Offshore wind power plant site selection and cable route planning method considering navigation influence

Abstract

The invention provides an offshore wind power plant site selection and cable route planning method considering navigation influence. The method comprises the following steps: step S11, carrying out area gridding on an area to be subjected to site selection; s12, constructing a risk evaluation index system based on the natural environment factors and the traffic environment factors; s13, calculating a navigation comprehensive risk value of each grid based on the risk evaluation index system; s14, constraint conditions are set based on the risk space distribution condition, and an offshore wind plant site selection multi-objective optimization model is constructed according to the cost and the wind energy density function; and S15, solving the offshore wind power plant site selection multi-objective optimization model to obtain an optimal offshore wind power plant site selection scheme. On the basis of comprehensively considering the ship navigation safety influence, the site selection of the offshore wind plant is scientifically planned by using the multi-objective optimization method. And economic benefits and sustainability of offshore wind power projects are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm site selection and planning, and in particular to an offshore wind farm site selection and cable route planning method taking into account the impact of navigation. Background Art

[0002] With the rapid development of the offshore wind power industry, the conflict between offshore wind power and ship navigation has gradually become prominent. After the construction of the wind farm is completed, it will exist as a long-term offshore facility, which will inevitably occupy a part of the navigable waters, and then have a certain impact on the safe navigation of ships. According to statistics, about 40% of accidents in offshore wind power waters around the world are related to ship navigation. In addition, the submarine cable pipelines laid by offshore wind farms to transmit electricity to the shore often inevitably need to cross waterways, customary routes and other marine traffic function areas, and anchor damage accidents occur from time to time, causing huge social and economic losses.

[0003] It can be seen that with the rapid development of offshore wind power, more attention needs to be paid to the contradiction between offshore wind power projects and ship navigation safety. From the perspective of offshore wind power development and construction units, it is necessary to focus on coordinating the relationship between the cost, benefit and safety of offshore wind farms. Reasonable planning of offshore wind farm site selection is a key step in resolving the contradiction between offshore wind power development and maritime traffic safety. On the basis of determining the appropriate site location, further planning of submarine cable routes should be done to reduce the occurrence of anchor damage accidents and ensure navigation safety.

[0004] At present, the research on the site selection of offshore wind farms basically adopts the method of multi-attribute decision-making, and lacks the research method of solving the problem by building mathematical models and using multi-objective optimization methods. The research on the problem of submarine cable routing planning is based on the evaluation of existing routes, and rarely combines optimization algorithms to make the best choice of cable routes while considering navigation safety. Therefore, it is necessary to design a method for offshore wind farm site selection and cable routing planning that takes into account the impact of navigation. Summary of the invention

[0005] The present invention proposes a method for offshore wind farm site selection and cable routing planning that takes into account the impact of navigation, so as to solve the technical problem that the existing offshore wind farm site selection and cable routing planning schemes do not take into account the impact of navigation during design.

[0006] In order to solve the above technical problems, the present invention provides a method for selecting an offshore wind farm site taking into account the impact of navigation, comprising the following steps:

[0007] Step S11: gridding the area to be selected;

[0008] Step S12: constructing a risk assessment index system based on natural environment factors and traffic environment factors;

[0009] Step S13: Calculating the comprehensive navigation risk value of each grid based on the risk assessment index system;

[0010] Step S14: setting constraints based on the risk spatial distribution, and constructing a multi-objective optimization model for offshore wind farm site selection based on cost and wind energy density functions;

[0011] Step S15: Solve the multi-objective optimization model for offshore wind farm site selection to obtain an optimal offshore wind farm site selection plan.

[0012] Preferably, the natural environmental factors in step S12 include: visibility, wind speed, wave height and flow rate; the traffic environmental factors include: ship traffic flow, anchorage distance and route distance.

