A wind turbine design parameter optimization method considering environmental benefits

Abstract

The present invention relates to a method for optimizing design parameters of a wind turbine generator set considering environmental benefits. The technical scheme is: by analyzing the components of the initial investment cost of the wind turbine generator set, an expression for the relationship between the costs of each component and its quality is established; an expression for the annual power generation of the wind turbine generator set is established from the two aspects of wind resource distribution and unit power, and then an expression for the annual operation and maintenance cost is obtained; a wind turbine generator set design parameter optimization model is established from the four aspects of initial investment cost, annual fixed rate of return, annual operation and maintenance cost, and annual power generation; considering the environmental benefits of the wind turbine generator set, a particle swarm algorithm is used to optimize the four design parameters of the wind rotor diameter, hub height, rated power, and rated wind speed, so as to obtain the minimum actual investment cost under the optimal wind turbine generator set design parameters, thereby avoiding over-optimization of the investment cost of a certain design parameter and neglect of the investment cost of other design parameters.

Description

Technical Field

[0001] The invention relates to the field of wind turbine design, and in particular to a method for optimizing design parameters of a wind turbine taking environmental benefits into consideration. Background Art

[0002] Under the "dual carbon" goal, wind power generation as a clean energy has been widely used in all walks of life.

[0003] In the design and manufacturing process of wind turbines, the same design parameters often play opposite roles. For example, increasing the rotor diameter and hub height can increase the annual power generation of the wind turbine, but it will also increase the manufacturing cost of the wind turbine. If the rated power and rated wind speed of the designed wind turbine are large, but the probability of the unit reaching the rated power or rated wind speed during actual operation is very small, then the manufacturing cost and power generation of the wind turbine will not match. Therefore, from the perspective of investment cost, appropriate wind turbine design parameters are very important. At present, in the process of optimizing the design parameters of wind turbines, the impact of a single design parameter on the investment cost is often considered, which will result in over-optimization of the investment cost of a certain design parameter and neglect of the investment costs of other design parameters. At the same time, compared with traditional thermal power generation, wind power generation has the advantages of "reducing carbon and reducing NO x In view of this, it is urgent to find a wind turbine design parameter optimization method that takes into account both investment costs and environmental benefits, as well as the influence of multiple factors. Summary of the invention

[0004] In view of the above situation, in order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method for optimizing the design parameters of a wind turbine generator set taking environmental benefits into consideration, which can effectively solve the problem of optimizing the design parameters of a wind turbine generator set taking into consideration the impact of environmental benefits on the design parameters.

[0005] The technical solution adopted by the present invention is:

[0006] A wind turbine design parameter optimization method considering environmental benefits. In the design and manufacturing process of wind turbines, the design parameters have a great influence on the investment cost and environmental benefits. The investment cost (C COE ), and define environmental benefits as the “discounted cost” per unit of wind turbine power generation (C COD ), select the rotor diameter (D), hub height (H), rated power (P rate )、Rated wind speed(V R ) these four design parameters, a mathematical model for optimizing the design parameters of wind turbines taking environmental benefits into consideration is established; the mathematical model is used as the fitness function of the particle swarm algorithm and the particle swarm algorithm is used to quickly and efficiently obtain the optimized solution, thereby obtaining the minimum actual investment cost of the wind turbine and completing the optimization of the design parameters of the wind turbine.

[0007] The investment cost is calculated by selecting the initial investment cost, annual operation and maintenance cost, annual power generation, and annual fixed rate of return; the investment cost of a wind turbine is:

[0008]

[0009] Where: C ICC - Initial Cost of Capital; C FCR - Annual fixed rate of return (Factor of Capital Rate); C O&M -Annual operation and maintenance costs (Operations and Maintenance); C AEP -Annual Energy Production; C COE - Wind turbine investment cost (Cost of energy);

[0010] Preferably, the method of the present invention comprises the following steps:

[0011] S1: Select 18 components including wind turbine blades, hub, main shaft, gearbox, generator, coupling, nacelle chassis and cover, yaw system, pitch change mechanism, oil cooling and exhaust, shock absorber, controller, tower, brake system, foundation, assembly, transportation, and grid connection device to be included in the initial investment cost C of the wind turbine set ICC , the cost and weight of each component have an approximately linear proportional relationship, and the relationship expression is:

[0012]

[0013] Where: C(x)-component cost; m(x)-component weight; x-design parameter; C0-reference unit component design cost; m0-reference unit component weight; μ-reference factor;

[0014] S2: Calculate the annual power generation C of the wind turbine by selecting the wind turbine's own power and wind resource conditions. AEP , whose expression is:

[0015]

[0016] Where: v cut-in - Cut-in wind speed, which represents the wind speed when the fan blades just start to turn; v cut-out - Cut-out wind speed, which represents the wind speed at which the wind turbine blades automatically stop rotating due to excessive wind speed; f(v)-probability of wind speed v; P(v)-output power of wind turbine at wind speed v; Δv-speed interval, which is 0.01m / s;

[0017] Specifically, the Weibull probability density function is used to describe the wind speed distribution, and its expression is:

[0018]

[0019] Among them: c-size parameter, affecting the average wind speed; k-shape parameter, affecting the distribution width; e-natural constant; the wind speed change trend can be reflected by the average wind speed and its variance, and the variance estimation is used to calculate c and k, and the expression is:

[0020]

[0021]

[0022] Where: Γ(·) is the Gamma function, v mean is the average wind speed; σ is the variance of the average wind speed;

[0023] Specifically, during the actual operation of a wind turbine, the expression for the relationship between wind speed (v) and hub height (H) is:

[0024]

[0025] Where: v and v0 are the wind speeds at H and H0 respectively, v0 and H0 are the reference speed and reference height; α-wind shear exponent factor, its value varies with the terrain environment and is taken as 1 / 7;

[0026] Specifically, take the average power P ave Instead of output power P(v), the average power P ave It can be expressed as a function of rated wind speed and rotor diameter:

[0027]

[0028] Where: C p - Wind energy utilization coefficient, take the value at rated wind speed; ρ- air density, kg / m 3 , take the standard atmospheric pressure state; V R -Rated wind speed, m / s; D-wind rotor diameter, m; η1-power generation efficiency; η2-drive chain transmission efficiency;

[0029] S3: Select the sum of component renewal costs and operation and maintenance costs as the annual operation and maintenance cost C O&M , whose expression is:

[0030] C O&M =10.7·P rate +0.007 C AEP (7)

[0031] Where: P rate - Rated power of wind turbine;

[0032] The annual fixed rate of return C FCR is a constant;

[0033] S4: Compared with thermal power generation, wind power generation has the advantages of “reducing carbon and NO x "These environmental benefits, this method takes economic benefits into consideration from the perspective of design parameters during the design and manufacturing process of wind turbines; in the environmental benefits part, the cost of treating pollutants such as ash, slag and gas generated by thermal power units is equivalent to the "reduced cost" during the design and manufacturing of wind turbines, and its expression is:

[0034]

[0035] Where: C COD - Environmental benefits (Cost of Discount), β-discount coefficient, value range 0 to 1;

[0036] S5: Combine steps S1-S4, take investment cost and environmental benefit as optimization indicators, and establish a wind turbine design parameter optimization function. The specific expression is:

[0037] C ACI =C COE -C COD =F(D,H,P rate ,V R ) (9)

[0038] D min ≤D≤D max

[0039] H min ≤H≤H max

[0040] P rate,min ≤P rate ≤P rate，max

[0041] V R,min ≤V R ≤V R,max

[0042] Where: C ACI -Actual Cost of Investment; D min , D max - Minimum and maximum values ​​of the wind rotor diameter; H min , H max - minimum and maximum values ​​of wheel hub height; P rate，min, P rate,max -Minimum and maximum rated power values; V R,min 、V R,max -Minimum and maximum values ​​of rated wind speed;

[0043] It can be seen from the above formula that in the design and manufacturing process of wind turbines, the minimum actual investment cost of wind turbines can be obtained by optimizing the four design parameters of wind rotor diameter, hub height, rated power of the unit, and rated wind speed. The particle swarm algorithm can be used to quickly and efficiently obtain the optimized solution.

