Wind power stabilizing fuzzy control method considering energy storage output level

Abstract

The invention discloses a wind power stabilizing fuzzy control method and system considering the energy storage output level, and belongs to the field of new energy station energy storage operation control. The wind power stabilizing fuzzy control method comprises the steps that based on original wind power data, a self-adaptive improved complete ensemble empirical mode method is adopted to achieve wind power decomposition, and according to the wind power grid-connected power fluctuation requirement, the energy storage output level is calculated; reconstructing high-frequency and low-frequency power components to complete distribution of energy storage primary power instructions; in order to solve the problem that the state of charge is out of limit due to excessive charging and discharging of energy storage, a fuzzy control method is adopted to carry out secondary correction on an energy storage power instruction, and SOC optimization of an energy storage system is achieved. The regulation and control requirements of wind power stabilization are considered, the minimum output capacity and the maximum output capacity of energy storage are taken as a composite target, and a genetic algorithm is adopted to optimize fuzzy membership parameters. By combining the ICEEMDAN, the fuzzy control and the genetic algorithm, the wind power grid-connected power fluctuation is effectively stabilized, the energy storage output demand is reduced, and the output capability of the energy storage system is improved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage operation control for new energy power stations, and particularly to a fuzzy control method for suppressing wind power fluctuations considering the energy storage output level. Background Art

[0002] In recent years, with the large-scale grid connection of new energy power stations such as wind power, due to its strong characteristics of randomness, volatility, and uncertainty, it has affected the safe and stable operation of the power system. At present, energy storage technology has developed rapidly. Relying on its characteristics of fast response speed and high regulation accuracy, the wind farm can effectively reduce the wind power fluctuation through the flexible charge and discharge power response ability of energy storage. Therefore, in order to regulate the wind power grid connection power fluctuation, improve the penetration rate of wind power grid connection, and offset the negative impact of high-penetration wind power, the energy storage system control strategy has become the key to wind power grid connection research for a long time.

[0003] Currently, the research on the control strategy for energy storage to suppress wind power fluctuations mainly includes two parts. First, a suitable power decomposition method needs to be adopted to complete the allocation of energy storage power commands; second, the charge and discharge control strategy of the energy storage system is designed to complete the response to the power commands. For the problem of energy storage power command allocation, power signal decomposition methods are often used, such as: first-order low-pass filtering, empirical mode decomposition, and wavelet packet decomposition, etc. However, the time constant of first-order low-pass filtering is difficult to accurately control, and there are mode mixing problems in empirical mode decomposition and wavelet packet decomposition methods. For the design of the energy storage charge and discharge power strategy, the wind power fluctuation suppression strategy is often studied from the perspective of protecting the energy storage, ignoring the demand of the power grid for the output capacity of the energy storage system to suppress wind power fluctuations. Combining the problems existing in the above research, a fuzzy control strategy for suppressing wind power fluctuations considering the energy storage output level is proposed. First, in order to reduce the mode mixing phenomenon existing in the traditional decomposition method, ICEEMDAN is used to decompose and reconstruct the wind power, and the allocation of energy storage power commands is completed. Second, in order to solve the problem that the SOC of the energy storage exceeds the limit due to overcharge and over-discharge, a fuzzy control method is used to perform secondary correction on the energy storage power command to realize the SOC optimization of the energy storage system. Finally, considering the regulation demand of wind power fluctuation suppression, with the minimum and maximum output capabilities of the energy storage as the composite target, a genetic algorithm is used to optimize the fuzzy membership parameters to achieve the goal of protecting the energy storage and improving the output capacity of the energy storage system to suppress wind power fluctuations. Summary of the Invention

[0004] In view of the above existing problems, the present invention designs an energy storage charge and discharge control strategy including ICEEMDAN decomposition, fuzzy control, and genetic algorithm, which realizes the correction of energy storage power commands and SOC optimization on the basis of meeting the requirements of wind power fluctuation grid connection, protects the energy storage system, and improves the energy supply quality of the wind farm.