[0013] Preferably, step S13 includes:

[0014] Step S131: Based on the risk assessment indicator system, collect data and normalize them;

[0015] Step S132: Determine the weight of each risk assessment indicator;

[0016] Step S133: Calculate the comprehensive risk value of each grid unit based on the weighted comprehensive method.

[0017] Preferably, step S132 includes:

[0018] Step S1321: Construct a decision matrix X:

[0019]

[0020] In the formula, x nm Indicates the value of the mth evaluation indicator of the nth object to be evaluated;

[0021] Step S1322: Normalize the decision matrix:

[0022]

[0023] In the formula, set B is a positive indicator, and set H is a negative indicator;

[0024] Step S1323: Calculate the overall performance S of each alternative solution i :

[0025]

[0026] Step S1324: Remove each criterion in turn and calculate the performance of the alternative solution again:

[0027]

[0028] Step S1325: Calculate the removal effect E of the jth index j :

[0029]

[0030] Step S1326: Determine the final weight of the standard, the weight w of the jth indicator j The calculation formula is as follows:

[0031]

[0032] Preferably, the expression of the multi-objective optimization model for offshore wind farm site selection constructed in step S14 includes:

[0033] Cost objective function f1:

[0034]

[0035] Where I is the set of candidate offshore wind farm grids; Q is the comprehensive cost per kilometer of AC cable; d i is the offshore distance of grid unit i; P is the AC cable transmission power; U is the voltage of the submarine cable used from the offshore booster station of the offshore wind farm to the onshore centralized control center; is the power factor; R is the equivalent resistance of the AC cable; T is the annual operating hours of the traffic cable; u is the electricity price; P G is the rated power of the turbine; h i is the water depth of grid cell i (m); V is the service speed of the maintenance ship; W oil P is the fuel consumption per unit time of the maintenance ship at the service speed; r-oil P is the unit price of fuel oil for operation and maintenance ship; r-ele is the unit price of offshore wind power; x i is a decision variable. If the site is selected in the i-th grid, then x i =1; otherwise x i =0;

[0036] Wind energy density objective function f2:

[0037]

[0038] Where ρ is the air density; v i is the wind speed of grid cell i;

[0039] Constraints:

[0040]

[0041] x i =0,d i <30hi <30;

[0042]

[0043]

[0044] In the formula, R i represents the comprehensive navigation risk value of the i-th candidate grid point; s i Represents the distance from the i-th candidate grid point to the nearest route.

[0045] The present invention also provides a cable routing planning method for an offshore wind farm considering the impact of navigation, and based on the above-mentioned offshore wind farm site selection method considering the impact of navigation, an optimal offshore wind farm site selection scheme is obtained, which includes the following steps:

[0046] Step S21: constructing a shortest path objective function based on the distance from the offshore wind farm to the cable routing junction and the distance from the routing junction to the submarine cable landing point;

[0047] Step S22: Based on the shortest path objective function, an optimization algorithm is used to search for the best path, and then an A* algorithm is used to find the best path.

[0048] Preferably, the expression of the shortest path objective function minL is:

[0049]

[0050] Where S is the set of offshore wind farms, S = {S1, S2, …, S n}, n represents the number of offshore wind farms; M is the routing junction of adjacent wind farms; T is the set of submarine cable landing points, T = {T1, T2, …, T m}, m represents the number of submarine cable landing points; is the distance from the sth offshore wind farm to the cable routing junction M; is the distance from the routing junction M to the tth submarine cable landing point.

[0051] Preferably, the sparrow algorithm is used to search for the optimal solution in step S22.

