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Lin Ye
College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

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Journal article
Published: 09 August 2021 in Renewable Energy
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To achieve a high penetration of renewable energy integration, an effective solution is to explore the interdependence between numerical weather prediction (NWP) data and historical wind power to improve prediction accuracy. This paper proposes a novel combined approach for wind power prediction. The characteristics of NWP and historical wind power data are extracted by using the feature extraction technique, the predictor is designed based on extreme learning machine (ELM) and least squares support vector machine (LSSVM) model, and then key parameters of the prediction models are optimized by improving cuckoo search (ICS) to obtain a reliable value, which is defined as the pre-combined prediction value (PPA). To obtain a reliable result, a variance strategy is developed to allocate the weights of the pre-combined prediction model to obtain the final predicted values. Four seasons dataset collected from regional wind farms in China is utilized as a benchmark experiment to evaluate the effectiveness of the proposed approach. The results of comprehensive numerical cases with different seasons show that the proposed approach, which considers multiple-error metrics, including error metrics, accuracy rate, qualification rate, and improvement percentages, achieves higher accuracy than other benchmark prediction models.

ACS Style

Peng Lu; Lin Ye; Yongning Zhao; Binhua Dai; Ming Pei; Zhuo Li. Feature extraction of meteorological factors for wind power prediction based on variable weight combined method. Renewable Energy 2021, 179, 1925 -1939.

AMA Style

Peng Lu, Lin Ye, Yongning Zhao, Binhua Dai, Ming Pei, Zhuo Li. Feature extraction of meteorological factors for wind power prediction based on variable weight combined method. Renewable Energy. 2021; 179 ():1925-1939.

Chicago/Turabian Style

Peng Lu; Lin Ye; Yongning Zhao; Binhua Dai; Ming Pei; Zhuo Li. 2021. "Feature extraction of meteorological factors for wind power prediction based on variable weight combined method." Renewable Energy 179, no. : 1925-1939.

Review
Published: 06 August 2021 in Applied Energy
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The integration of large-scale wind power introduces issues in modern power systems operations due to its strong randomness and volatility. These issues can be resolved via wind power forecasting that can provide comprehensive future information about wind power uncertainties. This paper presents a timely and comprehensive review of meta-heuristic algorithms in the framework of wind power forecasting. The framework is based on the auxiliary layer, forecasting base layer, and core layer. The auxiliary layer, such as the data-decomposition layer, decomposes the wind power time series into many relatively stationary subseries, and uses prediction models, including artificial neural networks (ANNs) and machine learning (ML). The core layer is based on meta-heuristic algorithms, which include evolutionary-based algorithms, physics-based algorithms, human-based algorithms, swarm-based algorithms, hybrid algorithms, and multi-objective optimization algorithms. These algorithms aim to search for the optimal solutions under constraints, which is highly significant for optimizing the key parameters of the prediction models. Besides, multiple error evaluation metrics, e.g., deterministic, uncertainty, and testing methods used in the field of wind power prediction are described. A quantitative analysis focusing on their advantages, disadvantages, forecasting accuracy, and computational costs are also provided. Finally, a few open research issues and trends related to the topic are discussed, which can contribute to improving the understanding of each wind power forecasting method. In general, this review paper provides valuable insights to wind power engineers.

ACS Style

Peng Lu; Lin Ye; Yongning Zhao; Binhua Dai; Ming Pei; Yong Tang. Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges. Applied Energy 2021, 301, 117446 .

AMA Style

Peng Lu, Lin Ye, Yongning Zhao, Binhua Dai, Ming Pei, Yong Tang. Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges. Applied Energy. 2021; 301 ():117446.

Chicago/Turabian Style

Peng Lu; Lin Ye; Yongning Zhao; Binhua Dai; Ming Pei; Yong Tang. 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges." Applied Energy 301, no. : 117446.