[0005] To solve the above technical problems, a fuzzy control method for suppressing wind power considering the energy storage output level is proposed, including:

[0006] Based on the original wind power data, an adaptive improved complete ensemble empirical mode method is used for wind power decomposition; according to the requirements of wind power grid connection power fluctuation, the high- and low-frequency power components are reconstructed, and the primary power command of the energy storage is allocated; a fuzzy control method is used to perform secondary correction on the energy storage power command, and the SOC of the energy storage system is optimized; considering the regulation requirements of wind power suppression, a genetic algorithm is used to optimize the fuzzy membership parameters.

[0007] As a preferred solution of the fuzzy control method for suppressing wind power considering the energy storage output level according to the present invention, wherein: the wind power decomposition includes adding the IMF component of white noise to the original power sequence to construct a new sequence;

[0008] The local mean is calculated for the new sequence respectively to obtain the first group of residuals;

[0009] Calculate the first modal component;

[0010] Continue to add white noise, and use local mean decomposition to calculate the residuals and modal components;

[0011] Until the calculation and decomposition are completed, all modal and residual numbers are obtained.

[0012] As a preferred solution of the fuzzy control method for suppressing wind power considering the energy storage output level according to the present invention, wherein: the allocation of the primary power command of the energy storage includes reconstruction based on the wind power decomposition result according to the requirements of wind power fluctuation grid connection. When any decomposition meets the grid connection requirements, the sum of the high-frequency components is used as the energy storage power command, and the remaining low-frequency components and residuals are used as the grid connection power, as follows:

[0013]

[0014] Wherein, P ref Is the energy storage wind power suppression command, P grid Is the grid connection power, m is the imf component demarcation point, n0 is the total number of imf components, Is the residual, and i is the variable index.

[0015] As a preferred solution of the fuzzy control method for suppressing wind power considering the energy storage output level according to the present invention, wherein: the secondary correction includes normalizing the primary power command of the fuzzy control input quantity;

[0016] The input quantity energy storage SOC, considering the charge and discharge efficiency of the energy storage system, measures the energy storage SOC;

[0017] Set the fuzzy control output as the correction coefficient to correct the initial power command and optimize the SOC.

[0018] As a preferred solution of the fuzzy control method for suppressing wind power considering the energy storage output level of the present invention, wherein: the SOC optimization includes taking the active power command of the energy storage and the state of charge of the energy storage as inputs, and the output is the power command correction coefficient, and performing fuzzy processing;

[0019] Construct a fuzzy control rule table for the active power command of the energy storage, the state of charge of the energy storage, and the power command adjustment coefficient;

[0020] And formulate a fuzzy control strategy based on the fuzzy rule table, and obtain the real-time control strategy of the energy storage system through defuzzification processing.

[0021] As a preferred solution of the fuzzy control method for suppressing wind power considering the energy storage output level of the present invention, wherein: the optimization of the fuzzy membership parameters includes constructing a genetic algorithm fitness function;

[0022] Considering the protection of the energy storage system and the response requirements of the wind power suppression command, with the minimum output of the energy storage system and the maximum energy storage output capacity as the goals, construct a genetic algorithm fitness function:

[0023]

[0024] Wherein, J is the genetic algorithm fitness function, SOC fix (t) is the state of charge of the energy storage after optimization, is the normalized active power command of the energy storage, and t is the time point.

[0025] As a preferred solution of the fuzzy control method for suppressing wind power considering the energy storage output level of the present invention, wherein: the optimization of the fuzzy membership parameters further includes constructing a genetic algorithm penalty constraint;

[0026] Apply a large penalty to individuals with out-of-limit wind power grid-connected power fluctuations and out-of-limit energy storage SOC to accelerate the iteration speed of the genetic algorithm. The specific constraints include grid-connected power fluctuation constraints and SOC constraints;

[0027] The grid-connected power fluctuation constraint is:

[0028]

[0029] The SOC constraint is:

[0030] SOC min ≤SOC fix (t)≤SOC max

[0031] where, ΔP 1min is the 1-minute power fluctuation of the grid-connected power, and ΔP 10min is the 10-minute power fluctuation of the grid-connected power, and P w,rate is the installed capacity of the wind farm.

