Wind farm primary frequency regulation redeemable capacity evaluation and coordinated control method, system and medium

Abstract

The present application relates to the technical field of smart grid, and discloses a wind farm primary frequency modulation redeemable capacity evaluation and collaborative control method, system and medium, which comprehensively considers current backup capacity, rotor speed margin and future short-time wind speed change, calculates the difference between future short-time predicted wind speed and current wind speed, and obtains a trend quantity reflecting the downward risk and upward potential of future wind speed; a dynamic capacity coefficient is calculated according to the real-time rotor speed, the load reduction target speed and the support threshold speed, and the single-machine short-time redeemable support capacity is obtained in combination with the steady-state backup capacity, the dynamic capacity coefficient and the capacity correction coefficient; finally, the total support command of the wind farm is generated according to the system frequency deviation, the product of the redeemable support capacity of each unit and the priority weight is taken as the distribution basis, the field-level support command is differentially distributed to each wind turbine, the collaboration and consistency of the capacity evaluation and the field-level distribution are realized, and the executability of the wind farm primary frequency modulation power instruction and the system frequency support effect are improved.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and in particular to wind farm primary frequency regulation capability control technology, belonging to power system control technology. Specifically, it relates to a method, system, and medium for assessing and coordinating the realization of wind farm primary frequency regulation capability. Background Technology

[0002] As the grid-connected capacity of wind power continues to increase, the equivalent inertia level of the power system decreases, relatively weakening the ability of traditional synchronous generators to provide primary frequency regulation and inertia support. In high-proportion renewable energy power systems, wind farms are no longer merely passive grid-connected power generation units, but are gradually becoming important regulation resources that need to actively participate in grid frequency support. For doubly-fed induction generators, there is a certain decoupling relationship between their rotor mechanical state and the grid frequency, and they do not have natural primary frequency regulation capability under normal maximum power point tracking operation. Therefore, in order for wind turbines to provide active power support when the system frequency drops, additional control methods such as overspeed load shedding, pitch load shedding, virtual inertia control, and droop control are usually required.

[0003] In existing technologies, wind turbines can reserve some active power during normal operation by operating at overspeed and reducing load. When the system frequency drops, the turbine can release reserve power and some rotor kinetic energy by increasing electromagnetic power commands, providing rapid active power support to the grid. However, the frequency regulation capability of wind turbines is not fixed, but closely related to the current wind speed, rotor speed, the point of load reduction operation, and wind speed changes in the next few seconds during the support process. If the frequency regulation capability is assessed solely based on the static reserve capacity at the moment of frequency disturbance, the aerodynamic input power of the turbine will decrease over time in a downwind wind scenario, and the originally estimated reserve capacity may not be fully realized, resulting in the farm-level allocation command exceeding the actual executable capacity of a single turbine.

[0004] On the other hand, from the perspective of wind farms, the current wind speed, future wind speed trends, and speed margins of each turbine within a wind farm are not consistent due to spatial location, wake effects, and differences in local wind conditions. Traditional equal distribution strategies or allocation strategies based on rated capacity implicitly assume that all turbines have the same frequency regulation capability, making it difficult to reflect the actual capability differences between different turbines. If the total frequency regulation command is evenly distributed to all turbines, some low-capacity turbines may be unable to fulfill the support command due to excessive demands, or even experience problems such as rapid speed drops and premature withdrawal from support; while some turbines with increased future wind speeds and more sufficient capacity are unable to undertake more frequency regulation tasks, resulting in underutilization of available resources. Summary of the Invention

[0005] To address the issues of overestimation of individual turbine capacity and over-allocation of farm-level commands when doubly-fed induction generator (DFIG) wind farms participate in primary frequency regulation of the power grid under conditions of continuous wind speed variation in existing technologies, this invention aims to provide a wind farm primary frequency regulation capacity assessment and collaborative control method that comprehensively considers current reserve capacity, rotor speed margin, and future short-term wind speed variation trends within the critical response time window of primary frequency regulation. This method can transform future wind speed variation information into control variables that can be used for individual turbine capacity correction and farm-level allocation. Furthermore, it establishes a hierarchical assessment mechanism from static reserve capacity to dynamic, verifiable support capacity, and performs weighted allocation based on the differences in verifiable capacity of each unit in farm-level control to avoid systemic over-allocation, improve the wind farm frequency regulation command fulfillment rate, and enhance the executability of wind farm primary frequency regulation power commands and the system frequency support effect.

[0006] According to a first aspect of the present invention, a method for assessing and coordinating the primary frequency regulation capability of a wind farm is proposed, comprising the following steps:

[0007] Step 1: Establish an overspeed load reduction operation model for doubly-fed wind turbine units in the wind farm, and calculate the maximum generating power, load reduction operation power, steady-state reserve capacity, target speed for load reduction, and support threshold speed for each wind turbine unit based on the current actual wind speed.

[0008] Step 2: Obtain the future short-term wind speed prediction sequence for each wind turbine, and calculate the difference between the current actual wind speed and the future short-term wind speed prediction value to obtain the multi-step predicted wind speed difference used to characterize the future wind speed change trend.

[0009] Step 3: Calculate the downside risk score and upside potential score based on the multi-step predicted wind speed difference, and obtain a unified trend score from the downside risk score and upside potential score;

[0010] Step 4: Calculate the capacity correction coefficient and priority weight of the wind turbine based on the unified trend score. The capacity correction coefficient is used to reduce the short-term support capacity of the turbine when the wind speed is down, and the priority weight is used to increase the farm-level allocation priority of the turbine when the wind speed is up.

[0011] Step 5: Calculate the dynamic capability coefficient based on the real-time rotor speed of the wind turbine, the target speed for load reduction, and the support threshold speed, and determine the short-term support capability of a single unit based on the dynamic capability coefficient, steady-state reserve capacity, and capability correction coefficient.

[0012] Step 6: Generate a total support command for the wind farm based on the system frequency deviation and the field-level droop gain, and limit the total support command for the wind farm based on the sum of the short-term support capabilities of all wind turbines in the field.

[0013] Step 7: Based on the product of the short-term available support capacity and priority weight of each wind turbine, the total support command of the wind farm after the limit is differentially allocated to each wind turbine, and each wind turbine executes local active power support control.

[0014] According to a second aspect of the present invention, a computer system is provided, comprising:

[0015] One or more processors;

[0016] The memory stores operable instructions that, when executed by the one or more processors, cause the one or more processors to perform the wind farm primary frequency regulation feasibility assessment and coordinated control method of the foregoing embodiments.

[0017] In a third aspect of the present invention, a computer-readable storage medium is provided having executable instructions that, when executed, cause a computer to perform the wind farm primary frequency regulation feasibility assessment and coordinated control method of the foregoing embodiments.

