A method, device and electronic equipment for optimizing energy storage capacity of a wind farm

Abstract

The present invention discloses a method, device and electronic device for optimizing the energy storage capacity of a wind farm, wherein the method comprises: obtaining an energy storage planning and operation model of the wind farm; wherein the objective function of the energy storage planning and operation model is to minimize the energy storage investment cost, and its constraints include energy storage equipment state of charge constraints, energy storage equipment hourly charging and discharging linear constraints, transmission capacity constraints, wind farm output constraints and wind power abandonment rate linear constraint group; based on the energy storage planning and operation model, the projection method is used to calculate the parameter feasible domain of the energy storage equipment; and the optimal configuration parameters of the energy storage equipment are calculated according to the parameter feasible domain of the energy storage equipment. The above method can obtain the parameter feasible domain of the energy storage equipment by solving the energy storage planning and operation model of the wind farm, and the optimal configuration parameters can be further calculated according to the parameter feasible domain, so that the wind farm energy storage system can reasonably absorb the wind power output based on the configuration parameters, and balance the economy of energy storage investment and the safety of wind power abandonment constraints.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a method, device and electronic equipment for optimizing energy storage capacity of a wind farm. Background Art

[0002] In order to alleviate the global energy crisis and vigorously promote green energy, the domestic offshore wind power industry is currently developing rapidly. Due to the influence of meteorological factors, offshore wind power output is often highly volatile and random. Considering that energy storage has the function of smoothing wind power fluctuations and improving power supply reliability, the joint operation of wind and storage has become an important model for wind power development.

[0003] However, the construction and maintenance costs of energy storage systems are often high. If the energy storage capacity is configured too large, the scheduling and storage costs will increase. In addition, the randomness of wind power output can easily lead to wind abandonment problems, increasing the pressure on wind power absorption.

[0004] Therefore, how to configure the optimal wind power supporting energy storage capacity is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method, device and electronic equipment for optimizing energy storage capacity of a wind farm, which can obtain the optimal configuration parameters of the energy storage equipment of the wind farm based on linear programming calculation.

[0006] In a first aspect, the present invention provides a method for optimizing energy storage capacity of a wind farm, comprising:

[0007] Obtain the energy storage planning and operation model of the wind farm; where:

[0008] The objective function of the energy storage planning and operation model is to minimize the energy storage investment cost, and its constraints include energy storage equipment charge state constraints, energy storage equipment hourly charging and discharging linear constraints, transmission capacity constraints, wind farm output constraints and wind power abandonment rate linear constraints.

[0009] Based on the energy storage planning operation model, a projection method is used to calculate the feasible domain of parameters of the energy storage device;

[0010] The optimal configuration parameters of the energy storage device are calculated according to the feasible domain of parameters of the energy storage device.

[0011] Optionally, the energy storage device has a linear constraint on charging and discharging within one hour, specifically:

[0012] The sum of the charging power and discharging power of the energy storage device within one hour shall not exceed the power limit of the energy storage device.

[0013] Optionally, the linear constraint group of wind power curtailment rate is determined by:

[0014] Obtaining an initial wind power curtailment rate constraint, where the initial wind power curtailment rate constraint is determined based on output information and curtailment information of the wind farm;

[0015] According to the strong duality property of linear programming, the initial wind power curtailment rate constraint is transformed into a wind power curtailment rate linear constraint group.

[0016] Optionally, the feasible domain of parameters of the energy storage device is calculated by using the projection method, specifically:

[0017] Based on the energy storage planning and operation model, determine the full parameters of the constraint conditions and the energy storage equipment parameters;

[0018] Using a projection method to obtain a projection result of the total parameter on the energy storage device parameter;

[0019] The feasible domain of parameters of the energy storage device is determined according to the projection result.

