Wind Power Smoothing Research Articles

A hybrid energy storage array group control strategy for wind power smoothing

A hybrid energy storage array group control strategy for wind power smoothing

A Wind Power Fluctuation Smoothing Control Strategy for Energy Storage Systems Considering the State of Charge

With the significant increase in the scale of energy storage configuration in wind farms, improving the smoothing capability and utilization of energy storage has become a key focus. Therefore, a wind power fluctuation smoothing control strategy is proposed for battery energy storage systems (BESSs), considering the state of charge (SOC). First, a BESS smoothing wind power fluctuation system model based on model predictive control (MPC) is constructed. The objective function aims to minimize the deviation of grid-connected power from the target power and the deviation of the BESS’s remaining capacity from the ideal value by comprehensively considering the smoothing effect and the SOC. Second, when the wind power’s grid-connected power exceeds the allowable fluctuation value, the weight coefficients in the objective function are adjusted in real time using the first layer of fuzzy control rules combined with SOC partitioning. This approach smooths wind power fluctuations while preventing overcharging and overdischarging of the BESS. When the grid-connected power is within the allowable fluctuation range, the charging and discharging power of the BESS is further refined using a second layer of fuzzy control rules. This enhances the BESS’s capability and utilization for smoothing future wind power fluctuations by preemptively charging and discharging. Finally, the proposed control strategy is simulated using MATLAB R2021b with actual operational data from a wind farm as a case study. Compared to the traditional MPC control method, the simulation results demonstrate that the proposed method effectively controls the SOC within a reasonable range, prevents the SOC from entering the dead zone, and enhances the BESS’s ability to smooth wind power fluctuations.

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.

Hybrid energy storage system control and capacity allocation considering battery state of charge self-recovery and capacity attenuation in wind farm

Hybrid energy storage system (HESS) can cope with the complexity of wind power. But frequent charging and discharging will accelerate its life loss, and affect the long-term wind power smoothing effect and economy of HESS. Firstly, for the operational control of HESS, a bi-objective model predictive control (MPC) -weighted moving average (WMA) strategy for energy storage target power controlling is proposed, considering both energy storage state of charge (SOC) self-recovery and grid-connected power stabilization. The strategy can quickly adjust the SOC of HESS in the wind power smoothing process and reduce the battery's life loss. Then, since the energy storage capacity determines its power smoothing ability, this paper proposes a battery life model considering the effective capacity attenuation caused by calendar aging, and introduces it into the HESS cost calculation model to optimize the capacity allocation. Finally, combined with the wind power data, the simulation verifies that the proposed strategy can effectively balance the contradiction between energy storage lifetime and wind power smoothing effect. Simultaneously, the HESS optimized capacity allocation results considering battery's effective capacity attenuation can ensure the long-term wind power smoothing effect and better HESS operational states, contributing to the long-term and stable operation of wind-storage combined system.

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.

Optimal Scheduling of Multi-energy System Considering Economy and Energy Conservation

In recent years, the optimal scheduling of multienergy has become the focus of the research. Against this background, this paper builds a model of a multienergy flow system of cooling, heating, and power, and advanced adiabatic compressed air energy storage (AA-CAES) is introduced to smooth wind power generation (WPG) and supply heating/cooling energy. Simulated annealing algorithm (SAA) is employed to energy-saving scheduling of the system with “exergy assessment” method. The energy-saving index and the exergy efficiency are compared in different cases. SAA is compared with the particle swarm optimization (PSO) algorithm in solving the optimal scheduling strategy. The cooling, heating, and power demands of an industrial park and WPG in typical days are employed to the simulation. The scheduling results of exergy input of SAA are far less than those of PSO in typical days of different seasons. The multienergy system without AA-CAES is also modeled, and energy-saving economic scheduling is carried out. The exergy efficiency of the system with AA-CAES is between 38% and 58% while the exergy efficiency of the system without AA-CAES is merely between 27% and 48%.

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.

