CN110033204B - Combined scheduling method for power generation maintenance considering fatigue distribution uniformity of offshore wind farms - Google Patents

Abstract

The present invention relates to a power generation maintenance joint scheduling method considering the fatigue distribution uniformity of offshore wind farms, comprising the following steps: 1) establishing a new wake model through the wake influence correlation matrix and the maintenance state of the wind turbine; 2) establishing a new wake model considering the offshore wind farm The mathematical model of power generation and maintenance joint dispatching for the uniformity of wind farm fatigue distribution; 3) The mixed integer linear programming method and the mixed integer second-order cone programming method are used to convert the nonlinear parts of the power generation and maintenance joint dispatch mathematical model into linear, and The mixed integer linear programming model is formed by the relaxation method; 4) The multi-objective function is processed by the constraint method, the model is converted into a single-objective model, and the single-objective model is solved, and finally the power generation and maintenance scheduling scheme with the optimal economic benefit is obtained. Then the joint dispatch of offshore wind farm units is carried out. Compared with the prior art, the present invention has the advantages of high efficiency in solving, many selectivity, taking into account the wake of any wind direction, and wide application range.

Description

Combined scheduling method for power generation maintenance considering fatigue distribution uniformity of offshore wind farms

technical field

The invention relates to the field of maintenance and scheduling of offshore wind farms, in particular to a combined scheduling method for power generation and maintenance considering the uniformity of fatigue distribution of offshore wind farms.

Background technique

The main purpose of the joint dispatch of offshore wind power generation and maintenance is to arrange an appropriate maintenance strategy during the maintenance dispatch period to reduce the cost of overseas maintenance and ensure that the wind farm generates a large amount of power, thereby bringing significant economic benefits to the wind farm. However, due to the impact of the harsh offshore environment, wind turbines need to consider factors such as weather, tides, and personnel arrangements when performing maintenance operations. During the operation, the offshore wind direction changes constantly, the wake effect between the units changes, and the active power output of the units is changed. affected, which complicates the modeling and solution of this problem. In order to simplify the influence of the wake effect on the active power output of the wind farm, some literatures only calculate the wake effect in a single wind direction or several fixed wind directions to obtain the overall active power output of the wind farm. This is too simplistic for the actual situation where the offshore wind direction changes arbitrarily and cannot more accurately express the overall power generation of the wind farm during the maintenance period.

In order to make the operation of offshore wind power have better economic benefits, some scholars and experts have put forward the maintenance strategy of offshore wind power or discussed the methods of improving the active power output of offshore wind farms. However, none of these models combine maintenance schedules with power generation schedules to consider the economics of offshore wind operation. In addition, due to the fatigue of wind turbines during power generation, excessive fatigue will affect the reliability of turbine operation. Considering the fatigue distribution of wind farms in the maintenance strategy will provide decision makers with more decision support.

In addition, the unit is out of operation during maintenance and does not absorb wind energy. The downwind fan is less affected by the wake, and considering the arbitrary wind direction, when calculating the active power output, the relative position of the wind turbine changes dynamically. The model contains both continuous variables and discrete variables, which increases the difficulty of modeling and makes it more difficult to solve the model. Some scholars use intelligent algorithms to solve such multi-constrained nonlinear complex problems, but it is easy to make the solutions fall into local extreme points. The mixed integer programming method has sufficient theoretical support in dealing with such problems, and has great advantages in dealing with problems with discrete variables. The key lies in the processing of nonlinear models.

Therefore, there is an urgent need for a combined power generation and maintenance scheduling method that considers the fatigue distribution uniformity of offshore wind farms, which can reasonably arrange the power generation and maintenance plans of offshore wind power while considering the fatigue distribution uniformity of wind farms, so as to obtain better performance for wind farms. economic benefits and provide decision-makers with more decision-making support.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a combined scheduling method for power generation and maintenance considering the uniformity of fatigue distribution of offshore wind farms in order to overcome the above-mentioned defects of the prior art.

The object of the present invention can be realized through the following technical solutions:

A joint scheduling method for power generation maintenance considering the uniformity of fatigue distribution of offshore wind farms, comprising the following steps:

1) Establish a new wake model through the wake influence correlation matrix and the maintenance status of the wind turbine;

2) Establish a mathematical model of combined power generation and maintenance scheduling considering the uniformity of fatigue distribution of offshore wind farms;

3) The mixed integer linear programming method and the mixed integer second-order cone programming method are used to convert the nonlinear parts of the model into linear ones, and the mixed integer linear programming model is formed by the relaxation method;

4) The multi-objective function is processed by the constraint method, the model is converted into a single-objective model, and the single-objective model is solved, and finally the power generation and maintenance scheduling plan with the optimal economic benefit is obtained, and then the joint dispatching of offshore wind farm units is carried out.

