Windmill arrangement optimization device, windmill arrangement optimization method, and program

Abstract

To provide a windmill arrangement optimization device which can derive a proper arrangement spot of a windmill in a short time.SOLUTION: A windmill arrangement optimization device according to one embodiment includes a simulation unit, a prediction model construction unit, a windmill arrangement determination unit, and a determination unit. The simulation unit simulates a speed of wind received by the windmill when the windmill is arranged in an area. The prediction model construction unit calculates, based on the wind speed, an amount of electric power generated by the windmill and an extreme value wind speed by regression analysis and creates a relational expression between a position coordinate of the windmill and at least one of the amount of generated electric power and the extreme value wind speed as a prediction model. The windmill arrangement determination unit determines a position coordinate, at which the amount of generated electric power is larger than a value of a preset amount of generated electric power, as an arrangement candidate spot based on the predication model. The determination unit determines that a spot, where the amount of generated electric power is maximum and the extreme value wind speed is smaller than a threshold value, among the arrangement candidate spots is proper.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Embodiments of the present invention relate to a wind turbine layout optimization device, a wind turbine layout optimization method, and a program.

Background Art

[0002] A wind turbine generator (WTG) using a windmill is generally known to have high cost performance in reducing fossil fuel and CO 2 Therefore, wind power generation facilities are increasing, from several wind turbines to large wind farms (WF) composed of dozens or more wind turbines.

[0003] In a wind power generation facility, the layout of wind turbines is important to realize the sound operation of the wind turbines. There are various constraints on the layout of wind turbines, such as wind conditions. Therefore, when considering the layout location of wind turbines, it is necessary to take such constraints into account. As a result, it takes time and effort to derive an appropriate layout location for wind turbines.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] The problem to be solved by the present invention is to provide a wind turbine layout optimization device, a wind turbine layout optimization method, and a program capable of deriving an appropriate layout location for wind turbines in a short time.

Means for Solving the Problems

[0006] The wind turbine layout optimization device according to an embodiment includes a simulation unit that simulates the wind conditions in an area where the layout of wind turbines is considered, a prediction model construction unit that creates a prediction model based on the wind conditions for the layout of wind turbines using the simulation results of the simulation unit, a wind turbine layout determination unit that determines at least one or more candidate locations for the layout of wind turbines based on the prediction model, and a determination unit that determines whether the candidate locations for the layout are appropriate. The simulation unit simulates the wind speed received by the wind turbines when the wind turbines are arranged in the area. The prediction model construction unit calculates the generated power and extreme wind speed obtained from the wind turbines by regression analysis based on the wind speed, and creates a relational expression between the position coordinates of the wind turbines and at least one of the generated power and the extreme wind speed as the prediction model. The wind turbine layout determination unit determines, based on the prediction model, the position coordinates where the generated power is greater than a preset value of the generated power as the candidate locations for the layout. The determination unit determines that a location where the generated power is maximum and the extreme wind speed is less than a threshold value among the candidate locations for the layout is appropriate.

Advantages of the Invention

[0007] According to one embodiment, it is possible to derive an appropriate location for the layout of wind turbines in a short time.

Brief Description of the Drawings

[0008]

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Mode for Carrying Out the Invention

[0009] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Note that the embodiments shown below are examples of embodiments of the present invention, and the present invention is not construed as being limited to these embodiments. In the drawings referred to in the present embodiment, the same parts or parts having the same function are denoted by the same reference numerals or similar reference numerals, and repeated description thereof may be omitted. In addition, the dimensional ratios in the drawings may be different from the actual ratios for convenience of explanation, or a part of the configuration may be omitted from the drawings.

