A wind-storage combined frequency control method that takes voltage stability into account under weak power grid conditions

Abstract

This invention provides a wind power and energy storage joint frequency control method for weak power grids that takes voltage stability into account. It relates to the power supply system field and includes: collecting the operating status of the power grid and the wind power and energy storage; identifying the power grid state based on these operating statuses; calculating the maximum allowable active power increment and speed recovery adjustment coefficient under voltage constraints when the power grid state is weak; solving for the optimal power allocation coefficient using an improved gray wolf optimization algorithm based on the maximum allowable active power increment and speed recovery adjustment coefficient; and executing a frequency response based on the optimal power allocation coefficient. This method has the advantage of providing high-quality frequency support while ensuring voltage safety in weak power grids.

Description

Technical Field

[0001] This invention relates to the field of power supply systems, and in particular to a wind power and energy storage combined frequency control method that takes into account voltage stability under weak power grid conditions. Background Technology

[0002] With the large-scale integration of wind power into regional power grids, the coordinated control of frequency stability and voltage security in weak grid environments has become a key challenge.

[0003] The existing technology has the following limitations:

[0004] First, the single-unit control strategy of inertia technology achieves rapid frequency support by releasing the kinetic energy of the wind turbine rotor. However, limited by the rotor's energy storage capacity, a speed recovery phase must begin after the inertia response ends. During this process, the wind turbine needs to reduce its active power output or even absorb power to restore its speed. Existing control strategies struggle to precisely coordinate the recovery timing with the system frequency state, leading to a power gap before the grid frequency stabilizes, resulting in severe secondary frequency drops. This dynamic performance degradation is particularly pronounced in weak grid environments, where frequency fluctuations are more easily amplified due to the lower system inertia.

[0005] Secondly, traditional wind-storage coordinated control strategies neglect the strong PV coupling characteristics of weak grids. Existing methods are mostly based on the "single-unit infinite power" system assumption, employing active power-frequency (Pf) single-loop control, failing to consider the low short-circuit ratio (SCR) characteristic commonly found at the grid ends in wind-rich areas. In weak grids, line resistance is not negligible; rapid changes in active power can cause significant voltage fluctuations through line impedance, forming a strong PV coupling effect. To pursue frequency response indicators, existing strategies often aggressively inject active power into wind turbines and energy storage, but lack real-time monitoring and constraint mechanisms for the grid connection voltage safety boundary. This active power support method easily leads to significant voltage drops, even triggering the unit's low-voltage ride-through protection and disconnecting it from the grid, resulting in a trade-off between frequency support and voltage safety.

[0006] Furthermore, regional power grid modeling and control technologies suffer from a lack of frequency-voltage coordinated protection. Existing regional power grid research presents a disconnect between frequency control and voltage control: frequency response studies often simplify voltage dynamic models, while voltage control studies fail to consider the active power impact during frequency dips. In multi-node regional power grids, this disconnect leads to a lack of cross-dimensional coordinated protection mechanisms. Specifically, this manifests as: the inability to monitor voltage status in real time and limit active power commands during virtual inertia response; the inability to prioritize reactive power support resources when system frequency deteriorates; and the potential for single-node frequency regulation behavior to disrupt local voltage stability, thereby causing regional power flow imbalances. Particularly in weak power grids with high wind power penetration, this lack of coordination significantly reduces the system's disturbance immunity.

[0007] Therefore, there is a need for a wind-storage combined frequency control method that takes into account voltage stability under weak power grid conditions, in order to provide high-quality frequency support while ensuring voltage safety in weak power grids. Summary of the Invention

[0008] This invention provides a wind-storage joint frequency control method for weak power grids that takes voltage stability into account, comprising: collecting the power grid operating status and the wind-storage operating status; identifying the power grid status based on the power grid operating status and the wind-storage operating status; when the power grid status is a weak grid status, calculating the maximum allowable active power increment and the speed recovery adjustment coefficient under voltage constraints; using an improved gray wolf optimization algorithm, solving for the optimal power allocation coefficient based on the maximum allowable active power increment and the speed recovery adjustment coefficient; and executing the frequency response according to the optimal power allocation coefficient.

[0009] Furthermore, based on the power grid operating status and the wind and energy storage operating status, the power grid status is identified, including: calculating the short-circuit ratio based on the power grid operating status and the wind and energy storage operating status; and identifying the power grid status based on the short-circuit ratio and the short-circuit ratio threshold.

[0010] Furthermore, the calculation of the maximum permissible active power increment under voltage constraints includes: constructing a sensitivity mathematical model between grid connection point voltage fluctuation and injected power; and calculating the maximum permissible active power increment under voltage constraints based on the grid connection point voltage, the grid connection point voltage safety threshold, and the sensitivity mathematical model between grid connection point voltage fluctuation and injected power under grid operation conditions.

[0011] Furthermore, the maximum permissible active power increment under voltage constraints is calculated using the following formula:

[0012] ,

[0013] in, This represents the maximum permissible active power increment under voltage constraints. The voltage at the grid connection point. The grid connection point voltage safety threshold, The rated voltage at the grid connection point, This is the voltage safety margin factor. This is the equivalent resistance on the grid side.

[0014] Further, the calculation of the speed recovery adjustment coefficient includes: constructing a fuzzy inference rule base for speed recovery; and calculating the speed recovery adjustment coefficient based on the regional power grid wind power penetration rate, the real-time speed of the wind turbine rotor, and the fuzzy inference rule base for speed recovery.

[0015] Furthermore, using an improved gray wolf optimization algorithm, the optimal power allocation coefficient is solved based on the maximum allowable active power increment and the speed recovery adjustment coefficient. This includes: generating an initial gray wolf population using chaotic mapping, where each gray wolf in the population represents a power allocation coefficient; constructing a comprehensive fitness function based on the maximum allowable active power increment and the speed recovery adjustment coefficient; and solving for the optimal power allocation coefficient based on the initial gray wolf population and the comprehensive fitness function.

