With the increasing participation of wind turbines (WTs) in reactive power regulation, voltage stability can be improved. However, such involvement significantly increases the fatigue load on the low-speed shaft (LSS). Since the fatigue load of WTs varies dynamically with the degree of reactive power support, this paper proposes a hierarchical response control (HRC) method for voltage support in doubly fed induction generators (DFIGs) that considers fatigue load. First, the power requirements of WTs under different grid voltage conditions are clarified. Second, a fatigue load sensitivity model is established to reveal the coupling mechanism between reactive power support and torque fluctuations in the drivetrain. Finally, with reactive power support capability and minimised fatigue load as optimisation objectives, and considering load fluctuation factors, filter parameters are adjusted in real time to avoid the inherent oscillation frequency of the shaft system, and the voltage regulation power is reasonably allocated among the units. Experimental results based on a 10 MW DFIG show that the proposed HRC method ensures reactive power support during grid voltage faults while effectively reducing the fatigue load on the drivetrain and tower. Specifically, under a voltage dip to 0.2 p.u., the proposed strategy reduces the torque fluctuation of the LSS by 3.36% and the fluctuation of the fore-aft bending moment at the tower base by 35.48%; under a voltage swell to 1.25 p.u., the load fluctuation of the LSS is decreased by 23.46% and the mean value of the tower-base fore-aft bending moment is lowered by 31.79%; under a cascading fault (0.2-1.25 p.u.), the standard deviation of the tower-base side-side bending moment is reduced by 42.19%. Furthermore, rainflow counting and the Miner linear cumulative damage rule reveal that the equivalent fatigue load of the LSS is reduced by 7.04%, 28.61%, and 10.65% under the voltage dip, voltage swell, and cascaded fault conditions, respectively.

As wind turbines increasingly participate in Primary Frequency Regulation (PFR) to support grid frequency stability, the accompanying mechanical load variations pose a potential threat to structural reliability. However, the underlying aero-electro-mechanical coupling mechanisms by which PFR-induced power and torque fluctuations affect tower dynamics have not been fully clarified. This study develops a multidimensional analytical model of a wind turbine with PFR to reveal the transmission path from grid frequency deviations to generator torque variations and subsequently to tower side-to-side (SS) bending moments. Frequency-domain and time-domain analyses show that generator torque, rather than aerodynamic thrust, is the dominant excitation source for tower SS vibration during PFR. The results further indicate that this excitation is highly phase-sensitive: the amplification or suppression of tower vibration depends on the instantaneous phase alignment between the torque disturbance and the tower’s natural sway cycle. Therefore, identical PFR commands may lead to substantially different structural responses when activated at different instants. The analytical conclusions are validated using high-fidelity FAST co-simulations under stochastic wind and grid-disturbance scenarios. Quantitative fatigue evaluation shows that uncoordinated PFR may increase the tower SS Damage Equivalent Load (DEL) by up to 600% in low-wind-speed regimes. These findings demonstrate that future wind turbine PFR controllers should incorporate phase-aware coordination strategies to reduce structural resonance and fatigue risks while maintaining effective grid frequency support.

Addressing the issue that traditional optimal torque control algorithms struggle to fully harness the power generation potential of wind turbine generators under actual operating conditions due to neglecting the dynamic changes in air density, this paper proposes an optimal torque control algorithm based on a Convolutional Neural Network (CNN)-Bidirectional Long ShortTerm Memory (BiLSTM) for air density correction. Firstly, the mechanism by which air density affects the optimal torque control algorithm is deeply analyzed. Then, a CNN-BiLSTM model is constructed to achieve high-precision prediction of air density. By combining the correlation between air density and torque gain coefficient, dynamic correction of the torque gain coefficient is completed. Finally, an optimal torque control algorithm incorporating real-time correction of air density is designed, and its specific implementation process and key parameter design are clarified. Finally, by comparing the model prediction accuracy, wind energy utilization coefficient, and power output characteristics of the traditional fixed-parameter optimal torque control algorithm and the proposed improved algorithm, the effectiveness of the improved algorithm is verified. The results show that the proposed algorithm can significantly enhance the wind energy capture efficiency and power output stability of the unit, demonstrating good engineering application value.

