Method for site selection and cable routing planning of offshore wind farm considering navigation impact

Abstract

The application provides a kind of offshore wind farm siting considering navigation influence and its cable route planning method, step S11: the region gridding is carried out to the region to be sited;Step S12: based on natural environment factors and traffic environment factors, a risk evaluation index system is constructed;Step S13: based on the risk evaluation index system, the navigation comprehensive risk value of each grid is calculated;Step S14: based on the risk space distribution, the constraint condition is set, and according to the cost and wind energy density function, an offshore wind farm siting multi-objective optimization model is constructed;Step S15: the offshore wind farm siting multi-objective optimization model is solved, and the optimal offshore wind farm siting scheme is obtained.The application scientifically plans the siting of offshore wind farm by using multi-objective optimization method on the basis of comprehensively considering the influence of ship navigation safety, and ensures the economic benefit and sustainability of offshore wind power project.

Description

Technical Field

[0001] This invention relates to the field of wind farm site selection and planning technology, specifically to a method for offshore wind farm site selection and cable routing planning that takes into account the impact of navigation. Background Technology

[0002] With the rapid development of the offshore wind power industry, the conflict between offshore wind power and ship navigation has become increasingly prominent. Once completed, wind farms exist as long-term offshore facilities, inevitably occupying a portion of navigable waters and thus impacting the safe navigation of ships. Furthermore, the submarine cables laid by offshore wind farms to transmit electricity to shore often inevitably cross waterways and established shipping routes, leading to frequent anchoring accidents and causing significant socio-economic losses.

[0003] Therefore, with the rapid development of large-scale offshore wind power, greater attention needs to be paid to the conflict between offshore wind power projects and maritime navigation safety. From the perspective of offshore wind power developers, it is crucial to coordinate the relationship between cost, efficiency, and safety of offshore wind farms. Rational planning of offshore wind farm site selection is a key step in resolving the conflict between offshore wind power development and maritime traffic safety. Based on determining suitable site locations, further planning of submarine cable routes is essential to reduce anchor-related accidents and ensure navigational safety.

[0004] Current research on offshore wind farm site selection primarily employs multi-attribute decision-making methods, lacking approaches that utilize mathematical models and multi-objective optimization techniques. Research on submarine cable routing planning largely evaluates existing routes, rarely considering navigation safety while incorporating optimization algorithms for optimal cable route selection. Therefore, it is necessary to design a method for offshore wind farm site selection and cable routing planning that takes into account navigational impacts. Summary of the Invention

[0005] This invention proposes a method for offshore wind farm site selection and cable routing planning that takes into account the impact of navigation, in order to solve the technical problem that existing offshore wind farm site selection and cable routing planning schemes do not consider the impact of navigation in their design.

[0006] To address the aforementioned technical problems, this invention provides a method for selecting offshore wind farm sites that considers the impact of navigation, comprising the following steps:

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

[0008] Step S12: Construct a risk assessment indicator system based on natural environmental factors and traffic environmental factors;

[0009] Step S13: Based on the risk assessment index system, calculate the comprehensive navigation risk value for each grid;

[0010] Step S14: Set constraints based on the risk spatial distribution, and construct 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 the optimal offshore wind farm site selection scheme.

[0012] Preferably, the natural environmental factors mentioned in step S12 include: visibility, wind speed, wave height, and current speed; 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 index system, collect data and normalize it;

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

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

[0017] Preferably, step S132 includes:

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

[0019]

[0020] In the formula, x nm This represents the value of the m-th evaluation indicator for the n-th object to be evaluated;

[0021] Step S1322: Normalize the decision matrix:

[0022]

[0023] In the formula, set B represents the positive index, and set H represents the negative index;

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

[0025]

[0026] Step S1324: Remove each standard in turn and recalculate the performance of the alternative solutions:

[0027]

[0028] Step S1325: Calculate the removal effect E of the j-th indicator. j :

[0029]

