Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network

Predicting wind power gen eration over the medium and long term is helpful for dispatchi ng departme nts,as it aids in constructing generation plans and electricity market transactions.This study presents a monthly wind power gen eration forecast!ng method based on a climate model and long short-term memory(LSTM)n eural n etwork.A non linear mappi ng model is established between the meteorological elements and wind power monthly utilization hours.After considering the meteorological data(as predicted for the future)and new installed capacity planning,the monthly wind power gen eration forecast results are output.A case study shows the effectiveness of the prediction method.

Frequency Regulation of Power Systems With a Wind Farm by Sliding-Mode-Based Design

Load frequency regulation is an essential auxiliary service used in dealing with the challenge of frequency stability in power systems that utilize an increasing proportion of wind power.We investigate a load frequency control method for multiarea interconnected power systems integrated with wind farms,aimed to eliminate the frequency deviation in each area and the tie-line power deviation between different areas.The method explores the derivative and integral terminal sliding mode control technology to solve the problem of load frequency regulation.Such technology employs the concept of relative degrees.However,the subsystems of wind-integrated interconnected power systems have different relative degrees,complicating the control design.This study develops the derivative and integral terminal sliding-mode-based controllers for these subsystems,realizing the load frequency regulation.Meanwhile,closed-loop stability is guaranteed with the theory of Lyapunov stability.Moreover,both a thermal power system and a wind power system are applied to provide frequency support in this study.Considering both constant and variable external disturbances,several numerical simulations were carried out in a two-area thermal power system with a wind farm.The results demonstrate the validity and feasibility of the developed method.

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy

As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting.The methods for forecasting wind power and PV production.The physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production.Moreover,the experiments demonstrated that cloud map identification has a significant impact on PV generation.With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

Feasibility Analysis of Constructing Solar Power Plant by Combining Large Scale Wind Farm

Hybrid utilization of renewable energy is one of effective method which can solve the problem that unstable of renewable energy so as not to substitute traditional fossil energy. As the typical renewable energy, solar energy and wind energy are in the van of renewable energy utilization. With the large scale utilization of solar and wind energy in the world, constructing large scale solar power plant in the large scale wind farm can make the most of ground resource combining the wind energy with solar energy. Feasibility of constructing large scale solar power plant in the large scale wind farm was analyzed in this paper, and come to a conclusion that constructing large scale solar power plant in the large scale wind farm can not also achieved the goal of mutual support of resource advantages and economizing money but also improved significantly the seasonal mismatch by combining solar with wind.

Protection of Zero-Sequence Power Variation in Mountain Wind Farm Collector Lines Based on Multi-Mode Grounding

The arc-suppression coil(ASC)in parallel low resistance(LR)multi-mode grounding is adopted in the mountain wind farm to cope with the phenomenon that is misoperation or refusal of zero-sequence protection in LR grounding wind farm.If the fault disappears before LR is put into the system,it is judged as an instantaneous fault;while the fault does not disappear after LR is put into the system,it is judged as a permanent fault;the single-phase grounding fault(SLG)protection criterion based on zerosequence power variation is proposed to identify the instantaneous-permanent fault.Firstly,the distribution characteristic of zero-sequence voltage(ZSV)and zero-sequence current(ZSC)are analyzed after SLGfault occurs in multi-mode grounding.Then,according to the characteristics that zero-sequence power variation of non-fault collector line is small,while the zero-sequence power variation of fault collector line can reflect the active power component of fault resistance,the protection criterion based on zero-sequence power variation is constructed.The theoretical analysis and simulation results show that the protection criterion can distinguish the property of fault only by using the single terminal information,which has high reliability.

Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.

A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows.Traditional approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts.The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

Quantitative method for evaluating detailed volatility of wind power at multiple temporal-spatial scales

With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.

A Literature Review of Wind Forecasting Methods

In this paper, an overview of new and current developments in wind forecasting is given where the focus lies upon principles and practical implementations. High penetration of wind power in the electricity system provides many challenges to the power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help the power system operators reduce the risk of unreliability of electricity supply. This paper gives a literature survey on the categories and major methods of wind forecasting. Based on the assessment of wind speed and power forecasting methods, the future development direction of wind forecasting is proposed.

A Review of Power Electronics for Wind Power

The paper reviews the power electronic applications for wind energy systems.Main wind turbine systems with different generators and power electronic converters are described.The electrical topologies of wind farms with power electronic conversion are discussed.Power electronic applications for improving the performance of wind turbines and wind farms in power systems have been illustrated.

Small-Signal Stability Analysis of Wind Power System Based on DFIG

This paper focuses on the small-signal stability of power system integrated with DFIG-based wind farm. The model of DFIG for small-signal stability analysis has built;the 3-generator 9-bus WECC test system is modified to investigate the impacts of large scale integration of wind power on power system small-signal stability. Different oscillatory modes are obtained with their eigenvalue, frequency and damping ratio, the results from eigenvalue analysis are presented to demonstrate the small-signal stability of power system is enhanced with the increasing output of the wind farm.

Achievements and Prospects of Wind Power Prediction

Wind power prediction is crucial to the operation of the power system accommodating a large amount of wind power. From the perspective of power dispatch, this paper discusses the current situations of the technology, system building, prediction errors, the index for evaluating wind power prediction system and the main bodies responsible for the prediction. It delves into the existing problems such as incomplete basic data, poor prediction accuracy, short prediction time scale, as well as lacking of prediction in most wind farms. Suggestions on improvement are proposed including enhancing the construction of wind power prediction system on both the grid side and the wind farm side, speeding up the development of ultra-short term wind power prediction system, deepening the research on wind power prediction technology, strengthening the construction of technical standard system and carrying out cross-sector cooperation.

