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Author: Vivienne Jiao Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Wind energy plays an important role in decarbonizing the economy and increasingly accounts for a growing share of electricity supply in the United States. However, availability of wind resource is highly dependent on variable factors such as weather and local geographies, making wind power generation forecast a particularly difficult task. This adds to the challenge of grid management, which requires that the supply of electricity equates the demand at all times. Complicating the effort to improve wind power predicitability is a lack of empirical data, since wind power generation data are proprietary and often considered business secrets. To address this lack of empirical study, this thesis uses actual generation data between 2016 to 2021 from seven anonymized wind farms in Midwestern United States that range from 50MW to 235MW in size. The experiments demonstrate how machine learning methods can be used to forecast wind power generation at different time intervals, and how the accuracy of forecasting can be significantly improved when using a combination of newly extracted weather forecast data and weather measurement data. The economic benefits of more accurate forecasting are then studied using a using a simulation with market data from the Midcontinent Independent System Operator and the Southwest Power Pool. The thesis then explores whether predictability of wind power generation can be improved by placing weather stations closer to the wind forecast sites. Implications of these findings can inform investment decisions regarding weather monitoring stations and forecasting models, which can help electricity market participants adapt to a grid with an increasing share of renewable resources.
Author: Vivienne Jiao Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Wind energy plays an important role in decarbonizing the economy and increasingly accounts for a growing share of electricity supply in the United States. However, availability of wind resource is highly dependent on variable factors such as weather and local geographies, making wind power generation forecast a particularly difficult task. This adds to the challenge of grid management, which requires that the supply of electricity equates the demand at all times. Complicating the effort to improve wind power predicitability is a lack of empirical data, since wind power generation data are proprietary and often considered business secrets. To address this lack of empirical study, this thesis uses actual generation data between 2016 to 2021 from seven anonymized wind farms in Midwestern United States that range from 50MW to 235MW in size. The experiments demonstrate how machine learning methods can be used to forecast wind power generation at different time intervals, and how the accuracy of forecasting can be significantly improved when using a combination of newly extracted weather forecast data and weather measurement data. The economic benefits of more accurate forecasting are then studied using a using a simulation with market data from the Midcontinent Independent System Operator and the Southwest Power Pool. The thesis then explores whether predictability of wind power generation can be improved by placing weather stations closer to the wind forecast sites. Implications of these findings can inform investment decisions regarding weather monitoring stations and forecasting models, which can help electricity market participants adapt to a grid with an increasing share of renewable resources.
Author: Vivienne Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Yu Ding
Publisher: CRC Press
ISBN: 0429956517
Category : Business & Economics
Languages : en
Pages : 400
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Book Description
Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml.engr.tamu.edu/book-dswe. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights
Author: Ümit Cali
Publisher: kassel university press GmbH
ISBN: 3862190315
Category :
Languages : en
Pages : 174
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Author: Melih Kurt
Publisher: kassel university press GmbH
ISBN: 3737603464
Category :
Languages : en
Pages : 200
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Book Description
This study explains the data preparation processes, plausibility checking of meteorological parameters, correction of met-mast wind speed, and also the determination of the nominal power of a wind farm using met-mast measurements. The wind speed correction of met-mast FINO1 is evaluated from the perspective of power produced by alpha ventus by using uncorrected and corrected measurements from this met-mast. Afterwards this data is used for the determination of nominal power for alpha ventus.
