CN118378921B - Offshore wind farm site selection method, equipment and medium for offshore multi-island area - Google Patents

Abstract

The present invention discloses a method, device and medium for selecting an offshore wind farm site for a near-shore multi-island area, comprising: obtaining measured wind direction data and measured wind speed data of a windward open area and a leeward sheltered area in a to-be-selected site, calculating the error of a wind farm data set with respect to the measured wind direction data and the measured wind speed data, thereby screening out a first wind farm data set; inputting the first wind farm data set into a mesoscale climate model, and outputting second wind farm data; calculating a mean wind power density based on the second wind farm data; and calculating a wind farm site selection score for the to-be-selected site, wherein the wind farm site selection score is the product of the mean wind power density, an energy factor index and an environmental factor index; wherein the energy factor index is taken according to the second wind farm data, and the environmental factor index is taken according to the coastline and sounding.

Description

Offshore wind farm site selection methods, equipment, and media for near-shore multi-island areas

Technical Field

The present invention belongs to the field of wind energy or wind resource assessment, and in particular relates to an offshore wind farm site selection method, equipment and medium for near-shore multi-island areas.

Background Art

Wind energy assessment is the basis for wind farm site selection. Accurate wind farm data is the premise of wind energy assessment, which in turn provides reliable energy-oriented support for wind farm site selection. The high-resolution output results of the mesoscale climate model (WRF, Weather Research and Forecasting Model) based on global or regional wind farm data are a common method for obtaining wind farm data. Selecting the global or regional wind farm data (dataset) that best fits the actual local wind farm can improve the accuracy of the WRF simulation results. Therefore, before WRF simulation, it is necessary to ensure the applicability of the background wind farm data in the study area. However, the performance of various types of wind farm data is easily affected by the underlying surface topography, especially in the nearshore areas with tortuous and broken coastlines and many islands, and their performance varies greatly. Therefore, it is often difficult to reflect the actual accuracy of wind farm data in the nearshore area by verifying it with only a few measured site data. By selecting multiple measured points and conducting long-term verification of multiple wind farm data, it is more effective to evaluate the performance of wind farm data in different regions, comprehensively screen out the wind farm data that best fits the actual local wind farm, and lay a solid foundation for wind farm site selection.

At present, the method based on comprehensive evaluation of multiple factors is widely used in wind farm site selection. Commonly used wind farm site selection calculation methods are mainly divided into the following two types:

(1) Weighted calculation method: The weighted calculation method often requires the use of statistical methods such as the analytic hierarchy process (AHP) and the Delphi method to determine the weights of various influencing factors through expert scoring, and then weightedly add multiple indicators. This type of method is highly subjective and often requires comprehensive consideration of the guidance and policy orientation of multiple experts from all walks of life with sufficient knowledge. Therefore, the calculation results are highly biased towards humanistic and social factors. Depending on the scores of different experts at different times, the calculation results are prone to deviations and are uncertain.

(2) Index method: The index method uses multiplication and division to avoid the shortcomings of the weighted calculation method. The calculation is simple and objective. For example, a study proposed an optimal hotspot identification index for quickly locating and describing offshore wind farm sites. This index combines the availability of wind resources, the monthly change rate, and the frequency of exploitable wind energy. It is a simple calculation method to assist decision-making. However, since this method is not suitable for considering the influence of more than three factors at the same time, otherwise the calculation result may appear infinite value, making the result meaningless.

The above evaluation methods have been widely used in coastal or offshore areas with monotonous coastlines. However, in coastal areas with tortuous coastlines and numerous islands, the local spatial differences in environmental factors such as water depth and topography are strong. Conventional calculation methods often fail to highlight the spatial heterogeneity of various factors, especially environmental factors, resulting in low differentiation of evaluation results, and the best construction area is difficult to be effectively identified, and the efficiency of site selection needs to be improved.

In summary, the site selection of offshore wind farms needs to be based on the best wind farm data. In addition, it is urgent to propose an offshore wind farm site selection method that can emphasize the spatial heterogeneity of factors, consider multiple types of factors at the same time, and has an objective and convenient calculation method.

Summary of the invention

The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, device and medium for selecting an offshore wind farm site for near-shore multi-island areas.

In a first aspect, an embodiment of the present invention provides a method for selecting an offshore wind farm site for a near-shore multi-island area, the method comprising:

Obtaining wind direction measured data and wind speed measured data in a windward open area and a leeward sheltered area in the to-be-selected site area, calculating the error of the wind field data set with respect to the wind direction measured data and the wind speed measured data, thereby screening out a first wind field data set;

Inputting the first wind field data set into the mesoscale climate model and outputting the second wind field data; calculating the mean wind power density based on the second wind field data;

Calculate the wind farm site selection score of the area to be sited, where the wind farm site selection score is the product of the mean wind power density, the energy factor index and the environmental factor index; wherein the energy factor index is taken according to the second wind farm data, and the environmental factor index is taken according to the coastline and the depth measurement.