[0020] Optionally, the calculating the optimal configuration parameters of the energy storage device according to the feasible domain of parameters of the energy storage device is specifically:

[0021] Establishing a linear programming function according to the feasible domain of parameters of the energy storage device and the energy storage investment cost;

[0022] By solving the linear programming function, the optimal configuration parameters of the energy storage device are obtained.

[0023] Optionally, the energy storage investment cost includes energy storage power cost and energy storage capacity cost;

[0024] The parameters of the energy storage device include energy storage power and energy storage capacity.

[0025] In a second aspect, the present invention further provides a wind farm energy storage capacity optimization system, comprising:

[0026] The model acquisition unit is used to obtain the energy storage planning and operation model of the wind farm; wherein,

[0027] The objective function of the energy storage planning and operation model is to minimize the energy storage investment cost, and its constraints include energy storage equipment charge state constraints, energy storage equipment hourly charging and discharging linear constraints, transmission capacity constraints, wind farm output constraints and wind power abandonment rate linear constraints.

[0028] A first calculation unit, configured to calculate a parameter feasible domain of the energy storage device by using a projection method based on the energy storage planning operation model;

[0029] The second calculation unit is used to calculate the optimal configuration parameters of the energy storage device according to the feasible domain of parameters of the energy storage device.

[0030] Optionally, the first computing unit is specifically configured to:

[0031] Determine the full parameters of the constraint conditions and the parameters of the energy storage equipment based on the energy storage planning and operation model;

[0032] Using a projection method to obtain a projection result of the total parameter on the energy storage device parameter;

[0033] The feasible domain of parameters of the energy storage device is determined according to the projection result.

[0034] Optionally, the second computing unit is specifically configured to:

[0035] Establishing a linear programming function according to the feasible domain of parameters of the energy storage device and the energy storage investment cost;

[0036] By solving the linear programming function, the optimal configuration parameters of the energy storage device are obtained.

[0037] In a third aspect, the present invention also provides an electronic device, comprising one or more processors; a memory coupled to the processor and used to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the wind farm energy storage capacity optimization method as described in the first aspect.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The wind farm energy storage capacity optimization method provided by the present invention can obtain the parameter feasible domain of the energy storage equipment by solving the wind farm energy storage planning and operation model based on linear constraints, and then further calculate according to the parameter feasible domain of the energy storage equipment to obtain the optimal configuration parameters of the energy storage equipment, so that the wind farm energy storage system can reasonably absorb the wind power output based on the configuration parameters, and effectively balance the economy of energy storage investment and the safety of wind power abandonment constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the implementation mode will be briefly introduced below. Obviously, the drawings described below are only some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a schematic diagram of a flow chart of a method for optimizing energy storage capacity of a wind farm provided by an embodiment of the present invention;

[0042] Figure 2 It is a schematic diagram of the structure of an offshore wind farm supporting energy storage system provided by an embodiment of the present invention;

[0043] Figure 3 It is a structural schematic diagram of a wind farm energy storage capacity optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, in a first aspect, an embodiment of the present invention provides a method for optimizing energy storage capacity of a wind farm, comprising the following steps.

[0046] S1: Obtain the energy storage planning and operation model of the wind farm.

[0047] Specifically, the objective function of the energy storage planning and operation model is to minimize the energy storage investment cost, and its constraints include energy storage equipment charge state constraints, energy storage equipment hourly charging and discharging linear constraints, transmission capacity constraints, wind farm output constraints and wind power abandonment rate linear constraints.

[0048] Among them, the linear constraint of charging and discharging of the energy storage device within an hour is that the sum of the charging power and the discharging power of the energy storage device within an hour does not exceed the power limit of the energy storage device.

[0049] In this embodiment, the linear constraint group of wind power curtailment rate can be obtained in the following way:

[0050] An initial wind power curtailment rate constraint is obtained, where the initial wind power curtailment rate constraint is determined based on output information and curtailment information of the wind farm; and according to the strong duality property of linear programming, the initial wind power curtailment rate constraint is converted into a wind power curtailment rate linear constraint group.