[0016] Furthermore, the comprehensive fitness function is:

[0017] ,

[0018] in, The overall fitness value, The system's rated frequency, For the power allocation factor Below, the simulation shows the lowest system frequency. As the frequency normalization reference value, Frequency recovery time, As the time-normalized reference value, In a voltage hard-constraint state, if the power allocation coefficient is... If the total output power is greater than the maximum allowable active power increment, then ,otherwise, , In a soft-constraint state of rotational speed, if the power distribution coefficient is... If the speed recovery process violates the speed recovery adjustment coefficient, then... ,otherwise, , As a penalty factor, , These are the weighting coefficients.

[0019] Furthermore, the position update of the gray wolf introduces a nonlinear convergence factor adjustment strategy.

[0020] Furthermore, based on the optimal power allocation coefficient, a frequency response is executed, including: calculating the inertia support power required to smooth frequency changes based on the frequency change rate in the grid operating state; determining the total power reference value based on the maximum allowable active power increment under voltage constraints and the inertia support power; and generating doubly-fed wind turbine power commands and energy storage system power commands based on the optimal power allocation coefficient and the total power reference value.

[0021] Furthermore, it also includes: calculating the rate of decline of the grid connection point voltage; determining whether to freeze the power command of the doubly-fed induction generator (DFIG) based on the rate of decline of the grid connection point voltage and the grid connection point voltage safety threshold; and determining whether to adjust the operating mode of the DFIG and the energy storage system based on the grid connection point voltage and the minimum critical value of the grid connection point voltage.

[0022] Compared with existing technologies, the wind-storage joint frequency control method provided by this invention, which takes into account voltage stability under weak power grid conditions, has at least the following beneficial effects:

[0023] 1. Existing wind power and energy storage frequency control methods often assume a rigid power grid and neglect the impact of line impedance on voltage. This leads to a sharp drop in the point-of-connection voltage (PCC) under weak grid conditions when wind turbines increase active power to support the frequency, potentially triggering undervoltage disconnection protection. This method innovatively introduces a safe threshold constraint for the PCC into the frequency control loop. By constructing a PV sensitivity model under weak grid conditions, it quantifies voltage safety requirements into a specific maximum allowable active power increment. The system can sense the voltage floor in real time and dynamically limit the active power output of wind turbines during frequency adjustments, preventing voltage collapse due to power overload.

[0024] 2. Traditional control strategies employ a fixed-rate speed recovery after the wind turbine releases its inertia. This causes the turbine to absorb a large amount of power from the grid to quickly recover its speed, exacerbating grid vulnerability and triggering a secondary frequency sag. This method designs an adaptive speed recovery mechanism based on fuzzy logic, intelligently adjusting the speed recovery adjustment coefficient according to wind power penetration and real-time speed. When the grid is vulnerable or the frequency is unstable, the system automatically slows down the speed recovery rate, smoothing power fluctuations and avoiding frequency degradation caused by the turbine absorbing power. Compared to the traditional fixed-rate recovery strategy, this mechanism significantly reduces the depth of the secondary frequency sag, achieving a smooth system frequency transition while ensuring the turbine does not stall. This improves transient frequency indicators and enhances the grid's immunity in high wind power penetration scenarios.

[0025] 3. Existing technologies do not fully consider hard voltage constraints, leading to unreasonable power distribution under complex operating conditions, resulting in idle energy storage resources or excessive wind turbine output. This method, through an improved Grey Wolf optimization algorithm, constructs a wind-storage collaborative model incorporating hard voltage constraints. This model directs the energy storage system to actively fill the power gap caused by voltage limiting or speed recovery of the wind turbine, utilizing its millisecond-level response characteristics. This collaborative mechanism leverages the rapid and precise regulation capabilities of energy storage while ensuring the safe operation of the wind turbine. Under the premise of ensuring voltage safety, it significantly raises the minimum system frequency, achieving comprehensive optimization of frequency regulation performance. Simultaneously, it improves the utilization rate of wind and energy storage resources, providing technical support for the efficient consumption of new energy sources under weak grid conditions. Attached Figure Description

[0026] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0027] Figure 1This is a flowchart illustrating a wind-storage combined frequency control method that takes voltage stability into account under weak power grid conditions, as shown in some embodiments of this specification.

[0028] Figure 2 This is a flowchart illustrating an improved gray wolf optimization algorithm according to some embodiments of this specification;

[0029] Figure 3 This is a schematic diagram of the simulation test system architecture and signal flow according to some embodiments of this specification;

[0030] Figure 4 This is a comparison diagram of the system frequency response shown in some embodiments of this specification;

[0031] Figure 5 This is a voltage waveform diagram of the grid connection point after voltage constraint control, as shown in some embodiments of this specification;

[0032] Figure 6 This is a diagram of the grid connection point voltage waveform without applied voltage constraint, as shown in some embodiments of this specification.

[0033] Figure 7 This is a waveform diagram of the active power output of a doubly-fed wind turbine shown in some embodiments of this specification;

[0034] Figure 8 This is an active power output waveform diagram of an energy storage system shown in some embodiments of this specification;

[0035] Figure 9 This is a fan speed curve diagram shown according to some embodiments of this specification. Detailed Implementation

[0036] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0037] Figure 1 This is a flowchart illustrating a wind-storage combined frequency control method that balances voltage stability under weak power grid conditions, based on some embodiments of this specification. Figure 1 As shown, a wind-storage joint frequency control method that takes into account voltage stability under weak power grid conditions may include the following steps.

[0038] Step 110: Collect the power grid operation status and wind and energy storage operation status.

