Uncertainty Analysis for Maximum Power at SRC Using Hierarchical Monte Carlo Simulation

Mark Campanelli; Keith Emery; Ryan Elmore; Brian Zaharatos

doi:10.1109/PVSC.2014.6924903

Uncertainty Analysis for Maximum Power at SRC Using Hierarchical Monte Carlo Simulation

Mark Campanelli, Keith Emery, Ryan Elmore, Brian Zaharatos

Chemistry and Nanoscience

Research output: Contribution to conference › Paper › peer-review

1 Scopus Citations

Abstract

We present an uncertainty analysis (UA) for the maximum power of photovoltaic devices at standard reporting conditions, denoted P_max0. The UA employs a hierarchical Monte Carlo (MC) simulation to sample from the state-of-knowledge probability distribution for P_max0, based upon an irradiance and spectrally corrected current-voltage curve measured (nominally) at 1-sun according to ASTM E948-09 or E1036-12. A conditional factoring of the joint distribution of the parameters that determine P_max0 allows rigorous consideration of both systematic (e.g., calibration chain) and random (e.g., solar simulator noise) sources of uncertainty in P_max0. The corresponding hierarchical MC simulation is computationally efficient and parallelizable, allowing enough samples to control MC sampling error. The UA method complies with the Guide to the Expression of Uncertainty in Measurement.

Original language	American English
Pages	3670-3675
Number of pages	6
DOIs	https://doi.org/10.1109/PVSC.2014.6924903 https://doi.org/10.1109/PVSC.2014.6924903
State	Published - 15 Oct 2014
Event	40th IEEE Photovoltaic Specialist Conference, PVSC 2014 - Denver, United States Duration: 8 Jun 2014 → 13 Jun 2014

Conference

Conference	40th IEEE Photovoltaic Specialist Conference, PVSC 2014
Country/Territory	United States
City	Denver
Period	8/06/14 → 13/06/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

NREL Publication Number

NREL/CP-5J00-61244

Keywords

calibration
hierarchical modeling
maximum power
measurement uncertainty
Monte Carlo simulation
noise
uncertainty analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

Cite this

@conference{33e2130bcc3742a880d0c1cdca59d805,

title = "Uncertainty Analysis for Maximum Power at SRC Using Hierarchical Monte Carlo Simulation",

abstract = "We present an uncertainty analysis (UA) for the maximum power of photovoltaic devices at standard reporting conditions, denoted Pmax0. The UA employs a hierarchical Monte Carlo (MC) simulation to sample from the state-of-knowledge probability distribution for Pmax0, based upon an irradiance and spectrally corrected current-voltage curve measured (nominally) at 1-sun according to ASTM E948-09 or E1036-12. A conditional factoring of the joint distribution of the parameters that determine Pmax0 allows rigorous consideration of both systematic (e.g., calibration chain) and random (e.g., solar simulator noise) sources of uncertainty in Pmax0. The corresponding hierarchical MC simulation is computationally efficient and parallelizable, allowing enough samples to control MC sampling error. The UA method complies with the Guide to the Expression of Uncertainty in Measurement.",

keywords = "calibration, hierarchical modeling, maximum power, measurement uncertainty, Monte Carlo simulation, noise, uncertainty analysis",

author = "Mark Campanelli and Keith Emery and Ryan Elmore and Brian Zaharatos",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 40th IEEE Photovoltaic Specialist Conference, PVSC 2014 ; Conference date: 08-06-2014 Through 13-06-2014",

year = "2014",

month = oct,

day = "15",

doi = "10.1109/PVSC.2014.6924903",

language = "American English",

pages = "3670--3675",

}

TY - CONF

T1 - Uncertainty Analysis for Maximum Power at SRC Using Hierarchical Monte Carlo Simulation

AU - Campanelli, Mark

AU - Emery, Keith

AU - Elmore, Ryan

AU - Zaharatos, Brian

PY - 2014/10/15

Y1 - 2014/10/15

N2 - We present an uncertainty analysis (UA) for the maximum power of photovoltaic devices at standard reporting conditions, denoted Pmax0. The UA employs a hierarchical Monte Carlo (MC) simulation to sample from the state-of-knowledge probability distribution for Pmax0, based upon an irradiance and spectrally corrected current-voltage curve measured (nominally) at 1-sun according to ASTM E948-09 or E1036-12. A conditional factoring of the joint distribution of the parameters that determine Pmax0 allows rigorous consideration of both systematic (e.g., calibration chain) and random (e.g., solar simulator noise) sources of uncertainty in Pmax0. The corresponding hierarchical MC simulation is computationally efficient and parallelizable, allowing enough samples to control MC sampling error. The UA method complies with the Guide to the Expression of Uncertainty in Measurement.

AB - We present an uncertainty analysis (UA) for the maximum power of photovoltaic devices at standard reporting conditions, denoted Pmax0. The UA employs a hierarchical Monte Carlo (MC) simulation to sample from the state-of-knowledge probability distribution for Pmax0, based upon an irradiance and spectrally corrected current-voltage curve measured (nominally) at 1-sun according to ASTM E948-09 or E1036-12. A conditional factoring of the joint distribution of the parameters that determine Pmax0 allows rigorous consideration of both systematic (e.g., calibration chain) and random (e.g., solar simulator noise) sources of uncertainty in Pmax0. The corresponding hierarchical MC simulation is computationally efficient and parallelizable, allowing enough samples to control MC sampling error. The UA method complies with the Guide to the Expression of Uncertainty in Measurement.

KW - calibration

KW - hierarchical modeling

KW - maximum power

KW - measurement uncertainty

KW - Monte Carlo simulation

KW - noise

KW - uncertainty analysis

UR - http://www.scopus.com/inward/record.url?scp=84912081165&partnerID=8YFLogxK

U2 - 10.1109/PVSC.2014.6924903

DO - 10.1109/PVSC.2014.6924903

M3 - Paper

AN - SCOPUS:84912081165

SP - 3670

EP - 3675

T2 - 40th IEEE Photovoltaic Specialist Conference, PVSC 2014

Y2 - 8 June 2014 through 13 June 2014

ER -

Uncertainty Analysis for Maximum Power at SRC Using Hierarchical Monte Carlo Simulation

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this