[0010] (First Embodiment) FIG. 1 is a block diagram showing the configuration of a wind turbine layout optimization device according to the first embodiment. The wind turbine layout optimization device 1 shown in FIG. 1 is a device that calculates and outputs an appropriate layout of wind turbines from wind conditions, land conditions, and social conditions. Here, the wind conditions include, for example, the annual energy production (AEP: Annual Energy Production) obtained from the wind turbines, the extreme wind speed V ref , V e50 , an index I indicating the state of turbulence, the angle of attack of the wind flowing into the wind turbine, an index indicating the change in wind speed in the height direction during a storm, the wake effect received by the wind turbine on the leeward side from the wind turbine on the windward side, and the like. The land conditions include, for example, distance restrictions from buildings, roads, rivers, etc., restrictions on light and noise pollution, construction feasibility restrictions such as slopes and ground, and restrictive conditions such as land rights. Furthermore, the social conditions include restrictive conditions such as animal and plant protection areas, landscape protection areas, and cultural property protection areas.

[0011] As shown in FIG. 1, the wind turbine layout optimization device 1 according to this embodiment includes a wind turbine layout calculation unit 10, an input unit 20, and a display unit 30. The wind turbine layout calculation unit 10 calculates the layout of wind turbines. The detailed configuration of the wind turbine layout calculation unit 10 will be described later.

[0012] The input unit 20 is realized by a mouse, keyboard, trackball, switch, button, joystick, etc., which receives various input operations from the operator and outputs the received input information to the wind turbine layout calculation unit 10. The input unit 20 receives input operations such as, for example, terrain data, designation of an area to consider the wind turbine layout, wind inflow conditions such as wind direction, wind speed, and turbulence intensity, information related to wind turbines such as wind turbine shape and the number of installed wind turbines, and social conditions.

[0013] The display unit 30 is composed of a liquid crystal display, a CRT (Cathode Ray Tube) display, etc., which display various information. The display unit 30 displays, for example, an image showing the wind turbine layout calculated by the wind turbine layout calculation unit 10.

[0014] Hereinafter, the detailed configuration of the wind turbine layout calculation unit 10 will be described. The wind turbine layout calculation unit 10 includes an arithmetic processing unit 102, a determination unit 103, and a storage unit 104. The arithmetic processing unit 102 further includes a simulation unit 102a, a prediction model construction unit 102b, and a wind turbine layout determination unit 102c. In this embodiment, each processing function performed by the simulation unit 102a, the prediction model construction unit 102b, and the wind turbine layout determination unit 102c is stored in the storage unit 104 in the form of a program executable by a computer.

[0015] The arithmetic processing unit 102 is a processor that reads a program from the storage unit 104 and executes it to realize the functions corresponding to the respective programs. In other words, the processing circuit in the state where each program is read has each function shown in the arithmetic processing unit 102. Here, the term "processor" means, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or an application specific integrated circuit (ASIC), a programmable logic device (for example, a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA), etc.).

[0016] The simulation unit 102a simulates the wind conditions in the area where the placement of wind turbines is considered, for example, using MASCOT, RIAM-COMPACT (registered trademark), etc. At this time, the simulation unit 102a samples the position coordinates for placing a plurality of wind turbines using random numbers (quasi-random numbers). Further, for any wind turbine type, the simulation unit 102a samples the placement with a relatively large total generated power amount among the wind turbine placements by a greedy method or the like, or samples the position where the distance from the current optimal placement among the sampled wind turbine placements is the shortest.

[0017] FIG. 2 is a diagram showing an example of a wind condition simulation model used in the simulation unit 102a. In the wind condition simulation model 40 shown in FIG. 2, an inflow wind 50, an analysis center 41, a minimum analysis grid range 42, a target area 43, an analysis area 44, an additional area 45, an upstream buffer area 46, a downstream buffer area 47, and a side buffer area 48 are shown. The inflow wind 50 is the wind that flows into the windmill from the upwind side. The analysis center 41 is the central position of the analysis of the wind condition model and corresponds to the windmill position 60. The minimum analysis grid range 42 is the area of the minimum constituent unit of the target area 43 of the wind condition simulation. The analysis area 44 is the area that entirely surrounds the target area 43. The additional area 45 is the area added to the upwind side of the analysis area 44. The upstream buffer area 46 is the area adjacent to the additional area 45 on the upwind side. The downstream buffer area 47 is the area adjacent to the analysis area 44 on the downwind side. The side buffer area 48 is the area adjacent to the sides of the analysis area 44 and the additional area 45. In each area of the wind condition simulation model 40, the topographic data received by the input unit 20 and the designation of the area for considering the windmill arrangement are reflected. Also, for the analysis, the inflow conditions of the wind such as the wind direction, wind speed, and turbulence intensity received by the input unit 20 are used.

