Simulation method and device for frequency modulation of wind power plant
By using a multi-dimensional simulation model, combined with unit cluster aggregation, coupled resource coordination, and power grid equivalent model, the problem of not considering the characteristics of multiple units in wind farm frequency regulation simulation is solved, realizing high-precision simulation of wind farm frequency regulation characteristics, and supporting frequency regulation strategy optimization and power grid stability analysis.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA RESOURCES NEW ENERGY INVESTMENT CO LTD SHANXI BRANCH
- Filing Date
- 2026-02-06
- Publication Date
- 2026-05-08
AI Technical Summary
Existing wind farm frequency regulation simulation methods fail to effectively consider the co-modulation characteristics and aggregation effects of multiple units within a wind farm, resulting in significant deviations between simulation results and actual scenarios. Furthermore, traditional models are highly complex and have low simulation efficiency.
A multi-dimensional simulation model is adopted, including a unit group aggregation model, a coupled resource coordination model, and a power grid equivalent model. By acquiring real-time parameters, the simulation is performed to describe the frequency regulation characteristics of the wind farm and improve the simulation accuracy.
It significantly improves the simulation accuracy of wind farm frequency regulation characteristics, provides high-quality simulation basis for frequency regulation strategy optimization and power grid stability analysis, and avoids the one-sidedness and insufficient accuracy of traditional models.
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Abstract
Description
Technical Field
[0001] This invention relates to the field of simulation technology, and in particular to a simulation method and apparatus for wind farm frequency regulation. Background Technology
[0002] With the continuous increase in wind power penetration, the randomness and volatility of wind farm output have an increasingly significant impact on grid frequency stability. Wind turbines are connected to the grid via power electronic converters, which naturally lack the rotational inertia support of traditional synchronous turbines, thus limiting their ability to participate in grid frequency regulation. Therefore, it is urgent to use simulation technology to describe the frequency regulation characteristics of wind farms, providing support for frequency regulation strategy optimization and grid security and stability analysis.
[0003] Existing wind farm frequency regulation simulation methods have many shortcomings: some methods only model a single wind turbine and do not consider the co-regulation characteristics and aggregation effect of multiple turbines in the wind farm, resulting in a large deviation between the simulation results and the actual scenario; some co-simulation methods take into account both mechanical and electrical characteristics, but the model is complex and the simulation efficiency is low.
[0004] Based on this, the present invention proposes a simulation method and device for wind farm frequency regulation to solve the above-mentioned technical problems. Summary of the Invention
[0005] This invention describes a simulation method and apparatus for wind farm frequency regulation, which can improve the simulation accuracy of wind farm frequency regulation characteristics.
[0006] According to a first aspect, the present invention provides a simulation method for wind farm frequency regulation, comprising: Obtain wind farm frequency regulation simulation parameters for a preset duration; wherein, the frequency regulation simulation parameters include real-time air density, real-time wind speed, real-time generator angular velocity, real-time wind turbine angular velocity, real-time grid frequency, real-time energy storage temperature, number of grid nodes, and real-time voltage of the nth node; The frequency regulation simulation parameters of the wind farm are input into a preset multi-dimensional simulation model for simulation to obtain simulation results; wherein, the simulation results are used to describe the frequency regulation characteristics of the wind farm. The multi-dimensional simulation model includes a generator group aggregation model, a coupled resource coordination model, and a power grid equivalent model. The simulation results include the equivalent output curve of the generator group, the total control torque curve, the real-time aerodynamic power of the wind turbine, the real-time charge and discharge power curve of the energy storage, the state of charge evolution curve, the system dynamic equivalent inertia time constant curve, and the equivalent load power time domain curve. The generator group aggregation model is used to simulate the real-time aerodynamic power of the wind turbine, the equivalent output curve of the generator group, and the total control torque curve. The coupled resource coordination model is used to simulate the real-time charge and discharge power curve of the energy storage and the state of charge evolution curve. The power grid equivalent model is used to simulate the system dynamic equivalent inertia time constant curve and the equivalent load power time domain curve.
