Wind power frequency modulation parameter optimization method considering frequency modulation performance and energy loss

Abstract

The invention discloses a wind power frequency modulation parameter optimization method considering frequency modulation performance and energy loss. The method comprises the following steps: constructing a power system frequency response model aggregating a single-machine synchronous unit and a wind turbine generator, and constructing a double-index fitting model of a fan rotating speed recovery process and a corresponding energy loss quantification model based on a rotor motion equation; deriving an analytic relationship between the technical index and the energy loss index of the wind power frequency modulation parameter pair; determining the internal weight of the technical index and the weight between the technical index and the energy loss index by adopting a combined weight method, and constructing a comprehensive evaluation index comprising the technical index and the energy loss index; establishing a dual-objective optimization model taking minimization of a technical index and minimization of an energy loss index as a core, and outputting a group of Pareto leading edge optimal solution sets; and according to the calculated relative closeness of each candidate scheme, selecting a solution with the maximum relative closeness as a control parameter for setting the frequency modulation performance and energy loss of the wind turbine generator.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, specifically to a parameter optimization method for wind turbine generator sets participating in grid frequency regulation, and particularly to a wind power frequency regulation parameter optimization method that takes into account frequency regulation performance and energy loss. Background Technology

[0002] In recent years, the penetration rate of renewable energy, represented by wind power, in the power system has been continuously increasing, while the proportion of traditional synchronous generators has decreased accordingly. However, because the rotor speed of wind turbines is decoupled from the grid frequency, they do not possess the inertial response and primary frequency regulation characteristics of traditional synchronous generators. The high proportion of wind power integration significantly reduces the equivalent inertia of the power system, posing a serious threat to the safety and stability of the grid frequency.

[0003] To improve frequency stability, doubly-fed induction generator (DFIG) wind turbines typically employ additional inertia control and load shedding control to participate in grid frequency regulation. However, the use of inertia and load shedding control can cause a secondary frequency drop when the turbine exits frequency regulation, resulting in energy loss during speed recovery. Therefore, the selection of frequency regulation parameters is crucial to both regulation performance and energy loss, and complex coupling mechanisms exist between these parameters. Current technologies primarily focus on optimizing the frequency regulation process itself or addressing the secondary frequency drop during speed recovery, but they still have the following shortcomings: Firstly, in terms of frequency regulation parameter optimization, most existing methods focus on a single objective or local components. For example, CN117674189B proposes a method for setting wind turbine frequency regulation parameters based on available frequency regulation energy constraints. This method uses available frequency regulation energy constraints to tune virtual inertia and droop coefficients. Its core is to treat energy as a hard constraint, with the goal of maximizing the minimum frequency point and steady-state frequency. CN114629133A provides a method for optimizing wind turbine frequency regulation parameters considering two frequency drops. Although it considers the minimum points of the first and second frequency drops simultaneously and establishes relevant analytical expressions for parameter optimization, its method focuses on starting from the system frequency response model and balancing the depth of the two drop points by adjusting the frequency regulation parameters. It does not involve a refined assessment of the internal energy loss of the wind turbine during the speed recovery process.

[0004] Secondly, regarding speed recovery control strategies, existing research has recognized the contradiction between rapid recovery and suppressing secondary drops. After a wind turbine exits frequency regulation and experiences a secondary drop, speed recovery is necessary. This process involves not only frequency safety but also energy loss. Most existing research focuses on improving primary frequency regulation performance or reducing the magnitude of the secondary drop through improved control strategies, but it fails to consider the energy losses caused by measures taken to suppress the secondary drop. This one-sided optimization often prioritizes better frequency regulation performance, leading to excessively long turbine speed recovery times or excessive power losses, resulting in significant waste of wind resources.

[0005] In summary, existing technologies treat frequency regulation (energy release) and recovery (energy replenishment) as two relatively separate stages and optimize them separately: either focusing on frequency regulation parameters to optimize the initial frequency drop (e.g., CN117674189B), or attempting to balance the two drops at the frequency level (e.g., CN114629133A). Therefore, the key challenge is to find a wind turbine frequency regulation parameter optimization method that can fully leverage the frequency regulation performance of wind power, coordinate frequency regulation effects with energy losses while ensuring grid frequency security (considering both primary and secondary drops and rates of change), achieve synergistic optimization throughout the entire process, and possess strong robustness under multi-dimensional objectives. Summary of the Invention

[0006] The present invention aims to address the shortcomings of the prior art by providing a wind power frequency regulation parameter optimization method that takes into account frequency regulation performance and energy loss. It aims to solve the problem that the analysis of the influence of frequency regulation parameters in the prior art is not comprehensive enough, and that it fails to effectively take into account the energy loss during the speed recovery process while suppressing the secondary drop.

