Double-layer intelligent optimization method and device for wind farms considering environmental impact

Abstract

The present invention provides a two-layer intelligent optimization method for a wind farm with environmental impact, which belongs to the technical field of wind power generation. The two-layer intelligent optimization method for a wind farm with environmental impact of the present invention includes the following specific steps: obtaining the power of the wind turbines in the wind farm; setting the wake model of the wind turbines; calculating the output power of the wind farm; setting the two-layer structural optimization model of the wind farm; optimizing the two-layer structural optimization model based on the coyote optimization algorithm. The two-layer optimization model proposed by the present invention can reduce the adverse impact of the wind farm on the environment in the planning stage compared with the single-layer optimization model.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and in particular relates to a double-layer intelligent optimization method and device for a wind farm under environmental influence. Background Art

[0002] The optimization of wind farm capacity and layout is a primary issue for wind farm designers and grid planners. First, the active and reactive power output of the grid-connected wind farm will have a great impact on the power flow, and in turn affect the network losses and bus voltage. Second, due to the so-called wake effect, the total power output of the wind farm will be affected by the interaction between the internal wind turbines. The optimization problem involves various economic costs and environmental impacts. The prior art proposes a two-wind turbine layout planning model in grid and rectangular coordinates, which is solved by random key genetic algorithm and particle swarm optimization algorithm. Others have studied the optimization of wind farms and their application in power system reliability analysis, which is solved by particle swarm optimization algorithm (PSO). Others have studied the use of differential evolution (DE) algorithm in wind farm layout optimization problems, and designed a new encoding mechanism to effectively solve the problem. Others have proposed a wind turbine selection method for multi-objective optimization wind farms, which is solved by harmonious search algorithm and NSGA-II.

[0003] There are two important flaws in current research. First, the capacity of a wind farm, or the number of installed wind turbines, is always determined separately. However, in practice, the determination of wind farm capacity and the layout planning of wind turbines are mutually influential and need to be jointly optimized to achieve a low-cost wind farm. Second, the objective function is usually to find a balance between minimizing the cost of the wind farm itself and maximizing the power generation. The system operation costs and the environmental impacts brought by wind farms are rarely considered, which are usually reflected in the following two aspects: 1) Due to the existence of wind farms, network losses, power generation costs and pollutant emissions in the power grid will be reduced; 2) The operation of wind farms will generate noise, which affects the health of people and animals around. Therefore, in the planning stage, the positive and negative impacts of wind farms on the environment should not be ignored.

[0004] Therefore, in order to overcome the defects in the prior art, the present invention proposes a double-layer intelligent optimization method and device for a wind farm based on environmental impact. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art and to provide a double-layer intelligent optimization method and device for a wind farm under environmental influence.

[0006] In one aspect of the present invention, a two-layer intelligent optimization method for a wind farm considering environmental impact is provided, comprising the following specific steps:

[0007] Obtain the power of wind turbines in wind farms;

[0008] Setting a wake model of the wind turbine;

[0009] Calculating the output power of the wind farm;

[0010] Setting a double-layer structural optimization model of the wind farm;

[0011] The double-layer structure optimization model is optimized based on the coyote optimization algorithm.

[0012] Optionally, obtaining the power of a wind turbine in a wind farm includes:

[0013] The logistic function is used to represent the wind turbine power curve, as follows:

[0014]

[0015] in, P ip and v ip is the power and wind speed at the inflection point of the wind power curve; P wt r Indicates the rated active power of the wind turbine, P wt (v) represents the active power generated by the wind turbine at wind speed v.

[0016] Optionally, the wake model of the wind turbine generator set is set as follows:

[0017]

[0018] Among them, v x,r is the wind speed at a distance x and radius r from the wind turbine, σ represents the characteristic width of the wake, K(x) is the undetermined coefficient of x, and C T is the thrust coefficient;

[0019] v 0 Represents the wind speed of the wind turbine, r r Indicates the rotor radius of the wind turbine, z 0 represents the surface roughness length, z h Indicates the hub height of the wind turbine.

[0020] Optionally, the calculation of the output power of the wind farm is expressed as follows:

[0021]

[0022] Among them, P wf Represents the total output power of the wind farm, P wt (v i ) represents the output power of the i-th wind turbine, N tIndicates the total number of wind turbines in the wind farm;

[0023] The total reactive power injected by the wind farm is Q wf According to the following calculation:

[0024] Q wf =P wf tanφ

[0025] Where φ is the power factor angle of the wind farm.

[0026] Optionally, the setting of the double-layer structure optimization model of the wind farm includes:

[0027] The first-tier model is set up to optimize wind farm capacity and locality based on the comprehensive cost of wind energy generation;

[0028] The second layer model is set up to plan the power generation schedule of the generators in the power grid.