[0018] The simulation unit 102a generates a wind condition simulation model 40 as shown in FIG. 2. More specifically, the simulation unit 102a generates a wind condition simulation model 40 for calculating physical quantities based on the Navier-Stokes equation, which is the governing equation according to the present embodiment. Here, the governing equation is a mathematical equation representing the physical laws within the mesh model.

[0019] FIG. 3 is a diagram showing an example of a governing equation. As shown in FIG. 3, the governing equation is the Navier-Stokes equation. In the equation shown in FIG. 3, ρ represents the density of the fluid, ν represents the kinematic viscosity of the fluid, and μ represents the viscosity of the fluid. In addition, an external force term F of the force received from the windmill rotor described later is added to the Navier-Stokes equation. The external force term F includes external forces Fx, Fy, and Fz in the x, y, and z directions. The x direction is a direction parallel to the wind direction, the y direction is a direction orthogonal to the x direction, and the z direction is a vertical direction orthogonal to the x direction and the y direction.

[0020] FIG. 4 is a diagram showing an example of a windmill. The windmill 200 shown in FIG. 4 includes a plurality of blades 200a, a nacelle 200b, a tower 200c, and a hub 200d. The plurality of blades 200a are installed on the hub 200d at intervals in the rotational direction. The nacelle 200b is installed on the upper part of the tower 200c. The tower 200c extends in the vertical direction (z direction) at the windmill position 60. The hub 200d is attached to the front surface of the nacelle 200b. In the windmill 200 configured as described above, when each blade 200a rotates by wind force, a generator provided in the nacelle 200b generates electricity by the rotational force.

[0021] The prediction model construction unit 102b constructs a prediction model of wind conditions using Gaussian process regression, neural networks, random forests, support vector regression, etc. The wind condition parameters of this prediction model are models with the position coordinates of one windmill as explanatory variables. For example, the above-mentioned AEP, extreme wind speed V ref 、V e50 、an index I indicating the state of turbulence, the angle of attack, an exponent indicating the change in wind speed in the height direction during a storm, and models of wind conditions such as the wake effect received by a windmill on the leeward side from a windmill on the windward side. The wake effect is a model with the position coordinates of the windmill on the leeward side and the distance and angle between the windmill on the leeward side and the nearest windmill as explanatory variables.

[0022] The wind turbine layout determination unit 102c discretizes the position coordinates of wind turbines using random numbers (quasi-random numbers), arbitrary grid points, or points parallel to the contour lines of the terrain, formulates it as an integer linear programming problem in the discretized space, and obtains the position coordinates of multiple wind turbines. Specifically, 0-1 variables indicating whether to use wind turbines at multiple candidate coordinates are used as design variables. The AEP, V ref 、V e50 are obtained using a prediction model. As a result, more coordinates can be used as candidates than the number of simulated wind turbine coordinates, and a wind turbine layout with a larger generated power can be obtained with fewer simulation times. Alternatively, the wind turbine layout determination unit 102c can also solve it using a greedy method. Specifically, one wind turbine is selected one by one from the wind turbine layout, and without changing the other wind turbine coordinates, the selected wind turbine coordinates are sequentially and repeatedly changed so that the total AEP increases, and the process is repeated until the wind turbine layout no longer changes. Furthermore, it is also possible to solve it using a gradient method or the like without discretization. For example, since the AEP prediction model can obtain the gradient (the change amount of AEP when the wind turbine position coordinates move slightly), the wind turbine layout with the maximum AEP can be obtained using the gradient method with this gradient. The gradient method cannot be used in simulation because the gradient cannot be obtained, but the gradient method can be used by using the prediction model, so a wind turbine layout with a larger generated power can be obtained more quickly.

