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Title: Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

Abstract

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurementmore » data collected locally in our lab.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1];  [2]
  1. ORNL
  2. University of Tennessee, Knoxville (UTK), Department of Electrical Engineering and Computer Science
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1399377
DOE Contract Number:
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: North American Power Symposium - Morgantown, West Virginia, United States of America - 9/17/2017 4:00:00 AM-9/19/2017 4:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Melin, Alexander M., Olama, Mohammed M., Dong, Jin, Djouadi, Seddik M., and Zhang, Yichen. Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output. United States: N. p., 2017. Web.
Melin, Alexander M., Olama, Mohammed M., Dong, Jin, Djouadi, Seddik M., & Zhang, Yichen. Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output. United States.
Melin, Alexander M., Olama, Mohammed M., Dong, Jin, Djouadi, Seddik M., and Zhang, Yichen. Fri . "Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output". United States. doi:. https://www.osti.gov/servlets/purl/1399377.
@article{osti_1399377,
title = {Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output},
author = {Melin, Alexander M. and Olama, Mohammed M. and Dong, Jin and Djouadi, Seddik M. and Zhang, Yichen},
abstractNote = {The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Sep 01 00:00:00 EDT 2017},
month = {Fri Sep 01 00:00:00 EDT 2017}
}

Conference:
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