Search

The actuator disc model (ADM) incorporated into the k-ε turbulence model of EI Kasmi is able to simulate the wind farm wakes with high fidelity, which is essential for layout optimization and the evaluation of power production in modern wind farms. However, the effects of the parameter C4ε in the k-ε model of EI Kasmi on turbulent kinetic energy (TKE) in the superposition areas and the recovery of wind farm wake velocity have rarely been studied. To this end, the k-ε model of EI Kasmi is coupled with the advanced ADM in PHOENICS to investigate the influence of the parameter C4ε on the wake simulation of wind farms (e.g., two offshore wind farms, one onshore wind farm). It is concluded that a decreased parameter C4ε makes the generation range of TKE in the vicinity of the rotor smaller, but the TKE near the rotor becomes larger, and the wake recovery rate of the downstream turbine is less affected by the near wake. As the interwind turbine spacing increases, the influence area of TKE in the wake region of each downstream machine gradually reduces, and atmospheric turbulence plays a dominant role in wake recovery. A decreased parameter C4ε can effectively promote the inflow wind velocity and power output of downstream turbines. The power output simulated compares well with the measured power value when the parameter C4ε = 0.15. [ABSTRACT FROM AUTHOR]

Multivariable wind turbine power curve model is crucial for the performance degradation evaluation of wind turbine. However, the existing models have low modeling accuracy without considering the wind energy capture difference caused by segment control, and the inputs cannot represent the actual wind energy resources owing to the neglect of the short-term self-dependence of environmental parameters, which makes it difficult to ensure the performance degradation evaluation accuracy of wind turbine. To address these problems, a multivariable wind turbine power curve modeling method considering segment control differences and short-time self-dependence is proposed. First, an abnormal data cleaning method based on timing matching and bidirectional quartile algorithm is proposed to clean the abnormal data. Then, the multivariate wind turbine power curve model based on piece-wise regression of multiple environmental parameters is constructed, and applied to the performance degradation evaluation of wind turbine. The results demonstrated that the proposed abnormal data cleaning method can effectively solve the problem that the data categories of transition region between abnormal and normal data cannot be identified. Meanwhile, the proposed multivariable wind turbine power curve model can effectively improve the modelling accuracy and ensure the performance degradation evaluation accuracy of wind turbine under different wind energy resources. • The timing matching and bidirectional quartile algorithm for abnormal data cleaning. • A multivariable power curve model by piece-wise regression of multiple parameters. • The proposed model considers wind energy capture difference of segment control. • The proposed model includes short-time self-dependence of environmental parameters. • Correct evaluation of wind turbine performance degradation under different resources. [ABSTRACT FROM AUTHOR]

Accessibility estimation is significant to the offshore wind farm operation and maintenance (O&M) due to the extremely limited weather window and its sensitive effects on O&M tasks. Wave forecasting can be one solution to help maintenance decision‐making. However, the uncertain and dynamic properties of wave forecasts are seldom considered in the accessibility estimation process. This paper presents an uncertain accessibility estimation method based on a multi‐step probabilistic wave height forecasting (MPWHF) model and Monte Carlo simulation. First, an MPWHF model is proposed using the wavelet decomposition and the sequence to sequence (Seq2Seq) network with quantile outputs. Second, the O&M missions are randomly given a start time and simulated in the O&M flow chart by the Monte Carlo method. Finally, several access indexes, including accessibility probability, delay time, and delay probability, are evaluated based on the simulation results. Verification of the proposed MPWHF model and uncertain accessibility estimation is based on 7‐year observation data of a buoy station. The results show that the MPWHF model outperforms other counterparts and the probability of offshore accessibility is nonlinearly dependent on the weather limits and the O&M required time. [ABSTRACT FROM AUTHOR]