[0052] Preferably, the method of optimizing using the sparrow algorithm includes:

[0053] Step S221: Randomly generate the initial state of the group:

[0054] X i,j = rand·(UB j -LB j );

[0055] Where, i = 1, 2, ..., pop, j = 1, 2, ..., dim; UB j and LB j They represent the upper and lower bounds of the j-th dimension in the search space respectively; rand is a random number in the range (0,1) that follows a uniform distribution;

[0056] Step S222: Update the explorer's position:

[0057]

[0058] Among them, X i，j is the individual position of the sparrow, that is, the position information of the i-th sparrow in the j-th dimension; t is the current iteration number, iter max is the maximum number of iterations; α is a random number in [0,1]; R2∈[0,1], ST∈[0.5,1] are the warning value and safety value respectively; Q is a random number that obeys the normal distribution; L is a 1×d matrix, in which each element is 1;

[0059] Step S223: Update follower position:

[0060]

[0061] A + =A T (AA T ) -1 ;

[0062] In the formula, X p is the position of the optimal finder, X worst is the current global worst position; N is the population size; A is a 1×d matrix, each element is randomly assigned a value of 1 or -1;

[0063] Step S224: Update the position of the alerter:

[0064]

[0065] In the formula, X best represents the currently known global optimal position, represents the global worst position; the step size control parameter β is a random number that obeys a normal distribution with a mean of 0 and a variance of 1; K is a uniformly distributed random number in the range of [-1,1]; the fitness value f i represents the fitness value of the i-th sparrow, fg and f worst Represent the current optimal and worst fitness values ​​in the population respectively; in order to avoid the denominator being zero, a very small constant ε is added;

[0066] Step S225: Repeat steps S222 to S224 to search for the best solution.

[0067] Preferably, in step S22, when the A* algorithm is used to find the optimal path, the cost function is adjusted by the navigation comprehensive risk value, and the cost function expression F(n) is:

[0068] F(n)=DG(n)+DH(n);

[0069] Where D represents the comprehensive risk value of navigation, G(n) represents the cost value from the center of the offshore wind farm as the starting point to the current node, and H(n) represents the cost value from the current node to the landing point of the submarine cable.

[0070] The beneficial effects of the present invention include at least: the present invention uses a multi-objective optimization method to scientifically plan the site selection of offshore wind farms on the basis of comprehensive consideration of the impact on ship navigation safety. This method not only focuses on the technical and economic feasibility of wind farm site selection, but also comprehensively evaluates the potential impact of site selection on the navigation environment. Through multi-objective optimization, the cost, energy efficiency and navigation safety of wind power projects can be balanced under multiple constraints. In addition, after the site selection plan is determined, the route planning of submarine cables is further carried out to achieve the optimal design of the submarine cable laying path, thereby reducing the project cost and construction difficulty, while minimizing interference with ship navigation and ensuring navigation safety. Through this series of optimization processes, the economic benefits and sustainability of offshore wind power projects are ensured, and their negative impact on the surrounding navigation environment is effectively alleviated, achieving the coordinated development of energy development and maritime traffic safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0072] Figure 2 This is a schematic diagram of the site selection results of an offshore wind farm according to an embodiment of the present invention;

[0073] Figure 3 This is a schematic diagram of submarine cable routing planning results according to an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0075] Example 1

[0076] like Figure 1As shown, an embodiment of the present invention provides a method for selecting an offshore wind farm site considering the impact of navigation, comprising the following steps:

[0077] Step S11: gridding the area to be selected.

[0078] Specifically, the potential offshore wind farm site selection area is gridded so that the study area consists of several continuous adjacent grids of the same size. In the marine meteorological data used in this embodiment, the spatial resolution of the water depth data is the smallest, which is about 1 / 60°×1 / 60°. Based on the spatial resolution of the water depth data, the longitude and latitude coordinates are determined as a grid unit every 0.033° in combination with the research needs, and the study area is gridded.

[0079] Step S12: Constructing a risk assessment index system based on natural environmental factors and traffic environmental factors.

[0080] Specifically, a navigation risk assessment index system for offshore wind farms is established by comprehensively considering natural environmental factors and traffic environmental factors; a submarine cable risk assessment index system is established by focusing on submarine cable anchor damage risk factors caused by ship activities.

[0081] In this embodiment, the risk assessment index system includes natural environment factors and traffic environment factors.

[0082] Natural environmental factors include visibility, wind speed, wave height and current speed.

