Wind power electric field micro-siting method and device based on multi-objective optimization

Abstract

The invention provides a wind power field micro-siting method and device based on multi-objective optimization, and relates to the technical field of wind power field micro-siting, and the method comprises the steps: determining an initial fan siting scheme according to the wind energy resource data of a wind power field region; sequencing the optimization indexes including the current collection line cost, the operation and maintenance road cost and the fan cost to obtain an optimization sequence of the optimization indexes; and sequentially optimizing the initial fan site selection schemes according to the optimization sequence to obtain a target fan site selection scheme. According to the scheme, micro-siting of the wind power electric field is realized, and the efficiency and accuracy of micro-siting of the wind power electric field are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind farm micro-site selection, and in particular to a wind farm micro-site selection method and device based on multi-objective optimization. Background Art

[0002] With the vigorous promotion of the new energy industry, the wind power industry has been continuously and rapidly developed due to its short construction period, low environmental requirements, rich reserves, and high utilization rate. The micro-site selection of wind farms is an important task in the early stage of wind power project construction. Moreover, its site selection is not only about the layout of wind turbines, but also about the construction of related supporting facilities such as collector lines and operation and maintenance roads. It is a systematic planning and site selection process. Although the existing wind farm unit layout optimization method can use mathematical models and artificial intelligence algorithms to optimize the layout of wind turbines, its local search ability is low and the optimization time required is long. In addition, the current optimization method does not fully consider related supporting facilities such as collector lines and operation and maintenance roads. Therefore, a new technical solution is needed to solve the above technical problems. Summary of the invention

[0003] The present invention provides a method and device for micro-site selection of a wind farm based on multi-objective optimization, which systematically considers relevant supporting facilities such as collector lines, operation and maintenance roads, and also improves the efficiency and accuracy of micro-site selection of a wind farm.

[0004] In a first aspect, the present invention provides a wind farm micro-site selection method based on multi-objective optimization, comprising: Determine the initial wind turbine site selection plan based on the wind energy resource data of the wind farm area; Sorting the optimization indicators including the cost of the collection line, the cost of the operation and maintenance road, and the cost of the wind turbine to obtain the optimization order of the optimization indicators; The initial wind turbine site selection scheme is optimized in sequence according to the optimization sequence to obtain a target wind turbine site selection scheme.

[0005] Optionally, the optimization indicators including the collection line cost, the operation and maintenance road cost and the wind turbine cost are sorted to obtain the optimization order of the optimization indicators, including: Determining an evaluation level for comparing the fuzzy relationship between the optimization indicators; Determine the comparison result of pairwise comparison of any two optimization indicators according to the evaluation level; According to the comparison results, a fuzzy judgment matrix that meets the consistency requirements is constructed; Calculating the fuzzy judgment matrix to obtain the weight value of each optimization index; The weight values ​​are sorted from large to small to obtain the optimization order including the optimization index.

[0006] Optionally, optimizing the initial wind turbine site selection scheme in sequence according to the optimization order to obtain a target wind turbine site selection scheme includes: Determine in sequence from the optimization sequence a first optimization index ranked first, a second optimization index ranked second, and a third optimization index ranked third; Optimizing the initial wind turbine site selection plan according to the first optimization index to obtain a first wind turbine site selection plan; Optimizing the first wind turbine site selection plan according to the second optimization index to obtain a second wind turbine site selection plan; The second wind turbine site selection plan is optimized according to the third optimization index to obtain the target wind turbine site selection plan.

[0007] Optionally, the optimization is performed according to the collector line cost, including: Get the current wind turbine site selection plan during the current optimization; Using Dijkstra algorithm to determine the shortest collector line corresponding to each current wind turbine site selection scheme; Determining the equivalent collector line length of the shortest collector line according to the topography, geological conditions and obstacle areas of the wind farm area; The collector line cost of the equivalent collector line length is calculated, and the current wind turbine site selection scheme corresponding to the minimum collector line cost is determined as the optimized wind turbine site selection scheme.

[0008] Optionally, the current wind turbine site selection plan includes longitude and latitude coordinates of each device, and the devices include a number of wind turbines and booster stations; Determining the equivalent collector line length of the shortest collector line according to the topography, geological conditions, meteorological conditions and obstacle areas of the wind farm area includes: Obtaining the topography, geological conditions and obstacle areas of the wind farm area; Determine the slope influence factor between adjacent devices on the shortest collector line according to the topography; Determine the construction difficulty influencing factor on the shortest collector line and the cable laying method used between adjacent devices according to the topography, geological conditions and meteorological conditions, and determine the laying influencing factor of the cable laying method; Determine, according to the obstacle area, an equivalent length of each obstacle area between adjacent devices on the shortest collector line; Determine the equivalent length between adjacent devices on the shortest collector line according to the straight-line distance between adjacent devices on the shortest collector line, the slope influence factor, the construction difficulty influence factor, the laying influence factor, and the equivalent length of the obstacle area; The equivalent length between the booster station and the existing power grid and all the equivalent lengths on the shortest collector line are summed to obtain the equivalent collector line length.