Wake effect leads to the power loss and fatigue damage increase on wind turbines. Therefore, wind turbine wake is seriously considered as one of the key parameters in the micro-sitting and layout optimization. Interestingly, according to the recent research of observations and simulations, similar to individual wind turbine wake, the wake of large-scale wind farm also exists which has significant impact on surrounding wind farms and the local meteorology. However, few research focused on quantitative study of wind farm wake. As a result, in this research, firstly, the numerical simulation for the neutral atmospheric flow has been carried out and preliminarily verified. By using actuator disc model, the horizontal and vertical velocity profiles behind the wind farm are analyzed. Secondly, the effective wind-farm drag coefficient has been decomposed into two components, which represent the vertical and horizontal momentum flux, respectively. By analyzing the drag coefficient, the momentum transport of wind farm was studied. The average velocity behind wind farm was also calculated, from which it was found that, with the length scale increasing, the wind farm starts to absorb energy from upper atmosphere. Lastly, the velocity decay behind wind farm can also be modeled by the formulation of Jensen wake model. [ABSTRACT FROM AUTHOR]

The wind speed sequences at different spatial positions have a certain spatiotemporal coupling relationship. It is of great significance to analyze the clustering effect of the wind farm(s) and reduce the adverse impact of large-scale wind power integration if we can grasp this relationship at multiple scales. At present, the physical method cannot optimize the time-shifting characteristics in real time, and the research scope is concentrated on the wind farm. The statistical method cannot quantitatively describe the temporal relationship and the speed variation among wind speed sequences at different spatial positions. To solve the above problems, a quantification method of wind speed time-shifting characteristics based on wind process is proposed in this paper. Two evaluation indexes, the delay time and the decay speed, are presented to quantify the time-shifting characteristics. The effectiveness of the proposed method is verified from the perspective of the correlation between wind speed sequences. The time-shifting characteristics of wind speed sequences under the wind farms scale and the wind turbines scale are studied, respectively. The results show that the proposed evaluation method can effectively achieve the quantitative analysis of time-shifting and could improve the results continuously according to the actual wind conditions. Besides, it is suitable for any spatial scale. The calculation results can be directly applied to the wind power system to help obtain the more accurate output of the wind farm. [ABSTRACT FROM AUTHOR]

Accurate modeling of the wind turbine power curve (WTPC) is crucial for calculating the theoretical wind power for the integration of renewable energies and electric vehicles. However, existing WTPC modeling methods cannot simultaneously guarantee high modeling accuracy and efficiency for data samples with a large amount of accumulated abnormal data. To address this problem, this paper presents a WTPC modeling method based on interval extreme probability density, which does not require complicated and time-consuming abnormal data cleaning and can significantly improve the modeling efficiency while guaranteeing high modeling accuracy. To verify the applicability and validity of the proposed method, firstly, WTPC models were constructed using actual operation data from 12 wind turbines in a Chinese wind farm and were compared with the manufacturer's power curve and with WTPC modeling methods based on abnormal data cleaning algorithms. Secondly, the theoretical wind power was calculated and compared with the commonly used manufacturer's power curve. The results demonstrated that the proposed WTPC modeling method has high modeling accuracy and efficiency and can improve the calculation accuracy of the theoretical wind power effectively, providing more reliable data support for the integrated planning of renewable energies and electric vehicles in nearby regions. • Wind turbine power curve modeling based on interval extreme probability density. • A modeling method without complicated and time-consuming abnormal data cleaning. • Wind turbine power curves can be modeled with high accuracy and efficiency. • The proposed model supports the planning of renewable energies and electric vehicles. [ABSTRACT FROM AUTHOR]

Summary: Wind power prediction (WPP) is one of the key techniques to eliminate the adverse impact of large proportion of wind power on power grid, while it faces the main problem of low prediction accuracy. The main contribution of this paper is a new data sample division method based on China's 24 solar terms (24STs) for wind power prediction. The 24STs is an astronomical and meteorological calendar summarized by ancient Chinese, and it more accurately reflects the law of the seasonal changes, the phenomenon of phenology, and climate characteristics compared with the calendar month. In the paper, firstly, the reasonableness of the division of 24STs is verified from the point of temperature and wind speed; secondly, four meteorological factors related to wind power output are used for similarity calculation, and the results show that the data samples in the same STs have higher similarity than those in the same semi‐month based on Gregorian; finally, the WPP models are established with the 24STs sample division method and the semi‐month sample division method, and the case proves that the 24STs method has higher prediction accuracy because it can grasp the seasonal changes law of some meteorological elements more accurately. [ABSTRACT FROM AUTHOR]

