Offshore wind power site selection method and device

Abstract

The invention discloses an offshore wind power site selection method and device, and is used for solving the technical problem of poor accuracy of offshore wind power site selection caused by an existing offshore wind power site selection scheme, and the method comprises the steps: obtaining sea water depth data, historical wind speed data and shoreside distance data of an alternative offshore wind power site; on the basis of the historical wind speed data of the alternative offshore wind power sites, the annual energy output and power generation potential data of the alternative offshore wind power sites are calculated; according to the sea area water depth data, the shoreside distance data and the power generation potential data of the alternative offshore wind power sites and the fan material energy data corresponding to the alternative offshore wind power sites, cost carbon emission data of the alternative offshore wind power sites are calculated; and based on a preset data scoring method, the cost carbon emission data and the annual energy output of the alternative offshore wind power sites are adopted to score the alternative offshore wind power sites, and a target offshore wind power site is determined.

Description

Technical Field

[0001] The present invention relates to the technical field of prefabricated cabin substation, and in particular to an offshore wind power site selection method and device. Background Art

[0002] As one of the main forms of clean energy, offshore wind power technology has attracted widespread attention and developed rapidly in recent years. Offshore wind power has stable wind speeds, abundant space resources, and little impact on the land environment. It is an important direction for the development of renewable energy in the future.

[0003] At present, the development of offshore wind power technology is mainly concentrated in coastal areas. The construction of wind farms often needs to consider multiple factors, including wind speed, water depth, distance from shore, environmental impact and economic cost. The site selection of offshore wind farms is a key step that affects the economic efficiency, environmental benefits and power generation efficiency of the project. Reasonable site selection can not only effectively improve the power generation efficiency of wind farms, but also reduce the impact on the marine ecosystem, reduce costs and improve the economic feasibility of the project.

[0004] Most of the existing offshore wind power site selection schemes design wind farms based on single historical data analysis and calculated estimated power generation, lacking systematic multi-factor analysis, resulting in poor accuracy in offshore wind power site selection. Summary of the invention

[0005] The present invention provides an offshore wind power site selection method and device, which are used to solve the technical problem that the existing offshore wind power site selection scheme lacks systematic multi-factor analysis, resulting in poor accuracy of offshore wind power site selection.

[0006] A first aspect of the present invention provides a method for selecting an offshore wind power site, comprising:

[0007] Obtain sea depth data, historical wind speed data and shore distance data for multiple candidate offshore wind power sites;

[0008] Calculating the annual power generation and power generation potential data of each of the candidate offshore wind power sites based on the historical wind speed data of each of the candidate offshore wind power sites and the wind turbine type corresponding to each of the candidate offshore wind power sites;

[0009] Calculate the cost carbon emission data of each of the candidate offshore wind power sites by using a preset material consumption model based on the sea water depth data, shore distance data, power generation potential data of each of the candidate offshore wind power sites and the wind turbine material energy data of the wind turbine type corresponding to each of the candidate offshore wind power sites;

[0010] Based on a preset data scoring method, the cost carbon emission data and annual power generation of each candidate offshore wind power site are used to score each candidate offshore wind power site, and a site selection score value of each candidate offshore wind power site is output;

[0011] The alternative offshore wind power site corresponding to the largest site selection score is selected as the target offshore wind power site.

[0012] Optionally, the calculating the annual power generation and power generation potential data of each of the candidate offshore wind power sites based on the historical wind speed data of each of the candidate offshore wind power sites and the wind turbine type corresponding to each of the candidate offshore wind power sites includes:

[0013] Calculating the power generation potential data of each of the candidate offshore wind power sites based on the historical wind speed data of each of the candidate offshore wind power sites;

[0014] The annual power generation of each of the candidate offshore wind power sites is calculated by using the power generation potential data of each of the candidate offshore wind power sites and the wind turbine type corresponding to each of the candidate offshore wind power sites.

[0015] Optionally, the cost carbon emission data includes a target total cost and offshore wind turbine carbon emission data; the use of a preset material consumption model to calculate the cost carbon emission data of each candidate offshore wind power site according to the sea area water depth data, shore distance data, power generation potential data and wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site includes:

[0016] Calculate the target total cost of each of the candidate offshore wind power sites by using a preset material consumption model based on the sea area water depth data, shore distance data, power generation potential data of each of the candidate offshore wind power sites and wind turbine material energy data of the wind turbine type corresponding to each of the candidate offshore wind power sites;

[0017] The offshore wind turbine carbon emission data of each of the candidate offshore wind power sites is calculated based on the wind turbine material energy consumption in the wind turbine material energy data of the wind turbine type corresponding to each of the candidate offshore wind power sites.

[0018] Optionally, the wind turbine material energy data also includes economic cost of material consumption; the method of using a preset material consumption model to calculate the target total cost of each candidate offshore wind power site according to the sea area water depth data, shore distance data, power generation potential data of each candidate offshore wind power site and wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site includes:

[0019] Using the sea area water depth data and shore distance data of each of the candidate offshore wind power sites, calculate the wind turbine foundation transportation cost of each of the candidate offshore wind power sites;

[0020] Taking the wind turbine material energy consumption and material consumption economic cost corresponding to each of the candidate offshore wind power sites as inputs to the preset material consumption model, and outputting the material cost of each of the candidate offshore wind power sites;

[0021] Calculate the unit electricity cost of each of the candidate offshore wind power sites based on the power generation potential data of each of the candidate offshore wind power sites, the equipment cost and the maintenance cost in the material cost;

[0022] The unit electricity cost, material cost and wind turbine foundation transportation cost of each of the candidate offshore wind power sites are respectively summed up to output the target total cost of each of the candidate offshore wind power sites.