[0029] Optionally, the first-layer model expression is as follows:

[0030]

[0031]

[0032] in, The matrix representing the coordinates of the wind turbine, The matrix representing the coordinates of the noise receiver; F 1 is the unit wind power generation cost, which is calculated by the daily equivalent investment C wt , Grid operation cost C o and its environmental penalty cost C e composition; and Indicates the upper and lower limits of the x-axis coordinate of the wind turbine. and Indicates the upper and lower limits of the y-axis coordinate of the wind turbine. and represents the x-axis and y-axis coordinates of the i-th wind turbine, and represents the x-axis and y-axis coordinates of the j-th wind turbine, C wf represents the capacity of the wind farm, and Indicates the upper and lower limits of wind farm capacity, SPL indicates the sound pressure level, SPL lim Indicates the lower limit of the boost level, N t represents the number of wind turbines in the wind farm, represents the voltage of the m(n)th bus at the tth hour, and Indicates the upper and lower limits of the voltage of the m(n)th bus at the tth hour, represents the voltage phase difference between the mth and nth buses at the tth hour; G mn represents the conductivity of line mn, B mn represents the inductance of line mn, represents the reactive power output of the mth generator at the tth hour, and Indicates the upper and lower limits of the reactive power output of the mth generator at the tth hour, Q ld,m(j) represents the reactive load value of the m(j-th) bus at the tth hour; represents the reactive power output of the wind farm at hour t; represents the reactive power flow and its upper and lower limits of the mnth line at the tth hour, It represents the active power flow and its upper and lower limits of the mnth line at the tth hour;

[0033] Minimum number of WT and the maximum number Calculated by the following formula

[0034]

[0035] Among them, round is the rounding function; is the rated active power of the wind turbine; It is the upper and lower limits of wind farm output power.

[0036] Optionally, the constraints in the first-layer model consist of two parts, specifically including:

[0037] The boundaries of the wind farm planning area, the minimum permissible distances between wind turbines, the lower and upper limits of the wind farm capacity, and the wind farm’s SPL limits at each Nr receptor; and,

[0038] Active and reactive power balancing, bus voltage magnitude and phase angle constraints, active and reactive power output constraints for conventional generators, and bus active and reactive power flow constraints.

[0039] Optionally, the second layer model expression is as follows:

[0040]

[0041] in, Expressed as a quadratic function:

[0042] a o,i ,a 1,i ,a 2,i (ò / MW) is the cost coefficient of the ith generator;

[0043] is the penalty cost of pollutant emission in hour t, expressed as Among them, ρ i (ò / MW) is the unit penalty price;

[0044] is the amount of pollutants released by the ith generator at hour t, which is evaluated by the following formula:

[0045] Among them, b o,i ,b 1,i ,b 2,i (lb / MW) is the emission factor of the ith generator.

[0046] Optionally, the optimizing the double-layer structure optimization model based on the coyote optimization algorithm includes:

[0047] Coyote groups were randomly initialized and randomly divided into groups;

[0048] Coyotes grow within the group;

[0049] the life and death of coyotes;

[0050] Coyotes are driven away and embraced.

[0051] Another aspect of the present invention provides a wind farm double-layer intelligent optimization device for environmental impact, comprising: an acquisition module, a wake model module, a calculation module, a double-layer structure optimization model module and an optimization module; wherein:

[0052] The acquisition module is used to acquire the power of the wind turbines in the wind farm;

[0053] The wake model module is used to set the wake model of the wind turbine;

[0054] The calculation module is used to calculate the output power of the wind farm;

[0055] The double-layer structure optimization model is used to set the double-layer structure optimization model of the wind farm;

[0056] The optimization module is used to optimize the double-layer structure optimization model based on the coyote optimization algorithm.

[0057] The present invention provides a two-layer intelligent optimization method for a wind farm with respect to environmental impact, comprising the following specific steps: obtaining the power of a wind turbine in a wind farm; setting a wake model of the wind turbine; calculating the output power of the wind farm; setting a two-layer structural optimization model of the wind farm; and optimizing the two-layer structural optimization model based on a coyote optimization algorithm. The two-layer optimization model proposed by the present invention can reduce the adverse impact of a wind farm on the environment in the planning stage compared to a single-layer optimization model. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flowchart of a double-layer intelligent optimization method for a wind farm under environmental impact according to an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of a Gaussian wake model according to another embodiment of the present invention;

[0060] Figure 3 A schematic diagram of a double-layer intelligent optimization device for a wind farm under environmental impact according to another embodiment of the present invention;

[0061] Figure 4 A schematic diagram of a planning area of ​​a wind farm according to another embodiment of the present invention;

[0062] Figure 5 This is a graph showing power output and acoustic power emission of a wind turbine according to another embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0064] like Figure 1 As shown, in one aspect of the present invention, a two-layer intelligent optimization method S100 for a wind farm under environmental impact is provided, comprising the following specific steps S110 to S150:

[0065] S110: Obtain the power of wind turbines in the wind farm.

[0066] Specifically, this embodiment uses the Logistic function to represent the wind turbine power curve, which is as follows:

[0067]

[0068] in, P ip and v ip is the power and wind speed at the inflection point of the wind power curve; P wt r Indicates the rated active power of the wind turbine, P wt (v) represents the active power generated by the wind turbine at wind speed v.

[0069] S120, setting the wake model of the wind turbine generator set, such as Figure 2 shown.

