Reactive power optimization method, system and equipment for energy storage type wind power plant and medium

Abstract

The invention discloses an energy storage type wind power plant reactive power optimization method, system and device and a medium, and belongs to the technical field of wind power reactive power optimization, and the method comprises the steps: collecting the parameters of a wind power plant, and constructing an energy storage type fan model; performing fuzzy droop control on the fan through a reactive power-voltage droop coefficient according to the voltage deviation of the grid-connected point of the wind power plant and the real-time wind speed in combination with a reactive power-voltage droop control equation; if the reactive power demand of the fan exceeds the capacity limit of the inverter after fuzzy droop control, carrying out partition load shedding according to the voltage deviation of the grid-connected point, and obtaining an active power margin; and constructing a reactive power optimization model of the wind power plant, and solving to obtain an optimal power instruction of the fan and the reactive power compensation device. The droop coefficient is adjusted according to the parameters such as the grid-connected point voltage deviation and the wind speed, so that the reactive output of the fan is optimized, and the stability of the power grid voltage is improved. Partition load shedding voltage regulation control is implemented based on the grid-connected point voltage deviation, the active power output of the fan is adjusted, the reactive power margin is increased, and the grid-connected point voltage deviation is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power reactive power optimization, in particular to a kind of energy storage type wind farm reactive power optimization method, system, equipment and medium. BACKGROUND

[0002] With the increasing proportion of wind power in China, the power grid gradually presents the characteristics of "weak support, low disturbance", and the voltage stability problem is increasingly prominent, and fully utilizing the reactive power regulation capability of wind power to support the voltage of power grid is an important way to solve this problem. With the help of the advantages of electrochemical energy storage such as precise control, fast response and flexible adjustment, configuring energy storage battery at the DC bus of wind turbine can smooth the output of wind turbine and stabilize the DC voltage. At the same time, the traditional grid-connected control of wind turbine is transformed into network-constructed control, which is based on the principle of virtual synchronous control (VSG) and simulates the excitation regulation characteristics of traditional synchronous generator to adjust the system voltage, which is beneficial to improve the stability of system voltage.

[0003] However, in actual application, the wind power converter is limited by the capacity of power electronic devices, and when the required reactive compensation of wind turbine is too high, the active power needs to be reduced to provide more space for reactive adjustment. Unlike single unit control, actual wind farm contains various reactive power sources, and the operating states of each reactive power source are different. Therefore, it is necessary to carry out reactive power coordinated control on each wind turbine and reactive compensation devices such as SVG and SVC to improve the overall operating efficiency and stability of wind farm. However, the traditional method of controlling wind turbine by using virtual synchronous technology often does not fully consider the influence of virtual synchronous droop coefficient on control effect, which leads to the difficulty in fully exerting the advantages of virtual synchronous control. In addition, the current control means is mostly aimed at frequency regulation control for load shedding, and the research on wind turbine load shedding strategy for reactive voltage control is relatively less. In the reactive power distribution optimization strategy, the main concern is often focused on the voltage stability of key nodes, while the economic problem brought by load shedding voltage regulation is ignored. This limits the improvement of overall operating efficiency of wind farm, and may have adverse effects on the stability and economy of power grid. SUMMARY

[0004] In view of the above problems, the present application is proposed.

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

[0006] To solve the above technical problems, the present application provides the following technical scheme: a kind of energy storage type wind farm reactive power optimization method, which comprises, collecting voltage deviation of wind farm grid-connected point, real-time wind speed and wind turbine related parameters; construct a wind turbine model based on the wind turbine related parameters, and establish a reactive power-voltage droop control equation based on the wind turbine model; According to the voltage deviation of the grid-connected point of the wind farm and the real-time wind speed, combined with the reactive power-voltage droop control equation, the reactive power-voltage droop coefficient is obtained, and the wind turbine is controlled by fuzzy droop control through the reactive power-voltage droop coefficient. If the reactive power demand of the wind turbine after fuzzy droop control exceeds the capacity limit of the inverter, the active power margin is obtained by partitioning and reducing the load according to the voltage deviation of the grid-connected point. Based on the active power margin, a wind farm reactive power optimization model is constructed, and the wind turbine and the optimal power instruction of the reactive power compensation device are obtained by solving the wind farm reactive power optimization model using the sea squirt algorithm.