[0044] The invention discloses a method for optimizing design parameters of a wind turbine generator set considering environmental benefits. The method considers the environmental benefits of the design parameters of the wind turbine generator set, enriches the contents of the theoretical design and cost accounting of the wind turbine generator set, establishes a relationship expression between the costs of each part and its quality by analyzing the components of the initial investment cost of the wind turbine generator set; establishes a relationship expression between the annual power generation of the wind turbine generator set from the two aspects of wind resource distribution and unit power, and then obtains an expression for the annual operation and maintenance cost; establishes a wind turbine generator set design parameter optimization model from the four aspects of initial investment cost, annual fixed rate of return, annual operation and maintenance cost, and annual power generation; considers the environmental benefits of the wind turbine generator set, uses a particle swarm algorithm to optimize the four design parameters of the wind rotor diameter, hub height, rated power, and rated wind speed, and obtains the minimum actual investment cost under the optimal wind turbine generator set design parameters. The present invention is a method for optimizing the design parameters of a wind turbine generator set which takes into account both investment costs and environmental benefits as well as the influence of multiple factors. The method of the present invention utilizes a particle swarm algorithm to collaboratively optimize four wind turbine generator set design parameters, namely, rotor diameter, hub height, rated power of the unit, and rated wind speed, thereby avoiding over-optimization of the investment cost of a certain design parameter while ignoring the investment costs of other design parameters. The method has good use effect, and the actual cost is reduced by 20% after optimization. The method is an innovation in the optimization method of wind turbine generator set design parameters and has good social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is the flow chart of the particle swarm algorithm of the present invention;

[0046] Figure 2 This is a fitness curve diagram of the particle swarm algorithm according to an embodiment of the present invention;

[0047] Figure 3 This is a wind rotor diameter optimization curve diagram of an embodiment of the present invention. In the figure, the horizontal axis is the wind rotor diameter (m) and the vertical axis is the cost. DETAILED DESCRIPTION

[0048] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples.

[0049] The present invention discloses a method for optimizing design parameters of a wind turbine generator set considering environmental benefits. In the design and manufacturing process of a wind turbine generator set, its design parameters have a great influence on the investment cost and environmental benefits. The investment cost (C COE ), and define environmental benefits as the “discounted cost” per unit of wind turbine power generation (C COD ), select the rotor diameter (D), hub height (H), rated power (P rate )、Rated wind speed(V R ) These four design parameters are used to establish a mathematical model for optimizing the design parameters of wind turbines taking environmental benefits into consideration;

[0050] The mathematical model is used as the fitness function of the particle swarm algorithm and the particle swarm algorithm is used to quickly and efficiently obtain an optimized solution, thereby obtaining the minimum actual investment cost of the wind turbine.

[0051] The investment cost is calculated by selecting four parts: initial investment cost, annual operation and maintenance cost, annual power generation, and annual fixed rate of return. Specifically, the initial investment cost includes component cost, assembly cost, and transportation cost. The annual operation and maintenance cost includes labor, consumables, material cost, maintenance cost, component failure rate, and spare parts. The annual power generation is affected by four conditions: wind speed distribution, unit power curve, power generation efficiency, and wind turbine drive chain transmission efficiency. It can be obtained that the investment cost of the wind turbine is:

[0052]

[0053] Where: C ICC - Initial Cost of Capital; C FCR - Annual fixed rate of return (Factor of Capital Rate); C O&M -Annual operation and maintenance costs (Operations and Maintenance); C AEP -Annual Energy Production; C COE - Wind turbine investment cost (Cost of energy);

[0054] The specific steps include:

[0055] S1: Select 18 components including wind turbine blades, hub, main shaft, gearbox, generator, coupling, nacelle chassis and cover, yaw system, pitch change mechanism, oil cooling and exhaust, shock absorber, controller, tower, brake system, foundation, assembly, transportation, and grid connection device to be included in the initial investment cost C of the wind turbine set ICCThe proportion of each component in the total cost varies due to market, technology and other factors. The cost of wind turbine components is greatly affected by their weight. The cost and weight of each component have an approximately linear proportional relationship, and the relationship expression is:

[0056]

[0057] Where: C(x)-component cost; m(x)-component weight; x-design parameter; C0-reference unit component design cost; m0-reference unit component weight; μ-reference factor, whose value depends on the wind turbine model and components;

[0058] S2: Calculate the annual power generation C of the wind turbine by selecting the wind turbine's own power and wind resource conditions. AEP , whose expression is:

[0059]

[0060] Where: v cut-in - Cut-in wind speed, which represents the wind speed when the fan blades just start to turn; v cut-out - Cut-out wind speed, which represents the wind speed at which the wind turbine blades automatically stop rotating due to excessive wind speed; f(v)-probability of wind speed v; P(v)-output power of wind turbine at wind speed v; Δv-speed interval, which is 0.01m / s;

[0061] Specifically, the Weibull probability density function is used to describe the wind speed distribution, and its expression is:

[0062]

[0063] Among them: c-size parameter, affecting the average wind speed; k-shape parameter, affecting the distribution width; e-natural constant; the wind speed change trend can be reflected by the average wind speed and its variance, and the variance estimation is used to calculate c and k, and the expression is:

[0064]

[0065]

[0066] Where: Γ(·) is the Gamma function, v mean is the average wind speed; σ is the variance of the average wind speed;

[0067] Specifically, during the actual operation of a wind turbine, the expression for the relationship between wind speed (v) and hub height (H) is:

[0068]

[0069] Where: v and v0 are the wind speeds at H and H0 respectively, v0 and H0 are the reference speed and reference height; α-wind shear exponent factor, its value varies with the terrain environment and is taken as 1 / 7;

[0070] Specifically, take the average power P ave Instead of output power P(v), the average power P ave It can be expressed as a function of rated wind speed and rotor diameter:

[0071]

[0072] Where: C p - Wind energy utilization coefficient, take the value at rated wind speed; ρ- air density, kg / m 3 , take the standard atmospheric pressure state; V R -Rated wind speed, m / s; D-wind rotor diameter, m; η1-power generation efficiency; η2-drive chain transmission efficiency;

[0073] S3: Select the sum of component renewal costs and operation and maintenance costs as the annual operation and maintenance cost C O&M , whose expression is:

[0074] C O&M =10.7·P rate +0.007 C AEP (7)

[0075] Where: P rate - Rated power of wind turbine;

[0076] The annual fixed rate of return C FCR is a constant, with a value of 0.1158;

[0077] S4: Compared with thermal power generation, wind power generation has the advantages of “reducing carbon and NO x "These environmental benefits, this method takes economic benefits into consideration from the perspective of design parameters during the design and manufacturing process of wind turbines; in the environmental benefits part, the cost of treating pollutants such as ash, slag and gas generated by thermal power units is equivalent to the "reduced cost" during the design and manufacturing of wind turbines, and its expression is:

[0078]

[0079] Where: C COD - Environmental benefits (Cost of Discount), β-discount coefficient, the value is 0.1;

[0080] Specifically, SO2, NO xThe wastes such as CO2, CO, TSP, fly ash and slag are used as environmental management projects for thermal power units, and their management costs are equivalent to the environmental benefits of wind turbines, as shown in Table 1. As can be seen from Table 1, compared with thermal power generation, wind turbines can obtain 0.1CNY environmental benefits for every 1kW·h of electricity produced.