[0070] The Gaussian wake model is Figure 2 As shown, the mathematical model is expressed as follows:

[0071]

[0072] Among them, v x,r is the wind speed at a distance x and radius r from the wind turbine, σ represents the characteristic width of the wake, K(x) is the undetermined coefficient of x, and C T is the thrust coefficient;

[0073] v 0 Represents the wind speed of the wind turbine, r r Indicates the rotor radius of the wind turbine, z 0 represents the surface roughness length, z h Indicates the hub height of the wind turbine.

[0074] In this model, the radius of the wake increases infinitely due to the exponential nature of the function, so all wind turbines located downstream of a given wind turbine are in the wake, regardless of their distance, and no wind turbine is partially in the wake of another wind turbine.

[0075] S130: Calculate the output power of the wind farm.

[0076] For a given inflow wind vector v = (v0, θ), wind speed v0 (m / s), and wind direction θ (°), the coordinates of the wind turbine should be converted from the original layout [X, Y] to the new layout [X′, Y′] according to the following formula.

[0077]

[0078] When determining the relative distance between wind turbines in the new coordinates, the i-th wind turbine (i=1,2,…,N t ) wind speed v i It can be calculated by the following formula:

[0079]

[0080] Among them, t ij The value is 1. The speed penalty depends only on the location of each wind turbine in the wind farm and the wind direction. ij is the wake loss of the i-th wind turbine caused by the j-th wind turbine.

[0081] The total power output of the wind farm is P wf It is expressed as follows:

[0082]

[0083] Among them, P wf Represents the total output power of the wind farm, P wt (v i ) represents the output power of the i-th wind turbine, N t Represents the total number of wind turbines in the wind farm.

[0084] When connected to the grid, the wind farm can be regarded as a PQ load bus. The total active power injected by the wind farm is P wf and reactive power Q wf Calculated based on and respectively.

[0085] Q wf =P wf tanφ (6)

[0086] Where φ is the power factor angle of the wind farm.

[0087] S140: Setting a double-layer structural optimization model of the wind farm.

[0088] When dealing with complex optimization problems with multiple levels of variables, two-level optimization has two advantages over single-level optimization. First, it significantly reduces the computational scale and the complexity of the developed model. In addition, it significantly reduces the load on the communication network. The interaction mechanism between the first-level and second-level models is as follows. The first level formulates an overall optimization plan for wind farm scale and wind farm micro-site selection, with the goal of minimizing the comprehensive generation cost of wind power and determining the optimal wind farm capacity, number of wind farms, and layout. The second level is nested in the first level, generating the minimum operating and environmental penalty costs for the first-level model by procuring the optimal generation plan of other generators in the grid. The generation plan obtained from the second-level model is affected by the results of the first-level model, including the integration and layout of wind turbines. In addition, the optimization results of the first-level model are related to the power generation and environmental penalty costs obtained from the second-level model.

[0089] First, set up the first-level model. In order to optimize the capacity and layout of wind farms with the minimum comprehensive wind power generation cost, the first-level problem is expressed as follows:

[0090]

[0091]

[0092] in, The matrix representing the coordinates of the wind turbine, The matrix representing the coordinates of the noise receiver; F 1 is the unit wind power generation cost, which is calculated by the daily equivalent investment C wt , Grid operation cost C o and its environmental penalty cost C e composition. and Indicates the upper and lower limits of the x-axis coordinate of the wind turbine. and Indicates the upper and lower limits of the y-axis coordinate of the wind turbine. and represents the x-axis and y-axis coordinates of the i-th wind turbine, and represents the x-axis and y-axis coordinates of the j-th wind turbine, C wf represents the capacity of the wind farm, and Indicates the upper and lower limits of wind farm capacity, SPL indicates the sound pressure level, SPL lim Indicates the lower limit of the boost level, N t represents the number of wind turbines in the wind farm, represents the voltage of the m(n)th bus at the tth hour, and Indicates the upper and lower limits of the voltage of the m(n)th bus at the tth hour, It represents the voltage phase angle difference between the mth and nth buses at the tth hour. mn Indicates the conductivity of line mn, B mn represents the inductance of line mn, represents the reactive power output of the mth generator at the tth hour, and Indicates the upper and lower limits of the reactive power output of the mth generator at the tth hour, Q ld,m(j) It represents the reactive load value of the m(j-th) bus at the tth hour. It represents the reactive power output of the wind farm at the tth hour. represents the reactive power flow and its upper and lower limits of the mnth line at the tth hour, It represents the active power flow and its upper and lower limits of the mnth line at the tth hour;

[0093] Minimum number of WT and the maximum number Calculated by the following formula:

[0094]

[0095] Among them, round is the rounding function, is the rated active power of the wind turbine. It is the upper and lower limits of wind farm output power.

[0096] The constraints in the first-level model consist of two parts.

[0097] Cstr.1 concerns 1) the boundaries of the wind farm planning area, 2) the minimum permitted distance between wind turbines, 3) the lower and upper limits of the wind farm capacity and 4) the SPL limit of the wind farm at each Nr receptor, which is limited to less than 40 dB(A).