[0030] Step S1326: Determine the final weights of the standards, the weight w of the j-th 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] In the formula, I represents the set of candidate offshore wind farm grids; Q represents the comprehensive cost per kilometer of AC cable; d i denoted as , where is the distance from the shore to grid cell i; P is the AC cable transmission power; U is the voltage of the submarine cable used to connect the offshore wind farm's offshore booster station to the onshore control center. Power factor; R is the equivalent resistance of the AC cable; T is the annual operating hours of the transportation cable; u is the electricity price; P G The rated power of the turbine; h i V represents the water depth (m) of grid cell i; V represents the service speed of the maintenance vessel; W represents the water depth (m) of grid cell i. oil P represents the fuel consumption per unit time of the maintenance vessel at its service speed. r-oil P is the unit price of fuel oil for maintenance ships; r-ele This refers to the unit price of offshore wind power; x i Let x be the decision variable. If the location is chosen in the i-th grid, then... i =1; otherwise x i =0;

[0036] Wind energy density objective function f2:

[0037]

[0038] In the formula, ρ is the air density; v i Let i be the wind speed of grid cell i;

[0039] Constraints:

[0040]

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

[0042]

[0043] In the formula, R i s represents the comprehensive navigation risk value of the i-th candidate grid point; i It represents the distance from the i-th candidate grid point to the nearest flight path.

[0044] This invention also provides a cable routing planning method for offshore wind farms that considers navigation impacts. Based on the above-mentioned offshore wind farm site selection method considering navigation impacts, the optimal offshore wind farm site selection scheme is obtained, including the following steps:

[0045] Step S21: Based on the distance from the offshore wind farm to the cable route convergence point and the distance from the route convergence point to the submarine cable landing point, construct the shortest path objective function;

[0046] Step S22: Based on the shortest path objective function, after optimization using an optimization algorithm, the A* algorithm is used to find the optimal path.

[0047] Preferably, the expression for the shortest path objective function minL is:

[0048]

[0049] In the formula, S is the set of offshore wind farms, S = {S1, S2, ..., S} n}, where n represents the number of offshore wind farms; M is the route convergence point of adjacent wind farms; and T is the set of submarine cable landing points, T = {T1, T2, ..., T}. m}, where m represents the number of submarine cable landing points; Let be the distance from the s-th offshore wind farm to the cable route junction point M; Let M be the distance from the route convergence point M to the t-th submarine cable landing point.

[0050] Preferably, the Sparrow Algorithm is used for optimization in step S22.

[0051] Preferably, the method of using the sparrow algorithm for optimization includes:

[0052] Step S221: Randomly generate the initial state of the population:

[0053] X i,j =rand·(UB) j -LB j );

[0054] In the formula, i = 1, 2, ..., pop, j = 1, 2, ..., dim; UB j and LB jrepresents 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.

[0055] Step S222: Update Explorer Locations:

[0056]

[0057] Among them, X i，j Let represent the individual sparrow position, specifically the position of the i-th sparrow in the j-th dimension; t represents the current iteration number, iter max α is the maximum number of iterations; α is a random number within [0,1]; R2∈[0,1] and ST∈[0.5,1] are the warning value and the safety value, respectively; Q is a random number that follows a normal distribution; L is a 1×d matrix where each element is 1;

[0058] Step S223: Update follower positions:

[0059]

[0060] A + =A T (AA T ) -1 ;

[0061] In the formula, X p X represents the position of the optimal discoverer. worst The current worst position is N; the population size is N; A is a 1×d matrix, with each element randomly assigned a value of 1 or -1.

[0062] Step S224: Update the location of the vigilant:

[0063]

[0064] In the formula, X best This represents the currently known global optimal position. The value represents the worst-case 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 [-1, 1]; the fitness value f i Let fg and f represent the fitness value of the i-th sparrow. worst These represent the current best and worst fitness values ​​in the population, respectively; to avoid the denominator being zero, a very small constant ε is added.

[0065] Step S225: Repeat steps S222 to S224 to perform optimization.