In a second aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the above-mentioned offshore wind farm site selection method for near-shore multi-island areas.

In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned offshore wind farm site selection method for near-shore multi-island areas.

In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program/instruction, which, when executed by a processor, implements the above-mentioned offshore wind farm site selection method for near-shore multi-island areas.

The beneficial effects of the present invention are as follows: by evaluating the wind field data set in the open windward area and the sheltered leeward area, the most suitable wind field data set for the nearshore multi-island area can be comprehensively obtained, thereby improving the accuracy of the high-precision wind field simulation of the mesoscale climate model WRF, thereby effectively improving the accuracy of wind energy assessment; the multi-factor exponential correction calculation method based on wind power density is objective and convenient to calculate, and can effectively highlight the spatial differences of the influencing factors while ensuring the independence of various factors, thereby improving the spatial differentiation of the assessment results.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

FIG1 is a schematic flow chart of a method for selecting an offshore wind farm site for a near-shore multi-island area provided by an embodiment of the present invention;

FIG2 is a schematic diagram of a process for screening a first wind field data set provided by an embodiment of the present invention;

FIG. 3 is a schematic diagram of an electronic device provided according to an embodiment of the present invention.

DETAILED DESCRIPTION

The present invention is further described below in conjunction with embodiments. The description of the following embodiments is only used to help understand the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principles of the present invention, several improvements and modifications may be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

As shown in FIG1 , the present invention provides a method, device, and medium for selecting an offshore wind farm site for a near-shore multi-island area, the method comprising:

Step S1, obtaining measured wind direction data and wind speed data in the windward open area and the leeward sheltered area in the site selection area, calculating the error of the wind field data set with respect to the measured wind direction data and the measured wind speed data, thereby screening out a first wind field data set.

It should be noted that the performance of various types of wind field data is closely affected by the underlying surface topography, especially in coastal areas with tortuous and broken coastlines and many islands. The performance of wind field data in the open windward area and the leeward and sheltered area is quite different. Therefore, it is necessary to select as many actual measurement sites as possible to conduct long-term verification of the wind speed and wind direction of various wind field data, and summarize the wind field data that best fits the actual wind field in the area to be selected. This is an important basis for accurate wind energy assessment.

Furthermore, in the windward open area and the leeward sheltered area of the site to be selected, actual wind direction data and wind speed data at different heights and different time spans are obtained.

In this example, in order to comprehensively and reasonably evaluate the performance of wind field data in different terrain areas, the measured sites need to be evenly distributed in open windward areas and shielded areas. In order to reduce the error of the wind profile function in the process of wind speed height conversion, the long-period wind direction and wind speed measured data at a height of 10m recorded by buoys, automatic stations, anemometers, etc. are used to verify the accuracy of various wind field data sets; secondly, since the hub height of wind turbines is mostly around 100m, the wind speed and wind direction data at heights of 70m, 90m, and 100m recorded by the wind tower should be obtained as much as possible to verify the accuracy of the wind field at the corresponding height output by the WRF model.

Furthermore, global wind data sets can be divided into three categories: analysis data (such as FNL), reanalysis data (such as ERA5, CFSv2, JRA-55, MERRA-2, CAMS, etc.), and multi-satellite fusion data (such as CCMP).

In terms of the quality assessment of wind field datasets, it is necessary to divide the research area into windward open areas and leeward sheltered areas, and verify the accuracy of various types of wind field data at three time scales: monthly, seasonal, and annual, to obtain a wind field dataset with the smallest comprehensive error and the strongest correlation.

Further, as shown in FIG2 , the absolute error, root mean square error, deviation, standard deviation and/or dispersion index of each wind field data set with respect to the wind direction measured data and the wind speed measured data are calculated;

The absolute values of absolute error, root mean square error, deviation, standard deviation and/or discrete index are weighted averaged to obtain the comprehensive error index corresponding to each wind field data set; the expression is as follows:

Where E represents the comprehensive error index, _k1 represents the weight corresponding to the absolute error, MAE represents the absolute error, _k2 represents the weight corresponding to the root mean square error, RMSE represents the root mean square error, _k3 represents the weight corresponding to the bias, BIAS represents the bias, _k4 represents the weight corresponding to the standard deviation, STDE represents the standard deviation, _k5 represents the weight corresponding to the dispersion index, and SI represents the dispersion index.