[0098] SPL(T,R k ) is the equivalent continuous downwind octave band SPL of the entire wind farm, calculated as follows:

[0099]

[0100] in, is the equivalent continuous downwind octave SPL at each receiver location, calculated for each sound source in each of the eight octaves j of nominal mid-frequency (63 Hz-8 kHz).

[0101] Each octave (L f ) can be expressed as follows:

[0102] L f =L w +D c -A w (f) (10)

[0103] Among them, L w is the octave band sound power emitted by the noise source, D c is the directivity correction for non-omnidirectional sources (in this study, D c is set to 0), A w (f) is the octave band attenuation.

[0104] Cstr.2 is the set of power flow constraints at the tth hour, including 1) active and reactive power balance; 2) bus voltage amplitude and phase angle constraints; 3) active and reactive power output constraints of conventional generators; 4) bus active and reactive power flow constraints.

[0105] C wt Represents the daily equivalent investment cost of wind turbines, expressed as follows:

[0106]

[0107] Among them, C o is the total operating cost, including the daily operation and maintenance costs of the wind farm, the generation costs of other generators in the grid and the grid loss costs, expressed as follows:

[0108]

[0109] in is the daily operation and maintenance cost of the wind farm, which can be calculated by the following formula:

[0110]

[0111] OPEX unit (ò / MW / yr) is the unit annual operation and maintenance cost of the wind farm. It can be obtained from the second layer model. The network loss cost is calculated by the following formula.

[0112]

[0113] in is the network loss at hour t, which can be calculated as follows:

[0114]

[0115] in, It can be obtained from the second layer model. It can be obtained from a typical daily curve of wind speed and direction.

[0116] C e is the penalty cost for pollutant emissions, which can be expressed as follows:

[0117]

[0118] C o and C e are determined by the second-tier model and are sensitive to the power output of the wind farm. The first-tier and second-tier models are connected by C o and C e Interact with each other.

[0119] E wf is the expected daily energy production of the wind farm and can be estimated by:

[0120]

[0121] Where JPDF(v,θ) represents the joint probability distribution function of wind speed and wind direction. in and v out It is the cut-in and cut-out wind speed of the wind turbine.

[0122] Second, the second layer model

[0123] The purpose of the second-level model is to plan the generation schedule of other generators in the grid. It is expressed as an optimal economic dispatch model:

[0124]

[0125] in, Expressed as a quadratic function:

[0126] a o,i ,a 1,i ,a 2,i (ò / MW) is the cost coefficient of the i-th generator, which is determined according to the properties of the generator. is the penalty cost of pollutant emission in hour t, expressed as:

[0127]

[0128] Among them, ρ i (ò / MW) is the unit penalty price;

[0129] is the amount of pollutants released by the ith generator at hour t, which is evaluated by the following formula:

[0130] Among them, b o,i ,b 1,i ,b 2,i (lb / MW) is the emission factor of the ith generator.

[0131] The three constraints represent the grid power balance, spinning reserve capacity limitation and generator output power limitation. The optimal generation dispatch can be determined by the second-level model. The first-level model F 1 The component C 0 and C e can be obtained from the second-level model accordingly.

[0132] S150, optimizing the double-layer structure optimization model based on the coyote optimization algorithm.

[0133] It should be noted that the coyote optimization algorithm (COA) was proposed in 2018. It is a new intelligent optimization algorithm that simulates the living, growth, life and death, expulsion and acceptance of coyotes. In COA, each coyote represents a candidate solution, and each solution vector is composed of the social state factors of the coyote. These state factors include internal and external factors of the coyote. Each state factor represents a decision variable. D state factors constitute a solution vector containing decision variables, and each coyote is measured by social adaptability. COA is mainly divided into four steps: randomly initialize the coyote group and randomly group it, the growth of coyotes in the group, the life and death of coyotes, and the expulsion and acceptance of coyotes by the group. The optimization process of the algorithm is as follows:

[0134] First, randomly initialize the coyote group and randomly divide them into groups

[0135] Set the parameter number of coyote groups N p、N number of coyotes in the group c and the maximum number of iterations N gen , the initial social status factors of each coyote are randomly set as shown in the following relationship, and the social adaptability of each coyote is calculated, and random grouping is performed. The specific relationship is as follows:

[0136] soc j =lb j +r j *(ub j -lb j ) (twenty two)

[0137] Among them, lb j andub j denote the lower and upper bounds of the jth state factor of the coyote, j = 1, 2..., D, r j is a random number uniformly distributed in the range [0,1].

[0138] Second, the growth of coyotes within the group

[0139] Determine the optimal wolf alpha in the group, calculate the group cultural trend, and randomly select two coyotes. The four factors affect the growth of the coyotes. The calculation of the group cultural trend is as follows:

[0140] cult j =median(A j ) (twenty three)

[0141] Where A is an N c A matrix with D rows and D columns, representing N c A solution vector, A j represents the j-th column of matrix A, and median represents the median; where,

[0142] During the growth of the coyote, the difference δ between the best coyote alpha in the group and a randomly selected coyote in the group is first calculated. 1 , the difference between the group cultural trend and another random coyote in the group δ 2 , and the coyotes in the group are in δ 1 and δ 2 The growth is affected by the following relationship:

[0143] δ 1 =Lbest-soc r1 ,δ 2 =cult-soc r2 (twenty four)

[0144] new_soc c =soc c +s 1 *δ 1 +s2 *δ 2 (25)

[0145] Among them, r 1 and r 2 represent two different random coyote labels at both ends, and L best represents the optimal coyote alpha wolf within the group; s 1 and s 2 are the random weights of δ 1 and δ 2 respectively, and s 1 and s 2 are random numbers uniformly distributed within [0, 1].