[0081] Table 1 Environmental benefits of wind turbines equivalent to environmental management costs of thermal power units

[0082]

[0083] S5: Combine steps S1-S4, take investment cost and environmental benefit as optimization indicators, and establish a wind turbine design parameter optimization function. The specific expression is:

[0084] C ACI =C COE -C COD =F(D,H,P rate ,V R ) (9)

[0085] D min ≤D≤D max

[0086] H min ≤H≤H max

[0087] P rate,min ≤P rate ≤P rate，max

[0088] V R,min ≤V R ≤V R,max

[0089] Where: C ACI -Actual Cost of Investment; D min , D max - Minimum and maximum values ​​of the wind rotor diameter; H min , H max - minimum and maximum values ​​of wheel hub height; P rate，min , P rate,max -Minimum and maximum rated power values; V R,min 、V R,max -Minimum and maximum values ​​of rated wind speed;

[0090] It can be seen from the above formula that in the design and manufacturing process of wind turbines, the minimum actual investment cost of wind turbines can be obtained by optimizing the four design parameters of wind rotor diameter, hub height, rated power of the unit, and rated wind speed. The particle swarm algorithm can be used to quickly and efficiently obtain the optimized solution.

[0091] Particle swarm algorithm is an evolutionary algorithm for optimizing multivariable functions. It has the characteristics of few parameters, good convergence and high efficiency. The specific method of using particle swarm algorithm to obtain the minimum actual investment cost of wind turbines includes the following steps:

[0092] Step 1: Use the wind turbine design parameter optimization function as the fitness function of the particle swarm algorithm; there are N particles, each particle represents a solution to the design parameters, and the initial flight speed and initial position of the given particle are:

[0093] v i =[v i,1 ,v i,2 ,v i,3 ,......v i,n ] T

[0094] x i =[x i,1 ,x i,2 ,x i,3 ......x i,n ] T

[0095] Step 2: Evaluate the fitness function value of the particle and update the particle's current optimal position and global optimal position:

[0096] pbest i =[p i,1 ,p i,2 p i,3 ......p i,n ]

[0097] gbest=[g1,g2,g3,......g n ]

[0098] Step 3: In order to obtain the optimal solution, the particle updates its current speed and position through its own and the group's optimal position:

[0099] v i,j (t+1)=ω·v i,j (t)+c1·r1(t)·(pbest i,j (t)-x i,j (t))+c2·r2(t)·(pbest g,j (t)-x i,j (t))

[0100] x i,j (t+1)=x i,j (t)+v i,j (t+1)

[0101] Among them: ω-inertia weight, affecting the global optimization ability; c1, c2-acceleration factors, expressed as self-cognition and social cognition abilities; r1, r2-random numbers, ranging from 0 to 1.

[0102] Through iterative calculation of particle swarm algorithm, the minimum actual investment cost of wind turbines can be obtained.

[0103] The method of the present invention has achieved good technical effects through practical application, and the application examples are as follows:

[0104] Taking the design parameter optimization of a doubly-fed variable-speed constant-frequency wind turbine as an example, considering environmental benefits, the minimum actual investment cost is obtained by optimizing its rotor diameter, hub height, unit rated power and rated wind speed.

[0105] S1: The design parameters of the reference unit are known: rotor diameter D0 = 60m, rated power P rate，0 =1000kW, rated wind speed V R，0 =12m / s; hub height H0 =40m; the cost ratio of each component of the reference unit is shown in Table 2.

[0106] Table 2 Cost ratio of each component of the unit

[0107]

[0108] S2: Determine the mathematical relationship between the cost of each component of the wind turbine (C1) and its rotor diameter. It is known that the cost of three components, namely the generator, coupling, and grid-connected device, is proportional to the square of the rotor diameter and accounts for 14.38% of the total cost. The mathematical relationship is expressed as follows:

[0109]

[0110] The cost of the controller is fixed, accounting for 13.51% of the total cost; while the cost of the remaining components (C2) is proportional to the cube of the wind wheel diameter, and its mathematical expression is:

[0111]