[0009] Optionally, the equivalent length is determined by the following formula: in, For the i The device and the adjacent j The equivalent distance between devices; For the i The device and the adjacent j The straight-line distance between the devices; is the slope influencing factor; is the construction difficulty influencing factor; The cable laying method for the laying influencing factor; For the i The device and the adjacent j The first m The equivalent length of the obstacle area, M For the i The device and the adjacent j The number of obstruction areas between devices.

[0010] Optionally, optimizing according to the operation and maintenance road cost includes: Get the current wind turbine site selection plan during the current optimization; Determine the operation and maintenance road cost corresponding to each of the current wind turbine site selection plans; The current wind turbine site selection plan corresponding to the minimum operation and maintenance road cost is determined as the optimized wind turbine site selection plan.

[0011] Optionally, optimizing according to the wind turbine cost includes: Get the current wind turbine site selection plan during the current optimization; Determining the wind turbine cost corresponding to each of the current wind turbine site selection plans; The current wind turbine site selection plan corresponding to the minimum wind turbine cost is determined as the optimized wind turbine site selection plan.

[0012] Optionally, the current wind turbine site selection plan includes longitude and latitude coordinates of each device, and the devices include a number of wind turbines and booster stations; The determining of the operation and maintenance road cost corresponding to each of the current wind turbine site selection schemes includes: Determine equipment maintenance points according to the distribution density of each of the equipment in the current wind turbine site selection plan; Determine the roads to be built and the roads to be repaired according to the obtained existing road distribution, road surface damage conditions of the existing roads, obstacle areas and the equipment maintenance points in the current wind turbine site selection plan; Determine the construction difficulty coefficient according to the topography, geological conditions and meteorological conditions; The road operation and maintenance cost is determined based on the area of ​​the road to be built, the cost of new road construction per unit area, the area of ​​the road to be repaired, the cost of road repair per unit area, the construction difficulty coefficient and the repair coefficient.

[0013] Optionally, the current wind turbine site selection plan includes longitude and latitude coordinates of each device, and the devices include a number of wind turbines and booster stations; The determining of the wind turbine cost corresponding to each of the current wind turbine site selection schemes includes: Determine the cost of the wind turbine body and the cost of supporting facilities for the wind turbine in the current wind turbine site selection plan; The cost of the fan is obtained by summing up the transportation cost, installation cost, commissioning cost, and subsequent maintenance cost of the fan and its supporting facilities, as well as the fan body cost and the supporting facilities cost.

[0014] In a second aspect, the present invention provides a wind farm micro-site selection device based on multi-objective optimization, comprising: The preliminary selection module is used to determine the initial wind turbine site selection plan based on the wind energy resource data of the wind farm area; The optimization module is used to sort the optimization indicators including the collection line cost, the operation and maintenance road cost and the wind turbine cost to obtain the optimization order of the optimization indicators; and optimize the initial wind turbine site selection plan in turn according to the optimization order to obtain the target wind turbine site selection plan.

[0015] In a third aspect, an embodiment of the present invention further provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any first aspect of this specification is implemented.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the method described in any first aspect of this specification.

[0017] In a fifth aspect, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the method described in any first aspect of this specification are implemented.

[0018] The embodiment of the present invention provides a method and device for micro-site selection of a wind farm based on multi-objective optimization. The method first determines the initial wind turbine site selection plan with the best power generation, i.e., abundant wind energy, through wind energy resource data, and then completes secondary optimization through multiple optimization indicators such as collector line cost optimization, operation and maintenance road cost optimization, and wind turbine cost optimization, thereby obtaining the final target wind turbine site selection plan. In this way, the present invention preferably considers the initial wind turbine site selection plan that can obtain enough wind energy, and then performs multi-indicator optimization. Under the premise of ensuring the optimal power generation, the cost of related supporting facilities such as collector lines, operation and maintenance roads, and the cost of wind turbines are systematically considered, thereby realizing the micro-site selection of wind farms and improving the efficiency and accuracy of the micro-site selection of wind farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flow chart of a wind farm micro-site selection method based on multi-objective optimization provided by an embodiment of the present invention; Figure 2 is a flow chart of another method for microscopic site selection of a wind farm based on multi-objective optimization provided by an embodiment of the present invention; Figure 3 is a hardware architecture diagram of a computing device provided by an embodiment of the present invention; Figure 4 It is a structural schematic diagram of a wind farm micro-site selection device based on multi-objective optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, 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 described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] The specific implementation of the concept of this application is described below.

[0023] Please refer to Figure 1 The embodiment of the present invention provides a wind farm micro-site selection method based on multi-objective optimization, the method comprising: Step 100, determining an initial wind turbine site selection plan based on wind energy resource data in the wind farm area; Step 102, sorting the optimization indicators including the collection line cost, the operation and maintenance road cost and the wind turbine cost to obtain the optimization order of the optimization indicators; Step 104 , optimizing the initial wind turbine site selection scheme in sequence according to the optimization order to obtain a target wind turbine site selection scheme.

