The impact of wind field spatial heterogeneity and variability on short-term wind power forecast errors

Yang, Ming; Zhang, Luobin; Cui, Yang; Yang, Qiongqiong; Huang, Binyang

doi:10.1063/1.5064438

Cited by 12 publications

(10 citation statements)

References 35 publications

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“…When multiple wind farms are integrated into a power system, the dependency between different wind farms has a significant impact on the reliability [42]. WPFEs at different locations cannot be assumed to be independent if they are geographically close owing to the inertia of meteorological forecasting systems [30,44]. According to the case studies based on western Denmark [34] and Ireland [39], the correlation of the WPFEs is strongly dependent on the distance between the wind farms.…”

Section: Introductionmentioning

confidence: 99%

Reliability sensitivity of wind power system considering correlation of forecast errors based on multivariate NSTPNT method

Dong

2021

Prot Control Mod Power Syst

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The impact of wind power forecast errors (WPFEs) on power system reliability can be quantified by a sensitivity model, which helps to determine the importance of different wind farms. However, the unknown distribution and correlation of WPFEs make it difficult to calculate the reliability sensitivity. The existing univariate non-standard third-order polynomial normal transformation (NSTPNT) expresses the reliability sensitivity of WPFEs by a normal random variable with explicit distribution, and is not suitable for multiple wind farms with correlated forecast errors. In this paper, the univariate NSTPNT method is extended to the multivariate by deriving the analytical expression of the correlation coefficients before and after the transformation, to establish the transformation between the WPFEs and a normal random vector (RV) with the specific correlation. A reliability sensitivity model to the WPFEs expressed to the normal RV is then proposed. The numerical results validate the accuracy of the proposed multivariate NSTPNT and the sensitivity model. The maximum relative error for using the sensitivity to approximate the change of reliability with distribution parameters of the WPFEs is less than 2.42%. The necessity of considering the correlation of WPFEs is analyzed. The maximum relative error of the sensitivity reaches 83% when the correlation is ignored.

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Section: Introductionmentioning

confidence: 99%

Reliability sensitivity of wind power system considering correlation of forecast errors based on multivariate NSTPNT method

Dong

2021

Prot Control Mod Power Syst

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“…Because these atmospheres are subject to dramatic changes in the velocity and direction of wind, it is difficult to anticipate how the resulting wakes will form and what kind of power output should be expected. In Yang et al (2019), an analysis of the impact of spatial heterogeneity in wind farm flow is presented for a site within complex terrain. This study showed that using averaged values of wind conditions caused short-term wind power forecasting to be less accurate, due to spatial heterogeneity within the wind field and the variability of wind turbine power curves.…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of a wake model for spatially heterogeneous flow

et al. 2021

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Abstract. Methods of turbine wake modeling are being developed to more accurately account for spatially variant atmospheric conditions within wind farms. Most current wake modeling utilities are designed to apply a uniform flow field to the entire domain of a wind farm. When this method is used, the accuracy of power prediction and wind farm controls can be compromised depending on the flow-field characteristics of a particular area. In an effort to improve strategies of wind farm wake modeling and power prediction, FLOw Redirection and Induction in Steady State (FLORIS) was developed to implement sophisticated methods of atmospheric characterization and power output calculation. In this paper, we describe an adapted FLORIS model that features spatial heterogeneity in flow-field characterization. This model approximates an observed flow field by interpolating from a set of atmospheric measurements that represent local weather conditions. The objective of this method is to capture heterogeneous atmospheric effects caused by site-specific terrain features, without explicitly modeling the geometry of the wind farm terrain. The implemented adaptations were validated by comparing the simulated power predictions generated from FLORIS to the actual recorded wind farm output from the supervisory control and data acquisition (SCADA) recordings and large eddy simulations (LESs). When comparing the performance of the proposed heterogeneous model to homogeneous FLORIS simulations, the results show a 14.6 % decrease for mean absolute error (MAE) in wind farm power output predictions for cases using wind farm SCADA data and a 18.9 % decrease in LES case studies. The results of these studies also indicate that the efficacy of the proposed modeling techniques may vary with differing site-specific operational conditions. This work quantifies the accuracy of wind plant power predictions under heterogeneous flow conditions and establishes best practices for atmospheric surveying for wake modeling.

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“…This method is easy to model and has strong adaptability to sample learning. It has been widely used in the wind power industry and other projects that require prediction [11]. An et al used the particle swarm optimization algorithm (PSO) to optimize the extreme learning machine (ELM) and combined them with the Adaboost integrated learning model to make a short-term prediction of wind power [12].…”

Section: Introductionmentioning

confidence: 99%

Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

Jiang

Chen

et al. 2021

Sustainability

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Improving the accuracy of wind power forecasting is an important measure to deal with the uncertainty and volatility of wind power. Wind speed and wind direction are the most important factors affecting the power generation of wind turbines. In this paper, we propose a wind power forecasting method that combines the sparrow search algorithm (SSA) with the deep extreme learning machine (DELM). Based on the DELM model, the length of the time series’ influence on the performance of the neural network is validated through the comparison of the forecast error indexes, and the optimal time series length of the wind power is determined. The sparrow search algorithm is used to optimize its parameters to solve the problem of random changes in model input weights and thresholds. The proposed SSA-DELM model is validated using the measured data of a certain wind turbine, and various forecasting indexes are compared with several current wind power forecasting methods. The experimental results show that the proposed model has better performance in ultra-short-term wind power forecasting, and its coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) are 0.927, 69.803, and 115.446, respectively.

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The impact of wind field spatial heterogeneity and variability on short-term wind power forecast errors

Cited by 12 publications

References 35 publications

Reliability sensitivity of wind power system considering correlation of forecast errors based on multivariate NSTPNT method

Reliability sensitivity of wind power system considering correlation of forecast errors based on multivariate NSTPNT method

Design and analysis of a wake model for spatially heterogeneous flow

Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

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