[0112] The initial investment cost of the whole unit (C x ) and the reference unit cost (C0) is as follows:

[0113]

[0114] S3: The mathematical expression of the annual power generation of a wind turbine is:

[0115]

[0116] Considering the annual operation and maintenance costs (CO&M ), its mathematical expression is:

[0117]

[0118] Fixed rate of return C FCR The value of is 0.1158;

[0119] S4: Environmental benefits (C COD ), its mathematical expression is:

[0120]

[0121] In summary, the relationship between the minimum actual investment cost of a wind turbine and its four design parameters, namely, rotor diameter, hub height, rated power of the unit, and rated wind speed, can be obtained as follows:

[0122]

[0123] S5: The particle swarm algorithm is used to optimize the relational expressions of the design parameters of the wind turbine. The results are as follows: Figure 2 , Figure 3 As shown in Table 3, it can be seen from the figure that the particle swarm algorithm iterated 1500 steps and converged stably; assuming that the actual investment cost of the reference unit is 1, the optimal wind rotor diameter after optimization is 53m, and the optimal rated wind speed is 12.5m / s; compared with the reference unit, if the hub height and rated power are kept unchanged, its actual investment cost is reduced by 20%; at the same time, the design parameter of reducing the wind rotor diameter is also conducive to process manufacturing and installation and maintenance.

[0124] Table 3 Optimal optimization values ​​of wind turbine design parameters considering environmental benefits

[0125]

Claims

1. A method for optimizing design parameters of a wind turbine generator system considering environmental benefits, characterized in that: In the design and manufacturing process of wind turbines, the investment cost is defined by the investment cost per unit of wind turbine power generation, and the environmental benefit is defined by the "discounted cost" per unit of wind turbine power generation. The rotor diameter D, hub height H, and rated power P are selected. rate , Rated wind speed V R These four design parameters are used to establish a mathematical model for wind turbine design parameter optimization considering environmental benefits; The mathematical model is used as the fitness function of the particle swarm algorithm and the particle swarm algorithm is used to quickly and efficiently obtain an optimized solution, thereby obtaining the minimum actual investment cost of the wind turbine generator set; The investment cost is calculated by selecting the initial investment cost, annual operation and maintenance cost, annual power generation, and annual fixed rate of return; the investment cost of a wind turbine is: Where: C ICC - Initial investment cost; C FCR -Annual fixed rate of return; C O&M -Annual operation and maintenance costs; C AEP -Annual power generation; C COE - Investment cost of wind turbines; The method specifically comprises the following steps: S1: Select 18 components including wind turbine blades, hub, main shaft, gearbox, generator, coupling, nacelle chassis and cover, yaw system, pitch change mechanism, oil cooling and exhaust, shock absorber, controller, tower, brake system, foundation, assembly, transportation, and grid connection device to be included in the initial investment cost C of the wind turbine set ICC , the cost and weight of each component have an approximately linear proportional relationship, and the relationship expression is: Where: C(x)-component cost; m(x)-component weight; x-design parameter; C0-reference unit component design cost; m0-reference unit component weight; μ-reference factor; S2: Calculate the annual power generation C of the wind turbine by selecting the wind turbine's own power and wind resource conditions. AEP , whose expression is: Where: v cut-in - Cut-in wind speed, which represents the wind speed when the fan blades just start to turn; v cut-out - Cut-out wind speed, which represents the wind speed at which the wind turbine blades automatically stop rotating due to excessive wind speed; f(v)-probability of wind speed v; P(v)-output power of wind turbine at wind speed v; Δv-speed interval, which is 0.01m / s; Specifically, the Weibull probability density function is used to describe the wind speed distribution, and its expression is: Among them: c-size parameter, affecting the average wind speed; k-shape parameter, affecting the distribution width; e-natural constant; the wind speed change trend can be reflected by the average wind speed and its variance, and the variance estimation is used to calculate c and k, and the expression is: Where: Γ(·) is the Gamma function, v mean is the average wind speed; σ is the variance of the average wind speed; Specifically, during the actual operation of a wind turbine, the expression for the relationship between the wind speed v and the hub height H is: Where: v and v0 are the wind speeds at H and H0 respectively, v0 and H0 are the reference speed and reference height; α-wind shear exponent factor, its value varies with the terrain environment and is taken as 1 / 7; Specifically, take the average power P ave Instead of output power P(v), the average power P ave It can be expressed as a function of rated wind speed and rotor diameter: Where: C p - Wind energy utilization coefficient, take the value at rated wind speed; ρ- air density, kg / m 3 , take the standard atmospheric pressure state; V R -Rated wind speed, m / s; D-wind rotor diameter, m; η1-power generation efficiency; η2-drive chain transmission efficiency; S3: Select the sum of component renewal costs and operation and maintenance costs as the annual operation and maintenance cost C O&M , whose expression is: C O&M =10.7·P rate +0.007·C AEP (7) Where: P rate - Rated power of wind turbine; The annual fixed rate of return C FCR is a constant; S4: Compared with thermal power generation, wind power generation has the advantages of "reducing carbon and NO x "These environmental benefits, this method takes economic benefits into consideration from the perspective of design parameters during the design and manufacturing process of wind turbines; in the environmental benefits part, the cost of treating pollutants such as ash, slag and gas generated by thermal power units is equivalent to the "reduced cost" during the design and manufacturing of wind turbines, and its expression is: Where: C COD -Environmental benefits, β-reduction factor; S5: Combine steps S1-S4, take investment cost and environmental benefit as optimization indicators, and establish a wind turbine design parameter optimization function. The specific expression is: C ACI =C COE -C COD =F(D,H,P rate ,V R ) (9) D min ≤D≤D max H min ≤H≤H max P rate,min ≤P rate ≤P rate，max In R,min ≤V R ≤V R,max Where: C ACI - Actual investment cost; D min , D max - Minimum and maximum values ​​of the wind rotor diameter; H min , H max - minimum and maximum values ​​of wheel hub height; P rate，min , P rate,max -Minimum and maximum rated power values; V R,min 、V R,max -Minimum and maximum values ​​of rated wind speed; It can be seen from the above formula that in the design and manufacturing process of wind turbines, the minimum actual investment cost of wind turbines can be obtained by optimizing the four design parameters of wind rotor diameter, hub height, rated power of the unit, and rated wind speed. The particle swarm algorithm can be used to quickly and efficiently obtain the optimized solution.