Unsteady motion of the atmosphere incurs nonlinear and spatiotemporally coupled uncertainties in the wind power prediction (WPP) of multiple wind farms. This brings both opportunities and challenges to wind power probabilistic forecasting (WPPF) of a wind farm cluster or region, particularly when wind power is highly penetrated within the power system. This paper proposes an Improved Deep Mixture Density Network (IDMDN) for short-term WPPF of multiple wind farms and the entire region. In this respect, a deep multi-to-multi (m2m) mapping Neural Network model, which adopts the beta kernel as the mixture component to avoid the density leakage problem, is established to produce probabilistic forecasts in an end-to-end manner. A novel modified activation function and several general training procedures are then introduced to overcome the unstable behavior and NaN (Not a Number) loss issues of the beta kernel function. Verification of IDMDN is based on an open-source dataset collected from seven wind farms, and comparison results show that the proposed model improves the WPPF performance at both wind farm and regional levels. Furthermore, a laconic and accurate probabilistic expression of predicted power at each time step is produced by the proposed model. [ABSTRACT FROM AUTHOR]

Highlights • Mid-to-long term wind and solar joint generation prediction model established. • Copula for extracting nonlinear and non-Gaussian distributed key weather factors. • Long short term memory network to construct the prediction model with fewer data. • The proposed method is able to improve prediction accuracy validated by real cases. Abstract The accurate estimation of mid-to-long term wind and photovoltaic power generation is important to the power grid's plan improvement, dispatching optimization, management development, and consumption enhancement. These constitute key factors for the realization of power mutual assistance and complementary dispatch of power generation in the broad area of renewable energy. However, owing to the large time scale of mid-to-long term prediction, the low accuracy of weather prediction, the limited data samples of historical power generation, and the significant difference between power generation prediction and short-term power prediction, short-term power prediction technology cannot be directly copied. Thus, the industry has not established yet an effective approach for mid-to-long term wind and photovoltaic power generation predictions. To solve these problems, this study proposed a method for the mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network to achieve an effective extraction of the key meteorological factors that affect power generation owing to nonlinear effects and tendencies, and to deeply exploit the long-term dependencies and tendencies from the limited available data samples. Therefore, the proposed approach is suitable for mid-to-long term wind and photovoltaic power generation prediction using limited data samples. Firstly, the non-linear effects and tendency correlation measurements of the copula function were used to extract the key meteorological factors that influence wind and photovoltaic power generation. Secondly, independent wind/photovoltaic prediction models were established based on long short term memory network using the best input condition obtained by comparing these models to the persistence model. Additionally, the independent wind/photovoltaic models were further compared to support vector machine model with the optimal input condition. Thirdly, the joint prediction models of wind and photovoltaic power generation based on long short term memory network were established using different inputs. The persistence model and the support vector machine model were used as benchmarks to compare the elicited performances. Finally, the validity and applicability of the proposed approach were extensively evaluated using actual data from wind farms and photovoltaic power stations in China and the United States. The independent and joint power generation prediction results demonstrated that the proposed approach outperforms both the persistence model and the support vector machine model, and can have widespread applicability in limited data sample cases. [ABSTRACT FROM AUTHOR]