[0007] According to a second aspect, the present invention provides a simulation device for wind farm frequency regulation, comprising: The acquisition unit is configured to acquire wind farm frequency regulation simulation parameters for a preset duration; wherein, the frequency regulation simulation parameters include real-time air density, real-time wind speed, real-time generator angular velocity, real-time wind turbine angular velocity, real-time grid frequency, real-time energy storage temperature, number of grid nodes, and real-time voltage of the nth node; The simulation unit is configured to input the wind farm frequency regulation simulation parameters into a preset multi-dimensional simulation model to perform simulation and obtain simulation results; wherein, the simulation results are used to describe the frequency regulation characteristics of the wind farm. The multi-dimensional simulation model includes a generator group aggregation model, a coupled resource coordination model, and a power grid equivalent model. The simulation results include the equivalent output curve of the generator group, the total control torque curve, the real-time aerodynamic power of the wind turbine, the real-time charge and discharge power curve of the energy storage, the state of charge evolution curve, the system dynamic equivalent inertia time constant curve, and the equivalent load power time domain curve. The generator group aggregation model is used to simulate the real-time aerodynamic power of the wind turbine, the equivalent output curve of the generator group, and the total control torque curve. The coupled resource coordination model is used to simulate the real-time charge and discharge power curve of the energy storage and the state of charge evolution curve. The power grid equivalent model is used to simulate the system dynamic equivalent inertia time constant curve and the equivalent load power time domain curve.
[0008] Thirdly, embodiments of this specification also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of this specification.
[0009] Fourthly, embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of this specification.
[0010] According to the simulation method and apparatus for wind farm frequency regulation provided by the present invention, firstly, wind farm frequency regulation simulation parameters are collected for a preset duration. These parameters include real-time air density, real-time wind speed, real-time generator angular velocity, real-time wind turbine angular velocity, real-time grid frequency, real-time energy storage temperature, number of grid nodes, and real-time voltage of the nth node. During the data collection process, various parameters undergo time-series alignment, outlier filtering, and missing value completion to ensure the continuity, consistency, and accuracy of the parameter data, avoiding simulation deviations due to data interference or incompleteness. The wind farm frequency regulation simulation parameters are then input into a preset multi-dimensional simulation model for simulation, yielding simulation results. These simulation results are used to describe the frequency regulation characteristics of the wind farm. The multi-dimensional simulation model includes three sub-models: a unit cluster aggregation model, a coupled resource coordination model, and a grid equivalent model. The unit cluster aggregation model is used to simulate the characteristics of the wind farm's internal unit cluster, outputting the equivalent output curve and total control torque curve of the unit cluster, reflecting the frequency regulation response capability and control law of the wind farm cluster. The coupled resource coordination model is used for the coordinated frequency regulation process of coupled resources such as energy storage and wind farms. It simulates and outputs real-time charging and discharging power curves and state-of-charge evolution curves of energy storage, quantifying the power regulation law and state change characteristics of energy storage during frequency regulation. The grid equivalent model is used for the dynamic characteristics of the grid side, simulating and outputting the system's dynamic equivalent inertia time constant curve and equivalent load power time domain curve, capturing the dynamic change law of the grid under the frequency regulation of wind farms. In this way, the present invention effectively avoids the one-sidedness and insufficient accuracy problems of traditional single models or simplified models in the simulation process, significantly improves the simulation accuracy of wind farm frequency regulation characteristics, and provides high-quality simulation basis for wind farm frequency regulation strategy optimization, grid stability analysis, and engineering practice applications. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 A flowchart illustrating a simulation method for wind farm frequency regulation according to one embodiment is shown. Figure 2 A schematic block diagram of a simulation apparatus for wind farm frequency regulation according to one embodiment is shown. Detailed Implementation
[0013] The solution provided by the present invention will now be described with reference to the accompanying drawings.
[0014] Figure 1This diagram illustrates a simulation method for wind farm frequency regulation according to one embodiment. It is understood that this method can be executed by any device, equipment, platform, or cluster of devices with computing and processing capabilities. Figure 1 As shown, the method includes: Step 100: Obtain the wind farm frequency regulation simulation parameters for a preset duration; wherein, the frequency regulation simulation parameters include real-time air density, real-time wind speed, real-time generator angular velocity, real-time wind turbine angular velocity, real-time grid frequency, real-time energy storage temperature, number of grid nodes, and real-time voltage of the nth node; Step 102: Input the wind farm frequency regulation simulation parameters into the preset multi-dimensional simulation model to perform simulation and obtain the simulation results; wherein, the simulation results are used to describe the frequency regulation characteristics of the wind farm; The multi-dimensional simulation models include a generator group aggregation model, a coupled resource coordination model, and a power grid equivalent model. The simulation results include the equivalent output curve of the generator group, the total control torque curve, the real-time aerodynamic power of the wind turbine, the real-time charging and discharging power curve of the energy storage, the state of charge evolution curve, the system dynamic equivalent inertia time constant curve, and the equivalent load power time domain curve. The generator group aggregation model is used to simulate the real-time aerodynamic power of the wind turbine, the equivalent output curve of the generator group, and the total control torque curve. The coupled resource coordination model is used to simulate the real-time charging and discharging power curve of the energy storage and the state of charge evolution curve. The power grid equivalent model is used to simulate the system dynamic equivalent inertia time constant curve and the equivalent load power time domain curve.