[0083] Traffic environment factors include ship traffic flow, commercial ship traffic flow, fishing vessel traffic flow, ship length, ship draft, anchorage distance and route distance.

[0084] Step S13: Based on the risk assessment index system, the comprehensive navigation risk value of each grid is calculated.

[0085] Specifically, historical marine meteorological data, ship AIS data and other research data of the research area are obtained and processed, and then the comprehensive risk value of navigation is calculated, including the following steps:

[0086] Standardized risk indicators, specifically for positive indicators, are standardized using formula (1):

[0087]

[0088] For the reverse indicator, formula (2) is used for standardization:

[0089]

[0090] In formulas (1) and (2), x ij is the value of the jth evaluation index of the i-th object to be evaluated, maxxj is the maximum value of the j-th evaluation index, minx j is the minimum value of the jth evaluation index. ij It can reflect the risk level of the jth evaluation indicator of the ith evaluation object. The larger the value, the greater the risk brought by the evaluation indicator.

[0091] Determine the weight of each risk assessment indicator, specifically:

[0092] 1) Construct a decision matrix. Assuming there are n alternatives and m risk assessment indicators, the matrix can be expressed as:

[0093]

[0094] 2) Normalized decision matrix:

[0095]

[0096] Set B is a positive indicator and set H is a negative indicator.

[0097] 3) Calculate the overall performance S of each alternative i :

[0098]

[0099] 4) Remove each criterion in turn and calculate the performance of the alternatives again:

[0100]

[0101] 5) Calculate the removal effect E of the jth standard j :

[0102]

[0103] 6) Determine the final weight of the standard, the weight w of the jth standard j The calculation formula is as follows:

[0104]

[0105] Finally, the comprehensive risk value R of each grid unit is calculated based on the weighted comprehensive method. i , the calculation formula is as follows:

[0106]

[0107] In this embodiment, the following method is also provided for risk reference: Based on k-means clustering, the risk value of each grid unit is clustered to determine different risk level thresholds and judge the risk level of each grid unit.

[0108] Specifically, the elbow method is used to determine the number of k-means clustering clusters, that is, to determine how many risk levels the risk value of each grid unit is divided into; after determining the optimal number of clusters, k-means clustering is performed on the risk values ​​to divide the risk level of each grid unit.

[0109] Step S14: setting constraints based on the spatial distribution of risks, and constructing a multi-objective optimization model for offshore wind farm site selection based on cost and wind energy density functions.

[0110] Specifically, the constructed offshore wind farm site selection model is as follows: the cost objective function is expressed by equation (10), the wind energy density objective function is expressed by equation (11), and the constraints are expressed by equations (12) to (16).

[0111] Cost objective function f1:

[0112]

[0113] Where I is the set of candidate offshore wind farm grids; Q is the comprehensive cost per kilometer of AC cable; d i is the offshore distance of grid unit i; P is the AC cable transmission power; U is the voltage of the submarine cable used from the offshore booster station of the offshore wind farm to the onshore centralized control center; is the power factor; R is the equivalent resistance of the AC cable; T is the annual operating hours of the traffic cable; u is the electricity price; P G is the rated power of the turbine; h i is the water depth of grid cell i (m); V is the service speed of the maintenance ship; W oil P is the fuel consumption per unit time of the maintenance ship at the service speed; r-oil P is the unit price of fuel oil for operation and maintenance ship; r-ele is the unit price of offshore wind power; x i is a decision variable. If the site is selected in the i-th grid, then x i =1; otherwise x i =0;

[0114] Wind energy density objective function f2:

[0115]

[0116] Where ρ is the air density; v i is the wind speed of grid cell i;

[0117] Constraints:

[0118]

[0119] x i =0,d i <30h i<30(13)

[0120]

[0121]

[0122]

[0123] In the formula, R i represents the comprehensive navigation risk value of the i-th candidate grid point; s i Represents the distance from the i-th candidate grid point to the nearest route.