[0066] Preferably, in step S22, when using the A* algorithm to find the optimal path, the cost function is adjusted using the comprehensive navigation risk value, and the cost function expression F(n) is:

[0067] F(n) = DG(n) + DH(n);

[0068] In the formula, D represents the comprehensive navigation risk value, G(n) represents the cost from the center of the offshore wind farm as the starting point to the current node, and H(n) represents the cost from the current node to the submarine cable landing point.

[0069] The beneficial effects of this invention include at least the following: Based on a comprehensive consideration of the impact on ship navigation safety, this invention utilizes a multi-objective optimization method to scientifically plan the site selection of offshore wind farms. This method not only focuses on the techno-economic feasibility of wind farm site selection but also comprehensively assesses 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. Furthermore, after the site selection scheme is determined, the routing of submarine cables is further planned to achieve the optimal design of the submarine cable laying path, thereby reducing engineering costs 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 mitigated, achieving coordinated development of energy development and maritime traffic safety. Attached Figure Description

[0070] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0071] Figure 2 This is a schematic diagram of the offshore wind farm site selection results according to an embodiment of the present invention;

[0072] Figure 3 This is a schematic diagram of the submarine cable routing planning results according to an embodiment of the present invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0074] Example 1

[0075] like Figure 1 As shown, this embodiment of the invention provides a method for selecting offshore wind farm sites that takes into account the impact of navigation, including the following steps:

[0076] Step S11: Grid the area to be selected.

[0077] Specifically, the potential offshore wind farm site selection area is gridded, so that the study area consists of several consecutive adjacent grids of the same size. Among the marine meteorological data used in this embodiment, the spatial resolution of water depth data is the smallest, approximately 1 / 60° × 1 / 60°. Based on the spatial resolution of the water depth data and combined with the research requirements, the longitude and latitude coordinates are determined to be used as a grid unit every 0.033°, and the study area is gridded accordingly.

[0078] Step S12: Construct a risk assessment index system based on natural environmental factors and traffic environmental factors.

[0079] Specifically, a navigation risk assessment index system for offshore wind farms is established by comprehensively considering natural and transportation environmental factors; a submarine cable risk assessment index system is established by focusing on the risk factors of submarine cable anchor damage caused by ship activities.

[0080] In this embodiment, the risk assessment index system includes natural environmental factors and traffic environmental factors.

[0081] Natural environmental factors include visibility, wind speed, wave height, and current speed.

[0082] Traffic environment factors include ship traffic flow, merchant ship traffic flow, fishing vessel traffic flow, ship length, ship draft, anchorage distance, and route distance.

[0083] Step S13: Calculate the comprehensive navigation risk value for each grid based on the risk assessment index system.

[0084] Specifically, historical marine meteorological data, ship AIS data, and other research data for the study area are acquired and processed, followed by the calculation of the comprehensive navigation risk value, including the following steps:

[0085] Standardized risk indicators, specifically for positive indicators, are standardized using formula (1):

[0086]

[0087] For the reverse indicator, standardization is performed using formula (2):

[0088]

[0089] In formulas (1) and (2), x ij It is the value of the j-th evaluation indicator for the i-th object to be evaluated, maxx j minx is the maximum value of the j-th evaluation index. jp represents the minimum value of the j-th evaluation indicator. ij It can reflect the risk level of the j-th evaluation indicator of the i-th evaluation object. The larger the value, the greater the risk brought by the evaluation indicator.

[0090] The weights of each risk assessment indicator are determined as follows:

[0091] 1) Constructing the decision matrix. Assuming there are n alternative solutions and m risk assessment indicators, this can be represented by a matrix as follows:

[0092]

[0093] 2) Normalized decision matrix:

[0094]

[0095] Set B represents positive indicators, and set H represents negative indicators.

[0096] 3) Calculate the overall performance S of each alternative scheme. i :

[0097]

[0098] 4) Remove each criterion in turn and recalculate the performance of the alternative solutions:

[0099]

[0100] 5) Calculate the removal effect E of the j-th standard. j :

[0101]

[0102] 6) Determine the final weights of the standards, the weight w of the j-th standard. j The calculation formula is as follows:

[0103]

[0104] Finally, the comprehensive risk value R of each grid cell is calculated based on the weighted synthesis method. i The calculation formula is as follows:

[0105]

[0106] In this embodiment, the following method is also provided for risk reference: Based on the risk value of each grid cell in k-means clustering, different risk level thresholds are determined, and the risk level of each grid cell is judged.