[0015] In this embodiment, firstly, wind farm frequency regulation simulation parameters are collected for a preset duration. These parameters include real-time air density, real-time wind speed, generator real-time angular velocity, wind turbine real-time angular velocity, grid real-time frequency, energy storage real-time temperature, number of grid nodes, and the real-time voltage of the nth node. During the data collection process, various parameters undergo time-series alignment, outlier filtering, and missing value completion to ensure the continuity, consistency, and accuracy of the parameter data, avoiding simulation deviations caused by data interference or incompleteness. The wind farm frequency regulation simulation parameters are then input into a preset multi-dimensional simulation model for simulation, yielding simulation results. These simulation results are used to describe the frequency regulation characteristics of the wind farm. The multi-dimensional simulation model includes three sub-models: a unit cluster aggregation model, a coupled resource coordination model, and a grid equivalent model. The unit cluster aggregation model is used to simulate the characteristics of the wind farm's internal unit cluster, outputting the equivalent output curve and total control torque curve of the unit cluster, reflecting the frequency regulation response capability and control law of the wind farm cluster. The coupled resource coordination model is used for the coordinated frequency regulation process of coupled resources such as energy storage and wind farms. It simulates and outputs real-time charging and discharging power curves and state-of-charge evolution curves of energy storage, quantifying the power regulation law and state change characteristics of energy storage during the frequency regulation process. The grid equivalent model is used for the dynamic characteristics of the grid side. It simulates and outputs the dynamic equivalent inertia time constant curve and equivalent load power time domain curve of the system, capturing the dynamic change law of the grid under the frequency regulation of wind farms.
[0016] Thus, this invention effectively avoids the problems of one-sidedness and insufficient accuracy in the simulation process of traditional single or simplified models, significantly improves the simulation accuracy of wind farm frequency regulation characteristics, and provides high-quality simulation basis for wind farm frequency regulation strategy optimization, power grid stability analysis and engineering practice applications.
[0017] In one embodiment of the present invention, the unit group aggregation model is constructed using the following formula:
[0018] In the formula, This represents the real-time aerodynamic power of the wind turbine. For real-time air density, Where is the radius of the wind turbine. The time-varying wind energy capture coefficient, For real-time wind speed, The equivalent moment of inertia of the wind turbine side. For the real-time aerodynamic torque of the wind turbine, This is the torsional stiffness coefficient of the drive shaft. This refers to the lateral displacement of the wind turbine. This refers to the generator side angular displacement. This is the damping coefficient on the wind turbine side. This refers to the real-time angular velocity of the wind turbine. For the electromagnetic torque of the generator, The real-time angular velocity of the generator. The equivalent moment of inertia on the generator side. This is the generator-side damping coefficient. The total control torque curve is shown. For dynamic virtual inertia coefficient, This is the dynamic droop coefficient. The rated frequency of the power grid. For the real-time frequency of the power grid, The damping coefficient is the rate of change of power. To increase the power output of the unit group The equivalent output curves for the unit group. For the initial equivalent output, The dead-zone power loss of the k-th wind turbine converter is... This refers to the DC bus voltage of the converter. Let the dead zone equivalent current of the k-th wind turbine converter be . Let be the power factor angle of the k-th wind turbine.
[0019] In this embodiment, the calculation formula for the real-time aerodynamic power of the wind turbine introduces a time-varying wind energy capture coefficient, which can describe the aerodynamic response of the wind turbine under complex wind conditions. By incorporating the difference between the wind turbine side angular displacement and the generator side angular displacement into the torque balance calculation, it can capture torque fluctuations caused by the elastic deformation of the drive shaft, significantly improving the accuracy of transient simulation. A new formula for converter dead-zone power loss is also added. To quantify the impact of the nonlinear characteristics of power electronic equipment on aggregate output and avoid errors caused by idealized assumptions, a new power change rate damping term is added. It effectively suppresses control oscillations caused by sudden changes in output, and improves the stability of cluster response.
