A method, system, device and medium for reactive power optimization of energy storage type wind farm

Abstract

The application discloses a kind of energy storage type wind farm reactive power optimization method, system, equipment and medium, belong to wind power reactive power optimization technical field, including: collection wind farm parameter constructs energy storage type wind turbine model;According to wind farm grid-connected point voltage deviation and real-time wind speed, combine reactive power-voltage droop control equation, by reactive power-voltage droop coefficient to wind turbine is fuzzy droop control;If reactive power demand of wind turbine after fuzzy droop control exceeds inverter capacity limit, according to grid-connected point voltage deviation is partitioned and is unloaded, obtains active power margin;Construct wind farm reactive power optimization model, solve and obtain the optimal power instruction of wind turbine and reactive power compensation device.The application adjusts droop coefficient according to grid-connected point voltage deviation and wind speed and other parameters, thereby optimizing the reactive power output of wind turbine, improves the stability of power grid voltage. Based on grid-connected point voltage deviation implementation partitioned and unloaded voltage control, adjust the active power output of wind turbine, increase reactive power margin, reduce grid-connected point voltage deviation.

Description

Technical Field

[0001] This invention relates to the field of reactive power optimization technology, specifically to a reactive power optimization method, system, equipment, and medium for energy storage wind farms. Background Technology

[0002] With the increasing proportion of wind power in my country, the power grid is gradually exhibiting characteristics of "weak support and low disturbance immunity," making voltage stability issues increasingly prominent. Fully utilizing the reactive power regulation capabilities of wind power to support grid voltage is a crucial approach to addressing this problem. Leveraging the advantages of electrochemical energy storage, such as precise control, rapid response, and flexible adjustment, configuring energy storage batteries at the DC bus of wind turbines can smooth turbine output and stabilize DC voltage. Simultaneously, transforming the traditional grid-following control of wind turbines into grid-connected control, based on the principle of Virtual Synchronous Control (VSG), simulates the excitation regulation characteristics of traditional synchronous generators to regulate system voltage, which is beneficial for improving system voltage stability.

[0003] However, in practical applications, wind power converters are limited by the capacity of power electronic devices. When the reactive power compensation required by the wind turbine is too high, it is necessary to reduce the active power load to free up more reactive power regulation space. Unlike the control of a single unit, a real wind farm contains multiple reactive power sources, and the operating states of each reactive power source are different. Therefore, it is necessary to coordinate the reactive power control of each wind turbine and reactive power compensation devices such as SVG and SVC to improve the overall operating efficiency and stability of the wind farm. However, traditional methods of controlling wind turbines using virtual synchronization technology often do not fully consider the impact of the virtual synchronization droop coefficient on the control effect, making it difficult to fully utilize the advantages of virtual synchronization control. In addition, most current control methods are geared towards frequency regulation control for load reduction, and there is relatively little research on wind turbine load reduction strategies for reactive power voltage control. In reactive power distribution optimization strategies, the main focus is often on the voltage stability of key nodes, while neglecting the economic issues brought about by load reduction and voltage regulation. This limits the improvement of the overall operating efficiency of the wind farm and may have an adverse impact on the stability and economy of the power grid. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: how to solve the voltage support and reactive power coordination optimization problem between units after large-scale wind power is connected to the grid.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a reactive power optimization method for energy storage wind farms, comprising,

[0007] Collect voltage deviation at the grid connection point of the wind farm, real-time wind speed, and relevant parameters of the wind turbine;

[0008] A wind turbine model for energy storage is constructed based on the relevant parameters of the wind turbine. Based on the wind turbine model for energy storage, a reactive power-voltage droop control equation is established.

[0009] Based on the voltage deviation at the grid connection point of the wind farm and the real-time wind speed, and combined with the reactive power-voltage droop control equation, the reactive power-voltage droop coefficient is obtained, and the wind turbine is subjected to fuzzy droop control through the reactive power-voltage droop coefficient.

[0010] If the reactive power demand of the wind turbine exceeds the inverter capacity limit after fuzzy droop control, the active power margin is obtained by dividing the load according to the voltage deviation at the grid connection point.