[0023] Optionally, the preset data scoring method includes an entropy weight method and a weighted comprehensive scoring method; the preset data scoring method uses the cost carbon emission data and annual power generation of each candidate offshore wind power site to score each candidate offshore wind power site, and outputs a site selection score value of each candidate offshore wind power site, including:

[0024] The entropy weight method is used to weight the target total cost, carbon emission data of offshore wind turbines and annual power generation of each of the candidate offshore wind power sites, so as to determine the cost weight, carbon emission weight and power weight of each of the candidate offshore wind power sites;

[0025] Normalizing the target total cost, offshore wind turbine carbon emission data and annual power generation of each of the candidate offshore wind power sites respectively to generate a normalized target total cost, normalized offshore wind turbine carbon emission data and normalized annual power generation of each of the candidate offshore wind power sites;

[0026] Based on the weighted comprehensive scoring method, each of the alternative offshore wind power sites is scored using the normalized target total cost, normalized offshore wind turbine carbon emission data, normalized annual power generation, cost weight, carbon emission weight and electricity weight of each of the alternative offshore wind power sites, and the site selection score value of each of the alternative offshore wind power sites is output.

[0027] Optionally, the calculation process of the offshore wind turbine carbon emission data is specifically as follows:

[0028] ;

[0029] Among them, CE is the carbon emission data of offshore wind turbines; is the emission of stage i ( ), from i = 1 to 7, they represent the production, construction, transportation, operation, maintenance, disassembly, end-of-life disposal and reuse stages respectively; is the consumption of the jth material and energy in the i-th stage (the material and energy consumption of the fan); is the carbon emission factor corresponding to the consumption of the j-th substance and energy.

[0030] A second aspect of the present invention provides an offshore wind power site selection device, comprising:

[0031] An acquisition module, used to acquire sea depth data, historical wind speed data and shore distance data of multiple candidate offshore wind power sites;

[0032] A first calculation module is used to calculate the annual power generation and power generation potential data of each of the candidate offshore wind power sites based on the historical wind speed data of each of the candidate offshore wind power sites and the wind turbine type corresponding to each of the candidate offshore wind power sites;

[0033] The second calculation module is used to calculate the cost carbon emission data of each of the candidate offshore wind power sites by using a preset material consumption model according to the sea water depth data, shore distance data, power generation potential data of each of the candidate offshore wind power sites and the wind turbine material energy data of the wind turbine type corresponding to each of the candidate offshore wind power sites;

[0034] An output module is used to score each of the candidate offshore wind power sites based on a preset data scoring method, using the cost carbon emission data and annual power generation of each of the candidate offshore wind power sites, and output a site selection score value for each of the candidate offshore wind power sites;

[0035] The selection module is used to select the candidate offshore wind power site corresponding to the largest site selection score as the target offshore wind power site.

[0036] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the offshore wind power site selection method as described in any one of the above items.

[0037] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the steps of the offshore wind power site selection method as described in any one of the above items.

[0038] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the offshore wind power site selection method as described in any one of the above items.

[0039] It can be seen from the above technical solutions that the present invention has the following advantages:

[0040] The above technical solution of the present invention provides a method for selecting an offshore wind power site, which first obtains sea water depth data, historical wind speed data and shore distance data of multiple candidate offshore wind power sites; then, based on the historical wind speed data of each candidate offshore wind power site and the wind turbine type corresponding to each candidate offshore wind power site, calculates the annual power generation and power generation potential data of each candidate offshore wind power site; uses a preset material consumption model to calculate the cost carbon emission data of each candidate offshore wind power site based on the sea water depth data, shore distance data, power generation potential data of each candidate offshore wind power site and the wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site; based on the preset data scoring method, adopts Each candidate offshore wind power site is scored using its cost carbon emission data and annual power generation, and the site selection score of each candidate offshore wind power site is output; finally, the candidate offshore wind power site corresponding to the largest site selection score is selected as the target offshore wind power site; based on the above scheme, each candidate offshore wind power site is scored according to the acquired sea water depth data, historical wind speed data and shore distance data, and the site selection process of the target offshore wind power site is completed, which can realize multi-factor analysis of multiple key factors such as annual power generation and cost, avoids the limitations of over-reliance on experience judgment or single factor decision-making in the previous site selection process, and improves the accuracy of offshore wind power site selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art 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 paying creative labor.

[0042] Figure 1 A flowchart of the steps of an offshore wind power site selection method provided in Embodiment 1 of the present invention;

[0043] Figure 2 A flowchart of the steps of an offshore wind power site selection method provided in Embodiment 2 of the present invention;

[0044] Figure 3 An algorithm framework diagram of an offshore wind power site selection method provided in Embodiment 2 of the present invention;

[0045] Figure 4 This is a structural block diagram of an offshore wind power site selection device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0046] The embodiments of the present invention provide an offshore wind power site selection method and device, which are used to solve the technical problem that the existing offshore wind power site selection scheme lacks systematic multi-factor analysis, resulting in poor accuracy of offshore wind power site selection.

[0047] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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 are within the scope of protection of the present invention.

[0048] See also Figure 1 , Figure 1 A flowchart of the steps of an offshore wind power site selection method provided in Embodiment 1 of the present invention.

[0049] The present invention provides an offshore wind power site selection method, comprising:

[0050] Step 101: Obtain sea area water depth data, historical wind speed data, and shore distance data for multiple candidate offshore wind power sites.

[0051] It should be noted that the sea area water depth data of the alternative offshore wind power site is the water depth data of the sea area, which can be used for spatial analysis in the Geographic Information System (GIS); the shore distance data of the alternative offshore wind power site is the distance between the alternative offshore wind power site and the shore, which can be used to calculate the cable length and related installation costs; the historical wind speed data can be used to calculate the long-term average wind speed and wind speed fluctuations.