[0020] In one embodiment of the present invention, the unit group aggregation model further includes the following formula:
[0021] In the formula, This represents the initial time-varying wind energy capture coefficient. To correct the tip speed ratio, The pitch angle is the propeller angle. The fitting coefficients for the first aerodynamic characteristic are: The fitting coefficients for the second aerodynamic characteristic are... The fitting coefficients for the third aerodynamic characteristic are... The fitting coefficients for the fourth aerodynamic characteristic are... The fitting coefficient for the fifth aerodynamic characteristic is... The fitting coefficient for the sixth aerodynamic characteristic. The fitting coefficient for the seventh aerodynamic characteristic. For dynamic tip speed ratio, This is the correction factor for the rate of change of wind speed. As the baseline virtual inertia coefficient, It is the Sigmoid activation function. The rated angular velocity of the wind turbine. As the baseline droop coefficient, The equivalent output curves for the unit group. The rated equivalent output of the unit group.
[0022] In this embodiment, The dynamic virtual inertia coefficient, using the Sigmoid function, dynamically adjusts the inertial support strength according to the unit speed, suppressing frequency drops while avoiding speed oscillations. The dynamic droop coefficient, combined with the real-time output adaptive correction of the unit group, enables precise matching between power response and frequency deviation, shortening the adjustment time and reducing overshoot. All aerodynamic characteristic fitting coefficients are calibrated using aerodynamic characteristic curves provided by the wind turbine manufacturer, with a reference value range of 0.35~0.48.
[0023] In one embodiment of the present invention, the real-time air density is verified using the following formula:
[0024] In the formula, The density of air under standard conditions. This is the altitude correction factor. This represents the real-time altitude deviation. This is the temperature correction factor. This represents the real-time ambient temperature deviation.
[0025] In this embodiment, instead of the idealized assumption of fixed air density in the simulation, this invention incorporates the dynamic real-time changes in altitude and temperature into the basic parameter verification of wind farm cluster simulation. Traditional models often use standard air density for static calculations, neglecting the impact of altitude deviations and temperature fluctuations on air density at the wind farm site, leading to systematic biases in aerodynamic power calculations. This formula introduces altitude and temperature correction coefficients to construct a dynamic verification mechanism, which can accurately correct air density based on real-time environmental data. This makes the calculation of wind turbine aerodynamic power and time-varying wind energy capture coefficient more closely reflect the actual site conditions, improving the simulation accuracy of the cluster model.
[0026] In one embodiment of the present invention, the coupled resource coordination model is constructed using the following formula:
[0027] In the formula, For real-time charge and discharge power curves of energy storage, For dynamic rated power of energy storage, For the real-time frequency of the power grid, The rated frequency of the power grid. For the maximum permissible frequency deviation, This is the SOC correction factor. This is the rated power of the energy storage. This represents the energy storage aging degradation coefficient. The coefficient representing the influence of energy storage temperature. For real-time temperature of energy storage, The rated operating temperature for energy storage. This is the charge state evolution curve. This is the limit of the state of charge of energy storage. This is the intermediate value of the energy storage state of charge. This represents the upper limit of the energy storage state of charge. This represents the initial state of charge of the energy storage. To improve the real-time charging and discharging efficiency of energy storage, For the rated capacity of energy storage, For energy storage self-discharge rate, For time, For integration time variable, This refers to the real-time charging and discharging power of energy storage.
[0028] In this embodiment, the calculation formula for the dynamic rated power of energy storage incorporates an aging degradation coefficient and a temperature influence coefficient to dynamically correct the energy storage charging and discharging capability. It describes the impact of energy storage performance degradation and temperature characteristics on frequency regulation throughout the entire life cycle. The SOC correction coefficient replaces the traditional linear correction. In the extreme SOC range (close to the lower or upper limit of the energy storage state of charge), the weight of charging and discharging power is automatically reduced to avoid safety risks and sudden changes in frequency regulation capability caused by overcharging / over-discharging of energy storage. At the same time, it maintains full response in the middle SOC range, balancing frequency regulation performance and energy storage life. The state of charge evolution curve is constructed by introducing the self-discharge rate and dynamic charging and discharging efficiency to calculate the SOC evolution from the initial state to the real time, including the entire process of energy storage charging and discharging, self-discharge, and efficiency changes.