[0007] The first aspect of this invention provides a method for optimizing wind power frequency regulation parameters, taking into account frequency regulation performance and energy loss, comprising: Step 1: Construct a power system frequency response model that integrates single-unit synchronous turbines and wind turbines. This model is used to characterize the inertia control behavior and overspeed control behavior of wind turbines using a comprehensive frequency regulation control strategy for wind turbines. Step 2: Based on the rotor motion equation, a double exponential model including the fan speed during normal operation and the fan speed when the frequency regulation is stopped is used for fitting, and a double exponential fitting model of the fan speed recovery process is constructed. Based on this, a quantitative model of energy loss during the speed recovery period is established. Step 3: Based on the frequency response model obtained in Step 1 and the energy loss quantification model obtained in Step 2, derive the analytical relationship between wind power frequency regulation parameters and technical indicators and energy loss indicators; wherein, the wind power frequency regulation parameters include inertia control parameters. droop control parameters Overspeed control coefficient and the time to exit FM Technical indicators include the maximum rate of change of frequency. Maximum value of a single drop and the maximum value of the second drop The energy loss index refers to the energy loss caused during the speed recovery process. ; Step 4: Use the combined weighting method to determine the internal weights of the technical indicators and the weights between the technical indicators and the energy loss indicators, and construct a comprehensive evaluation index that includes both the technical indicators and the energy loss indicators; among which, the internal weights of the technical indicators include the maximum value of the frequency change rate, the maximum value of the first frequency drop, and the maximum value of the second frequency drop; Step 5: Establish a dual-objective optimization model with minimizing technical indicators and minimizing energy loss indicators as the core; and use a multi-objective genetic algorithm to achieve convergence through population initialization, non-dominated sorting, and crowding calculation, outputting a set of Pareto front optimal solutions after multiple iterations; each solution in the optimal solution set corresponds to a set of matching control parameters for technical indicators and energy loss indicators. Step Six: Based on the Pareto front optimal solution set obtained from the multi-objective optimization solution in Step Five, and combined with the weights between the technology and energy loss indices obtained in Step Four, the superior solution distance method is used for decision-making. Based on the calculated relative proximity of each candidate scheme, the solution with the largest relative proximity is selected as the control parameter for adjusting the frequency regulation performance and energy loss of the wind turbine.

[0008] Furthermore, the integrated frequency modulation control strategy includes an inertia control module and an overspeed control module, which output electromagnetic power commands for the wind turbine. The inertia control module simulates the inertial response of a synchronous generator and uses the grid frequency change rate and frequency deviation as inputs to obtain the inertia control output power. The overspeed control module simulates a frequency to deviate the fan speed from the maximum power point, thereby reducing the load and output of the fan. It obtains reserve capacity through overspeed control, thus achieving overspeed control output power.

[0009] Furthermore, in step one, after the wind turbine obtains reserve capacity through overspeed, it can possess frequency regulation characteristics similar to conventional synchronous turbines. Therefore, the electromagnetic power increment equation for the wind turbine using inertia control and overspeed control is:

[0010] in, It is the rate of change of frequency. It's a frequency deviation. These are inertia control parameters. These are overspeed control parameters. It is the droop control parameter.

[0011] Furthermore, step two specifically includes: A bi-exponential fitting model is constructed based on the rotor motion equation to describe the change of rotational speed over time during the speed recovery process:

[0012] in, , and These are the fitting parameters; This is the rotational speed during normal operation. It is the fan speed when the frequency regulation is stopped; t is the time variable; Based on the aforementioned double-exponential fitting model, a quantitative model for energy loss during speed recovery is established as follows:

[0013] in, It is the rotational speed recovery time. It refers to the mechanical power of the fan. It is the electromagnetic power of the fan; It is the load reduction power tracking coefficient, and dt is the integral infinitesimal element over time.

[0014] Furthermore, the analytical relationship between the wind power frequency regulation parameters derived in step three and the technical indicators and energy loss indicators includes: The maximum rate of change of frequency decreases as the inertia control parameter increases; The maximum value of a single drop decreases as the inertia control parameter, droop control parameter, and overspeed control parameter increase; The maximum value of the second drop increases with the increase of the inertia control parameter, droop control parameter, and overspeed control parameter; Energy loss increases with the increase of inertia control parameters, droop control parameters, overspeed control parameters, and frequency modulation exit time.