Highlights • Quantitative complementarity evaluation indices for combined power generation. • Evaluation indices are presented from two perspectives: variability and ramp. • Typical day screening method for multi-dimension correlated meteorological data. • Complementarity can be improved by changing the ratio of solar and wind power. Abstract Complementarity between wind power, photovoltaic, and hydropower is of great importance for the optimal planning and operation of a combined power system. However, less attention has been paid to quantify the level of complementarity of wind power, photovoltaic and hydropower. Therefore, this paper proposes a complementarity evaluation method for wind power, photovoltaic and hydropower by thoroughly examining the fluctuation of the independent and combined power generation. To begin with, a series of evaluation indices are presented from two perspectives: random fluctuation between adjacent time slots and ramp within continuous time windows. Secondly, a method for screening the typical meteorological day is proposed by combining t-distributed stochastic neighbor embedding with K-means clustering algorithm to improve the evaluation of daily complementarity. This method can deal with the feature extraction of high-dimension and correlated data sequences. A case study is conducted for the validation of the proposed method, and the evaluation result of the proposed method was compared with that of the Kendall' rank correlation coefficient based on the Gaussian copula function. It is found that the proposed method has higher accuracy because it describes the complementarity from two aspects including fluctuation and ramp. It is also found from the study case that the optimum complementarity level for a certain case can be achieved by changing the ratio of photovoltaic and wind power. This work will provide reference for the optimization of power grid dispatch and power supply planning of combined power stations. [ABSTRACT FROM AUTHOR]

Highly wind penetrated future power system will couple to the variabilities and nonlinear correlations of wind. Reliable wind power forecasting (WPF) for a region is critical to the security and economics of the power system operation. Therefore, this paper proposes a multiscale WPF method by establishing a multi-to-multi (m2m) mapping network and the use of stacked denoising autoencoder (SDAE). The concerned forecast time horizon is 24–72 hours. First, multi-NWPs in a region are corrected based on SDAE to generate better inputs for the following regional WPF. Second, a number of SDAEs with diverse model parameters and input features are integrated into ensemble SDAE for predicting the wind power generated from various wind farms in a region. Two sets of data are utilized in this case study to validate the proposed method. The results show that the proposed m2m mapping and SDAE are able to capture the real correlations of wind at multiple sites, and outperform the other counterparts in terms of multi-NWPs correction as well as the WPF for both the region and individual concerned wind farm. Moreover, the ensemble SDAE performs better than any other individual regional WPF model. [ABSTRACT FROM PUBLISHER]

The accurate evaluation and fair comparison of wind farms power generation performance is of great significance to the technical transformation and operation and maintenance management of wind farms. However, problems exist in the evaluation indicator systems such as confusion, coupling and broadness, and the influence of wind energy resource differences not being able to be effectively eliminated, which makes it difficult to achieve the fair comparison of power generation performance among different wind farms. Thus, the evaluation indicator system and comprehensive evaluation method of wind farm power generation performance, including the influence of wind energy resource differences, are proposed in this paper to address the problems above, to which some new concepts such as resource conditions, ideal performance, reachable performance, actual performance, and performance loss are introduced in the proposed indicator system; the combination of statistical and comparative indicators are adopted to realize the quantitative evaluation, indicator decoupling, fair comparison, and loss attribution of wind farm power generation performance. The proposed comprehensive evaluation method is based on improved CRITIC (Criteria Importance though Intercrieria Correlation) weighting method, in which the uneven situation of different evaluation indicators and the comprehensive comparison of power generation performance among different wind farms shall be overcome and realized. Several sets of data from Chinese wind farms in service are used to validate the effectiveness and applicability of the proposed method by taking the comprehensive evaluation models based on CRITIC weighting method and entropy weighting method as the benchmarks. The results demonstrated that the proposed evaluation indicator system works in the quantitative evaluation and fair comparison of wind farm design, operation, and maintenance and traces the source of power generation performance loss. In addition, the results of the proposed comprehensive evaluation model are more in line with the actual power generation performance of wind farms and can be applied to the comprehensive evaluation and comparison of power generation performance of different wind farms. [ABSTRACT FROM AUTHOR]