[0146] After each coyote in the group grows, the following relationship is used to calculate its social adaptability and greedy selection is adopted. By retaining high-quality coyotes to participate in the growth of the remaining coyotes in the group, the convergence speed of the algorithm is accelerated:

[0147]

[0148] Third, the birth and death of coyotes

[0149] It should be understood that two important evolutionary processes in nature are birth and death, and the age of coyotes in COA is in years. After each group of coyotes grows, a new-born coyote is produced. The birth and death of coyotes are shown in Table 1 below.

[0150] Table 1 Birth and death process of coyotes

[0151]

[0152] Specifically, the birth of a new coyote is jointly affected by the social conditions and social environment of two randomly selected parent coyotes. The way of generating a new-born coyote is as follows:

[0153]

[0154] Among them, cr 1 and cr 2 are two randomly different coyotes within the p-th group, j 1 and j 2 are two random dimensions of the new-born coyote, P s is the dispersion probability, P a is the correlation probability, and the magnitudes of the dispersion and correlation probabilities affect the diversity of the new-born coyote. R j is a random number of the decision variable in the value range of the d-th dimension, and rnd j is a random number uniformly distributed within [0, 1].

[0155] P s = 1 / D

[0156] P a =(1-P s ) / 2 (28)

[0157] where ω represents a coyote pack that is less well-adapted than newborn wolves, The number of coyotes in the group, the age of newborn coyotes is 0.

[0158] Fourth, coyotes are driven away and accepted

[0159] Initially, coyotes are randomly assigned to groups, but sometimes they leave and join other groups. The probability of a coyote being excluded or accepted by a group is denoted by P. e This mechanism helps to exchange information between COA groups and promotes mutual influence between coyotes in the population.

[0160]

[0161] After initialization and random grouping, the three steps of coyote growth, coyote life and death, and coyote expulsion and acceptance are carried out in sequence. If the iteration termination condition is met, the optimal coyote is output. Otherwise, jump to the second step.

[0162] like Figure 3 As shown, another aspect of the present invention provides a double-layer intelligent optimization device 200 for a wind farm affected by an environment, comprising: an acquisition module 210, a wake model module 220, a calculation module 230, a double-layer structure optimization model module 240 and an optimization module 250; wherein the acquisition module 210 is used to acquire the power of the wind turbine in the wind farm; the wake model module 220 is used to set the wake model of the wind turbine; the calculation module 230 is used to calculate the output power of the wind farm; the double-layer structure optimization model 240 is used to set the double-layer structure optimization model of the wind farm; the optimization module 250 is used to optimize the double-layer structure optimization model based on the coyote optimization algorithm.

[0163] It should be noted that the method adopted by the device of this embodiment is described above and will not be repeated here.

[0164] The following is an explanation of the double-layer intelligent optimization method for wind farms with regard to environmental impact using a specific embodiment:

[0165] 1. Test system and data source

[0166] The wind farm to be optimized occupies a rectangular area, and its basic information is given in Table 2. The planning area of ​​the wind farm is Figure 4The red dots on the boundary represent noise receivers. During the wind farm layout planning stage, all nearby houses are considered as potential noise receivers. Receivers 1-8 marked with blue circles are located in eight typical directions (NW, N, NE, E, SE, S, SW, W).

[0167] Table 2 Wind farm information

[0168]

[0169] The parameters of the wind turbine installed in the wind farm are given in Table 3. The power output and acoustic power emission rate curves of the wind turbine are shown in Figure 5 shown.

[0170] Table 3 Wind turbine parameters

[0171]

[0172] The wind farm should be integrated in the IEEE 30 bus test system. The parameters of the generators in the system are given in Table 4.

[0173] Table 4 Generator set parameters

[0174]

[0175] Use in z ref = Turbulent wind with an average wind speed of 10m / s and a turbulence intensity of 20% at 62m. The parameters of WGA are set as follows:

[0176] N wg =120,N g =10,w=0.7298,R=1.5931,m=0.85,It max =250 (30)

[0177] 2. Capacity and layout optimization of grid-connected wind farms considering environmental impacts

[0178] Firstly, the present invention establishes a two-layer optimization model of capacity and layout of grid-connected wind farms considering environmental impacts. The model is specifically expressed as follows:

[0179] 1) First layer model

[0180] In order to optimize the capacity and layout of wind farms with the minimum comprehensive wind power generation cost, the first-level problem is formulated as follows:

[0181]

[0182]

[0183] in, The matrix representing the wind turbine coordinates, The matrix representing the coordinates of the noise receiver; F 1 is the unit wind power generation cost, which is calculated by the daily equivalent investment C wt , Grid operation cost C o and its environmental penalty cost C e The minimum and maximum number of WT are calculated by the following formula

[0184]

[0185] Here, round is the rounding function. is the rated active power of the wind turbine. It is the upper and lower limits of wind farm output power.