Described step 1) specifically comprises the following steps:

11) Decompose the input wind speed of the wind turbine into two directions, horizontal and vertical, as follows:

Among them, j is the wind turbine index variable in the wind farm, t is the time period index variable, v _j,t is the input wind speed of the jth wind turbine WT _j in the time period T _t , vh _j,t , vv _j,t represent the j the input wind speed of the wind turbine WT _j in the horizontal direction and the vertical direction in the time period T _t ;

12) Construct the wake influence correlation matrix, and combine the maintenance status of the wind turbine in the wake model to obtain the input wind speed in the horizontal and vertical directions, as follows:

为第i台风电机组WT_i在时段T_t的推力系数，与风速有关，其值可由推力系数拟合曲线得到，x_i,t为第i台风电机组WT_i在时段T_t的检修状态变量，1表示处于检修，0表示正常运行。Among them, i is the index variable of the wind turbines in the wind farm, v _t is the offshore wind speed in the time period T _t , α _t is the wind direction in the time period T _t , U1 _j,t , U3 _j,t are the boundary units in the time period T _t , respectively The correlation matrix of , when the jth wind turbine is a boundary unit in a certain time period, the corresponding element in the matrix is 1, otherwise it is 0, U2 _j,i,t is the ith wind turbine under the time period T _t to the jth typhoon The correlation matrix of the wake effect of the wind turbine in the horizontal direction. When the ith wind turbine has a horizontal wake effect on the jth wind turbine, the element in the matrix takes the value of 1, otherwise it is 0, where the ith wind turbine The typhoon generator unit and the jth wind generator unit are always adjacent units, U4 _{j,i,t is} the correlation matrix with wake effects in the vertical direction at the time period T _t , ki is a constant determined by the unit spacing and impeller diameter ,

is the thrust coefficient of the ith wind turbine WT _i in the time period T _t , which is related to the wind speed, and its value can be obtained from the thrust coefficient fitting curve, x _i,t is the maintenance state variable of the i th wind turbine WT _i in the time period T _t , 1 means in maintenance, 0 means normal operation.

In the described step 2), the objective function of the mathematical model of power generation and maintenance joint dispatch considering the uniformity of the fatigue distribution of the offshore wind farm is to minimize the maintenance cost f ₁ in the entire dispatch time range, maximize the power generation f ₂ and make the offshore wind farm. The wind farm fatigue distribution _f3 is the most uniform, then:

为材料设备成本，

为环境监测成本，

为基础设施成本，

为运输成本，

为人力成本，

为停机损失成本，LP_i为检修WT_i的持续时间段数量；Among them, m is the total number of wind turbines in the wind farm, n is the total number of time periods in the dispatch cycle,

for the cost of materials and equipment,

For environmental monitoring costs,

for infrastructure costs,

for transportation costs,

for labor cost,

is the cost of downtime loss, and LP _i is the number of duration periods for overhauling WT _i ;

Among them, P _i,t is the output power of the i-th wind turbine WT _i in the time period T _t , and t _t is the duration of the time period T _t ;

为时段T_n风电场各机组疲劳系数的平均值，F_i(t₀)为第i台风电机组在调度周期开始前自身所累积的疲劳系数值，γ为机组的紊流疲劳损伤和发电疲劳损伤的比值，W_i,t为第i台风电机组WT_i在时段T_t的发电量，P_i ^rate为风电机组的额定有功出力，

为第i台风电机组WT_i的服务寿命，

为第i台风电机组WT_i的维护补偿系数。Among them, f ₃ is the standard deviation of the fatigue coefficient of each wind farm in the time period T _n , F _i (n) is the fatigue coefficient of the i-th wind turbine WT _i in the time period T _n ,

F _i (t ₀ ) is the fatigue coefficient value accumulated by the i- _th wind turbine itself before the dispatch cycle begins, γ is the turbulent fatigue damage and power generation fatigue of the unit The damage ratio, Wi _,t is the power generation of the i-th wind turbine WT _i in the time period T _t , P _i ^rate is the rated active power output of the wind turbine,

is the service life of the i-th wind turbine WT _i ,

is the maintenance compensation coefficient of the i-th wind turbine WT _i .

