Smoothing Wind Power Fluctuations Research Articles

Strategy of Flywheel–Battery Hybrid Energy Storage Based on Optimized Variational Mode Decomposition for Wind Power Suppression

The fluctuation and intermittency of wind power generation seriously affect the stability and security of power grids. Aiming at smoothing wind power fluctuations, this paper proposes a flywheel–battery hybrid energy storage system (HESS) based on optimal variational mode decomposition (VMD). Firstly, the grid-connected power and charging–discharging power of the HESS are determined based on the sliding average algorithm. Secondly, the VMD algorithm, optimized using long short-term memory (LSTM), is used to decompose the hybrid energy storage power (HESP) into a series of sub-modes with frequencies from low to high. Then, the state of charge of the battery energy storage system and the speed of the flywheel energy storage system are monitored in real time, and the primary power of the HESS is modified according to the rules formulated by fuzzy control. Finally, through a simulation example, it is concluded that the method meets the requirements of smoothing wind power fluctuations and gives full play to the characteristics of energy storage battery and flywheel energy storage to ensure the stable operation of the energy storage system. The method presented in this paper can provide a reference for HESP configuration and control operation strategy formulation.

Coordinated Power Smoothing Control Strategy of Multi-Wind Turbines and Energy Storage Systems in Wind Farm Based on MADRL

The randomness and volatility of wind power greatly affect the safety and economy of the power systems, and the wake effect of the wind farm aggravates the wind energy loss and the wind power fluctuation. Taking into consideration the wake effect of the wind farm, a new coordinated wind power smoothing control strategy for multi-wind turbines (M-WT) and energy storage systems (ESS) is proposed. The proposed method is based on a multi-agent deep reinforcement learning (MADRL), in which the relationship between output power and wake effect is firstly analyzed, and a power smoothing control model of the M-WT and ESS is established. MADRL is then introduced to optimize the power control of M-WT and ESS. In order to further increase the learning and training efficiency, an improved MADRL algorithm based on the partitioned experience buffer and prioritized experience replay is proposed, where the experience buffer is divided into positive, negative, and neutral experiences, and the experiences are sampled according to experience priority. The effectiveness of the proposed strategy is verified on the SimWindFarm platform. The results show that the proposed control strategy can maximize the economic benefits while further smoothing wind power fluctuations and increasing power generation.

Optimal Allocation of Hybrid Energy Storage System Based on Smoothing Wind Power Fluctuation and Improved Scenario Clustering Algorithm

Against the backdrop of the global energy transition, wind power generation has seen rapid development. However, the intermittent and fluctuating nature of wind power poses a challenge to the stability of grid operation. To solve this problem, a solution based on a hybrid energy storage system is proposed. The hybrid energy storage system is characterized by fast and precise control and bidirectional energy throughput, which can improve the impact of wind power fluctuations on grid stability. An ensemble empirical modal decomposition method was used to assign the raw wind power data to the grid-connected power and energy storage power commands with two reasonable corrections to meet the power allocation of the hybrid energy storage characteristics. In addition, a hybrid energy storage system model considering the whole life cycle cost was developed, and the optimal energy storage power cutoff was determined by exhaustively enumerating the high- and low-frequency power cutoffs. Finally, a comparison with a single storage capacity optimization model was carried out to verify the technical and economic advantages of hybrid energy storage in smoothing wind power fluctuations. To address the shortcomings of the traditional fuzzy c-means clustering algorithm, such as the need to specify the number of clusters in advance and sensitivity to the selection of the initial clustering centers, a combination of the cloud modeling theory and fuzzy c-means was used to make the process more automated and efficient. The improved clustering method algorithmic scheme had capacity error, power error, and cost error of around 3%, and the computational time was also significantly reduced and was computationally efficient compared to the full-year time series simulation. Through MATLAB (2020b) experimental simulation, it was found that the algorithm had a better balance of computational accuracy and efficiency.

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.