[0186] The constraints in the first-level model consist of two parts.

[0187] Cstr.1 concerns 1) the boundaries of the wind farm planning area, 2) the minimum permitted distance between wind turbines, 3) the lower and upper limits of the wind farm capacity and 4) the SPL limit of the wind farm at each Nr receptor, which is limited to less than 40 dB(A).

[0188] SPL(T,R k ) is the equivalent continuous downwind octave band SPL of the entire wind farm, calculated as follows:

[0189]

[0190] in, is the equivalent continuous downwind octave SPL at each receiver location, calculated for each sound source in each of the eight octaves j of nominal mid-frequency (63 Hz-8 kHz).

[0191] Each octave (L f ) can be expressed as follows:

[0192] L f =L w +D c -A w (f) (34)

[0193] Among them, L w is the octave band sound power emitted by the noise source, D c is the directivity correction for non-omnidirectional sources (in this study, D c is set to 0), A w (f) is the octave band attenuation.

[0194] Cstr.2 is the set of power flow constraints at the tth hour, including 1) active and reactive power balance; 2) bus voltage amplitude and phase angle constraints; 3) active and reactive power output constraints of conventional generators; 4) bus active and reactive power flow constraints.

[0195] C wt Represents the daily equivalent investment cost of wind turbines, expressed as follows:

[0196]

[0197] Among them, C o is the total operating cost, including the daily operation and maintenance costs of the wind farm, the generation costs of other generators in the grid and the grid loss costs, expressed as follows:

[0198]

[0199] in is the daily operation and maintenance cost of the wind farm, which can be calculated by the following formula:

[0200]

[0201] OPEX unit (ò / MW / yr) is the unit annual operation and maintenance cost of the wind farm. It can be obtained from the second layer model. The network loss cost is calculated by the following formula.

[0202]

[0203] in is the network loss at hour t, which can be calculated as follows:

[0204]

[0205] in, It can be obtained from the second layer model. It can be obtained from a typical daily curve of wind speed and direction.

[0206] C e is the penalty cost for pollutant emissions, which can be expressed as follows:

[0207]

[0208] C o and C e are determined by the second-tier model and are sensitive to the power output of the wind farm. The first-tier and second-tier models are connected by C o and C e Interact with each other.

[0209] E wf is the expected daily energy production of the wind farm and can be estimated by:

[0210]

[0211] Where JPDF(v,θ) represents the joint probability distribution function of wind speed and wind direction. in and v out It is the cut-in and cut-out wind speed of the wind turbine.

[0212] Second layer model

[0213] The purpose of the second-level model is to plan the generation schedule of other generators in the grid. It is expressed as an optimal economic dispatch model:

[0214]

[0215]

[0216] in, Expressed as a quadratic function:

[0217] a o,i ,a 1,i ,a 2,i (ò / MW) is the cost coefficient of the i-th generator, which is determined according to the properties of the generator.

[0218] is the penalty cost of pollutant emission in hour t, expressed as:

[0219]

[0220] Among them, ρ i (ò / MW) is the unit penalty price;

[0221] is the amount of pollutants released by the ith generator at hour t, which is evaluated by the following formula:

[0222] Among them, b o,i ,b 1,i ,b 2,i (lb / MW) is the emission factor of the ith generator.

[0223] The three constraints represent the grid power balance, spinning reserve capacity limitation and generator output power limitation. The optimal generation dispatch can be determined by the second-level model. The first-level model F 1 The component C 0 and C ecan be obtained from the second-level model accordingly.

[0224] Afterwards, the two-layer structure optimization model is optimized based on the coyote optimization algorithm: 1) Initialization and random grouping

[0225] Set the parameter number of coyote groups N p 、N number of coyotes in the group c and the maximum number of iterations N gen , the initial social status factors of each coyote are randomly set as shown in the following relationship, and the social adaptability of each coyote is calculated, and random grouping is performed. The specific relationship is as follows:

[0226] soc j =lb j +r j *(ub j -lb j ) (46)

[0227] Among them, lb j andub j denote the lower and upper bounds of the jth state factor of the coyote, j = 1, 2..., D, r j is a random number uniformly distributed in the range [0,1].

[0228] 2) Growth of coyotes within the group.