In recent years, the accuracy of the wind power prediction has been urgently studied and improved to satisfy the requirements of power system operation. In this paper, the relevance vector machine (RVM)-based models are established to predict the wind power and its interval for a given confidence level. An NWP improvement module is presented considering the characteristic of NWP error. Moreover, two parameter optimization algorithms are applied to further improve the prediction model and to compare each performance. To take three wind farms in China as examples, the performance of two RVM-based models optimized, respectively, by genetic algorithm (GA) and particle swarm optimization (PSO) are compared with predictions based on a genetic algorithm-artificial neural network (GA-ANN) and support vector machine. Results show that the proposed models have better prediction accuracy with GA-RVM model and more efficient calculation with PSO-RVM. [ABSTRACT FROM AUTHOR]

Abstract: Wind power prediction is of great importance for the safety and stabilization of grids. The most important and difficult problem now is to enhance the prediction precision. BP (Backward Propagation) neural network has been used extensively in wind power prediction. But BP network is apt to getting into local minima and its convergence rate is slow. Tabu search is a kind of intelligent algorithm, which can achieve the global optimizations. This paper put forward a wind power prediction model of BP neural network optimized by tabu search algorithm with memory function. The result shows that with appropriate input parameters, the wind power prediction model of neural network based on tabu search algorithm can improve the prediction precision as well as the convergence rate. [Copyright &y& Elsevier]

Based on the characteristics of the power curves of wind turbine generator systems and the principles of the support vector machine (SVM), a piecewise support vector machine (PSVM) model is proposed in this article to improve the precision of short-term wind-power prediction systems. The operation data from a wind farm in north China are used to verify the proposed model, and the average mean error and root mean squared error of the PSVM model are 4.76% and 68.83 kW less than that of an SVM model respectively. Results of parameter optimization confirm the robustness of the PSVM model. [ABSTRACT FROM AUTHOR]

The quantitative evaluation of cluster wind power output volatility and source-load timing matching is vital to the planning and operation of the future power system dominated by new energy. However, the existing volatility evaluation methods of cluster wind power output do not fully consider timing volatility, or are not suitable for small sample data scenarios. Meanwhile, the existing source-load timing matching evaluation indicator ignores the impact of wind power permeability on the timing matching degree between wind power output and load. Therefore, the authors propose quantitative evaluation methods of cluster wind power output volatility and source-load timing matching in regional power grid. Firstly, the volatility-based smoothing coefficient is defined to quantitatively evaluate the smoothing effect of wind-farm cluster power output. Then, the source-load timing matching coefficient considering wind power permeability is proposed to quantitatively evaluate the timing matching degree of regional wind power output and load, and the corresponding function model of volatility-based smoothing coefficient and source-load timing matching coefficient is established. Finally, the validity and applicability of the proposed methods are verified by MATLAB software based on the actual power output of 10 wind farms and actual grid load in a certain grid dispatching cross-section of northeast China. The results demonstrated that the proposed volatility-based smoothing coefficient can accurately represent the smoothing effect of wind farm cluster power output while maintaining the volatility continuity of wind power output time series and without affect from the data sample size. The source-load timing matching coefficient can accurately characterize the difference in the timing matching degree between wind power output and grid load under different wind power permeability and the influence degree on grid load. [ABSTRACT FROM AUTHOR]

Wind speed spatial variation in the whole wind wheel rotation plane caused by wind shear and tower shadow effect becomes more obvious with the increase of rotor diameter and tower height, thus the hub height wind speed is not sufficient to represent the actual wind speed effect and the rotor equivalent wind speed should be used. However, the existing rotor equivalent wind speed calculation models do not consider wind turbine operation characteristics, which cannot match the actual power control effect, thus ignoring the power loss caused by actual power control deviation. To address this problem, a rotor equivalent wind speed calculation method based on equivalent power is proposed in this paper, which comprehensively considers wind shear, tower shadow and wind turbine operation characteristics. The rationality of the proposed model was qualitatively evaluated from different power control stages of wind turbine and the validity was quantitatively verified from the aspect of theoretical power calculation. The results demonstrated that the proposed model can effectively reflect the variation law of rotor equivalent wind speed under the actual control effect of different power control stages, which contains the power loss caused by actual power control deviation, thus improving the theoretical power calculation accuracy. • Rotor equivalent wind speed calculation model based on equivalent power is proposed. • Wind shear, tower shadow and wind turbine operation characteristics are considered. • The proposed model matches the actual power control effect of wind turbine. • The proposed model contains the power loss caused by actual power control deviation. • The proposed method can improve theoretical power accuracy verified by real cases. [ABSTRACT FROM AUTHOR]