[0024] In the embodiment of the present invention, the initial wind turbine site selection scheme with the best power generation, i.e., abundant wind energy, is determined through wind energy resource data, and then secondary optimization is completed through multiple optimization indicators such as collection line cost optimization, operation and maintenance road cost optimization, and wind turbine cost optimization, thereby obtaining the final target wind turbine site selection scheme. In this way, the present invention preferably considers the initial wind turbine site selection scheme that can obtain enough wind energy, and then performs multi-index optimization. Under the premise of ensuring the optimal power generation, the cost of related supporting facilities such as collection lines, operation and maintenance roads, and the cost of wind turbines are systematically considered, and the micro-site selection of wind farms is realized while improving the efficiency and accuracy of the micro-site selection of wind farms.

[0025] In the present invention, after giving priority to power generation, the three optimization indicators of the second optimization are sorted again to determine the optimization indicators that should be given priority in the current wind turbine site selection plan, so as to select layer-by-layer optimization and layer-by-layer screening according to the actual situation of the wind farm area to determine the safest and most suitable target wind turbine site selection plan for the current wind farm area, so that the wind turbine site selection method is more scientific, efficient and accurate.

[0026] Described below Figure 1 How the various steps are performed.

[0027] First, in step 100, the wind energy resource data of the wind farm area can be a wind energy resource distribution map, annual wind resource data, quarterly wind resource data, monthly wind resource data and annual meteorological sequence resource characteristic data. By considering the optimal principle of power generation, the wind turbine location and booster station location of the wind farm area are preliminarily arranged to determine the initial wind turbine site selection plan. The initial wind turbine site selection plan gives priority to wind resource conditions. The machine position arrangement can be manually selected by hand or realized by calculation software. Manual arrangement means manually arranging the wind turbine in a location with good resource conditions according to the wind energy resource distribution conditions, while software arrangement is to set boundary conditions, distance requirements between wind turbines, inflow angle and turbulence intensity upper limit, and the optimal location plan is obtained by software iterative calculation. It should be noted that by setting the power generation threshold, considering other factors and adjusting relevant parameters such as the distance requirements between wind turbines, several initial wind turbine site selection plans with a total power generation greater than the lower limit of power generation can be obtained.

[0028] In a preferred embodiment, for step 102, the optimization indicators including the collection line cost, the operation and maintenance road cost and the wind turbine cost are sorted to obtain the optimization sequence of the optimization indicators, including: Determine the evaluation level for comparing the fuzzy relationship between optimization indicators; Determine the comparison results of any two optimization indicators in pairwise comparison according to the evaluation level; According to the comparison results, a fuzzy judgment matrix that meets the consistency requirements is constructed; Calculate the fuzzy judgment matrix to obtain the weight value of each optimization index; The weight values ​​are sorted from large to small to obtain an optimization order including optimization indicators.

[0029] It should be noted that any two optimization indicators form an optimization indicator pair, and the comparison results of the same optimization indicator pair in different wind farm areas are different.

[0030] It should be noted that before constructing the fuzzy judgment matrix, it is necessary to establish a hierarchical model including a target layer, a criterion layer and a scheme layer, wherein the target layer is to determine the target wind turbine site selection scheme; the criterion layer is the collector line cost, the operation and maintenance road cost and the wind turbine cost; the scheme layer is the optional initial wind turbine site selection scheme. The evaluation level and the comparison result determined according to the evaluation level can be determined by the expert group based on experience. The calculation of the weight value includes but is not limited to the use of methods such as the geometric mean, the arithmetic mean, and the eigenvalue. Among them, the comparison results of each pair of indicators in different wind farm areas are not the same, so the optimization order determined by different wind farm areas is also different, which can also achieve different optimization indicators that are prioritized in different wind farm areas, that is, the optimization order of each optimization indicator is not the same, so the method of the present invention is applicable to any wind farm area and can flexibly adapt to the actual wind farm micro-site selection requirements.

[0031] In the present invention, the fuzzy judgment matrix meets the definition of the consistency matrix, that is, the constructed fuzzy judgment matrix does not have logical problems and meets the consistency requirements. For example, the fuzzy judgment matrix is ​​constructed with the optimization indicators of Ⅰ, Ⅱ, and Ⅲ. If it is judged that Ⅰ is 5 relative to Ⅱ (that is, the comparison result is 5, Ⅰ is slightly more important than Ⅱ), Ⅰ is 1 / 5 relative to Ⅲ (Ⅲ is slightly more important than Ⅰ), when judging Ⅱ relative to Ⅲ, according to the above logic, Ⅲ should be more important than Ⅱ; at this time, if it is judged that Ⅱ is 1 / 3 relative to Ⅲ, the consistency requirements are met.

[0032] In the present invention, a simulation evaluation method is used to establish a fuzzy judgment matrix, and based on the fuzzy judgment matrix, the optimization ranking of the three optimization indicators of the collection line cost, operation and maintenance road cost and wind turbine cost in the secondary optimization is realized, thereby improving the efficiency and accuracy of determining the target wind turbine site selection plan.

[0033] For step 104, if Figure 2 As shown, the initial wind turbine site selection scheme is optimized in turn according to the optimization order to obtain the target wind turbine site selection scheme, including: S0, determining in sequence from the optimization sequence a first optimization index ranked first, a second optimization index ranked second, and a third optimization index ranked third; S1, optimizing the initial wind turbine site selection plan according to the first optimization index to obtain a first wind turbine site selection plan; S2, optimizing the first wind turbine site selection plan according to the second optimization index to obtain a second wind turbine site selection plan; S3, optimizing the second wind turbine site selection plan according to the third optimization index to obtain a target wind turbine site selection plan.