[0039] Specifically, the power grid operation status and wind-storage operation status are collected in real time using a wide area measurement system (WAMS) deployed in the regional power grid or a phasor measurement unit (PMU) located at the point of common coupling (PCC) of the wind farm. The sampling period is set to a preset time (e.g., 10 ms / time, 15 ms / time, 20 ms / time, etc.).

[0040] The power grid operating status can include at least:

[0041] Grid connection point common connection voltage (U pcc ): The effective voltage value at the connection point of the wind-storage system. This parameter directly reflects the voltage stability level. In a weak grid environment, the line impedance is relatively large, and rapid changes in active power will cause significant voltage fluctuations through impedance voltage drop.

[0042] System frequency (f) and its rate of change (df / dt): These reflect the active power balance of the regional power grid. When generation and load are unbalanced, f deviates from its rated value (e.g., 50Hz), and df / dt (frequency change rate) further quantifies the severity of the imbalance. The larger df / dt is, the more severe the power deficit and the less system inertia it has. For example, during periods of high wind power generation and sudden load drop, f may rise rapidly, requiring the wind-storage system to absorb active power to suppress frequency overruns. Conversely, during periods of sudden load increase or generation failure, f decreases and df / dt becomes negative, necessitating the release of rotor kinetic energy or energy storage power to provide inertia support.

[0043] The operating status of wind and storage systems can include at least the following:

[0044] Doubly fed wind turbine operating status parameters: including the real-time rotor speed ω of the wind turbine. r and the current active power P output by the wind turbine wind It is used to evaluate the kinetic energy reserve and adjustable range of the wind turbine during the subsequent speed recovery phase.

[0045] Energy storage system state variables: including the current output P of the energy storage system ESS The State of Charge (SOC) is used to assess the power throughput and sustained operating time of an energy storage system.

[0046] Step 120: Identify the grid status based on the grid operation status and the wind and energy storage operation status.

[0047] Specifically, it includes:

[0048] Calculate the short-circuit ratio based on the power grid operation status and the wind and energy storage operation status;

[0049] The power grid status is identified based on the short-circuit ratio and short-circuit ratio threshold.

[0050] Specifically, based on Thevenin's equivalence principle, the short-circuit capacity S at the grid connection point is estimated in real time using the collected voltage and current data. SC The calculation formula is as follows:

[0051] ,

[0052] in, The rated voltage at the grid connection point, It is the equivalent impedance magnitude on the grid side, including the transmission line resistance R and inductance L.

[0053] Based on the estimated short-circuit capacity, the short-circuit ratio is further calculated:

[0054] ,

[0055] in, The rated installed capacity of the wind farm (or wind farm group) connected to the grid connection point.

[0056] The short-circuit ratio threshold is a preset value, for example, the short-circuit ratio threshold SCR. th =3.0. Based on simulation analysis, when the SCR is below 3.0, the sensitivity of the grid connection point voltage to changes in active power exhibits a nonlinear surge characteristic, entering a strong coupling region. At this point, without voltage constraints, the conventional frequency response can easily trigger a low-voltage grid disconnection accident. Meanwhile, referring to IEEE and relevant national standards, this value is selected as the short-circuit ratio threshold. This ensures a safe voltage baseline while minimizing the waste of the wind turbine's frequency regulation capability due to premature limiting, achieving an optimal balance between safety and regulation performance.

[0057] If SCR≥SCR th The current power grid is determined to be in a strong grid state. At this time, the line impedance is relatively low, the voltage support capacity is strong, and the system will execute the conventional virtual inertia control strategy.

[0058] If SCR < SCR th The current power grid is determined to be in a weak grid state. At this time, the line impedance is relatively high, and the injection of active power will cause significant voltage fluctuations (enhanced PV coupling effect), automatically activating the voltage stability constraint mode.

[0059] Step 130: When the power grid is in a weak state, calculate the maximum allowable active power increment and speed recovery regulation coefficient under voltage constraints.

[0060] In some embodiments, calculating the maximum permissible active power increment under voltage constraints includes:

[0061] Construct a sensitivity mathematical model between grid connection point voltage fluctuation and injected power;

[0062] Based on the mathematical model of grid connection point voltage, grid connection point voltage safety threshold, and sensitivity between grid connection point voltage fluctuation and injected power in the power grid operation state, the maximum allowable active power increment under voltage constraints is calculated.

[0063] Specifically, at the end of a weak power grid, the voltage fluctuation at the grid connection point Changes in active power injected into the wind storage system and reactive power variation The following sensitivity mathematical model is approximately satisfied.

[0064] ,

[0065] in, and These are the equivalent resistance and reactance on the grid side, respectively. This refers to the rated voltage at the grid connection point. Specifically, the frequency response process addressed in this invention primarily relies on active power regulation. In weak grid environments, due to the long transmission lines and small cross-sections, the equivalent resistance... The large value results in an active power term in the equation. The weighting of the impact on voltage is significantly increased. This sensitivity mathematical model clearly quantifies the potential threat of active power surges to voltage stability, providing a physical basis for subsequent limiting calculations.

[0066] Based on the above sensitivity model, the maximum active power that can be injected at the current moment is calculated in real time to establish the voltage safety boundary.

[0067] In some embodiments, the maximum permissible active power increment under voltage constraints is calculated according to the following formula:

[0068] ,

[0069] in, This represents the maximum permissible active power increment under voltage constraints. The voltage at the grid connection point. The grid connection point voltage safety threshold, The rated voltage at the grid connection point, This is the voltage safety margin factor. This is the equivalent resistance on the grid side.

[0070] Preferably, the grid connection point voltage safety threshold is set to a per-unit value of 0.90. This value is set slightly higher than the grid undervoltage ride-through protection action value to allow for a margin. The value ranges from 1.05 to 1.1, and is used to offset the effects of measurement errors and system nonlinearity.