[0011] Based on the active power margin, a reactive power optimization model for the wind farm is constructed. The Zunhaishao algorithm is used to solve the reactive power optimization model for the wind farm to obtain the optimal power command for the wind turbine and reactive power compensation device.

[0012] As a preferred embodiment of the reactive power optimization method for an energy storage wind farm described in this invention, the method includes: constructing an energy storage wind turbine model based on the relevant wind turbine parameters, comprising:

[0013] Obtain parameters of the direct-drive wind turbine, turbine-side converter, grid-side converter, and electrochemical energy storage, and construct an energy storage-type wind turbine model.

[0014] As a preferred embodiment of the reactive power optimization method for an energy storage wind farm described in this invention, the reactive power-voltage droop control equation is established based on the energy storage wind turbine model. The reactive power-voltage droop control equation is expressed as follows:

[0015] ,

[0016] in, This is the reactive power reference value for VSG. The reactive power setting value for VSG. This is the reactive power-voltage droop factor. The rated voltage at the grid connection point, This is the actual voltage at the grid connection point.

[0017] As a preferred embodiment of the reactive power optimization method for an energy storage wind farm described in this invention, the reactive power-voltage droop coefficient is obtained based on the voltage deviation at the grid connection point of the wind farm and the real-time wind speed, combined with the reactive power-voltage droop control equation, including:

[0018] Using the voltage deviation at the grid connection point of the wind farm and the real-time wind speed as inputs, the reactive power-voltage droop coefficient is adaptively adjusted through a fuzzy controller.

[0019] According to the rules of the fuzzy controller, the universe of discourse for the voltage deviation at the grid connection point is set to [0, 1.5, 2.5, 3.3, 5, 6.7, 8.5, 10], and the corresponding fuzzy subset is {NL, NM, NS, ZER-O, PS, PM, PL}, with a quantization factor of 0.5.

[0020] The universe of discourse for real-time wind speed is set to [8, 9, 10, 11, 12], and the corresponding fuzzy subsets are {NL, NS, ZERO, PS, PL}.

[0021] The universe of discourse for the reactive power-voltage droop coefficient is set to [1, 1.7, 2.3, 3, 3.7, 4.3, 5], the corresponding fuzzy subset is {NL, NM, NS, ZERO, PS, PM, PL}, and the corresponding scaling factor is 1000.

[0022] As a preferred embodiment of the reactive power optimization method for energy storage wind farms described in this invention, if the reactive power demand of the wind turbine exceeds the inverter capacity limit after fuzzy droop control, zoned load shedding is performed based on the grid connection point voltage deviation to obtain an active power margin, including:

[0023] A virtual synchronous control virtual speed governor based on variable power tracking is adopted to execute the zoned load reduction control strategy. After the zoned load reduction operation is triggered, the active power command of the grid-side converter changes from the maximum power tracking value of the wind turbine to the variable power tracking value, thereby obtaining the active power margin.

[0024] As a preferred embodiment of the reactive power optimization method for an energy storage wind farm described in this invention, the reactive power optimization model for the wind farm is constructed based on the active power margin, including:

[0025] With the objectives of minimizing grid connection point voltage deviation, minimizing generator terminal voltage deviation, and maximizing wind turbine active power margin, a multi-objective optimization objective function is designed. Based on the objective function and constraints, a reactive power optimization model for the wind farm is constructed.

[0026] The constraints include power flow equation constraints, voltage amplitude constraints, reactive power compensation device output constraints, and wind turbine reactive power output constraints.

[0027] As a preferred embodiment of the reactive power optimization method for an energy storage wind farm described in this invention, the method employs the *Zunhaishao* algorithm to solve the reactive power optimization model of the wind farm and obtain the optimal power command for the wind turbine and reactive power compensation device, including:

[0028] Input the objective function and constraints of the wind farm reactive power optimization model into the Slime slug algorithm; initialize the Slime slug swarm, randomly generate a set of initial data within the constraint range, and run the wind farm model to calculate the fitness value corresponding to the initial data;

[0029] The non-dominated solution set in the food source storage is updated through iterative calculation. The iteration ends when the preset termination condition is met, and the optimal Pareto solution set of the wind farm reactive power allocation optimization is output to obtain the optimal power command of each wind turbine and reactive power compensation device.