[0032] Another object of the present invention is to provide a fuzzy control system for suppressing wind power fluctuations considering the energy storage output level. The present invention solves the problem of the impact of wind power fluctuations on the stability of the power grid. By decomposing the wind power signal and distributing commands, combining the fuzzy control method to dynamically correct the energy storage output command, and using the genetic algorithm to optimize the parameters of the fuzzy controller, it ensures that the energy storage system can effectively suppress wind power fluctuations without overcharging and discharging, and improves the stability and reliability of wind power grid connection.

[0033] As a preferred embodiment of the fuzzy control system for suppressing wind power fluctuations considering the energy storage output level according to the present invention, it is characterized in that it includes a wind power decomposition module, a command distribution module, a command correction module, and an optimization module;

[0034] The wind power decomposition module decomposes the original wind power signal into modal components and residual components of different frequencies based on an improved modal method. By adding white noise, calculating the local mean and residual, it extracts the high-frequency fluctuation component and the low-frequency steady component, and outputs the decomposed data to the command distribution module;

[0035] The command distribution module receives the output of the decomposition module. According to the requirements of wind power grid-connected power fluctuations, it takes the decomposed data as the fluctuating power that needs to be suppressed by the energy storage, and generates a primary energy storage power command; the remaining low-frequency component and the residual are used as the grid-connected power to ensure that the grid-connected fluctuations meet the standards. It transmits the generated primary energy storage power command to the command correction module, and at the same time outputs the grid-connected power to the power grid;

[0036] The command correction module dynamically corrects the energy storage output command based on the fuzzy control method, combines the current state of charge of the energy storage and the primary power command, adjusts the correction coefficient through fuzzy rules and membership functions, optimizes the SOC state, and avoids overcharging and discharging of the energy storage leading to over-limit;

[0037] The optimization module uses the genetic algorithm to optimize the membership function parameters of the fuzzy controller with the minimum and maximum output capabilities of the energy storage as the composite objective. Through the fitness function and penalty constraints, it ensures the global optimality of the system.

[0038] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, it realizes the steps of the fuzzy control method for suppressing wind power fluctuations considering the energy storage output level.

[0039] A computer-readable storage medium, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of a fuzzy control method for suppressing wind power considering the energy storage output level are implemented.

[0040] Advantages of the present invention: By analyzing the characteristics of power decomposition methods, the present invention uses ICEEMDAN to decompose wind power into low-frequency grid-connected power and high-frequency energy storage power commands, providing support for subsequent control strategies, having high engineering compatibility, and reducing the mode mixing phenomenon of traditional decomposition methods.

[0041] In view of the characteristics of fast response speed and strong power support ability of the energy storage system, the present invention proposes a fuzzy control strategy for high-frequency power command correction and energy storage SOC optimization, solves the problem of SOC over-limit caused by excessive charge and discharge of the energy storage, and improves the output ability of the energy storage system.

[0042] In order to reduce the impact of command correction on grid-connected power, the present invention optimizes the fuzzy membership parameters based on the genetic algorithm, eliminates individuals with wind power grid-connected power fluctuations and energy storage SOC over-limit, protects the energy storage system while meeting the basic requirements of suppressing wind power, and takes into account the output ability requirements of the grid for the energy storage system to suppress wind power fluctuations. Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is the overall flowchart of a fuzzy control method for suppressing wind power considering the energy storage output level provided by an embodiment of the present invention.

[0045] Figure 2 It is the wind power decomposition diagram based on ICEEMDAN of a fuzzy control method for suppressing wind power considering the energy storage output level provided by an embodiment of the present invention.

[0046] Figure 3 It is the effect diagram of the energy storage system suppressing wind power of a fuzzy control method for suppressing wind power considering the energy storage output level provided by an embodiment of the present invention.

