Large-scale Wind Power Integration Research Articles

PMSG Wind turbine grid-connected pre-synchronization technology based on virtual impedance

With the integration of large-scale wind power, the inertia of the whole grid system is reduced. Therefore, virtual synchronous machine (VSG) technology has become a new strategy for wind power integration. In view of the fact that the VSG grid connected control of permanent magnet direct drive wind turbine does not consider the constraints of grid connection conditions, a scheme combining VSG technology with pre synchronization technology is proposed in this paper. VSG technology is still used in the PWM control of grid connected inverter. However, considering that there is no deviation between the phase angle of output voltage and the phase angle of grid voltage, it is impossible to judge whether the grid connection conditions are met or not when there is no deviation between the phase angle of output voltage and that of grid voltage.

ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified.

Ultra‐short‐term multi‐step wind power forecasting based on CNN‐LSTM

The fluctuation and intermission of large-scale wind power integration is a serious threat to the stability and security of the power system. Accurate prediction of wind power is of great significance to the safety of wind power grid connection. This study proposes a novel spatio-temporal correlation model (STCM) for ultra-short-term wind power prediction based on convolutional neural networks-long short-term memory (CNN-LSTM). The original meteorological factors at multi-historical time points of different sites throughout the target wind farm can be reconstructed into the input window of the model, and thus a new data reconstruction method is represented. CNN is used to extract the spatial correlation feature vectors of meteorological factors of different sites and the temporal correlation vectors of the meteorological features in ultra-short term, which are reconstructed in time series and used as the input data of LSTM. Then, LSTM extracts the temporal feature relationship between the historical time points for multi-step wind power forecasting. The STCM based on CNN-LSTM proposed in this study is suitable for wind farms that can collect meteorological factors at different locations. Taking the measured meteorological factors and wind power dataset of a wind farm in China as an example, four evaluation metrics of the CNN-LSTM model, CNN and LSTM individually used for multi-step wind power prediction, are obtained. The results show that the proposed STCM based on CNN-LSTM has better spatial and temporal characteristics extraction ability than the traditional structure model and can forecast the power of wind farm more accurately.

Frequency-Voltage Characteristics Concerned Optimal Dispatch

The rapid development of integrated wind power bring serious challenges to power system operation. In order to address the increasing wind power curtailment problem, the frequency-voltage characteristics concerned optimal dispatch for power system with large-scale wind power integration is proposed in this paper. Through integration of the optimization of frequency characteristics and voltage characteristics into the traditional power system economic dispatch model, the flexibility resources of generation side and load sides in power systems are mobilized to promote large-scale wind power accommodation. The formulated frequency-voltage characteristics concerned optimal dispatch mathematical model is a nonlinear programming model, which is solved by the GAMS platform. The numerical analysis for an actual provincial power grid indicates that the proposed frequency-voltage characteristics concerned optimal dispatch method can promote wind power utilization efficiency and improve the economy of power system operation.

Risk-averse robust interval economic dispatch for power systems with large-scale wind power integration

Risk-averse robust interval economic dispatch for power systems with large-scale wind power integration

Wind Speed Forecasting Using the Stationary Wavelet Transform and Quaternion Adaptive-Gradient Methods

Accurate wind speed forecasting is a fundamental requirement for advanced and economically viable large-scale wind power integration. The hybridization of the quaternion-valued neural networks and stationary wavelet transform has not been proposed before. In this paper, we propose a novel wind-speed forecasting model that combines the stationary wavelet transform with quaternion-valued neural networks. The proposed model represents wavelet subbands in quaternion vectors, which avoid separating the naturally correlated subbands. The model consists of three main steps. First, the wind speed signal is decomposed using the stationary wavelet transform into sublevels. Second, a quaternion-valued neural network is used to forecast wind speed components in the stationary wavelet domain. Finally, the inverse stationary wavelet transform is applied to estimate the predicted wind speed. In addition, a softplus quaternion variant of the RMSProp learning algorithm is developed and used to improve the performance and convergence speed of the proposed model. The proposed model is tested on wind speed data collected from different sites in China and the United States, and the results demonstrate that it consistently outperforms similar models. In the meteorological terminal aviation routine (METAR) dataset experiment, the proposed wind speed forecasting model reduces the mean absolute error, and root mean squared error of predicted wind speed values by 26.5% and 33%, respectively, in comparison to several existing approaches.