[0018] The wind farm primary frequency regulation capability assessment and collaborative control method described in the above embodiments of the present invention is particularly suitable for application scenarios where doubly fed wind turbines operate at overspeed and reduce load while participating in grid primary frequency regulation. By coordinating and organically linking future forecast information, single-unit capability assessment, and farm-level allocation control, a control chain is formed from future wind speed prediction, single-unit capability correction, farm-level collaborative allocation to frequency response improvement. The product of the feasible support capability of each unit and its priority weight is used as the allocation basis, and farm-level support commands are differentiatedly allocated to each wind turbine. This enables the risk of overestimation of the unit's capability when the wind speed drops after frequency disturbances occur, reduces unfeasible frequency regulation energy, avoids systemic over-allocation caused by the equal-distribution strategy, improves the execution reliability of wind farm primary frequency regulation commands, and improves the system's lowest frequency point and recovery speed.

[0019] Compared with the prior art, the significant advantages of the wind farm primary frequency regulation feasibility assessment and coordinated control method of the present invention are as follows:

[0020] (1) The wind farm primary frequency regulation coordinated control of the present invention no longer evaluates the primary frequency regulation capability of wind turbines based solely on static reserve capacity, but simultaneously considers the future short-term wind speed trend and rotor speed margin, making the capability assessment result closer to the actual achievable capability and achieving coordinated consistency between capability assessment and farm-level allocation.

[0021] (2) The coordinated control method of the present invention reduces the support capacity of the wind speed down-wind unit in advance by reducing the capacity correction coefficient, thereby reducing the unrealizable frequency regulation energy, realizing the coordinated realization of the capacity of high-realizable capacity units and low-capacity units, and avoiding the field-level control command from exceeding the actual capacity of a single unit.

[0022] (3) The coordination control method of the present invention improves the allocation priority of wind speed up-up units by prioritizing weights, which can make full use of the frequency regulation potential brought about by future wind condition improvement, improve the overall support resource utilization rate of wind farms, and use the realizable support capacity and priority weights as the allocation basis. Compared with the equal allocation strategy and the static reserve capacity ratio allocation strategy, it can avoid systematic over-allocation.

[0023] It should be understood that all combinations of the foregoing concepts and the additional concepts described in more detail below may be considered part of the inventive subject matter of this disclosure, provided that such concepts do not contradict each other. Furthermore, all combinations of the claimed subject matter are considered part of the inventive subject matter of this disclosure.

[0024] The foregoing and other aspects, embodiments, and features of the teachings of the present invention will be more fully understood from the following description in conjunction with the accompanying drawings. Other additional aspects of the invention, such as features and / or beneficial effects of exemplary embodiments, will become apparent from the following description or may be learned through practice of specific embodiments according to the teachings of the present invention. Attached Figure Description

[0025] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component shown in the various figures may be denoted by the same reference numeral. For clarity, not every component is labeled in each figure. Embodiments of various aspects of the invention will now be described by way of example and with reference to the accompanying drawings.

[0026] Figure 1 This is a flowchart illustrating the wind farm primary frequency regulation capability assessment and coordinated control method according to an example of the present invention.

[0027] Figure 2 This is a simplified DFIG model structure diagram according to an example of the present invention.

[0028] Figure 3 This is a block diagram illustrating the principle of a single-machine frequency modulation control chain according to an example of the present invention.

[0029] Figure 4 This is a flowchart of the unified trend assessment according to an example of the present invention.

[0030] Figure 5 This is a schematic diagram of the adaptive load reduction rate curve under different wind speeds according to an example of the present invention.

[0031] Figure 6 The dynamic capability coefficient k is an example of the present invention. cap A schematic diagram showing the relationship between rotor speed and rotational speed.

[0032] Figure 7The capability correction coefficient λ according to the example of the present invention ref With priority weight μ ref Schematic diagram of time-domain curves.

[0033] Figure 8 This is a schematic diagram of the complete closed-loop simulation platform architecture according to an example of the present invention.

[0034] Figure 9 This is a schematic diagram comparing the active power command curves of three allocation schemes according to an example of the present invention.

[0035] Figure 10 This is a schematic diagram comparing the total over-allocated amount with the remaining unallocated amount under three allocation schemes according to the present invention.

[0036] Figure 11 This is a schematic diagram comparing the system frequency curves of various schemes according to the example of the present invention. Detailed Implementation

[0037] To better understand the technical content of the present invention, specific embodiments are described below in conjunction with the accompanying drawings.

[0038] Various aspects of the invention are described in this disclosure with reference to the accompanying drawings, which illustrate numerous illustrative embodiments. The embodiments of this disclosure are not necessarily intended to encompass all aspects of the invention. It should be understood that the various concepts and embodiments described above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed herein are not limited to any particular implementation. Furthermore, some aspects of the invention disclosed may be used alone or in any suitable combination with other aspects of the invention disclosed.

[0039] {Example 1}

[0040] According to embodiments of the present invention, addressing the problems of overestimation of single-unit capacity and over-allocation of farm-level commands when doubly-fed induction generator (DFIG) wind farms participate in primary frequency regulation of the power grid under continuously varying wind speeds in the prior art, the present invention aims to propose a method for assessing and coordinating the fulfillment of primary frequency regulation capabilities in wind farms. This method utilizes short-term wind speed forecasts to pre-correct the frequency regulation capabilities of wind turbine units and performs farm-level coordinated allocation based on the fulfillment support capabilities and priority weights of each unit. This ensures that units with high fulfillment capabilities naturally undertake more support tasks, while units with low capabilities undertake fewer tasks. Units with higher future wind speeds receive more allocation due to increased priority weights, while units with lower future wind speeds receive less allocation due to reduced capability correction coefficients. This achieves consistency between capability assessment and farm-level allocation, thereby improving the executability of primary frequency regulation power commands and the system frequency support effect. It also solves the problems in existing wind farm primary frequency regulation control where single-unit capability assessment relies on static reserve capacity, fails to consider short-term wind speed changes, and easily leads to over-allocation of farm-level frequency regulation commands.

[0041] Combination Figure 1 The example shown, according to the wind farm primary frequency regulation feasibility assessment and coordinated control method of the present invention, includes the following process:

[0042] Step 1: Establish an overspeed load reduction operation model for doubly-fed wind turbine units in the wind farm, and calculate the maximum generating power, load reduction operation power, steady-state reserve capacity, target speed for load reduction, and support threshold speed for each wind turbine unit based on the current actual wind speed.

[0043] Step 2: Obtain the future short-term wind speed prediction sequence for each wind turbine, and calculate the difference between the current actual wind speed and the future short-term wind speed prediction value to obtain the multi-step predicted wind speed difference used to characterize the future wind speed change trend.