In the step 2), the constraints of the mathematical model for the joint scheduling of power generation and maintenance considering the uniformity of the fatigue distribution of the offshore wind farm include:

Wind turbine active output constraints:

为第i台风电机组WT_i在时段T_t输出功率的最小值，

为第i台风电机组WT_i在时段T_t输出功率的预测值；in,

is the minimum value of the output power of the i-th wind turbine WT _i in the time period T _t ,

is the predicted value of the output power of the i-th wind turbine WT _i in the time period T _t ;

Overhaul necessity constraints:

Among them, b _i,t represents the decision variable indicating whether the i-th wind turbine WT _i starts to enter the maintenance state at T _t , 1 means entering, 0 means not entering;

Overhaul persistence constraints:

x _i,t ≥b _i,t

x _i,t -x _i,t-1 ≤bi _,t

x _i,t +x _i,t-1 +b _i,t ≤2

Wherein, when t=1, x _i,t-1 =0;

Overhaul Duration Constraints:

Deadline constraints:

Among them, Li is the serial number of the last time period during which the _i -th wind turbine WT _i needs to complete the maintenance operation;

Weather constraints:

Among them, U is the set of time periods during which wind turbine maintenance is not allowed due to offshore weather;

Manpower constraints:

和

分别为检修第i台风电机组WT_i在运维船、直升机和陆地口岸上的人力需求量，AM_t表示在时段T_t可用的人力数量；in,

and

are the manpower requirements for overhauling the i-th wind turbine WT _i on the operation and maintenance ship, helicopter and land port, respectively, AM _t represents the available manpower in the time period T _t ;

Vehicle Constraints:

Among them, _Vi and H _i are respectively the number of operation and maintenance ships and helicopters required to overhaul the i-th wind turbine WT _i , and AV _t and AH _t respectively represent the number of available operation and maintenance ships and helicopters in time period T _t ;

Greenhouse Gas Emissions Constraints:

为员工的平均体重，

和

分别为维护第i台风电机组WT_i由运维船和直升机运载的设备重量，GHG为行业制定的温室气体排放标准；Among them, D _i represents the distance from the port stop to the i-th wind turbine WT _i , q ^V and q ^H are the kilograms of greenhouse gases emitted per kilogram of operating and maintenance ships and helicopters traveling per kilogram, respectively,

is the average weight of employees,

and

The weight of the equipment carried by the operation and maintenance ship and the helicopter for the maintenance of the i-th wind turbine WT _i , the GHG emission standard for the industry formulated by GHG;

Marine Environmental Constraints:

Among them, LV _t is the number of operation and maintenance ships allowed to move in the maritime space during the period T _t ;

Flock Constraints:

Among them, LH _t is the number of operation and maintenance helicopters allowed to move in the maritime space during the period T _t ;

Night maintenance restrictions:

Among them, Y is the time period of each day and night, and AL indicates the restriction on the maintenance allowed to go to sea at night.

Described step 4) specifically comprises the following steps:

41) Solve the single-objective model that minimizes the maintenance cost objective function f ₁ , maximizes the power generation objective function f ₂ and minimizes the wind farm fatigue distribution uniformity f ₃ , and obtains the objective function values and decision variables under different objectives, forming The decision attribute table of the objective function;

42) Determine the value range of the constraint value through the value of each single objective function in the decision attribute table, and substitute it into the corresponding objective function for constraint restriction, so that the objective function is converted into the corresponding constraint condition, thereby forming a single objective model;

43) Solve the single-objective model, and finally obtain the most economical power generation and maintenance scheduling scheme, and then carry out the joint scheduling of offshore wind farm units.

Compared with the prior art, the present invention has the following advantages:

1. Efficient solution: As a traditional solution algorithm, the mixed integer linear programming method has sufficient theoretical support, and the calculation amount is small and the speed is fast.

2. More choices: The combined dispatching scheme of power generation and maintenance obtained by the present invention will have more choices considering the uniformity of the fatigue distribution of the offshore wind farm, and the economic benefits corresponding to the pursuit of a more uniform fatigue distribution will be lower, which is Policymakers offer choice.