[0051] It should be noted that the energy storage investment cost includes energy storage power cost and energy storage capacity cost.

[0052] S2: Based on the energy storage planning operation model, a projection method is used to calculate a feasible domain of parameters of the energy storage device.

[0053] Specifically, the full parameters of the constraint conditions and the energy storage device parameters can be determined based on the energy storage planning operation model, and then the projection method is used to obtain the projection results of the full parameters on the energy storage device parameters, and the parameter feasible domain of the energy storage device is determined according to the projection results.

[0054] Among them, the parameters of the energy storage equipment include energy storage power and energy storage capacity.

[0055] S3: Calculate the optimal configuration parameters of the energy storage device according to the feasible domain of parameters of the energy storage device.

[0056] In this embodiment, a linear programming function may be established according to the parameter feasible domain of the energy storage device and the energy storage investment cost, and the optimal configuration parameters of the energy storage device may be obtained by solving the linear programming function.

[0057] The following will describe the specific process of applying the wind farm energy storage capacity optimization method to the configuration of supporting energy storage capacity for offshore wind farms through an embodiment. The structure of the supporting energy storage system for offshore wind farms is as follows: Figure 2 shown.

[0058] In this embodiment, a pre-built energy storage planning and operation model of an offshore wind farm is firstly obtained.

[0059] Specifically, the constraints of the energy storage planning and operation model obtained include energy storage equipment charge state constraints, energy storage equipment intra-hour charging and discharging linear constraints, transmission capacity constraints, wind farm output constraints and wind power abandonment rate linear constraints.

[0060] Among them, the state of charge constraint of energy storage equipment can be specifically expressed as:

[0061]

[0062] α l e m ≤e nt ≤α h e m ,

[0063] In the formula, e nt represents the state of charge of the energy storage device at hour t on day n, η c / η d Respectively represent the charging and discharging efficiency of the energy storage device, Δ t represents the duration of time period t, and They represent the charging power and discharging power of the energy storage device at hour t on day n, respectively. m represents the energy storage capacity, α l ∈(0,0.5) and α h ∈(0.9,1) are the upper and lower limit constant constraints of the energy storage equipment operation.

[0064] For the linear constraints of energy storage equipment charging and discharging within one hour, it can be specifically expressed as:

[0065]

[0066] In the formula, and They represent the charging power and discharging power of the energy storage device at hour t on day n, respectively. m Indicates the energy storage power.

[0067] It should be noted that, unlike the energy storage charging and discharging constraints of the traditional model, the linear constraints on energy storage device charging and discharging within an hour provided in this embodiment can avoid the appearance of integer variables and effectively simplify the calculation. At the same time, according to the linear constraints on energy storage device charging and discharging within an hour, the linearized within-hour scheduling of the energy storage device can be represented, that is, the energy storage device can be divided into different time periods within an hour for charging and discharging.

[0068] For the transmission capacity constraint, it can be specifically expressed as:

[0069] In the formula, represents the discharge power of the energy storage device at hour t on day n, represents the output power of the offshore wind farm at hour t on day n, F m The upper limit of the capacity of long-distance transmission lines.

[0070] For the wind farm output constraint, it can be specifically expressed as:

[0071]

[0072] In the formula, represents the output power of the offshore wind farm at hour t on day n, represents the charging power of the energy storage device at hour t on day n, represents the amount of power abandoned by the offshore wind farm at hour t on day n, It represents the output of offshore wind farm at hour t on day n.

[0073] For the linear constraint group of wind power curtailment rate, in this embodiment, the linear constraint group of wind power curtailment rate can be obtained by linearly transforming the initial wind power curtailment rate constraint.