Author: Aubryn Cooperman
Publisher:
ISBN:
Category : Numerical weather forecasting
Languages : en
Pages : 86
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Book Description
Author: Cungang Hu
Publisher: Springer Nature
ISBN: 9819739403
Category :
Languages : en
Pages : 822
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Book Description
Author: Fushuan Wen
Publisher: Springer Nature
ISBN: 9819770475
Category :
Languages : en
Pages : 644
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Book Description
Author: Horst Rinne
Publisher: CRC Press
ISBN: 1420087444
Category : Business & Economics
Languages : en
Pages : 812
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Book Description
The Most Comprehensive Book on the SubjectChronicles the Development of the Weibull Distribution in Statistical Theory and Applied StatisticsExploring one of the most important distributions in statistics, The Weibull Distribution: A Handbook focuses on its origin, statistical properties, and related distributions. The book also presents various ap
Author: Jin Hur
Publisher:
ISBN:
Category :
Languages : en
Pages : 392
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Book Description
Wind generating resources have been increasing more rapidly than any other renewable generating resources. Wind power forecasting is an important issue for deploying higher wind power penetrations on power grids. The existing work on power output forecasting for wind farms has focused on the temporal issues. As wind farm outputs depend on natural wind resources that vary over space and time, spatial analysis and modeling is also needed. Predictions about suitability for locating new wind generating resources can be performed using spatial modeling. In this dissertation, we propose a new approach to spatial prediction of wind farm outputs for grid integration based on Kriging techniques. First, we investigate the characteristics of wind farm outputs. Wind power is variable, uncontrollable, and uncertain compared to traditional generating resources. In order to understand the characteristics of wind power outputs, we study the variability of wind farm outputs using correlation analysis. We estimate the Power Spectrum Density (PSD) from empirical data. Following Apt[1], we classify the estimated PSD into four frequency ranges having different slopes. We subsequently focus on phenomena relating to the slope of the estimated PSD at a low frequency range because our spatial prediction is based on the period over daily to monthly timescales. Since most of the energy is in the lower frequency components (the second, third, and fourth slope regions have much lower spectral density than the first), the conclusion is that the dominant issues regarding energy will be captured by the low frequency behavior. Consequently, most of the issues regarding energy (at least at longer timescales) will be captured by the first slope, since relatively little energy is in the other regions. We propose the slope estimation model of new wind farm production. When the existing wind farms are highly correlated and the slope of each wind farm is estimated at a low frequency range, we can predict the slope with low frequency components of a new wind farm through the proposed spatial interpolation techniques. Second, we propose a new approach, based on Kriging techniques, to predict wind farm outputs. We introduce Kriging techniques for spatial prediction, modeling semivariograms for spatial correlation, and mathematical formulation of the Kriging system. The aim of spatial modeling is to calculate a target value of wind production at unmeasured or new locations based on the existing values that have already been measured at locations considering the spatial correlation relationship between measured values. We propose the multivariate spatial approach based on Co-Kriging to consider multiple variables for better prediction. Co-Kriging is a multivariate spatial technique to predict spatially distributed and correlated variables and it adds auxiliary variables to a single variable of interest at unmeasured locations. Third, we develop the Augmented Kriging-based Model, to predict power outputs at unmeasured or new wind farms that are geographically distributed in a region. The proposed spatial prediction model consists of three stages: collection of wind farm data for spatial analysis, performance of spatial analysis and prediction, and verification of the predicted wind farm outputs. The proposed spatial prediction model provides the univariate prediction based on Universal Kriging techniques and the multivariate prediction based on Universal and Co-Kriging techniques. The proposed multivariate prediction model considers multiple variables: the measured wind power output as a primary variable and the type or hub height of wind turbines, or the slope with low frequency components as a secondary variable. The multivariate problem is solved by Co-Kriging techniques. In addition, we propose $p$ indicator as a categorical variable considering the data configuration of wind farms connected to electrical power grids. Although the interconnection voltage does not influence the wind regime, it does affect transmission system issues such as the level of curtailments, which, in turn, affect power production. Voltage level is therefore used as a proxy to the effect of the transmission system on power output. The Augmented Kriging-based Model (AKM) is implemented in the R system environments and the latest Gstat library is used for the implementation of the AKM. Fourth, we demonstrate the performance of the proposed spatial prediction model based on Kriging techniques in the context of the McCamey and Central areas of ERCOT CREZ. Spatial prediction of ERCOT wind farms is performed in daily, weekly, and monthly time scales for January to September 2009. These time scales all correspond to the lowest frequency range of the estimated PSD. We propose a merit function to provide practical information to find optimal wind farm sites based on spatial wind farm output prediction, including correlation with other wind farms. Our approach can predict what will happen when a new wind farm is added at various locations. Fifth, we propose the Augmented Sequential Outage Checker (ASOC) as a possible approach to study the transmission system, including grid integration of wind-powered generation resources. We analyze cascading outages caused by a combination of thermal overloads, low voltages, and under-frequencies following an initial disturbance using the ASOC.