[0071] The above formula transforms the abstract voltage stability requirement into a specific active power limit.

[0072] In some embodiments, calculating the speed recovery adjustment coefficient includes:

[0073] Construct a fuzzy inference rule base for speed recovery;

[0074] The speed recovery adjustment coefficient is calculated based on the regional power grid wind power penetration rate, the real-time speed of the wind turbine rotor, and the fuzzy inference rule base for speed recovery.

[0075] Specifically, the wind power penetration rate of the regional power grid can be calculated using the following formula:

[0076] ,

[0077] in, For the regional power grid wind power penetration rate, For the total wind power output in the region, This represents the total load of the regional power grid. This indicator is used to characterize the degree to which synchronous generator units in the system have been replaced, and the penetration rate. The higher the value, the weaker the system's frequency disturbance immunity, and the more gradual the subsequent speed recovery strategy should be.

[0078] The regional power grid wind power penetration rate and the real-time rotor speed of the wind turbines are selected as input variables for the fuzzy controller. The regional power grid wind power penetration rate reflects the current inertia level of the system. A higher penetration rate means a more fragile system and a weaker ability to withstand power backflow. The real-time rotor speed of the wind turbines reflects the current kinetic energy reserve state of the turbines. A lower speed means the turbines are closer to stall and the need to restore speed is more urgent.

[0079] Using triangular or trapezoidal membership functions, the above input variables are fuzzified and mapped to fuzzy language sets such as {low (L), medium (M), high (H)}. For example, when wind power penetration rate... When the value is high, its membership degree on the "high (H)" linguistic variable approaches 1.

[0080] Based on expert experience and system operating characteristics, fuzzy inference rules are formulated. The core control logic follows these principles:

[0081] When wind power penetration is high: regardless of the speed status, the system should adopt a "slow recovery" strategy, outputting a smaller adjustment coefficient to delay the speed recovery process and avoid the already fragile grid frequency from dropping sharply again due to rapid power absorption.

[0082] When wind power penetration is low and the rotational speed is extremely low, it indicates that the grid inertia is sufficient but the wind turbine is at risk of stalling. In this case, a "rapid recovery" strategy is allowed, with a larger regulation coefficient output to prioritize the physical safety of the wind turbine.

[0083] When both are in an intermediate state: adopt a "moderate recovery" strategy to seek a balance between maintaining frequency stability and restoring equipment status.

[0084] For the wind power penetration rate of the regional power grid, its actual physical range is set to the universe of discourse [0,1] (i.e., 0% to 100%). It is divided into three fuzzy language sets: {Low (L), Medium (M), High (H)}. For example, Low (L): uses a trapezoidal membership function with a parameter range of [0,0,0.25,0.45]. Medium (M): uses a triangular membership function with a parameter range of [0.25,0.5,0.75]. High (H): uses a trapezoidal membership function with a parameter range of [0.55,0.75,1.0,1.0].

[0085] For the real-time rotor speed of the wind turbine, combined with the safe operating range of the doubly-fed induction generator (DFIG), its per-unit domain is set to [0.7, 1.2] pu. This is divided into three fuzzy language sets: {Low (L), Medium (M), High (H)}. For example, Low (L): parameter range is [0.7, 0.7, 0.8, 0.95]. Medium (M): parameter range is [0.2, 0.5, 0.8]. High (H): parameter range is [0.5, 0.8, 1.0, 1.0].

[0086] For the output variable, the speed recovery adjustment coefficient, its normalized universe of discourse is set to [0,1]. It is divided into three fuzzy language sets: {Small (S), Medium (M), Large (B)}. For example, Small (S): parameter range [0,0,0.2,0.5], representing slow recovery. Medium (M): parameter range [0.2,0.5,0.8], representing moderate recovery. High (H): parameter range [0.5,0.8,1.0,1.0], representing fast recovery.

[0087] Based on the system's operational characteristics, a 3×3 fuzzy inference rule matrix is ​​constructed. The core logical principle is: the more vulnerable the power grid (the larger ρ is), the slower the recovery to maintain frequency; the more dangerous the wind turbine (… The lower the permeability, the faster the recovery to ensure physical safety. When the two conflict (e.g., high permeability and low rotation speed), a compromise strategy is adopted. Specific fuzzy control rules are shown in Table 1:

[0088] ,

[0089] The inference results are defuzzified using the center of gravity method to output a precise speed recovery adjustment coefficient. Its value range is usually normalized to 0 to 1. The smaller the speed recovery adjustment coefficient, the more stringent the power lock-in limit will be imposed on the wind turbine when the algorithm performs power allocation, forcing it to maintain the current output for a longer period of time or only slowly decrease it, thereby eliminating the risk of secondary frequency drop at the physical level.

[0090] Specifically, the speed recovery adjustment coefficient can be calculated using the following formula. :

[0091] ,

[0092] in, To output the discrete values ​​within the universe of discourse of the variable. The corresponding membership value is obtained after calculation using fuzzy inference rules. n The number of sampling levels.

[0093] Step 140: Using the improved Grey Wolf optimization algorithm, the optimal power allocation coefficient is solved based on the maximum allowable active power increment and the speed recovery adjustment coefficient.

[0094] Specifically, it includes:

[0095] The initial gray wolf population is generated using chaotic mapping. Compared to pseudo-random initialization, this strategy enhances the ergodicity of the initial solution within the feasible region (0~1), effectively preventing the algorithm from getting trapped in local optima and ensuring that a globally optimal power allocation ratio can be found. Each gray wolf in the population represents a power allocation coefficient. ,in, , This represents the proportion of power handled by the wind turbine side. This represents the proportion of power supplied by the energy storage side;

[0096] A comprehensive fitness function is constructed based on the maximum permissible active power increment and the speed recovery adjustment coefficient;

[0097] Based on the initial gray wolf population and the overall fitness function, the optimal power allocation coefficient is solved.