Providing Frequency Support of Hydro-Pumped Storage to Taiwan Power System with Wind Power Integration

The combination of wind and pumped storage is a useful method to compensate the fluctuation of wind power generation, which would exploit the abundant wind potential and increase wind power penetration. Taiwan Power Company (TPC) develops renewable energy actively in recent years. Moreover, TPC has started planning a high penetration wind power system and building offshore wind farms around the coast of Zhangbin, Yunlin and Penghu. The target of the offshore wind power installed capacity is up to 3 GW by 2025. However, the integration of the large scale of wind power would give huge challenges to the system operator because wind is randomly characterized. In this study, after high penetration wind power is integrated, the impacts of system frequency and the dispatch of conventional units will be discussed. Additionally, the hybrid system combing wind power with pumped-storage will be planning to reduce the effect of system frequency.

Wind Power Forecasting Methods Based on Deep Learning:A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid.Aiming to provide reference strategies for relevant researchers as well as practical applications,this paper attempts to provide the literature investigation and methods analysis of deep learning,enforcement learning and transfer learning in wind speed and wind power forecasting modeling.Usually,wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state,which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure,temperature,roughness,and obstacles.As an effective method of high-dimensional feature extraction,deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design,such as adding noise to outputs,evolutionary learning used to optimize hidden layer weights,optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting.The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness,instantaneity and seasonal characteristics.

Wind farm active power dispatching algorithm based on Grey Incidence

This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced.

Two-stage ADMM-based distributed optimal reactive power control method for wind farms considering wake effects

Since the connection of small-scale wind farms to distribution networks,power grid voltage stability has been reduced with increasing wind penetration in recent years,owing to the variable reactive power consumption of wind generators.In this study,a two-stage reactive power optimization method based on the alternating direction method of multipliers(ADMM)algorithm is proposed for achieving optimal reactive power dispatch in wind farm-integrated distribution systems.Unlike existing optimal reactive power control methods,the proposed method enables distributed reactive power flow optimization with a two-stage optimization structure.Furthermore,under the partition concept,the consensus protocol is not needed to solve the optimization problems.In this method,the influence of the wake effect of each wind turbine is also considered in the control design.Simulation results for a mid-voltage distribution system based on MATLAB verified the effectiveness of the proposed method.

Wind Farm Coordinated Control for Power Optimization

以降低风电场尾流损失、优化风场出力为目标,设计基于Laguerre函数非线性预测控制（nonlinear modelpredictive control,NLMPC）方案的风场集群控制器。该控制器应用风场动态尾流模型,通过NLMPC统一调整风场内各机组转速以提升风场功率。在控制器设计中,使用有效风速预测误差校正对预测模型失配及超短期风速预测误差进行补偿,引入Laguerre函数降低滚动时域优化计算负担并分析了控制器对风速预测误差的鲁棒性能。仿真研究表明,集群控制器能够在不同风速条件下提升风场功率、降低优化计算负担,且对风速预测模型失配与风场自然风速预测误差具有鲁棒性。

Characteristic Aerodynamic Loads and Load Effects on the Dynamics of a Floating Vertical Axis Wind Turbine

The development of offshore wind farms in deep water favors floating wind turbine designs, but floating horizontal axis wind turbines are facing the challenge of high cost of energy (CoE). The development of innovative designs to reduce the CoE is thus desirable, such as floating vertical axis wind turbines (VAWTs). This study demonstrates the characteristics of aerodynamic loads and load effects of a two-bladed floating VAWT supported by a semi-submersible platform. Fully coupled simulations are performed using the time-domain aero-hydro-servo-elastic code SIMO-RIFLEX-AC. It is found that thrust, lateral force, and aerodynamic torque vary considerably and periodically with the rotor azimuth angle. However, the variation in the generator torque can be alleviated to some extent by the control strategy applied. Moreover, the variations of platform motions and tensions in the mooring lines are strongly influenced by turbulent winds, whereas those of tower-base bending moments are not. The tower-base bending moments exhibit notable two-per-revolution (2P) response characteristics. © 2017 Tianjin University and Springer-Verlag GmbH Germany

Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data.This article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.The incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial networks.After augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power.Therefore,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset.An incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s performance.Compared with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.

Perspectives of China’s wind energy development

Wind energy is a kind of clean renewable energy, which is also relatively mature in technology, with largescale development conditions and prospect for the commercialization. The development of wind energy is a systematic project, involving policy, law, technology, economy, society, environment, education and other aspects. The relationship among all the aspects should be well treated and coordinated. This paper has discussed the following relationships which should be well coordinated: relationship between wind resources and wind energy development, relationship between the wind turbine generator system and the components, relationship between wind energy technology and wind energy industry, relationship between off-grid wind power and grid-connected wind power, relationship between wind farm and the power grid, relationship between onshore wind power and offshore wind power, relationship between wind energy and other energies, relationship between technology introduction and self-innovation, relationship among foreign-funded, joint ventured and domestic-funded enterprises and relationship between the government guidance and the market regulation, as well as giving out some suggestions.