Wind power interval forecasting based on adaptive decomposition and probabilistic regularised extreme learning machine

The uncertainty of wind power brings great challenges to large-scale wind power integration. The conventional point forecasting of wind power is difficult to meet the demand of power grid planning and operation. A novel two-stage short-term hybrid wind power interval forecasting model is proposed in this study. In the first stage, the original wind power data is automatically decomposed and divided into three different classes based on the data preprocessing method combining variational mode decomposition with sample entropy. In the second stage, the prediction model is established using the probabilistic regularised extreme learning machine (PRELM) and particle swarm optimisation (PSO). In view of the different characteristics of the subseries in the above three classes, prediction intervals (PIs) are constructed for each subseries. A novel interval evaluation index is used as the objective function of PSO to optimise the PRELM output weight matrix to find the optimal PIs. Also the prediction results of each subseries are reconstructed to obtain the final wind power prediction results. The numerical results based on actual wind power data show that the proposed model shows better performance compared with other methods and can effectively improve the prediction accuracy.

Evaluation Method of Wind Speed Time-Shifting Characteristics at Multiple Scales and Its Application in Wind Power System

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.

Exploiting the Operational Flexibility of Wind Integrated Hybrid AC/DC Power Systems

Due to inherent uncertainty and volatility, large-scale wind power integration brings new challenges to the flexible operation of power systems. It calls for more flexibility not only from the generation side but also from the grid side, especially the hybrid HVAC/HVDC transmission grid. This paper proposes to exploit potential flexibility by controlling power flows through HVDC by the merits of its flexible regulation capability, as well as HVAC transmission switching (TS). To synergistically schedule them with generator units, a security-constrained economic dispatch (SCED) model for wind integrated hybrid AC/DC power system is presented to realize the co-optimization of generation and TS. Wherein, multiple uncertainties (wind power fluctuation and generator failure) are considered, and the linear power flow model of the DC grid is adopted. Then the model is transformed into a two-stage (normal state optimization and corrective dispatch verification) robust optimization (RO) model for iterative solution by the aid of the column-and-constraint generation (C&CG) algorithm. Case studies on the modified IEEE 14-bus and IEEE 118-bus systems show that TS can assist HVDC to optimize power flow distribution and provide more flexibility, meanwhile optimizing HVDC transmission power and TS can greatly reduce the operating cost of the whole system.

Wind power prediction using a novel model on wavelet decomposition-support vector machines-improved atomic search algorithm

Wind power output is highly volatile and intermittent owing to the characteristics of wind energy. Large-scale wind power integration affects the stability of entire power system. Accurate wind power prediction facilitates to utilize wind energy, improve power supply quality and maintain stable operation of power grid. A hybrid prediction model combining wavelet decomposition, support vector machine and improved atom search algorithm is proposed to predict wind power output. Dynamic sinusoidal wave adaptive weight is introduced to improve the position update equation, and crossover and mutation operations are added to the end of each iteration to improve the search ability of atomic search algorithm. In the prediction process, the original wind power data is processed by wavelet decomposition and the non-stationary signal is decomposed into several detail sequences of different frequencies and approximate sequence to extract important wind power features to reduce the prediction error caused by data fluctuation. Prediction results of actual wind farm data prove the proposed model has prominent advantages in predicting performance. Three evaluation indexes of mean absolute error, mean absolute percent error and root mean square error decreased by at least 1.14%, 2.60% and 1.52%. The proposed method is conductive to reduce wind curtailment and improve the economic benefit of wind farm.

Analysis of pumped hydroelectric energy storage for large-scale wind energy integration

Renewable energy sources are becoming a popular choice of energy, due to their sundry advantages and more convenient environmental impacts. Wind certainly emerges as one of the most plausible energy sources in modern power generation. However, large-scale wind energy is associated with fluctuations in voltage and power due to its intermittent nature. The proposed pumped hydroelectric energy storage system offers one of the best solutions in solving this problem. It is shown that large-scale integration of wind energy becomes more feasible and efficient when a proper energy storage system is added to achieve appropriate energy charging or discharging. Upon comparing the potential of different storage systems, pumped hydroelectric energy storage is found to be highly promising for wind integration. In order to demonstrate the behavior of the integrated system with respect to time constant and to verify the feasibility of the energy storage in different case studies, simulations are performed in Matlab/Simulink. This work would open new doors toward the investigation of large-scale wind power integration. It is also expected that pumped hydroelectric energy storage would become a possible alternative for energy reserves for large-scale wind power applications.

