A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation

A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and seasonality in wind speed observations. No d...

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Published in:Energies
Main Authors: Birgir Hrafnkelsson, Gudmundur Oddsson, Runar Unnthorsson
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2016
Subjects:
Online Access:https://doi.org/10.3390/en9040286
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spelling ftmdpi:oai:mdpi.com:/1996-1073/9/4/286/ 2023-08-20T04:07:31+02:00 A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation Birgir Hrafnkelsson Gudmundur Oddsson Runar Unnthorsson 2016-04-13 application/pdf https://doi.org/10.3390/en9040286 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/en9040286 https://creativecommons.org/licenses/by/4.0/ Energies; Volume 9; Issue 4; Pages: 286 wind speed wind energy Monte Carlo (MC) simulation modified Weibull simulation annual energy production (AEP) method Text 2016 ftmdpi https://doi.org/10.3390/en9040286 2023-07-31T20:52:21Z A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and seasonality in wind speed observations. No distributional assumptions are made, and this approach is relatively simple in comparison to simulation methods that aim at including the autocorrelation and seasonal effects. Annual energy production (AEP) is simulated by transforming the simulated wind speed values via the power curve of the wind turbine at the site. The proposed Monte Carlo approach is generic and is applicable for all sites provided that a sufficient amount of wind speed data and information on the power curve are available. The simulated AEP values based on the Monte Carlo approach are compared to both actual AEP and to simulated AEP values based on a modified Weibull approach for wind speed simulation using data from the Burfell site in Iceland. The comparison reveals that the simulated AEP values based on the proposed Monte Carlo approach have a distribution that is in close agreement with actual AEP from two test wind turbines at the Burfell site, while the simulated AEP of the Weibull approach is such that the P50 and the scale are substantially lower and the P90 is higher. Thus, the Weibull approach yields AEP that is not in line with the actual variability in AEP, while the Monte Carlo approach gives a realistic estimate of the distribution of AEP. Text Iceland MDPI Open Access Publishing Energies 9 4 286
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic wind speed
wind energy
Monte Carlo (MC) simulation
modified Weibull simulation
annual energy production (AEP)
method
spellingShingle wind speed
wind energy
Monte Carlo (MC) simulation
modified Weibull simulation
annual energy production (AEP)
method
Birgir Hrafnkelsson
Gudmundur Oddsson
Runar Unnthorsson
A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation
topic_facet wind speed
wind energy
Monte Carlo (MC) simulation
modified Weibull simulation
annual energy production (AEP)
method
description A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and seasonality in wind speed observations. No distributional assumptions are made, and this approach is relatively simple in comparison to simulation methods that aim at including the autocorrelation and seasonal effects. Annual energy production (AEP) is simulated by transforming the simulated wind speed values via the power curve of the wind turbine at the site. The proposed Monte Carlo approach is generic and is applicable for all sites provided that a sufficient amount of wind speed data and information on the power curve are available. The simulated AEP values based on the Monte Carlo approach are compared to both actual AEP and to simulated AEP values based on a modified Weibull approach for wind speed simulation using data from the Burfell site in Iceland. The comparison reveals that the simulated AEP values based on the proposed Monte Carlo approach have a distribution that is in close agreement with actual AEP from two test wind turbines at the Burfell site, while the simulated AEP of the Weibull approach is such that the P50 and the scale are substantially lower and the P90 is higher. Thus, the Weibull approach yields AEP that is not in line with the actual variability in AEP, while the Monte Carlo approach gives a realistic estimate of the distribution of AEP.
format Text
author Birgir Hrafnkelsson
Gudmundur Oddsson
Runar Unnthorsson
author_facet Birgir Hrafnkelsson
Gudmundur Oddsson
Runar Unnthorsson
author_sort Birgir Hrafnkelsson
title A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation
title_short A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation
title_full A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation
title_fullStr A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation
title_full_unstemmed A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation
title_sort method for estimating annual energy production using monte carlo wind speed simulation
publisher Multidisciplinary Digital Publishing Institute
publishDate 2016
url https://doi.org/10.3390/en9040286
genre Iceland
genre_facet Iceland
op_source Energies; Volume 9; Issue 4; Pages: 286
op_relation https://dx.doi.org/10.3390/en9040286
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/en9040286
container_title Energies
container_volume 9
container_issue 4
container_start_page 286
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