[0074] Specifically, the initial wind power curtailment rate constraint is expressed as:

[0075]

[0076] Among them, ρ n represents the empirical probability of a typical scenario under random output of an offshore wind farm, represents the output of the offshore wind farm at hour t on day n, represents the amount of power abandoned by the offshore wind farm at hour t on day n, and σ represents the power abandonment rate.

[0077] In this embodiment, for a given power abandonment rate, the following conditions should be met:

[0078]

[0079] stρ≤1·Γ+ρ 0 :μ +

[0080] ρ≥ρ 0 -1·Γ:μ -

[0081] 1 T ρ=1:λ

[0082] ρ ≥ 0

[0083] Among them, ρ n ∈Π.μ + ,μ - and λ are the dual variables of the corresponding constraints, and the corresponding dual problem is expressed as:

[0084]

[0085]

[0086] μ + ≥0,μ - ≤0

[0087] It is understandable that the strong duality in linear programming always holds, so the optimal values ​​of the above two optimization problems must be equal.

[0088] Correspondingly, the initial wind power curtailment rate constraint can be equivalent to searching for a feasible solution to the following linear problem:

[0089] 1 T (μ + -μ - )Γ+(μ + +μ - ) T ρ 0 +λ≤0

[0090]

[0091] μ + ≥0,μ - ≤0

[0092] Therefore, in this embodiment, the feasible solution of the above linear problem can be set as a linear constraint group of wind power curtailment rate, where ρ∈Π can be represented by a finite number of dual variables.

[0093] This embodiment uses the strong duality of linear programming to convert the initial wind power curtailment rate constraint into a set of linear constraints, thereby obtaining a wind power curtailment rate linear constraint group, thereby achieving the effect of simplifying the constraint conditions and improving the calculation efficiency.

[0094] In this embodiment, the objective function of the energy storage planning and operation model of the offshore wind farm is set to minimize the energy storage investment cost, wherein the energy storage investment cost includes the energy storage capacity cost and the energy storage power cost.

[0095] Furthermore, after obtaining the energy storage planning and operation model of the offshore wind farm, this embodiment can calculate the feasible domain of parameters of the supporting energy storage equipment of the offshore wind farm based on the projection method.

[0096] Specifically, the parameter x can be set to represent the set of offshore wind farm scheduling variables, including e nt , and the dual variable μ + , μ - , λ; at the same time, the parameter θ is set to represent the parameters of the energy storage device.

[0097] It should be noted that the parameter vector θ includes the energy storage power and capacity parameters, which can be expressed as θ = [p m ,e m ] T .

[0098] Furthermore, the above constraints of the offshore wind farm are transformed into a feasibility problem for solving the parameter θ.

[0099]

[0100] Since all constraints in the above feasibility problem are linear constraints, the above formula can be expressed in matrix form:

[0101] Λ(θ)={x|Ax+Bθ≤b}

[0102] Among them, an equality constraint can be represented by two inequality constraints with opposite signs.

[0103] Furthermore, the unit capacity cost of the energy storage device is set to c e , the unit power cost is c p , the energy storage equipment investment budget is m , then the available vector c=[c p ,c e ] T To express.

[0104] In this embodiment, the feasible domain of parameter θ can be defined as:

[0105]

[0106] At the same time, define the polyhedron with respect to parameter θ and parameter x:

[0107]

[0108] Among them, Θ is Projection onto parameter θ.

[0109] According to the definition of projection, Θ is a polyhedron and can be expressed as:

[0110]

[0111] D = {γ|A T γ=0,-1≤γ≤0}

[0112] Among them, vert(D) represents all the vertices of D.

[0113] After completing the setting and definition of the above parameters, this embodiment further calculates the parameter feasible region of the supporting energy storage equipment of the offshore wind farm through the following steps.

[0114] S21: Set the initial value of the feasible domain of energy storage device parameters: Θ temp ={θ|θ≥0,c T θ≤ξ m}.

[0115] S22: Update Θ temp The set of all extreme points And record the new poles that have not yet participated in the calculation.