[0015] Furthermore, in step four, a combined weighting method combining fuzzy hierarchical analysis and entropy weighting is used to calculate the internal weights of technical indicators, as well as the weights of technical indicators and energy loss indicators between technical indicators and energy loss indicators.

[0016] A second aspect of this application is to provide an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the wind power frequency regulation parameter optimization method as described above.

[0017] A third aspect of this application is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the wind power frequency regulation parameter optimization method described above.

[0018] A fourth aspect of this application is to provide a computer program product, including a computer program that, when executed, is used to implement the wind power frequency regulation parameter optimization method described above.

[0019] The beneficial effects of this invention are as follows: The wind power frequency regulation parameter optimization method described in this invention not only derives the inertia control parameters, but also derives the analytical relationship between the wind power frequency regulation parameters and the technical indicators and energy loss indicators. It breaks through the limitation of existing research focusing on a single parameter and provides a complete theoretical basis for multi-parameter synergistic optimization. The wind power frequency regulation parameter optimization method described in this invention breaks through the limitations of traditional methods that only focus on reducing the magnitude of secondary frequency drops. It innovatively incorporates the energy loss generated during speed recovery into the optimization model, constructing a comprehensive evaluation function that integrates frequency regulation technical indicators and energy loss indicators. This method can effectively suppress system frequency drops and control the rate of frequency change while significantly reducing power generation losses caused by frequency regulation, achieving optimal coordination between system safety and economy. A dual-objective optimization model is established with minimizing frequency modulation performance and minimizing energy loss as the core. A multi-objective evolutionary algorithm is used to obtain the Pareto optimal solution set. The decision is made by combining the combined weight method and the superior-inferior solution distance method. The optimal parameter combination can be scientifically and flexibly selected according to the different preferences of actual systems for safety and economy, which enhances the engineering applicability of the tuning strategy. Attached Figure Description

[0020] Figure 1 This is a flowchart of a wind power frequency regulation parameter optimization method that takes into account frequency regulation performance and energy loss, provided by the present invention. Figure 2 This is a schematic diagram illustrating the principle of the integrated frequency regulation control strategy for wind turbine units in step one of the wind power frequency regulation parameter optimization method. Figure 3 This is a block diagram of the power system frequency response model, which includes aggregated single-unit synchronous generators and wind turbines, constructed in step one of the wind power frequency regulation parameter optimization methods described herein. Figure 4 This is a schematic diagram comparing the double-exponential fitting curve of the double-exponential fitting model constructed in step two of the wind power frequency regulation parameter optimization method with the actual simulation curve. Figure 5 This is a schematic diagram of the topology of a three-machine nine-node power system model containing wind turbines, constructed by the wind power frequency regulation parameter optimization method described in the numerical example verification. Figure 6 This is a comparison curve of the system frequency response of four different calculation examples under typical wind speed (9m / s) and typical permeability (21%) scenarios according to the embodiments of the present invention; Figure 7 This is a comparison curve of the system frequency response of four different calculation examples under high wind speed (11m / s) and typical permeability (21%) scenarios according to the embodiments of the present invention; Figure 8This is a comparison curve of the system frequency response of four different calculation examples under typical wind speed (9m / s) and high permeability (36%) scenarios according to the embodiments of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, beneficial effects, and significant advancements of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings provided in the examples of the present invention. Obviously, all the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this application, unless otherwise expressly specified and limited, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance; the term "multiple" refers to two or more; unless otherwise specified or explained, the terms "connected," "fixed," etc., should be interpreted broadly. For example, "connected" can be a fixed connection, a detachable connection, an integral connection, or an electrical connection; "connected" can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0023] like Figure 1 As shown, a method for optimizing wind power frequency regulation parameters considering frequency regulation performance and energy loss includes: Step 1: Construct a power system frequency response model including wind turbine generators. This frequency response model is used to analyze the frequency regulation behavior of the wind turbine generators using methods such as... Figure 2 The integrated frequency regulation control strategy of the wind turbine is described. The control strategy consists of two parts: an inertia control module and an overspeed control module, and the final output is the electromagnetic power command of the wind turbine.

[0024] S11: Calculation of output power for inertia control The inertia control module is used to simulate the frequency regulation process of a synchronous generator when the grid frequency vibrates, providing rapid power support. Specifically, it includes: The inertia control module receives the power grid frequency. f and rated frequency f 0 ; For power grid frequency f By differentiation, the rate of change of frequency is obtained. And multiply by the inertia coefficient K d ; According to the formula Δf = f f 0 Calculate frequency deviation Δf ; the rate of change of power grid frequency and frequency deviation Introduced as an input to the active power control system, its inertia control output power increment for:

[0025] in, These are inertia control parameters. These are droop control parameters. It is the time to exit frequency modulation.