[0007] As a preferred scheme of the energy storage type wind farm reactive power optimization method, wherein: a wind turbine model is constructed based on the wind turbine related parameters, including: The parameters of the direct-drive wind turbine, the machine-side converter, the grid-side converter and the electrochemical energy storage are obtained, and the energy storage type wind turbine model is constructed.

[0008] As a preferred scheme of the energy storage type wind farm reactive power optimization method, wherein: based on the wind turbine model, a reactive power-voltage droop control equation is established, and the reactive power-voltage droop control equation is expressed as: , Wherein, VSG is the reactive power reference value of the VSG, VSG is the reactive power set value of the VSG, K is the reactive power-voltage droop coefficient, V is the rated voltage of the grid-connected point, V is the actual voltage of the grid-connected point.

[0009] As a preferred scheme of the energy storage type wind farm reactive power optimization method, wherein: according to the voltage deviation of the grid-connected point of the wind farm and the real-time wind speed, combined with the reactive power-voltage droop control equation, the reactive power-voltage droop coefficient is obtained, including: The voltage deviation of the grid-connected point of the wind farm and the real-time wind speed are taken as inputs, and the reactive power-voltage droop coefficient is adaptively adjusted by the fuzzy controller; According to the rules of the fuzzy controller, the domain of the voltage deviation of the grid-connected point is set as [0, 1.5, 2.5, 3.3, 5, 6.7, 8.5, 10], and the corresponding fuzzy subsets are {NL, NM, NS, ZERO, PS, PM, PL}, and the quantization factor is 0.5. The argument domain of the real-time wind speed is set as [8, 9, 10, 11, 12], and the corresponding fuzzy subset is {NL, NS, ZERO, PS, PL}. The argument domain of the reactive power-voltage droop coefficient is set as [1, 1.7, 2.3, 3, 3.7, 4.3, 5], and the corresponding fuzzy subset is {NL, NM, NS, ZERO, PS, PM, PL}, and the corresponding proportional factor is 1000.

[0010] As a preferred scheme of the energy storage type wind farm reactive power optimization method, if the wind turbine reactive power demand exceeds the inverter capacity limit after the fuzzy droop control, the active power margin is obtained by performing partition load shedding according to the grid connection point voltage deviation, and the active power margin includes: The virtual speed governor of the virtual synchronous control based on the variable power tracking is used to execute the partition load shedding control strategy, and after the partition load shedding operation is triggered, the active power instruction of the grid-side converter is changed from the wind turbine maximum power tracking value to the variable power tracking value, so as to obtain the active power margin.

[0011] As a preferred scheme of the energy storage type wind farm reactive power optimization method, based on the active power margin, a wind farm reactive power optimization model is constructed, and the wind farm reactive power optimization model includes: A multi-objective optimization target function is designed with the minimum grid connection point voltage deviation, the minimum unit terminal voltage deviation and the maximum wind turbine active power margin as targets, and a wind farm reactive power optimization model is constructed according to the target function and the constraint condition; The constraint condition includes a power flow equation constraint, a voltage amplitude constraint, a reactive power compensation device output constraint and a wind turbine reactive power output constraint.

[0012] As a preferred scheme of the energy storage type wind farm reactive power optimization method, the tunicate algorithm is used to solve the wind farm reactive power optimization model, and the optimal power instruction of the wind turbine and the reactive power compensation device is obtained, and the wind farm reactive power optimization model includes: The target function and the constraint condition of the wind farm reactive power optimization model are input into the tunicate algorithm, a set of initial data is randomly generated within the constraint condition limit range, and the wind farm model is run to calculate the fitness value corresponding to the initial data; The non-dominated solution set in the food source storage library is updated through iterative calculation, and the iteration is ended when the preset termination condition is met, and the optimal Pareto solution set of the wind farm reactive power distribution optimization is output, and the optimal power instruction of each wind turbine and reactive power compensation device is obtained.

[0013] The application provides an energy storage type wind farm reactive power optimization system.

[0014] 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; 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. 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.

[0015] 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.

[0016] 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.

[0017] 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

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0019] Figure 1 A general flowchart of a reactive power optimization method of a storage type wind farm according to an embodiment of the present application is provided.