[0098] In some embodiments, the comprehensive fitness function is:

[0099] ,

[0100] in, The overall fitness value, The system's rated frequency, For the power allocation factor Below, the simulation shows the lowest system frequency. As the frequency normalization reference value, Frequency recovery time, As the time-normalized reference value, In a voltage hard-constraint state, if the power allocation coefficient is... If the total output power is greater than the maximum allowable active power increment, then ,otherwise, This ensures that no gray wolf causes the voltage to exceed the limit. In a soft-constraint state of rotational speed, if the power distribution coefficient is... If the speed recovery process violates the speed recovery adjustment coefficient, then... ,otherwise, , The penalty factor is a sufficiently large positive number, for example, 10⁶. This penalty factor is much larger than the normal calculated value and is used to impose a very large fitness penalty on solutions that violate the constraints. , These are the weighting coefficients.

[0101] Specifically, it can be based on the power allocation factor. Calculate the power distribution factor Corresponding target reference power of wind turbine ,in, For the total power reference value, the following inequality conditions must be satisfied:

[0102] ,

[0103] in, This represents the actual active power output of the doubly-fed wind turbine in the previous control cycle. This represents the maximum allowable active power reduction step size constant for a doubly-fed induction generator (DFIG) at the physical level. Otherwise, specify the power distribution coefficient. This will cause a severe secondary frequency drop, at this power distribution factor. The speed recovery process violates the speed recovery adjustment coefficient, and therefore this process is directly eliminated in the current iteration. The corresponding gray wolf individual. Through this quantization inequality, the fuzzy controller outputs... Successfully mapped to the search boundary of the optimization algorithm: when When the power grid is smaller (more vulnerable), the algorithm is forced to find methods that cause the wind turbines to gradually reduce their output. This value enables a closed-loop control logic at the mathematical and algorithmic level to suppress secondary frequency drops.

[0104] Traditional algorithms typically search the entire space, easily generating solutions that violate voltage safety. This invention embeds the PV sensitivity equation derived in step 2 into the algorithm's fitness evaluation stage, constructing a dynamic hard boundary. When the power allocation coefficient generated in a certain iteration... When the total power exceeds the maximum permissible active power increment under voltage constraints, the algorithm imposes a large penalty value. This "physical constraint embedding" mechanism ensures that each iteration of the algorithm is performed within the voltage safety domain, avoiding invalid searches.

[0105] fitness value The smaller the value, the better the power allocation coefficient. The better the control effect, the higher the security. The algorithm will continuously update the wolf pack's position to find the optimal location. The minimum global optimal solution, i.e., the optimal power allocation coefficient. .

[0106] Figure 2 This is a flowchart illustrating an improved gray wolf optimization algorithm based on some embodiments of this specification, such as... Figure 2 As shown, in some embodiments, solving for the optimal power allocation coefficient based on the initial gray wolf population and the overall fitness function may specifically include the following steps:

[0107] Social hierarchy: The overall fitness value of each gray wolf is calculated based on the aforementioned comprehensive fitness function. The three wolves with the best overall fitness values ​​are marked as follows: Wolf (optimal) Wolves (second best) and Wolves (third best), the remaining individuals are defined as... Wolf.

[0108] Encirclement and Hunting (Location Update): wolves , , Guided by the three alpha wolves, the algorithm continuously approaches the global optimum based on the position update formula. During this process, the position update of the gray wolves incorporates a nonlinear convergence factor adjustment strategy. In the standard gray wolf optimization algorithm, the convergence factor... The ability to balance the global exploration and local exploitation of the algorithm typically decreases linearly with the number of iterations. However, this purely mathematical decrease is divorced from the physical reality of power systems. To improve the optimization efficiency and safety of the algorithm in complex weak power grid environments, this invention proposes directly mapping the physical state of the power grid sensed in the preceding steps to the algorithm's control parameters.

[0109] First, based on the short-circuit ratio (SCR) and the maximum allowable active power increment under voltage constraints... and current inertia requirements Real-time calculation of the current power grid system urgency index :

[0110] ,

[0111] In the formula: The threshold for determining a weak power grid (e.g., 3.0); , , which is the weighting coefficient (the value range is usually [0.5, 1.5]);

[0112] The physical meaning of this index is: the weaker the power grid (the smaller the SCR), and the inertia demand approaches or even exceeds the maximum allowable active power increment under voltage constraints. hour, The larger the value, the higher the risk of voltage collapse the system faces, making it extremely urgent to optimize the environment.

[0113] Based on the system urgency index ψ, the convergence factor is reconstructed. Nonlinear decay model:

[0114] ,

[0115] In the formula, This represents the current iteration number. This represents the maximum number of iterations.

[0116] This formula achieves deep coupling and linkage between the algorithm's optimization behavior and the physical state of the power grid: under severely weak / high-risk grid conditions... The system is in a high-risk and urgent state. The curve will exhibit rapid exponential decay in the early stages of iteration. This forces the gray wolf population to quickly abandon large-scale global blind search, preventing the generation of overly aggressive tentative power commands, and to enter the local precision development stage early, ensuring that the algorithm can converge to a feasible solution near the safety boundary as quickly as possible, meeting the millisecond-level real-time requirements of frequency response. Under relatively strong network / safe conditions ( Or <1): The system voltage margin is sufficient. The curve decays gradually flattening out or even exhibiting a convex function decline. At this point, the algorithm is given a longer global exploration window, allowing the population to fully explore the optimal allocation ratio of wind-storage synergy in a broad solution space, achieving the best economic efficiency and frequency regulation performance.