A dual-layer cooperative control strategy of battery energy storage units for smoothing wind power fluctuations

The large-scale integration of wind power with intermittent characteristics into grids brings a challenge to the power system. Installation of the battery storage energy system (BESS) in a wind farm (WF) can effectively smooth wind power fluctuation. However, the BESS units may face the problem of over-charge/over-discharge if the power dispatch strategy for multiple units of BESS is improper, which results in lifetime degradation. In this paper, a dual-layer cooperative control strategy of multiple BESS units is proposed. In the first layer, the time-varying characteristics of wind power are introduced to define a power fluctuation coefficient. The coefficient can adjust the time constant of the first-order low pass filter (FLF) to avoid the deep energy utilization of BESS and decrease the over-charge/over-discharge possibility. To facilitate more power output for units with high SOC and absorb more energy for units with low SOC, the second layer calculates a SOC distribution factor by a function of the SOC of each unit to allocate the power command. The two layers of control operate cooperatively to reduce the SOC variation and unbalance degrees, thus the over-charge/over-discharge can be avoided, which is good for prolonging the lifetime of BESS with multiple units. Taking an actual WF in the Hunan province of China as the research object, the effectiveness of the proposed strategy is verified by the co-simulation of DIgSILENT/PowerFactory and Matlab/Simulink.

Adaptive Double Kalman Filter Method for Smoothing Wind Power in Multi-Type Energy Storage System

At present, in the situation that wind power penetration is increasing year by year, the use of a hybrid energy storage system (HESS) to smooth out wind power fluctuations becomes an effective method. However, the existing control strategy has the problem of inadequate utilization of fluctuating power. In this paper, we propose a control strategy for smoothing wind power fluctuations based on double Kalman filters with adaptive adjustment of the state of charge (SOC). Firstly, considering the wind power’s active power grid constraint, the parameters of the dual Kalman filter are adaptively adjusted according to the original output power to obtain the target grid power synchronously, and the total smoothing command of the energy storage system is determined with the goal of improving the SOC of the lithium battery. On this basis, the SOC of the supercapacitor is considered to be improved, and the adaptive low-pass filter is used for the secondary distribution of the energy storage power command to achieve fine-grained management of the output power of HESS. The final simulation results show that the obtained smoothed wind power satisfies the 1 min and 10 min fluctuation criteria, and the minimum capacity required for lithium batteries is reduced by 0.07 MW·h under the same initial conditions as in the proposed method in this paper; it can use the fluctuating power when the SOC crosses the limit, and has a regulating effect on the SOC of HESS to improve the wind power smoothing ability and realize the stable grid-connection requirement of wind power.

Control Strategy for Energy-Storage Systems to Smooth Wind Power Fluctuation Based on Interval and Fuzzy Control

The anti-peak shaving characteristics of wind power is an important factor that limits the consumption of wind power. The use of the space-time translation capability of a battery energy storage system is one of the effective means for promoting wind power consumption. Thus, this study proposes an energy storage system smoothing wind power fluctuation control strategy considering wind power consumption to improve the utilization level and economy of an energy storage system. First, the statistical characteristics of wind power are analyzed, and the scene division principle of a multiscenario regulation of an energy storage system is formulated using a correlation coefficient. On this basis, the charging and discharging intervals of the energy storage system in each scenario are determined by comprehensively considering the wind power and the time domain distribution law of load. Then, the control strategy of energy storage system based on inter-zone control is proposed based on the division of charging and discharging intervals. During the charging interval, smoothing the upward fluctuation of wind power or reducing the amount of abandoned wind power is aimed at storing electricity. During the discharge interval, electricity is released to smooth the downward fluctuation of wind power. Finally, considering the real-time state of charge and wind power consumption of the energy storage system, a method of charging and discharging power correction based on fuzzy control is proposed. Taking the actual data of a wind farm as an example, a test is performed on a simulation platform of a combined wind storage power generation system to verify the feasibility and superiority of the proposed control strategy.

An energy storage coordinated control strategy based on model predictive control for smoothing wind power fluctuations

Based on the nature of wind, wind power fluctuations can cause significant problems in the distribution network. One of the solutions is to integrate an energy storage system with wind farm to mitigate the output power fluctuations. Therefore, an energy storage coordinated control strategy based on model predictive control is proposed to smooth minute-scale fluctuations of wind power. By analysing the limitations of traditional control strategy, four operating modes of battery energy storage system which are determined by the predicted state of charge obtained by model predictive control, are designed to avoid violating the state of charge limitation, and an energy state feedback control is designed to adjust the initial power allocation orders of batteries. Finally, the effectiveness of the proposed control strategy can be verified by the real-time digital simulator. The results indicate that the developed approach reduces the switching times of batteries and improves the ability of the battery energy storage system to smooth wind power fluctuations significantly.