[0052] Step 102: Calculate the annual power generation and power generation potential data of each candidate offshore wind power site based on the historical wind speed data of each candidate offshore wind power site and the wind turbine type corresponding to each candidate offshore wind power site.

[0053] Specifically, based on the historical wind speed data of each alternative offshore wind power site, the power generation potential data of each alternative offshore wind power site is calculated; using the power generation potential data of each alternative offshore wind power site and the wind turbine type corresponding to each alternative offshore wind power site, the annual power generation of each alternative offshore wind power site is calculated.

[0054] It is worth mentioning that the type of wind turbine corresponding to each alternative offshore wind power site can be determined based on actual needs, environmental factors, etc., and the material energy data of each wind turbine type is different.

[0055] Step 103: Calculate the cost carbon emission data of each candidate offshore wind power site using a preset material consumption model based on the sea water depth data, shore distance data, power generation potential data of each candidate offshore wind power site, and wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site.

[0056] Cost carbon emissions data includes target total cost and offshore wind turbine carbon emissions data.

[0057] Specifically, a preset material consumption model is used to calculate the target total cost of each alternative offshore wind power site based on the sea water depth data, shore distance data, power generation potential data of each alternative offshore wind power site and the wind turbine material energy data of the wind turbine type corresponding to each alternative offshore wind power site; the offshore wind turbine carbon emission data of each alternative offshore wind power site is calculated based on the wind turbine material energy consumption in the wind turbine material energy data of the wind turbine type corresponding to each alternative offshore wind power site.

[0058] It is worth mentioning that the present invention quantifies the impact of each alternative offshore wind power site on the environment through offshore wind turbine carbon emission data based on different environmental factor assessments (such as carbon emissions and impacts on underwater ecosystems), thereby completing the environmental benefit assessment.

[0059] Step 104: Based on a preset data scoring method, each candidate offshore wind power site is scored using the cost carbon emission data and annual power generation of each candidate offshore wind power site, and a site selection score value of each candidate offshore wind power site is output.

[0060] The preset data scoring methods include entropy weight method and weighted comprehensive scoring method.

[0061] Specifically, the entropy weight method is used to assign weights to the target total cost, carbon emission data of offshore wind turbines and annual power generation of each alternative offshore wind power site, and the cost weight, carbon emission weight and electricity weight of each alternative offshore wind power site are determined; the target total cost, carbon emission data of offshore wind turbines and annual power generation of each alternative offshore wind power site are normalized respectively, and the normalized target total cost, normalized carbon emission data of offshore wind turbines and normalized annual power generation of each alternative offshore wind power site are generated;

[0062] Furthermore, based on the weighted comprehensive scoring method, the normalized target total cost of each alternative offshore wind power site, the normalized offshore wind turbine carbon emission data, the normalized annual power generation, the cost weight, the carbon emission weight and the electricity weight are used to score each alternative offshore wind power site, and the site selection score of each alternative offshore wind power site is output.

[0063] Step 105: Select the candidate offshore wind power site corresponding to the largest site selection score as the target offshore wind power site.

[0064] It should be noted that after scoring each candidate offshore wind power site based on the above steps, the score is a comprehensive assessment based on cost, power generation potential and emission benefits. All site selection scores are sorted in descending order to determine the order of merit of different candidate sites.

[0065] Furthermore, the weighted sum model (WSM) is used as a multi-criteria decision analysis (MCDA). Through the results of MCDA, decision makers can select the optimal offshore wind farm site selection plan based on the order of advantages and disadvantages of different criteria, comprehensively considering economic, environmental and technical factors, that is, selecting the alternative offshore wind power site corresponding to the largest site selection score as the target offshore wind power site.

[0066] In this embodiment, based on the above steps, the present invention can comprehensively analyze the power generation potential, economic cost and environmental benefits of different offshore wind power sites, so as to find the best balance point among multiple objectives.

[0067] In an embodiment of the present invention, the present invention provides an offshore wind power site selection method, firstly obtaining sea water depth data, historical wind speed data and shore distance data of multiple candidate offshore wind power sites; then, based on the historical wind speed data of each candidate offshore wind power site and the wind turbine type corresponding to each candidate offshore wind power site, calculating the annual power generation and power generation potential data of each candidate offshore wind power site; using a preset material consumption model according to the sea water depth data, shore distance data, power generation potential data of each candidate offshore wind power site and the wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site, calculating the cost carbon emission data of each candidate offshore wind power site; based on the preset data scoring method, The cost carbon emission data and annual power generation of each alternative offshore wind power site are used to score each alternative offshore wind power site, and the site selection score of each alternative offshore wind power site is output; finally, the alternative offshore wind power site corresponding to the largest site selection score is selected as the target offshore wind power site; based on the above scheme, each alternative offshore wind power site is scored according to the acquired sea water depth data, historical wind speed data and shore distance data, and the site selection process of the target offshore wind power site is completed, which can realize multi-factor analysis of multiple key factors such as annual power generation and cost, avoids the limitations of over-reliance on experience judgment or single factor decision-making in the previous site selection process, and improves the accuracy of offshore wind power site selection.

[0068] See also Figure 2 , Figure 2 A flowchart of the steps of an offshore wind power site selection method provided in Embodiment 2 of the present invention.

[0069] The present invention provides an offshore wind power site selection method, comprising:

[0070] Step 201: Acquire sea area water depth data, historical wind speed data, and shore distance data of multiple candidate offshore wind power sites.

[0071] It should be noted that water depth and distance from shore (sea depth data and shore distance data) will directly affect the material consumption of construction according to the material consumption model, and then affect the construction cost and emissions. Therefore, the amount of required materials (such as wind turbine foundation, length of submarine cables, etc.) should be evaluated according to the difference in water depth and distance from shore, and then the cost and emissions should be calculated in combination with the material consumption model. Use wind speed data (such as the ERA5 dataset), that is, use historical wind speed data with the help of existing wind power generation potential models to calculate the power generation potential of different locations to reflect the relationship between wind speed and power generation potential.

