scholarly journals Uncertainty Analysis in MCP-Based Wind Resource Assessment and Energy Production Estimation

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Matthew A. Lackner ◽  
Anthony L. Rogers ◽  
James F. Manwell
Keyword(s):  
Wind Energy ◽  
Energy Production ◽  
Closed Form Solution ◽  
Resource Assessment ◽  
Form Solution ◽  
Mathematical Framework ◽  
Site Assessment ◽  
Wind Resource Assessment ◽  
Uncertainty Sources ◽  
Wind Resource

This paper presents a mathematical framework to properly account for uncertainty in wind resource assessment and wind energy production estimation. A meteorological tower based wind measurement campaign is considered exclusively, in which measure-correlate-predict is used to estimate the long-term wind resource. The evaluation of a wind resource and the subsequent estimation of the annual energy production (AEP) is a highly uncertain process. Uncertainty arises at all points in the process, from measuring the wind speed to the uncertainty in a power curve. A proper assessment of uncertainty is critical for judging the feasibility and risk of a potential wind energy development. The approach in this paper provides a framework for an accurate and objective accounting of uncertainty and, therefore, better decision making when assessing a potential wind energy site. It does not investigate the values of individual uncertainty sources. Three major aspects of site assessment uncertainty are presented here. First, a method is presented for combining uncertainty that arises in assessing the wind resource. Second, methods for handling uncertainty sources in wind turbine power output and energy losses are presented. Third, a new method for estimating the overall AEP uncertainty when using a Weibull distribution is presented. While it is commonly assumed that the uncertainty in the wind resource should be scaled by a factor between 2 and 3 to yield the uncertainty in the AEP, this work demonstrates that this assumption is an oversimplification and also presents a closed form solution for the sensitivity factors of the Weibull parameters.

scholarly journals Offshore Wind Energy Resource Assessment from Satellite Data Observations and WRF in Porto Santo Island

2020 ◽  
Author(s):  
Fabiola S. Pereira ◽  
Carlos S. Silva
Keyword(s):  
Wind Energy ◽  
Weibull Distribution ◽  
Energy Production ◽  
Energy Resource ◽  
Resource Assessment ◽  
Offshore Wind ◽  
Electricity Production ◽  
Offshore Wind Energy ◽  
Wind Resource Assessment ◽  
Wind Resource

Abstract. The vast majority of isolated electricity production systems such as Islands depends on fossil fuels. Porto Santo Island, a Portuguese UNESCO Biosphere Reserve candidate from Madeira Archipelago situated in the Atlantic Ocean, aims to become a sustainable territory in order to reduce its carbon footprint. A sustainable pathway goes through the integration of renewable energy in the electricity production system, in particular, the potential of offshore wind energy. The scope of this work has three main purposes: (1) the offshore wind resource assessment in Porto Santo Island, (2) the determination of a zone of interest regarding the combination of different parameters such us the bathymetry, distance to the coastline and integrated in the national situation plan of maritime space (3) the estimation of the annual energy production from the best-fitted Weibull Distribution. In the first place, a methodology for data analysis was defined processing netcdf data regarding a ten year wind hindcast from WRF (Weather Research and Forecasting) atmospheric model at 100 m above mean sea level from Ocean Observatory, annual and monthly mean offshore wind energy resource maps were created and a comparison with about 20 year times series of surface winds derived from remotely satellite scatterometer observations at different locations was made. Results show that the average annual mean wind speeds reach the range of 6.6–7.6 m/s in specific areas, situated in the northern part of Porto Santo Island with a Weibull distribution shape parameter (k) of 2.4–2.9. Based on the results, the wind resource assessment, the estimation of the annual wind energy production and capacity factors were calculated from the best-fitted Weibull distribution for each of the geographical coordinates selected. Comparisons with observational data show that WRF model is a proficient wind generating tool. The technical energy production potential and a priority zoning for offshore wind power development is performed using wind turbine generators of 3.3 MW–8.0 MW capacity, that could generate between 12 and 26 GWh of energy per year, while avoiding CO2 emissions. The results show that an offshore wind farm plan is an eligible choice, with an average annual wind power density reaching about 300  W/m2 at 100 m height in the north region.

scholarly journals An overview of wind-energy-production prediction bias, losses, and uncertainties

2021 ◽  
Vol 6 (2) ◽  
pp. 311-365
Author(s):  
Joseph C. Y. Lee ◽  
M. Jason Fields
Keyword(s):  
Wind Energy ◽  
Prediction Error ◽  
Energy Production ◽  
Resource Assessment ◽  
Energy Industry ◽  
Energy Losses ◽  
Energy Yield ◽  
Prediction Errors ◽  
Wind Resource Assessment ◽  
Wind Resource

Abstract. The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the reported average energy prediction bias is declining. Herein, we present a literature review of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary of how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the article.

scholarly journals An Overview of Wind Energy Production Prediction Bias, Losses, and Uncertainties

2020 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
M. Jason Fields
Keyword(s):  
Wind Energy ◽  
Prediction Error ◽  
Energy Production ◽  
Resource Assessment ◽  
Energy Industry ◽  
Energy Losses ◽  
Energy Yield ◽  
Prediction Errors ◽  
Wind Resource Assessment ◽  
Wind Resource