[0047] Figure 4 It is the charge and discharge power diagram of the energy storage system of a fuzzy control method for suppressing wind power considering the energy storage output level provided by an embodiment of the present invention.

[0048] Figure 5 The state-of-charge diagram of the energy storage system for a method of suppressing wind power fluctuations considering the energy storage output level provided by an embodiment of the present invention.

[0049] Figure 6 The fuzzy membership function diagram of the state of charge SOC(t) of the energy storage in the fuzzy control system for a method of suppressing wind power fluctuations considering the energy storage output level provided by an embodiment of the present invention.

[0050] Figure 7 The fuzzy membership function diagram of the primary power command Pref*(t) of the energy storage in the fuzzy control system for a method of suppressing wind power fluctuations considering the energy storage output level provided by an embodiment of the present invention.

[0051] Figure 8 The fuzzy membership function diagram of the energy storage power command correction coefficient α(t) in the fuzzy control system for a method of suppressing wind power fluctuations considering the energy storage output level provided by an embodiment of the present invention.

[0052] Figure 9 The iterative convergence diagram of the genetic algorithm for a method of suppressing wind power fluctuations considering the energy storage output level provided by an embodiment of the present invention.

[0053] Figure 10 The system scheme module diagram of a fuzzy control system for suppressing wind power fluctuations considering the energy storage output level provided by an embodiment of the present invention. Detailed implementation manners

[0054] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0055] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0056] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments alone or selectively.

[0057] The present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0058] Meanwhile, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner, and outer" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0059] Unless otherwise clearly defined and limited in the present invention, the terms "mounted, connected, and coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0060] Example 1, referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a fuzzy control method for suppressing wind power considering the energy storage output level, including:

[0061] S1: Based on the original wind power data, an adaptive improved complete ensemble empirical mode decomposition method is used for wind power decomposition.

[0062] Furthermore, an improved complete ensemble empirical mode decomposition method (ICEEMDAN) for wind power decomposition based on adaptivity is constructed.

[0063] Step 1.1: Add k groups of IMF components E1(W(k)) of white noise to the original power sequence to construct a new sequence as follows:

[0064] P(k) = P + ε0E1(W(k))

[0065] Where ε0 represents the coefficient multiplied when adding the first IMF component E1(W(k)) of Gaussian white noise; P is defined as the power signal to be decomposed; E1 represents the first-order modal component generated by EMD decomposition; and W(k) represents Gaussian white noise.

[0066] Step 1.2: Calculate the local mean of the new sequence respectively to obtain the first group of residuals, as shown below.

[0067]

[0068] Among them, R1 is the first group of residuals; K is the number of sequence groups; N is the local mean of the power signal.

[0069] Step 1.3: Calculate the first mode component imf1, as shown below:

[0070] imf1 = P - R1

[0071] Step 1.4: Continue to add white noise and calculate the mth group of residuals R m and the mth mode component imf m , as shown below:

[0072]

[0073] imf m = R m-1 - R m

[0074] S2: According to the requirements of wind power grid-connected power fluctuation, reconstruct the high and low frequency power components and allocate the primary power command of the energy storage.

[0075] Furthermore, for the decomposition result of wind power, reconstruct it according to the requirements of wind power fluctuation grid connection in China. When the mth decomposition meets the grid connection requirements, the sum of the high frequency components imf1~imf m is used as the energy storage power command, and the remaining low frequency components and residuals are used as the grid-connected power, as shown below:

[0076]

[0077]

[0078] In the formula, P ref is the energy storage wind power smoothing command, P grid is the grid-connected power, m is the imf component demarcation point, n0 is the total number of imf components, is the residual, and i is the variable index.

[0079] S3: To solve the problem that the SOC of the energy storage exceeds the limit due to overcharge and over-discharge, a fuzzy control method is used to perform secondary correction on the energy storage power command to realize the SOC optimization of the energy storage system.