3. Taking into account the wake of any wind direction: the wake effect between wind turbines has a great influence on the output power of the wind turbines. Considering the wake of any wind direction can more accurately express the output power of the offshore wind turbines under the actual working conditions. A more reasonable joint scheduling scheme for power generation and maintenance.

4. Wide range of application: The built wake model and constraints are applicable to offshore wind farms with large unit spacing, and the constraints are also applicable to onshore wind farms.

Description of drawings

Figure 1 is the flow chart for solving the joint dispatch scheme of offshore wind power generation and maintenance.

Figure 2 shows the layout of offshore wind turbines.

Figure 3 is a graph showing the relationship between the output power of the wind turbine and the wind speed.

Fig. 4 is a distribution diagram of wind speed in the dispatch period.

Figure 5 is a histogram of wind direction distribution in the dispatch period.

Figure 6 shows the active power output diagram of the wind turbine in each scenario.

Figure 7 shows the comprehensive economic benefits of power generation and maintenance under different fatigue distribution uniformity.

Detailed ways

The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

Example

As shown in Fig. 1, the present invention proposes a joint scheduling method for power generation and maintenance considering the uniformity of fatigue distribution of offshore wind farms. The present invention first establishes a wake influence correlation matrix and combines the maintenance status of wind turbines to establish the wake of any wind direction. Flow model, the specific modeling steps are as follows:

Step 1: Decompose the input wind speed of any wind direction of the wind turbine into two directions, horizontal and vertical, then the calculation formula of wind speed at this time is:

Step 2: Build the wake influence correlation matrix, and combine the maintenance status of the wind turbine in the wake model to obtain the input wind speed in the horizontal and vertical directions:

Step 3: The layout of the wind farm is shown in Figure 2. The wind speed of each unit can be expressed. The first part on the right side of the equation (2) is the wind speed in the horizontal direction of the non-intermediate units in the boundary of the wind farm, and the second part is the non-boundary middle unit. The wind speed in the horizontal direction of the unit, the sum of these two parts is the wind speed in the horizontal direction of the middle unit in the boundary of the wind farm, and Equation (3) is the description of the wind speed in the vertical direction.

Secondly, a mathematical model for the joint dispatch of offshore wind power generation and maintenance is constructed. _The model aims at the minimum maintenance cost f1, the maximum power generation _f2 and the most uniform wind farm fatigue distribution _f3 within the dispatching time range, while satisfying the requirements of power generation and maintenance. Multiple constraints.

Then, linearize the established joint scheduling model of power generation and maintenance considering the uniformity of fatigue distribution of offshore wind farms, so as to make it a mixed integer linear programming model for solving.

The above linearization processing is mainly carried out for the output power of the wind turbine, the uniformity of the fatigue distribution of the wind farm and the linearization of the wake model. The specific process is as follows:

Step 4: According to the relationship between the output power of the wind turbine and the wind speed, as shown in Figure 3, use the mixed integer linear programming method to linearize the output power of the wind turbine:

P=δ ₃ P ^rate +IF ₂ z ₂ (23)

stv=δ ₂ v _in +δ ₃ v _r +z ₁ +z ₂ +z ₃ (24)

0≤z ₁ ≤v _in δ ₁ (25)

0≤z ₂ ≤(v _r -v _in )δ ₂ (26)

0≤z ₃ ≤(v _out -v _r )δ ₃ (27)

δ ₁ +δ ₂ +δ ₃ =1 (28)

δ ₁ , δ ₂ , δ ₃ ={0,1} (29)

Step 5: Linearize the fatigue distribution uniformity of wind turbines in the wind farm, and first use the absolute value function instead of the standard deviation to reflect the fatigue distribution uniformity, then the function _f3 becomes:

Step 6: Linearize the above absolute value, then you can get

0≤a1 _i ≤Md1 _i (34)

0≤a2 _i ≤Md2 _i (35)

d1 _i +d2 _i =1 (36)

Step 7: Wind farm fatigue distribution uniformity _f3 can be described as

与风速的关系，为简化计算，令Step 8: Linearize the wake model, first obtain from the thrust coefficient fitting curve

The relationship with wind speed, in order to simplify the calculation, let

Step 9: Substitute the above formula into formula (2), then the second part on the right side of the equal sign of formula (2) becomes:

U2 _j,i,t vh _i,t (1-k _i (k _w vh _i,t +b _w )(1-x _i,t )) (39)

The nonlinear part obtained after expanding this formula is:

vh _i,t (1-x _i,t ) (40)

Step 10: Linearize the above formula (40),

vh1 _i,t = vh _i,t (1-x _i,t ) (42)

st 0≤vh1 _i,t ≤vh _i,t (43)

(1-x _i,t )M+vh _i,t -M≤vh1 _i,t ≤(1-x _i,t )M (44)

Step 11: Use the mixed-integer second-order cone programming method to linearize the square term in equation (41), and use the relaxation method to approximate the second-order cone to form a mixed-integer linear programming model, let

So,

Step 12: After the square term is linearized, the equation (41) is linearized with reference to the processing of the equation (40). Meanwhile, the linearization of formula (1) and formula (3) is processed in the same way as formula (2).