[0023] The determination unit 103 determines whether the wind turbine layout determined by the arithmetic processing unit 102 is an appropriate layout.

[0024] The storage unit 104 is realized by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, hard disks, optical disks, etc. The storage unit 104 includes a wind condition data storage unit 104a, a land condition data storage unit 104b, a social condition data storage unit 104c, and a set value storage unit 104d. In addition, the storage unit 104 stores various programs.

[0025] Wind condition data is stored in the wind condition data storage unit 104a, which indicates the simulation results of the simulation unit 102a. Land condition data is stored in advance in the land condition data storage unit 104b. Social condition data is stored in advance in the social condition data storage unit 104c. Various values input through the operation of the input unit 20 are stored in the set value storage unit 104d.

[0026] Figure 5 is an example of a flowchart of the arithmetic processing executed by the wind turbine layout optimization device 1. Hereinafter, the arithmetic processing of the wind turbine layout optimization device 1 will be described.

[0027] In the flowchart shown in Figure 5, first, the operator operates the input unit 20 to input wind turbine information used in the arithmetic processing, an area to consider the layout of the wind turbines 200, the number of wind turbine layouts to consider, and a threshold value of the extreme wind speed, etc. (step S100). Here, the wind turbine information includes, for example, the height of the hub 200d of the wind turbine 200 and the diameter of the rotor surface combining the blade 200a and the hub 200d. The values input to the input unit 20 are stored in the set value storage unit 104d.

[0028] Subsequently, the simulation unit 102a executes the wind condition simulation model 40 based on the conditions set in step S100 (step S101). The wind condition data obtained by the wind condition simulation model 40 is stored in the wind condition data storage unit 104a.

[0029] Figure 6 is a diagram showing an example of wind condition data. The wind condition data shown in Figure 6 shows the wind speed V at a certain point where the inside of the layout consideration area of the wind turbines 200 is divided into a grid pattern with the minimum analysis grid range 42, by wind direction and altitude. In the present embodiment, the simulation unit 102a creates wind condition data for each position coordinate of the analysis center 41 of the minimum analysis grid range 42.

[0030] When the simulation by the simulation unit 102a is completed, subsequently, the prediction model construction unit 102b processes the wind condition data read from the wind condition data storage unit 104a using a regression analysis method such as Gaussian process regression, etc., to create a prediction model for wind conditions (step S102).

[0031] In step S102, the prediction model construction unit 102b creates a relational expression between the position coordinates of the analysis center 41 within the minimum analysis grid range 42, in other words, the coordinates of the wind turbine position 60, and the AEP or the extreme wind speed.

[0032] FIG. 7 is a diagram showing an example of the distribution of AEP and extreme wind speed created using the prediction model for wind conditions. In FIG. 7, each of the AEP and extreme wind speed within the layout consideration area is shown in association with the coordinates of the wind turbine position 60. FIG. 7 shows the magnitudes of the AEP and extreme wind speed at the wind turbine position 60. The AEP and extreme wind speed are calculated by the prediction model construction unit 102b using the wind condition data stored in the wind condition data storage unit 104a. At this time, the prediction model construction unit 102b may calculate either one of the AEP and extreme wind speed instead of both, and the prediction model construction unit 102b may calculate not only the AEP and extreme wind speed but also, for example, an index I indicating the state of turbulence based on the wind condition data.

[0033] When the construction of the prediction model by the prediction model construction unit 102b is completed, subsequently, the wind turbine layout determination unit 102c determines at least one or more wind turbine layout candidate points within the layout consideration area using the prediction model (step S103). In step S103, the wind turbine layout determination unit 102c determines, for example, a point within the layout consideration area where the AEP is greater than a preset lower limit value as a wind turbine layout candidate point.