[0124] The above model constraints are explained as follows.

[0125] The location is not selected at the grid point with high navigation risk level, which is expressed by formula (12).

[0126] The “single thirty” standard means that new offshore wind power projects should, in principle, be located in sea areas 30 km offshore or with a water depth of more than 30 m. When the offshore distance of grid cell i is within 30 km and the water depth is less than 30 m, the grid point cannot be sited. The decision variable x i The value is 0, which is expressed by formula (13).

[0127] When the water depth of the candidate grid unit is less than 35 m, the cost of the support structure in the cost objective function is calculated using the cost formula of the single pile support structure; otherwise, the cost formula of the jacket support structure is used, which is expressed by formula (14).

[0128] In the i-th candidate grid cell site selection, then x i =1; if the site is not selected in the i-th candidate grid, then x i =0, expressed by formula (15).

[0129] The distance s from the i-th candidate grid cell to the nearest route i If it is less than 1 km, building an offshore wind farm here will cause a certain shadow area to form on nearby marine radars, so the site will not be selected in this candidate grid unit, as expressed by formula (16).

[0130] Step S15: Solve the multi-objective optimization model for offshore wind farm site selection to obtain the optimal offshore wind farm site selection plan.

[0131] Exemplarily, the method for solving the problem in this embodiment is as follows.

[0132] Initialize the population: randomly generate an initial population according to the set algorithm parameters, where each individual represents a potential solution;

[0133] Calculate fitness: For each individual in the population, calculate its fitness function value according to the characteristics of the problem. The fitness function reflects the degree of individual performance in solving multi-objective optimization problems;

[0134] Fast non-dominated sorting: Use fast non-dominated sorting to divide the individuals in the population into multiple non-dominated layers and determine the non-dominated level of each individual in the population;

[0135] Crowding distance calculation: In order to maintain the diversity of the Pareto frontier, the crowding distance of each individual is calculated. The crowding distance measures the density of individuals in the target space and reflects the relative superiority of individuals in the same non-dominated hierarchy. The larger the crowding distance, the greater the distance between individuals, which helps to maintain a uniform distribution on the Pareto frontier;

[0136] Selection operation: Use the binary tournament selection strategy to randomly select two individuals from the current population and select the one with a higher non-dominated level or a larger crowding distance. This process is repeated until a certain number of individuals are selected as parents;

[0137] Crossover operation: Perform a crossover operation on the selected parent individuals to generate new offspring individuals. Crossover operation is one of the key steps of genetic algorithms and is used to generate new solution sets;

[0138] Mutation operation: Perform mutation operations on offspring individuals to increase the diversity of the population. Mutation operations can be random mutations, uniform mutations, etc., which help the algorithm explore new solutions in the search space;

[0139] Iterative evolution: Repeat the above selection, crossover and mutation operations to form a new population, and continue the iterative evolution process until the termination condition is met.

[0140] Selection of the optimal solution: The TOPSIS method is used to arrange and select Pareto frontier individuals in order to obtain the best site selection solution.

[0141] Example 2

[0142] Based on the best site selection scheme obtained in the embodiment, this embodiment proposes a cable route planning method for an offshore wind farm taking into account the impact of navigation, including the following steps.

[0143] Step S21: construct a shortest path objective function based on the distance from the offshore wind farm to the cable routing junction and the distance from the routing junction to the submarine cable landing point.

[0144] Specifically, in this embodiment, the expression of the shortest path objective function is:

[0145]

[0146] Where S is the set of offshore wind farms, S = {S1, S2, …, S n}; M is the routing junction of adjacent wind farms; T is the set of submarine cable landing points, T = {T1, T2, …, T m}; is the distance from the sth offshore wind farm to the cable routing junction M; is the distance from the routing junction M to the tth submarine cable landing point.

[0147] Step S22: Based on the shortest path objective function, an optimization algorithm is used to search for the best path, and then an A* algorithm is used to find the best path.