[0072] In this embodiment, a material consumption model is used to quantify material consumption at different water depths and offshore distances, which can generate values ​​related to power generation potential, costs and emissions.

[0073] Step 202: Calculate the annual power generation and power generation potential data of each candidate offshore wind power site based on the historical wind speed data of each candidate offshore wind power site and the wind turbine type corresponding to each candidate offshore wind power site.

[0074] Further, step 202 may include the following sub-steps:

[0075] Step S21, calculating the power generation potential data of each candidate offshore wind power site according to the historical wind speed data of each candidate offshore wind power site;

[0076] Step S22: Calculate the annual power generation of each candidate offshore wind power site by using the power generation potential data of each candidate offshore wind power site and the wind turbine type corresponding to each candidate offshore wind power site.

[0077] It should be noted that the historical wind speed data of each candidate offshore wind power site is input into the wind speed-power generation potential model to calculate the power generation potential of each candidate offshore wind power site. Since it is necessary to consider the impact of wind speed fluctuations on power generation efficiency, the annual power generation of each candidate offshore wind power site is further calculated based on the wind turbine type and power generation potential, that is, the power generation potential data of each candidate offshore wind power site and the wind turbine type corresponding to each candidate offshore wind power site are used. According to the annual power generation of each candidate offshore wind power site, the annual power generation of the region can be obtained.

[0078] Step 203: Calculate the target total cost of each candidate offshore wind power site using a preset material consumption model based on the sea area water depth data, shore distance data, power generation potential data of each candidate offshore wind power site and the wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site.

[0079] The material and energy data of the wind turbine include the economic cost of material consumption and the material and energy consumption of the wind turbine.

[0080] Further, step 203 may include the following sub-steps:

[0081] Step S31, using the sea area water depth data and shore distance data of each candidate offshore wind power site to calculate the wind turbine foundation transportation cost of each candidate offshore wind power site;

[0082] Step S32: using the wind turbine material energy consumption and material consumption economic cost corresponding to each candidate offshore wind power site as inputs of a preset material consumption model, and outputting the material cost of each candidate offshore wind power site;

[0083] Step S33, calculating the unit electricity cost of each candidate offshore wind power site based on the power generation potential data of each candidate offshore wind power site, the equipment cost and the maintenance cost in the material cost;

[0084] Step S34: perform sum operations on the unit electricity cost, material cost and wind turbine foundation transportation cost of each candidate offshore wind power site, and output the target total cost of each candidate offshore wind power site.

[0085] It should be noted that the wind turbine foundation transportation cost of each alternative offshore wind power site is calculated based on the water depth and distance from shore, that is, the sea area water depth data and shore distance data of each alternative offshore wind power site are used. The wind turbine foundation transportation cost includes the cost of wind turbine foundation, submarine cable, and equipment transportation.

[0086] Furthermore, the material cost is estimated through the material consumption model, that is, the material energy consumption and economic cost of the wind turbine corresponding to each candidate offshore wind power site are used as inputs of the preset material consumption model to output the material cost of each candidate offshore wind power site.

[0087] Furthermore, based on the power generation potential of the candidate offshore wind power site, the equipment cost in the material cost, and the maintenance cost, the cost per unit of electricity of the candidate offshore wind power site (the cost per unit of electricity of the candidate offshore wind power site) is calculated. Finally, the calculated cost of each candidate offshore wind power site is added together to obtain the target total cost of each candidate offshore wind power site.

[0088] Step 204 : Calculate the offshore wind turbine carbon emission data of each candidate offshore wind power site according to the wind turbine material energy consumption in the wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site.

[0089] It should be noted that the calculation process of offshore wind turbine carbon emission data is as follows:

[0090] ;

[0091] Among them, CE is the carbon emission data of offshore wind turbines; is the emission of stage i ( ), from i = 1 to 7, they represent the production, construction, transportation, operation, maintenance, disassembly, end-of-life disposal and reuse stages respectively; is the consumption of the jth material and energy in the i-th stage (the material and energy consumption of the fan); is the carbon emission factor corresponding to the consumption of the j-th substance and energy, and the carbon emission factor is obtained by collating the environmental impact assessment literature of production processes and products.

[0092] It is worth mentioning that the material energy consumption of the wind turbine type corresponding to the candidate offshore wind power site and the corresponding emission factor are input into the preset material consumption model, and the emissions generated during the infrastructure construction process are output. (For example, carbon emissions during material production, transportation and installation, i.e., construction period emissions), combined with construction period emissions , calculate the emissions per kilowatt-hour of the alternative offshore wind power site (carbon emissions data of offshore wind turbines). The mathematical relationship between the emissions per kilowatt-hour (unit of power generation) and the power generation potential can be expressed as: Power generation potential Weight = economic cost per unit of electricity generated Weight = Carbon emissions per unit of electricity generated Weight.

[0093] Step 205: Based on a preset data scoring method, each candidate offshore wind power site is scored using the cost carbon emission data and annual power generation of each candidate offshore wind power site, and a site selection score value of each candidate offshore wind power site is output.

[0094] The preset data scoring methods include entropy weight method and weighted comprehensive scoring method.

[0095] Further, step 205 may include the following sub-steps:

[0096] S51. Use the entropy weight method to assign weights to the target total cost, carbon emission data of offshore wind turbines and annual power generation of each candidate offshore wind power site, and determine the cost weight, carbon emission weight and power weight of each candidate offshore wind power site;

[0097] S52, respectively normalizing the target total cost, offshore wind turbine carbon emission data, and annual power generation of each candidate offshore wind power site to generate a normalized target total cost, normalized offshore wind turbine carbon emission data, and normalized annual power generation of each candidate offshore wind power site;

[0098] S53. Based on the weighted comprehensive scoring method, each alternative offshore wind power site is scored using the normalized target total cost, normalized offshore wind turbine carbon emission data, normalized annual power generation, cost weight, carbon emission weight and electricity weight of each alternative offshore wind power site, and the site selection score of each alternative offshore wind power site is output.