Optimal Capacity Configuration of Hybrid Energy Storage System Considering Smoothing Wind Power Fluctuations and Economy

After comparing the economic advantages of different methods for energy storage system capacity configuration and hybrid energy storage system (HESS) over single energy storage system, a method based on improved moving average and ensemble empirical mode decomposition (EEMD) to smooth wind power fluctuations is proposed aiming at the optimal capacity configuration of HESS. Firstly, EEMD was used to decompose the HESS reference power which was derived by improved moving average filtering, and then several intrinsic mode functions (IMFs) were obtained. Each IMF instantaneous frequency-time profile was processed by Hilbert transform, the so- call gap frequency was identified, and then reconstruct the high and low frequency components to obtain the HESS reference power. Subsequently, taken the energy storage system charge-discharge efficiency and state of charge (SOC) into account, the rated power and capacities of each scheme was determined. Finally, based on Life Cycle Cost (LCC) theory, an energy storage system economic cost calculation model was established to compare the costs of each scheme and select the optimal one.

Modelling and control of vanadium redox flow battery for smoothing wind power fluctuation

Due to the negative impact of a highly stochastic wind power fluctuation on the power quality and stability during high penetration of wind power in power systems, there is growing interest in power smoothing and energy redistribution in wind power systems by using large-scale energy storage technologies. The aim of this work is to use a vanadium redox flow battery as an energy storage system (ESS) to smooth wind power fluctuation with two system configurations and corresponding control strategies. As the first step, a vanadium redox flow battery (VRFB) mathematical model, underlain by electrochemical theories, is built to describe the battery behaviours in charge/discharge cycles. Accordingly, the aforementioned system configuration and power fluctuation smoothing schemes are proposed. At the end of this work, simulations are conducted on a 3.6 MW doubly-fed induction generator (DFIG) at a diversity of wind speeds for an assessment of feasibility that this work can be applied to a high penetration of wind power in practice. Simulation results reveal that power fluctuations can be improved by power smoothing. The root mean square deviation (RMSD) percentage can be reduced to less than 1%.

Probabilistic Forecasting Based Sizing and Control of Hybrid Energy Storage for Wind Power Smoothing

With the increasing wind power integration, the security and economy of the power system operations are greatly influenced by the intermittency and fluctuation of wind power. Due to the flexible operational modes for charging/discharging, the hybrid energy storage system (HESS) is composed of battery energy storage system and super-capacitor can effectively mitigate the wind power uncertainty. This paper proposes a probabilistic forecasting-based HESS sizing and control scheme to cost-effectively smooth wind power fluctuations. First, probabilistic wind power forecasting is combined with multivariate Gaussian copula to generate temporally correlated wind power scenarios. Then, an adaptive variational mode decomposition (VMD) method is proposed to extract the frequency components of each wind scenario and determine the pre-scheduled power of wind-HESS system adaptively. Finally, a two-stage stochastic optimization model is constructed to determine the capacity and correct the pre-scheduled power of HESS with the objective of minimizing total cost. Case studies based on actual data from a Danish wind farm demonstrate that the proposed HESS sizing and control scheme can significantly reduce the installation cost and operation cost of HESS and prominently smooth wind power fluctuations.

Research On the Application of Superconducting Magnetic Energy Storage in the Wind Power Generation System For Smoothing Wind Power Fluctuations

As the output power of wind farm is fluctuating, it is one of the important ways to improve the schedule ability of wind power generation to predict the output power of wind farm. The operation mode of tracking planned output takes the planned value issued by the grid dispatching as the control basis of wind power generation. This operation mode is easy to control, which not only meets the needs of power grid dispatching, but also improves the reliability and controllability of new energy generation. This paper introduces the traditional PI control strategy of SMES and combines the advantages of SMES and the characteristics of application scenarios, and introduces sliding mode variable structure control into SMES. Through simulation and comparison of two different control modes, it is verified that SMES adopting sliding mode variable structure control have the advantages of fast response speed and strong robustness, which can make the wind farm output stable power according to the planned value.