This paper addresses a critical and often overlooked challenge: drivetrain fatigue in wind turbines (WTs) induced by primary frequency regulation (PFR) operations, particularly below rated wind speed. While existing PFR strategies focus on grid support, they rarely offer mechanisms to simultaneously mitigate the mechanical stress they impose. To overcome this, a Model Predictive Control (MPC) strategy is proposed that adaptively adjusts the PFR gain in real time. First, the boundary characteristics of the de-loading factor under varying wind speeds and PFR gains are systematically analyzed. This analysis bridges a critical gap in prior studies, which predominantly adopted fixed-gain strategies without comprehensively evaluating their fatigue implications. Building on these boundary characteristics, an MPC framework is developed to optimally adjust the frequency regulation gain in real time. This approach achieves an optimization by minimizing drivetrain torque fluctuations for fatigue mitigation, critically informed by a comprehensive boundary analysis of de-loading factors, while rigorously ensuring essential PFR support capability through explicit operational constraints. Extensive simulations demonstrate the superior performance of the proposed MPC, achieving a simultaneous reduction of up to 27.99% in drivetrain fatigue load and 14.44% in frequency deviations compared to conventional methods. This work significantly enhances the mechanical reliability of WTs and facilitates their more sustainable integration into modern power grids by offering a unique solution to a long-standing trade-off.

This paper proposes a flexible and safe model predictive control strategy for wind turbines to improve grid frequency support while reducing drivetrain fatigue loads. As wind power penetration increases, wind turbines are increasingly required to participate in frequency regulation, but frequent active power adjustment may intensify shaft fatigue. A discrete-time state-space model of the coupled wind turbine and power grid is first established, and a fatigue-load sensitivity model is introduced to describe the influence of power and pitch commands on drivetrain torque fluctuation. Based on real-time operating data, the controller performs rolling optimization of generator power and blade pitch angle under frequency support, power balance, and load constraints. Thus, the frequency response intensity is adjusted according to the real-time mechanical state of the turbine. Simulation results under +0.2 Hz and +1 Hz frequency disturbances show that the proposed strategy provides directionally consistent power support, with mean active-power differences below 0.01%. The lowspeed shaft fatigue load is reduced by up to 7.67%, and the tower base fore-aft fatigue load is reduced by up to 9.44%. Field tests on a 10 MW doubly-fed wind turbine validate its effectiveness.

Wind turbine (WT) engagement in frequency response enhances overall system frequency stability. However, it concurrently results in elevated fatigue loads on the low-speed shaft (LSS). To mitigate the fatigue loads on the LSS while maintaining system frequency stability, this paper introduces a comprehensive wind-storage primary frequency regulation (PFR) method, which takes into account the fatigue loads on the LSS of the WT. Initially, the impact of PFR on the fatigue loads of the LSS in the WT is analyzed. To mitigate the LSS's fatigue loads, an improved torsional vibration control method based on generator speed is introduced. This method markedly decreases LSS fatigue loads. The fatigue loads can be reduced by 20.94 %. However, torsional vibration methods may intensify WT output power fluctuations, which pose issues for LSS torsional vibration suppression. Building on this foundation, a combined wind-storage strategy utilizing a low-pass filtering algorithm is proposed. Supercapacitor (SC) storage undergoes charging and discharging in response to frequency changes to achieve WT power smoothing. The validity of proposed method is confirmed through time-domain simulation. The results demonstrate that the proposed method reduces system frequency fluctuations with the frequency deviation being reduced by around 50 %. Additionally, it can further decrease the damage equivalent load of the LSS by more than 9 %. Finally, by comparing the proposed method against a fuzzy logic-based wind-storage coordinated method, the effectiveness of the proposed method is verified.

To improve the economic efficiency of urban integrated energy systems (UIESs) and mitigate day-ahead dispatch uncertainty, this paper presents an interconnected UIES and transmission system (TS) model based on distributed robust optimization. First, interconnections are established between a TS and multiple UIESs, as well as among different UIESs, each incorporating multiple energy forms. The Bregman alternating direction method with multipliers (BADMM) is then applied to multi-block problems, ensuring the privacy of each energy system operator (ESO). Second, robust optimization based on wind probability distribution information is implemented for each ESO to address dispatch uncertainty. The column and constraint generation (C&CG) algorithm is then employed to solve the robust model. Third, to tackle the convergence and practicability issues overlooked in the existing studies, an external C&CG with an internal BADMM and corresponding acceleration strategy is devised. Finally, numerical results demonstrate that the adoption of the proposed model and method for absorbing wind power and managing its uncertainty results in economic benefits.

This study proposes a novel approach to address the issues of inadequate frequency regulation capabilities and increased fatigue loads in wind turbines operating below rated wind speeds. The limitations of conventional primary frequency regulation in wind turbines are initially analyzed. The research reveals that, below rated wind speeds, the output power fluctuations of wind turbines with high wind power penetration have a significant impact on system frequency deviation. Additionally, insufficient output power affects the frequency regulation capability of wind turbines. Furthermore, primary frequency regulation contributes to a substantial increase in fatigue loads on wind turbines. To overcome these challenges, we present a wind-energy storage coordinated primary frequency regulation method that integrates wind power prediction and model predictive control. The effectiveness of the proposed approach is validated through simulation studies.