Then, the multi-objective function is processed by the constraint method, and the model is transformed into a single-objective model. It can keep the most important objective function or the designer's most preferred objective function as the objective function of the single objective problem, and convert other objective functions into constraints by adding a restriction domain _εi . It can efficiently obtain the Pareto solution set, while ensuring the benefit of the rth objective, it can also take into account other objectives, and it is also popular in solving practical design problems. The specific process is as follows:

Step 13: Solve the above single-objective models of f ₁ , f ₂ , and f ₃ respectively, obtain the objective function values and decision variables under different objectives, and form the decision attribute table of the objective function, as shown in Table 1, where * indicates that the objective is The function solves the model for the target.

Table 1 Decision attribute table

objective function f₁ f₂ f₃ f₁^* f₁^min f₂(x₁^*) f₃(x₁^*) f₂^* f₁(x₂^*) f₂^max f₃(x₂^*) f₃^* f₁(x₃^*) f₂(x₃^*) f₃^min

Step 14: Determine the value range of ε _i (i=1, 2, 3) through the data in the table, and substitute it into the corresponding objective function for constraints, so that the objective function can be transformed into corresponding constraints, thereby forming a single objective The model is solved.

Step 15: Finally, the obtained mixed integer linear programming model is solved to obtain a joint dispatch scheme for offshore wind power generation and maintenance.

In this method, the wake effect of any wind direction is firstly modeled by the wake effect correlation matrix, and then a mathematical model for the joint dispatch of offshore wind power generation and maintenance considering the uniformity of fatigue distribution is constructed. The mixed integer linear programming method and the mixed integer two The order cone programming method processes the nonlinear part of the model, and finally forms a mixed integer linear programming model through the relaxation method, which improves the solution efficiency. At the same time, the constraint method is used to convert the multi-objective function into a single objective to obtain the Pareto solution set efficiently. The method proposed by the invention takes into account the change of the offshore wind direction during the dispatch period, and also takes the maintenance state into account when calculating the wake. By introducing the uniformity of the fatigue distribution of the offshore wind farm, it can provide decision makers with more options for selection. The joint scheduling scheme of power generation and maintenance shows the feasibility and effectiveness of the method proposed in the present invention.

Specific application scenario 1: Simulation of an offshore wind farm with a layout as shown in Figure 2. There are 30 wind turbines in 10 rows and 3 columns, and the offshore wind farm is subjected to power generation maintenance within a specified period (168 periods in a week). For joint dispatch, the distribution of wind speed in this week is shown in Figure 4, and the probability distribution of wind direction is shown in Figure 5. Select the wind speed on the 4th day and analyze the influence of the change of wind direction on the wake effect between units based on the following 4 scenarios. Scenario 1: The wind direction is 0°. Scenario 2: The wind direction is 90°. Scenario 3: The wind direction is 30°. Scenario 4: The wind direction varies between 0° and 360°, according to the probability distribution in Figure 5, as shown in Table 2. The active power output of each unit in the above scenario is solved by using the wake model proposed by the present invention, and the result is shown in Fig. 6 . It can be seen from the figure that the active power output of each wind turbine in scenario 4 is relatively uniform, and compared with scenario 2, it is more in line with the actual operating conditions of offshore wind turbines. situation, and using different wind speeds and directions can more objectively describe the active power output of the wind farm under the actual situation, so as to more accurately describe the power generation of the offshore wind farm in this period.