[0026] Controlled by logic switches, Increment of output power of inertia control after time output control ,exist At this time, the fan stops frequency regulation, and the inertia control output power value is 0.

[0027] S12: Calculation of overspeed control output power The overspeed control module reduces the load by deviating the fan speed from the maximum power point, thereby reducing the fan output and reserving rotational reserve capacity. When the load is reduced at a preset reduction rate... At that time, the fan speed exceeded the corresponding speed. .at this time You can find the wind energy utilization coefficient. The values ​​were obtained from the table of blade pitch angle and tip speed ratio, where... It can be calculated using the following formula:

[0028] in, It is the maximum utilization factor of wind energy. It is the propeller pitch angle. It is the optimal tip speed ratio. It is the fan speed. It is the radius of the wind turbine blades. It's wind speed.

[0029] Once the wind turbine obtains reserve capacity through overspeed control, it can possess frequency regulation characteristics similar to conventional synchronous units, with its overspeed control output power increment... for:

[0030] in, These are overspeed control parameters, which are related to the load reduction rate. The relationship is ( ), It is the load-reduction power tracking coefficient. It is the MPPT state power tracking coefficient. Is the load reduction rate The corresponding rotational speed.

[0031] According to the above formula, the overspeed control parameters are... With load reduction rate They are directly proportional, and their changing trends are consistent; therefore, this invention uses an overspeed control coefficient. Replacement load reduction rate The impact of wind power frequency regulation parameters on frequency regulation performance and energy loss indicators is analyzed.

[0032] S13: Based on the obtained inertia control output power increment and overspeed control output power increment, obtain the electromagnetic power increment. Specifically, the electromagnetic power increment It is the increment of the output power of the inertia control. With the overspeed control output power increment The arithmetic sum of .

[0033] S14: To more intuitively express the impact of wind power frequency regulation parameters on frequency regulation performance, synchronous generators are aggregated and replaced with a simplified frequency model of a single synchronous generator unit. The model is as follows:

[0034] in, It is the reheat time constant. It is the power ratio coefficient of the high-pressure cylinder in a traditional synchronous generator set. is the equivalent droop coefficient of the synchronous generator, and s is a complex frequency domain variable.

[0035] S15: Final build as follows Figure 3 The power system frequency response model shown in the figure includes load surges. It is input. It is the output. It is the system's damping system. It is the system's equivalent inertial time constant. When the system experiences a sudden increase in load, it will cause a decrease in frequency. At this time, the power increment of the fan and the power increment of the synchronous machine are simultaneously injected into the system, and a closed loop is formed under the combined effect of system inertia and damping effect.

[0036] Step 2: Based on the rotor motion equation, construct a double exponential fitting model of the wind turbine speed recovery process, and establish a quantitative model of energy loss during the speed recovery period accordingly.

[0037] During the speed recovery phase, the electromagnetic power of the fan drops sharply to less than the mechanical power. The fan recovers its speed according to the rotor motion equation, and solving its differential equation yields the following result. Implicit equation:

[0038]

[0039] in, This is the rotational speed during normal operation. This refers to the fan speed when frequency regulation is discontinued. It is the inertial time constant of the wind turbine, w r It is the fan speed. It is an intermediate variable, dw is the rotational speed element. It is the load reduction power tracking coefficient.

[0040] It can be seen that during the speed recovery process, the speed change curve over time is only related to the speed during normal operation. When the fan is out of frequency modulation, the speed Therefore, the present invention employs [the following]: and A bi-exponential fitting model was constructed to describe the change in rotational speed over time during the speed recovery process by fitting the model with the bi-exponential model:

[0041] in, , and t is the fitting parameter; t is the time variable.

[0042] Based on simulation data fitting, , , , fitted To meet the accuracy requirements, a comparison diagram of the double-exponential fitting curve of the double-exponential fitting model and the actual simulation curve is shown below. Figure 4 As shown in the figure. The curves in the figure represent the actual simulation speed-time curves, and the dots in the figure represent the fitting results of the double exponential fitting model to the actual simulation data, under three different... and Under the conditions (respectively) =0.92 and =1.04, =0.87 and =1.02, =0.83 and The fitting results were good (=0.98).