[0044] Step 3: Calculate the downside risk score and upside potential score based on the multi-step predicted wind speed difference, and obtain a unified trend score from the downside risk score and upside potential score;

[0045] Step 4: Calculate the capacity correction coefficient and priority weight of the wind turbine based on the unified trend score. The capacity correction coefficient is used to reduce the short-term support capacity of the turbine when the wind speed is down, and the priority weight is used to increase the farm-level allocation priority of the turbine when the wind speed is up.

[0046] Step 5: Calculate the dynamic capability coefficient based on the real-time rotor speed of the wind turbine, the target speed for load reduction, and the support threshold speed, and determine the short-term support capability of a single unit based on the dynamic capability coefficient, steady-state reserve capacity, and capability correction coefficient.

[0047] Step 6: Generate a total support command for the wind farm based on the system frequency deviation and the field-level droop gain, and limit the total support command for the wind farm based on the sum of the short-term support capabilities of all wind turbines in the field.

[0048] Step 7: Based on the product of the short-term available support capacity and priority weight of each wind turbine, the total support command of the wind farm after the limit is differentially allocated to each wind turbine, and each wind turbine executes local active power support control.

[0049] Below, we will further elaborate on the specific implementation process of steps 1 to 7 above, with reference to the accompanying drawings.

[0050] Step 1: Establish an overspeed load reduction operation model for wind turbine units and calculate the steady-state reserve capacity of a single unit.

[0051] In this embodiment, an overspeed load reduction operation model is established for each doubly-fed induction generator (DFIG) in the wind farm, and its aerodynamic capture power is expressed as:

[0052] ;

[0053] In the formula, P mec Where ρ is the aerodynamic capture power, R is the air density, v is the impeller radius, and C is the wind speed. p λ is the wind energy utilization coefficient, λ is the tip speed ratio, and β is the blade pitch angle.

[0054] The tip speed ratio is defined as follows:

[0055] ;

[0056] In the formula, ω is the angular velocity of the wind turbine.

[0057] Furthermore, at a given wind speed, the maximum generating power P corresponding to the maximum power point of the wind turbine's maximum power point tracking operation. opt .

[0058] To maintain primary frequency regulation reserve power, the unit operates in overspeed and load reduction mode, with the load reduction operating power P... del for:

[0059] ;

[0060] In the formula, d is the load reduction rate.

[0061] Steady-state reserve capacity is defined as:

[0062] ;

[0063] To ensure that the wind turbine retains primary frequency regulation reserve power, the wind turbine operates in an overspeed and load-reducing state, with the load-reducing operating power P... delDefined as:

[0064] ;

[0065] In the formula, d is the load reduction rate.

[0066] As an optional implementation, the load reduction rate d is determined based on the wind speed and using a wind speed-related variable load reduction rate strategy. The formula for the variable load reduction rate strategy is:

[0067] ;

[0068] In the formula, d min For the minimum load reduction rate, d max For the maximum load reduction rate, k d b is the variable load factor. d Let v be the intercept and v be the wind speed. Therefore, by using a variable load shedding strategy, a larger proportion of the wind speed units can be reserved for backup, while the wind speed units can reduce wind curtailment losses while ensuring a certain absolute reserve.

[0069] In this example, the minimum unloading rate d min =0.05, maximum load reduction rate d max = 0.2, variable load reduction factor k d = -0.0744, intercept b d =0.6952.

[0070] In this embodiment, the steady-state reserve capacity is obtained from the difference between the maximum generating power and the reduced operating power:

[0071] ;

[0072] In the formula, reserve i Let P be the steady-state reserve capacity of the i-th wind turbine. opt,i Let P be the maximum generating power of the i-th wind turbine. del,i Let be the unloaded operating power of the i-th wind turbine.

[0073] Furthermore, based on the current wind speed and load reduction power, the target rotational speed ω0 for load reduction and the support threshold rotational speed ω are calculated. st The target speed for load reduction is the speed corresponding to the unit's steady-state operation during overspeed load reduction, while the support threshold speed is the boundary at which the unit's speed can decrease when participating in frequency regulation support. This boundary is used to determine whether the unit still has sufficient speed margin to continue releasing rotor kinetic energy.

[0074] Step 2: Obtain the future short-term wind speed forecast sequence and construct a multi-step wind speed difference system.

[0075] Before a frequency disturbance occurs or at the arrival of the wind farm-level frequency regulation control cycle, the measured wind speed sequence of each unit in the wind farm is acquired, and the future short-term wind speed prediction sequence is obtained through a second-level wind speed prediction module. It should be understood that the aforementioned second-level wind speed prediction module can be implemented using existing second-level wind speed prediction methods or models. This invention does not limit the internal algorithm structure of the prediction module, but only uses its output future short-term wind speed prediction results as input for subsequent capability assessment.

[0076] Furthermore, combining the current actual wind speed V0, a ​​short-term wind speed prediction sequence is obtained based on the prediction time interval and prediction step size. In this example, typically taking a prediction time interval of 5 seconds and a prediction step size of 3 steps as an example, the predicted wind speed values ​​V5, V6, and V7 for the next 5 seconds, 10 seconds, and 15 seconds are obtained. 10 V 15 And based on the current actual wind speed V real Calculate the multi-step predicted wind speed differences d1, d2, d3, d4. tot Construct a wind speed difference system:

[0077] ;

[0078] Wherein, d1 reflects the wind speed change trend in the most recent 5 seconds, and d2 and d3 respectively reflect the wind speed change trends in the following two 5-second intervals. tot It reflects the overall direction and magnitude of wind speed change over the next 15 seconds. When d... tot When d < 0, it indicates that the overall short-term wind speed will decrease in the future; when d tot A value greater than 0 indicates that the overall short-term wind speed will increase in the future.

[0079] In a further optional embodiment, to avoid frequent fluctuations in the control quantity caused by second-level wind speed measurement noise, a trend discrimination threshold ε can be set during implementation. trend and the overall stability band threshold ε tot When the change in wind speed is less than the threshold, a clear trend is not considered to exist.

[0080] Step 3: Calculate the downside risk score, upside potential score, and uniform trend score.

[0081] In this embodiment, a downlink risk score is calculated based on the difference in predicted wind speeds over multiple steps. score The downside risk score is used to characterize the risk that a decrease in short-term wind speed will reduce the unit's support capacity. It is composed of a weighted downside main risk term and a downside trend persistence term.

[0082] Downside main risk item based on d tot Determine: When d tot When the value is within the stability zone, the downside primary risk term is 0; when d totWhen the preset downward saturation value is reached, the main downward risk term is 1; a linear transition is adopted in the intermediate interval. The downward trend persistence term is determined based on whether d2 and d3 decrease continuously, and is used to identify whether the future wind speed decrease is sustainable.

[0083] For example, based on the overall stability band threshold ε tot (Taking 0.10m / s as an example) and the preset downlink saturation value v down,sat (Taking -2m / s as an example), when d tot ≥-ε tot The downside primary risk term is 0; when d tot ≤v down,sat When d, the downside main risk term is 1; when d to Located at v down,sat With -ε tot When the transition is between these values, a linear transition value is used: (-ε) tot -d tot ) / ( -ε tot -v down,sat ).