[0029] In one embodiment of the present invention, the power grid equivalent model is constructed using the following formula:
[0030] In the formula, The curve represents the system's dynamic equivalent inertial time constant, where m is the number of synchronous generator types. Let be the inertial time constant of the i-th type of synchronous generator unit. Let k be the rated capacity of the i-th type of synchronous generator unit, and k be the number of distributed power source types. Let be the inertial time constant of the j-th type of distributed source. For the rated capacity of the j-th type of distributed power source, For the dynamic equivalent inertia of the wind farm, Determine the total capacity for wind farms. The average virtual inertia coefficient of the unit group. The total real-time capacity of the system is N, where N is the number of power grid nodes. The real-time voltage of the nth node. Let n be the impedance of the nth node. The power factor angle of the nth node. The equivalent load power time-domain curve is shown. The system's dynamic damping coefficient is... For the real-time frequency of the power grid, The rated frequency of the power grid. For random disturbance power, The system's dynamic damping coefficient is... This is the system's reference damping coefficient. This is the damping adjustment coefficient.
[0031] In this embodiment, for the first time, the virtual inertia of wind farms and the inertia of distributed power sources are incorporated into the calculation of the system's equivalent inertia. Through a weighted aggregation formula of the system's dynamic equivalent inertia time constant curves, the dynamic quantification of synchronous generators, distributed power sources, and wind farms is achieved, reflecting the inertial evolution law of a high-proportion renewable energy power grid. Quantifying the power supply of each node replaces the traditional equivalent simplification of the entire network, improving the simulation accuracy of frequency dynamic response. Introducing the system dynamic damping coefficient, the damping strength is adaptively adjusted according to the frequency deviation, avoiding the response lag of the fixed damping coefficient under complex disturbances, and improving the model's simulation capability for frequency oscillations. Among them, the inertial time constant of the i-th type of synchronous generator is the factory nameplate parameter or measured value of the synchronous generator, and the rated capacity of the i-th type of synchronous generator is the factory nominal value of the generator.
[0032] In one embodiment of the present invention, the power grid equivalent model further includes the following formula:
[0033] In the formula, This is the system's rated load power. This is the load frequency regulation effect coefficient. This is the load voltage regulation effect coefficient. The system average voltage, The system's rated voltage. This is the load voltage-frequency cross-coupling coefficient.
[0034] In this embodiment, traditional models only consider the independent response of the load to frequency or voltage, neglecting their combined influence. In scenarios with high wind power penetration, the coupling effect of voltage fluctuations and frequency deviations can lead to significant errors in load power calculation. This model introduces a load voltage-frequency cross-coupling coefficient through the solution formula of the equivalent load power time-domain curve, quantifying the load power change under joint voltage and frequency disturbances. Simultaneously, it achieves global aggregation of node-level voltages through the system average voltage, further improving the simulation accuracy of load dynamic response. The load voltage-frequency cross-coupling coefficient is obtained through simulation fitting or field measurement, quantifying the combined influence of load voltage and frequency.
[0035] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0036] According to another embodiment, the present invention provides a simulation device for wind farm frequency regulation. Figure 2 A schematic block diagram of a simulation apparatus for wind farm frequency regulation according to one embodiment is shown. It will be understood that this apparatus can be implemented using any device, equipment, platform, or cluster of devices with computing and processing capabilities. Figure 2 As shown, the device includes an acquisition unit 200 and a simulation unit 202. The main functions of each component are as follows: The acquisition unit 200 is configured to acquire wind farm frequency regulation simulation parameters for a preset duration; wherein, the frequency regulation simulation parameters include real-time air density, real-time wind speed, real-time generator angular velocity, real-time wind turbine angular velocity, real-time grid frequency, real-time energy storage temperature, number of grid nodes, and real-time voltage of the nth node; Simulation unit 202 is configured to input the wind farm frequency regulation simulation parameters into a preset multi-dimensional simulation model for simulation and obtain simulation results; wherein, the simulation results are used to describe the frequency regulation characteristics of the wind farm; The multi-dimensional simulation model includes a generator group aggregation model, a coupled resource coordination model, and a power grid equivalent model. The simulation results include the equivalent output curve of the generator group, the total control torque curve, the real-time aerodynamic power of the wind turbine, the real-time charge and discharge power curve of the energy storage, the state of charge evolution curve, the system dynamic equivalent inertia time constant curve, and the equivalent load power time domain curve. The generator group aggregation model is used to simulate the real-time aerodynamic power of the wind turbine, the equivalent output curve of the generator group, and the total control torque curve. The coupled resource coordination model is used to simulate the real-time charge and discharge power curve of the energy storage and the state of charge evolution curve. The power grid equivalent model is used to simulate the system dynamic equivalent inertia time constant curve and the equivalent load power time domain curve.