To meet the increasing wind power forecasting (WPF) demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD) pre-calculated flow fields (CPFF)-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC)-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems. [ABSTRACT FROM AUTHOR]

Utilizing the secondary wind resources in cities and countryside is a significant way to promote wind power consumption and sustainable transportation. However, the probability of wind speed near the cut-in wind speed increases in such area and resulting in frequent on/off switches as well as large fatigue load of the wind turbine. To address these problems, this paper proposes a switching strategy of the low wind speed wind turbine based on real-time wind process prediction. First, the wavelet decomposition and neural network are employed to predict the time series of wind speed in a real-time manner. Second, based on the historical and predicted wind speed, the typical wind processes are extracted by using the x-means algorithm. Third, seven indexes are defined to quantify the characteristics of the wind process set before developing the corresponding switching strategy for each type of it. Data from NREL are used to validate the proposed models. The results show that, the proposed strategy increases the energy yield, reduces the number of wind turbine switches as well as the power fluctuation. Therefore, the proposed strategy is beneficial to both wind power projects development and electric vehicles charging. • Low wind speed turbine switching by real-time wind process prediction is proposed. • Typical wind process is established by clustering the historical and future winds. • Strategy adjusts dynamically based on seven presented wind characteristic indexes. • The proposed strategy increases the energy yield and reduces the switches. • The proposed strategy improves quantity and quality of wind power for EVs charging. [ABSTRACT FROM AUTHOR]

Accurate and credible operation data sets of wind and solar power stations are the basis of many research works. However, such data sets often contain abnormal data due to failure, maintenance, energy curtailment, etc. The existing identification methods fail to consider the operating characteristics of power stations and the forms of abnormal data, resulting in low identification ability. Therefore, an adaptive identification method of abnormal data (AIMAD) in the wind and solar power stations is proposed in this paper, including the bidirectional one-sided quartile method and double DBSCAN method to deal with unevenly distributed abnormal data; the improved K-means clustering method based on the distance between the cluster center and benchmark power curve to process the abnormal data that are densely accumulated and closely connected with normal data in the power scatter diagram. The proposed method can adjust adaptively according to the forms of abnormal data to realize accurate identification and has strong robustness for power stations. The operation data of 30 wind farms and 8 solar plants in China are taken as examples to verify the effectiveness and superiority of the proposed method. • BOQ and d-DBSCAN are proposed to deal with unevenly distributed abnormal data. • Improved K-means method is proposed for identifying stacked abnormal data. • AIMAD is proposed to realize accurate identification of abnormal data. • Operation data in 38 stations are used to verify the effectiveness of AIMAD. [ABSTRACT FROM AUTHOR]

Wind power forecasting (WPF) brings about decision risk to power system operation, because of its certain deviations. The uncertainty analysis of wind power forecasting (WPFUA) or wind power probabilistic forecasting (WPPF) provides probabilistic and confidence levels for decision makers to increase the forecasting certainty. In order to deeply and comprehensively understand WPFUA or WPPF model and then to optimize the forecasting model, the uncertain factors are firstly introduced in this paper. Secondly, the current WPFUA and WPPF methods are classified in terms of model input, modeling principle, express way and forecasting horizons. It summarizes the characteristics, merits and demerits, and application conditions of different methods. Thirdly, two advanced optimization strategies are discussed respectively from temporal and spatial perspectives. Finally, the development direction of uncertainty analysis technology in future is summarized. The objective of this review is, on one hand, to help WPPF users understand the model characteristics from different perspectives and select the most suitable one for various applications. On the other hand, it helps WPPF providers learn and optimize WPPF according to different characteristics. [ABSTRACT FROM AUTHOR]