[0117] Iteration Termination: When the preset maximum number of iterations is reached or the fitness value no longer changes significantly, the optimal power allocation coefficient is finally output. .

[0118] Step 150: Perform frequency response based on the optimal power allocation factor.

[0119] Specifically, it includes:

[0120] Based on the frequency change rate in the power grid operation state, calculate the inertia support power required to smooth frequency changes;

[0121] The reference value of total power is determined based on the maximum allowable active power increment under voltage constraints and the power supported by inertia.

[0122] Based on the optimal power allocation coefficient and the total power reference value, generate power commands for the doubly-fed wind turbine and the energy storage system.

[0123] Specifically, frequency dynamic changes are driven by active power imbalances, while system inertia provides response time for primary frequency regulation by suppressing the rate of frequency change (df / dt). Inertia support power is a theoretical value calculated based on the real-time rate of frequency change and the system inertia constant, used to quantify the instantaneous active power support required to smooth frequency fluctuations.

[0124] The total power reference value is the smaller of the maximum allowable active power increment under voltage constraints and the inertia support power. Through this step, when the grid is in a strong grid state or has sufficient voltage margin, it outputs full power according to inertia demand; while when the grid is in a weak grid state and the voltage is approaching the safety limit, active power limiting is automatically implemented. This mechanism fundamentally solves the technical problem of grid disconnection caused by ignoring voltage constraints in weak grids, ensuring that the wind-storage system always provides frequency support within the voltage safety domain.

[0125] Doubly fed wind turbine power command Energy storage system power command .

[0126] The aforementioned instructions are sent to the doubly-fed induction generator (DFIG) converter and the energy storage converter, respectively, driving the physical devices to operate in coordination. Through this mechanism, the energy storage system, utilizing its rapid and precise response characteristics, proactively takes over the power shortfall caused by voltage limiting, thus providing high-quality frequency support while ensuring the voltage safety of the weak power grid.

[0127] After receiving the power reference value from the central controller, the bottom-level controllers of the doubly-fed wind turbine and energy storage system respectively perform the following actions:

[0128] Doubly fed wind turbine side: The rotor-side converter (RSC) of the wind turbine adopts a constant power control mode, based on the received power command from the doubly fed wind turbine. Adjusting the rotor excitation current. By changing the electromagnetic torque, the fan releases the kinetic energy stored in the rotor and converts it into electrical energy to be injected into the grid. Because a soft constraint for speed recovery has been introduced in the fourth stage, the fan's output process is smooth and controlled, avoiding excessive speed drops.

[0129] On the energy storage system side: the energy storage converter (PCS) operates according to the energy storage system power command. Rapidly adjusts output current. Due to the millisecond-level response speed of the energy storage system, it can fill the power gap caused by the "voltage limiting" of wind turbines, ensuring that the system's response to grid frequency changes remains at a high level.

[0130] In some embodiments, a wind-storage joint frequency control method that takes voltage stability into account under weak power grid conditions may further include:

[0131] Calculate the rate of voltage drop at the grid connection point, and based on the rate of voltage drop at the grid connection point and the grid connection point voltage safety threshold, determine whether to freeze the power command of the doubly-fed induction generator (DFIG).

[0132] Based on the grid connection point voltage and the minimum critical value of the grid connection point voltage, determine whether to adjust the operating mode of the doubly fed wind turbine and the energy storage system.

[0133] Specifically, the system calculates the rate of voltage drop at the grid connection point, i.e., the amount of voltage change per unit time, through high-frequency real-time monitoring (e.g., a sampling period of 10ms). When a rapid voltage drop is detected and the voltage approaches a preset first-level safety threshold (e.g., 0.90pu, where pu is an abbreviation for "Per Unit"), the system immediately triggers the dynamic limiting function, specifically calculating the voltage change rate in real time. Preset first-level voltage warning threshold (defined as) ,in As a preset safety margin, in this embodiment, it is taken as 0.02 to 0.05 pu. Use 0.92 to 0.95 pu and voltage drop rate threshold. (In this embodiment, -0.5 pu / s is used). If real-time monitoring meets the logical condition: ( )and( This means that the voltage is determined to be approaching the safety threshold and showing a rapid deterioration trend. At this point, based on the dynamic comparison between the voltage drop rate and the threshold, the controller forcibly freezes the active power command of the current doubly-fed induction generator (DFIG) and suspends its response frequency adjustment action to prevent further voltage deterioration due to continuous absorption of active power. This mechanism is independent of the command priority of the Grey Wolf optimization algorithm and acts directly on the underlying actuators, ensuring the speed and reliability of emergency voltage protection.

[0134] Secondly, if extreme circumstances such as grid failure cause the grid connection point voltage to drop below the secondary minimum critical value (e.g., 0.85 pu), the system will activate emergency blocking protection logic. At this time, the doubly-fed induction generator (DFIG) immediately exits frequency response mode, and its control strategy switches from active power priority to voltage priority, for example, switching to reactive power priority mode or maximum voltage support mode. It adjusts the reactive power output of the rotor-side converter to quickly raise the grid connection point voltage. Simultaneously, the energy storage system switches to full reactive power output, utilizing its rapid power response capability (millisecond level) to provide additional support for voltage recovery. The coordinated operation of the DFIG and the energy storage system continues until the voltage recovers to a safe range (e.g., 0.85 pu), thereby avoiding the risk of system instability caused by voltage collapse.

[0135] The triggering conditions of the above two-level protection logic clearly distinguish the severity of voltage drops: the first-level early warning system suppresses voltage deterioration trends through dynamic limiting, while the second-level blocking system provides fallback protection against extreme faults, forming a gradient defense system from mild to severe. Its core design philosophy is to treat voltage stability as a priority constraint independent of frequency control, achieving proactive risk intervention through real-time monitoring and threshold comparison. For example, when the rate of voltage drop exceeds a preset slope threshold and the voltage is close to the threshold, the system will still prioritize freezing the active power commands of the wind turbines, even if the frequency has not yet fallen out of the dead zone; and when the voltage drops below the minimum critical value of the grid connection point voltage, regardless of the current frequency state, a forced switch to voltage support mode is implemented.