[0116] S23: Calculate the following linear programming problem based on the new poles that have not yet been calculated:

[0117]

[0118] sA T γ=0,-1≤γ≤0

[0119] In the calculation process, the optimal solution and optimal value of the k-th problem are recorded as and

[0120] It is understandable that among the K problems, the largest is the global optimal solution. This embodiment calculates the global optimal solution v according to the following formula * :

[0121]

[0122]

[0123]

[0124] S24: For the global optimal solution v * Specifically, if v* =0, terminate the calculation; if v * > 0, then in the set Θ temp Add a cutting plane (γ * ) T Bθ≥(γ * ) T b, and returns to S22 to continue calculation.

[0125] By calculating through the above steps, the feasible domain Θ of the parameters of the final energy storage device can be obtained.

[0126] In this embodiment, after obtaining the feasible domain Θ of the parameters of the energy storage device, the energy storage investment parameter c=[c p ,c e ] T , and further calculate the optimal configuration parameters of offshore wind farm energy storage equipment.

[0127] It should be noted that, in order to simplify the calculation process, in this embodiment, the parameter feasible domain of the energy storage device is recorded as Θ = {θ|Hθ≤g}, that is: Θ = {θ|H p p m +H e e m ≤g}.

[0128] Specifically, by solving the following linear programming problem, the optimal configuration parameters of the offshore wind farm energy storage equipment can be obtained:

[0129] min cθ

[0130] stHθ≤g

[0131] In this embodiment, the above linear programming problem can be further expanded:

[0132] min c p p m +c e e m

[0133] H p p m +H e e m ≤g

[0134] It should be noted that if other parameters of the offshore wind farm are adjusted, such as the investment cost parameter c = [c p ,c e ] T , wind power curtailment rate parameter σ or long-distance transmission line capacity parameter F m Etc., different optimal energy storage configuration parameters can be calculated accordingly.

[0135] In actual planning and operation, the selected values ​​of parameters can be adjusted according to actual needs, so as to plan a more reasonable parameter configuration plan and provide more accurate guidance for the demand assessment of supporting energy storage equipment for offshore wind farms.

[0136] In the wind farm energy storage capacity optimization method provided by the above embodiment of the present invention, the energy storage planning operation model obtained takes into account the linear constraints of intra-hour energy storage scheduling, which can effectively avoid the application of integer variables in conventional energy storage models; and for the wind power curtailment rate constraints of offshore wind farms, the strong duality property of linear programming is applied to realize the conversion of the wind power curtailment rate constraints into a set of linear constraints, thereby effectively simplifying the calculation process of the model.

[0137] At the same time, the above method also adopts a projection algorithm based on linear programming, and generates a tangent plane by iteratively solving multiple groups of linear programming, thereby obtaining a more accurate parameter feasible domain of the energy storage equipment; then based on the parameter feasible domain of the energy storage equipment, the optimal configuration parameters of the energy storage equipment are further calculated, so that the obtained optimal configuration parameters can meet the constraints of wind power abandonment rate, energy storage investment budget constraints and offshore wind farm operation constraints, effectively taking into account the feasibility of offshore wind farm operation and the safety of energy storage construction investment.

[0138] like Figure 3 As shown, in the second aspect, another embodiment of the present invention further provides a wind farm energy storage capacity optimization system, including a model acquisition unit 101, a first calculation unit 102 and a second calculation unit 103.

[0139] The model acquisition unit 101 is used to acquire the energy storage planning and operation model of the wind farm.

[0140] Among them, the objective function of the energy storage planning and operation model is to minimize the energy storage investment cost, and its constraints include energy storage equipment charge state constraints, energy storage equipment hourly charging and discharging linear constraints, transmission capacity constraints, wind farm output constraints and wind power abandonment rate linear constraints.

[0141] The first calculation unit 102 is used to calculate the feasible domain of parameters of the energy storage device by using a projection method based on the energy storage planning operation model.