[0099] Please note that Figure 3 The process of offshore wind power site selection of the present invention consists of four parts, namely data collection, power generation evaluation, economic evaluation and environmental benefit evaluation; wherein, based on the historical wind speed data and the wind turbine type corresponding to the alternative offshore wind power site, the power generation evaluation of the alternative offshore wind power site is completed; based on the sea area water depth data, shore distance data, power generation potential data and wind turbine material energy data of the wind turbine type corresponding to the alternative offshore wind power site, the economic evaluation of the alternative offshore wind power site is completed; based on the wind turbine material energy consumption in the wind turbine material energy data of the wind turbine type corresponding to the alternative offshore wind power site, the environmental evaluation of the alternative offshore wind power site is completed.

[0100] Furthermore, the main criteria for selecting offshore wind power sites are first determined. The decision criteria can include the following categories: 1) Power generation factors: such as power generation potential (affected by average wind speed and wind speed fluctuations), wind turbine type, foundation type, and the layout of submarine cables. 2) Economic factors: such as cost per kilowatt-hour, installation cost (foundation, wind turbine, cable), long-term maintenance cost, etc. 3) Environmental factors: environmental impact of carbon emissions, material consumption (involving materials consumed during infrastructure construction), etc.

[0101] Furthermore, the present invention selects the Weighted Sum Model (WSM) as the MCDA method, and uses the entropy weight method to assign weights to each criterion (power generation potential, economic cost, environmental impact) according to the degree of fluctuation and uncertainty of the data (i.e., the target total cost of the candidate offshore wind power site, the carbon emission data of the offshore wind turbine, and the annual power generation). Data with large fluctuations often represent more important criteria, that is, the entropy weight method is used to assign weights to the target total cost of each candidate offshore wind power site, the carbon emission data of the offshore wind turbine, and the annual power generation, and the cost weight, carbon emission weight, and electricity weight of each candidate offshore wind power site are determined. Each candidate offshore wind power site is scored, and the score can be based on a comprehensive evaluation of cost, power generation potential, and emission benefits. In order to ensure that the criteria of different dimensions can be compared at the same scale, it is usually necessary to normalize each criterion, that is, normalize the target total cost, carbon emission data of offshore wind turbines and annual power generation of each candidate offshore wind power site, and generate the normalized target total cost, normalized carbon emission data of offshore wind turbines and normalized annual power generation of each candidate offshore wind power site. The scores of each decision criterion (such as power generation potential, economic cost and environmental impact) are weighted and summed by the assigned weights to obtain the comprehensive score of each site selection scheme (i.e., site selection score value); the calculation process of the site selection score value can be expressed as:

[0102] ;

[0103] in, is the site selection score of the i-th candidate offshore wind power site; is the jth weight, which includes cost weight, carbon emission weight and electricity weight, indicating the weight of the jth criterion; is the j-th normalized data of the ith alternative offshore wind power site, the normalized data includes the normalized target total cost, the normalized offshore wind turbine carbon emission data, and the normalized annual power generation, which represents the score of the ith alternative offshore wind power site on the j-th criterion.

[0104] Step 206: Select the candidate offshore wind power site corresponding to the largest site selection score as the target offshore wind power site.

[0105] It should be noted that through the results of MCDA, decision makers can select the optimal offshore wind farm site selection plan based on the order of merit of different criteria and comprehensive consideration of economic, environmental and technical factors, that is, select the alternative offshore wind power site corresponding to the largest site selection score as the target offshore wind power site.

[0106] As a comparison of technical effects, it can be combined with existing technologies for reference. In the existing offshore wind power site selection process, commonly used evaluation factors include: wind energy resources (such as wind speed, wind direction), sea conditions (such as water depth, distance from shore), infrastructure costs (such as wind turbine foundations, submarine cable laying, equipment transportation), environmental impacts (such as carbon emissions, impacts on marine ecology), etc. Generally, the power generation efficiency of a wind farm is directly affected by wind speed and wind speed fluctuations, while the construction cost and emissions of infrastructure are closely related to factors such as water depth and distance from shore. In addition, the economic feasibility of a wind power project includes not only the initial construction investment, but also the long-term operation and maintenance costs and power generation costs.

[0107] In the development of offshore wind power, with the advancement of development technology, the size of wind turbines continues to increase, and the infrastructure required to build wind farms is becoming increasingly complex, especially in deep water areas, which require the use of special infrastructure and long-distance transmission systems. At the same time, the site selection of offshore wind power also faces many environmental protection challenges. The construction and operation of wind farms may have potential negative impacts on the marine ecosystem, such as affecting the migration and habitat of marine organisms. In addition, with the strengthening of carbon emission restrictions worldwide, how to effectively reduce the carbon emissions of offshore wind power projects through technical means is also a factor that needs to be considered.

[0108] To meet the above challenges, more and more studies have begun to explore wind power site selection models based on multi-source data and combined with multi-objective optimization algorithms in order to achieve the best balance between power generation efficiency, economic costs and environmental benefits. Current wind power site selection research usually relies on multi-criteria decision analysis (MCDA) technology to provide scientific decision support for wind farm site selection by weighting and comprehensively scoring multiple influencing factors. However, due to the large number of data sources involved and the mutual influence of various factors, how to build a scientific, reasonable and efficient site selection model has become one of the focuses of current technical research.