[0034] Subsequently, the determination unit 103 determines whether there is a point among the wind turbine layout candidate points that satisfies the condition that the AEP is the maximum and the extreme wind speed is less than the threshold value set in step S100 (step S104).

[0035] If there is a candidate location for wind turbine placement that satisfies the above conditions, the wind turbine placement decision unit 102c determines that location as the wind turbine placement location based on wind conditions (step S105). Note that in step S105, multiple wind turbine placement locations may be determined. On the other hand, if in step S104 there is no candidate location for wind turbine placement that satisfies the above conditions, the wind turbine placement decision unit 102c returns to step S103. In this case, the decision conditions for candidate locations for wind turbine placement and the conditions for wind turbine placement locations may be relaxed.

[0036] After determining the wind turbine placement location based on wind conditions, the wind turbine placement decision unit 102c calculates the degree of influence restricted by land conditions and social conditions within the wind turbine placement study area set in step S100, using the land condition data pre-stored in the land condition data storage unit 104b and the social condition data pre-stored in the social condition data storage unit 104c (step S106). In step S106, for example, in the land condition data and social condition data, the constraint conditions are pre-digitized according to their content, and the wind turbine placement decision unit 102c calculates the numerical values of the land condition data and social condition data (for example, by referring to the flags assigned to coordinates to determine installability, setting restrictions on the number of installations for sub-areas, setting distance restrictions from buildings and roads, etc.) to calculate the degree of influence.

[0037] Subsequently, the wind turbine placement decision unit 102c determines at least one or more candidate locations for wind turbine placement taking into account land conditions and social conditions (step S107). In step S107, for example, the wind turbine placement decision unit 102c determines, within the wind turbine placement study area, locations where the degree of influence calculated in step S106 is smaller than a preset reference value as candidate locations for wind turbine placement taking into account land conditions and social conditions.

[0038] Next, the determination unit 103 determines whether the wind turbine placement point determined in step S105 is appropriate for the wind turbine placement candidate point determined in step S107 (step S108). In step S108, when the wind turbine placement point determined in step S105 also corresponds to the wind turbine placement candidate point determined in step S107, the determination unit 103 determines that the wind turbine placement point determined in step S105 is a point that also satisfies the land conditions and social conditions. On the other hand, when the wind turbine placement point determined in step S105 does not correspond to the wind turbine placement candidate point determined in step S107, the determination unit 103 determines that the wind turbine placement point determined in step S105 is a point that does not satisfy the land conditions or social conditions.

[0039] In step S108, when the wind turbine placement point based on the wind conditions also satisfies the land conditions and social conditions, the display unit 30 performs a display operation that allows the operator to confirm that the wind turbine placement point is an appropriate placement point within the wind turbine placement consideration area. For example, the display unit 30 displays a point that satisfies the wind conditions, land conditions, and social conditions in a different color from other points within the wind turbine placement consideration area.

[0040] According to the present embodiment described above, since the prediction model construction unit 102b constructs a prediction model based on the wind conditions, it is possible to save the labor required when considering an appropriate wind turbine placement point. As a result, it becomes possible to derive an appropriate wind turbine placement point in a short time.

[0041] Also, in the present embodiment, the wind turbine placement determination unit 102c determines the wind turbine placement candidate points using the land condition data and social condition data stored in the storage unit 104. Therefore, not only the wind conditions but also the constraints that need to be considered when actually placing the wind turbine, such as the land conditions and social conditions, are taken into account, so that it becomes possible to further optimize the placement of the wind turbine.

[0042] In this embodiment, after the wind turbine layout determination unit 102c determines the wind turbine layout locations that satisfy the wind conditions within the wind turbine layout consideration area, it determines the wind turbine layout locations that satisfy the land conditions and social conditions. However, the wind turbine layout determination unit 102c may determine the wind turbine layout locations that satisfy the wind conditions after determining the wind turbine layout locations that satisfy the land conditions and social conditions. That is, in the flowchart shown in FIG. 5, the operations of steps S106 to S108 may be executed between step S100 and step S101. Also in this case, since not only the wind conditions but also the constraints that need to be considered when actually arranging the wind turbines, such as the land conditions and social conditions, are taken into account, it is possible to further optimize the layout of the wind turbines.