[0030] This invention provides a reactive power optimization system for energy storage wind farms.

[0031] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an energy storage type wind farm reactive power optimization system, comprising: a data acquisition module, used to collect voltage deviation at the grid connection point of the wind farm, real-time wind speed and wind turbine related parameters;

[0032] The data model module is used to construct an energy storage wind turbine model based on the relevant parameters of the wind turbine, and to establish a reactive power-voltage droop control equation based on the energy storage wind turbine model.

[0033] The control module is used to obtain the reactive power-voltage droop coefficient based on the voltage deviation at the grid connection point of the wind farm and the real-time wind speed, combined with the reactive power-voltage droop control equation, and to perform fuzzy droop control on the wind turbine through the reactive power-voltage droop coefficient.

[0034] The load shedding module is used to perform zoned load shedding based on the grid connection point voltage deviation if the reactive power demand of the wind turbine exceeds the inverter capacity limit after fuzzy droop control, thereby obtaining the active power margin.

[0035] The reactive power optimization module is used to construct a reactive power optimization model for the wind farm based on the active power margin, and to solve the reactive power optimization model for the wind farm using the Zunhaishao algorithm to obtain the optimal power command for the wind turbine and reactive power compensation device.

[0036] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the aforementioned reactive power optimization method for an energy storage wind farm.

[0037] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the aforementioned reactive power optimization method for an energy storage wind farm.

[0038] The beneficial effects of this invention are as follows: This invention introduces a fuzzy controller to adaptively adjust the virtual synchronization droop coefficient. Through the design of the fuzzy controller, the droop coefficient can be adjusted in real time according to parameters such as grid connection point voltage deviation and wind speed, thereby optimizing the reactive power output of the wind turbine and improving the stability of the grid voltage. It implements zoned load shedding and voltage regulation control based on grid connection point voltage deviation. When the grid connection point voltage fluctuates significantly, the reactive power demand of the wind turbine is reduced by adjusting the active power output, thereby increasing the reactive power margin and reducing the grid connection point voltage deviation. At the wind farm level, a multi-objective "slug-shaped" algorithm is used to allocate reactive power commands to each wind turbine and reactive power compensation device. This not only meets the stability requirements of the wind farm's grid connection point voltage and turbine terminal voltage but also improves the overall active power margin, thereby enhancing the operational economy and stability of the wind farm. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the overall process of a reactive power optimization method for an energy storage wind farm, as provided in one embodiment of the present invention.

[0041] Figure 2 This is a schematic diagram of the grid-connected structure of a direct-drive wind turbine for an energy storage type wind farm, which is provided as an embodiment of the present invention for a reactive power optimization method for energy storage type wind farms.

[0042] Figure 3 This is a schematic diagram of the load reduction process of a direct-drive wind turbine for an energy storage type wind farm, which is provided as an embodiment of the present invention for a reactive power optimization method.

[0043] Figure 4 This is a schematic diagram of a wind farm voltage regulation control strategy for a reactive power optimization method for an energy storage wind farm, provided as an embodiment of the present invention.

[0044] Figure 5 This is a schematic diagram of the reactive power optimization allocation strategy for a wind farm, provided as an embodiment of the present invention. Detailed Implementation

[0045] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0046] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a reactive power optimization method for energy storage wind farms, including:

[0047] S100: Collects voltage deviation at the grid connection point of the wind farm, real-time wind speed, and relevant parameters of the wind turbine;

[0048] S101: Construct an energy storage wind turbine model based on relevant wind turbine parameters, and establish reactive power-voltage droop control equations based on the energy storage wind turbine model;

[0049] S102: Based on the voltage deviation at the grid connection point of the wind farm and the real-time wind speed, and combined with the reactive power-voltage droop control equation, obtain the reactive power-voltage droop coefficient, and use the reactive power-voltage droop coefficient to perform fuzzy droop control on the wind turbine.

[0050] S103: If the reactive power demand of the wind turbine exceeds the inverter capacity limit after fuzzy droop control, the active power margin is obtained by partitioning the load according to the voltage deviation at the grid connection point.