[0037] In one embodiment of the present invention, the unit group aggregation model is constructed using the following formula:
[0038] In the formula, This represents the real-time aerodynamic power of the wind turbine. The real-time air density, Where is the radius of the wind turbine. The time-varying wind energy capture coefficient, The real-time wind speed is... The equivalent moment of inertia of the wind turbine side. For the real-time aerodynamic torque of the wind turbine, This is the torsional stiffness coefficient of the drive shaft. This refers to the lateral displacement of the wind turbine. This refers to the generator side angular displacement. This is the damping coefficient on the wind turbine side. This refers to the real-time angular velocity of the wind turbine. For the electromagnetic torque of the generator, The real-time angular velocity of the generator is... The equivalent moment of inertia on the generator side. This is the generator-side damping coefficient. The total control torque curve is shown below. For dynamic virtual inertia coefficient, This is the dynamic droop coefficient. The rated frequency of the power grid. The real-time frequency of the power grid, The damping coefficient is the rate of change of power. To increase the power output of the unit group The equivalent output curve of the unit group is shown below. For the initial equivalent output, The dead-zone power loss of the k-th wind turbine converter is... This refers to the DC bus voltage of the converter. Let the dead zone equivalent current of the k-th wind turbine converter be . Let be the power factor angle of the k-th wind turbine.
[0039] In one embodiment of the present invention, the unit group aggregation model further includes the following formula:
[0040] In the formula, This represents the initial time-varying wind energy capture coefficient. To correct the tip speed ratio, The pitch angle is the propeller angle. The fitting coefficients for the first aerodynamic characteristic are: The fitting coefficients for the second aerodynamic characteristic are... The fitting coefficients for the third aerodynamic characteristic are... The fitting coefficients for the fourth aerodynamic characteristic are... The fitting coefficient for the fifth aerodynamic characteristic is... The fitting coefficient for the sixth aerodynamic characteristic. The fitting coefficient for the seventh aerodynamic characteristic. For dynamic tip speed ratio, This is the correction factor for the rate of change of wind speed. As the baseline virtual inertia coefficient, It is the Sigmoid activation function. The rated angular velocity of the wind turbine. As the baseline droop coefficient, The equivalent output curves for the unit group. The rated equivalent output of the unit group.
[0041] In one embodiment of the present invention, the real-time air density is verified using the following formula:
[0042] In the formula, The density of air under standard conditions. This is the altitude correction factor. This represents the real-time altitude deviation. This is the temperature correction factor. This represents the real-time ambient temperature deviation.
[0043] In one embodiment of the present invention, the coupled resource coordination model is constructed using the following formula:
[0044] In the formula, The real-time charge and discharge power curve of the energy storage is shown. For dynamic rated power of energy storage, For the real-time frequency of the power grid, The rated frequency of the power grid. For the maximum permissible frequency deviation, This is the SOC correction factor. This is the rated power of the energy storage. This represents the energy storage aging degradation coefficient. The coefficient representing the influence of energy storage temperature. The real-time temperature of the energy storage is... The rated operating temperature for energy storage. This is the charge state evolution curve. This is the limit of the state of charge of energy storage. This is the intermediate value of the energy storage state of charge. This represents the upper limit of the energy storage state of charge. This represents the initial state of charge of the energy storage. To improve the real-time charging and discharging efficiency of energy storage, For the rated capacity of energy storage, For energy storage self-discharge rate, For time, For integration time variable, This refers to the real-time charging and discharging power of energy storage.