[0043] Energy loss is defined as the integral of the difference between mechanical power and electromagnetic power during the speed recovery time. Based on the aforementioned double exponential fitting model, the quantitative model formula for energy loss during speed recovery is established as follows:

[0044] in, It is the rotational speed recovery time. It refers to the mechanical power of the fan. dt is the electromagnetic power of the wind turbine, and dt is the time element.

[0045] This step provides a mathematical basis for subsequent analysis of the specific impact of frequency modulation parameters on energy loss.

[0046] Step 3: Based on the frequency response model obtained in Step 1 and the energy loss quantification model obtained in Step 2, derive the mathematical analytical relationship between wind power frequency regulation parameters and technical indicators and energy loss indicators. The wind power frequency regulation parameters include inertia control parameters. K d droop control parameters K p Overspeed control coefficient K os and the time to exit FM Technical indicators include the maximum rate of change of frequency. Maximum value of a single drop and the maximum value of the second drop The energy loss index refers to the energy loss caused during the speed recovery process. .

[0047] Based on the frequency response model obtained in step one, the complex frequency domain expression for the frequency deviation can be derived, and the maximum rate of frequency change occurs at the moment of sudden load increase, i.e. At this point, the maximum value of the rate of change of frequency can be obtained. :

[0048] in, .

[0049] M is the system's equivalent inertia time constant.

[0050] Its time-domain expression can be obtained from the expression of the frequency deviation in the complex frequency domain. ,when hour, The maximum value can be found by determining the time it takes for a single drop to reach this maximum value. and the maximum value of a single drop :

[0051]

[0052] When the wind turbine is out of frequency regulation, its output power drops sharply, and the system imbalance power becomes Since the fan no longer participates in frequency regulation, its frequency regulation parameters and inertia control parameters are... K d droop control parameters K p Overspeed control coefficient K os and the time to exit FM This can be equivalent to 0. In this case, the expression for the maximum value of the second drop is the same as the expression for the maximum value of the first drop, and the system frequency deviation satisfies... Therefore, the time when it reaches its maximum value. and the maximum value of the second drop :

[0053] The solution method for each parameter is the same as that for the maximum value of a single drop; only the sudden increase in load needs to be considered. Replace with unbalanced power Its satisfaction ( This refers to the output of the synchronous generator unit when the fan is out of frequency regulation mode. This refers to the power output after the fan stops frequency regulation.

[0054] For the energy loss model, when the wind speed is constant, The changes can be approximated as negligible; simply substitute the rotational speed equation from step two into the energy loss quantification equation.

[0055] Finally, the inertia control parameters can be obtained based on the derived equations. K d droop control parameters K p Overspeed control coefficient K os and the time to exit FM With the maximum rate of change of frequency Maximum value of a single drop Maximum value of the second drop Relationship with energy loss: Maximum rate of change of frequency: its relationship with inertia control parameters Related to inertia control parameters Increases and decreases.

[0056] Maximum value of a single drop: its relationship with inertia control parameters droop control parameters Overspeed control parameters Related to inertia control parameters droop control parameters Overspeed control parameters It decreases as it increases.

[0057] Maximum value of the second drop: its relationship with the inertia control parameter droop control parameters Overspeed control parameters and the time to exit FM Related to inertia control parameters droop control parameters Overspeed control parameters It increases with the increase of [something]. Appropriately increase the frequency withdrawal time. This can reduce the value of the second drop, but excessively long frequency exit time. On the contrary, it will increase the maximum value of the second drop.

[0058] Energy loss: its relationship with inertia control parameters droop control parameters Overspeed control parameters and the time to exit FM Related to inertia control parameters droop control parameters Overspeed control parameters and the time to exit FM It increases as it increases.

[0059] Step 4: Use the combined weighting method to determine the internal weights of the technical indicators and the weights between the technical indicators and the energy loss indicators, and construct a comprehensive evaluation index that includes both technical indicators and energy loss indicators.

[0060] To scientifically balance the effects of frequency modulation with economic losses, this invention employs a combined weighting method that combines fuzzy hierarchical analysis with entropy weighting to calculate the following two types of weights: 1. Internal weighting of technical indicators: Set technical specifications The maximum value of the normalized rate of change Maximum frequency drop and the maximum value of the second drop in frequency The weights are calculated by weighting. The internal weights, obtained through a combined weighting method using fuzzy hierarchical analysis and entropy weighting, are as follows: , , ,and .