[0043] (Second Embodiment) FIG. 8 is a block diagram showing the configuration of the wind turbine layout optimization device according to the second embodiment. The same components as those of the wind turbine layout optimization device 1 according to the first embodiment described above are denoted by the same reference numerals, and detailed descriptions thereof are omitted.

[0044] In the wind turbine layout optimization device 2 shown in FIG. 8, in addition to the components of the wind turbine layout optimization device 1 according to the first embodiment, a cost data storage unit 104e is newly provided in the storage unit 104. The cost data storage unit 104e stores cost data required for the installation and operation of the wind turbine 200. The cost data shows, for example, the material cost, construction cost, transportation cost, operation cost, etc. of the wind turbine 200.

[0045] FIG. 9 is an example of a flowchart of the arithmetic processing executed by the wind turbine layout optimization device 2. Hereinafter, the arithmetic processing of the wind turbine layout optimization device 2 will be described.

[0046] In this embodiment, first, the operations of steps S100 to S103 described in the first embodiment are executed. That is, various values are input to the input unit 20 by the operator, and subsequently, the simulation unit 102a executes a wind condition simulation. Subsequently, the prediction model construction unit 102b constructs a prediction model of the wind conditions. Subsequently, the wind turbine placement determination unit 102c determines candidate locations for wind turbine placement based on the wind conditions. In step S103, the wind turbine placement determination unit 102c can use, for example, NSGA-II. By solving a two-objective optimization problem of AEP and cost, a plurality of wind turbine placements with a trade-off between AEP and cost can be output. This installation cost can be derived based on the cost data stored in the cost data storage unit 104e. Also, the wind turbine placement determination unit 102c can also obtain the installation cost using a branch-and-bound method, Prim's method, or Esau-Williams method.

[0047] Next, in this embodiment, the determination unit 103 determines whether there is a location among the candidate locations for wind turbine placement that satisfies the conditions that the AEP is maximized, the extreme wind speed is less than the threshold set in step S100, and the installation cost of the wind turbine 200 is minimized (step S104).

[0048] After step S104, the operations of steps S105 to S108 described in the first embodiment are executed. That is, following the determination of the wind turbine placement location based on the wind conditions by the wind turbine placement determination unit 102c, candidate locations for wind turbine placement are determined based on land conditions and social conditions, and the determination unit 103 determines whether the wind turbine placement location is appropriate as the candidate location for wind turbine placement. Finally, the display unit 30 displays the wind turbine placement location that satisfies all of the wind conditions, land conditions, and social conditions.

[0049] Also in this embodiment described above, similar to the first embodiment, since the prediction model construction unit 102b constructs a prediction model of the wind conditions, the labor required for considering appropriate wind turbine placement locations can be saved. As a result, it becomes possible to derive appropriate wind turbine placement locations in a short time. In addition, in the present embodiment, the wind turbine layout determination unit 102c determines candidate locations for wind turbine layout based on the cost data stored in the cost data storage unit 104e. Therefore, with respect to wind turbine layout, it is possible to achieve optimization from the perspective of the entire business considering not only soundness but also profitability in consideration of wind conditions, land conditions, and social conditions.

[0050] In the present embodiment as well, the order of determining the wind turbine layout locations that satisfy the wind conditions, the wind turbine layout locations that satisfy the land conditions and social conditions is not restricted. Therefore, the wind turbine layout determination unit 102c may determine the wind turbine layout locations that satisfy the wind conditions after determining the wind turbine layout locations that satisfy the land conditions and social conditions.

[0051] (Third Embodiment) Hereinafter, the third embodiment will be described. Since the configuration of the wind turbine layout optimization device according to the present embodiment is the same as that of the wind turbine layout optimization device 1 according to the first embodiment or the wind turbine layout optimization device 2 according to the second embodiment described above, the description thereof will be omitted.