2. The method for optimizing design parameters of a wind turbine generator system considering environmental benefits according to claim 1, characterized in that: The specific method of using particle swarm algorithm to obtain the minimum actual investment cost of wind turbines includes the following steps: Step 1: Use the wind turbine design parameter optimization function as the fitness function of the particle swarm algorithm; there are N particles, each particle represents a solution to the design parameters, and the initial flight speed and initial position of the given particle are: v i =[v i,1 ,v i,2 ,v i,3 ,......v i,n ]T x i =[x i,1 ,x i,2 ,x i,3 ......x i,n ] T Step 2: Evaluate the fitness function value of the particle and update the particle's current optimal position and global optimal position: gbest=[g1,g2,g3,......g n ] Step 3: In order to obtain the optimal solution, the particle updates its current speed and position through its own and the group's optimal position: v i,j (t+1)=ω·v i,j (t)+c1·r1(t)·(pbest i,j (t)-x i,j (t))+c2·r2(t)·(pbest g,j (t)-x i,j (t)) x i,j (t+1)=x i,j (t)+v i,j (t+1) Where: ω-inertia weight; c1, c2-acceleration factors; r1, r2-random numbers, ranging from 0 to 1; Through iterative calculation of particle swarm algorithm, the minimum actual investment cost of wind turbines can be obtained.

3. The method for optimizing design parameters of a wind turbine generator system considering environmental benefits according to claim 1, characterized in that: The annual fixed rate of return C FCR The value of is 0.1158.

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