[0080] It should be noted that Step 3.1: Construct the calculation method of fuzzy control input and output quantities

[0081] Normalize the primary power command, which is the input quantity of the fuzzy control;

[0082] For the input quantity of energy storage SOC, consider the charge and discharge efficiency of the energy storage system and calculate the energy storage SOC;

[0083] Set the output quantity of the fuzzy control as the correction coefficient, correct the initial power command, and optimize the SOC. Specifically as follows:

[0084] Z c +Z d = 1

[0085]

[0086] P fix P(t) = α(t)P ref (t)

[0087]

[0088] Where, Z c and Z d are 0-1 variables, η c is the charging efficiency, η d is the discharging efficiency, η is the charge and discharge efficiency, SOC0 is the initial state of charge of the energy storage, P rate is the rated active power of the energy storage, E rate is the rated capacity of the energy storage, n is the number of sampling points, ΔT is the sampling time interval, P ref (t) is the active power command of the energy storage, is the normalized active power command of the energy storage, P fix (t) is the corrected power command of the energy storage, SOC fix (t) is the optimized state of charge of the energy storage, and α(t) is the correction coefficient of the energy storage power command.

[0089] Step 3.2: Fuzzify the input and output quantities of the energy storage system

[0090] The input quantities of the energy storage fuzzy control system are the state of charge of the energy storage and the initial power command, and the output quantity is the correction coefficient of the power command.

[0091] Fuzzify the input and output quantities.

[0092] Based on the fuzzy control theory, using the Gaussian function as the membership function, a design is made with the energy storage SOC(t) and the normalized power command As the input of the fuzzy control, the power command correction coefficient α(t) is the output fuzzy control strategy. For the input variable SOC(t), fuzzy sets are defined: Z, S, M, B, and L, corresponding to zero, small, medium, large, and larger respectively, representing the degree from small to large within the numerical range [0.1, 0.9].

[0093] Input variable defines fuzzy sets: NB, NS, Z, PS, and PB, corresponding to negative large, negative small, zero, positive small, and positive large respectively, representing the degree from small to large within the numerical range [-1, 1].

[0094] The output variable α(t) defines a fuzzy set of 5 linguistic values: Z, S, M, B, and L, corresponding to zero, small, medium, large, and larger respectively, representing the degree from small to large within the numerical range [0, 1].

[0095] Step 3.3: Construct the fuzzy control rule table

[0096] After fuzzifying the above input and output variables, construct the fuzzy rule table. The column parameter of the rule table is the fuzzy set of SOC(t), and the row parameter is the primary power command fuzzy set, and each cell parameter is the fuzzy control rule of the correction coefficient α(t):

[0097] Table 1 Wind power grid-connected power fluctuation requirement table

[0098]

[0099] Step 3.4: Defuzzification

[0100] Use defuzzification to clarify the fuzzy control output variable and obtain the accurate value of the energy storage output power.

[0101] In an embodiment of the present application, defuzzification can adopt the weighted average method. In this embodiment, the specific steps are as follows:

[0102] According to the input variables (such as energy storage SOC state, power deviation, etc.), calculate the membership degree of the premise condition of each fuzzy rule, and determine the set of activated rules; each activated rule corresponds to an output fuzzy set and its membership degree (trigger strength); for each activated rule, extract the central value of the output fuzzy set in its conclusion part (such as the vertex value of the triangular membership degree); calculate the weighted contribution of the output value of each rule, sum the weighted values of all activated rules, and divide by the sum of the trigger strengths to obtain the clarification result; the weighted average method directly calculates based on the rule trigger strength and the central value, with high calculation efficiency and suitable for real-time control.

[0103] S4: Considering the regulation requirements for suppressing wind power fluctuations, taking the minimum and maximum output capabilities of energy storage as the composite objective, the genetic algorithm is used to optimize the fuzzy membership parameters.