Calculate the correlation coefficient for each wind field data set;

The wind field data set with the smallest comprehensive error index and the strongest correlation is selected as the first wind field data set.

It should be noted that the smaller the error comprehensive index is, that is, the closer it is to 0, the better the performance of the wind farm data is; conversely, the worse the performance of the wind farm data is. The larger the correlation coefficient is, that is, the closer it is to 1, the better the performance of the wind farm data is; conversely, the worse the performance of the wind farm data is.

Step S2: input the first wind field data set into the mesoscale climate model, and output the second wind field data; and calculate the mean wind power density based on the second wind field data.

Furthermore, the first wind field data set is used as a background wind field simulated in the mesoscale climate model WRF to output second wind field data, which are multi-year high-precision wind field data.

In this example, in order to ensure the accuracy of the simulation results, it is necessary to compare and verify the second wind field data with the measured wind direction data and the measured wind speed data. The error is required to be less than the error of the input first wind field data set. By adjusting the parameterization scheme, the error can be minimized and the correlation can be improved. Finally, suitable high-precision wind field data can be output.

Furthermore, the expression of the mean wind power density is as follows:

Where N represents the number of wind speeds or moments, _vi represents the wind speed at moment i, and ρ represents the air density at a certain height.

Step S3, calculating the wind farm site selection score of the area to be selected, wherein the wind farm site selection score is the product of the mean wind power density, the energy factor index and the environmental factor index; wherein the energy factor index is determined according to the second wind farm data. The expression is as follows:

Where WPD represents the mean wind power density, K _energy represents the energy factor index, K _environment represents the environmental factor index, f _positives represents the positive index corresponding to each factor index, and f _negatives represents the negative index corresponding to each factor index.

Furthermore, the environmental factor index is obtained based on the coastline and bathymetry.

It should be noted that, assuming that a certain factor involves both positive and negative indicators, the Y value is equal to the product of the sum of the positive indicators and the sum of the differences between the negative indicators and 1, so as to ensure the monotonicity of the Y value calculation; if a certain factor does not consider the impact of positive or negative indicators, it is only expressed as the sum of the differences between the negative indicators and 1, or the sum of the positive indicators.

In this example, the energy factor index K _energy is the product of the energy positive index f _{positives-energy} and the energy negative index f _{negatives-energy} . The energy positive index f _{positives-energy} includes: the frequency of occurrence of the best wind power density, the frequency of occurrence of the available wind speed, the frequency of occurrence of the best wind speed, etc.; the energy negative index f _{negatives-energy} includes: the frequency of occurrence of the extreme wind speed, the annual change rate, seasonal change rate, monthly change rate of the wind speed, etc.

The environmental factor index K _environment is mainly used to measure the impact of environmental factors on wind turbine construction, installation, and maintenance through the negative environmental index f _{negatives-environment} and the negative environmental index f _{positives-environment. The negative environmental index f positives-environment} includes: depth measurement, offshore distance, maximum wave height, etc.

Furthermore, positive indicators refer to indicators that are beneficial to wind turbine power generation or construction. The closer the normalized value is to 1, the better the performance is and the more suitable it is for wind energy site selection. Conversely, negative indicators refer to indicators that are not conducive to wind turbine power generation or construction. The closer the normalized value is to 1, the worse the performance is and the less suitable it is for wind energy site selection. Before comprehensive evaluation of multiple factors, it is necessary to normalize the remaining indicators except the wind power density indicator to unify the magnitude and ensure the fairness of the calculation.

It should be noted that the multi-factor exponential correction calculation method based on wind power density in the present invention is objective and convenient to calculate, and is oriented to the small-area differences in environmental factors. While ensuring the independence of various factors, it can effectively highlight the spatial differences influencing factors and improve the spatial differentiation of evaluation results.

Furthermore, the method further comprises:

Step S4, querying the spatially restricted area; based on the area to be selected after eliminating the spatially restricted area and according to the wind farm site selection score, obtaining the candidate area for the offshore wind farm.

It should be noted that offshore wind farms must not only be built in areas with the richest wind resources, but also comply with spatial planning requirements. For example, they must avoid international waterways, bird paths, offshore distances that affect human life and work, and marine functional areas (such as recreational areas, shipping anchorage areas, marine protected areas) and other spatially restricted areas.

In this example, based on the ArcGIS platform, by superimposing the vector data of various spatial planning elements in the study area, the spatially restricted area and the spatially unrestricted area can be obtained; after eliminating the spatially restricted area, the spatially unrestricted area and the Y value result of the wind farm site selection score are superimposed to find the potential optimal wind farm development area.