[0020] Figure 2 A storage type direct drive wind turbine grid-connected structure diagram of a reactive power optimization method of a storage type wind farm according to an embodiment of the present application is provided.

[0021] Figure 3 A load shedding process diagram of a storage type direct drive wind turbine of a reactive power optimization method of a storage type wind farm according to an embodiment of the present application is provided.

[0022] Figure 4 A wind farm voltage regulation control strategy diagram of a reactive power optimization method of a storage type wind farm according to an embodiment of the present application is provided.

[0023] Figure 5 A wind farm reactive power optimization distribution strategy flowchart of a reactive power optimization method of a storage type wind farm according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0024] In order to make the above objectives, features and advantages of the present application more apparent and understandable, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort should fall within the protection scope of the present application.

[0025] Embodiment 1, refer to Figure 1 According to an embodiment of the present application, the embodiment provides a reactive power optimization method of a storage type wind farm, which comprises: S100: collecting wind farm grid-connected point voltage deviation, real-time wind speed and wind turbine related parameters; S101: constructing a storage type wind turbine model according to the wind turbine related parameters, and establishing a reactive power-voltage droop control equation based on the storage type wind turbine model; S102: obtaining a reactive power-voltage droop coefficient according to the wind farm grid-connected point voltage deviation and the real-time wind speed, combining the reactive power-voltage droop control equation, and performing fuzzy droop control on the wind turbine through the reactive power-voltage droop coefficient; S103: If the reactive power demand of the fan after the fuzzy droop control exceeds the inverter capacity limit, perform partition load shedding according to the voltage deviation of the grid-connected point to obtain an active power margin; S104: Based on the active power margin, construct a wind farm reactive power optimization model, solve the wind farm reactive power optimization model using the sea squirt algorithm, and obtain the optimal power instructions of the fan and the reactive power compensation device.

[0026] Embodiment 2, refer to Figures 1-5 For an embodiment of the present application, a kind of energy storage type wind farm reactive power optimization method based on the last embodiment includes: In the embodiment of the present application, the fan-related parameters collected in step S100 include the rated power of the direct-drive fan, the blade swept area, the wind energy utilization coefficient, the capacity and conversion efficiency of the machine-side converter (MSC) and the grid-side converter (GSC), the capacity, the upper and lower limits of the state of charge (SOC) and the charge-discharge efficiency of the electrochemical energy storage, etc.

[0027] In the embodiment of the present application, step S101 constructs an energy storage type fan model according to the fan-related parameters, and establishes a reactive power-voltage droop control equation based on the energy storage type fan model, which specifically includes: The size of wind energy captured by the direct-drive fan is related to air density, wind speed and the area swept by the wind turbine blades, which is expressed as: , Wherein, represents the mechanical energy captured by the wind turbine, represents the area swept by the blade, represents the wind speed, represents the pitch angle, represents the tip speed ratio, is the wind energy utilization coefficient, is the radius of the fan.

[0028] The power flow after adding electrochemical energy storage to the direct current side of the direct-drive fan, at this time the power balance equation is: , Wherein, is the power after adding electrochemical energy storage to the direct current side of the direct-drive fan, is the active power of MSC, is the active power of GSC, is the DC bus voltage, is the DC bus capacitor, is the active power absorbed or emitted by the electrochemical energy storage, is the derivative of the DC bus voltage with respect to time.

[0029] The power of the energy storage when the DC voltage is stable is: , wherein, is the power of energy storage when the direct current voltage is stable, is the active power of MSC, is the active power of GSC.

[0030] It should be noted that in order to avoid the influence of overcharge / overdischarge on the service life of the energy storage, the lower limit value of the state of charge (SOC) of the electrochemical energy storage is set to 10% and the upper limit value is set to 90%.

[0031] In the embodiment of the application, the virtual synchronous theory is constructed to realize reactive droop control, comprising: The classic second-order model of the synchronous generator is composed of mechanical characteristics and electromagnetic characteristics, wherein the mechanical characteristics are mainly expressed by the rotor motion equation as: , wherein, is the moment of inertia of VSG, is the active reference power of VSG, is the electromagnetic power of VSG, is the damping coefficient of VSG, is the angular velocity of VSG, is the rated angular velocity of VSG, is the electrical angle of VSG.