[0109] In the process of selecting sites for offshore wind power, different water depths and distances from the shore have a direct impact on the consumption of construction materials, which in turn affect the construction cost and carbon emissions of the project. In addition, with the continuous development of wind turbine technology, new wind turbines have also put forward higher requirements for the evaluation of power generation potential. Considering the complexity of these factors, it is crucial to develop a multi-objective optimization site selection model that can comprehensively consider power generation potential, economic costs, and environmental benefits for the sustainable development of offshore wind power.

[0110] Based on the above foundation, since offshore wind power projects are usually located in deep water areas far from the coast, the infrastructure costs during the construction process have increased significantly. In particular, the construction costs of wind turbine foundations and submarine cables rise sharply with the increase of water depth and offshore distance. Material consumption is related to water depth and offshore distance. Especially in deep water areas, floating foundations or more complex structures are required, which use a large amount of materials and are difficult to construct, further pushing up project costs; the infrastructure design and installation required for offshore wind farms are complex, especially in deep water and far from the coast, where specific floating foundation structures are required. The design and installation of such structures have extremely high technical requirements, and the subsequent maintenance is complex, which increases the overall technical difficulty and risk. The laying and maintenance of submarine cables are also technical difficulties, especially in areas with complex submarine terrain or harsh environmental conditions. Cables are easily affected by submarine tides, sediments and ship activities, resulting in an increased failure rate; the subsequent maintenance of offshore wind farms is relatively complex and costly, mainly because the location of wind farms is far from land, and it is difficult to dispatch maintenance ships and personnel. Weather and sea conditions may also have an adverse effect on daily maintenance. Especially in the maintenance of submarine cables and wind turbine foundations, the cost is often high. In addition, the material consumption of wind turbines, foundations, cables and other equipment is large, and they are prone to material loss and functional degradation after long-term operation, requiring regular replacement or maintenance; the existing technology is relatively inadequate for the environmental impact assessment of offshore wind farm construction. The construction of wind turbine foundations may cause certain interference to the marine ecosystem, especially in areas with greater water depths, and the construction process may affect the habitat of marine organisms and the balance of the seabed ecology. The noise and vibration generated by wind turbine operation may also have a negative impact on marine life (such as whales and dolphins), and existing technologies have not yet fully solved these problems; the current site selection decisions for offshore wind power often lack systematic multi-factor analysis, and factors such as water depth, offshore distance, wind speed fluctuations, and environmental impacts have not been fully combined for comprehensive evaluation. The existing site selection plan may ignore the balance between economic and environmental benefits, resulting in unreasonable site selection and affecting the long-term sustainability of the project.

[0111] In view of the above problems, the present invention proposes a method for selecting an offshore wind power site, which is used to solve the problems of high construction cost, low power generation efficiency, complex structure, high maintenance cost, insufficient environmental impact assessment, poor coordination between power generation and grid access, and insufficient site optimization in the current background technology of offshore wind power. Specifically, by integrating multiple key data such as wind speed, water depth, and offshore distance, a high-precision data basis is provided for power generation assessment, economic assessment, and environmental assessment. Based on these data, the wind speed-power generation potential model and material consumption model are used to accurately calculate the power generation potential, construction cost, carbon emissions, and other factors of different site selection schemes. This method effectively avoids the limitations of over-reliance on experience judgment or single factor decision-making in the previous site selection process, and significantly improves the scientificity and accuracy of site selection decisions; the present invention combines a multi-objective optimization algorithm and a multi-criteria decision analysis (MCDA) technology, which can balance and optimize the three factors of power generation potential, economic cost, and environmental benefit. Through the weighted comprehensive scoring method (WSM), different weights are assigned on the basis of fully considering the importance of each decision criterion, so as to achieve a balance between power generation efficiency, economic benefit, and environmental protection. This multi-objective optimization method overcomes the drawbacks of traditional site selection methods that focus too much on a single objective (such as power generation potential or cost), and provides a more comprehensive and sustainable site selection plan; by modeling the relationship between water depth, offshore distance and material consumption, the present invention can accurately estimate the consumption of construction materials under different sea conditions, thereby optimizing the construction plan and reducing the cost increase and carbon emissions caused by excessive use of materials. This technical means effectively reduces the construction cost of offshore wind farms in deepwater areas or far from the coast, while greatly reducing carbon emissions during material production and construction, and improving the environmental friendliness of the project; comprehensively considers the impact of wind speed fluctuations on power generation efficiency to ensure that the site selection plan can have a high power generation potential in long-term operation. In addition, the present invention not only takes into account the initial construction cost, but also evaluates the long-term maintenance cost and cost per kilowatt-hour. Through the comprehensive optimization of these factors, the present invention maximizes the economic benefits of the wind farm from construction to operation, and significantly improves the project's return on investment;

[0112] In summary, the present invention, by combining multi-source data, precise models and multi-objective optimization technology, not only effectively overcomes the problems of single site selection decision, high cost and large carbon emissions in the existing technology, but also greatly improves the scientific nature of site selection and the sustainability of the project. The application of these technical means has significantly enhanced the comprehensive benefits of offshore wind farms, provided strong technical support for future offshore wind power development, and achieved comprehensive analysis and optimization of multiple key factors such as power generation potential, economic cost and environmental benefits.

[0113] In an embodiment of the present invention, the present invention provides an offshore wind power site selection method, firstly obtaining sea water depth data, historical wind speed data and shore distance data of multiple candidate offshore wind power sites; then, based on the historical wind speed data of each candidate offshore wind power site and the wind turbine type corresponding to each candidate offshore wind power site, calculating the annual power generation and power generation potential data of each candidate offshore wind power site; using a preset material consumption model according to the sea water depth data, shore distance data, power generation potential data of each candidate offshore wind power site and the wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site, calculating the cost carbon emission data of each candidate offshore wind power site; based on the preset data scoring method, The cost carbon emission data and annual power generation of each alternative offshore wind power site are used to score each alternative offshore wind power site, and the site selection score of each alternative offshore wind power site is output; finally, the alternative offshore wind power site corresponding to the largest site selection score is selected as the target offshore wind power site; based on the above scheme, each alternative offshore wind power site is scored according to the acquired sea water depth data, historical wind speed data and shore distance data, and the site selection process of the target offshore wind power site is completed, which can realize multi-factor analysis of multiple key factors such as annual power generation and cost, avoids the limitations of over-reliance on experience judgment or single factor decision-making in the previous site selection process, and improves the accuracy of offshore wind power site selection.