[0107] Specifically, the elbow method is used to determine the number of k-means clusters, that is, to determine how many risk levels each grid cell's risk value is divided into; after determining the optimal number of clusters, k-means clustering is performed on the risk values ​​to classify the risk levels of each grid cell.

[0108] Step S14: Set constraints based on the risk spatial distribution and construct a multi-objective optimization model for offshore wind farm site selection based on cost and wind energy density functions.

[0109] Specifically, the offshore wind farm site selection model is as follows: the cost objective function is represented by equation (10), the wind energy density objective function is represented by equation (11), and the constraints are represented by equations (12) to (16).

[0110] Cost objective function f1:

[0111]

[0112] In the formula, I represents the set of candidate offshore wind farm grids; Q represents the comprehensive cost per kilometer of AC cable; d i denoted as , where is the distance from the shore to grid cell i; P is the AC cable transmission power; U is the voltage of the submarine cable used to connect the offshore wind farm's offshore booster station to the onshore control center. Power factor; R is the equivalent resistance of the AC cable; T is the annual operating hours of the transportation cable; u is the electricity price; P G The rated power of the turbine; h i V represents the water depth (m) of grid cell i; V represents the service speed of the maintenance vessel; W represents the water depth (m) of grid cell i. oil P represents the fuel consumption per unit time of the maintenance vessel at its service speed. r-oil P is the unit price of fuel oil for maintenance ships; r-ele This refers to the unit price of offshore wind power; x i Let x be the decision variable. If the location is chosen in the i-th grid, then... i =1; otherwise x i =0;

[0113] Wind energy density objective function f2:

[0114]

[0115] In the formula, ρ is the air density; v i Let i be the wind speed of grid cell i;

[0116] Constraints:

[0117]

[0118] x i =0,d i <30 and h i<30 (13)

[0119]

[0120]

[0121] In the formula, R i s represents the comprehensive navigation risk value of the i-th candidate grid point; i It represents the distance from the i-th candidate grid point to the nearest flight path.

[0122] The model constraints mentioned above are explained below.

[0123] The site selection is not to be located at grid points with a high risk level of navigation, as expressed by formula (12).

[0124] The "Single 30" standard stipulates that new offshore wind power projects should, in principle, be located at a distance of more than 30km from the shore or in waters with a depth of more than 30m. When grid cell i is within 30km of the shore and in waters less than 30m, that grid point cannot be selected. The decision variable x... i The value is 0, as represented by formula (13).

[0125] When the water depth of the candidate grid cell is less than 35m, 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, as expressed by formula (14).

[0126] If the i-th candidate grid cell is located, then x i =1; if not located in the i-th candidate grid, then x i =0, as expressed by formula (15).

[0127] The distance s from the i-th candidate grid cell to the nearest route i If the distance is less than 1km, constructing an offshore wind farm in this location would cause a certain shadow area to form on nearby marine radar, so the location will not be selected in this candidate grid cell, as expressed by formula (16).

[0128] Step S15: Solve the multi-objective optimization model for offshore wind farm site selection to obtain the optimal offshore wind farm site selection scheme.

[0129] For example, the solution method in this embodiment is as follows.

[0130] Initialize the population: Randomly generate an initial population based on the set algorithm parameters, where each individual represents a potential solution;

[0131] Fitness calculation: For each individual in the population, calculate its fitness function value based on the characteristics of the problem. The fitness function reflects the individual's performance in solving multi-objective optimization problems.

[0132] Fast nondominated sorting: Fast nondominated sorting is used to divide individuals in a population into multiple nondominated levels, determining the nondominated level of each individual in the population;

[0133] Crowding distance calculation: To maintain diversity on the Pareto front, the crowding distance for each individual is calculated. Crowding distance measures the density of individuals in the target space, reflecting their relative superiority or inferiority within the same non-dominated level. A larger crowding distance indicates greater distance between individuals, which helps maintain a uniform distribution on the Pareto front.

