participating gases in the atmosphere. The inherent variability of solar generation at higher grid penetration levels poses problems associated with the cost of reserves, dispatchable and ancillary generation, and grid reliability in general. As a result, high accuracy forecast systems are required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment. Here we review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:535 / 576
页数:42
相关论文
共 311 条
[1]   TAG - A TIME-DEPENDENT, AUTOREGRESSIVE, GAUSSIAN MODEL FOR GENERATING SYNTHETIC HOURLY RADIATION [J].
AGUIAR, R ;
COLLARESPEREIRA, M .
SOLAR ENERGY, 1992, 49 (03) :167-174
[2]   Clustered K Nearest Neighbor Algorithm for Daily Inflow Forecasting [J].
Akbari, Mahmood ;
van Overloop, Peter Jules ;
Afshar, Abbas .
WATER RESOURCES MANAGEMENT, 2011, 25 (05) :1341-1357
[3]   An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation [J].
Al-Alawi, SM ;
Al-Hinai, HA .
RENEWABLE ENERGY, 1998, 14 (1-4) :199-204
[4]   Stochastic modelling of global solar radiation measured in the state of Kuwait [J].
Al-Awadhi, SA ;
El-Nashar, N .
ENVIRONMETRICS, 2002, 13 (07) :751-758
[5]  
Allen RG, 1994, B INT COMM IRRIG DRA, V43, P35
[6]  
ANGSTROM A, 1962, TELLUS, V14, P435
[7]  
[Anonymous], J SOLAR ENERGY ENG
[8]  
[Anonymous], NASA C PUBLICATION
[9]  
[Anonymous], INT GEOPHYS
[10]  
[Anonymous], 2012, RENEWABLE ELECT GRID