[0142] The second calculation unit 103 is used to calculate the optimal configuration parameters of the energy storage device according to the parameter feasible domain of the energy storage device.

[0143] In this embodiment, the first calculation unit 102 is specifically used to: determine the full parameters and energy storage device parameters of the constraint conditions based on the energy storage planning operation model; obtain the projection results of the full parameters on the energy storage device parameters using the projection method; and determine the parameter feasible domain of the energy storage device according to the projection results.

[0144] In this embodiment, the second calculation unit 103 is specifically used to: establish a linear programming function according to the parameter feasible domain of the energy storage device and the energy storage investment cost; and obtain the optimal configuration parameters of the energy storage device by solving the linear programming function.

[0145] The information interaction, execution process and other contents between the units in the above system are based on the same concept as the embodiment of the wind farm energy storage capacity optimization method of the present invention. For specific contents, please refer to the description in the embodiment of the method of the present invention, and will not be repeated here.

[0146] In a third aspect, an embodiment of the present invention further provides an electronic device, which may include: one or more processors, and a memory. The memory is coupled to the processor and is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the wind farm energy storage capacity optimization method as described in any of the above embodiments, and achieve the same technical effect as the above method.

[0147] The processor is used to control the overall operation of the electronic device to complete all or part of the steps of the above-mentioned wind farm energy storage capacity optimization method. The memory is used to store various types of data to support the operation of the electronic device, and these data may include, for example, instructions for any application or method used to operate on the electronic device, as well as application-related data. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0148] In an exemplary embodiment, the electronic device may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, ASC), digital signal processors (Digital Signal Processor, DSP), digital signal processing devices (Digital Signal Processing Device, DSPD), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field Programmable Gate Array, FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned wind farm energy storage capacity optimization method and achieve the same technical effect as the above-mentioned method.

[0149] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned wind farm energy storage capacity optimization method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory including program instructions, and the above-mentioned program instructions can be executed by a processor of an electronic device to complete the above-mentioned wind farm energy storage capacity optimization method and achieve the same technical effect as the above-mentioned method.

[0150] It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. The terms "first", "second", etc. in the present invention are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0151] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0152] The above description is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing energy storage capacity of a wind farm, characterized in that: include: Obtain the energy storage planning and operation model of the wind farm; where: The objective function of the energy storage planning and operation model is to minimize the energy storage investment cost, and its constraints include energy storage equipment charge state constraints, energy storage equipment hourly charging and discharging linear constraints, transmission capacity constraints, wind farm output constraints and wind power abandonment rate linear constraints. Based on the energy storage planning operation model, a projection method is used to calculate the feasible domain of parameters of the energy storage device; Calculating the optimal configuration parameters of the energy storage device according to the feasible domain of parameters of the energy storage device; The linear constraints of charging and discharging of the energy storage device within one hour are specifically: The sum of the charging power and discharging power of the energy storage device within one hour shall not exceed the power limit of the energy storage device; The linear constraint group of wind power curtailment rate is determined by the following method: Acquire an initial wind power curtailment rate constraint, where the initial wind power curtailment rate constraint is determined based on output information and curtailment information of the wind farm; According to the strong duality property of linear programming, the initial wind power curtailment rate constraint is converted into a wind power curtailment rate linear constraint group; The feasible domain of parameters of the energy storage device is calculated based on the energy storage planning operation model by using the projection method, specifically: According to each dispatching variable of the wind farm, it is converted into a matrix form about the parameter vector θ: Λ(θ)={x|Ax+Bθ≤b}; Then we get the feasible domain Θ of the parameter vector θ, and the polyhedron about the parameter θ and the parameter x According to the definition of projection, we get a polyhedron about the feasible domain Θ: Among them, vert(D) represents all the vertices of D, c e is the unit capacity cost of energy storage equipment, c p is the unit power cost, ξ m is the energy storage equipment investment budget, x represents the set of dispatch variables of the wind farm, and c is the e and c p The vector of Set the initial value of the feasible region of the parameters of the energy storage device: Update Θ temp The set of all extreme points And record the new poles that have not yet participated in the calculation; Based on the new poles that have not yet been calculated, calculate the following linear programming problem: in, and are the optimal solution and optimal value of the k-th problem respectively; Then we can get the global optimal solution of the linear programming problem. For the global optimal solution v * Make a judgment: If v * =0, terminate the calculation; if v * > 0, then in the set Θ temp Adding a cutting plane and returns the updated Θ temp The steps of all pole sets are calculated and continued; thereby obtaining the feasible domain of parameters of the energy storage device; The calculating the optimal configuration parameters of the energy storage device according to the feasible domain of parameters of the energy storage device is specifically: The feasible domain of parameters of energy storage equipment is recorded as: θ={θH p p m +H e e m ≤g}; where e m Indicates energy storage capacity; Then the linear programming problem is expanded as: The optimal configuration parameters of the energy storage device are obtained.