[0084] Threshold ε determined by trend trend Taking 0.05 m / s as an example, the persistence of the downward trend is identified by whether the multi-step predicted wind speed differences d2 and d3 in the subsequent two prediction intervals continuously decrease: when d2 < -ε trend Furthermore, d3 < d2 indicates that the wind speed is showing an accelerating and continuous downward trend, with the downward trend duration term being 1.0; when d2 < -ε trend Or d3 < -ε trend If the wind speed decreases within a single interval, the term for the downward trend duration is 0.5; otherwise, the term for the downward trend duration is 0.

[0085] Then, the total downside risk score is calculated using the weighted coefficients of the respective main risk items and trend persistence items. In particular, the weighted coefficients of the optional main downside risk items and downside trend persistence items are 0.7 and 0.3, respectively (ensuring that the weight of the main risk items is greater than 0.5 and the weight of the trend persistence items is less than 0.5).

[0086] Similarly, the uplink potential score (up) is calculated based on the difference in predicted wind speeds over multiple steps. score The upside potential score is used to characterize the increased reliability of the realization of capabilities brought about by a rise in short-term wind speeds in the future. It is composed of a weighted average of the main upside component and the upside trend persistence component.

[0087] When d tot When the uplink stability band threshold is exceeded, the main uplink term begins to increase; when d tot When the upward saturation value is reached, the main upward term is 1. The upward trend continuation term is determined based on whether the subsequent forecast interval continues to rise.

[0088] For example, based on the preset uplink saturation value v up,sat (Taking 2m / s as an example), when d tot ≤ε tot When d, the ascending term is 0; when d tot ≥v up,sat When d, the ascending term is 1; when d tot Located at ε tot With v up,sat When the transition occurs, a linear transition strategy is adopted, with a value of (d). tot - ε tot ) / ( v up,sat - ε tot ).

[0089] Similarly, the sustainability of the upward trend can be identified by whether the multi-step predicted wind speed differences d2 and d3 within the subsequent two prediction intervals decrease continuously: if d2 > ε trend Furthermore, if d3 > d2, it indicates that the wind speed is accelerating and continuously increasing, with the duration of this upward trend being 1.0; if d2 > ε trend Or d3 > ε trend In the case of a single-range increase, the upward trend duration term is 0.5; in all other cases, the upward trend duration term is 0.

[0090] Then, the total downside risk score is calculated by weighting the respective main risk items and trend persistence items. In particular, the weighting coefficients of the optional upside main risk items and upside trend persistence items are 0.7 and 0.3, respectively (ensuring that the weight of the main risk items is greater than 0.5 and the weight of the trend persistence items is less than 0.5).

[0091] Furthermore, the downside risk score and upside potential score will be unified into a unified trend score:

[0092] trend score,i = up score,i - risk score,i ;

[0093] In the formula, trend score,i The uniform trend score for the i-th wind turbine unit is represented by up. score,i risk score,i Let represent the upside potential score and downside potential score of the i-th wind turbine, respectively.

[0094] When trend score,i < 0 indicates an unfavorable future wind speed trend, with downside risks dominating; when trend score,i >0 indicates a favorable future wind speed trend, with upward potential dominating; when trend... score,i ≈ 0 indicates that the wind speed will remain relatively stable in the short term.

[0095] Step 4: Calculate the correction coefficient and priority weight based on the ability derived from the unified trend score.

[0096] In this embodiment, the unified trend score of the i-th wind turbine is used as the basis for the calculation. score,i Further derive and separate the corresponding downlink and uplink components:

[0097] ;

[0098] ;

[0099] In the formula, down score,i with up score,i Let represent the downlink component and the uplink component of the i-th wind turbine, respectively;

[0100] According to the down component score,i Calculate the capacity correction factor for the i-th wind turbine:

[0101] λ ref,i = 1-k λ ·down score,i ;

[0102] In the formula, k λ This is the gain reduction factor for capability. Capability correction factor λ ref,i Limit the amplitude to [0.7, 1.0].

[0103] When there is no significant downside risk to future wind speed, λ ref,i = 1, no reduction in unit capacity; when there is a significant downside risk to future wind speeds, λ ref,i =<1, reduce the short-term realizable capacity of the unit in advance according to the degree of risk.

[0104] Based on the uplink component score,i Calculate priority weights:

[0105] μ ref,i = 1+k μ ·up score,i ;

[0106] In the formula, k μ Increase the gain coefficient for priority. Priority weight μ ref,i Limit [1.0, 1.1].

[0107] When the wind speed is expected to increase in the future, the unit's priority in the field-level allocation will increase. ref,i The value is greater than 1. When there is no obvious upward trend in future wind speed, μ ref,i =1.

[0108] In embodiments of the present invention, the capacity correction coefficient and the priority weight operate at different levels. The capacity correction coefficient is used to assess the deliverable capacity of a single unit, avoiding the allocation of support commands exceeding the actual capacity of units operating in downwind winds; the priority weight is used in the field-level allocation process, enabling units with higher future upwind wind speeds and more reliable capacity delivery to receive more frequency regulation tasks.

[0109] Step 5: Calculate the dynamic capability coefficient and obtain the short-term support capability of a single machine.

[0110] Traditional static reserve capacity only reflects the maximum release power under current steady-state conditions and cannot reflect the dynamic support capability of the unit after the rotor speed decreases during frequency regulation support. Therefore, this invention introduces a dynamic capability coefficient k. cap It is used to characterize the impact of rotor speed margin on the actual release capacity of the unit.

[0111] In this embodiment of the invention, in the low-to-medium wind speed zone, the dynamic capability coefficient is calculated using the following formula: ;

[0112] In the formula, ω r ω is the real-time rotor speed of the unit, ω0 is the target speed for load reduction, ω st To support the threshold speed, z is the normalized speed margin, limited to [0,1], and k min This represents the minimum capability coefficient.

[0113] As an optional example, the minimum capability coefficient k min = 0.20, ensuring that even when the speed margin is insufficient, the unit still retains the most basic support response capability, rather than completely exiting frequency regulation.

[0114] When ω r When ω = 0, z = 1, k cap = 1 indicates that the unit has complete dynamic support capability;

[0115] When ω r = ω st When z = 0, k cap = k min This indicates that the unit retains only basic support capabilities;

[0116] When ω r Further below ω st At that time, the unit's support capacity is further limited to avoid the risk of stall due to excessively low speed.

[0117] In an embodiment of the present invention, the theoretical dynamic support capability is as follows:

[0118] ;

[0119] The parameter reserve here represents the steady-state reserve capacity.