[0148] In this embodiment, the optimization solution is divided into two stages. The first stage is the optimization of the shortest path objective function in step S21, and the second stage is to use the A* algorithm to find the optimal path.

[0149] Specifically, in the first stage of solving, the sparrow algorithm is used to find the optimal solution, including:

[0150] Step S221: Randomly generate the initial state of the group:

[0151] X i,j = rand·(UB j -LB j ) (18)

[0152] Where, i = 1, 2, ..., pop, j = 1, 2, ..., dim; UB j and LB j They represent the upper and lower bounds of the j-th dimension in the search space respectively; rand is a random number in the range (0,1) that follows a uniform distribution;

[0153] Step S222: Update the explorer's position:

[0154]

[0155] Among them, X i，j is the individual position of the sparrow, that is, the position information of the i-th sparrow in the j-th dimension; t is the current iteration number, iter max is the maximum number of iterations; α is a random number in [0,1]; R2∈[0,1], ST∈[0.5,1] are the warning value and safety value respectively; Q is a random number that obeys the normal distribution; L is a 1×d matrix, in which each element is 1;

[0156] Step S223: Update follower position:

[0157]

[0158] A+ =A T (AA T ) -1 (twenty one)

[0159] In the formula, X p is the position of the optimal finder, X worst is the current global worst position; N is the population size; A is a 1×d matrix, each element is randomly assigned a value of 1 or -1;

[0160] Step S224: Update the position of the alerter:

[0161]

[0162] In the formula, X best represents the currently known global optimal position, represents the global worst position; the step size control parameter β is a random number that obeys a normal distribution with a mean of 0 and a variance of 1; K is a uniformly distributed random number in the range of [-1,1]; the fitness value f i represents the fitness value of the i-th sparrow, fg and f worst Represent the current optimal and worst fitness values ​​in the population respectively; in order to avoid the denominator being zero, a very small constant ε is added;

[0163] Step S225: Repeat steps S222 to S224 to search for the best solution.

[0164] In the second stage of solving, the methods of finding the optimal path through the A* algorithm include:

[0165] Initialize the open list openlist and the closed list closelist. The openlist stores all the retrievable nodes starting from the parent node and arranges them from small to large according to the estimated cost value. The closelist stores the nodes that have been traversed.

[0166] Add the starting node to the openlist. At the beginning of the search, the closelist is empty. After each node is visited, it is transferred to the closelist, and the A* algorithm continues to loop to locate the end point;

[0167] For each node that has not reached the end point, explore the nodes around it. For each newly discovered node, if it is not in the openlist, add it to it, calculate its cost value F(n), and mark it as a child node of the current node;

[0168] If the newly discovered node is already in the openlist, recalculate its cost value F(n). If the newly calculated cost value is lower, update the parent node information of the node and move it to the closelist, while updating the openlist;

[0169] Repeat the above steps until the end point is found. Once the end point is added to the openlist, the algorithm ends. Connect the nodes in the closelist in order to form the shortest path from the start point to the end point. If the openlist is cleared, it means there is no feasible path.

[0170] In this embodiment, the risk-based initial cable routing optimization considers both distance and risk objectives simultaneously, improves the cost function of the A* algorithm, and improves the cost function F(n) to be expressed by formula (18):

[0171] F(n)=DG(n)+DH(n)(23)

[0172] Where D represents the comprehensive risk value of navigation, G(n) represents the cost value from the center of the offshore wind farm as the starting point to the current node, and H(n) represents the cost value from the current node to the landing point of the submarine cable.

[0173] Example 3

[0174] This embodiment uses a certain sea area as an example to illustrate Embodiment 1 and Embodiment 2.

[0175] Examples of evaluation index data are shown in Table 1.

[0176] Table 1

[0177]

[0178] Standardized risk indicators are shown in Table 2.

[0179] Table 2

[0180]

[0181]

[0182] The weights of each risk assessment indicator are determined as follows.