[0051] S104: Based on the active power margin, a reactive power optimization model for the wind farm is constructed. The Zunhaishao algorithm is used to solve the reactive power optimization model of the wind farm and obtain the optimal power command for the wind turbine and reactive power compensation device.

[0052] Example 2, refer to Figures 1-5 As an embodiment of the present invention, based on the previous embodiment, a reactive power optimization method for energy storage wind farms is provided, comprising:

[0053] In this embodiment of the invention, the wind turbine-related parameters collected in step S100 include the rated power, blade swept area, and wind energy utilization coefficient of the direct-drive wind turbine, the capacity and conversion efficiency of the machine-side converter (MSC) and grid-side converter (GSC), and the capacity, state of charge (SOC) upper and lower limits and charge and discharge efficiency of the electrochemical energy storage, etc.

[0054] In this embodiment of the invention, step S101 constructs an energy storage wind turbine model based on relevant wind turbine parameters, and establishes reactive power-voltage droop control equations based on the energy storage wind turbine model, specifically including:

[0055] The amount of wind energy captured by a direct-drive wind turbine depends on air density, wind speed, and the area swept by the turbine blades, as expressed as:

[0056] ,

[0057] in, This represents the mechanical energy captured by the wind turbine. Indicates the area swept by the blade. Indicates wind speed. Indicates the pitch angle. Indicates the tip speed ratio, The wind energy utilization coefficient, The radius of the wind turbine.

[0058] The power flow after adding electrochemical energy storage to the DC side of a direct-drive fan is given by the following power balance equation:

[0059] ,

[0060] in, The power output after adding electrochemical energy storage to the DC side of the direct-drive fan. This represents the active power of the MSC. The active power of GSC This is the DC bus voltage. For DC bus capacitors, Active power absorbed or generated by electrochemical energy storage. This is the derivative of the DC bus voltage with respect to time.

[0061] When the DC voltage is stable, the stored energy power is:

[0062] ,

[0063] in, The power stored when the DC voltage is stable. This represents the active power of the MSC. This represents the active power of the GSC.

[0064] It should be noted that, in order to avoid the lifespan of energy storage being affected by overcharging / over-discharging, this paper sets the lower limit of the state of charge (SOC) of electrochemical energy storage to 10% and the upper limit to 90%.

[0065] In this embodiment of the invention, a virtual synchronization theory is constructed to achieve reactive power droop control, including:

[0066] The classical second-order model of a synchronous generator consists of two parts: mechanical characteristics and electromagnetic characteristics. The mechanical characteristics are mainly expressed by the rotor motion equations as follows:

[0067] ,

[0068] in, Let VSG be the moment of inertia. The active reference power of VSG, For the electromagnetic power of VSG, The damping coefficient of the VSG. The angular velocity of the VSG The rated angular velocity of the VSG, The electrical angle of the VSG.

[0069] The electromagnetic characteristics are expressed by the stator voltage equation as follows:

[0070] ,

[0071] in, To control the magnitude of the adjustment rate, This refers to the reactive power output value of the VSG. This is the reactive power reference value for VSG. For the total voltage, This is the dynamic adjustment part of the power system reference voltage. This is the reference voltage for VSG.

[0072] The reactive power-voltage droop control equation is expressed as:

[0073] ,

[0074] in, This is the reactive power reference value for VSG. The reactive power setting value for VSG. This is the reactive power-voltage droop factor. The rated voltage at the grid connection point, This is the actual voltage at the grid connection point.

[0075] In this embodiment of the invention, step S102, based on the voltage deviation at the wind farm's grid connection point and the real-time wind speed, and combined with the reactive power-voltage droop control equation, obtains the reactive power-voltage droop coefficient, and performs fuzzy droop control on the wind turbine using the reactive power-voltage droop coefficient. It also includes sub-steps A1-A4:

[0076] A1: Using the voltage deviation at the grid connection point of the wind farm and the real-time wind speed as inputs, the reactive power-voltage droop coefficient is adaptively adjusted through a fuzzy controller;

[0077] A2: According to the rules of the fuzzy controller, the universe of discourse for the voltage deviation at the grid connection point is set to [0, 1.5, 2.5, 3.3, 5, 6.7, 8.5, 10], and the corresponding fuzzy subset is {NL, NM, NS, ZER-O, PS, PM, PL}, with a quantization factor of 0.5.