[0120] By further adding a capability correction coefficient, the short-term support capability that a single machine can realize is obtained:

[0121] support cap,i = λ ref,i · reserve i · k cap,i ;

[0122] Among them, reserve i Let be the steady-state reserve capacity of the i-th wind turbine unit.

[0123] This single unit can provide support capabilities in a short period of time. cap,i Taking into account the current steady-state reserve capacity, the rotor speed margin during frequency regulation, and the future short-term wind speed change trend, it is the single-unit capacity boundary for field-level frequency regulation allocation.

[0124] Step 6: Generate the wind farm general support command and apply full field capacity constraints.

[0125] When the system frequency drops and exceeds the frequency regulation dead zone, the wind farm enters the primary frequency regulation support state.

[0126] first,

[0127] Calculate the system frequency deviation Δf:

[0128] Δf = f0 - f;

[0129] In the formula, f0 is the rated frequency, and f is the real-time frequency of the system;

[0130] The original support command ΔP for the wind farm is generated based on the field-level sagging control. farm,ref :

[0131] ΔP farm,ref = K farm ·Δf ;

[0132] In the formula, K farm This is for field-level droop gain.

[0133] Then, the total available support capability constraint of the wind farm is applied to the original support command of the wind farm. The total support command of the wind farm is limited by the sum of the short-term available support capabilities of all wind turbines in the wind farm, so as to ensure that the total support command of the wind farm can be actually fulfilled by each wind turbine.

[0134] Specifically, the total fulfillable capacity constraint imposed on the original support command of the wind farm is as follows:

[0135] ;

[0136] In the formula, ΔP farm,cmd This represents a wind farm support order after applying the total available-of-works constraint. N is the number of wind turbines in the wind farm. cap,i To ensure that the i-th wind turbine can provide support in the short term, P B,sys P is the system power base value. B,wtg This is the base value for single-unit power.

[0137] This overall constraint ensures that the wind farm will not issue total support orders exceeding the actual capacity of the entire wind farm to each wind turbine unit at the lower level, thus guaranteeing that the total support orders of the wind farm can be actually fulfilled by each wind turbine unit.

[0138] Step 7: Perform field-level differentiated allocation based on redeemability and priority weight.

[0139] After determining the overall support command for the wind farm, it is differentiated and allocated to each wind turbine, with each wind turbine executing local active power support control.

[0140] Specifically, the allocation weight of the i-th wind turbine is defined as follows:

[0141] ;

[0142] In the formula, N represents the number of wind turbines in the wind farm, support cap,i μ reflects the absolute redeemability of the i-th generating unit. ref,i The priority of the i-th unit reflects the upward trend of future wind speed. The product of the two takes into account both the unit's capacity boundary and the reliability of future capacity realization.

[0143] The local support power command for the i-th generating unit is:

[0144] ΔP local,i = α i ·ΔP farm,cmd ;

[0145] And it satisfies the amplitude limit: 0 ≤ ΔP local,i ≤ support cap,i .

[0146] Therefore, through the above allocation method, units with high availability naturally undertake more support tasks, while units with low availability undertake fewer tasks. Furthermore, units with higher wind speeds will receive more allocations due to their increased priority weight, while units with lower wind speeds will receive less allocations due to the reduction in capacity correction coefficients, thus achieving consistency between capacity assessment and field-level allocation.

[0147] {Example 2}

[0148] In this embodiment, we combine Figures 2 to 11 As shown, the implementation of various aspects of the wind farm primary frequency regulation feasibility assessment and coordinated control method in the above embodiments will be further explained.

[0149] Step 1: Establish an overspeed load reduction operation model for wind turbine units and calculate the steady-state reserve capacity of a single unit.

[0150] First, an overspeed load reduction operation model is established for each doubly-fed induction generator (DFIG) in the wind farm. The simplified DFIG model structure is as follows: Figure 2 As shown in the figure. This embodiment takes a real coastal wind farm as the object, which includes N=79 doubly-fed induction wind turbine units with a single unit capacity of 1.5 MW, and a total installed capacity of 118.5 MW. Each unit operates in an overspeed and load shedding state and has active power reserve capability to participate in the primary frequency regulation of the system. The wind farm is connected to the IEEE 3-machine 9-node test system, which includes synchronous generator units and load disturbances. The complete closed-loop simulation platform architecture is as follows. Figure 8 As shown.

[0151] Furthermore, in the calculation of the aerodynamic capture power of the wind turbine, the air density ρ = 1.225 kg / m³ 3 The impeller radius R = 29.64 m, and v is the actual measured wind speed.

[0152] Furthermore, the maximum generating power P is calculated based on the current actual wind speed. opt For low to medium wind speed areas (v < 12.41 m / s), the maximum renewable power is determined according to the aerodynamic power model described above. For high wind speed areas (v ≥ 12.41 m / s), the maximum renewable power is constrained by the rated power, i.e., P opt = 1.5 MW.

[0153] Furthermore, the reduced-load operating power is P del = P opt (1-d), where the load reduction rate d adopts a wind speed-dependent variable load reduction rate strategy, such as Figure 5 As shown: .

[0154] When the wind speed is low, the generating power of the unit is relatively small. In order to obtain sufficient absolute reserve capacity, the load shedding rate is taken as a larger value. When the wind speed is high, the generating power of the unit is relatively large. The load shedding rate is taken as a smaller value to reduce wind curtailment losses.

[0155] Furthermore, the steady-state reserve capacity is obtained from the difference between the maximum generating power and the reduced-load operating power.

[0156] Based on the current wind speed and load reduction power, the target rotational speed ω0 for load reduction and the support threshold rotational speed ω are calculated. st=0.923ω0. The support threshold speed is the boundary at which the unit's speed can decrease when participating in frequency regulation support. It is used to determine whether the unit still has sufficient speed margin to continue releasing rotor kinetic energy. Taking a typical unit WTG57 as an example, its current real wind speed v = 7.41m / s, and the real-time rotor speed ω0 is... r = 0.8320 pu, the steady-state parameter calculation process is as follows:

[0157] .

[0158] Step 2: Obtain the future short-term wind speed forecast sequence and construct a multi-step wind speed difference system.

[0159] Before a frequency disturbance occurs or at the start of a wind farm frequency regulation control cycle, the measured wind speed sequence of each turbine in the wind farm is acquired, and a short-term wind speed prediction sequence is obtained through a second-level wind speed prediction module. The second-level wind speed prediction module updates the prediction results every 5 seconds, outputting the predicted wind speeds for the next 5, 10, and 15 seconds. It should be noted that the second-level wind speed prediction module can employ any prediction method that meets the time scale requirements of primary frequency regulation. The objective of this invention is not to protect the wind speed prediction algorithm, but rather to use its wind speed prediction results for primary frequency regulation feasibility assessment and coordinated control.