[0034] It should be noted that after the first two optimizations, several wind turbine site selection schemes can be obtained, namely, several first wind turbine site selection schemes and several second wind turbine site selection schemes, and after the last optimization, at least one target wind turbine site selection scheme can be obtained. If at least two target wind turbine site selection schemes are obtained, the final target wind turbine site selection scheme can be determined by experts and technicians through comprehensive comparison of the target wind turbine site selection schemes.

[0035] In the present invention, after determining the optimization order, the first optimization, the second optimization and the third optimization in the secondary optimization are performed in sequence according to the indicators corresponding to the optimization order, and the optimization is performed step by step to finally obtain the target scheduling scheme. For example, if the optimization order is the collector line cost with a weight value of 0.7, the wind turbine cost with a weight value of 0.2, and the operation and maintenance road cost with a weight value of 0.1, the collector line cost optimization, the wind turbine cost optimization and the operation and maintenance road cost optimization are performed in sequence; if the optimization order is the wind turbine cost with a weight value of 0.5, the collector line cost with a weight value of 0.3, and the operation and maintenance road cost with a weight value of 0.2, the wind turbine cost optimization, the collector line cost optimization and the operation and maintenance road cost optimization are performed in sequence.

[0036] In a preferred embodiment, in step 104, when the optimization index is the collector line, the optimization is performed according to the collector line cost, including: Get the current wind turbine site selection plan during the current optimization; The Dijkstra algorithm is used to determine the shortest collector line corresponding to each current wind turbine site selection scheme; Determine the equivalent collector line length of the shortest collector line based on the topography, geological conditions and obstacle areas of the wind farm area; The collector line cost of the equivalent collector line length is calculated, and the current wind turbine site selection scheme corresponding to the minimum collector line cost is determined as the optimized wind turbine site selection scheme.

[0037] It should be noted that if the collector line cost is the first optimization indicator, the current wind turbine site selection plan is the initial wind turbine site selection plan; if the collector line cost is the second optimization indicator, the current wind turbine site selection plan is the first wind turbine site selection plan. Specifically, in order to ensure that the next optimization can proceed smoothly, the current wind turbine site selection plans corresponding to at least the first two collector line costs arranged from small to large can also be determined as the optimized wind turbine site selection plan.

[0038] Specifically, the current wind turbine site selection plan includes the longitude and latitude coordinates of each device, and the equipment includes several wind turbines and a booster station. For a wind turbine site selection plan that includes multiple booster stations, the wind turbine site selection plan can be grouped according to the booster station to obtain multiple groups of wind turbine groups centered on the booster station. For each wind turbine group, the collection line cost, operation and maintenance road cost, and wind turbine cost are calculated according to the method of the present invention. The Dijkstra algorithm mainly determines the shortest collection line by taking the booster station as the starting point and expanding outward layer by layer with the starting point as the center until it reaches the end point.

[0039] In the present invention, since the collector line cost is the product of the collector line length and the collector line cost per unit length, changes in the collector line length and the collector line cost per unit length will affect the collector line cost. However, in order to simplify the calculation of the collector line cost, the present invention converts the effects of topography, geological conditions and obstacle areas on the collector line cost into the effects on the shortest collector line length, so that these effects are reflected in the increase in length to obtain the equivalent collector line length. The collector line cost can be determined based on the product of the equivalent collector line length and the preset collector line cost per unit length. It should be noted that since the preset collector line cost per unit length is a fixed value that takes into account the investment cost and maintenance cost, this method can quickly compare and screen out the current wind turbine site selection plan with the lowest collector line cost.

[0040] In a preferred embodiment, determining the equivalent collector line length of the shortest collector line according to the topography, geological conditions, meteorological conditions and obstacle areas of the wind farm area includes: Obtain the topography, geological conditions and obstacle areas of the wind farm area; Determine the slope influence factor between adjacent devices on the shortest collector line based on the topography; Determine the factors affecting the construction difficulty on the shortest collector line and the cable laying method between adjacent equipment based on the topography, geological conditions and meteorological conditions, and determine the laying factors affecting the cable laying method; Based on the obstacle area, determine the equivalent length of each obstacle area between adjacent devices on the shortest collector line; Determine the equivalent length between adjacent devices on the shortest collector line based on the straight-line distance between adjacent devices on the shortest collector line, slope influence factor, construction difficulty influence factor, laying influence factor, and equivalent length of the obstacle area; The equivalent length between the booster station and the existing power grid and all equivalent lengths on the shortest collector line are summed to obtain the equivalent collector line length; the equivalent length is determined by the following formula: in, For thei The device and the adjacent j The equivalent distance between devices; For the i The device and the adjacent j The straight-line distance between the devices; For the i The device and the adjacent j Slope influence factor between devices; For the i The device and the adjacent j Factors affecting the construction difficulty between equipment; For the i The device and the adjacent j The laying influencing factors of the cable laying method used between the devices; l m(i,j) For the i The device and the adjacent j The first m The equivalent length of the obstacle area, M For the i The device and the adjacent j The number of obstruction areas between devices; where , , are all greater than or equal to 1.