Optimization of Energy Storage Capacity to Smooth Wind Power Fluctuation

The uncertainty and randomness of wind power generation bring hidden trouble to the safe operation of power distribution network. Combining energy storage system with wind power generation can effectively improve the output characteristics of wind power generation. In this paper, considering the investment cost of energy storage and the effect of suppressing the fluctuation of wind power output, the optimization of energy storage capacity under the scenario of wind power grid connection is studied. Firstly, the multi-objective capacity optimization model of the energy storage system is established to minimize the cost of the energy storage system and the variance of wind power system output equipped with energy storage. Then, the multi-objective optimization problem is transformed into a single objective optimization problem by introducing the weight coefficient of the objective function and the particle swarm optimization algorithm (PSO) is used to solve the problem. Finally, through the test of IEEE-33 bus distribution network system, the relationship curve between energy storage capacity and wind power fluctuation suppression effect is obtained. The results of numerical examples show the effectiveness of the proposed method.

Long-term stable operation control method of dual-battery energy storage system for smoothing wind power fluctuations

Since wind output power shows obvious fluctuations characteristics, direct grid-connection will bring great challenges to the safe and stable operation of the power system. In order to achieve better economic benefits, this paper adopts the dual-battery energy storage system (DBESS) operation mode which performs charge–discharge tasks separately considering the discharge depth and frequent charge–discharge conversion on battery life. However, the extreme operation will occur when considering the imbalance of charge and discharge energy of DBESS, which will make it difficult for DBESS to maintain long-term stable operation to smooth the wind output power. Besides this paper proposes a charge–discharge operational stability index for characterizing the system operation status, based on the real-time operating conditions of the energy storage system and wind power fluctuations. To ensure the DBESS long-term operation stability, the extreme operation problems caused by charge–discharge unbalanced energy in the long-term operating process must be solved. This paper used two proposed indicators as input factors and proposed a control strategy to adaptively fine-tune the first-order low-pass filtering time constant, to charge the battery throughput power in real-time and optimize two battery packs’ State of Charge (SOC). The effectiveness of the proposed control strategy is verified from two aspects by using MATLAB/Simulink software: the SOC optimizing control effect and the power fluctuation smoothing effect.

Model Predictive Control Method of hybrid Battery energy storage system for Smoothing Wind Power Fluctuation

In a large-scale wind power generation system, active power fluctuation caused by random wind speed will have a serious impact on the power grid. In order to limit the power fluctuation that wind farm transmits to the power grid and protect the energy storage battery, this paper has proposed a model predictive control method of hybrid energy storage by optimizing the objective function and constraint conditions. Firstly, the mathematical model of predictive control method has been established in a wind power system with hybrid energy storage. Then, with the goal of minimum energy storage output and maximum charging-discharging capacity of the super-capacitor, the predictive control process has been optimized. Meanwhile, the constraint on the output power of the battery has been dynamically changed to reduce its charging-discharging switching frequency, and the model predictive control strategy of the hybrid energy storage has been formed. Finally, compared with the model prediction control method of single energy storage, based on a wind farm data, the simulation results show that the proposed method can smooth wind power fluctuation, reduce the time that the power does not satisfy the fluctuation requirements, ensure the capability of the super-capacitor, and reduce the charging-discharging switching frequency of the energy storage battery.

Dispatching of a Wind Farm Incorporated With Dual-Battery Energy Storage System Using Model Predictive Control

The battery energy storage system (BESS) integrated with a wind farm is an efficient way to smooth wind power fluctuations and improve wind farm dispatchability. The presented study proposed a model predictive control (MPC)-based power dispatch strategy for a wind farm incorporated with dual-battery energy storage system (DBESS). The state space model of the active power balance of the wind farm incorporated with DBESS was established to analyze the operation mode of the DBESS. The MPC was then applied to acquire the control inputs of the two batteries, thereby enabling both batteries to respectively be in a state of charging and discharging to track the alternating dispatch order. Actual wind power data was applied in the simulation, wherein the results indicate that the proposed MPC-based dispatching strategy of the DBESS-incorporated wind farm effectively tracks the dispatching order using the output of the wind/battery storage hybrid system, thereby improve the dispatchability of the wind farm while simultaneously reducing battery switching times and extending the lifetime of battery.