Table 2 Variation of downwind direction at different time periods

period Wind direction/° period Wind direction/° period Wind direction/° 1 90 9 350 17 150 2 80 10 330 18 130 3 60 11 300 19 120 4 45 12 280 20 100 5 35 13 250 twenty one 110 6 25 14 210 twenty two 90 7 10 15 180 twenty three 80 8 0 16 160 twenty four 85

Specific application scenario 2: Use the model proposed in the present invention to perform power generation maintenance scheduling for 10 units in an offshore wind farm. Considering the uniformity of the fatigue distribution of the offshore wind farm, the comprehensive economic benefits of power generation maintenance are different. The Pareto The distribution of the solution is shown in Figure 7. The points with the best economic benefits under the uniformity of each fatigue distribution have been marked in the figure, and each point corresponds to a power generation maintenance plan. Obviously, when the scheme adopted in the dispatch period makes the fatigue distribution uniformity of the offshore wind farm better, the comprehensive economic benefits will be lower. Pursuing a good uniformity of fatigue distribution in offshore wind farms will limit the power generation of wind farms and require more maintenance costs to implement corresponding strategies, which will provide decision makers with more options when formulating dispatch plans and better decision support.

Claims (4)

1. A power generation and maintenance combined scheduling method considering fatigue distribution uniformity of an offshore wind farm is characterized by comprising the following steps:

1) establishing a new wake model through the wake influence incidence matrix and the maintenance state of the wind turbine generator, and specifically comprising the following steps of:

11) the wind speed input by the wind turbine generator is divided into a horizontal direction and a vertical direction, and then:

wherein j is an index variable of a wind turbine generator set in the wind power plant, t is a time interval index variable, v_j,tIs the jth wind generating set WT_jIn a time period T_tInput wind speed of vh_j,t、vv_j,tRespectively represent the jth wind turbine unit WT_jIn a time period T_tThe input wind speeds in the horizontal direction and the vertical direction;

12) constructing a wake flow influence incidence matrix, and combining the maintenance state of a wind turbine generator set in a wake flow model to obtain the input wind speeds in the horizontal direction and the vertical direction, so that the method comprises the following steps:

wherein i is an index variable of a wind turbine generator set in the wind power plant, and v is_tIs a period of time T_tOffshore wind speed, alpha_tIs a period of time T_tWind direction, U1_j,t，U3_j,tRespectively, is a period of time T_tWhether the lower part is the incidence matrix of the boundary unit or not, when the j-th wind turbine generator unit is the boundary unit in a certain period, the corresponding element in the matrix is 1, otherwise, the matrix is 0, and U2_j,i,tIs a period of time T_tThe lower ith wind turbine generator set has an incidence matrix with wake flow influence on the jth wind turbine generator set in the horizontal direction, when the ith wind turbine generator set has the wake flow effect in the horizontal direction on the jth wind turbine generator set, the value of an element in the matrix is 1, otherwise, the value is 0, wherein the ith wind turbine generator set and the jth wind turbine generator set are always adjacent units, and U4_j,i,tIs a period of time T_tLower correlation matrix with wake effect in vertical direction, k_iIs a constant determined by the unit spacing and the impeller diameter,

is the ith wind generating set WT_iIn a time period T_tThrust coefficient of (x)_i,tIs the ith wind generating set WT_iIn a time period T_t1 represents maintenance, and 0 represents normal operation;

2) establishing a power generation and maintenance combined scheduling mathematical model considering the fatigue distribution uniformity of the offshore wind farm;

3) converting each nonlinear part in the power generation and maintenance combined scheduling mathematical model into linearity by adopting a mixed integer linear programming method and a mixed integer second-order cone programming method, and forming a mixed integer linear programming model by a relaxation method;

4) and (3) processing the multi-target function by using a constraint method, converting the model into a single-target model, solving the single-target model, and finally performing combined dispatching on the offshore wind power plant unit after obtaining a power generation and maintenance dispatching scheme with optimal economic benefit.

2. The power generation and overhaul combined dispatching method considering fatigue distribution uniformity of the offshore wind farm as claimed in claim 1, wherein in the step 2), an objective function of a power generation and overhaul combined dispatching mathematical model considering the fatigue distribution uniformity of the offshore wind farm is considered to minimize overhaul cost f in the whole dispatching time range₁To maximize the power generation amount f₂And causes the fatigue distribution f of the offshore wind farm₃Most uniform, then:

wherein m is the total number of wind generating sets in the wind power plant, n is the total time period number of the dispatching cycle,

in order to reduce the cost of the material equipment,

in order to achieve the cost of monitoring the environment,

in the interest of the cost of the infrastructure,

in order to achieve the cost of transportation,

in order to reduce the labor cost,

for cost loss at shutdown, LP_iFor overhauling WT_iThe number of sustained periods of time;