[0160] Furthermore, with a prediction time interval of 5 seconds and a prediction step size of 3 steps, the predicted wind speeds V5, V6, and V7 are obtained for the next 5 seconds, 10 seconds, and 15 seconds. 10 V 15 Based on the current actual wind speed V0, a ​​wind speed difference system is constructed. d1 reflects the wind speed change trend over the last 5 seconds, and d2 and d3 respectively reflect the wind speed change trends over the next two 5-second intervals. tot It reflects the overall direction and magnitude of wind speed change over the next 15 seconds. When d... tot When d < 0, it indicates that the overall short-term wind speed will decrease in the future; when d tot A value greater than 0 indicates an overall increase in short-term wind speed. Furthermore, to avoid frequent fluctuations in control values ​​due to second-level wind speed measurement noise, a trend discrimination threshold ε is set. trend = 0.05m / s and the overall stability band threshold ε tot = 0.10 m / s. When the change in wind speed is less than the threshold, a clear trend is not considered to exist. Taking the WTG57 unit mentioned above as an example, its predicted wind speeds for the next 15 seconds are V5 = 7.02 m / s and V... 10 = 6.65 m / s, V 15 =6.42 m / s, the multi-step predicted wind speed difference is calculated as follows:

[0161] .

[0162] Step 3: Calculate the downside risk score, upside potential score, and uniform trend score.

[0163] A downside risk score is calculated based on the difference in multi-step predicted wind speeds. score The downside risk score characterizes the risk of reduced generator support capacity due to a decrease in short-term wind speed. It is composed of a weighted average of the main risk term and the trend persistence term. The main risk term is based on d... tot Determined, when d tot When the value is within the stability zone, the main risk term is 0; when d tot Reaching the preset saturation value Δv down,sat When the wind speed is -2.0 m / s, the main risk term is 1; a linear transition is used in the intermediate interval. The trend persistence term is determined based on whether d2 and d3 decrease continuously, and is used to identify whether the future wind speed decrease is sustainable. Similarly, the uplink potential score up is calculated based on the difference in wind speed predicted over multiple steps. score The upside potential score characterizes the increased reliability of the realization of capabilities resulting from a future short-term increase in wind speed; it is composed of a weighted average of the main upside term and the trend persistence term. When d tot When the upward stability band is exceeded, the main rising term begins to increase; when d tot Reaching the upward saturation value Δv up,sat When the speed is 2.0 m / s, the main upward term is 1. The trend continuation term is determined based on whether the subsequent forecast interval continues to rise.

[0164] Furthermore, the downside risk score and upside potential score will be unified into a unified trend score. score When trend score < 0 indicates an unfavorable future wind speed trend, with downside risks dominating; trend score > 0 indicates a favorable future wind speed trend, with upward potential dominating; trend score ≈ 0 indicates that the wind speed will remain relatively stable in the short term. The unified trend assessment process is as follows: Figure 4 As shown. Taking the WTG57 unit mentioned above as an example, its wind speed trend parameters are calculated as follows:

[0165] .

[0166] Step 4: Derive capability correction coefficients and priority weights from the unified trend score.

[0167] First, the downward and upward components are separated from the unified trend score; then, the capability correction coefficient is calculated based on the downward component, and the priority weight is calculated based on the upward component.

[0168] In this example, the capability reduction gain k λ = 0.30. Capacity correction coefficient λ refThe amplitude is limited to [0.7, 1.0]. When there is no significant downside risk to future wind speeds, λ ref = 1, no reduction in unit capacity; when there is a significant downside risk to future wind speeds, λ ref < 1. Reduce the short-term cash-in-flow capacity of the generating units in advance according to the degree of risk.

[0169] Priority boost gain k μ = 0.10. Priority weight μ ref The range is limited to [1.0, 1.1]. When there is an upward trend in future wind speed, the priority of this unit in the field-level allocation is increased; when there is no obvious upward trend in future wind speed, μ ref =1. The time-domain variation curves of the capability correction coefficient and priority weight are shown below. Figure 7 As shown.

[0170] Taking the WTG57 unit mentioned above as an example, its control coefficients are calculated as follows:

[0171] ;

[0172] Step 5: Calculate the dynamic capability coefficient and obtain the short-term support capability of a single machine.

[0173] In low-to-medium wind speed areas, the short-term achievable support capacity k of a single unit is calculated based on the real-time rotor speed, target speed for load reduction, and support threshold speed of the unit. cap Minimum dynamic capability coefficient k min = 0.20. When ω r When ω = 0, z = 1, k cap = 1, indicating that the unit has complete dynamic support capability; when ω r = ω st When z = 0, k cap 0.2 indicates that the unit retains only basic support capabilities; when ω r Further below ω st At this time, the unit's support capacity is further limited to avoid the risk of stall due to excessively low speed. The relationship between the dynamic capability coefficient and rotor speed is as follows: Figure 6 As shown.

[0174] Furthermore, in high wind speed areas (v ≥ 12.41 m / s), the unit operates at rated power. At this time, the aerodynamic capture power has reached its upper limit, and overspeed load reduction is no longer applicable. The main method is to reserve reserve capacity by adjusting the pitch load.

[0175] The dynamic capability coefficient is corrected to:

[0176] ;

[0177] The calculation methods for the capacity correction factor and priority weight are consistent with those for low-to-medium wind speed areas.

[0178] Furthermore, the theoretical dynamic support capability is as follows:

[0179] ;

[0180] After applying the capability correction factor, the short-term support capability that a single machine can realize is obtained:

[0181] .

[0182] Taking the aforementioned WTG57 unit as an example, its feasible support capacity is calculated as follows:

[0183] ;

[0184] The calculations above indicate that, under strong wind downhill conditions, the theoretical static reserve capacity of this unit is 32.9 kW. However, considering the risk of future wind speed decline, the actual available support capacity is only 24.5 kW. If support instructions are still allocated according to the static reserve capacity, approximately 8.4 kW of unavailable energy will result.

[0185] Step 6: Generate the wind farm general support command and apply full field capacity constraints.

[0186] When the system frequency drops and exceeds the frequency modulation dead zone Δf dead After reaching 0.03 Hz, the wind farm enters primary frequency regulation support mode. First, calculate the system frequency deviation:

[0187] ;

[0188] In the formula, f0 = 50 Hz is the rated frequency, and f is the real-time frequency of the system.

[0189] Furthermore, the original support command for the wind farm is generated based on the field-level sag control:

[0190] ;

[0191] In the formula, the field-level droop gain K farm = 15 MW / Hz.

[0192] Furthermore, to ensure that the overall support orders for the wind farm can be actually fulfilled by each turbine, a total farm-wide fulfillment capacity constraint is imposed on the original support orders for the wind farm:

[0193] ;

[0194] In the formula, N = 79 represents the number of wind turbines in the wind farm. cap,iTo ensure that the i-th wind turbine can provide support in the short term, P B,sys = 592.5 MW is the system power base value, P B,wtg = 1.5 MW is the base value for single-unit power. Through this constraint, the wind farm will not issue total support orders to its subordinate units that exceed the actual capacity of the entire farm.