[0104] Specifically, step 4.1: Construct the fitness function of the genetic algorithm;

[0105] Considering the protection of the energy storage system and the response requirements of the wind power fluctuation suppression command, taking the minimum output of the energy storage system and the maximum output capacity of the energy storage as the objectives, construct the fitness function of the genetic algorithm:

[0106]

[0107] where J is the fitness function of the genetic algorithm, SOC fix (t) is the state of charge of the energy storage after optimization, is the normalized active power command of the energy storage, and t is the time point.

[0108] Step 4.2: Construct the penalty constraints of the genetic algorithm;

[0109] Apply large penalties to individuals with out-of-limit fluctuations in the grid-connected wind power and out-of-limit SOC of the energy storage to accelerate the iteration speed of the genetic algorithm. The specific constraints include grid-connected power fluctuation constraints and SOC constraints;

[0110] The grid-connected power fluctuation constraint is:

[0111]

[0112] The SOC constraint is:

[0113] SOC min ≤SOC fix (t)≤SOC max

[0114] where ΔP 1min is the 1-minute power fluctuation amount of the grid-connected power, ΔP 10min is the 10-minute power fluctuation amount of the grid-connected power, and P w,rate is the installed capacity of the wind farm.

[0115] Example 2, referring to Figures 2 - 9 , the second embodiment of the present invention provides a fuzzy control method for suppressing wind power based on species counting and energy storage output level. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0116] By Figure 2Analysis shows that the present invention uses ICEEMDAN to decompose the wind power, which is decomposed into 9 IMF components and 1 residue according to the frequency range from high to low. According to the grid connection standard of wind power fluctuation, imf1-imf3 are reconstructed into the high-frequency energy storage power command, and the remaining IMF components and residues are reconstructed into the low-frequency components as the grid connection power.

[0117] It can be seen from Figure 3 Analysis shows that based on the fuzzy control strategy for suppressing wind power fluctuations considering the energy storage output level of the present invention, the fluctuation degree of the original wind power is effectively reduced, making the wind power grid connection power smoother and meeting the grid connection standard.

[0118] It can be seen from Figure 4 Analysis shows that the energy storage system charges and discharges according to the initially determined high-frequency command, and the output amplitude is large, which is likely to cause the problem of excessive suppression of wind power. The fuzzy control strategy for suppressing wind power fluctuations considering the energy storage output level of the present invention effectively reduces the output amplitude of the energy storage system, which is beneficial to the improvement of economic benefits.

[0119] It can be seen from Figure 5 Analysis shows that when the energy storage system outputs according to the initially determined high-frequency command, the SOC may approach the limit value or even cross the line, resulting in the problem that the energy storage cannot respond to the power suppression command due to insufficient output capacity. The fuzzy control strategy for suppressing wind power fluctuations considering the energy storage output level of the present invention uses the fuzzy control method to correct the energy storage power command and optimizes the fuzzy membership parameters based on the genetic algorithm. After the fuzzy control optimization, the SOC of the energy storage system fluctuates between 0.3 and 0.7, ensuring that the energy storage works within a reasonable range and improving the ability of the energy storage system to respond to the power command.

[0120] It can be seen from Figure 6 Analysis shows that based on the fuzzy control theory, the present invention uses the Gaussian function as the membership function, designs the energy storage SOC(t) as the input of the fuzzy control, and defines the fuzzy sets: Z, S, M, B, and L, corresponding to zero, small, medium, large, and larger respectively, indicating the degree of SOC(t) increasing from small to large in the number domain [0.1, 0.9].

[0121] It can be seen from Figure 7 Analysis shows that based on the fuzzy control theory, the present invention uses the Gaussian function as the membership function, designs the normalized energy storage power command P one (t) as the input of the fuzzy control. For the input quantity P one (t), the fuzzy sets are defined: NB, NS, Z, PS, and PB, corresponding to negative large, negative small, zero, positive small, and positive large respectively, indicating the degree of P one (t) increasing from small to large in the number domain [-1, 1] respectively.