[0229] Determine the optimal wolf alpha in the group, calculate the group cultural trend, and randomly select two coyotes. The four factors affect the growth of the coyotes. The calculation of the group cultural trend is as follows:

[0230] cult j =median(A j ) (47)

[0231] Where A is an N c A matrix with D rows and D columns, representing N c A solution vector, A j represents the j-th column of matrix A, and median represents the median; where,

[0232] During the growth of the coyote, the difference δ between the best coyote alpha in the group and a randomly selected coyote in the group is first calculated. 1 , the difference between the group cultural trend and another random coyote in the group δ 2 , and the coyotes in the group are in δ 1 and δ 2 The growth is affected by the following relationship:

[0233] δ 1 =Lbest-soc r1 ,δ2 =cult-soc r2 (48)

[0234] new - soc c =soc c +s 1 *δ 1 +s 2 *δ 2 (49)

[0235] Among them, r 1 and r 2 Represents two different random coyote numbers, L best Represents the best coyote alpha wolf in the group;s 1 and 2 They are δ 1 and δ 2 The random weights, s 1 and 2 is a random number uniformly distributed within [0,1];

[0236] After each coyote in the group grows up, the social adaptability is calculated using the following relationship and greedy selection is used to accelerate the convergence of the algorithm by retaining high-quality coyotes to participate in the growth of other coyotes in the group:

[0237]

[0238] 3) Coyotes Live and Die

[0239] Two important evolutionary processes in nature are birth and death. In COA, the age of coyotes is measured in years. After each group of coyotes grows up, a newborn coyote is produced. The birth and death of coyotes are shown in Table 5 below:

[0240] Table 5 Birth and death process of coyotes

[0241]

[0242] The birth of new coyotes is influenced by the social conditions and social environment of two randomly selected parent coyotes. The way new coyotes are produced is as follows:

[0243]

[0244] Among them, cr 1 and cr 2 are two random different coyotes in group p, j 1 and j 2 are the two random dimensions of the newborn coyote, P s is the probability of dispersion, P ais the association probability. The magnitude of dispersion and association probability affects the diversity of newborn coyotes. j is a random number within the value range of the decision variable dimension, rnd j is a random number uniformly distributed in [0,1].

[0245] P s =1 / D

[0246] P a =(1-P s ) / 2 (52)

[0247] where ω represents a coyote pack that is less adaptable than newborn wolves, The number of coyotes in the group, the age of newborn coyotes is 0.

[0248] 4) Coyotes are driven away and accepted

[0249] Initially, coyotes are randomly assigned to groups, but sometimes they leave and join other groups. The probability of a coyote being driven out and accepted by a group is represented by Pe. This mechanism helps to exchange information between COA groups and promotes mutual influence between coyotes in the population.

[0250]

[0251] After initialization and random grouping, the three steps of coyote growth, coyote life and death, and coyote expulsion and acceptance are carried out in sequence. If the iteration termination condition is met, the optimal coyote is output. Otherwise, jump to the second step.

[0252] Finally, the Coyote optimization algorithm obtains the optimal layout and capacity of the wind farm.

[0253] The present invention provides a two-layer intelligent optimization method and device for wind farms with environmental impact, which has the following beneficial effects compared with the prior art: the method of the present invention takes into account the capacity and layout optimization of grid-connected wind farms with environmental impact, and the two-level optimization model based on it can reduce the adverse impact of wind farms on the environment in the planning stage compared with the single-layer optimization model.

[0254] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A two-layer intelligent optimization method for wind farms considering environmental impacts, It is characterized in that The specific steps include: Obtain the power of wind turbines in wind farms; Setting a wake model for the wind turbine; Calculating the output power of the wind farm; The double-layer structural optimization model of the wind farm is set up, including: The first-tier model is set up to optimize wind farm capacity and locality based on the comprehensive cost of wind energy generation; Setting up the second-tier model to plan the power generation schedule of generators in the power grid; The first-layer model expression is as follows: in, The matrix representing the coordinates of the wind turbine, The matrix representing the coordinates of the noise receiver; F 1 is the unit wind power generation cost, which is calculated by the daily equivalent investment C wt , Grid operation cost C o and its environmental penalty cost C e composition; and Indicates the upper and lower limits of the x-axis coordinate of the wind turbine. and Indicates the upper and lower limits of the y-axis coordinate of the wind turbine. and represents the x-axis and y-axis coordinates of the i-th wind turbine, and represents the x-axis and y-axis coordinates of the j-th wind turbine, C wf represents the capacity of the wind farm, and Indicates the upper and lower limits of wind farm capacity, SPL indicates the sound pressure level, SPL lim Indicates the lower limit of the boost level, N t represents the number of wind turbines in the wind farm, represents the voltage of the mth bus at the tth hour, represents the nth bus voltage at the tth hour, and Indicates the upper and lower limits of the mth bus voltage at the tth hour, represents the voltage phase difference between the mth and nth buses at the tth hour; G mn Indicates the conductivity of line mn, B mn represents the inductance of line mn, represents the reactive power output of the mth generator at the tth hour, and Indicates the upper and lower limits of the reactive power output of the mth generator at the tth hour, It represents the reactive load value of the mth bus at the tth hour; represents the reactive power output of the wind farm at hour t; represents the reactive power flow and its upper and lower limits of the mnth line at the tth hour, represents the active power flow and its upper and lower limits of the mnth line at hour t; and, Minimum number of WT and the maximum number Calculated by the following formula Among them, round is the rounding function; is the rated active power of the wind turbine; are the upper and lower limits of the wind farm output power; The double-layer structure optimization model is optimized based on the coyote optimization algorithm.

2. The method according to claim 1, It is characterized in that The obtaining of the power of the wind turbine generator set in the wind farm comprises: The logistic function is used to represent the wind turbine power curve, as follows: in, P ip and v ip are the power and wind speed at the inflection point of the wind power curve; Indicates the rated active power of the wind turbine, P wt (v) represents the active power generated by the wind turbine at wind speed v.