Example 1

Taking a certain location in a near-shore multi-island area as an example, the specific implementation process of a method for selecting an offshore wind farm site for a near-shore multi-island area provided in this example is described in detail. The method specifically includes the following steps:

Step S1, obtaining measured wind direction data and wind speed data in the windward open area and the leeward sheltered area in the site selection area, calculating the error of the wind field data set with respect to the measured wind direction data and the measured wind speed data, thereby screening out a first wind field data set.

In this example, the wind speed and direction data at a height of 10m of the JRA-55 wind field dataset and CCMP wind field dataset are downloaded with a time interval of 6 hours from January 1, 2010 to December 31, 2020. At the same time, the measured wind direction data and wind speed data at the corresponding time and height are obtained.

The correlation coefficient, absolute error, root mean square error, deviation and comprehensive error index of the JRA-55 wind field data set and the CCMP wind field data set compared with the measured wind direction data and wind speed data are calculated, and the calculation results are shown in Tables 1 and 2. It should be noted that in this example, since the error values are at the same order of magnitude, the weight coefficients in the comprehensive error index are all 1.

Table 1: Calculation results of JRA-55 and CCMP wind field datasets compared with measured wind speed data

Table 2: Calculation results of JRA-55 and CCMP wind field datasets compared with wind direction measured data

It can be seen from Tables 1 and 2 that the wind speed and wind direction errors of the CCMP wind field dataset are smaller than those of the JRA-55 wind field dataset, and the correlation coefficient score of the CCMP wind field dataset is also higher than that of the JRA-55 wind field dataset. Therefore, in this example, the CCMP wind field dataset is selected as the best wind field data corresponding to the site selection area as the first wind field dataset.

Step S2, inputting the first wind field data set (i.e., the CCMP wind field data set with a time interval from January 1, 2010 to December 31, 2020) into the mesoscale climate model, and outputting the second wind field data;

Based on the data of the second wind field, the multi-year average wind power density WPD of this location is calculated to be 510W/m².

Step S3, calculating the wind farm site selection score of the area to be sited, wherein the wind farm site selection score is the product of the mean wind power density, the energy factor index and the environmental factor index; wherein the energy factor index is taken according to the second wind farm data, and the environmental factor index is taken according to the coastline and the depth measurement.

Specifically, based on the second wind farm data, the following normalized energy index values at the location are calculated, including:

The frequency of occurrence of optimal wind power density is 0.86; the frequency of occurrence of optimal wind speed is 0.85; the frequency of occurrence of extreme wind speed is 0.31; the seasonal variation rate SV is 0.14; the monthly variation rate MV is 0.26.

Calculate the total energy factor score K _energy , the expression is as follows:

K _energy =f _{positives-energy} ×f _{negatives-energy} =(0.91+0.86+0.85)×((1-0.31)+(1-0.14)+(1-0.26))=5.998

Environmental factor indicators are calculated based on the shoreline and bathymetric values, including:

Assume that only positive environmental indicators are set at this point, and no negative environmental indicators are set (i.e., the value of f _{negatives-environment} is 1). The normalized values of the positive indicators offshore distance and water depth are 0.6 and 0.5, respectively. The total score of environmental factors K _environment is calculated as follows:

K _environment =f _{positives-environment*} f _{negatives-environment} = (0.6+0.5)×1=1.1

Finally, the Y value of the wind farm site selection score is =510×5.998×1.1=3364.88.

Then the Y value is normalized within the study area, and the value is 0.87. According to the normalized Y value classification table listed below (Table 3), the normalized Y value of this point is at level 5, which means that the resources at this point are rich, and it is the best location for building offshore wind turbines, which is very suitable for building offshore wind farms.

By referring to the above method, the Y value of each point in a certain area to be selected can be obtained.

Table 3: Normalized wind farm site selection score Y value classification table

Step S4, querying the spatially restricted area, based on the area to be selected after eliminating the spatially restricted area and according to the wind farm site selection score, obtaining the candidate area for the offshore wind farm.

Specifically, based on the ArcGIS platform, the vector data of spatially restricted areas (such as international waterways, bird paths, offshore distances that affect human life and operations, and marine functional areas (such as leisure and entertainment areas, shipping anchorage areas, marine protected areas, etc.) and the normalized Y-value classification results of a certain area are spatially superimposed to find the best wind farm development location that meets spatial specifications.

As shown in Fig. 3, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs and a processor 102. When the one or more programs are executed by the processor 102, any method in the first aspect described above is implemented.