[0032] The electromagnetic characteristics are expressed by the stator voltage equation as: , wherein, is the amplitude of control adjustment rate, is the reactive power output value of VSG, is the reactive power reference value of VSG, is the total voltage, is the dynamic adjustment part of the reference voltage of the power system, is the reference voltage of VSG.

[0033] The reactive-voltage droop control equation is expressed as: , wherein, is the reactive power reference value of VSG, is the reactive power set value of VSG, is the reactive-voltage droop coefficient, is the rated voltage of the grid-connected point, is the actual voltage of the grid-connected point.

[0034] In the embodiment of the present application, step S102 obtains the reactive power-voltage droop coefficient according to the voltage deviation of the wind farm grid-connected point and the real-time wind speed, in combination with the reactive power-voltage droop control equation, and performs fuzzy droop control on the wind turbine through the reactive power-voltage droop coefficient, further comprising sub-steps A1-A4: A1: taking the voltage deviation of the wind farm grid-connected point and the real-time wind speed as inputs, the fuzzy controller is used to adaptively adjust the reactive power-voltage droop coefficient; A2: according to the rules of the fuzzy controller, the domain of the voltage deviation of the grid-connected point is set as [0, 1.5, 2.5, 3.3, 5, 6.7, 8.5, 10], and the corresponding fuzzy subsets are {NL, NM, NS, ZERO, PS, PM, PL}, and the quantization factor is 0.5; A3: the domain of the real-time wind speed is set as [8, 9, 10, 11, 12], and the corresponding fuzzy subsets are {NL, NS, ZERO, PS, PL}; A4: the domain of the reactive power-voltage droop coefficient is set as [1, 1.7, 2.3, 3, 3.7, 4.3, 5], and the corresponding fuzzy subsets are {NL, NM, NS, ZERO, PS, PM, PL}, and the corresponding proportional factor is 1000.

[0035] Specifically, the MSC controller of the direct-drive wind turbine uses the hill climbing search method to control the wind turbine speed, acquires the active power output value corresponding to the current wind turbine speed in real time, gradually adjusts the speed and compares the power change trend: if the active power increases after the speed is increased, the speed is continuously adjusted in the same direction; if the power decreases, the speed is adjusted in the opposite direction. This process is iterated until the speed point that makes the active power reach the maximum value under the current wind speed is found, so that the wind turbine always runs in the state of optimal wind energy utilization coefficient, realizes maximum power tracking, and keeps the wind turbine speed stable at the position of maximum power under the current wind speed. The improved GSC controller uses the VSG method to control the wind turbine grid-connected power, so that the direct-drive wind turbine has the voltage regulation characteristics of a pseudo-synchronous machine. The electrochemical energy storage controller is used to maintain the stability of the DC bus voltage and smooth the active power fluctuation of the wind turbine.

[0036] The energy storage type direct-drive wind turbine is also provided with a control mode judgment module and a power control module. The control mode judgment module selects the normal control mode and the partitioned load shedding control mode according to the acquired grid-connected point voltage information; and the power control module determines the active power instruction and the reactive power control instruction of the wind turbine according to the switching action instruction issued by the control mode judgment module.

[0037] The fuzzy control has good dynamic characteristics and robustness, and can cope with the uncertainty and nonlinear changes of the wind power system. In this embodiment, the voltage deviation of the grid-connected point and the wind speed are taken as inputs, and the fuzzy controller is used to adaptively adjust the reactive power droop coefficient.

[0038] In the embodiment of the present application, if the reactive power demand of the fan after the fuzzy droop control exceeds the inverter capacity limit, the step S103 further comprises sub-steps B1-B2: B1: The reactive power limit of the wind storage system flowing into the power grid is: wherein, is the maximum reactive power limit of the inverter, is the maximum capacity limit of the inverter, is the active power of the wind storage system flowing into the power grid.

[0039] B2: After the reactive power demand exceeds the maximum power limit, active power reduction can be considered to free up more reactive power regulation margin. Since the wind turbine speed cannot be infinitely increased, the traditional overspeed reduction strategy of the fan has certain regulation boundary, so the VSG virtual governor reduction control strategy based on variable power tracking is adopted. After triggering the reduction operation, the active power instruction of the grid-side GSC changes from the maximum power tracking value of the fan to the variable power tracking value, and the active power generated by the machine-side MSC is stored in the electrochemical energy storage.