[0061] Among them, the fuzzy hierarchical analysis method constructs a fuzzy judgment matrix, then performs a consistency test using the maximum eigenvalue and random consistency index of the fuzzy judgment matrix. If the test passes, the fuzzy weights are obtained after normalization using the geometric mean method, and then defuzzified using the centroid method. Finally, the subjective weights are obtained. The entropy weight method calculates the maximum value of the frequency change rate. Maximum frequency drop and the maximum value of the second drop in frequency The magnitude of the variation is determined, and then the information entropy and information utility value of each indicator are calculated. From this, the objective weights of each frequency modulation performance indicator can be obtained to determine the maximum value of the frequency change rate. Maximum frequency drop and the maximum value of the second drop in frequency The objective weight is calculated based on this information.

[0062] At this point, by weighting and integrating the subjective and objective factors, the resulting technical indicator expression is:

[0063] 2. Weighting of technical indicators and energy loss indicators: Set comprehensive evaluation indicators From technical indicators and energy loss index (i.e., energy loss) The weights are calculated as follows: , ,and .

[0064] At this point, the comprehensive evaluation indicators The expression is:

[0065] Step 5: Construction and solution of the dual-objective optimization model.

[0066] S51: Establish a dual-objective optimization model centered on minimizing technical indicators and minimizing energy loss indicators:

[0067] Decision variables include inertia control parameters droop control parameters Overspeed control coefficient and the time to exit FM The constraints include wind turbine speed constraints and frequency performance constraints. St. S52: Solving the Model A multi-objective genetic algorithm is used to solve the above model. The specific solution process includes: Population initialization: Randomly generate an initial population containing multiple different parameter combinations within the feasible region of the decision variables; Iterative evolution: In each generation iteration, the algorithm performs operations such as selection, crossover, and mutation to produce offspring populations; Non-dominated ordination and crowding calculation: The parent and offspring populations are merged, and non-dominated ordination is performed according to the objective function value to divide the population individuals into different Pareto levels; within the same non-dominated Pareto, the crowding of individuals is calculated to evaluate the distribution density of individuals in the objective space. Elite preservation and generation of the next generation: Based on non-dominance level and crowding level, individuals with lower crowding levels are prioritized within the same level to maintain population diversity, and the best individuals are selected to form the next generation population. After multiple iterations and convergence, the final output is a set of Pareto front optimal solutions; each solution in this set represents a technical indicator under different emphases. J tech Energy loss index J energy It is a non-dominated equilibrium state that decision-makers can ultimately choose based on actual operational needs.

[0068] Step Six: Optimal Parameter Decision.

[0069] Based on the Pareto front optimal solution set obtained from the multi-objective optimization solution in step five, and combined with the technical index weights obtained in step four, and energy loss index weight The decision-making process is based on the superior-inferior solution distance method, and the specific process is as follows: The candidate solutions in the Pareto front optimal solution set obtained in step five are regarded as decision solutions. Each solution contains two evaluation attributes: technical indicators. J tech Energy loss index J energy Construct an n×2 dimensional decision matrix; Determine the ideal solution and the negative ideal solution; Combine the technical indicator weights obtained in step four and energy loss index weight Determine the Euclidean distance; The relative proximity of each candidate solution is calculated based on the obtained Euclidean distance. The value of the relative proximity is between 0 and 1. The closer the value is to 1, the closer the solution is to the ideal solution, indicating better overall performance. Conversely, the closer the value is to 0, the worse the performance. Iterate through the relative proximity of all candidate solutions and select the solution with the highest relative proximity as the best compromise solution.

[0070] Numerical Example Verification Analysis To verify the wind power frequency regulation parameter optimization method that takes into account frequency regulation performance and energy loss, a three-machine nine-node power system model containing wind turbines was established in a simulation environment, such as... Figure 5 As shown, the system specifically includes two 100MW synchronous turbine units G1 and G2 and one 52.5MW wind turbine unit. The remaining parameter settings are shown in Table 1. At 120s, the system experienced a sudden load increase of 35MW at Load3. The wind turbine unit participated in frequency regulation using inertia control and overspeed control from Section 1, Wind Power Integrated Frequency Regulation Control.

[0071] Table 1: Operating parameters of the synchronous machine and wind turbine in the constructed three-machine nine-node model

[0072] To verify the proposed method for optimizing the frequency regulation parameters of wind turbine generators, the following four calculation examples are set up: Example 1: The wind turbine does not participate in frequency regulation and always operates in maximum power point tracking (MPPT) mode; Example 2: The wind turbine participates in frequency regulation with fixed frequency regulation parameters, which are set as follows: , , , .