[0195] This embodiment selects a typical strong downwind condition (average wind speed across the field is 9.13 m / s, average ΔV). 15 The system was verified using a speed of -1.02 m / s. Under this typical strong downtrend condition, the total short-term support capacity of all 79 generating units is 1094 kW. The system power disturbance increment is set to ΔP. d = 0.0283 pu (corresponding to 16.77 MW), verifying the differential distribution characteristics of the capability correction mechanism and μ ref The priority adjustment effect, and the calculation of the total field-level support command are as follows:

[0196] .

[0197] Step 7: Perform field-level differentiated allocation based on redeemability and priority weight.

[0198] After determining the total support order for the wind farm, it is allocated differentially to each wind turbine. The allocation weight of the i-th wind turbine is defined as follows (taking a wind farm with N=79 turbines as an example):

[0199] ;

[0200] In the formula, support cap,i μ reflects the absolute redeemability of the i-th generating unit. ref,i This reflects the priority corresponding to the upward trend of future wind speed for the i-th unit. The product of these two factors takes into account both the unit's capacity boundary and the reliability of future capacity fulfillment.

[0201] Furthermore, the local support power command ΔP of the i-th unit... local,i Determined in the following ways:

[0202] ΔP local,i = α i ·ΔP farm,cmd ;

[0203] Simultaneously satisfying the amplitude limit 0 ≤ ΔP local,i ≤ support cap,i .

[0204] Furthermore, after receiving the local support power command, each unit performs active power regulation through the individual unit frequency regulation control chain. The individual unit frequency regulation control chain adopts a typical existing control process, the principle of which is as follows: Figure 3 As shown, this control chain comprises two independent paths: an electromagnetic torque control path and a pitch angle control path. The electromagnetic torque control path is used for rapid response to power commands, releasing rotor kinetic energy to provide short-term support; the pitch angle control path is used for auxiliary power regulation and speed protection in high wind speed areas. The control process includes comprehensive speed protection and safe exit logic to ensure safe unit operation.

[0205] In this embodiment, all 79 generating units execute the above allocation steps respectively, generating local support power commands. A comparison of the active power command curves for the three allocation schemes is provided below. Figure 9 As shown, the total amount of excess allocation is compared with the remaining unallocated amount. Figure 10 As shown in the figure. The results indicate that the traditional equal allocation strategy resulted in over-allocation of 37 units, with a total over-allocation of 316 kW; the static reserve capacity proportional allocation strategy avoided over-allocation, but did not consider the impact of future wind speed changes; the method of this invention reduces the total over-allocation to 0, and the unfulfilled frequency regulation energy is reduced from 700.8 kW·s to 213.8 kW·s, a reduction of 69.5%. The remaining unallocated amount of 244 kW is a safety margin actively reserved to ensure the fulfillment of frequency regulation commands, and is not a loss of allocation efficiency.

[0206] like Figure 11 The diagram shows a comparison of system frequency response curves for three schemes: wind farm not participating in frequency regulation, traditional constant-load shedding control, and the collaborative control method of this invention. The results show that when the wind farm does not participate in frequency regulation, the lowest system frequency is 49.864 Hz; when using traditional constant-load shedding control, the lowest system frequency is 49.870 Hz; and after adopting the method of this invention, the lowest system frequency is raised to 49.873 Hz, and the time for the system frequency to first recover to 49.98 Hz is shortened from 137.58 s to 126.59 s, thus accelerating the frequency recovery speed.

[0207] It should be noted that the prediction step size used in this embodiment is 3 steps (15 s), which is suitable for the critical response time window of primary frequency regulation in power systems. If applied to a fast frequency response scenario (within 10 s), the prediction step size can be adjusted to 2 steps (10 s); if applied to a secondary frequency regulation scenario, the prediction step size can be appropriately extended to 5-10 steps. The method of this invention is also applicable to permanent magnet synchronous wind turbines; simply replace the rotor speed model of the doubly fed wind turbine with the corresponding model of the permanent magnet synchronous turbine.

[0208] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A method for assessing and coordinating the realization of primary frequency regulation capability in a wind farm, characterized in that, Includes the following steps: Step 1: Establish an overspeed load reduction operation model for doubly-fed wind turbine units in the wind farm, and calculate the maximum generating power, load reduction operation power, steady-state reserve capacity, target speed for load reduction, and support threshold speed for each wind turbine unit based on the current actual wind speed. Step 2: Obtain the future short-term wind speed prediction sequence for each wind turbine, and calculate the difference between the current actual wind speed and the future short-term wind speed prediction value to obtain the multi-step predicted wind speed difference used to characterize the future wind speed change trend. Step 3: Calculate the downside risk score and upside potential score based on the multi-step predicted wind speed difference, and obtain a unified trend score from the downside risk score and upside potential score; Step 4: Calculate the capacity correction coefficient and priority weight of the wind turbine based on the unified trend score. The capacity correction coefficient is used to reduce the short-term support capacity of the turbine when the wind speed is down, and the priority weight is used to increase the farm-level allocation priority of the turbine when the wind speed is up. Step 5: Calculate the dynamic capability coefficient based on the real-time rotor speed of the wind turbine, the target speed for load reduction, and the support threshold speed, and determine the short-term support capability of a single unit based on the dynamic capability coefficient, steady-state reserve capacity, and capability correction coefficient. Step 6: Generate a total support command for the wind farm based on the system frequency deviation and the field-level droop gain, and limit the total support command for the wind farm based on the sum of the short-term support capabilities of all wind turbines in the field. Step 7: Based on the product of the short-term available support capacity and priority weight of each wind turbine, the total support command of the wind farm after the limit is differentially allocated to each wind turbine, and each wind turbine executes local active power support control.

2. The method for assessing and coordinating the primary frequency regulation capability of a wind farm according to claim 1, characterized in that, In step 1, the aerodynamic capture power of the wind turbine in the overspeed load reduction operation model established for each doubly-fed induction generator in the wind farm is expressed as: ； Among them, P mec Where ρ is the aerodynamic capture power, R is the air density, v is the impeller radius, and C is the wind speed. p λ is the wind energy utilization coefficient, λ is the tip speed ratio, and β is the blade pitch angle; The tip speed ratio is defined as follows: ； In the formula, ω is the angular velocity of the wind turbine; At a given wind speed, the maximum power point tracking (MPPT) of the wind turbine corresponds to the maximum available power output P. opt ; To ensure that the wind turbine retains primary frequency regulation reserve power, the wind turbine operates in an overspeed and load-reducing state, with the load-reducing operating power P... del Defined as: ； In the formula, d is the load reduction rate; The steady-state reserve capacity is obtained from the difference between the maximum generating power and the reduced operating power: ； In the formula, reserve i Let P be the steady-state reserve capacity of the i-th wind turbine. opt,i Let P be the maximum generating power of the i-th wind turbine. del,i Let be the reduced-load operating power of the i-th wind turbine.