Model Predictive Control-Based Coordinated Control Algorithm with a Hybrid Energy Storage System to Smooth Wind Power Fluctuations

Stochastically fluctuating wind power has an escalating impact on the stability of power grid operations. To smooth out short- and long-term fluctuations, this paper presents a coordinated control algorithm using model predictive control (MPC) to manage a hybrid energy storage system (HESS) consisting of ultra-capacitor (UC) and lithium-ion battery (LB) banks. In the HESS-computing period, the algorithm minimizes HESS operating costs in the subsequent prediction horizon by optimizing the time constant of a flexible first-delay filter (FDF) to obtain the UC power output. In the LB-computing period, the algorithm keeps the optimal time constant of the FDF from the previous period to directly obtain the power output of the UC bank to minimize the power output of the LB bank in the next prediction horizon. A relaxation technique is deployed when the problem is unsolvable. Thus, the fluctuation mitigation requirements are fulfilled with a large probability even in extreme conditions. A state-of-charge (SOC) feedback control strategy is proposed to regulate the SOC of the HESS within its proper range. Case studies and quantitative comparisons demonstrate that the proposed MPC-based algorithm uses a lower power rating and storage capacity than other conventional algorithms to satisfy one-minute and 30-min fluctuation mitigation requirements (FMR).

Multi-Objective Day-Ahead Scheduling of Power Market Integrated With Wind Power Producers Considering Heat and Electricity Trading and Demand Response Programs

The large-scale penetration of renewable energy, such as wind power, brings a lot of economic and environmental benefits to the grid, and it also causes hidden dangers in the reliability and security of the power system due to its uncertainty. As an effective demand-side management method, demand response has unique advantages in smoothing wind power fluctuations and mitigating grid pressure. This paper proposes a new model for the demand response aggregator (DRA) that includes both combined heat and power systems (CHPS) and energy storage devices. DRA can interact with the Independent system operator (ISO) through combined heat and power (CHP) units, energy storage devices, and the heat buffer tank to benefit from the electricity market and the thermal market simultaneously. At the same time, wind power producers (WPP) are modeled to turn wind power that was initially passively consumed into active market participants. The problem is modeled using an improved weighted method, which aims to take the diverse objectives of multiple market participants into account. The proposed model is tested on the modified IEEE RTS-24 test system to analyze the optimal scheduling strategies of each participant in the power market.

Model predictive control and improved low-pass filtering strategies based on wind power fluctuation mitigation

The rapid development of renewable energy sources such as wind power has brought great challenges to the power grid. Wind power penetration can be improved by using hybrid energy storage (ES) to mitigate wind power fluctuation. We studied the strategy of smoothing wind power fluctuation and the strategy of hybrid ES power distribution. Firstly, an effective control strategy can be extracted by comparing constant-time low-pass filtering (CLF), variable-time low-pass filtering (VLF), wavelet packet decomposition (WPD), empirical mode decomposition (EMD) and model predictive control algorithms with fluctuation rate constraints of the identical grid-connected wind power. Moreover, the mean frequency of ES as the cut-off frequency can be acquired by the Hilbert Huang transform (HHT), and the time constant of filtering algorithm can be obtained. Then, an improved low-pass filtering algorithm (ILFA) is proposed to achieve the power allocation between lithium battery (LB) and supercapacitor (SC), which can overcome the over-charge and over-discharge of ES in the traditional low-pass filtering algorithm (TLFA). In addition, the optimized LB and SC power are further obtained based on the SC priority control strategy combined with the fuzzy control (FC) method. Finally, simulation results show that wind power fluctuation can be effectively suppressed by LB and SC based on the proposed control strategies, which is beneficial to the development of wind and storage system.

Optimal Sizing and Control Strategies for Hybrid Storage System as Limited by Grid Frequency Deviations

Frequency deviations of power systems caused by grid-connected wind power fluctuations is one of the key factors which restrains the increase of wind penetration level. This paper examines a combined wind and hybrid energy storage system (HESS, supercapacitor, and battery) to smooth wind power fluctuations. A fuzzy-based wind-HESS system (FWHS) controller is proposed to suppress the wind power fluctuations. The proposed controller takes full advantage of the complimentary characteristics of the supercapacitor and battery with the supercapacitor and battery in charge of high and middle frequency components of wind fluctuations, respectively. A differential evolution (DE)-based optimal sizing method for HESS systems is introduced to evaluate the minimum capacity of HESS as being limited by grid frequency deviation. The efficiency of the proposed scheme in the paper for wind-HESS system is evaluated by a real Chinese power system.