[0078] A3: Set the universe of discourse for real-time wind speed to [8, 9, 10, 11, 12], and the corresponding fuzzy subsets to {NL, NS, ZERO, PS, PL};

[0079] A4: Set the universe of discourse for the reactive power-voltage droop coefficient to [1, 1.7, 2.3, 3, 3.7, 4.3, 5], the corresponding fuzzy subset to {NL, NM, NS, ZERO, PS, PM, PL}, and the corresponding scaling factor to 1000.

[0080] Specifically, the direct-drive wind turbine's MSC controller uses a hill-climbing search method to control the turbine's speed. It collects real-time data on the active power output corresponding to the current turbine speed, gradually fine-tunes the speed, and compares the power change trend: if the active power increases after increasing the speed, the speed is adjusted in that direction; if the power decreases, the speed is fine-tuned in the opposite direction. This process iterates until the speed point that maximizes the active power at the current wind speed is found, thus keeping the turbine operating at its optimal wind energy utilization coefficient, achieving maximum power point tracking, and maintaining the turbine speed at the maximum power position at the current wind speed. The improved GSC controller uses a VSG method to control the turbine's grid-connected power, giving the direct-drive wind turbine the voltage regulation characteristics of a quasi-synchronous machine. The electrochemical energy storage controller is used to maintain DC bus voltage stability while smoothing out fluctuations in the turbine's active power output.

[0081] The energy storage direct-drive wind turbine is also equipped with a control mode determination module and a power control module. The control mode determination module selects between conventional control mode and zoned load shedding control mode based on the collected grid connection point voltage information; the power control module determines the active power command and reactive power control command of the wind turbine based on the switching action command issued by the control mode determination module.

[0082] Fuzzy control has good dynamic characteristics and robustness, and can cope with the uncertainty and nonlinear changes of wind power systems. In this embodiment, the grid connection point voltage deviation and wind speed are used as inputs, and the reactive power droop coefficient is adaptively adjusted by the fuzzy controller.

[0083] In this embodiment of the invention, if step S103 shows that the reactive power demand of the wind turbine exceeds the inverter capacity limit after fuzzy droop control, and load shedding is performed according to the grid connection point voltage deviation to obtain the active power margin, it also includes sub-steps B1-B2:

[0084] B1: The reactive power limit for the wind-storage system flowing into the power grid is:

[0085] ,

[0086] in, For the inverter's maximum reactive power limit, Due to the maximum capacity limit of the inverter, This refers to the active power flowing into the power grid from the wind-storage system.

[0087] B2: When reactive power demand exceeds the maximum power limit, active power reduction can be considered through load shedding to free up more reactive power adjustment margin. Since wind turbine speed cannot increase indefinitely, traditional wind turbine overspeed load shedding strategies have certain adjustment boundaries. Therefore, this invention adopts a load shedding control strategy based on variable power point tracking (VSG) virtual speed controllers. After triggering the load shedding operation, the active power command of the grid-side GSC changes from the wind turbine's maximum power point tracking value to the variable power point tracking value, and the excess active power generated by the turbine-side MSC is stored in electrochemical energy storage.

[0088] In this embodiment of the invention, step S103, based on the active power margin, constructs a reactive power optimization model for the wind farm, uses the Zunhaishao algorithm to solve the reactive power optimization model for the wind farm, and obtains the optimal power command for the wind turbine and reactive power compensation device. It also includes sub-steps C1-C4:

[0089] C1: With the objectives of minimizing the voltage deviation at the grid connection point, minimizing the voltage deviation at the generator terminal, and maximizing the active power margin of the wind turbine, design a multi-objective optimization objective function, and construct a reactive power optimization model for the wind farm based on the objective function and constraints.

[0090] C2: Constraints include power flow equation constraints, voltage amplitude constraints, reactive power compensation device output constraints, and wind turbine reactive power output constraints.

[0091] C3: Input the objective function and constraints of the wind farm reactive power optimization model into the Tulipa swarm algorithm; initialize the Tulipa swarm, randomly generate a set of initial data within the constraints, and run the wind farm model to calculate the fitness value corresponding to the initial data.

