Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression

To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending(SDT)algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm(IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance.

Wind Power Fluctuation Compensation by Variable Speed Pumped Storage Plants in Grid Integrated System:Frequency Spectrum Analysis

Electrical power generation from wind technology is the most rapidly growing technology due to its ample characteristics.Nevertheless,because of its stochastic feature,it has the unnecessary impact on the operations and stability of the power grid system.The fluctuation of the grid frequency problem,for example,is more pronounced.The fluctuation of the frequency in turn impacts even the collapse of the power system.To minimize such problems,a droop-vector control strategy applied on a doubly-fed induction machine based(DFIM)variable speed pumped storage(VSPS)system is proposed in this paper.This method is should be used as a wind power fluctuation compensation solution in the wind farm-grid integration system.The system model is made on the basis of the technique called a phasor model.The frequency spectrum analysis approach is used in the VSPS plant for determining the dynamic performances of the grid in case of contingencies including wind power fluctuation compensation.The software platform MATLAB/Simulink is used for verifying the performance of the proposed system.The results show that the method of the frequency spectrum analysis technique is effective for determining the wind power fluctuation and stability requirements in large power networks.The control strategy proposed in this paper implementing the VSC-DFIM based VSPS plant integrated with the power gird and wind farm network achieves a well-controlled power flow and stable grid frequency with the deviations being in acceptable ranges.

Analytic Mechanism for the Cumulative Effect of Wind Power Fluctuations from Single Wind Farm to Wind Farm Cluster

The use of wind power is rapidly growing worldwide as a means of reducing carbon emissions for the energy sector.China has the world’s largest wind power installation and multiple large-scale wind farm clusters,each comprising dozens of wind farms.For the planning and operation of the power system,it is important to understand the power fluctuation characteristics of wind farm clusters.Several studies demonstrate that the relative power fluctuation of a wind farm cluster is less than that of a single wind farm.Is this decreasing trend a random occurrence or does it have a regular pattern?This scientific question is addressed by investigating the mechanism of the cumulative effect of a wind farm cluster.In this study,a cumulative model is proposed by examining the spatiotemporal relationships of wind power variations and wind farm dispersion.Structural gain function and critical cumulative frequency are defined as the foundations to analytically describing the cumulative effect.By investigating the cumulative effect mechanism,the relationship between power fluctuation and spatiotemporal parameters of the wind farm cluster are revealed.The power fluctuation of a cluster can be predicted using the cumulative model even before it is completely built.The mechanism of the cumulative effect is validated on the basis of the data of two actual wind farm clusters.

A demand side controller of electrolytic aluminum industrial microgrids considering wind power fluctuations

Direct wind power purchase for large industrial users is a meaningful way to improve wind power consumption and decrease industrial production costs.Short-term wind power fluctuations may lead to large-scale wind power curtailment problems.To promote use of wind energy,a demand side control method is proposed based on output regulator theory for a grid-connected industrial microgrid with electrolytic aluminum loads to continuously track and respond to wind power fluctuations.The control model of the EALs and the dominant frequencies of the wind power fluctuation signals are analyzed and incorporated into the demand side control plant.The feedback control signals with active power deviations on the tie-line are used to design the demand side controller.Simulations are conducted for an actual industrial microgrid to validate the feasibility and effectiveness of the proposed method.The results demonstrate that the proposed controller based on output regulator theory is able to effectively track wind power fluctuations.

Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation

In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.

Bi-objective Layout Optimization for Multiple Wind Farms Considering Sequential Fluctuation of Wind Power Using Uniform Design

The fluctuation of wind power brings great challenges to the secure,stable,and cost-efficient operation of the power system.Because of the time-correlation of wind speed and the wake effect of wind turbines,the layout of wind farm has a significant impact on the wind power sequential fluctuation.In order to reduce the fluctuation of wind power and improve the operation security with lower operating cost,a bi-objective layout optimization model for multiple wind farms considering the sequential fluctuation of wind power is proposed in this paper.The goal is to determine the optimal installed capacity of wind farms and the location of wind turbines.The proposed model maximizes the energy production and minimizes the fluctuation of wind power simultaneously.To improve the accuracy of wind speed estimation and hence the power calculation,the timeshifting of wind speed between the wind tower and turbines’locations is also considered.A uniform design based two-stage genetic algorithm is developed for the solution of the proposed model.Case studies demonstrate the effectiveness of this proposed model.

Day-ahead allocation of operation reserve in composite power systems with large-scale centralized wind farms

This paper focuses on the day-ahead allocation of operation reserve considering wind power prediction error and network transmission constraints in a composite power system.A two-level model that solves the allocation problem is presented.The upper model allocates operation reserve among subsystems from the economic point of view.In the upper model,transmission constraints of tielines are formulated to represent limited reserve support from the neighboring system due to wind power fluctuation.The lower model evaluates the system on the reserve schedule from the reliability point of view.In the lower model,the reliability evaluation of composite power system is performed by using Monte Carlo simulation in a multi-area system.Wind power prediction errors and tieline constraints are incorporated.The reserve requirements in the upper model are iteratively adjusted by the resulting reliability indices from the lowermodel.Thus,the reserve allocation is gradually optimized until the system achieves the balance between reliability and economy.A modified two-area reliability test system (RTS) is analyzed to demonstrate the validity of the method.