[0114] See also Figure 4 , Figure 4 This is a structural block diagram of an offshore wind power site selection device provided in Embodiment 3 of the present invention.

[0115] The present invention provides an offshore wind power site selection device, comprising:

[0116] An acquisition module 401 is used to acquire sea depth data, historical wind speed data and shore distance data of multiple candidate offshore wind power sites;

[0117] A first calculation module 402 is used to calculate the annual power generation and power generation potential data of each candidate offshore wind power site based on the historical wind speed data of each candidate offshore wind power site and the wind turbine type corresponding to each candidate offshore wind power site;

[0118] The second calculation module 403 is used to calculate the cost carbon emission data of each candidate offshore wind power site by using a preset material consumption model according to the sea water depth data, shore distance data, power generation potential data of each candidate offshore wind power site and the wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site;

[0119] The output module 404 is used to score each candidate offshore wind power site based on a preset data scoring method using the cost carbon emission data and annual power generation of each candidate offshore wind power site, and output a site selection score value for each candidate offshore wind power site;

[0120] The selection module 405 is used to select the candidate offshore wind power site corresponding to the largest site selection score as the target offshore wind power site.

[0121] Furthermore, the first calculation module 402 is specifically configured to:

[0122] Calculate the power generation potential data of each candidate offshore wind power site based on the historical wind speed data of each candidate offshore wind power site;

[0123] The annual power generation of each alternative offshore wind power site is calculated using the power generation potential data of each alternative offshore wind power site and the wind turbine type corresponding to each alternative offshore wind power site.

[0124] Further, the cost carbon emission data includes the target total cost and the offshore wind turbine carbon emission data; the second calculation module 403 includes:

[0125] The first submodule is used to calculate the target total cost of each candidate offshore wind power site by using a preset material consumption model according to the sea water depth data, shore distance data, power generation potential data of each candidate offshore wind power site and the wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site;

[0126] The second submodule is used to calculate the offshore wind turbine carbon emission data of each candidate offshore wind power site according to the wind turbine material energy consumption in the wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power site.

[0127] Furthermore, the material energy data of the wind turbine also includes the economic cost of material consumption; the first submodule is specifically used for:

[0128] Using the sea depth data and shore distance data of each candidate offshore wind power site, calculate the basic transportation cost of wind turbines at each candidate offshore wind power site;

[0129] Taking the material energy consumption and economic cost of the wind turbines corresponding to each candidate offshore wind power site as inputs of the preset material consumption model, outputting the material cost of each candidate offshore wind power site;

[0130] Calculate the unit electricity cost of each candidate offshore wind power site based on the power generation potential data of each candidate offshore wind power site, the equipment cost and the maintenance cost in the material cost;

[0131] The unit electricity cost, material cost and wind turbine foundation transportation cost of each alternative offshore wind power site are respectively summed up and the target total cost of each alternative offshore wind power site is output.

[0132] Furthermore, the preset data scoring method includes an entropy weight method and a weighted comprehensive scoring method; the output module 404 is specifically used for:

[0133] The entropy weight method is used to assign weights to the target total cost, carbon emission data of offshore wind turbines and annual power generation of each alternative offshore wind power site, and the cost weight, carbon emission weight and power weight of each alternative offshore wind power site are determined;

[0134] Normalizing the target total cost, offshore wind turbine carbon emission data and annual power generation of each candidate offshore wind power location respectively to generate the normalized target total cost, normalized offshore wind turbine carbon emission data and normalized annual power generation of each candidate offshore wind power location;

[0135] Based on the weighted comprehensive scoring method, the normalized target total cost of each alternative offshore wind power site, the normalized offshore wind turbine carbon emission data, the normalized annual power generation, the cost weight, the carbon emission weight and the electricity weight are used to score each alternative offshore wind power site, and the site selection score of each alternative offshore wind power site is output.

[0136] Furthermore, the calculation process of offshore wind turbine carbon emission data is as follows:

[0137] ;

[0138] Among them, CE is the carbon emission data of offshore wind turbines; is the emission of stage i ( ), from i = 1 to 7, they represent the production, construction, transportation, operation, maintenance, disassembly, end-of-life disposal and reuse stages respectively; is the consumption of the jth material and energy in the i-th stage (the material and energy consumption of the fan); is the carbon emission factor corresponding to the consumption of the j-th substance and energy.

[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, modules and sub-modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0140] An embodiment of the present invention further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the offshore wind power site selection method as described in any of the above embodiments.

[0141] An embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the offshore wind power site selection method as in any of the above embodiments are implemented.

[0142] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the offshore wind power site selection method as described in any of the above embodiments.

[0143] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for selecting an offshore wind power site, characterized in that: include: Obtain sea depth data, historical wind speed data and shore distance data for multiple candidate offshore wind power sites; Calculating the annual power generation and power generation potential data of each of the candidate offshore wind power sites based on the historical wind speed data of each of the candidate offshore wind power sites and the wind turbine type corresponding to each of the candidate offshore wind power sites; Calculate the cost carbon emission data of each of the candidate offshore wind power sites by using a preset material consumption model based on the sea water depth data, shore distance data, power generation potential data of each of the candidate offshore wind power sites and the wind turbine material energy data of the wind turbine type corresponding to each of the candidate offshore wind power sites; Based on a preset data scoring method, the cost carbon emission data and annual power generation of each candidate offshore wind power site are used to score each candidate offshore wind power site, and a site selection score value of each candidate offshore wind power site is output; The alternative offshore wind power site corresponding to the largest site selection score is selected as the target offshore wind power site.