[0136] Furthermore, this method also includes a closed-loop reset mechanism for the control flow: when the system frequency recovers to the dead zone range (e.g., 50 Hz), the system frequency is reset to the dead zone range. + After the frequency reaches 0.03 Hz and remains stable for a set time (e.g., 2 seconds), the controller automatically resets all state variables (e.g., unfreezes power command and exits voltage support mode). Under maximum power point tracking (MPPT) control, the doubly-fed wind turbine slowly recovers its speed to the optimal value. The energy storage system enters standby or charging mode based on its remaining capacity, reserving energy for the next frequency disturbance. This reset logic ensures that the system can quickly return to normal economic operation mode after the disturbance ends, while avoiding subsequent control conflicts caused by residual states.

[0137] Through the aforementioned multi-level voltage protection and closed-loop reset mechanism, this method achieves synergistic optimization of frequency control and voltage stability in weak grid scenarios, significantly improving the anti-interference capability and operational safety of new energy-dominated power systems.

[0138] The following section, based on experiments, explains the beneficial effects of a wind-storage combined frequency control method that takes voltage stability into account under weak power grid conditions.

[0139] A system was built based on the MATLAB / Simulink platform, such as Figure 3The simulation model of the IEEE 9-node system with wind and energy storage shown is used to verify the control performance of the present invention in a weak grid environment. Specific parameters are as follows: the system base capacity is set to 100 MVA, and the base frequency is 50 Hz. The doubly-fed induction generator (DFIG) has a rated capacity of 30 MW (equivalent to approximately 30% penetration rate), an equivalent inertial time constant of 3.5 s, a safe operating speed range of 0.7–1.2 pu, and initially operates in maximum power point tracking (MPPT) mode with an output power of 0.8 pu. The associated energy storage system (ESS) has a rated power of 4 MW (i.e., 13% of the wind turbine capacity), a maximum discharge power limit of 0.2 pu, and millisecond-level power response capability. The regional grid side has a comprehensive equivalent inertial time constant of 5.0 s and a system damping coefficient of 1.0. A step load surge of 0.15 pu (i.e., 15 MW) is applied at simulation time t=5.0 s to simulate a typical operating condition of encountering a large load impact in a weak grid environment.

[0140] In this experiment, the experimental object was set as a typical weak power grid system with a short-circuit ratio (SCR) of 2.4. To simulate this condition, an equivalent line impedance Z = 0.08 + j0.25 (per unit value) was set between the wind farm grid connection point (PCC) and the main grid. Regarding system control parameters, the voltage safety threshold Ulimit was set to 0.90 pu, with a safety margin factor of 1.05. The population size and maximum number of iterations of the improved gray wolf optimization algorithm were set to 30 and 50, respectively. The experiment aims to verify whether the control strategy can provide effective frequency support while ensuring voltage safety when the system faces a 15% load surge disturbance.

[0141] The simulation triggered a disturbance at t=5.0s, with a sudden increase in the regional power grid load causing the system frequency to drop. Upon detecting the frequency anomaly, the monitoring device identified the current grid SCR as 2.4 (less than the short-circuit ratio threshold of 3.0) and immediately activated the voltage stability constraint mode. At this moment, the voltage at the PCC point dropped instantaneously to 0.964 pu due to the active power surge. The central controller immediately invoked the weak grid PV sensitivity model, combined with the current measured voltage and line impedance, to calculate that the maximum allowable increase in active power injected into the wind-storage system at the current moment was approximately 0.357 pu.

[0142] Although the theoretical inertia support requirement calculated based on the frequency drop depth is as high as 0.45 pu, the system enforces a hard upper limit of 0.357 pu for total power output to prevent voltage collapse. Simultaneously, the fuzzy logic controller detects that the current system is in a state of high wind power penetration (…). To prevent a secondary drop in frequency caused by subsequent speed recovery, a smaller speed recovery adjustment coefficient is intelligently output. The system instructs the wind turbines to adopt a slow recovery strategy.

[0143] Based on the aforementioned hard voltage constraint and soft speed constraint, the improved Grey Wolf optimization algorithm completes the optimization calculation in a very short time and outputs the globally optimal allocation coefficient.

[0144] α =0.6. This means that the system intelligently decides to have the doubly-fed wind turbine handle 60% of the limited power, while the energy storage system handles the remaining 40% of the power.

[0145] To verify the effectiveness of this method, a comparative experiment was conducted based on the aforementioned simulation platform. The experimental results show that when the system faces a sudden load surge, if the existing technology (corresponding to...) is used... Figure 4 (Dashed line) The system frequency drops to a minimum of around 49.4Hz. The existing technology refers to a conventional pure maximum power point tracking (MPPT) grid-connected control strategy without virtual inertia and frequency response components. Under this strategy, when the regional power grid experiences a 15% load surge, the doubly-fed induction generator (DFIG) wind turbines only maintain the conventional MPPT mode, keeping their active power output at a fixed value of 0.8 pu, without releasing rotor kinetic energy for inertia support (the rotor speed remains at 1.0 pu). Simultaneously, the energy storage system does not participate in regulation, and its active power output remains zero. Due to the complete lack of rapid active power support from wind and energy storage resources, the grid's active power gap cannot be filled in time, causing the system frequency to drop sharply to around 49.4Hz. However, using this method (corresponding to...) Figure 4 (Solid line) The system frequency minimum point was significantly increased to above 49.7Hz, exhibiting a smooth, monotonic recovery characteristic, effectively suppressing the secondary drop phenomenon. This advantage is attributed to the precise timing coordination between the wind turbine and energy storage. This method can quickly identify disturbances and, under the premise of strictly adhering to the weak grid voltage safety boundary (maintaining a safe level of 0.964 pu), precisely allocates wind and energy storage output through the Grey Wolf algorithm. This not only significantly increases the frequency minimum point to above 49.7Hz but also ensures a smooth recovery of the wind turbine speed through an adaptive speed mechanism, fully demonstrating the enormous value of this method in improving the overall stability of system frequency and voltage under harsh operating conditions.