[0040] In the embodiment of the present application, based on the active power margin, the step S103 constructs a reactive power optimization model of the wind farm, solves the reactive power optimization model of the wind farm by adopting the sea cucumber algorithm, and obtains the optimal power instruction of the fan and the reactive power compensation device, which further comprises sub-steps C1-C4: C1: A multi-objective optimization objective function is designed with the minimum voltage deviation of the grid connection point, the minimum voltage deviation of the unit terminal and the maximum active power margin of the fan as the target, and a reactive power optimization model of the wind farm is constructed according to the objective function and the constraint condition; C2: The constraint conditions include the power flow equation constraint, the voltage amplitude constraint, the reactive power compensation device output constraint and the fan reactive power output constraint; C3: The objective function and the constraint condition of the wind farm reactive power optimization model are input in the sea cucumber algorithm; the sea cucumber colony is initialized, a set of initial data is randomly generated within the constraint condition limit, and the wind farm model is run to calculate the fitness value corresponding to the initial data; C4: The non-dominated solution set in the food source storage is updated through iterative calculation, and the iteration is ended when the preset termination condition is met, the optimal Pareto solution set of the wind farm reactive power distribution optimization is output, and the optimal power instruction of each fan and reactive power compensation device is obtained.

[0041] ​In the embodiment of the present application, the wind farm control center calculates the lack of reactive power output of the current wind farm according to the deviation of the grid-connected point voltage from the given reference value. The reactive power compensation device is preferentially used in reactive power compensation. If the reactive power capacity of the reactive power compensation device is insufficient to support the overall reactive power lack of the wind farm, the multi-objective mussels algorithm is used to perform reactive power optimization and distribution on the wind turbines and the reactive power compensation device in the wind farm.

[0042] In the embodiment of the present application, the minimum grid-connected point voltage deviation, the minimum unit terminal voltage deviation and the maximum wind turbine active power margin are taken as the objective functions of multi-objective optimization, including: (1) The minimum grid-connected point voltage deviation. Improving the stability of the grid-connected point voltage of the wind farm is the primary goal of the reactive power distribution optimization of the wind farm. The objective function is defined as wherein, is the rated voltage of the grid-connected point of the wind farm, is the actual voltage of the grid-connected point of the wind farm,

[0043] (2) The minimum unit terminal voltage deviation. The terminal voltage of the wind turbine also needs to meet the national standard requirements. The objective function is wherein, , is the set of all wind turbine nodes in the wind farm, is the reference value of the terminal voltage of the wind turbine, is the measured value of the terminal voltage of the wind turbine, and is taken as .

[0044] (3) The maximum wind turbine active power margin. In order to ensure the economy of the wind farm during steady-state operation, sufficient active power margin needs to be ensured. The active power margin objective function of the wind farm is defined as wherein, , is the set of all wind turbine nodes in the wind farm, is the maximum capacity limit of the wind turbine converter, is the reactive power compensation of the wind turbine.

[0045] The constraint conditions include equality constraints and inequality constraints. The equality constraint power flow equation constraint is expressed as: wherein,​​​​​​​ is the total number of nodes in the wind farm, is the active power injected by the wind farm, is the reactive power injected by the wind farm, is the phase difference between the node and the node is the conductance between the node and the node is the susceptance between the node and the node is the conductance between the node and the node is the susceptance between the node and the node is the current voltage amplitude of the node in the wind farm, is the current voltage amplitude of the node in the wind farm.

[0046] The inequality constraints include voltage amplitude constraints, reactive power compensation device output constraints and wind turbine reactive power output constraints, and are expressed as: , wherein, is the current voltage amplitude of the node in the wind farm, is the lower limit of the voltage amplitude of the node in the wind farm, is the upper limit of the voltage amplitude of the node in the wind farm, is the set of nodes meeting the voltage constraints; is the current reactive power compensation value of the reactive power compensation device in the wind farm, is the lower limit of the reactive power compensation capacity of the reactive power compensation device in the wind farm, is the upper limit of the reactive power compensation capacity of the reactive power compensation device in the wind farm, is the set of reactive power compensation device equipment; is the sum of the current reactive power compensation of the wind turbine, is the lower limit of the current reactive power compensation capacity of the wind turbine, is the upper limit of the current reactive power compensation capacity of the wind turbine, is the set of wind turbines.