[0073] Example 3: The wind turbine adopts a sequential parameter tuning method. This method first initially tunes the frequency regulation exit time. Then tune the inertia control parameters. Then adjust the droop control parameters. Final adjustment The sequential parameter tuning method is referenced from Qiao Ying, Guo Xiaoqian, Lu Zongxiang, et al., A method for determining auxiliary frequency regulation parameters of wind turbines considering the second frequency drop of the system [J]. Power System Technology, 2020, 44(03): 807-15.

[0074] Example 4: The wind power frequency regulation parameter optimization method described in this invention is adopted.

[0075] The frequency modulation performance of four simulation examples was compared under different wind speeds and permeability scenarios. The typical wind speed was 9 m / s with a typical permeability of 21%, while the high wind speed was 11 m / s with a high permeability of 36%. The simulation curves for each example under different wind speeds and permeability scenarios are shown below. Figure 6-8 As shown in Table 2-4, the simulation results are as follows.

[0076] Table 2: Simulation results for four examples under typical wind speed and typical permeability scenarios:

[0077] Table 3: Simulation results of four examples under high wind speed and typical permeability scenarios:

[0078] Table 4: Simulation results of four examples under typical wind speed and high permeability scenarios:

[0079] Simulation results and curves for each scenario reveal that, compared to Example 1 where the wind turbine maintains MPPT operation, the additional control significantly improves the maximum first-order drop and frequency change rate in the other three scenarios. Example 2, using fixed parameters for frequency regulation, fails to fully utilize the turbine's frequency regulation capability, resulting in significant first-order drops and energy losses, leading to poor technical and energy loss indicators. In contrast to Example 2, Examples 3 and 4, through parameter tuning, improve the maximum first-order drop and frequency change rate while ensuring a better second-order drop, thus optimizing the technical indicators.

[0080] Compared to the sequential parameter tuning method used in Example 3, Example 4 demonstrates superior technical and energy loss performance, with a more significant improvement in energy loss. In typical wind speed and permeability scenarios, Example 4 achieves a 4.5% improvement in technical performance, a 21.6% improvement in energy loss, and a 10% improvement in overall performance compared to Example 3. In high wind speed and typical permeability scenarios, with a 13% improvement in technical performance, the energy loss improvement is 16.6%, and the overall performance is 15.7%. In typical wind speed and high permeability scenarios, with a 12% improvement in technical performance, the energy loss improvement is 17.9%, and the overall performance is 14.3%.

[0081] In summary, the wind power frequency regulation parameter optimization method proposed in this paper, which considers both frequency regulation performance and energy loss, improves both technical and energy indicators under typical wind speeds and penetration rates, as well as high wind speeds and high penetration rates, with the energy loss indicator showing the most significant improvement. Across the three scenarios, the technical indicators improved by an average of 9.8%, the energy loss indicator by an average of 18.7%, and the overall indicator by an average of 12.4%. This demonstrates that the wind power frequency regulation parameter optimization method described in this invention is more in line with the complex characteristics of power systems, adapts to the complex coupling mechanism between each parameter, and effectively reduces wind power energy loss while ensuring frequency regulation performance.

[0082] To address the issue that existing studies do not provide a comprehensive analysis of the impact of frequency modulation parameters, this invention derives a simplified mathematical model of the relationship between overspeed control and inertia control parameters and frequency modulation performance indicators and energy loss. It also provides a comprehensive quantitative analysis of the impact mechanism and limiting factors of each frequency modulation parameter on frequency modulation performance and energy loss.

[0083] To address the complex coupling mechanism between parameters in quantitative parameter optimization methods and comprehensively consider the secondary drop and energy loss during the speed recovery process, this wind power frequency regulation parameter optimization method proposes a method that considers both frequency regulation performance and energy loss, based on the impact of frequency regulation parameters on various indicators. Simulation examples further verify that this method can significantly reduce energy loss while ensuring effective frequency regulation.