Journal article
Published: 11 January 2020 in Journal of Cleaner Production
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The integration of a large number of wind farms poses big challenges to the secure and economical operation of power systems, and ultra-short-term wind power forecasting is an effective solution. However, traditional approaches can only predict an individual wind farm power at a time and ignore the spatio-temporal correlation of wind farms. In this paper, a novel ultra-short-term forecasting framework based on spatio-temporal (ST) analysis, multi-output support vector machine (MSVM) and grey wolf optimizer (GWO) which defined ST-GWO-MSVM model is proposed to predict the output wind power from multiple wind farms; the ST-GWO-MSVM model includes data analysis stage, parameters optimization stage, and modeling stage. In the data analysis stage, the person correlation coefficient and partial autocorrelation function are used to analyze the spatio-temporal correlation of wind power. In the parameters optimization stage, to avoid obtaining the unreliable forecasting results due to the parameters are chosen empirically, the GWO algorithm is used to optimize the kernel function parameters of the MSVM model. In the modeling stage, an innovative forecasting model with optimal parameter of MSVM is proposed to predict the output wind power of 15 wind farms. Results show that the performance of ST-GWO-MSVM is better than other benchmark models in terms of multiple-error metrics including fractional bias, direction accuracy, and improvement percentages.

ACS Style

Peng Lu; Lin Ye; Wuzhi Zhong; Ying Qu; Bingxu Zhai; Yong Tang; Yongning Zhao. A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. Journal of Cleaner Production 2020, 254, 119993 .

AMA Style

Peng Lu, Lin Ye, Wuzhi Zhong, Ying Qu, Bingxu Zhai, Yong Tang, Yongning Zhao. A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy. Journal of Cleaner Production. 2020; 254 ():119993.

Chicago/Turabian Style

Peng Lu; Lin Ye; Wuzhi Zhong; Ying Qu; Bingxu Zhai; Yong Tang; Yongning Zhao. 2020. "A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy." Journal of Cleaner Production 254, no. : 119993.

Journal article
Published: 25 November 2019 in Journal of Cleaner Production
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A multi-heterogeneous energy generation power system (MHEGPS) including wind power, solar power, hydro power, gas power, storage and so on, is an effective way to cope with the increasing demand for energy. In the paper, a novel framework of power hub is introduced to analyze the model of the multi-heterogeneous energy generation power system. A power flow coupled matrix model for the power hub of the multi-heterogeneous energy generation power system is proposed to represent characteristics and connection relationships of energy converters and storages. Further, an optimal operation model to minimize the operation cost is put forward considering the different scenes based on the power hub. Case studies are carried out in different scenes for the multi-heterogeneous energy generation power system. Power generation systems and storage units in different electricity tariff are also discussed. Results show that the electric energy storage units could realize the time sequence shifting of electric energy by charging and discharging to reduce the operation cost of the whole power hub. The real time price (RTP) is superior to the time-of-use price (TOUP) in all cases. Simulation results prove that the optimal operation model is effective and the proposed modeling method provides an alternative method for the complex hybrid energy power system.

ACS Style

Yanxiang Yao; Lin Ye; Xiaoxu Qu; Peng Lu; Yongning Zhao; Weisheng Wang; Yue Fan; Ling Dong. Coupled model and optimal operation analysis of power hub for multi-heterogeneous energy generation power system. Journal of Cleaner Production 2019, 249, 119432 .

AMA Style

Yanxiang Yao, Lin Ye, Xiaoxu Qu, Peng Lu, Yongning Zhao, Weisheng Wang, Yue Fan, Ling Dong. Coupled model and optimal operation analysis of power hub for multi-heterogeneous energy generation power system. Journal of Cleaner Production. 2019; 249 ():119432.

Chicago/Turabian Style

Yanxiang Yao; Lin Ye; Xiaoxu Qu; Peng Lu; Yongning Zhao; Weisheng Wang; Yue Fan; Ling Dong. 2019. "Coupled model and optimal operation analysis of power hub for multi-heterogeneous energy generation power system." Journal of Cleaner Production 249, no. : 119432.