[0045] In one embodiment of the present invention, the power grid equivalent model is constructed using the following formula:
[0046] In the formula, The curve represents the dynamic equivalent inertial time constant of the system, where m is the number of synchronous generator types. Let be the inertial time constant of the i-th type of synchronous generator unit. Let k be the rated capacity of the i-th type of synchronous generator unit, and k be the number of distributed power source types. Let be the inertial time constant of the j-th type of distributed source. For the rated capacity of the j-th type of distributed power source, For the dynamic equivalent inertia of the wind farm, Determine the total capacity for wind farms. The average virtual inertia coefficient of the unit group. The total real-time capacity of the system is N, where N is the number of power grid nodes. The real-time voltage of the nth node. Let n be the impedance of the nth node. The power factor angle of the nth node. The equivalent load power time-domain curve is shown. The system's dynamic damping coefficient is... For the real-time frequency of the power grid, The rated frequency of the power grid. For random disturbance power, The system's dynamic damping coefficient is... This is the system's reference damping coefficient. This is the damping adjustment coefficient.
[0047] In one embodiment of the present invention, the power grid equivalent model further includes the following formula:
[0048] In the formula, This is the system's rated load power. This is the load frequency regulation effect coefficient. This is the load voltage regulation effect coefficient. The system average voltage, The system's rated voltage. This is the load voltage-frequency cross-coupling coefficient.
[0049] According to another embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed in a computer, causes the computer to perform a combination Figure 1 The method described.
[0050] According to another embodiment, an electronic device is also provided, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements a combination... Figure 1 The method described.
[0051] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0052] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium.
[0053] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.
Claims
1. A simulation method for frequency regulation in a wind farm, characterized in that, include: Obtain wind farm frequency regulation simulation parameters for a preset duration; wherein, the frequency regulation simulation parameters include real-time air density, real-time wind speed, real-time generator angular velocity, real-time wind turbine angular velocity, real-time grid frequency, real-time energy storage temperature, number of grid nodes, and real-time voltage of the nth node; The frequency regulation simulation parameters of the wind farm are input into a preset multi-dimensional simulation model for simulation to obtain simulation results; wherein, the simulation results are used to describe the frequency regulation characteristics of the wind farm. The multi-dimensional simulation model includes a generator group aggregation model, a coupled resource coordination model, and a power grid equivalent model. The simulation results include the equivalent output curve of the generator group, the total control torque curve, the real-time aerodynamic power of the wind turbine, the real-time charge and discharge power curve of the energy storage, the state of charge evolution curve, the system dynamic equivalent inertia time constant curve, and the equivalent load power time domain curve. The generator group aggregation model is used to simulate the real-time aerodynamic power of the wind turbine, the equivalent output curve of the generator group, and the total control torque curve. The coupled resource coordination model is used to simulate the real-time charge and discharge power curve of the energy storage and the state of charge evolution curve. The power grid equivalent model is used to simulate the system dynamic equivalent inertia time constant curve and the equivalent load power time domain curve.
2. The method according to claim 1, characterized in that, The unit group aggregation model is constructed using the following formula: In the formula, This represents the real-time aerodynamic power of the wind turbine. The real-time air density, Where is the radius of the wind turbine. The time-varying wind energy capture coefficient, The real-time wind speed is... The equivalent moment of inertia of the wind turbine side. For the real-time aerodynamic torque of the wind turbine, This is the torsional stiffness coefficient of the drive shaft. This refers to the lateral displacement of the wind turbine. This refers to the generator side angular displacement. This is the damping coefficient on the wind turbine side. This refers to the real-time angular velocity of the wind turbine. For the electromagnetic torque of the generator, The real-time angular velocity of the generator is... The equivalent moment of inertia on the generator side. This is the generator-side damping coefficient. The total control torque curve is shown below. For dynamic virtual inertia coefficient, This is the dynamic droop coefficient. The rated frequency of the power grid. The real-time frequency of the power grid, The damping coefficient is the rate of change of power. To increase the power output of the unit group The equivalent output curve of the unit group is shown below. For the initial equivalent output, For the first Typhoon turbine converter dead-zone power loss This refers to the DC bus voltage of the converter. For the first Typhoon turbine converter dead zone equivalent current For the first Typhoon power factor angle.
3. The method according to claim 2, characterized in that, The unit group aggregation model also includes the following formula: In the formula, This represents the initial time-varying wind energy capture coefficient. To correct the tip speed ratio, The pitch angle is the propeller angle. The fitting coefficients for the first aerodynamic characteristic are: The fitting coefficients for the second aerodynamic characteristic are... The fitting coefficients for the third aerodynamic characteristic are... The fitting coefficients for the fourth aerodynamic characteristic are... The fitting coefficient for the fifth aerodynamic characteristic is... The fitting coefficient for the sixth aerodynamic characteristic. The fitting coefficient for the seventh aerodynamic characteristic. For dynamic tip speed ratio, This is the correction factor for the rate of change of wind speed. As the baseline virtual inertia coefficient, It is the Sigmoid activation function. The rated angular velocity of the wind turbine. As the baseline droop coefficient, The equivalent output curves for the unit group. The rated equivalent output of the unit group.