As an important part of high-proportion renewable energy power system, battery energy storage station (BESS) has gradually participated in the frequency regulation market with its excellent frequency regulation performance. However, the participation of BESS in the electricity market is constrained by its own state of charge (SOC). Due to the inability to accurately predict the next day's real-time SOC, the mismatch between bidding strategy and real-time scheduling is easy to occur. Aiming at the multi time scale clearing mechanism in the frequency regulation market, this paper divides the bidding strategy of the BESS participating in the frequency regulation market into two stages: the day ahead market (DAM) and the real time market (RTM). In the pre-clearing stage of the DAM, since it is impossible to accurately predict the frequency regulation command of the next day, the cost of dynamic frequency regulation of BESS is reasonably simplified to avoid the loss of BESS to the maximum extent. In the real-time clearing stage of the RTM, the capacity constraint factor is introduced according to the real-time SOC of BESS, and the initial quotation before the day is adjusted, so that the time-varying characteristic of the SOC of BESS is considered in the bidding strategy.

With the integration of massive distributed resources into the distribution network, the traditional centralized control approach struggles to meet the extensive energy and information interaction demands of numerous nodes. To address the developmental needs of new-generation distribution networks, this paper introduces a hierarchical control strategy based on source-load clusters. Initially, the paper defines the concept of source-load clusters and establishes a multi-tier control architecture consisting of distributed source-loads, distributed source-load aggregators, source-load cluster energy management centers, and distribution network operation centers. Subsequently, it designs a method for autonomous and hierarchical operation in each source-load cluster region, allowing each cluster to develop a day-ahead operation plan based on its unique source-load characteristics. Finally, through case studies, the paper demonstrates that the proposed control strategy effectively implements hierarchical energy control, reduces the control burden on distribution network operators, and achieves orderly power dispatch and maximal resource utilization.

Wind turbines' participation in frequency response is known to improve the frequency stability of power systems, but it can also have a negative impact on the fatigue load of wind turbines. The objective of this paper is to investigate the effect of frequency response on the fatigue loads experienced by various components of a wind turbine, including the low-speed shaft, tower, and blade. To achieve this goal, the authors develop a model of a variable speed horizontal-axis wind turbine based on a doubly fed induction generator. They derive explicit analytical equations of low-speed shaft torque, tower bending moment, and blade bending moment to describe the fluctuations of torque and moment related to the operating states of wind turbines, such as generator torque, rotor speed, and pitch angle, under frequency response. These equations allow for the evaluation of the impact of frequency response on torque and moment changes and fatigue load. Spectral density analysis and modal analysis are used to further analyze the analytical equations, examining the influence of frequency response on different operating conditions of wind turbines and determining the mechanism by which frequency response affects fatigue load qualitatively and quantitatively. The authors use the FAST V8 Code based on the NREL offshore 5-MW baseline wind turbine to demonstrate the effectiveness of the proposed analysis method in evaluating fatigue loads affected by frequency response. The results show that the fatigue load on the low-speed shaft and the lateral side of the tower will significantly increase due to wind turbine participation in frequency response.

This study primarily analyzes the frequency regulation capability and fatigue loads of wind turbines based on over-speed control. Initially, a small-signal model of the wind turbine is established, which describes the output characteristics of the wind turbine under different control modes and wind speeds. Next, the model is used to analyze the wind turbine’s frequency regulation capability and to calculate the optimal frequency regulation parameter range based on the phase margin. Finally, a combination of frequency domain and time domain analysis is used to examine the influence of over-speed control on the fatigue loads of low-speed shafts, towers, and blades, which determines the wind speed range suitable for frequency regulation. The Fast (Fatigue, Aerodynamics, Structures, and Turbulence) Code V8 is used to simulate the dynamic characteristics of the wind turbine.

The operational range of a wind turbine is typically divided into two regions based on wind speed: below and above the rated wind speed. The turbine switches between these two regions depending on the prevailing wind speed; however, during the transition, the generator may undergo transient shocks in torque, which can negatively impact both the mechanical load of the turbine and the reliability of the power system. This article presents a flexible torque control method for wind turbines, specifically designed to handle the transition between wind speed regions when the turbine is participating in frequency regulation. First, the anomalies in generator torque caused by traditional torque control methods during frequency response scenarios are analyzed. Next, two methods—dynamic deloading and flexible torque control—are developed to address these issues. The developed methods set transition regions based on generator speed, which helps to reduce the impact of transient changes in generator torque. Importantly, the addition of transition regions does not require additional feedback, making the controller easy to implement. The response characteristics of the proposed methods are then analyzed under different deloading factors and wind speeds using model linearization. Simulation studies are presented to verify the effectiveness of the proposed methods. Overall, this study demonstrates the potential value of flexible torque control methods for wind turbines, which can help to mitigate the negative impact of torque shocks and improve the reliability and efficiency of wind power systems.