[0047] The wind farm reactive power optimization model is solved by using the TSP algorithm to obtain the optimal power instructions of the wind turbine and the reactive power compensation device, specifically including:

[0048] ​In the wind farm reactive power distribution optimization algorithm based on the multi-objective sea squirt algorithm, the input wind farm reactive power optimization model setting value is set; the sea squirt colony is initialized, a set of data is randomly generated in the given constraint range, and then the wind farm model is run to obtain the fitness value of the sea squirt colony.

[0049] Iterative calculation, updating the non-dominated solution set in the food source repository, ending the algorithm loop when the termination condition is met, and outputting the optimal Pareto solution set of the wind farm reactive power distribution optimization.

[0050] Embodiment 3 is an embodiment of the present application, which provides a kind of energy storage type wind farm reactive power optimization system, comprising: data acquisition module, for collecting wind farm grid connection point voltage deviation, real-time wind speed and fan related parameters; Data model module, for constructing energy storage type fan model according to fan related parameters, and establishing reactive power-voltage droop control equation based on energy storage type fan model; Control module, for obtaining reactive power-voltage droop coefficient according to wind farm grid connection point voltage deviation and real-time wind speed, and combining reactive power-voltage droop control equation, to carry out fuzzy droop control on fan by reactive power-voltage droop coefficient; Load reduction module, for if the reactive power demand of fan after fuzzy droop control exceeds the capacity limit of inverter, according to the voltage deviation of grid connection point, to carry out partition load reduction and obtain active power margin; Reactive power optimization module, for constructing wind farm reactive power optimization model based on active power margin, solving wind farm reactive power optimization model by sea squirt algorithm, and obtaining the optimal power instruction of fan and reactive power compensation device.

[0051] The embodiment also provides an electronic device suitable for a kind of energy storage type wind farm reactive power optimization method, comprising: memory and processor;Memory is used to store computer executable instructions, and processor is used to execute computer executable instructions, to realize a kind of energy storage type wind farm reactive power optimization method as described above embodiment proposes.

[0052] The embodiment also provides a storage medium, which stores a computer program, and the program is executed by processor to realize a kind of energy storage type wind farm reactive power optimization method as described above embodiment proposes.

[0053] The storage medium proposed in the embodiment and the implementation of a kind of energy storage type wind farm reactive power optimization method proposed in the above embodiment belong to the same inventive concept, and the technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment and the above embodiment have the same beneficial effects.

[0054] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and necessary universal hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A method for reactive power optimization of energy storage type wind farm, characterized in that: The application relates to a wind farm reactive power optimization method based on fuzzy voltage and reactive power droop control. The wind farm grid-connected point voltage deviation, real-time wind speed and fan-related parameters are collected; An energy storage type fan model is constructed according to the fan-related parameters, and a reactive power-voltage droop control equation is established based on the energy storage type fan model; According to the wind farm grid-connected point voltage deviation and real-time wind speed, the reactive power-voltage droop coefficient is obtained by combining the reactive power-voltage droop control equation, and the fuzzy droop control is performed on the fan through the reactive power-voltage droop coefficient; If the reactive power demand of the fan after the fuzzy droop control exceeds the inverter capacity limit, the active power margin is obtained by performing partition load shedding according to the grid-connected point voltage deviation. Based on the active power margin, a wind farm reactive power optimization model is constructed, and the dahlia algorithm is used to solve the wind farm reactive power optimization model to obtain the optimal power instruction of the fan and the reactive power compensation device.

2. The energy storage type wind farm reactive power optimization method of claim 1, wherein: The energy storage type fan model is constructed according to the fan-related parameters, and a reactive power-voltage droop control equation is established based on the energy storage type fan model. The energy storage type fan model is constructed according to the fan-related parameters, and a reactive power-voltage droop control equation is established based on the energy storage type fan model.

3. The energy storage type wind farm reactive power optimization method of claim 2, wherein: According to the wind farm grid-connected point voltage deviation and real-time wind speed, the reactive power-voltage droop coefficient is obtained by combining the reactive power-voltage droop control equation, and the fuzzy droop control is performed on the fan through the reactive power-voltage droop coefficient. ， wherein, is a reactive power reference value for the VSG, is a reactive power setpoint value for the VSG, is a reactive-voltage droop coefficient, is a point of common coupling nominal voltage, is a point of common coupling actual voltage.