[0134] Selection operation: A binary tournament selection strategy is used to randomly select two individuals from the current population, and the individual with a higher non-dominant level or a greater crowding distance is selected. This process is repeated until a certain number of individuals are selected as parents;

[0135] Crossover operation: A crossover operation is performed on selected parent individuals to generate new offspring individuals. Crossover is one of the key steps in genetic algorithms, used to produce a new solution set.

[0136] Mutation operation: Mutating offspring individuals increases population diversity. Mutation operations can be random mutation, uniform mutation, etc., which helps the algorithm explore new solutions in the search space;

[0137] Iterative evolution: Repeat the selection, crossover, and mutation operations described above to form a new population and continue the iterative evolution process until the termination condition is met.

[0138] Optimal solution selection: The TOPSIS method is used to arrange and select individuals at the Pareto front in an orderly manner to obtain the optimal site selection scheme.

[0139] Example 2

[0140] Based on the optimal site selection scheme obtained in the previous embodiment, this embodiment proposes a cable routing planning method for offshore wind farms that considers the impact of navigation, including the following steps.

[0141] Step S21: Based on the distance from the offshore wind farm to the cable route convergence point and the distance from the route convergence point to the submarine cable landing point, construct the shortest path objective function.

[0142] Specifically, in this embodiment, the expression for the shortest path objective function is:

[0143]

[0144] In the formula, S is the set of offshore wind farms, S = {S1, S2, ..., S} n}; M is the route convergence point of adjacent wind farms; T is the set of submarine cable landing points, T = {T1, T2, ..., T} m}; Let be the distance from the s-th offshore wind farm to the cable route junction point M; Let M be the distance from the route convergence point M to the t-th submarine cable landing point.

[0145] Step S22: Based on the shortest path objective function, after optimization using an optimization algorithm, the A* algorithm is used to find the optimal path.

[0146] 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.

[0147] Specifically, in the first stage of the solution process, the sparrow algorithm is used for optimization, including:

[0148] Step S221: Randomly generate the initial state of the population:

[0149] X i,j =rand·(UB) j -LB j (18)

[0150] In the formula, i = 1, 2, ..., pop, j = 1, 2, ..., dim; UB j and LB j represents 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.

[0151] Step S222: Update Explorer Locations:

[0152]

[0153] Among them, X i，j Let represent the individual sparrow position, specifically the position of the i-th sparrow in the j-th dimension; t represents the current iteration number, iter max α is the maximum number of iterations; α is a random number within [0,1]; R2∈[0,1] and ST∈[0.5,1] are the warning value and the safety value, respectively; Q is a random number that follows a normal distribution; L is a 1×d matrix where each element is 1;

[0154] Step S223: Update follower positions:

[0155]

[0156] A + =A T (AA T) -1 (twenty one)

[0157] In the formula, X p X represents the position of the optimal discoverer. worst The current worst position is N; the population size is N; A is a 1×d matrix, with each element randomly assigned a value of 1 or -1.

[0158] Step S224: Update the location of the vigilant:

[0159]

[0160] In the formula, X best This represents the currently known global optimal position. The value represents the worst-case 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 [-1, 1]; the fitness value f i Let fg and f represent the fitness value of the i-th sparrow. worst These represent the current best and worst fitness values ​​in the population, respectively; to avoid the denominator being zero, a very small constant ε is added.

[0161] Step S225: Repeat steps S222 to S224 to perform optimization.

[0162] In the second stage of solving the problem, the methods for finding the optimal path using the A* algorithm include:

[0163] Initialize an open list (openlist) and a closed list (closelist). The open list stores all searchable nodes starting from the parent node and sorts them in ascending order according to the estimated cost value. The closed list stores the nodes that have been traversed.

[0164] Add the starting node to the openlist. The closelist is empty at the start of the search. After each node is visited, it is moved to the closelist, and the A* algorithm continues its iterative process to locate the endpoint.