Journal article
Published: 01 May 2019 in IEEE Transactions on Power Systems
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Large-scale wind power cluster with distributed wind farms has generated the active power dispatch and control problems in the power system. In this paper, a novel hierarchical model predictive control (HMPC) strategy based on dynamic active power dispatch is proposed to improve wind power schedule and increase wind power accommodation. The strategy consists of four layers with refined time scales, including intra-day dispatch, real-time dispatch, cluster optimization and wind farm modulation layer. A dynamic grouping strategy is specifically developed to allocate the schedule for wind farms in cluster optimization layer. In order to maximize wind power output, downward spinning reserve and transmission pathway utilization are developed in wind farm modulation layer. Meanwhile, a stratification analysis approach for ultra-short-term wind power forecasting error is presented as feedback correction to increase forecasting accuracy. The proposed strategy is evaluated by a case study in the IEEE network with wind power cluster integration. Results show that wind power accommodation has been enhanced by use of the proposed HMPC strategy, compared with the conventional dispatch and allocation methods.

ACS Style

Lin Ye; Cihang Zhang; Yong Tang; Wu Zhi Zhong; Yongning Zhao; Peng Lu; Bing Xu Zhai; Hai Bo Lan; Zhi Li; Ying Qu; Bohao Sun; Huadong Sun; Boyu He. Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration. IEEE Transactions on Power Systems 2019, 34, 4617 -4629.

AMA Style

Lin Ye, Cihang Zhang, Yong Tang, Wu Zhi Zhong, Yongning Zhao, Peng Lu, Bing Xu Zhai, Hai Bo Lan, Zhi Li, Ying Qu, Bohao Sun, Huadong Sun, Boyu He. Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration. IEEE Transactions on Power Systems. 2019; 34 (6):4617-4629.

Chicago/Turabian Style

Lin Ye; Cihang Zhang; Yong Tang; Wu Zhi Zhong; Yongning Zhao; Peng Lu; Bing Xu Zhai; Hai Bo Lan; Zhi Li; Ying Qu; Bohao Sun; Huadong Sun; Boyu He. 2019. "Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration." IEEE Transactions on Power Systems 34, no. 6: 4617-4629.

Journal article
Published: 30 January 2019 in Computers & Electrical Engineering
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With the large-scale wind power penetration, probabilistic power flow plays an important role in power system uncertainty analysis. This paper proposes a novel Gaussian Mixture Model to fit the probability density distribution of short-term wind power forecasting errors with the multimodal and asymmetric characteristics. Cumulants are used to calculate mean value and deviation of state variables for each random combination result of Gaussian components. Probabilistic power flow is acquired by summing up all the Gaussian probability density functions with weights counted by the product of Gaussian components in each random combination. Parallel probabilistic power flow computation by use of the Gaussian Mixture Model and cumulants could simplify the calculation procedure in large scale of integrated wind power network. Case studies are carried out in modified IEEE 57-bus test system to verify advantages of the novel approach. Results show that the computational efficiency and accuracy are well improved in the proposed method.

ACS Style

Lin Ye; Yali Zhang; Cihang Zhang; Peng Lu; Yongning Zhao; Boyu He. Combined Gaussian Mixture Model and cumulants for probabilistic power flow calculation of integrated wind power network. Computers & Electrical Engineering 2019, 74, 117 -129.

AMA Style

Lin Ye, Yali Zhang, Cihang Zhang, Peng Lu, Yongning Zhao, Boyu He. Combined Gaussian Mixture Model and cumulants for probabilistic power flow calculation of integrated wind power network. Computers & Electrical Engineering. 2019; 74 ():117-129.

Chicago/Turabian Style

Lin Ye; Yali Zhang; Cihang Zhang; Peng Lu; Yongning Zhao; Boyu He. 2019. "Combined Gaussian Mixture Model and cumulants for probabilistic power flow calculation of integrated wind power network." Computers & Electrical Engineering 74, no. : 117-129.