[0092] C4: Update the non-dominated solution set in the food source storage through iterative calculation. The iteration ends when the preset termination condition is met. Output the optimal Pareto solution set for reactive power allocation optimization of the wind farm and obtain the optimal power command for each wind turbine and reactive power compensation device.

[0093] In this embodiment of the invention, the wind farm control center calculates the current reactive power output deficit of the wind farm based on the deviation between the grid connection point voltage and a given reference value. Reactive power compensation devices are preferentially used during reactive power compensation. If the reactive power capacity of the reactive power compensation devices is insufficient to support the overall reactive power deficit of the wind farm, a multi-objective tunic algorithm is used to optimize the allocation of reactive power between the wind turbine generators and reactive power compensation devices in the wind farm.

[0094] In this embodiment of the invention, the objective function for multi-objective optimization is to minimize the grid connection point voltage deviation, minimize the generator terminal voltage deviation, and maximize the active power margin of the wind turbine, including:

[0095] (1) Minimize the voltage deviation at the grid connection point. Improving the stability of the voltage at the grid connection point of the wind farm is the primary goal of optimizing the reactive power distribution of the wind farm. Define the objective function. for:

[0096] ,

[0097] in, The rated voltage at the grid connection point of the wind farm. This is the actual voltage at the wind farm's grid connection point.

[0098] (2) Minimize the deviation of the generator terminal voltage. The generator terminal voltage of the wind turbine also needs to meet the national standard requirements. Objective function for:

[0099] ,

[0100] in, , This is the set of all wind turbine nodes in the wind farm. This is a reference value for the terminal voltage of the wind turbine. The measured value of the wind turbine terminal voltage is taken as... .

[0101] (3) The wind turbine has the largest active power margin. To ensure the economic efficiency of the wind farm during steady-state operation, a sufficient active power margin needs to be guaranteed. Therefore, the objective function of the active power margin of the wind farm is... Defined as:

[0102] ,

[0103] in, , This is the set of all wind turbine nodes in the wind farm. The maximum capacity limit for wind turbine converters, The reactive power compensated for by the wind turbine.

[0104] Constraints include equality constraints and inequality constraints. Equality constraints in the power flow equations are expressed as follows:

[0105] ,

[0106] in, This represents the total number of wind farm nodes. For injecting nodes active power, For injection nodes reactive power, For nodes and nodes The phase difference between them For nodes and nodes The electrical conductance between them For nodes and nodes The susceptance between them Nodes in a wind farm The current voltage amplitude, Nodes in a wind farm The current voltage amplitude.

[0107] Inequality constraints include voltage amplitude constraints, reactive power compensation device output constraints, and wind turbine reactive power output constraints, expressed as:

[0108] ,

[0109] in, Nodes in a wind farm The current voltage amplitude, Nodes in a wind farm The lower limit of voltage amplitude, Nodes in a wind farm The upper limit of voltage amplitude, The set of nodes that satisfy the voltage constraint; This represents the current reactive power compensation value of the reactive power compensation device in the wind farm. This represents the lower limit of the reactive power compensation capacity of reactive power compensation devices in wind farms. This represents the upper limit of reactive power compensation capacity for reactive power compensation devices in wind farms. A collection of reactive power compensation devices; This is the sum of the current reactive power compensation of the wind turbine units. This represents the lower limit of the current reactive power compensation capacity of wind turbine units. This represents the upper limit of the current reactive power compensation capacity of wind turbine units. A collection of wind turbine units.

[0110] The *Slug-like Algorithm* is used to solve the reactive power optimization model of a wind farm, obtaining the optimal power command for the wind turbines and reactive power compensation devices. Specifically, this includes:

[0111] In the wind farm reactive power allocation optimization algorithm based on the multi-objective tunicate algorithm, input the set value of the wind farm reactive power optimization model; initialize the tunicate swarm, randomly generate a set of data within the given constraints, and then run the wind farm model to obtain the fitness value of the tunicate swarm.