3. The method for assessing and coordinating the primary frequency regulation capability of a wind farm according to claim 2, characterized in that, In step 1, the load reduction rate d is determined based on the wind speed and using a wind speed-dependent variable load reduction rate strategy. The formula for the variable load reduction rate strategy is: ； In the formula, d min For the minimum load reduction rate, d max For the maximum load reduction rate, k d b is the variable load factor. d is the intercept, and v is the wind speed.

4. The method for assessing and coordinating the primary frequency regulation capability of a wind farm according to claim 1, characterized in that, In step 2, the short-term wind speed prediction sequence for each wind turbine includes predicted wind speed values ​​for the next 5 seconds, 10 seconds, and 15 seconds, and calculates the multi-step predicted wind speed differences d1, d2, d3, and d4 based on the current actual wind speed. tot : ； Wherein, d1 reflects the wind speed change trend in the most recent 5 seconds, and d2 and d3 respectively reflect the wind speed change trends in the next two consecutive 5-second intervals. tot It reflects the overall direction and magnitude of wind speed changes over the next 15 seconds; When d tot When d < 0, it indicates a general downward trend in short-term wind speed; when d tot A value greater than 0 indicates an overall upward trend in short-term wind speeds.

5. The method for assessing and coordinating the primary frequency regulation capability of a wind farm according to claim 4, characterized in that, In step 3, the calculation of the downside risk score and the upside potential score includes: A downside risk score is calculated based on the difference in multi-step predicted wind speeds. score The downside risk score score It is used to characterize the risk of reduced unit support capacity due to a future short-term decrease in wind speed, and is composed of a weighted average of the downside main risk term and the downside trend persistence term: The downside main risk term is determined based on the overall wind speed decrease in the next 15 seconds, and the downside trend persistence term is determined based on whether the wind speed decreases continuously in the subsequent forecast interval, which is used to identify whether the future wind speed decrease is sustainable. Upward potential score is calculated based on the difference in multi-step predicted wind speeds. score The upside potential score is up score It is used to characterize the improved reliability of capability realization brought about by a future short-term increase in wind speed, and is composed of a weighted average of the main upward term and the upward trend continuation term: The upward main upward term is determined based on the overall wind speed increase in the next 15 seconds, and the upward trend persistence term is determined based on whether the wind speed continues to rise in the subsequent prediction interval, which is used to identify whether the future wind speed increase is sustainable.

6. The method for assessing and coordinating the primary frequency regulation capability of a wind farm according to claim 5, characterized in that, In step 3, a unified trend score is obtained based on the downside risk score and the upside potential score: trend score,i = up score,i - risk score,i ; In the formula, trend score,i The uniform trend score for the i-th wind turbine unit is represented by up. score,i risk score,i Let represent the upside potential score and downside potential score of the i-th wind turbine, respectively. Among them, when trend score,i < 0 indicates an unfavorable future wind speed trend, with downside risks dominating; when trend score,i >0 indicates a favorable future wind speed trend, with upward potential dominating; when trend... score,i ≈ 0 indicates that the wind speed will remain relatively stable in the short term.

7. The method for assessing and coordinating the primary frequency regulation capability of a wind farm according to claim 6, characterized in that, In step 4, the capacity correction factor and priority weight of the wind turbine are calculated based on the unified trend score, including: From the unified trend score of the i-th wind turbine unit score,i Further derive and separate the corresponding down component. score,i and uplink component up score,i : ； ； The capability correction factor is calculated according to λ. ref,i = 1-k λ ·down score,i Calculate and limit its amplitude to [0.7, 1.0]; The priority weight is in μ ref,i = 1+k μ ·up score,i Calculate and limit it to [1.0, 1.1]; Where, λ ref,i μ ref,i These are the capacity correction factor and priority weight for the i-th wind turbine, respectively; k λ k μ These represent the capability reduction gain coefficient and the priority enhancement gain coefficient, respectively.

8. The method for assessing and coordinating the primary frequency regulation capability of a wind farm according to claim 7, characterized in that, In step 5, in the low-to-medium wind speed zone, a dynamic capacity coefficient is calculated based on the real-time rotor speed, the target speed for load reduction, and the support threshold speed. This coefficient is used to characterize the impact of rotor speed margin on the actual release capacity of the unit. ； In the formula, k cap,i ω represents the dynamic capability coefficient of the i-th wind turbine. r ω is the real-time rotor speed of the unit, ω0 is the target speed for load reduction, ω st To support the threshold speed, z is the normalized speed margin, limited to [0,1], and k min This is the minimum capability coefficient; Based on the dynamic capability coefficient of the i-th wind turbine, the short-term support capacity of a single turbine is calculated as follows: support cap,i = λ ref,i · reserve i · k cap,i ; Among them, support cap,i This indicates the short-term support capacity of the i-th wind turbine unit, reserve i This represents the steady-state reserve capacity of the i-th wind turbine unit.

9. The method for assessing and coordinating the primary frequency regulation capability of a wind farm according to claim 8, characterized in that, In step 6, when the system frequency drops and exceeds the frequency regulation dead zone, the wind farm enters the primary frequency regulation support state. First, the system frequency deviation Δf is calculated: Δf = f0 - f; In the formula, f0 is the rated frequency, and f is the real-time frequency of the system; The original support command ΔP for the wind farm is generated based on the field-level sagging control. farm,ref : ΔP farm,ref = K farm ·Δf ; In the formula, K farm For field-level droop gain; Original support command ΔP for wind farm farm,ref A total available support capacity constraint is applied to limit the total support command of the wind farm by using the sum of the short-term available support capacity of all wind turbines in the field, so as to ensure that the total support command of the wind farm can be actually fulfilled by each wind turbine.

10. The method for assessing and coordinating the primary frequency regulation capability of a wind farm according to claim 9, characterized in that, In step 7, the field-level allocation weight α of the i-th wind turbine is... i Defined as: ； In the formula, N represents the number of wind turbines in the wind farm, support cap,i μ represents the short-term support capacity of the i-th wind turbine unit. ref,i This indicates the priority weight of the i-th wind turbine unit; The local support power command ΔP actually executed locally by the i-th wind turbine unit local,i Determined in the following ways: ΔP local,i = a i ·ΔP farm,cmd ; Simultaneously satisfying the amplitude limit 0 ≤ ΔP local,i ≤ support cap,i ; Wherein, ΔP farm,cmd This indicates a wind farm support command after applying a total farm availability constraint.

11. A computer system, characterized in that, include: One or more processors; A memory that stores operable instructions, which, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 1 to 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium has executable instructions that, when executed, cause a computer to perform the method according to any one of claims 1 to 10.

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