[0122] It can be seen from Figure 8Analysis shows that the present invention is based on the fuzzy control theory, uses the Gaussian function as the membership function, and designs a fuzzy control strategy with the correction coefficient K(t) of the energy storage power command as the output. For the output quantity K(t), a fuzzy set with 5 linguistic values is defined: Z, S, M, B, and L, corresponding to zero, small, medium, large, and larger respectively, and the number domain range is [0 1].

[0123] It can be seen from Figure 9 Analysis shows that the present invention is based on the genetic algorithm, takes the minimum output and maximum output capabilities of the energy storage as the composite target, optimizes the membership parameters in the fuzzy control, and the curve starts to converge at the 6th iteration and finally tends to be stable.

[0124] Embodiment 3, the third embodiment of the present invention, which is different from the previous two embodiments in that:

[0125] If the said function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. And the aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks and other various media that can store program codes.

[0126] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0127] More specific examples (nonexhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0128] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0129] Example 4, referring to Figure 10 ..., is the fourth embodiment of the present invention. This embodiment provides a fuzzy control system for suppressing wind power considering the energy storage output level, including a wind power decomposition module 100, an instruction allocation module 200, an instruction correction module 300, and an optimization module 400;

[0130] The wind power decomposition module 100 decomposes the original wind power signal into modal components and residual components of different frequencies based on an improved modal method. By adding white noise, calculating the local mean, and the residual, the high-frequency fluctuation component and the low-frequency steady component are extracted, and the decomposed data is output to the instruction allocation module 200;

[0131] The instruction allocation module 200 receives the output of the decomposition module 100. According to the requirements of the wind power grid connection power fluctuation, the decomposed data is used as the fluctuating power that needs to be suppressed by the energy storage, and a primary energy storage power instruction is generated; the remaining low-frequency component and the residual are used as the grid connection power to ensure that the grid connection fluctuation meets the standard. The generated primary energy storage power instruction is transmitted to the instruction correction module 300, and at the same time, the grid connection power is output to the power grid;

[0132] The instruction correction module 300 dynamically corrects the energy storage output instruction based on the fuzzy control method, combines the current state of charge of the energy storage and the primary power instruction, adjusts the correction coefficient through fuzzy rules and membership functions, optimizes the SOC state, and avoids over-limit caused by overcharging and over-discharging of the energy storage;

[0133] The optimization module 400 uses the genetic algorithm to optimize the membership function parameters of the fuzzy controller with the minimum and maximum output capabilities of the energy storage as the composite objective, and ensures the global optimality of the system through the fitness function and penalty constraints.

[0134] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A fuzzy control method for stabilizing wind power taking into account the energy storage output level, characterized in that: include, Based on the original wind power data, an adaptive improved complete ensemble empirical mode method is used to decompose wind power. According to the power fluctuation requirements of wind power grid connection, high and low frequency power components are reconstructed and primary power instructions of energy storage are allocated; The fuzzy control method is used to perform secondary correction on the energy storage power command to optimize the SOC of the energy storage system; Considering the regulation demand of wind power leveling, genetic algorithm is used to optimize the fuzzy membership parameters.

2. A fuzzy control method for stabilizing wind power taking into account the energy storage output level according to claim 1, characterized in that: The wind power decomposition includes adding an IMF component of white noise to the original power sequence to construct a new sequence; Calculate the local mean of the new sequence to obtain the first set of residuals; Calculate the first modal component; Continue to add white noise and use local mean decomposition to calculate the residual and modal components; Until the calculation decomposition is completed, all modes and residual numbers are obtained.

3. A fuzzy control method for stabilizing wind power taking into account the energy storage output level as claimed in claim 2, characterized in that: The allocation of the energy storage primary power command includes, based on the wind power decomposition result, reconstruction according to the wind power fluctuation grid connection requirements, when any decomposition meets the grid connection requirements, the sum of the high-frequency components is used as the energy storage power command, and the remaining low-frequency components and the residual are used as the grid connection power, as shown below: Among them, P ref is the energy storage wind power smoothing instruction, P grid is the grid-connected power, m is the imf component demarcation point, n0 is the total number of imf components, is the residual, and i is the variable index.