[0084] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style of the specification is merely for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in the embodiments can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for optimizing wind power frequency regulation parameters considering frequency regulation performance and energy loss, characterized in that, include: Step 1: Construct a power system frequency response model that integrates single-unit synchronous turbines and wind turbines. This model is used to characterize the inertia control behavior and overspeed control behavior of wind turbines using a comprehensive frequency regulation control strategy for wind turbines. Step 2: Based on the rotor motion equation, a double exponential model including the fan speed during normal operation and the fan speed when the frequency regulation is stopped is used for fitting, and a double exponential fitting model of the fan speed recovery process is constructed. Based on this, a quantitative model of energy loss during the speed recovery period is established. Step 3: Based on the frequency response model obtained in Step 1 and the energy loss quantification model obtained in Step 2, derive the analytical relationship between wind power frequency regulation parameters and technical indicators and energy loss indicators; wherein, the wind power frequency regulation parameters include inertia control parameters, droop control parameters, overspeed control coefficient and frequency regulation exit time, the technical indicators include the maximum value of frequency change rate, the maximum value of the first drop and the maximum value of the second drop, and the energy loss indicator refers to the energy loss caused during the speed recovery process; Step 4: Use the combined weighting method to determine the internal weights of the technical indicators and the weights between the technical indicators and the energy loss indicators, and construct a comprehensive evaluation index that includes both the technical indicators and the energy loss indicators; among which, the internal weights of the technical indicators include the maximum value of the frequency change rate, the maximum value of the first frequency drop, and the maximum value of the second frequency drop; Step 5: Establish a dual-objective optimization model with minimizing technical indicators and minimizing energy loss indicators as the core; and use a multi-objective genetic algorithm to achieve convergence through population initialization, non-dominated sorting, and crowding calculation, outputting a set of Pareto front optimal solutions after multiple iterations; each solution in the optimal solution set corresponds to a set of matching control parameters for technical indicators and energy loss indicators. Step Six: Based on the Pareto front optimal solution set obtained from the multi-objective optimization solution in Step Five, and combined with the weights between the technology and energy loss indices obtained in Step Four, the superior solution distance method is used for decision-making. Based on the calculated relative proximity of each candidate scheme, the solution with the largest relative proximity is selected as the control parameter for adjusting the frequency regulation performance and energy loss of the wind turbine.

2. The wind power frequency regulation parameter optimization method considering frequency regulation performance and energy loss according to claim 1, characterized in that, The integrated frequency modulation control strategy includes an inertia control module and an overspeed control module, which outputs electromagnetic power commands for the wind turbine. The inertia control module simulates the inertial response of a synchronous generator and uses the grid frequency change rate and frequency deviation as inputs to obtain the inertia control output power. The overspeed control module simulates a frequency to deviate the fan speed from the maximum power point, thereby reducing the load and output of the fan. It obtains reserve capacity through overspeed control, thus achieving overspeed control output power.

3. The wind power frequency regulation parameter optimization method considering frequency regulation performance and energy loss according to claim 1, characterized in that, In step one, after the wind turbine obtains reserve capacity through overspeed, the electromagnetic power increment equation of the wind turbine unit using inertia control and overspeed control is as follows: in, It is the rate of change of frequency. It's a frequency deviation. These are inertia control parameters. These are overspeed control parameters. It is the droop control parameter.

4. The wind power frequency regulation parameter optimization method considering frequency regulation performance and energy loss according to claim 1, characterized in that, Step two specifically includes: A bi-exponential fitting model is constructed based on the rotor motion equation to describe the change of rotational speed over time during the speed recovery process: in, , and These are the fitting parameters; This is the rotational speed during normal operation. It is the fan speed when the frequency regulation is stopped; t is the time variable; Based on the aforementioned double-exponential fitting model, a quantitative model for energy loss during speed recovery is established as follows: in, It is the rotational speed recovery time. It refers to the mechanical power of the fan. It is the electromagnetic power of the fan; It is the load reduction power tracking coefficient, and dt is the integral infinitesimal element over time.

5. The wind power frequency regulation parameter optimization method considering frequency regulation performance and energy loss according to claim 1, characterized in that, The analytical relationship between the wind power frequency regulation parameters derived in step three and the technical and energy loss indicators includes: The maximum rate of change of frequency decreases as the inertia control parameter increases; The maximum value of a single drop decreases as the inertia control parameter, droop control parameter, and overspeed control parameter increase; The maximum value of the second drop increases with the increase of the inertia control parameter, droop control parameter, and overspeed control parameter; Energy loss increases with the increase of inertia control parameters, droop control parameters, overspeed control parameters, and frequency modulation exit time.

6. The wind power frequency regulation parameter optimization method considering frequency regulation performance and energy loss according to claim 1, characterized in that, In step four, a combined weighting method combining fuzzy hierarchical analysis and entropy weighting is used to calculate the internal weights of technical indicators, as well as the weights of technical indicators and energy loss indicators between technical indicators and energy loss indicators.

7. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the wind power frequency regulation parameter optimization method according to any one of claims 1-6.

8. A computer-readable storage medium storing a computer program, characterized in that, When executed by a processor, the program implements the wind power frequency regulation parameter optimization method according to any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the wind power frequency regulation parameter optimization method according to any one of claims 1-6.

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