2. A wind farm energy storage capacity optimization system, characterized in that: include: The model acquisition unit is used to obtain the energy storage planning and operation model of the wind farm; wherein, The objective function of the energy storage planning and operation model is to minimize the energy storage investment cost, and its constraints include energy storage equipment charge state constraints, energy storage equipment hourly charging and discharging linear constraints, transmission capacity constraints, wind farm output constraints and wind power abandonment rate linear constraints. A first calculation unit, configured to calculate a parameter feasible domain of the energy storage device by using a projection method based on the energy storage planning operation model; A second calculation unit, used to calculate the optimal configuration parameters of the energy storage device according to the parameter feasible domain of the energy storage device; The linear constraints of charging and discharging of the energy storage device within one hour are specifically: The sum of the charging power and discharging power of the energy storage device within one hour shall not exceed the power limit of the energy storage device; The linear constraint group of wind power curtailment rate is determined by the following method: Acquire an initial wind power curtailment rate constraint, where the initial wind power curtailment rate constraint is determined based on output information and curtailment information of the wind farm; According to the strong duality property of linear programming, the initial wind power curtailment rate constraint is converted into a wind power curtailment rate linear constraint group; The first computing unit is specifically: According to each dispatching variable of the wind farm, it is converted into a matrix form about the parameter vector θ: Λ(θ)={x|Ax+Bθ≤b}; Then we get the feasible domain Θ of the parameter vector θ, and the polyhedron about the parameter θ and the parameter x According to the definition of projection, we get a polyhedron about the feasible domain Θ: Among them, vert(D) represents all the vertices of D, c e is the unit capacity cost of energy storage equipment, c p is the unit power cost, ξ m is the energy storage equipment investment budget, x represents the set of dispatch variables of the wind farm, and c is the e and c p The vector of Set the initial value of the feasible region of the parameters of the energy storage device: Update Θ temp The set of all extreme points And record the new poles that have not yet participated in the calculation; Based on the new poles that have not yet been calculated, calculate the following linear programming problem: in, and are the optimal solution and optimal value of the k-th problem respectively; Then we can get the global optimal solution of the linear programming problem. For the global optimal solution v * Make a judgment: If v * =0, terminate the calculation; if v * > 0, then in the set Θ temp Adding a cutting plane and returns the updated Θ temp The steps of all pole sets are calculated and continued; thereby obtaining the feasible domain of parameters of the energy storage device; The second calculation unit is specifically: The feasible domain of parameters of energy storage equipment is recorded as: Θ={θ|H p p m +H e e m ≤g}; where e m Indicates energy storage capacity; Then the linear programming problem is expanded as: The optimal configuration parameters of the energy storage device are obtained.

3. An electronic device, characterized in that: include, one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the wind farm energy storage capacity optimization method as described in claim 1.

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