Novel stochastic methods to predict short-term solar radiation and photovoltaic power

RENEWABLE ENERGY

卷:145

Article

Dong, Jin¹  Olama, Mohammed M.²  Kuruganti, Teja²  Melin, Alexander M.³  Djouadi, Seddik M.⁴  Zhang, Yichen⁵  Xue, Yaosuo³ 

[1] Oak Ridge Natl Lab, Energy & Transportat Sci Div, Oak Ridge, TN 37831 USA

[2] Oak Ridge Natl Lab, Computat Sci & Engn Div, Oak Ridge, TN 37831 USA

[3] Oak Ridge Natl Lab, Elect & Elect Syst Res Div, Oak Ridge, TN 37831 USA

[4] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA

[5] Argonne Natl Lab, Energy Syst Div, Lemont, IL 60439 USA

关键词: Renewable energy;    Solar forecasting;    Photovoltaics;    Solar variability;    Stochastic forecasting;    Basis functions;   

DOI  :  10.1016/j.renene.2019.05.073

来源: Elsevier

【 摘 要 】

Solar forecasting has evolved towards becoming a key component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents two novel stochastic forecasting models for solar PV by utilizing historical measurement data to outline a short-term high-resolution probabilistic behavior of solar. First, an uncertain basis functions method is used to forecast both solar radiation and PV power. Three possible distributions are considered for the uncertain basis functions Gaussian, Laplace, and Uniform distributions. Second, stochastic state-space models are applied to characterize the behaviors of solar radiation and PV power output. A filter-based expectation-maximization and Kalman filtering mechanism is employed to recursively estimate the system parameters and state variables. This enables the system to accurately forecast small as well as large fluctuations of the solar signals. The introduced forecasting models are suitable for real-time tertiary dispatch controllers and optimal power controllers. The PV forecasting models are tested using solar radiation and PV power measurement data collected from a 13.5 kW PV panel installed on the rooftop of our laboratory. The results are compared with standard time series forecasting mechanisms and show a substantial improvement in the forecasting accuracy of the total energy produced. (C) 2019 Published by Elsevier Ltd.

【 授权许可】

Free