2. The offshore wind power site selection method according to claim 1, characterized in that: The calculating of the annual power generation and power generation potential data of each of the candidate offshore wind power sites based on the historical wind speed data of each of the candidate offshore wind power sites and the wind turbine type corresponding to each of the candidate offshore wind power sites comprises: Calculating the power generation potential data of each of the candidate offshore wind power sites based on the historical wind speed data of each of the candidate offshore wind power sites; The annual power generation of each of the candidate offshore wind power sites is calculated by using the power generation potential data of each of the candidate offshore wind power sites and the wind turbine type corresponding to each of the candidate offshore wind power sites.

3. The offshore wind power site selection method according to claim 1, characterized in that: The cost carbon emission data includes the target total cost and the offshore wind turbine carbon emission data; the cost carbon emission data of each candidate offshore wind power location is calculated by using the preset material consumption model according to the sea water depth data, shore distance data, power generation potential data and wind turbine material energy data of the wind turbine type corresponding to each candidate offshore wind power location, including: Calculate the target total cost of each of the candidate offshore wind power sites by using a preset material consumption model based on the sea area water depth data, shore distance data, power generation potential data of each of the candidate offshore wind power sites and wind turbine material energy data of the wind turbine type corresponding to each of the candidate offshore wind power sites; The offshore wind turbine carbon emission data of each of the candidate offshore wind power sites is calculated based on the wind turbine material energy consumption in the wind turbine material energy data of the wind turbine type corresponding to each of the candidate offshore wind power sites.

4. The offshore wind power site selection method according to claim 3, characterized in that: The material and energy data of the wind turbine also includes the economic cost of material consumption; the preset material consumption model is used to calculate the target total cost of each candidate offshore wind power site according to the sea water depth data, shore distance data, power generation potential data of each candidate offshore wind power site and the material and energy data of the wind turbine type corresponding to each candidate offshore wind power site, including: Using the sea area water depth data and shore distance data of each of the candidate offshore wind power sites, calculate the wind turbine foundation transportation cost of each of the candidate offshore wind power sites; Taking the wind turbine material energy consumption and material consumption economic cost corresponding to each of the candidate offshore wind power sites as inputs to the preset material consumption model, and outputting the material cost of each of the candidate offshore wind power sites; Calculate the unit electricity cost of each of the candidate offshore wind power sites based on the power generation potential data of each of the candidate offshore wind power sites, the equipment cost and the maintenance cost in the material cost; The unit electricity cost, material cost and wind turbine foundation transportation cost of each of the candidate offshore wind power sites are respectively summed up, and the target total cost of each of the candidate offshore wind power sites is output.

5. The offshore wind power site selection method according to claim 3, characterized in that: The preset data scoring method includes an entropy weight method and a weighted comprehensive scoring method; the preset data scoring method uses the cost carbon emission data and annual power generation of each candidate offshore wind power site to score each candidate offshore wind power site, and outputs a site selection score value for each candidate offshore wind power site, including: The entropy weight method is used to weight the target total cost, carbon emission data of offshore wind turbines and annual power generation of each of the candidate offshore wind power sites, so as to determine the cost weight, carbon emission weight and power weight of each of the candidate offshore wind power sites; Normalizing the target total cost, offshore wind turbine carbon emission data and annual power generation of each of the candidate offshore wind power sites respectively to generate a normalized target total cost, normalized offshore wind turbine carbon emission data and normalized annual power generation of each of the candidate offshore wind power sites; Based on the weighted comprehensive scoring method, each of the alternative offshore wind power sites is scored using the normalized target total cost, normalized offshore wind turbine carbon emission data, normalized annual power generation, cost weight, carbon emission weight and electricity weight of each of the alternative offshore wind power sites, and the site selection score value of each of the alternative offshore wind power sites is output.

6. The offshore wind power site selection method according to claim 3, characterized in that: The calculation process of the offshore wind turbine carbon emission data is specifically as follows: ； Among them, CE is the carbon emission data of offshore wind turbines; is the emission of stage i ( ), from i = 1 to 7, they represent the production, construction, transportation, operation, maintenance, disassembly, end-of-life disposal and reuse stages respectively; is the consumption of the jth material and energy in the i-th stage (the material and energy consumption of the fan); is the carbon emission factor corresponding to the consumption of the j-th substance and energy.

7. An offshore wind power site selection device, characterized in that: include: An acquisition module, used to acquire sea depth data, historical wind speed data and shore distance data of multiple candidate offshore wind power sites; A first calculation module is used to calculate the annual power generation and power generation potential data of each of the candidate offshore wind power sites based on the historical wind speed data of each of the candidate offshore wind power sites and the wind turbine type corresponding to each of the candidate offshore wind power sites; The second calculation module is used to calculate the cost carbon emission data of each of the candidate offshore wind power sites by using a preset material consumption model according to the sea water depth data, shore distance data, power generation potential data of each of the candidate offshore wind power sites and the wind turbine material energy data of the wind turbine type corresponding to each of the candidate offshore wind power sites; An output module is used to score each of the candidate offshore wind power sites based on a preset data scoring method, using the cost carbon emission data and annual power generation of each of the candidate offshore wind power sites, and output a site selection score value for each of the candidate offshore wind power sites; The selection module is used to select the candidate offshore wind power site corresponding to the largest site selection score as the target offshore wind power site.

8. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the offshore wind power site selection method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the offshore wind power site selection method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the offshore wind power site selection method according to any one of claims 1 to 6.

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