4. The energy storage type wind farm reactive power optimization method of claim 3, wherein: The wind farm grid-connected point voltage deviation and real-time wind speed are taken as inputs, and the reactive power-voltage droop coefficient is adaptively adjusted through the fuzzy controller; According to the rules of the fuzzy controller, the domain of the grid-connected point voltage deviation is set as [0, 1.5, 2.5, 3.3, 5, 6.7, 8.5, 10], and the corresponding fuzzy subsets are {NL, NM, NS, ZERO, PS, PM, PL}; the quantization factor is 0.5; The domain of the real-time wind speed is set as [8, 9, 10, 11, 12], and the corresponding fuzzy subsets are {NL, NS, ZERO, PS, PL}; The domain of the reactive power-voltage droop coefficient is set as [1, 1.7, 2.3, 3, 3.7, 4.3, 5], and the corresponding fuzzy subsets are {NL, NM, NS, ZERO, PS, PM, PL}; the corresponding proportional factor is 1000. If the reactive power demand of the fan after the fuzzy droop control exceeds the inverter capacity limit, the active power margin is obtained by performing partition load shedding according to the grid-connected point voltage deviation.

5. The energy storage type wind farm reactive power optimization method of claim 4, wherein: The virtual speed governor of the virtual synchronous control based on variable power tracking is used to execute the partition load shedding control strategy; after the partition load shedding operation is triggered, the active instruction of the grid-side converter changes from the maximum power tracking value of the fan to the variable power tracking value, and the active power margin is obtained. Based on the active power margin, a wind farm reactive power optimization model is constructed, including:

6. The energy storage type wind farm reactive power optimization method of claim 4, wherein: The minimum grid-connected point voltage deviation, the minimum unit terminal voltage deviation and the maximum fan active power margin are taken as targets to design a target function of multi-objective optimization, and a wind farm reactive power optimization model is constructed according to the target function and the constraint condition; The constraint conditions include the power flow equation constraint, the voltage amplitude constraint, the reactive power compensation device output constraint and the fan reactive power output constraint. ​ 7. The energy storage type wind farm reactive power optimization method of claim 4, wherein: The barrel sea slug algorithm is used to solve the reactive power optimization model of the wind farm, and optimal power instructions of the wind turbine and the reactive power compensation device are obtained, including: In the barrel sea slug algorithm, the objective function and the constraint condition of the reactive power optimization model of the wind farm are input; the barrel sea slug colony is initialized, and a set of initial data is randomly generated within the range limited by the constraint condition; the wind farm model is run to calculate the fitness value corresponding to the initial data; The non-dominated solution set in the food source storage library is updated through iterative calculation, and the iteration is ended when the preset termination condition is met; the optimal Pareto solution set of the reactive power distribution optimization of the wind farm is output, and the optimal power instructions of each wind turbine and the reactive power compensation device are obtained.

8. A reactive power optimization system for energy storage type wind farm, applying the method of any one of claims 1-7, characterized in that, It includes: The data acquisition module is used to collect the voltage deviation of the wind farm grid-connected point, the real-time wind speed and the wind turbine related parameters; The data model module is used to construct an energy storage type wind turbine model according to the wind turbine related parameters, and establish a reactive power-voltage droop control equation based on the energy storage type wind turbine model; The control module is used to obtain the reactive power-voltage droop coefficient according to the voltage deviation of the wind farm grid-connected point and the real-time wind speed, and combine the reactive power-voltage droop control equation to perform fuzzy droop control on the wind turbine; The load reduction module is used to perform partition load reduction according to the voltage deviation of the grid-connected point if the reactive power demand of the wind turbine after fuzzy droop control exceeds the inverter capacity limit, and obtain the active power margin; The reactive power optimization module is used to construct a wind farm reactive power optimization model based on the active power margin, solve the wind farm reactive power optimization model by using the barrel sea slug algorithm, and obtain the optimal power instructions of the wind turbine and the reactive power compensation device. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the energy storage type wind farm reactive power optimization method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the energy storage type wind farm reactive power optimization method in any one of claims 1 to 7.

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