3. The method according to claim 1, It is characterized in that The wake model of the wind turbine generator set is set according to the following relationship: Among them, v x,r is the wind speed at a distance x and radius r from the wind turbine, σ represents the characteristic width of the wake, K(x) is the undetermined coefficient of x, and C T is the thrust coefficient; v 0 Represents the wind speed of the wind turbine, r r Indicates the rotor radius of the wind turbine, z 0 Represents the surface roughness length, Z h Indicates the hub height of the wind turbine.

4. The method according to claim 1, It is characterized in that The calculation of the output power of the wind farm is expressed as follows: Among them, P wf Represents the total output power of the wind farm, P wt (v i ) represents the output power of the i-th wind turbine, N t Indicates the total number of wind turbines in the wind farm; The total reactive power injected by the wind farm is Q wf According to the following calculation: Q wf =P wf tanφ Where Φ is the power factor angle of the wind farm.

5. The method according to claim 1, It is characterized in that The constraints in the first-layer model consist of two parts, including: The boundaries of the wind farm planning area, the minimum permissible distances between wind turbines, the lower and upper limits of the wind farm capacity, and the wind farm’s SPL limits at each Nr receptor; and, Active and reactive power balancing, bus voltage magnitude and phase angle constraints, active and reactive power output constraints for conventional generators, and bus active and reactive power flow constraints.

6. The method according to claim 1, It is characterized in that The second layer model expression is as follows: H represents the total number of hours in a day, N gen Represents the total number of conventional generators in the grid, N ld Indicates the total number of bus loads in the power grid, represents the lower limit of the active power output of the ith generator in the tth hour, represents the active power output of the ith generator at the tth hour, represents the upper limit of the active power output of the ith generator in the tth hour, represents the active load value of the jth bus at the tth hour, represents the active power output of the wind farm at the tth hour; in, Expressed as a quadratic function: a o,i ,a 1,i ,a 2,i (ò / MW) is the cost coefficient of the ith generator; is the penalty cost of pollutant emission in hour t, expressed as Among them, ρ i (ò / MW) is the unit penalty price; is the amount of pollutants released by the ith generator at hour t, which is evaluated by the following formula: Among them, b o,i ,b 1,i ,b 2,i (lb / MW) is the emission factor of the ith generator.

7. The method according to any one of claims 1 to 4, It is characterized in that The step of optimizing the double-layer structure optimization model based on the coyote optimization algorithm includes: Coyote groups were randomly initialized and randomly divided into groups; Coyotes grow within the group; the life and death of coyotes; Coyotes are driven away and embraced.

8. A double-layer intelligent optimization device for wind farms with environmental impact, It is characterized in that include: Acquisition module, wake model module, calculation module, double-layer structure optimization model module and optimization module; wherein, The acquisition module is used to acquire the power of the wind turbines in the wind farm; The wake model module is used to set the wake model of the wind turbine; The calculation module is used to calculate the output power of the wind farm; The double-layer structure optimization model is used to set the double-layer structure optimization model of the wind farm, including: The first-tier model is set up to optimize wind farm capacity and locality based on the comprehensive cost of wind energy generation; Setting up the second-tier model to plan the power generation schedule of generators in the power grid; The first-layer model expression is as follows: in, The matrix representing the coordinates of the wind turbine, The matrix representing the coordinates of the noise receiver; F 1 is the unit wind power generation cost, which is calculated by the daily equivalent investment C wt , Grid operation cost C o and its environmental penalty cost C e composition; and Indicates the upper and lower limits of the x-axis coordinate of the wind turbine. and Indicates the upper and lower limits of the y-axis coordinate of the wind turbine. and represents the x-axis and y-axis coordinates of the i-th wind turbine, and represents the x-axis and y-axis coordinates of the j-th wind turbine, C wf represents the capacity of the wind farm, and Indicates the upper and lower limits of wind farm capacity, SPL indicates the sound pressure level, SPL lim Indicates the lower limit of the boost level, N t represents the number of wind turbines in the wind farm, represents the voltage of the mth bus at the tth hour, represents the nth bus voltage at the tth hour, and Indicates the upper and lower limits of the mth bus voltage at the tth hour, represents the voltage phase difference between the mth and nth buses at the tth hour; G mn Indicates the conductivity of line mn, B mn represents the inductance of line mn, represents the reactive power output of the mth generator at the tth hour, and Indicates the upper and lower limits of the reactive power output of the mth generator at the tth hour, It represents the reactive load value of the mth bus at the tth hour; represents the reactive power output of the wind farm at hour t; represents the reactive power flow and its upper and lower limits of the mnth line at the tth hour, represents the active power flow and its upper and lower limits of the mnth line at hour t; and, Minimum number of WT and the maximum number Calculated by the following formula Among them, round is the rounding function; is the rated active power of the wind turbine; are the upper and lower limits of the wind farm output power; The optimization module is used to optimize the double-layer structure optimization model based on the coyote optimization algorithm.

Images