[0041] It should be noted that the latitude and longitude coordinates do not take into account the altitude, so the straight-line distance It is only the distance between two points in a two-dimensional plane. However, the different altitudes will cause a certain slope between the locations of different equipment. At this time, it is necessary to consider the increase in the length of the cables laid between different equipment due to the slope. The equivalent length between the booster station and the existing power grid is also determined using the above formula, where the first i Device, j The devices are a booster station and an existing power grid. The straight-line distance between the booster station and the existing power grid is specifically the straight-line distance between the location of the booster station and the location of the access port of the existing power grid. For the i The device and the adjacent j The first m The equivalent length of an obstacle area is specifically the length required to bypass the obstacle area.

[0042] It should be noted that the slope influence factors of different slopes affected by topography are different, the construction difficulty influence factors under different topography, geological conditions, and meteorological conditions are different, the cable laying methods (including overhead and direct burial) under different topography, geological conditions, and meteorological conditions are different, and the laying influence factors of different cable laying methods are different. Among them, the slope influence factor will directly affect the distance between adjacent equipment. The higher the slope, the greater the slope influence factor, and it is positively correlated with the equivalent length. If the slope is 0° or 180°, the slope influence factor is 1, that is, the terrain is flat; the construction difficulty influence factor and the laying influence factor will directly affect the cost of the unit length of the collector line, and are positively correlated with the equivalent length. Specifically, the slope influence factor, the construction difficulty influence factor, and the laying influence factor can be set by the user.

[0043] In the present invention, the calculation of the equivalent collector line length allows theoretically unifying different slopes, different cable laying methods, different construction difficulties, and different obstacle areas under the same preset unit length collector line cost, only considering the impact of length on the collector line cost, thereby providing a more efficient minimum collector line cost evaluation method. At the same time, the collector line cost of the present invention also takes into account the distance between the booster station and the existing power grid. The closer to the existing power grid, the shorter the length of the transmission line, the lower the transmission loss, and the lower the wind abandonment rate, while further reducing the collector line cost.

[0044] In a preferred embodiment, in step 104, when the optimization index is the operation and maintenance road cost, the optimization is performed according to the operation and maintenance road cost, including: Get the current wind turbine site selection plan during the current optimization; Determine the operation and maintenance road costs corresponding to each current wind turbine site selection option; The current wind turbine site selection plan corresponding to the minimum operation and maintenance road cost is determined as the optimized wind turbine site selection plan.

[0045] Specifically, in order to ensure that the next optimization can proceed smoothly, the current wind turbine site selection schemes corresponding to at least the first two operation and maintenance road costs arranged from small to large may also be determined as the optimized wind turbine site selection scheme.

[0046] In a more preferred embodiment, the current wind turbine site selection plan includes the longitude and latitude coordinates of each device, and the devices include a number of wind turbines and booster stations; Determine the O&M road costs for each current wind turbine siting option, including: Determine equipment maintenance points based on the distribution density of each device in the current wind turbine site selection plan; Determine the roads to be built and repaired based on the existing road distribution, road surface damage conditions, obstacle areas, and equipment maintenance points in the current wind turbine site selection plan; Determine the construction difficulty coefficient based on topography, geological conditions and meteorological conditions; The cost of operating and maintaining roads is determined based on the area of ​​roads to be built, the cost of building new roads per unit area, the area of ​​roads to be repaired, the cost of repairing roads per unit area, the construction difficulty coefficient and the repair coefficient.

[0047] It should be noted that the road to be built can be connected to the existing road, and the equipment maintenance point can be reached through the road to be built and the existing road. The road to be repaired is an existing road that has road surface damage and is in urgent need of repair. The present invention includes but is not limited to using the Dijkstra algorithm to determine the road to be built corresponding to the current wind turbine site selection plan.

[0048] Specifically, it is determined by the following formula: in, Cost of repairing roads for operation; For the q The construction difficulty coefficient of the road to be built; s q For the q The area of ​​the road to be built; p 1 is the cost of new road construction per unit area; is the total number of sections of road to be constructed; For the p The construction difficulty coefficient of the road section to be repaired; For the p The repair coefficient of the road to be repaired; s p For the p The area of ​​the road to be repaired; p 2 is the cost of road repair per unit area; P is the total number of sections of road to be repaired; , , Both are greater than 1; the construction difficulty coefficient and the repair coefficient are positively correlated with the road operation and maintenance cost, and the greater the construction difficulty, the higher the construction difficulty coefficient, and the more serious the road surface disease, the higher the repair coefficient.

[0049] It should be noted that, in the present invention, the obstacle areas include nature reserves, water source protection areas, ecological and environmental sensitive areas, military sensitive areas, cultural relics and site protection areas, residential areas, etc.

[0050] In the present invention, the new roads to be built and the roads to be repaired are determined by comprehensively considering the distribution density of each equipment in the current wind turbine site selection plan, the distribution of existing roads, the pavement damage conditions of existing roads, and the obstacle areas. Then, the construction difficulty coefficient is determined based on the topography, geological conditions and meteorological conditions of the wind farm area, and the repair coefficient is determined based on the pavement damage conditions of existing roads. Finally, the construction difficulty coefficient and the repair coefficient are comprehensively considered to determine the road operation and maintenance cost.