Short-Term Scheduling of Expected Output-Sensitive Cascaded Hydro Systems Considering the Provision of Reserve Services

The integration of large-scale wind and solar power into the power grid brings new challenges to the security and stability of power systems because of the uncertainty and intermittence of wind and solar power. This sensitive expected output has real implications for short-term hydro scheduling (STHS), which provides reserve services to alleviate electrical perturbations from wind and solar power. This paper places an emphasis on the sensitive expected output characteristics of hydro plants and their effect on reserve services. The multi-step progressive optimality algorithm (MSPOA) is used for the STHS problem. An iterative approximation method is proposed to determine the forebay level and output under the condition of the sensitive expected output. Cascaded hydro plants in the Wujiang River are selected as an example. The results illustrate that the method can achieve good performance for peak shaving and reserve services. The proposed approach is both accurate and computationally acceptable so that the obtained hydropower schedules are in accordance with practical circumstances and the reserve can cope with renewable power fluctuations effectively.

An Optimal Day-Ahead Thermal Generation Scheduling Method to Enhance Total Transfer Capability for the Sending-Side System with Large-Scale Wind Power Integration

The rapidly increasing penetration of wind power into sending-side systems makes the wind power curtailment problem more severe. Enhancing the total transfer capability (TTC) of the transmission channel allows more wind power to be delivered to the load center; therefore, the curtailed wind power can be reduced. In this paper, a new method is proposed to enhance TTC, which works by optimizing the day-ahead thermal generation schedules. First, the impact of thermal generation plant/unit commitment on TTC is analyzed. Based on this, the day-ahead thermal generation scheduling rules to enhance TTC are proposed herein, and the corresponding optimization models are established and solved. Then, the optimal day-ahead thermal generation scheduling method to enhance TTC is formed. The proposed method was validated on the large-scale wind power base sending-side system in Gansu Province in China; the results indicate that the proposed method can significantly enhance TTC, and therefore, reduce the curtailed wind power.

Extreme scenario extraction of a grid with large scale wind power integration by combined entropy-weighted clustering method

Large-scale integration of wind power into a power system introduces uncertainties to its operation and planning, making the power system operation scenario highly diversified and variable. In conventional power system planning, some key operation modes and most critical scenarios are typically analyzed to identify the weak and high-risk points in grid operation. While these scenarios may not follow traditional empirical patterns due to the introduction of large-scale wind power. In this paper, we propose a weighted clustering method to quickly identify a system’s extreme operation scenarios by considering the temporal variations and correlations between wind power and load to evaluate the stability and security for system planning. Specifically, based on an annual time-series data of wind power and load, a combined weighted clustering method is used to pick the typical scenarios of power grid operation, and the edge operation points far from the clustering center are extracted as the extreme scenarios. The contribution of fluctuations and capacities of different wind farms and loads to extreme scenarios are considered in the clustering process, to further improve the efficiency and rationality of the extreme-scenario extraction. A set of case studies was used to verify the performance of the method, providing an intuitive understanding of the extreme scenario variety under wind power integration.

Development of an Equivalent Wind Farm Model for Frequency Regulation

The operational security of power systems for wind farm (WF) integration becomes more important as the penetration of wind generation increases. A WF may contain hundreds of wind turbines (WT) and thus the power system simulations based on the complex model of a WT consume much time. This article proposes an equivalent WF model with the frequency-regulation function. The main research works include the establishment of the generic model of a WT, the development of single- and multimachine equivalent models for a large WF, the clustering of WTs based on the developed nonlinear wake model and the fuzzy C-means (FCM) clustering algorithm, the integration of the frequency-regulation function into the equivalent WF model, and the validation of the developed equivalent WF model by various operating scenarios. The results demonstrate that the proposed multimachine equivalent method based on the FCM clustering provides a more accurate model than a single-WT equivalent model. Consequently, the research results provide a solution for simulating a large-scale wind power integration, providing efficient, accurate, and fast steady-state and dynamic analyses.

Enhancing Hosting Capacity of Uncertain and Correlated Wind Power in Distribution Network With ANM Strategies

To settle the large-scale integration of wind power, it is necessary to enhance the wind power hosting capacity of distribution network. To this end, this paper proposes a stochastic mixed-integer linear programming (MILP) model to enhance the hosting capacity of distribution network with active network management (ANM) strategies. The considered ANM strategies include power factor control, reactive power compensation and network reconfiguration. The coordination of these strategies enables the distribution network to accommodate more wind power without violating its constraints. The proposed model relaxes the original nonlinear power flow equations into linear format, so that a near optimal solution can be obtained with moderate computational burden. The wind power and load demand are inherently variable due to the influence of various factors. Moreover, the output powers of multiple wind turbines (WTs) located at adjacent sites of a wind farm are spatially correlated. To represent the uncertain loads, the roulette wheel mechanism is used to generate the load samples. In addition, a combined method of inverse transformation and Nataf transformation is established to deal with the uncertain and correlated wind power. Then, scenario combination and reduction are conducted to generate representative scenarios. Finally, the feasibility and accuracy of the proposed method are verified via numerical tests on the modified IEEE 33-bus test system.