[0112] The algorithm iterates through the non-dominated solution set in the food source repository, updates the solution set, terminates the algorithm loop when the termination condition is met, and outputs the optimal Pareto solution set for reactive power allocation optimization of the wind farm.

[0113] Example 3 is an embodiment of the present invention, which provides a reactive power optimization system for an energy storage wind farm, including: a data acquisition module for collecting voltage deviation at the grid connection point of the wind farm, real-time wind speed and wind turbine related parameters;

[0114] The data model module is used to construct an energy storage wind turbine model based on relevant wind turbine parameters, and to establish reactive power-voltage droop control equations based on the energy storage wind turbine model.

[0115] The control module is used to obtain the reactive power-voltage droop coefficient based on the voltage deviation at the grid connection point of the wind farm and the real-time wind speed, combined with the reactive power-voltage droop control equation, and to perform fuzzy droop control on the wind turbine through the reactive power-voltage droop coefficient.

[0116] The load shedding module is used to perform zoned load shedding based on the grid connection point voltage deviation if the reactive power demand of the wind turbine exceeds the inverter capacity limit after fuzzy droop control, thereby obtaining the active power margin.

[0117] The reactive power optimization module is used to construct a reactive power optimization model for wind farms based on the active power margin. The model is solved using the Zunhaishao algorithm to obtain the optimal power command for wind turbines and reactive power compensation devices.

[0118] This embodiment also provides an electronic device applicable to a reactive power optimization method for an energy storage wind farm, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the reactive power optimization method for an energy storage wind farm as proposed in the above embodiment.

[0119] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a reactive power optimization method for an energy storage wind farm as proposed in the above embodiment.

[0120] The storage medium proposed in this embodiment belongs to the same inventive concept as the reactive power optimization method for energy storage wind farms proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0121] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A reactive power optimization method for energy storage wind farms, characterized in that: include, Collect voltage deviation at the grid connection point of the wind farm, real-time wind speed, and relevant parameters of the wind turbine; Based on the relevant parameters of the wind turbine, an energy storage wind turbine model is constructed. Based on the energy storage wind turbine model, the reactive power-voltage droop control equation is established: ； in, This is the reactive power reference value for VSG. The reactive power setting value for VSG. This is the reactive power-voltage droop factor. The rated voltage at the grid connection point, This refers to the actual voltage at the grid connection point. Based on the voltage deviation at the grid connection point of the wind farm and the real-time wind speed, and combined with the reactive power-voltage droop control equation, the reactive power-voltage droop coefficient is obtained, and the wind turbine is subjected to fuzzy droop control through the reactive power-voltage droop coefficient. If the reactive power demand of the wind turbine exceeds the inverter capacity limit after fuzzy droop control, the active power margin is obtained by dividing the load according to the voltage deviation at the grid connection point. Based on the active power margin, a reactive power optimization model for the wind farm is constructed. The Zunhaishao algorithm is used to solve the reactive power optimization model for the wind farm to obtain the optimal power command for the wind turbine and reactive power compensation device. With the objectives of minimizing grid connection point voltage deviation, minimizing generator terminal voltage deviation, and maximizing wind turbine active power margin, a multi-objective optimization objective function is designed. Based on the objective function and constraints, a reactive power optimization model for the wind farm is constructed. The constraints include power flow equation constraints, voltage amplitude constraints, reactive power compensation device output constraints, and wind turbine reactive power output constraints. Input the objective function and constraints of the wind farm reactive power optimization model into the Slime slug algorithm; initialize the Slime slug swarm, randomly generate a set of initial data within the constraint range, and run the wind farm model to calculate the fitness value corresponding to the initial data; The non-dominated solution set in the food source storage is updated through iterative calculation. The iteration ends when the preset termination condition is met, and the optimal Pareto solution set of the wind farm reactive power allocation optimization is output to obtain the optimal power command of each wind turbine and reactive power compensation device.

2. The reactive power optimization method for an energy storage wind farm as described in claim 1, characterized in that: Based on the relevant parameters of the wind turbine, an energy storage wind turbine model is constructed, including: Obtain parameters of the direct-drive wind turbine, turbine-side converter, grid-side converter, and electrochemical energy storage, and construct an energy storage-type wind turbine model.