Journal article
Published: 13 October 2018 in Renewable Energy
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With the development of large-scale wind power integration, wind curtailment appears around the world, especially in China. It is essential to perform the assessment on capability of wind power accommodation (ACWPA) by calculating the maximum admissible wind power which plays an important role in system planning and operation. This paper proposes a long-term assessment on the maximum level of wind power installed capacity in future years based on peak power regulation, with consideration of potential wind curtailment. Meanwhile, a short-term assessment based on wind power forecasting is developed through day-ahead unit commitment to get admissible zone of wind power in grid operation. In particular, the extreme wind variation scenario (EWVS) calculated by quadratic programming (QP) is applied to optimize upper limit of admissible zone. Case studies are carried out to analyze wind power characteristics in a province in Southern China. Results show that the proposed approaches can effectively and accurately evaluate the capability of wind power accommodation in regional power grids.

ACS Style

Lin Ye; Cihang Zhang; Hui Xue; Jiachen Li; Peng Lu; Yongning Zhao. Study of assessment on capability of wind power accommodation in regional power grids. Renewable Energy 2018, 133, 647 -662.

AMA Style

Lin Ye, Cihang Zhang, Hui Xue, Jiachen Li, Peng Lu, Yongning Zhao. Study of assessment on capability of wind power accommodation in regional power grids. Renewable Energy. 2018; 133 ():647-662.

Chicago/Turabian Style

Lin Ye; Cihang Zhang; Hui Xue; Jiachen Li; Peng Lu; Yongning Zhao. 2018. "Study of assessment on capability of wind power accommodation in regional power grids." Renewable Energy 133, no. : 647-662.

Journal article
Published: 19 June 2018 in Energies
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With large scale wind integration and increasing wind penetration in power systems, relying solely on conventional generators for frequency control is not enough to satisfy system frequency stability requirements. It is imperative that wind power have certain capabilities to participate in frequency control by cooperating with conventional power sources. Firstly, a multi-area interconnected power system frequency response model containing wind power clusters and conventional generators is established with consideration of several nonlinear constraints. Moreover, a distributed model predictive control (DMPC) strategy considering Laguerre functions is proposed, which implements online rolling optimization by using ultra-short-term wind power forecasting data in order to realize advanced frequency control. Finally, a decomposition-coordination control algorithm considering Nash equilibrium is presented, which realizes online fast optimization of multivariable systems with constraints. Simulation results demonstrate the feasibility and effectiveness of the proposed control strategy and algorithm.

ACS Style

Bohao Sun; Yong Tang; Lin Ye; Chaoyu Chen; Cihang Zhang; Wuzhi Zhong. A Frequency Control Strategy Considering Large Scale Wind Power Cluster Integration Based on Distributed Model Predictive Control. Energies 2018, 11, 1600 .

AMA Style

Bohao Sun, Yong Tang, Lin Ye, Chaoyu Chen, Cihang Zhang, Wuzhi Zhong. A Frequency Control Strategy Considering Large Scale Wind Power Cluster Integration Based on Distributed Model Predictive Control. Energies. 2018; 11 (6):1600.

Chicago/Turabian Style

Bohao Sun; Yong Tang; Lin Ye; Chaoyu Chen; Cihang Zhang; Wuzhi Zhong. 2018. "A Frequency Control Strategy Considering Large Scale Wind Power Cluster Integration Based on Distributed Model Predictive Control." Energies 11, no. 6: 1600.

Journal article
Published: 21 March 2018 in Energies
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Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models.

ACS Style

Peng Lu; Lin Ye; Bohao Sun; Cihang Zhang; Yongning Zhao; Jingzhu Teng. A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA. Energies 2018, 11, 697 .

AMA Style

Peng Lu, Lin Ye, Bohao Sun, Cihang Zhang, Yongning Zhao, Jingzhu Teng. A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA. Energies. 2018; 11 (4):697.