A wind power smoothing strategy based on two-layer model algorithm control

The inherent uncertainty of wind power generation leads to a mismatch between actual wind power generation and electric grid demand power, while the occurrence of sudden loads makes real-time scheduling of wind farms difficult. To address these issues, a cooperative compensation strategy of battery is proposed to compensate for the unbalanced power and sudden loads. Positive unbalanced power is used directly to compensate for sudden loads, and the energy storage absorbs the remaining positive unbalanced power to indirectly compensate for negative unbalanced power and sudden loads when the energy storage has excess power. The fluctuation rate of wind power after compensation is considered, and a smoothing model based on two-layer model algorithm control (MAC) is established. A variable mode decomposition (VMD) adaptive frequency division method is proposed to decompose the compensated power in order to smooth the high-frequency and low-frequency parts of the compensated power using different components of the hybrid energy storage to achieve accurate compensation.

Beneficial role of diurnal smoothing for grid integration of wind power

Smoothing of generation variability, i.e. reduction of variance in the aggregate generation is crucial for grid integration of large-scale wind power plants. Prior studies of smoothing have focused on geographical smoothing, based on distance. In contrast, we propose a novel concept ‘diurnal smoothing’ that depends on spatial variations in the timing of seasonal-mean diurnal cycle peak. Considering the case of India, which experiences a strong diurnal cycle of wind-speed, we show how spatial heterogeneity in the wind diurnal cycle can be exploited to smooth wind power variability over and above geographical smoothing. For any given separation distance between sites, the hourly wind speed correlation is highly variable. Difference in timing of the diurnal cycle peak is an important factor for explaining this variability and we define smoothing from differently timed seasonal-mean diurnal cycle as ‘diurnal smoothing’. We show that apart from separation distance, the diurnal cycle is crucial for correlation among sites separated by 200 km or more with strong diurnal cycles (amplitude more than approximately 0.5 m s−1). Thus, diurnal smoothing is a vital factor in the aggregation of large wind power plants, and grid integration is benefited by considering (in addition to distance) new wind plant sites with largely separated diurnal cycles, especially those differing by roughly 12 h. Such diurnal smoothing is relevant for regions across the world with strong wind speed diurnal cycles. Ultimately grid integration depends on variations in total wind and solar generation and demand. Hence, their combined effects must be studied.

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.

Coordinated control of wind turbine and hybrid energy storage system based on multi-agent deep reinforcement learning for wind power smoothing

Due to the inherent fluctuation, wind power integration into the large-scale grid brings instability and other safety risks. In this study by using a multi-agent deep reinforcement learning, a new coordinated control strategy of a wind turbine (WT) and a hybrid energy storage system (HESS) is proposed for the purpose of wind power smoothing, where the HESS is combined with the rotor kinetic energy and pitch control of the wind turbine. Firstly, the wind power output is forecasted and decomposed into high, medium, and low-frequency components through an adaptive variational mode decomposition (VMD). The optimal secondary allocation of the reference power of the high-frequency and medium-frequency is then performed through a multi-agent twin-delay deep deterministic policy gradient algorithm (MATD3) to smooth the power output. To improve the exploration ability of the learning, a new type of α-“stable” Lévy noises is injected into the action space of the MATD3 and the noises are dynamically adjusted. Simulation and RT-LAB semi-physical real-time experimental results show that the proposed control strategy can make full use of the smoothing output power of the WT and HESS combined generation system reasonably, extend the life of the energy storage elements and reduce the wear of the WT.

Fault Ride-Through Techniques for Permanent Magnet Synchronous Generator Wind Turbines (PMSG-WTGs): A Systematic Literature Review

Global warming and rising energy demands have increased renewable energy (RE) usage globally. Wind energy has become the most technologically advanced renewable energy source. Wind turbines (WTs) must ride through faults to ensure power system stability. On the flip side, permanent magnet synchronous generators (PMSG)-based wind turbine power plants (WTPPs) are susceptible to grid voltage fluctuations and require extra regulations to maintain regular operations. Due to recent changes in grid code standards, it has become vital to explore alternate fault ride-through (FRT) methods to ensure their capabilities. This research will ensure that FRT solutions available via the Web of Science (WoS) database are vetted and compared in hardware retrofitting, internal software control changes, and hybrid techniques. In addition, a bibliometric analysis is provided, which reveals an ever-increasing volume of works dedicated to the topic. After that, a literature study of FRT techniques for PMSG WTs is carried out, demonstrating the evolution of these techniques over time. This paper concludes that additional research is required to enhance FRT capabilities in PMSG wind turbines and that further attention to topics, such as machine learning tools and the combination of FRT and wind power smoothing approaches, should arise in the following years.