随着以新能源为供给主体的新型电力系统建设思想提出,确保电网供电可靠性、提升新能源消纳率成为两大难题,从需求侧出发,开发长时间尺度电力市场不失为一种新的破局方法.山东电网提出参考需求侧资源参与容量市场方式,结合现有备用辅助市场,建立以负荷资源为主体的长周期备用市场,实现电网消纳新能源与参与主体创收的双赢.该文参考容量市场出清结果,基于K均值聚类算法,采用七大物理特征指标降维,针对调节潜力最大的工业负荷建立多时间长度响应潜力量化评估模型,并采用交付年与日负荷曲线采样年的行业用电量之比作为长周期修正系数,将求得的需求响应潜力映射至长周期市场,为负荷资源参与长周期备用市场能力的评估提供一定的参考.

The primary frequency regulation of the wind turbine can reduce the frequency deviation of the power system to improve the stability of the frequency. However, the primary frequency regulation increases the vibration of the WT's drive train. In this study, auxiliary damping control is proposed to mitigate the vibration of the drive train caused by primary frequency regulation. Firstly, the influence of frequency regulation on the torque of the drive train is analyzed. Then an additional damping control method is proposed, and the influence of the control parameters on the control effect is analyzed. Finally, the effectiveness of the control method is verified by simulation under different wind speeds. The results show that under the same frequency performance, the proposed method can reduce the standard deviation of the drive train torque by more than 50%.

Wind turbine (WT) have attracted more and more attention as a potential way to compensate the fall of the power system inertia by participating in inertial control (IC). However, such inertial control would frequently change the generator torque of the WT according to the system frequency, which increases the fatigue load of the main shaft. However, previous studies have failed to demonstrate a connection between inertia control and WT fatigue load. Thus, this study mainly focuses on the shaft fatigue load caused by inertial control. First, the impact of inertial control on WT fatigue load is analyzed in details. A WT small signal model is established, by which the impact mechanism of inertial control on the shaft torque and tower bending moment could be illustrated. Then, to reduce the impact of inertial control on the WT fatigue load, a PI based mitigation control (PIMC) strategy for the WT fatigue load mitigation is proposed. Simulations in both frequency and time domain are performed to demonstrate the correctness of the proposed method. The results show that the proposed method could greatly reduce the fatigue load of the main shaft while improving the inertia response capacity of the WT.

随着新能源机组的不断接入,目前我国电力系统存在充裕性灵活性不足、电力市场部分主体收益降低等问题.亟需进一步引入电力容量市场机制,保障市场主体收益及电力系统稳定运行.借鉴美国容量市场建设成功经验,基于"碳达峰、碳中和"的节能降碳目标以及我国电力市场发展现状,提出容量市场机制设计方案及相应的竞标策略,设置算例仿真验证其合理性,并通过与纯能量市场运行结果进行对比探究容量市场的重要意义.仿真结果表明,该机制及竞标策略能够激励各类市场主体积极参与容量市场,在最大化市场主体收益的同时为电力系统储备充足能源,从而提高电力系统整体充裕性与灵活性.

The increasing integration of large-scale wind power aggravates the difficulty of maintaining system frequency deviations in a certain range. The frequency regulation pressure of conventional generators increases, which requires wind farms to participate in system frequency regulation. In this paper, a multi-area interconnected power system frequency response model with wind power is established. Based on the frequency response model, the state space model of regional interconnected power system is presented. Then, the wind power variogram characteristics are introduced for estimating wind power variations in different time-scales. By predicting the wind power variations in AGC time-scale, a strategy of wind farm participating in AGC system is proposed and performed based on model predictive control (MPC). The control strategy makes the conventional units and wind farms to participate in AGC system coordinately. Simulation results are provided which verifies the feasibility and validity of the proposed strategy.

Frequency regulation adjusts the power or torque of the wind turbine generator (WTG) without considering the operating dynamics of WTG's shaft and tower, which results in a significant increase in the fatigue load experienced by the shaft torque and tower bending moment. In that, this paper proposes an optimal ancillary control (OAC) method to mitigate the above-mentioned fatigue load. The establishment of the OAC method includes the improvement of fatigue load sensitivity calculation accuracy and the formulation of optimization objective and constraints. Most importantly, the dual-mass drive train and wind speed fluctuations are considered by the fatigue load sensitivity. Moreover, the minimization of the fluctuation of main shaft torque and tower fore-aft bending moment is employed as the optimal objective. Finally, the dynamic adjustment of the power reference is reflected through the constraints. As a result, the OAC method can significantly reduce the WTG fatigue load and thus improve the system frequency stability. Case studies are conducted under various working conditions. By analyzing the damage equivalent load, rainflow cycles and frequency response, the effectiveness of the proposed method is verified.