[0183] The results of the weights of the navigation risk assessment indicators for offshore wind farms are shown in Table 3.

[0184] Table 3

[0185] Wind speed Wave height Current speed Ship traffic visibility Anchorage distance Route distance Weight 0.1403 0.076 0.0514 0.189 0.2154 0.2321 0.0959

[0186] The results of the weights of submarine cable risk assessment indicators are shown in Table 4.

[0187] Anchorage distance Route distance Fishing vessel traffic Merchant shipping traffic Length of merchant ship Commercial ship draft Weight 0.1658 0.1764 0.2986 0.085 0.1297 0.1446

[0188] The results of navigation risk assessment for offshore wind farms in grid units are shown in Table 5.

[0189] Table 5

[0190] Grid Value at Risk Risk Level 1 0.628474 High risk 2 0.499663 Low risk 3 0.437298 Low risk 4 0.622739 High risk 5 0.520741 Medium risk 6 0.591322 High risk 7 0.479672 Low risk 8 0.533366 Medium risk 9 0.529739 Medium risk … …

[0191] Finally, the best site selection solution is obtained as Figure 2 As shown in Figure 2, the solution of the cable routing planning model is as follows: Figure 3 shown.

[0192] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as the scope recorded in this specification.

[0193] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for selecting an offshore wind farm site considering the impact of navigation, characterized in that: The following steps are involved: Step S11: gridding the area to be selected; Step S12: constructing a risk assessment index system based on natural environment factors and traffic environment factors; Step S13: Calculating the comprehensive navigation risk value of each grid based on the risk assessment index system; Step S14: setting constraints based on the risk spatial distribution, and constructing a multi-objective optimization model for offshore wind farm site selection based on cost and wind energy density functions; Step S15: Solve the multi-objective optimization model for offshore wind farm site selection to obtain an optimal offshore wind farm site selection plan.

2. The method for selecting an offshore wind farm site considering the impact of navigation according to claim 1, characterized in that: The natural environmental factors in step S12 include: visibility, wind speed, wave height and flow rate; the traffic environmental factors include: ship traffic flow, anchorage distance and route distance.

3. The method for selecting an offshore wind farm site considering the impact of navigation according to claim 1, characterized in that: Step S13 includes: Step S131: Based on the risk assessment indicator system, collect data and normalize them; Step S132: Determine the weight of each risk assessment indicator; Step S133: Calculate the comprehensive risk value of each grid unit based on the weighted comprehensive method.

4. The method for selecting an offshore wind farm site considering the impact of navigation according to claim 3, characterized in that: Step S132 includes: Step S1321: Construct a decision matrix X: In the formula, x nm Indicates the value of the mth evaluation indicator of the nth object to be evaluated; Step S1322: Normalize the decision matrix: In the formula, set B is a positive indicator, and set H is a negative indicator; Step S1323: Calculate the overall performance S of each alternative solution i : Step S1324: Remove each criterion in turn and calculate the performance of the alternative solution again: Step S1325: Calculate the removal effect E of the jth index j : Step S1326: Determine the final weight of the standard, the weight w of the jth indicator j The calculation formula is as follows:

5. The method for selecting an offshore wind farm site considering the impact of navigation according to claim 1, characterized in that: The expression of the multi-objective optimization model for offshore wind farm site selection constructed in step S14 includes: Cost objective function f1: Where I is the set of candidate offshore wind farm grids; Q is the comprehensive cost per kilometer of AC cable; d i is the offshore distance of grid unit i; P is the AC cable transmission power; U is the voltage of the submarine cable used from the offshore booster station of the offshore wind farm to the onshore centralized control center; is the power factor; R is the equivalent resistance of the AC cable; T is the annual operating hours of the traffic cable; u is the electricity price; P G is the rated power of the turbine; h i is the water depth of grid cell i (m); V is the service speed of the maintenance ship; W oil P is the fuel consumption per unit time of the maintenance ship at the service speed; r-oil P is the unit price of fuel oil for operation and maintenance ship; r-ele is the unit price of offshore wind power; x i is a decision variable. If the site is selected in the i-th grid, then x i =1; otherwise x i =0; Wind energy density objective function f2: Where ρ is the air density; v i is the wind speed of grid cell i; Constraints: x i =0,d i <30and h i <30; In the formula, R i represents the comprehensive navigation risk value of the i-th candidate grid point; s i Represents the distance from the i-th candidate grid point to the nearest route.