The memory 101, the processor 102 and the communication interface 103 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules, and the processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 101. The communication interface 103 can be used to communicate signaling or data with other node devices.

Among them, the memory 101 can be, but is not limited to, a random access memory 101 (Random Access Memory, RAM), a read only memory 101 (Read Only Memory, ROM), a programmable read-only memory 101 (Programmable Read-Only Memory, PROM), an erasable programmable read-only memory 101 (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable read-only memory 101 (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

The processor 102 may be an integrated circuit chip with signal processing capability. The processor 102 may be a general-purpose processor 102, including a central processing unit 102 (CPU), a network processor 102 (NP), etc.; it may also be a digital signal processor 102 (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

In the embodiments provided in the present application, it should be understood that the disclosed method and system can also be implemented in other ways. The method and system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the method and system, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and/or the flowchart, and the combination of boxes in the block diagram and/or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

On the other hand, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by the processor 102, a method as described in any one of the first aspects above is implemented. If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory 101 (ROM, Read-Only Memory), random access memory 101 (RAM, RandomAccess Memory), disk or optical disk and other media that can store program codes.

The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims (10)

1. A method for selecting an offshore wind farm site for a near-shore multi-island area, characterized in that the method comprises: Obtaining wind direction measured data and wind speed measured data in a windward open area and a leeward sheltered area in the to-be-selected site area, calculating the error of the wind field data set with respect to the wind direction measured data and the wind speed measured data, thereby screening out a first wind field data set; Input the first wind field data set as a background wind field into the mesoscale climate model, and output the second wind field data; calculate the mean wind power density based on the second wind field data; Calculate the wind farm site selection score of the area to be sited, where the wind farm site selection score is the product of the mean wind power density, the energy factor index and the environmental factor index; wherein the energy factor index is taken according to the second wind farm data, and the environmental factor index is taken according to the coastline and the depth measurement. 2. A method for selecting an offshore wind farm site for a near-shore multi-island area according to claim 1, characterized in that obtaining measured wind direction data and measured wind speed data in a windward open area and a leeward sheltered area in the site selection area comprises: In the windward open area and leeward sheltered area of the site to be selected, obtain the actual wind direction data and wind speed data at different heights and time spans. 3. According to a method for selecting an offshore wind farm site for a near-shore multi-island area in claim 1, it is characterized in that the wind farm data set includes FNL, ERA5, CFSv2, JRA-55, MERRA-2, CAMS, and CCMP. 4. A method for selecting an offshore wind farm site for a nearshore multi-island area according to claim 1 or 3, characterized in that calculating the error of the wind farm data set with respect to the wind direction measured data and the wind speed measured data, thereby screening and obtaining the first wind farm data set comprises: Calculate the absolute error, root mean square error, bias, standard deviation and/or dispersion index of each wind field data set with respect to the wind direction measured data and the wind speed measured data; The absolute values of the absolute error, root mean square error, bias, standard deviation and/or dispersion index are weighted averaged to obtain the comprehensive error index corresponding to each wind field data set; Calculate the correlation coefficient for each wind field data set; The wind field data set with the smallest comprehensive error index and the strongest correlation is selected as the first wind field data set. 5. The method for selecting an offshore wind farm site for a near-shore multi-island area according to claim 1, wherein calculating the wind farm site selection score for the area to be selected comprises: The energy factor index is the product of the positive energy index and the negative energy index, and the environmental factor index is the negative environmental index. 6. A method for selecting an offshore wind farm site for nearshore multi-island areas according to claim 1 or 5, characterized in that the energy positive indicators include: the frequency of occurrence of optimal wind power density, the frequency of occurrence of available wind speed, and the frequency of occurrence of optimal wind speed; The negative energy indicators include: frequency of extreme wind speed, annual change rate of wind speed, seasonal change rate of wind speed, and monthly change of wind speed; The negative environmental indicators include: depth, distance from shore, and maximum wave height. 7. The method for selecting an offshore wind farm site for a near-shore multi-island area according to claim 1, characterized in that the method further comprises: Query spatially restricted areas; Based on the area to be sited after excluding the space-restricted area and according to the wind farm site selection score, the candidate area for offshore wind farm is obtained. 8. An electronic device, comprising a memory and a processor, characterized in that the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the offshore wind farm site selection method for near-shore multi-island areas as described in any one of claims 1-7. 9. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for selecting an offshore wind farm site for a near-shore multi-island area as described in any one of claims 1 to 7 is implemented. 10. A computer program product, comprising a computer program/instruction, characterized in that when the computer program/instruction is executed by a processor, the method for selecting an offshore wind farm site for a near-shore multi-island area as described in any one of claims 1 to 7 is implemented.