4. The method according to claim 2, characterized in that, The real-time air density is verified using the following formula: In the formula, The density of air under standard conditions. This is the altitude correction factor. This represents the real-time altitude deviation. This is the temperature correction factor. This represents the real-time ambient temperature deviation.
5. The method according to claim 1, characterized in that, The coupled resource coordination model is constructed using the following formula: In the formula, The real-time charge and discharge power curve of the energy storage is shown. For dynamic rated power of energy storage, For the real-time frequency of the power grid, The rated frequency of the power grid. For the maximum permissible frequency deviation, This is the SOC correction factor. This is the rated power of the energy storage. This represents the energy storage aging degradation coefficient. The coefficient representing the influence of energy storage temperature. The real-time temperature of the energy storage is... The rated operating temperature for energy storage. This is the charge state evolution curve. This is the limit of the state of charge of energy storage. This is the intermediate value of the energy storage state of charge. This represents the upper limit of the energy storage state of charge. This represents the initial state of charge of the energy storage. To improve the real-time charging and discharging efficiency of energy storage, For the rated capacity of energy storage, For energy storage self-discharge rate, For time, For integration time variable, This refers to the real-time charging and discharging power of energy storage.
6. The method according to claim 1, characterized in that, The power grid equivalent model is constructed using the following formula: In the formula, The curve represents the dynamic equivalent inertial time constant of the system, where m is the number of synchronous generator types. Let be the inertial time constant of the i-th type of synchronous generator unit. Let k be the rated capacity of the i-th type of synchronous generator unit, and k be the number of distributed power source types. Let be the inertial time constant of the j-th type of distributed source. For the rated capacity of the j-th type of distributed power source, For the dynamic equivalent inertia of the wind farm, Determine the total capacity for wind farms. The average virtual inertia coefficient of the unit group. The total real-time capacity of the system is N, where N is the number of power grid nodes. The real-time voltage of the nth node. Let n be the impedance of the nth node. The power factor angle of the nth node. The equivalent load power time-domain curve is shown. The system's dynamic damping coefficient is... For the real-time frequency of the power grid, The rated frequency of the power grid. For random disturbance power, The system's dynamic damping coefficient is... This is the system's reference damping coefficient. This is the damping adjustment coefficient.
7. The method according to claim 6, characterized in that, The power grid equivalent model also includes the following formula: In the formula, This is the system's rated load power. This is the load frequency regulation effect coefficient. This is the load voltage regulation effect coefficient. The system average voltage, The system's rated voltage. This is the load voltage-frequency cross-coupling coefficient.
8. A simulation device for wind farm frequency regulation, characterized in that, include: The acquisition unit is configured to acquire wind farm frequency regulation simulation parameters for a preset duration; wherein, the frequency regulation simulation parameters include real-time air density, real-time wind speed, real-time generator angular velocity, real-time wind turbine angular velocity, real-time grid frequency, real-time energy storage temperature, number of grid nodes, and real-time voltage of the nth node; The simulation unit is configured to input the wind farm frequency regulation simulation parameters into a preset multi-dimensional simulation model to perform simulation and obtain simulation results; wherein, the simulation results are used to describe the frequency regulation characteristics of the wind farm. The multi-dimensional simulation model includes a generator group aggregation model, a coupled resource coordination model, and a power grid equivalent model. The simulation results include the equivalent output curve of the generator group, the total control torque curve, the real-time aerodynamic power of the wind turbine, the real-time charge and discharge power curve of the energy storage, the state of charge evolution curve, the system dynamic equivalent inertia time constant curve, and the equivalent load power time domain curve. The generator group aggregation model is used to simulate the real-time aerodynamic power of the wind turbine, the equivalent output curve of the generator group, and the total control torque curve. The coupled resource coordination model is used to simulate the real-time charge and discharge power curve of the energy storage and the state of charge evolution curve. The power grid equivalent model is used to simulate the system dynamic equivalent inertia time constant curve and the equivalent load power time domain curve.
9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.