[0165] For each node that has not reached its destination, explore its surrounding nodes. For each newly discovered node, if it is not in the openlist, add it to the openlist, calculate its cost value F(n), and mark it as a child node of the current node;

[0166] 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 node's parent node information and move it to the closedlist, while also updating the openlist.

[0167] Repeat the above steps until the destination is found. Once the destination 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 destination. If the openlist is cleared, it means there is no feasible path.

[0168] In this embodiment, the risk-based initial cable route optimization simultaneously considers both distance and risk objectives, and improves the cost function of the A* algorithm. The improved cost function F(n) is expressed by equation (18):

[0169] F(n)=DG(n)+DH(n))(23)

[0170] In the formula, D represents the comprehensive navigation risk value, G(n) represents the cost from the center of the offshore wind farm as the starting point to the current node, and H(n) represents the cost from the current node to the submarine cable landing point.

[0171] Example 3

[0172] This embodiment uses a certain sea area as an example to illustrate Embodiment 1 and Embodiment 2.

[0173] Examples of evaluation indicator data are shown in Table 1.

[0174] Table 1

[0175]

[0176] Standardized risk indicators are shown in Table 2.

[0177] Table 2

[0178]

[0179]

[0180] The weights of each risk assessment indicator are determined as follows.

[0181] The weighting results of the navigation risk assessment indicators for offshore wind farms are shown in Table 3.

[0182] Table 3

[0183] wind speed High waves Ocean current velocity Ship traffic visibility Anchorage distance Flight route distance Weight 0.1403 0.076 0.0514 0.189 0.2154 0.2321 0.0959

[0184] The weighting results of the risk assessment indicators for submarine cables are shown in Table 4.

[0185] Anchorage distance Flight route distance Fishing boat traffic Merchant ship traffic Merchant ship length Merchant ship draft Weight 0.1658 0.1764 0.2986 0.085 0.1297 0.1446

[0186] The navigation risk assessment results for grid-unit offshore wind farms are shown in Table 5.

[0187] Table 5

[0188] Grid Risk Value 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 … …

[0189] Finally, the optimal location scheme obtained by solving is as follows: Figure 2 As shown, the solution results of the cable routing planning model are as follows: Figure 3 As shown.

[0190] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; only preferred embodiments of the present invention are illustrated. The descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. As long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0191] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for siting offshore wind farms taking into account navigational impact, characterized in that: The method comprises the following steps: Step S11: region gridding is performed on a region to be selected as a site; Step S12: a risk evaluation index system is constructed based on natural environment factors and traffic environment factors; Step S13: a navigation comprehensive risk value of each grid is calculated based on the risk evaluation index system; Step S14: a constraint condition is set based on a risk spatial distribution condition, and a sea wind farm site selection multi-objective optimization model is constructed according to a cost and a wind energy density function; Step S15: the sea wind farm site selection multi-objective optimization model is solved to obtain an optimal sea wind farm site selection scheme; An expression of the sea wind farm site selection multi-objective optimization model constructed in step S14 comprises: A cost objective function f1: where I is the set of candidate offshore wind farm grid; Q is the integrated cost of AC cable per km; d i is the offshore distance of grid cell i; P is the transmission power of AC cable; U is the voltage of submarine cable used from offshore substation of offshore wind farm to onshore control center; is the power factor; R is the equivalent resistance of AC cable; T is the annual operation hours of transmission cable; u is the electricity price; P G is the rated power of turbine; h i is the water depth of grid cell i in m; V is the service speed of the service ship; W oil P is the unit time fuel consumption of the service ship at the service speed; P r-oil P is the unit price of the fuel oil of the service ship; P r-ele P is the unit price of the offshore wind power price; x i x is the decision variable, if the i-th grid is selected, x i = 1; otherwise x i = 0; A wind energy density objective function f2: where p is the air density; v i is the wind speed for grid cell i; A constraint condition: x i = 0, d i <30km and h i <30m; In the formula, R i represents the navigation comprehensive 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.