Chicago/Turabian Style

Peng Lu; Lin Ye; Bohao Sun; Cihang Zhang; Yongning Zhao; Jingzhu Teng. 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA." Energies 11, no. 4: 697.

Journal article
Published: 17 January 2018 in IEEE Transactions on Power Systems
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The ever-increasing number of wind farms has brought challenges and opportunities in wind power forecasting techniques to take advantage of interdependencies between hundreds of spatially distributed wind farms, e.g., over a region. In this paper, a Sparsity-Controlled Vector Autoregressive (SC-VAR) model is introduced to obtain sparse model structures in a spatio-temporal wind power forecasting framework by reformulating the original VAR model into a constrained Mixed Integer Non-Linear Programming (MINLP) problem. It allows controlling the sparsity of the coefficient matrices in direct manner. However this original SC-VAR is difficult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to benefit model building and forecasting, the original SC-VAR is modified and a Correlation-Constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms. Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set of traditional local methods and spatio-temporal methods. Results show the proposed CCSC-VAR has better overall performance than both the original SC-VAR and other benchmark methods, taking into account all evaluation indicators, including sparsity-control ability, sparsity, accuracy and efficiency.

ACS Style

Yongning Zhao; Lin Ye; Pierre Pinson; Yong Tang; Peng Lu. Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting. IEEE Transactions on Power Systems 2018, 33, 5029 -5040.

AMA Style

Yongning Zhao, Lin Ye, Pierre Pinson, Yong Tang, Peng Lu. Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting. IEEE Transactions on Power Systems. 2018; 33 (5):5029-5040.

Chicago/Turabian Style

Yongning Zhao; Lin Ye; Pierre Pinson; Yong Tang; Peng Lu. 2018. "Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting." IEEE Transactions on Power Systems 33, no. 5: 5029-5040.

Journal article
Published: 19 June 2017 in IEEE Transactions on Sustainable Energy
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Regardless of the rapid development of wind power capacity installation around the world, wind curtailment is a severe problem to be solved. Wind curtailment can cause abundant outliers and change the original characteristics of operation data in wind farms. Power curve cannot be accurately modeled with these outliers and consequently wind power forecasting as well as other applications in power system will be negatively affected. In this paper, the characteristics of the outliers caused by wind curtailment are analyzed. Then, a data-driven outlier elimination approach combining quartile method and density-based clustering method is proposed. First, the quartile method is used twice for eliminating sparse outliers. Then density-based spatial clustering of applications with noise method is applied to eliminate stacked outliers. A case study is carried out by modeling the power curves of a wind farm and 20 wind turbines in this wind farm. The accuracy of power curve modeling is significantly improved and the elimination procedure can be completed in a very short time, indicating that the proposed methods are effective and efficient for eliminating outliers. The performance of the methods is insensitive to their parameters and can be directly used in different cases without tuning parameters, both for wind turbines and wind farms.

ACS Style

Yongning Zhao; Lin Ye; Weisheng Wang; Huadong Sun; Yuntao Ju; Yong Tang. Data-Driven Correction Approach to Refine Power Curve of Wind Farm Under Wind Curtailment. IEEE Transactions on Sustainable Energy 2017, 9, 95 -105.

AMA Style

Yongning Zhao, Lin Ye, Weisheng Wang, Huadong Sun, Yuntao Ju, Yong Tang. Data-Driven Correction Approach to Refine Power Curve of Wind Farm Under Wind Curtailment. IEEE Transactions on Sustainable Energy. 2017; 9 (1):95-105.

Chicago/Turabian Style

Yongning Zhao; Lin Ye; Weisheng Wang; Huadong Sun; Yuntao Ju; Yong Tang. 2017. "Data-Driven Correction Approach to Refine Power Curve of Wind Farm Under Wind Curtailment." IEEE Transactions on Sustainable Energy 9, no. 1: 95-105.