4. A fuzzy control method for stabilizing wind power taking into account the energy storage output level as claimed in claim 3, characterized in that: The secondary correction includes normalizing the fuzzy control input quantity primary power instruction; Input energy storage SOC, consider the charging and discharging efficiency of the energy storage system, and calculate the energy storage SOC; The fuzzy control output is set as the correction coefficient, the initial power command is corrected, and the SOC is optimized.

5. A fuzzy control method for stabilizing wind power taking into account the energy storage output level as claimed in claim 4, characterized in that: The SOC optimization Including, taking the energy storage active power instruction and the energy storage charge state as input, the output is the power instruction correction coefficient, and performing fuzzy processing; Constructing a fuzzy control rule table of energy storage active power command, energy storage charge state and power command adjustment coefficient; A fuzzy control strategy is formulated based on the fuzzy rule table, and the real-time control strategy of the energy storage system is obtained through defuzzification processing.

6. A fuzzy control method for stabilizing wind power taking into account the energy storage output level as claimed in claim 5, characterized in that: The optimization of the fuzzy membership parameters includes constructing a genetic algorithm fitness function; Considering the response requirements of energy storage system protection and wind power smoothing instructions, with the goal of minimizing the output of the energy storage system and maximizing the energy storage output capacity, the fitness function of the genetic algorithm is constructed: Among them, J is the fitness function of the genetic algorithm, SOC fix (t) is the state of charge after energy storage optimization, is the normalized active power command of energy storage, and t is the time point.

7. A fuzzy control method for stabilizing wind power taking into account the energy storage output level as claimed in claim 6, characterized in that: The optimization of the fuzzy membership parameters also includes constructing a genetic algorithm penalty constraint; A large penalty is imposed on individuals whose wind power grid-connected power fluctuation exceeds the limit and whose energy storage SOC exceeds the limit to speed up the iteration speed of the genetic algorithm. The specific constraints include grid-connected power fluctuation constraint and SOC constraint. The grid-connected power fluctuation constraint is: The SOC constraint is: SOC min ≤SOC fix (t)≤SOC max Among them, ΔP 1min ΔP is the 1-minute power fluctuation of the grid-connected power. 10min is the 10-minute power fluctuation of the grid-connected power, P w,rate Installed capacity of wind farm.

8. A system using a fuzzy control method for stabilizing wind power taking into account the energy storage output level as claimed in any one of claims 1 to 7, characterized in that: It includes wind power decomposition module, instruction allocation module, instruction correction module and optimization module; The wind power decomposition module decomposes the original wind power signal into modal components and residual components of different frequencies based on an improved modal method, extracts high-frequency fluctuation components and low-frequency stable components by adding white noise, calculating local means and residuals, and outputs the decomposed data to the instruction allocation module; The command distribution module receives the output of the decomposition module, and uses the decomposed data as the fluctuating power that the energy storage needs to smooth out according to the requirements of the wind power grid-connected power fluctuation, and generates the energy storage primary power command; the remaining low-frequency component and the residual are used as the grid-connected power to ensure that the grid-connected fluctuation meets the standard, and the generated energy storage primary power command is transmitted to the command correction module, and the grid-connected power is output to the power grid; The instruction correction module, based on the fuzzy control method, combines the current state of charge of the energy storage and the primary power instruction, dynamically corrects the energy storage output instruction, adjusts the correction coefficient through fuzzy rules and membership functions, optimizes the SOC state, and avoids over-limit caused by excessive charging and discharging of the energy storage; The optimization module adopts a genetic algorithm, takes the minimum output and maximum output capacity of energy storage as a composite goal, optimizes the membership function parameters of the fuzzy controller, and ensures the global optimality of the system through fitness function and penalty constraints.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a fuzzy control method for leveling wind power taking into account the energy storage output level according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a fuzzy control method for stabilizing wind power taking into account the energy storage output level according to any one of claims 1 to 7 are implemented.

Images