3. The reactive power optimization method for an energy storage wind farm as described in claim 2, characterized in that: Based on the voltage deviation at the grid connection point of the wind farm and the real-time wind speed, and in conjunction with the reactive power-voltage droop control equation, the reactive power-voltage droop coefficient is obtained, including: Using the voltage deviation at the grid connection point of the wind farm and the real-time wind speed as inputs, the reactive power-voltage droop coefficient is adaptively adjusted through a fuzzy controller. According to the rules of the fuzzy controller, the universe of discourse for the voltage deviation at the grid connection point is set to [0, 1.5, 2.5, 3.3, 5, 6.7, 8.5, 10], and the corresponding fuzzy subset is {NL, NM, NS, ZER-O, PS, PM, PL}, with a quantization factor of 0.

5. The universe of discourse for real-time wind speed is set to [8, 9, 10, 11, 12], and the corresponding fuzzy subsets are {NL, NS, ZERO, PS, PL}. The universe of discourse for the reactive power-voltage droop coefficient is set to [1, 1.7, 2.3, 3, 3.7, 4.3, 5], the corresponding fuzzy subset is {NL, NM, NS, ZERO, PS, PM, PL}, and the corresponding scaling factor is 1000.

4. The reactive power optimization method for an energy storage wind farm as described in claim 3, characterized in that: If the reactive power demand of the wind turbine exceeds the inverter capacity limit after fuzzy droop control, load shedding is performed in zones based on the grid connection point voltage deviation to obtain the active power margin, including: A virtual synchronous control virtual speed governor based on variable power tracking is adopted to execute the zoned load reduction control strategy. After the zoned load reduction operation is triggered, the active power command of the grid-side converter changes from the maximum power tracking value of the wind turbine to the variable power tracking value, thereby obtaining the active power margin.

5. A reactive power optimization system for an energy storage wind farm, employing the reactive power optimization method for an energy storage wind farm as described in any one of claims 1 to 4, characterized in that, include: The data acquisition module is used to collect voltage deviation at the grid connection point of the wind farm, real-time wind speed, and wind turbine-related parameters. The data model module is used to construct an energy storage wind turbine model based on the relevant wind turbine parameters, and to establish reactive power-voltage droop control equations based on the energy storage wind turbine model. ； in, This is the reactive power reference value for VSG. The reactive power setting value for VSG. This is the reactive power-voltage droop factor. The rated voltage at the grid connection point, This refers to the actual voltage at the grid connection point. The control module is used to obtain the reactive power-voltage droop coefficient based on the voltage deviation at the grid connection point of the wind farm and the real-time wind speed, combined with the reactive power-voltage droop control equation, and to perform fuzzy droop control on the wind turbine through the reactive power-voltage droop coefficient. The load shedding module is used to perform zoned load shedding based on the grid connection point voltage deviation if the reactive power demand of the wind turbine exceeds the inverter capacity limit after fuzzy droop control, thereby obtaining the active power margin. The reactive power optimization module is used to construct a reactive power optimization model for the wind farm based on the active power margin, and to solve the reactive power optimization model for the wind farm using the Zunhaishao algorithm to obtain the optimal power command for the wind turbine and reactive power compensation device. With the objectives of minimizing grid connection point voltage deviation, minimizing generator terminal voltage deviation, and maximizing wind turbine active power margin, a multi-objective optimization objective function is designed. Based on the objective function and constraints, a reactive power optimization model for the wind farm is constructed. The constraints include power flow equation constraints, voltage amplitude constraints, reactive power compensation device output constraints, and wind turbine reactive power output constraints. Input the objective function and constraints of the wind farm reactive power optimization model into the Slime slug algorithm; initialize the Slime slug swarm, randomly generate a set of initial data within the constraint range, and run the wind farm model to calculate the fitness value corresponding to the initial data; The non-dominated solution set in the food source storage is updated through iterative calculation. The iteration ends when the preset termination condition is met, and the optimal Pareto solution set of the wind farm reactive power allocation optimization is output to obtain the optimal power command of each wind turbine and reactive power compensation device.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the reactive power optimization method for an energy storage wind farm as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the reactive power optimization method for an energy storage wind farm as described in any one of claims 1 to 4.

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