[0051] In a preferred embodiment, in step 104, when the optimization index is the wind turbine cost, the optimization is performed according to the wind turbine cost, including: Get the current wind turbine site selection plan during the current optimization; Determine the wind turbine cost corresponding to each current wind turbine site selection option; The current wind turbine site selection plan corresponding to the minimum wind turbine cost is determined as the optimized wind turbine site selection plan.

[0052] Specifically, in order to ensure that the next optimization can proceed smoothly, the current wind turbine site selection schemes corresponding to at least the first two wind turbine costs arranged from small to large may also be determined as the optimized wind turbine site selection schemes.

[0053] In a more preferred embodiment, the current wind turbine site selection plan includes the longitude and latitude coordinates of each device, and the devices include a number of wind turbines and booster stations; Determine the wind turbine cost associated with each current wind turbine siting option, including: Determine the cost of the wind turbine body and the cost of supporting facilities for the wind turbine in the current wind turbine site selection plan; The cost of the fan is obtained by summing up the transportation cost, installation cost, commissioning cost, subsequent maintenance cost, fan body cost and supporting facilities cost of the fan and its supporting facilities.

[0054] In the present invention, the fan cost is obtained by comprehensively considering the fan model and power to determine the fan body cost, the cost of supporting facilities supporting the fan, the transportation cost of the fan and its supporting facilities, the installation cost, the commissioning cost, and the subsequent maintenance cost.

[0055] In the present invention, by optimizing the collection line cost, operation and maintenance road cost and wind turbine cost, it can be determined that the optimization of each optimization index is a process of solving the minimum value of the objective function. Therefore, after determining the optimization order, no matter which optimization index is optimized, it is only necessary to solve the minimum objective function corresponding to the optimization, thereby converting the original multi-objective optimization problem into a method of multiple single-objective optimization problems, thereby improving the efficiency of determining the target wind turbine site selection plan.

[0056] The multi-objective optimization micro-site selection of the present invention takes power generation as the first optimization, and realizes three optimization indicators in the second optimization. While ensuring the optimal power generation, it also takes into account the minimum collection line cost, the minimum operation and maintenance road cost optimization, and the minimum wind turbine cost. This not only systematically considers the relevant supporting facilities such as collection lines and operation and maintenance roads, but also improves the efficiency and accuracy of wind farm micro-site selection.

[0057] like Figure 3 , Figure 4 As shown, the embodiment of the present invention provides a wind farm micro-site selection device based on multi-objective optimization. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 3 As shown in FIG. 1 , a hardware architecture diagram of a computing device in which a wind farm micro-site selection device based on multi-objective optimization is provided in an embodiment of the present invention is located. Figure 3 In addition to the processor, memory, network interface, and non-volatile memory shown in the figure, the computing device in which the device is located in the embodiment may also generally include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 4 As shown, as a device in a logical sense, the CPU of the computing device in which it is located reads the corresponding computer program in the non-volatile memory into the memory and runs it. This embodiment provides a wind farm micro-site selection device based on multi-objective optimization, and the device includes: The preliminary selection module 400 is used to determine the initial wind turbine site selection plan based on the wind energy resource data of the wind farm area; The optimization module 402 is used to sort the optimization indicators including the collection line cost, the operation and maintenance road cost and the wind turbine cost to obtain the optimization order of the optimization indicators; and optimize the initial wind turbine site selection plan in turn according to the optimization order to obtain the target wind turbine site selection plan.

[0058] In some specific implementations, the preliminary selection module 400 may be used to execute the above step 100 , and the optimization module 402 may be used to execute the above steps 102 and 104 .

[0059] In one embodiment of the present invention, the optimization module 402 is further configured to perform the following operations: Determine the evaluation level for comparing the fuzzy relationship between optimization indicators; Determine the comparison results of any two optimization indicators in pairwise comparison according to the evaluation level; According to the comparison results, a fuzzy judgment matrix that meets the consistency requirements is constructed; Calculate the fuzzy judgment matrix to obtain the weight value of each optimization index; The weight values ​​are sorted from large to small to obtain an optimization order including optimization indicators.

[0060] In one embodiment of the present invention, the optimization module 402 is further configured to perform the following operations: Determine in order from the optimization sequence a first optimization index ranked first, a second optimization index ranked second, and a third optimization index ranked third; Optimizing the initial wind turbine site selection plan according to the first optimization index to obtain a first wind turbine site selection plan; Optimizing the first wind turbine site selection plan according to the second optimization index to obtain a second wind turbine site selection plan; The second wind turbine site selection plan is optimized according to the third optimization index to obtain a target wind turbine site selection plan.

[0061] In one embodiment of the present invention, the optimization module 402 is further configured to perform the following operations: Optimize based on collector line costs, including: Get the current wind turbine site selection plan during the current optimization; The Dijkstra algorithm is used to determine the shortest collector line corresponding to each current wind turbine site selection scheme; Determine the equivalent collector line length of the shortest collector line based on the topography, geological conditions and obstacle areas of the wind farm area; The collector line cost of the equivalent collector line length is calculated, and the current wind turbine site selection scheme corresponding to the minimum collector line cost is determined as the optimized wind turbine site selection scheme.