wherein, P_i,tIs the ith wind generating set WT_iIn a time period T_tInternal output power, t_tIs a period of time T_tThe length of time of;

wherein f is₃Is a period of time T_nFatigue coefficient standard deviation, F, of each unit of wind power plant_i(n) is the ith wind turbine unit WT_iIn a time period T_nThe fatigue coefficient of (a) is,

is a period of time T_nAverage value of fatigue coefficients of each unit of wind power plant, F_i(t₀) The fatigue coefficient value of the ith wind turbine generator set accumulated before the dispatching cycle begins, gamma is the ratio of the turbulent fatigue damage and the power generation fatigue damage of the generator set, W_i,tIs the ith wind generating set WT_iIn a time period T_tGenerated power of P_i ^rateRated active power output, T, of the wind turbine_i ^serIs the ith wind generating set WT_iThe service life of the system (c) is,

is the ith wind generating set WT_iThe maintenance compensation factor.

3. The power generation and overhaul joint scheduling method considering the fatigue distribution uniformity of the offshore wind farm according to claim 2, wherein the constraint conditions of the power generation and overhaul joint scheduling mathematical model considering the fatigue distribution uniformity of the offshore wind farm in the step 2) comprise:

active power output constraint of the wind turbine generator:

wherein,

is the ith wind generating set WT_iIn a time period T_tThe minimum value of the output power is set,

is the ith wind generating set WT_iIn a time period T_tA predicted value of the output power;

and (4) constraint of maintenance necessity:

wherein, b_i,tIndicating the ith wind turbine WT_iWhether or not at T_tStarting to enter a decision variable of a maintenance state, wherein 1 is entering and 0 is not entering;

and (4) maintenance continuity constraint:

x_i,t≥b_i,t

x_i,t-x_i,t-1≤b_i,t

x_i,t+x_i,t-1+b_i,t≤2

wherein, when t is 1, x_i,t-1＝0；

And (4) maintenance duration constraint:

the deadline constraint:

wherein L is_iIs the ith wind generating set WT_iThe sequence number of the last time period required to finish the maintenance operation;

weather restraint:

the U is a time period set which does not allow wind turbine generator maintenance due to offshore weather;

manpower restraint:

wherein,

and

respectively for overhauling ith wind turbine set WT_iManpower demand, AM, on maintenance ships, helicopters and land ports_tIs shown in the time period T_tThe amount of manpower available;

vehicle restraint:

wherein, V_iAnd H_iRespectively for overhauling ith wind turbine set WT_iNumber of ships and helicopters required, AV_tAnd AH_tRespectively, in the time period T_tThe number of operational vessels and helicopters available;

greenhouse gas emission constraint:

wherein D is_iIndicating port docking points to the ith wind turbine unit WT_iA distance of (q)^VAnd q is^HThe weight of greenhouse gases discharged per kilogram of load of the operation and maintenance ship and the helicopter and per kilometer of running load of the helicopter respectively,

is the average weight of the employee,

and

for maintaining the ith wind turbine units WT respectively_iThe weight of equipment carried by an operation and maintenance ship and a helicopter, GHG is the greenhouse gas emission standard established by the industry;

marine environment constraints:

wherein, LV_tIs a period of time T_tAccommodation of sea spaceThe number of the movable operation and maintenance ships;

and (3) bird group constraint:

wherein LH_tIs a period of time T_tThe number of operation and maintenance helicopters of which the offshore space allows activities;

and (4) maintenance and restraint at night:

where Y is the time period of the day night and AL represents the limit of allowed offshore service at night.

4. The power generation overhaul joint scheduling method considering the fatigue distribution uniformity of the offshore wind farm according to claim 3, wherein the step 4) specifically comprises the following steps:

41) respectively solving the objective function f of the overhaul cost₁Minimization, power generation objective function f₂Maximization and wind farm fatigue distribution uniformity f₃The minimized single-target model obtains objective function values and decision variables under different targets to form a decision attribute table of the objective function;

42) determining the value range of the constraint value through each single objective function value in the decision attribute table, substituting the value range into the corresponding objective function for constraint limitation, and converting the objective function into the corresponding constraint condition so as to form a single objective model;

43) and solving the single-target model, and finally performing combined dispatching of the offshore wind power plant units after obtaining the power generation and maintenance dispatching scheme with the optimal economic benefit.

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