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Title: Photovoltaic System Modeling. Uncertainty and Sensitivity Analyses

Abstract

We report an uncertainty and sensitivity analysis for modeling AC energy from ph otovoltaic systems . Output from a PV system is predicted by a sequence of models. We quantify u ncertainty i n the output of each model using empirical distribution s of each model's residuals. We propagate uncertainty through the sequence of models by sampli ng these distributions to obtain a n empirical distribution of a PV system's output. We consider models that: (1) translate measured global horizontal, direct and global diffuse irradiance to plane - of - array irradiance; (2) estimate effective irradiance; (3) predict cell temperature; (4) estimate DC voltage, current and power ; (5) reduce DC power for losses due to inefficient maximum power point tracking or mismatch among modules; and (6) convert DC to AC power . O ur analysis consider s a notional PV system com prising an array of FirstSolar FS - 387 modules and a 250 kW AC inverter ; we use measured irradiance and weather at Albuquerque, NM. We found the uncertainty in PV syste m output to be relatively small, on the order of 1% for daily energy. We found that unce rtainty in the models for POA irradiancemore » and effective irradiance to be the dominant contributors to uncertainty in predicted daily energy. Our analysis indicates that efforts to reduce the uncertainty in PV system output predictions may yield the greatest improvements by focusing on the POA and effective irradiance models.« less

Authors:
 [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1211576
Report Number(s):
SAND2015-6700
598837
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Hansen, Clifford W., and Martin, Curtis E.. Photovoltaic System Modeling. Uncertainty and Sensitivity Analyses. United States: N. p., 2015. Web. doi:10.2172/1211576.
Hansen, Clifford W., & Martin, Curtis E.. Photovoltaic System Modeling. Uncertainty and Sensitivity Analyses. United States. doi:10.2172/1211576.
Hansen, Clifford W., and Martin, Curtis E.. Sat . "Photovoltaic System Modeling. Uncertainty and Sensitivity Analyses". United States. doi:10.2172/1211576. https://www.osti.gov/servlets/purl/1211576.
@article{osti_1211576,
title = {Photovoltaic System Modeling. Uncertainty and Sensitivity Analyses},
author = {Hansen, Clifford W. and Martin, Curtis E.},
abstractNote = {We report an uncertainty and sensitivity analysis for modeling AC energy from ph otovoltaic systems . Output from a PV system is predicted by a sequence of models. We quantify u ncertainty i n the output of each model using empirical distribution s of each model's residuals. We propagate uncertainty through the sequence of models by sampli ng these distributions to obtain a n empirical distribution of a PV system's output. We consider models that: (1) translate measured global horizontal, direct and global diffuse irradiance to plane - of - array irradiance; (2) estimate effective irradiance; (3) predict cell temperature; (4) estimate DC voltage, current and power ; (5) reduce DC power for losses due to inefficient maximum power point tracking or mismatch among modules; and (6) convert DC to AC power . O ur analysis consider s a notional PV system com prising an array of FirstSolar FS - 387 modules and a 250 kW AC inverter ; we use measured irradiance and weather at Albuquerque, NM. We found the uncertainty in PV syste m output to be relatively small, on the order of 1% for daily energy. We found that unce rtainty in the models for POA irradiance and effective irradiance to be the dominant contributors to uncertainty in predicted daily energy. Our analysis indicates that efforts to reduce the uncertainty in PV system output predictions may yield the greatest improvements by focusing on the POA and effective irradiance models.},
doi = {10.2172/1211576},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {8}
}

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