6. A cable routing planning method for an offshore wind farm considering the impact of navigation, based on the offshore wind farm site selection method considering the impact of navigation as claimed in any one of claims 1 to 5 to obtain the optimal offshore wind farm site selection scheme, characterized in that: The following steps are involved: Step S21: constructing a shortest path objective function based on the distance from the offshore wind farm to the cable routing junction and the distance from the routing junction to the submarine cable landing point; Step S22: Based on the shortest path objective function, an optimization algorithm is used to search for the best path, and then an A* algorithm is used to find the best path.

7. A cable routing planning method for an offshore wind farm considering the impact of navigation according to claim 6, characterized in that: The expression of the shortest path objective function minL is: Where S is the set of offshore wind farms, S = {S1, S2, …, S n }, n represents the number of offshore wind farms; M is the routing junction of adjacent wind farms; T is the set of submarine cable landing points, T={T1,T2,…,T m }, m represents the number of submarine cable landing points; is the distance from the sth offshore wind farm to the cable routing junction M; is the distance from the routing junction M to the tth submarine cable landing point.

8. The cable routing planning method for an offshore wind farm considering the impact of navigation according to claim 6 is characterized in that: In step S22, the sparrow algorithm is used to search for the optimal solution.

9. A cable routing planning method for an offshore wind farm considering the impact of navigation according to claim 8, characterized in that: The methods of using the sparrow algorithm for optimization include: Step S221: Randomly generate the initial state of the group: X i,j =rand·(UB j -LB j ); Where, i = 1, 2, ..., pop, j = 1, 2, ..., dim; UB j and LB j They represent the upper and lower bounds of the j-th dimension in the search space respectively; rand is a random number in the range (0,1) that follows a uniform distribution; Step S222: Update the explorer's position: Among them, X i，j is the individual position of the sparrow, that is, the position information of the i-th sparrow in the j-th dimension; t is the current iteration number, iter max is the maximum number of iterations; α is a random number in [0,1]; R2∈[0,1], ST∈[0.5,1] are the warning value and safety value respectively; Q is a random number that obeys the normal distribution; L is a 1×d matrix, in which each element is 1; Step S223: Update follower position: A + =A T (CHALLENGE ACCEPTED T ) -1 ; Where, X p is the position of the optimal finder, X worst is the current global worst position; N is the population size; A is a 1×d matrix, each element is randomly assigned a value of 1 or -1; Step S224: Update the position of the alerter: Where, X best represents the currently known global optimal position, represents the global worst position; the step size control parameter β is a random number that follows a normal distribution with a mean of 0 and a variance of 1; K is a uniformly distributed random number in the range of [-1,1]; f i represents the fitness value of the i-th sparrow, fg and f worst Represent the current optimal and worst fitness values ​​in the population respectively; in order to avoid the denominator being zero, a very small constant ε is added; Step S225: Repeat steps S222 to S224 to search for the best solution.

10. A cable routing planning method for an offshore wind farm considering the impact of navigation according to claim 6, characterized in that: In step S22, when the A* algorithm is used to find the optimal path, the cost function is adjusted by the navigation comprehensive risk value, and the cost function expression F(n) is: F(n)=DG(n)+DH(n); Where D represents the comprehensive risk value of navigation, G(n) represents the cost value from the center of the offshore wind farm as the starting point to the current node, and H(n) represents the cost value from the current node to the landing point of the submarine cable.

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