Abstract: Relevance vector machine, a sparse probabilistic learning machine based on the kernel function, has excellent ability of prediction and generalization. It is proposed by this paper that the optimized relevance vector machine (ORVM) is a wind power interval forecasting model which is able to provide a certain prediction value and its possible fluctuation range at a given confidence level. The proposed model characterizes in insufficient sample training and uncertainty analysis and is greatly suitable to most of wind farms in China (newly built or large scale wind farms). First, a grouping mechanism has been used to divide wind turbines into several groups to establish forecasting model separately. Second, a selection method properly taking the characteristics of NWP error distribution into consideration was presented to improve forecasting accuracy of each group. Third, the parameters of the kernel function and initial value of iteration are determined by particle swarm optimization to further enhance forecasting accuracy. Two wind farms in China are involved in the process of primary data collection. The performance data obtained from ORVM models are tested against the predicted data generated by GA–ANN and SVM. Results show that the proposed model has better prediction accuracy, wider application scope and more efficient calculation. [Copyright &y& Elsevier]

The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%–18% and 3%–13%, respectively, which verified the effectiveness of the method. • Two-layer clustering strategy with variable time windows identifies wind power fluctuation types. • A simulation model of power system operating costs is established based on a data-driven approach. • Power system operating costs simulation accuracy varies with seasons and wind power fluctuations types. [ABSTRACT FROM AUTHOR]

Highlights • Probabilistic power curve model to quantify uncertainty of wind energy conversion. • Data clearing methods based on intuitive rules and density-based clustering. • Use of new model inputs – pitch angle and wind direction – improves model accuracy. • Novel index is proposed to evaluate performance of probabilistic power curve model. Abstract This paper proposes probabilistic wind turbine power curve (WTPC) models to quantify the uncertainties of energy conversion and highly scattered relationships of actual wind speed to power. First, new model inputs (i.e. pitch angle and wind direction) and novel data clearing methods are presented to improve the model accuracy, which is rare in the previous studies. Second, the models are established based on three nonparametric algorithms, i.e. Monte Carlo, neural network, and fuzzy clustering. Third, to fill the research gap on model evaluation, the desirable properties of a probabilistic WTPC model are defined as expected variance ratio (EVR), and this index is formulated by calculating the cumulative gaps between the simulated and actual power distribution in each wind speed segment. Data from two Chinese wind farms are used to validate and compare the proposed methods using the mainstream deterministic index and the proposed EVR. Results show that (i) new model inputs and data clearing methods are able to improve the accuracy for probabilistic models regardless of the afterwards modelling method; (ii) fuzzy outperforms other probabilistic models. [ABSTRACT FROM AUTHOR]

• Concept of quantile power curve of the wind turbine is proposed. • Quantile loss based neural network algorithm to establish the quantile power curve. • Index to evaluate the performance and uncertainty of a wind turbine is proposed. Wind turbine power curve is an indicator of wind turbine performance and important input of wind farm design or power prediction, therefore can serve the system planning and operation. However, a good power curve model is difficult to obtain because of the uncertain relationship between wind speed and its power output. Existing works focus on a deterministic model or use probabilistic distribution to represent such uncertain relation, which is not easy to be employed by the following decision-makers. This paper presents a novel concept termed as quantile power curve, which generates a series of power curves under any confidence level. Quantile loss based neural network algorithm is proposed to establish the quantile power curve. Index to measure the wind turbine performance and power generation uncertainty is also proposed based on the quantile power curve. Based on the operational data of a Chinese wind farm, the proposed model and index are validated and employed to estimate the wind energy yield when planning a system with wind, solar and electric vehicle charging loads. The results show that quantile power curve provides more comprehensive information about the uncertainty during the power generation process and helps to improve the renewable supply rate to charging loads. [ABSTRACT FROM AUTHOR]