[0062] In one embodiment of the present invention, the current wind turbine site selection plan includes the longitude and latitude coordinates of each device, and the equipment includes a number of wind turbines and booster stations.

[0063] In one embodiment of the present invention, the optimization module 402 is further configured to perform the following operations: Obtain the topography, geological conditions and obstacle areas of the wind farm area; Determine the slope influence factor between adjacent devices on the shortest collector line based on the topography; Determine the factors affecting the construction difficulty on the shortest collector line and the cable laying method between adjacent equipment based on the topography, geological conditions and meteorological conditions, and determine the laying factors affecting the cable laying method; Based on the obstacle area, determine the equivalent length of each obstacle area between adjacent devices on the shortest collector line; Determine the equivalent length between adjacent devices on the shortest collector line based on the straight-line distance between adjacent devices on the shortest collector line, slope influence factor, construction difficulty influence factor, laying influence factor, and equivalent length of the obstacle area; The equivalent length between the booster station and the existing power grid and all equivalent lengths on the shortest collector line are summed to obtain the equivalent collector line length; the equivalent length is determined by the following formula: in, For the i The device and the adjacent j The equivalent distance between devices; For the i The device and the adjacent j The straight-line distance between the devices; is the slope influencing factor; is the influencing factor of construction difficulty; To lay the impact factor; For the i The device and the adjacent j The first m The equivalent length of the obstacle area, M For the i The device and the adjacent j The number of obstruction areas between devices.

[0064] In one embodiment of the present invention, the optimization module 402 is further configured to perform the following operations: Get the current wind turbine site selection plan during the current optimization; For each current wind turbine site selection option, perform: Determine equipment maintenance points based on the distribution density of each device in the current wind turbine site selection plan; Determine the roads to be built and repaired based on the existing road distribution, road surface damage conditions, obstacle areas, and equipment maintenance points in the current wind turbine site selection plan; Determine the construction difficulty coefficient based on topography, geological conditions and meteorological conditions; The cost of operating and repairing roads shall be determined based on the area of ​​roads to be built, the cost of new roads per unit area, the area of ​​roads to be repaired, the cost of repairing roads per unit area, the construction difficulty coefficient and the repair coefficient; The current wind turbine site selection plan corresponding to the minimum operation and maintenance road cost is determined as the optimized wind turbine site selection plan.

[0065] In one embodiment of the present invention, the optimization module 402 is further configured to perform the following operations: Optimization based on wind turbine costs, including: Get the current wind turbine site selection plan during the current optimization; Determine the cost of the wind turbine body and the cost of supporting facilities for the wind turbine in each current wind turbine site selection plan; The transportation cost, installation cost, commissioning cost, post-maintenance cost of the wind turbine and its supporting facilities are summed up as well as the cost of the wind turbine body and supporting facilities to obtain the wind turbine cost of each current wind turbine site selection plan; The current wind turbine site selection plan corresponding to the minimum wind turbine cost is determined as the optimized wind turbine site selection plan.

[0066] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a wind farm micro-site selection device based on multi-objective optimization. In other embodiments of the present invention, a wind farm micro-site selection device based on multi-objective optimization may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0067] The information interaction, execution process and other contents between the modules in the above-mentioned device are based on the same concept as the embodiment of the method of the present invention. For the specific contents, please refer to the description in the embodiment of the method of the present invention, and no further description is given here.

[0068] An embodiment of the present invention further provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a wind farm micro-site selection method based on multi-objective optimization in any embodiment of the present invention is implemented.

[0069] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes a wind farm micro-site selection method based on multi-objective optimization in any embodiment of the present invention.

[0070] An embodiment of the present application also provides a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes a wind farm micro-site selection method based on multi-objective optimization as described in any of the above embodiments.

[0071] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can read and execute the program code stored in the storage medium.

[0072] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0073] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0074] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling the operating system on the computer to complete part or all of the actual operations based on the instructions of the program code.

[0075] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or expansion module is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0076] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical factors in the process, method, article or device including the elements.

[0077] A person of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various media that can store program codes.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 wind farm micro-site selection method based on multi-objective optimization, characterized in that: include: Determine the initial wind turbine site selection plan based on the wind energy resource data of the wind farm area; Sorting the optimization indicators including the cost of the collection line, the cost of the operation and maintenance road, and the cost of the wind turbine to obtain the optimization order of the optimization indicators; The initial wind turbine site selection scheme is optimized in sequence according to the optimization sequence to obtain a target wind turbine site selection scheme.

2. The method according to claim 1, characterized in that The optimization indicators including the collection line cost, the operation and maintenance road cost and the wind turbine cost are sorted to obtain the optimization sequence of the optimization indicators, including: Determining an evaluation level for comparing the fuzzy relationship between the optimization indicators; Determine the comparison result of pairwise comparison of any two optimization indicators according to the evaluation level; According to the comparison results, a fuzzy judgment matrix that meets the consistency requirements is constructed; Calculating the fuzzy judgment matrix to obtain the weight value of each optimization index; The weight values ​​are sorted from large to small to obtain the optimization order including the optimization index.