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

3. The method for offshore wind farm site selection considering navigation impact according to claim 1, characterized in that: Step S13 comprises: Step S131: data is collected and normalized based on the risk evaluation index system; Step S132: weights of the risk evaluation indexes are determined; Step S133: a comprehensive risk value of each grid unit is calculated based on a weighted comprehensive method.

4. The method for offshore wind farm site selection considering navigation impact according to claim 3, characterized in that: Step S132 comprises: Step S1321: a decision matrix X is constructed: In the formula, x nm represents the value of the mth evaluation index of the nth object to be evaluated; Step S1322: the decision matrix is normalized: In the formula, set B is a positive index, and set H is a reverse index; Step S1323: Calculate the overall performance S of each alternative i : Step S1324: each standard is removed in turn, and the performance of the alternative scheme is calculated again: Step S1325: Calculate the removal effect E of the jth index j : Step S1326: determining the maximum weight of the standard, the weight w of the jth index j The calculation formula is as follows:

5. A cable routing planning method for a marine wind farm considering navigation impact, based on the optimal marine wind farm site selection scheme achieved by the marine wind farm site selection method considering navigation impact according to any one of claims 1 to 4, characterized in that: The method comprises the following steps: Step S21: a shortest path objective function is constructed based on a distance from a sea wind farm to a cable routing convergence point and a distance from the routing convergence point to a submarine cable landing point; Step S22: after optimization is performed by using an optimization algorithm based on the shortest path objective function, an A* algorithm is used to find an optimal path.

6. A method of cable routing planning for an offshore wind farm taking into account navigation impacts according to claim 5, characterized in that: An expression of the shortest path objective function minL is as follows: In the formula, S is a set of offshore wind farms, S = {S1, S2, …, S n}, n represents the number of offshore wind farms; M is a route convergence point of adjacent wind farms; T is a 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 s-th offshore wind farm to the cable routing junction point M; is the distance from the routing junction point M to the t-th submarine cable landing point.

7. A method of cable routing planning for offshore wind farms considering navigation impact according to claim 5, characterized in that: In step S22, a sparrow algorithm is used for optimization.

8. A method of cable routing planning for an offshore wind farm taking into account navigation impacts according to claim 7, characterized in that: The method of optimization by using the sparrow algorithm comprises: Step S221: an initial state of a population is randomly generated: X i,j = rand · (UB j - LB j ) ; where i = 1, 2,..., pop, j = 1, 2,..., dim; UB j and LB j denote the upper bound and lower bound in the jth dimension of the search space, respectively; rand is a random number obeying uniform distribution in the range (0, 1). Step S222: a position of an explorer is updated: wherein X i，j is the sparrow individual position, i.e., the position information of the ith sparrow in the jth dimension; t is the current iteration number, iter max is the maximum iteration number; a is a random number in [0, 1]; R2 e [0, 1] and ST e [0.5, 1] are respectively a warning value and a safety value; Q1 is a random number subject to a normal distribution; and L is a 1 x d matrix, wherein each element is 1. Step S223: a position of a follower is updated: where X p is the position of the best found solution, X worst is the current global worst position; N is the population size; A is a 1 x d matrix with each element randomly assigned to be either 1 or -1. Step S224: a position of a guard is updated: where X best represents the current known global optimal position, represents the global worst position; the step control parameter β is a random number obeying normal distribution with mean 0 and variance 1; K is a random number uniformly distributed in the range [-1, 1]; f i represents the fitness value of the i-th sparrow; f g and f worst respectively represent the current optimal and worst fitness values in the population; in order to avoid zero denominator, a very small constant ε is added. Step S225: steps S222 to S224 are repeated to perform optimization.

9. A method of cable routing planning for offshore wind farms considering navigation impact according to claim 5, characterized in that: In step S22, when the A* algorithm is used to find the optimal path, a cost function is adjusted by using the navigation comprehensive risk value, and an expression of the cost function F(n) is as follows: F(n)=DG(n)+DH(n); In the formula, D represents the navigation comprehensive risk value, G(n) represents a generation value of the center of the sea wind farm as a starting point to a current node, and H(n) represents a generation value of the current node to the submarine cable landing point.

Images