Design of A Two-Stage Control Strategy of Vanadium Redox Flow Battery Energy Storage Systems for Grid Application

The low energy conversion efficiency of the vanadium redox flow battery (VRB) system poses a challenge to its practical applications in grid systems. The low efficiency is mainly due to the considerable overpotentials and parasitic losses in the VRB cells when supplying highly dynamic charging and discharging power for grid regulation. Apart from material and structural advancements, improvements in operating strategies are equally essential for achieving the expected high-performance VRB system, although an optimized solution has not been fully exploited in the existing studies. In this paper, a two-stage control strategy is thus developed based on a proposed and experimental validated multi-physics multi-time-scale electro-thermo-hydraulic VRB model. Specifically, in the first stage, the optimal flow rate of the VRB is obtained based on online optimization to reduce parasitic loss and enhance instantaneous system efficiency, and the result serves as the set point of a feedback flow rate controller. In the second stage, dual time scales are specifically considered. And the current and flow rate controllers are designed to meet the highly varying power demands for grid-connected applications. The effectiveness of the proposed control strategy is verified under a scenario to smooth wind power generation. Comparative studies show that compared to the prevailing approaches, higher efficiency can be achieved in tracking the theoretical optimal power profiles for online battery control.

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.

New Stability Concept for Primary Controlled Variable Speed Wind Turbines Considering Wind Fluctuations and Power Smoothing

With a further expansion of the wind energy sector, wind turbines (WTs) have an increasing impact on grid stability. Requirements for a grid connection are adapted continuously, especially for frequency support and the fault ride through (FRT) behavior of WTs. Besides the objectives of robust operational stability and maximum energy yield, a smooth of active power is also of interest to reach efficient load to generation balancing of the grid. With stochastic wind fluctuations, power smoothing methods gain importance, but in combination with primary power control, operation points (OPs) with the risk of operational instability occur. In these OPs with speeds below the maximum power point (MPP), a deceleration of the drive train to standstill is possible. While, so far, to ensure a stable operation especially variable droop is preferred for a WT with power controlled generator, a method to guarantee stability on conventional controller principles with constant droop is missing. Therefore, in this article, a control structure with power smoothing, constant droop, and a stabilizing limitation called MPP-limiter is presented, which ensure robust operational stability of the WT without requirements for additional safety margins. Therewith, the provision of the full primary control power is possible. Based on theoretical investigations, parameter studies, individual scenarios, and test bench results, the demand for the MPP-limiter from a stability perspective is shown. Advantages of the power smoothing and its effect on the energy yield are explained, like an increase of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6\,\mathrm{\%}$</tex-math></inline-formula> of total energy in the transition range between partial and full load.

A Neuro-Adaptive Control Scheme to Improve Dynamic Stability of Wind Power System using Battery Energy Storage

This paper investigates the capability of the battery energy storage system (BESS) to enhance the stability of wind power systems using an improved intelligent control strategy based on neural identification and simultaneous control. The proposed neural identifer comprises of only a few adaptive parameters, which reduce the computational burden and make it easier to implement. It faithfully estimates the local linear model of a dynamic system and adjusts its parameters online for varying operating states of the system. The proposed neuro-adaptive controller needs local measurements only and is model-free. The performance of the proposed controller is compared with the lead-lag controller whose parameters are tuned using teacher learning-based optimization. The performance of the controllers is compared on the basis of various power system stability indices such as oscillation damping, wind-power smoothing and improving the voltage profile of the wind farm under varying operating conditions.

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.