Monte Carlo Simulations of Wind Speed Data

Gallagher, Ron; Elmore, Andrew Curtis

doi:10.1260/0309-524x.33.6.661

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…Others have looked at improving AEP forecasting using Monte–Carlo simulations (Gallagher and Elmore, 2009; Hrafnkelsson et al, 2016). This method improved the forecast of wind turbines over normal Weibull distributions because it accounted for seasonal variations and autocorrelations of wind speeds.…”

Section: Discussionmentioning

confidence: 99%

Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines

Nielson

Bhaganagar

2019

Wind Engineering

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A novel and a robust high-fidelity numerical methodology has been developed to realistically estimate the net energy production of full-scale horizontal axis wind turbines in a convective atmospheric boundary layer, for both isolated and multiple wind turbine arrays by accounting for the wake effects between them. Large eddy simulation has been used to understand the role of atmospheric stability in net energy production (annual energy production) of full-scale horizontal axis wind turbines placed in the convective atmospheric boundary layer. The simulations are performed during the convective conditions corresponding to the National Renewable Energy Laboratory field campaign of July 2015. A mathematical framework was developed to incorporate the field-based measurements as boundary conditions for the large eddy simulation by averaging the surface flux over multiple diurnal cycles. The objective of the study is to quantify the role of surface flux in the calculation of energy production for an isolated, two and three wind turbine configuration. The study compares the mean value, +1 standard deviation, and −1 standard deviation from the measured surface flux to demonstrate the role of surface heat flux. The uniqueness of the study is that power deficits from large eddy simulation were used to determine wake losses and obtain a net energy production that accounts for the wake losses. The frequency of stability events, from field measurements, is input into the calculation of an ensemble energy production prediction with wake losses for different wind turbine arrays. The increased surface heat flux increases the atmospheric turbulence into the wind turbines. Higher turbulence results in faster wake recovery by a factor of two. The faster wake recovery rates result in lowering the power deficits from 46% to 28% for the two-turbine array. The difference in net energy production between the +1 and −1 standard deviation (with respect to surface heat flux) simulations was 10% for the two-turbine array and 8% for the three-turbine array. An ensemble net energy production by accounting for the wake losses indicated the overestimation of annual energy production from current practices could be corrected by accounting for variation of surface flux from the mean value.

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Section: Discussionmentioning

confidence: 99%

Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines

Nielson

Bhaganagar

2019

Wind Engineering

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“…Different approaches have been used to generate synthetic wind data with hourly timesteps. Kaminsky et al 7 present and compare several such methods including independent and identically distributed values [8][9][10][11][12] one-or two-step Markov chain models, [13][14][15][16] Box-Jenkins or auto-regressive (moving average) (AR(MA)) models, 17 which have been used for synthetic wind speed data, 18 and Markov chain models. 7 They pointed out that most methods lack lower frequency information, i.e., diurnal effects are typically neglected even though autocorrelation for several hours may be included.…”

Section: B Synthetic Wind Speed Data In Energy Modellingmentioning

confidence: 99%

A method for including daily patterns to synthetic wind speed data for energy systems planning: Validation in the Azores Islands

Suomalainen

Silva

Ferrão

et al. 2012

Journal of Renewable and Sustainable Energy

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A high penetration of renewable electricity generation requires an improved understanding and modelling capability of renewable energy resource dynamics. This paper develops such a capability for wind. Using historical hourly wind speed data, an enhanced methodology for modelling wind, which includes seasonal, daily, hourly, and location related factors, has been developed. The methodology uses a set of day types, evaluating their frequency in real wind data, to generate hourly synthetic wind speed data, which is critical to designing a high wind penetration energy system. The methodology is applied to three different locations and the results compared to a commonly used method for synthetic wind speed data. It is concluded that the new method constitutes an important advance in simulating the occurrence of different types of wind days, which is particularly relevant in energy systems planning, especially in evaluating the frequency and scale of surpluses and shortages of wind power, and the necessity for and dimensioning of a storage system. V C 2012 American Institute of Physics.

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“…A new Monte Carlo simulation procedure and nearby regional weather station data are used to predict wind speed and turbine energy. The results indicated that the replacement of on-site wind data can replace provide accurate predictions of proposed nearby wind turbine [5]. Another computational model using one year of wind speed data of a weather station located downwind of the wind turbine site resulted in the greatest match of simulation results to the measured values.…”

Section: Introductionmentioning

confidence: 97%

Wind Energy Reliability Analysis based on Monte Carlo Simulation Method

Abdusamad

2018

Proceedings of First Conference for Engineering Sciences and Technology: Vol. 2

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Reliability is defined as the probability that an item will continue to achieve its intended function without failure for a specified period of time under operation conditions. The challenges of addressing energy supply and climate change are expected to drive the growth of renewable-based electrical generation such as from wind. Consequently, worldwide growth in wind energy is expected to increase as more wind turbines are installed to meet clean electricity demand and decrease the usage of fossil fuel. However, wind has risks and uncertainties that need to be addressed, perhaps the most significant challenge wind speed variability, which emphasizes that it cannot be considered as a reliable means of meeting the electrical loads. This paper suggests a Monte Carlo summation method, which can be utilized to apply reliability analysis in order to obtain the most-likely resistance-load relationship and the system probability of failure. A case study, which is based upon collected data from actual measurements, is presented in order to study the performance of a group of wind-turbines that deliver electricity to satisfy the demand of a certain load in a suggested area.

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Monte Carlo Simulations of Wind Speed Data

Cited by 10 publications

References 6 publications

Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines

Using field data–based large eddy simulation to understand role of atmospheric stability on energy production of wind turbines

A method for including daily patterns to synthetic wind speed data for energy systems planning: Validation in the Azores Islands

Wind Energy Reliability Analysis based on Monte Carlo Simulation Method

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