Clustered Hybrid Wind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering Methods

Wind power prediction is the key technology to the safe dispatch and stable operation of power system with large-scale integration of wind power. In this work, based on the historical data of wind power, wind speed and temperature, the autoregressive moving average (ARMA) prediction model and the support vector machine (SVM) prediction model are established, particle swarm optimization (PSO) algorithm is involved for parameter optimization of SVM model. Furthermore, a hybrid PSO-SVM-ARMA prediction model based on ARMA and PSO-SVM model is illustrated for wind power prediction, and the covariance minimization method and PSO are employed to find the optimal weights. Moreover, with the basis of clustering theory, time series are clustered to examine the effective dataset for wind power prediction, and a clustered hybrid PSO-SVM-ARMA (C-PSO-SVM-ARMA) wind power prediction model is prospectively proposed. In case study, different prediction models are carried out and the prediction performance is examined based on different evaluation indices, the C-PSO-SVM-ARMA model shows better performance for wind power prediction with computational efficiency and satisfying precision.

Scenario-Set-Based Economic Dispatch of Power System With Wind Power and Energy Storage System

In view of uncertainties caused by large-scale wind power integration, energy storage system (ESS) is being considered to stabilize the fluctuation of wind power. In this paper, the influence of ESS on power system operation with wind power is analyzed in detail, and an economic dispatch (ED) model with wind power and ESS is proposed based on scenario set. First, the initial scenario set of wind power output is generated by the Monte Carlo sampling. To overcome the shortcoming of heavy dependence on the initial clustering centers, which usually leads to unstable clustering results, the k-means clustering is improved by combining self-organizing feature map neural network and particle swarm optimization (PSO). Then, the initial scenario set is reduced based on this improved k-means clustering method. Finally, an ED model solved by PSO is used to minimize the comprehensive power generation cost based on the reduced scenario set. Taking IEEE-39 bus system as an example, the scenario-set-based ED model is implemented in this paper. The simulation results show that, when solving the ED problem with wind power and ESS, the proposed method considering scenario reduction makes not only the clustering index better, but also the results of ED more reasonable.

Multi-Flexibility Option Integration to Cope With Large-Scale Integration of Renewables

Conventional electrical networks are slowly changing. A strong sense of policy urges as well as commitments have recently been surfacing in many countries to integrate more environmentally friendly energy sources into electrical systems. In particular, stern efforts have been made to integrate more and more solar and wind energy sources. One of the major setbacks of such resources arises as a result of their intermittent nature, creating several problems in the electrical systems from a technical, market, operation, and planning perspectives. This work focuses on the operation of an electrical system with large-scale integration of solar and wind power. In order to cope with the intermittency inherent to such power sources, it is necessary to introduce more flexibility into the system. In this context, demand response, energy storage systems, and dynamic reconfiguration of the system are introduced, and the operational performance of the resulting system is thoroughly analyzed. To carry out the required analysis, a stochastic mixed-integer linear programming operational model is developed, whose efficacy is tested on an IEEE 119-bus standard network system. Numerical results indicate that the joint deployment and management of various flexibility mechanisms into the system can support a seamless integration of large-scale intermittent renewable energies.

Probability-Interval-Based Optimal Planning of Integrated Energy System With Uncertain Wind Power

Owing to a higher energy supply efficiency and operational flexibility, integrated energy system (IES), including the power, heating, and gas systems, will be the primary form of energy supply in the future. However, with the increase of large-scale stochastic wind power integration, the IES planning will face a significant challenge as the traditional power system. Therefore, a probability-interval-based IES planning considering wind power integration is proposed in this article. First, a conditional value-at-risk (CVaR) based probability-interval method is developed to describe the uncertain wind power. Second, beside traditional facilities, electricity storage system is introduced to improve the flexibility of IES. Then, an expansion planning model for IES is established to minimize the total cost including investment, operation, CVaR cost, and unserved energy cost. Moreover, the piecewise linearization method is used to deal with the nonlinear integral terms of the proposed model to improve the solution efficiency. Finally, IEEE14-NGS14 and IEEE118-NGS40 systems are constructed and the planning model is solved by GAMS/CPLEX. The numerical results illustrate the correctness and effectiveness of the proposed method.