3. The method according to claim 1, characterized in that The step of optimizing the initial wind turbine site selection scheme in sequence according to the optimization sequence to obtain a target wind turbine site selection scheme includes: Determine in sequence from the optimization sequence a first optimization index ranked first, a second optimization index ranked second, and a third optimization index ranked third; Optimizing the initial wind turbine site selection plan according to the first optimization index to obtain a first wind turbine site selection plan; Optimizing the first wind turbine site selection plan according to the second optimization index to obtain a second wind turbine site selection plan; The second wind turbine site selection plan is optimized according to the third optimization index to obtain the target wind turbine site selection plan.

4. The method according to claim 1, characterized in that: Optimization is performed based on the collector line cost, including: Get the current wind turbine site selection plan during the current optimization; Using Dijkstra algorithm to determine the shortest collector line corresponding to each current wind turbine site selection scheme; Determining the equivalent collector line length of the shortest collector line according to the topography, geological conditions and obstacle areas of the wind farm area; The collector line cost of the equivalent collector line length is calculated, and the current wind turbine site selection scheme corresponding to the minimum collector line cost is determined as the optimized wind turbine site selection scheme.

5. The method according to claim 4, characterized in that The current wind turbine site selection plan includes the longitude and latitude coordinates of each device, and the devices include a number of wind turbines and booster stations; Determining the equivalent collector line length of the shortest collector line according to the topography, geological conditions, meteorological conditions and obstacle areas of the wind farm area includes: Obtaining the topography, geological conditions and obstacle areas of the wind farm area; Determine the slope influence factor between adjacent devices on the shortest collector line according to the topography; Determine the construction difficulty influencing factor on the shortest collector line and the cable laying method used between adjacent devices according to the topography, geological conditions and meteorological conditions, and determine the laying influencing factor of the cable laying method; Determine, according to the obstacle area, an equivalent length of each obstacle area between adjacent devices on the shortest collector line; Determine the equivalent length between adjacent devices on the shortest collector line according to the straight-line distance between adjacent devices on the shortest collector line, the slope influence factor, the construction difficulty influence factor, the laying influence factor, and the equivalent length of the obstacle area; The equivalent length between the booster station and the existing power grid and all the equivalent lengths on the shortest collector line are summed to obtain the equivalent collector line length; Wherein, the equivalent length is determined by the following formula: in, For the i The device and the adjacent j The equivalent distance between devices; For the i The device and the adjacent j The straight-line distance between the devices; is the slope influencing factor; is the construction difficulty influencing factor; is the laying impact factor; For the i The device and the adjacent j The first m The equivalent length of the obstacle area, M For the i The device and the adjacent j The number of obstruction areas between devices.

6. The method according to any one of claims 1 to 5, characterized in that: Optimization is performed based on the operation and maintenance road costs, including: Get the current wind turbine site selection plan during the current optimization; Determine the operation and maintenance road cost corresponding to each of the current wind turbine site selection plans; The current wind turbine site selection scheme corresponding to the minimum operation and maintenance road cost is determined as the optimized wind turbine site selection scheme; and / or, Optimization based on the wind turbine cost includes: Get the current wind turbine site selection plan during the current optimization; Determining the wind turbine cost corresponding to each of the current wind turbine site selection plans; The current wind turbine site selection plan corresponding to the minimum wind turbine cost is determined as the optimized wind turbine site selection plan.

7. The method according to claim 6, characterized in that The current wind turbine site selection plan includes the longitude and latitude coordinates of each device, and the devices include a number of wind turbines and booster stations; The determining of the operation and maintenance road cost corresponding to each of the current wind turbine site selection schemes includes: Determine equipment maintenance points according to the distribution density of each of the equipment in the current wind turbine site selection plan; Determine the roads to be built and the roads to be repaired according to the obtained existing road distribution, road surface damage conditions of the existing roads, obstacle areas and the equipment maintenance points in the current wind turbine site selection plan; Determine the construction difficulty coefficient according to the topography, geological conditions and meteorological conditions of the wind farm area; Determine the road operation and maintenance cost according to the area of ​​the road to be built, the cost of the new road per unit area, the area of ​​the road to be repaired, the cost of repairing the road per unit area, the construction difficulty coefficient and the repair coefficient; and / or, The current wind turbine site selection plan includes the longitude and latitude coordinates of each device, and the devices include a number of wind turbines and booster stations; The determining of the wind turbine cost corresponding to each of the current wind turbine site selection schemes includes: Determine the cost of the wind turbine body and the cost of supporting facilities for the wind turbine in the current wind turbine site selection plan; The cost of the fan is obtained by summing up the transportation cost, installation cost, commissioning cost, and subsequent maintenance cost of the fan and its supporting facilities, the fan body cost, and the supporting facilities cost.

8. A wind farm micro-site selection device based on multi-objective optimization, characterized in that: include: The preliminary selection module is used to determine the initial wind turbine site selection plan based on the wind energy resource data of the wind farm area; The optimization module is used to sort the optimization indicators including the collection line cost, the operation and maintenance road cost and the wind turbine cost to obtain the optimization order of the optimization indicators; and optimize the initial wind turbine site selection plan in turn according to the optimization order to obtain the target wind turbine site selection plan.

9. A computing device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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