[0146] like Figures 5-9 As shown, when a doubly fed wind turbine moderately reduces its active power output to prevent stall ( Figure 7 The energy storage system responded quickly and increased its power output to approximately 0.13 pu ( Figure 8 This precisely filled the power gap caused by the recovery of the fan speed, and the fan speed subsequently recovered smoothly without any fan disconnection. Figure 9Meanwhile, regarding voltage stability, as shown in the attached figure, compared to existing technologies, the grid connection point voltage is less prone to drops to 0.885 pu (below the safety line) due to active power surges. Figure 6 This method, through a PV sensitivity constraint mechanism, successfully maintained the grid connection point voltage at a safe level of 0.964 pu. Figure 5 This fully verifies the effectiveness of the method in providing high-quality frequency support while ensuring the voltage safety of weak power grids.

[0147] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A wind-storage combined frequency control method that takes voltage stability into account under weak power grid conditions, characterized in that, include: Collect data on the power grid operation status and wind and energy storage operation status; Identify the grid status based on the grid operation status and wind and energy storage operation status; When the power grid is in a weak state, calculate the maximum allowable active power increment and speed recovery regulation coefficient under voltage constraints; Using the improved Grey Wolf optimization algorithm, the optimal power allocation coefficient is solved based on the maximum allowable active power increment and the speed recovery adjustment coefficient; Execute the frequency response based on the optimal power allocation factor; Among these, the identification of grid status is based on the grid operation status and wind and energy storage operation status, including: Calculate the short-circuit ratio based on the power grid operation status and the wind and energy storage operation status; Identify power grid status based on short-circuit ratio and short-circuit ratio threshold; Using an improved gray wolf optimization algorithm, the optimal power allocation coefficient is solved based on the maximum permissible active power increment and the speed recovery adjustment coefficient, including: An initial gray wolf population is generated using chaotic mapping, where each gray wolf in the population represents a power allocation coefficient. A comprehensive fitness function is constructed based on the maximum permissible active power increment and the speed recovery adjustment coefficient; Based on the initial gray wolf population and the overall fitness function, the optimal power allocation coefficient is solved. The comprehensive fitness function is: ， in, The overall fitness value, The system's rated frequency, For the power allocation factor Below, the simulation shows the lowest system frequency. As the frequency normalization reference value, Frequency recovery time, As the time-normalized reference value, In a voltage hard-constraint state, if the power allocation coefficient is... If the total output power is greater than the maximum allowable active power increment, then ,otherwise, , In a soft-constraint state of rotational speed, if the power distribution coefficient is... If the speed recovery process violates the speed recovery adjustment coefficient, then... ,otherwise, , As a penalty factor, , These are the weighting coefficients.

2. The wind-storage combined frequency control method for weak power grids while considering voltage stability, as described in claim 1, is characterized in that... Calculate the maximum permissible active power increment under voltage constraints, including: Construct a sensitivity mathematical model between grid connection point voltage fluctuation and injected power; Based on the mathematical model of grid connection point voltage, grid connection point voltage safety threshold, and sensitivity between grid connection point voltage fluctuation and injected power in the power grid operation state, the maximum allowable active power increment under voltage constraints is calculated.

3. The wind-storage combined frequency control method for weak power grids while considering voltage stability, as described in claim 2, is characterized in that... The maximum permissible active power increment under voltage constraints is calculated using the following formula: ， in, This represents the maximum permissible active power increment under voltage constraints. The voltage at the grid connection point. The grid connection point voltage safety threshold, The rated voltage at the grid connection point, This is the voltage safety margin factor. This is the equivalent resistance on the grid side.

4. The wind-storage combined frequency control method for weak power grids that takes voltage stability into account, as described in claim 3, is characterized in that... Calculate the speed recovery adjustment coefficient, including: Construct a fuzzy inference rule base for speed recovery; The speed recovery adjustment coefficient is calculated based on the regional power grid wind power penetration rate, the real-time speed of the wind turbine rotor, and the fuzzy inference rule base for speed recovery.

5. The wind-storage combined frequency control method for weak power grids while considering voltage stability, as described in claim 4, is characterized in that... The gray wolf's position update introduces a nonlinear convergence factor adjustment strategy.

6. The wind-storage combined frequency control method for weak power grids while considering voltage stability, as described in claim 5, is characterized in that... Based on the optimal power allocation factor, the frequency response is executed, including: Based on the frequency change rate in the power grid operation state, calculate the inertia support power required to smooth frequency changes; The reference value of total power is determined based on the maximum allowable active power increment under voltage constraints and the power supported by inertia. Based on the optimal power allocation coefficient and the total power reference value, generate power commands for the doubly-fed wind turbine and the energy storage system.

7. The wind-storage combined frequency control method for weak power grids while considering voltage stability, as described in claim 6, is characterized in that... Also includes: Calculate the rate of voltage drop at the grid connection point, and based on the rate of voltage drop at the grid connection point and the grid connection point voltage safety threshold, determine whether to freeze the power command of the doubly-fed induction generator (DFIG). Based on the grid connection point voltage and the minimum critical value of the grid connection point voltage, determine whether to adjust the operating mode of the doubly fed wind turbine and the energy storage system.

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