Abstract. The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the industry is recording near-zero average energy prediction bias. Herein, we present an overview of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including the improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight the opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty, and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary on how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the manuscript.

scholarly journals Lidar uncertainty and beam averaging correction

2015 ◽  
Vol 12 (1) ◽  
pp. 85-89 ◽  
Author(s):  
A. Giyanani ◽  
W. Bierbooms ◽  
G. van Bussel
Keyword(s):  
Remote Sensing ◽  
Wind Energy ◽  
Complex Terrain ◽  
New Technology ◽  
Resource Assessment ◽  
International Guideline ◽  
Wind Resource Assessment ◽  
Draft Version ◽  
Wind Resource ◽  
Atmospheric Variables

Abstract. Remote sensing of the atmospheric variables with the use of Lidar is a relatively new technology field for wind resource assessment in wind energy. A review of the draft version of an international guideline (CD IEC 61400-12-1 Ed.2) used for wind energy purposes is performed and some extra atmospheric variables are taken into account for proper representation of the site. A measurement campaign with two Leosphere vertical scanning WindCube Lidars and metmast measurements is used for comparison of the uncertainty in wind speed measurements using the CD IEC 61400-12-1 Ed.2. The comparison revealed higher but realistic uncertainties. A simple model for Lidar beam averaging correction is demonstrated for understanding deviation in the measurements. It can be further applied for beam averaging uncertainty calculations in flat and complex terrain.

Distributed wind resource assessment: Comparing measured annual energy production with predictions from computational fluid dynamics

2019 ◽  
Vol 43 (6) ◽  
pp. 657-672
Author(s):  
Devon L Martindale ◽  
Thomas L Acker
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Turbine ◽  
Energy Production ◽  
Resource Assessment ◽  
Energy Output ◽  
Wind Resource Assessment ◽  
Production Values ◽  
The Us ◽  
Wind Resource

The US Department of Energy’s Distributed Wind Resource Assessment Workshop identified predicting the annual energy production of a kilowatt-sized wind turbine as a key challenge. This article presents the methods and results for predicting the annual energy production of two 2.1 kW Skystream 3.7 wind turbines using computational fluid dynamics, in this case Meteodyn WT. When compared with actual production data, annual energy production values were uniformly underpredicted, with errors ranging from 1% to in excess of 30%, depending on the solver settings and boundary conditions. The most accurate of the simulations with errors consistently less than 10% were achieved when using recommended solver settings of neutral atmospheric stability, and roughness values derived from the US National Land Cover Database. The software was used to create an annual energy production map for the modeling domain, which could be a valuable tool in estimating the energy output and economic value of a proposed wind turbine.

Wind Resource Assessment for the Kingdom of Bahrain

2008 ◽  
Vol 32 (5) ◽  
pp. 439-448 ◽  
Author(s):  
Hanan Al Buflasa ◽  
David Infield ◽  
Simon Watson ◽  
Murray Thomson
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Resource Assessment ◽  
Measured Data ◽  
Shape Parameters ◽  
Main Island ◽  
Wind Resource Assessment ◽  
Mean Wind Speed ◽  
Wind Resource ◽  
Hourly Basis

The geographical distribution of wind speed (the wind atlas) for the kingdom of Bahrain is presented, based on measured data and on calculations undertaken using WAsP,. The data used were recorded by the Meteorological Directorate at a weather station situated at Bahrain International Airport, taken on an hourly basis for a period of time extended for ten years. These data indicate an annual mean wind speed of 4.6 m/s at 10 m height and mean Weibull scale and shape parameters C and k of 5.2 m/s and 1.9 respectively. At a typical wind turbine hub height of sixty metres, these values are extrapolated to 6.9 m/s, 7.8 m/s and 1.8 respectively, which suggests that the area has a good wind resource. The wind atlas shows that several locations in the less populated central and southern regions of the main island of the archipelago of Bahrain are potentially suitable for wind energy production.

Urban wind resource assessment in changing climate: Case study

2016 ◽  
Vol 41 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Djordje Romanic ◽  
Ashkan Rasouli ◽  
Horia Hangan
Keyword(s):  
Climate Change ◽  
Wind Energy ◽  
Resource Assessment ◽  
Urban Environments ◽  
Wind Resource Assessment ◽  
Changing Climate ◽  
Energy Maps ◽  
Wind Resource ◽  
The City

Urban wind resource assessment in changing climate has not been studied so far. This study presents a methodology for microscale numerical modelling of urban wind resource assessment in changing climate. The methodology is applied for a specific urban development in the city of Toronto, ON, Canada. It is shown that the speed of the southwest winds, that is, the most frequent winds increased for .8 m s−1 in the period from 1948 to 2015. The generated wind energy maps are used to estimate the influences of climate change on the available wind energy. It is shown that the geometry of irregularly spaced and located obstacles in urban environments has to be taken into consideration when performing studies on urban wind resource assessment in changing climate. In the analysed urban environment, peak speeds are more affected by climate change than the mean speeds.

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