[0052] When the land conditions and social conditions described in the first embodiment are changed, the land condition data stored in the land condition data storage unit 104b is updated, and the social condition data stored in the social condition data storage unit 104c is updated. After the data is updated, when steps S100 to S108 of the flowchart shown in FIG. 5 or FIG. 9 are executed, it becomes possible to optimize the layout of the wind turbine 200 based on the latest land conditions and social conditions.

[0053] However, land conditions and social conditions may change frequently. Therefore, every time the land conditions and social conditions change, repeating all the operations from step S100 to step S108 of the flowchart shown in FIG. 5 or FIG. 9 requires a lot of time for arithmetic processing and also increases the processing load. On the other hand, changes in land conditions and social conditions may not affect the simulation results of the simulation unit 102a executed in step S101. In this case, the wind condition prediction model created by the prediction model construction unit 102b in step S102 is also not affected by changes in land conditions and social conditions.

[0054] Therefore, in the above case, in the present embodiment, when the land condition data stored in the land condition data storage unit 104b and the social condition data stored in the social condition data storage unit 104c are updated along with changes in land conditions and social conditions, the wind turbine placement optimization device skips steps S100 to S105 and starts operating from step S106. That is, without performing a simulation of wind conditions, the wind turbine placement location based on wind conditions is determined using the previously created prediction model.

[0055] According to the present embodiment described above, even if the land conditions and social conditions change frequently, it is possible to shorten the time required for arithmetic processing to optimize the placement of wind turbines and reduce the processing load.

[0056] In the first to third embodiments described above, the wind turbine placement optimization device for appropriately placing the wind turbine 200 on land has been described. However, the wind turbine placement optimization device according to each embodiment can also be applied when the wind turbine 200 is appropriately placed offshore. When the wind turbine 200 is placed offshore, the content of land conditions and social conditions becomes related to offshore, such as fishing rights.

[0057] Although several embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel system described in this specification can be implemented in various other forms. Also, various omissions, substitutions, and changes can be made to the form of the system described in this specification without departing from the gist of the invention. The appended claims and the equivalents thereof are intended to include such forms and modifications within the scope and gist of the invention.

Explanation of Reference Numerals

[0058] 1, 2: Windmill Arrangement Optimization Device 40: Wind Condition Simulation Model 102a: Simulation Unit 102b: Prediction Model Construction Unit 102c: Windmill Arrangement Determination Unit 103: Judgment Unit 200: Windmill

Claims

1. A simulation department that simulates wind conditions in the area where the placement of wind turbines is being considered; a prediction model construction unit that uses a simulation result of the simulation unit to create a prediction model based on wind conditions related to the arrangement of the wind turbines; a wind turbine location determination unit that determines at least one candidate location for the wind turbine based on the prediction model; and A determination unit that determines whether the arrangement candidate point is appropriate; Equipped with The simulation unit simulates a wind speed that the wind turbine will experience when the wind turbine is placed within the area, the prediction model construction unit calculates a generated power amount and an extreme wind speed obtained from the wind turbine by regression analysis based on the wind speed, and creates a relational expression between a position coordinate of the wind turbine and at least one of the generated power amount and the extreme wind speed as the prediction model; the wind turbine placement determination unit determines, based on the prediction model, the position coordinates at which the amount of generated power is greater than a preset value of the amount of generated power, as the placement candidate point; The determination unit determines, among the candidate placement sites, a site where the amount of generated power is maximum and the extreme wind speed is smaller than a threshold value as appropriate.

2. The simulation unit samples the position coordinates when a plurality of wind turbines are arranged, 2. The wind turbine placement optimization device according to claim 1, wherein the wind turbine placement determination unit discretizes the position coordinates when the multiple wind turbines are placed, and determines the position coordinates when the multiple wind turbines are placed in the discretized virtual space using a linear programming method.

3. 3. The wind turbine location optimization device according to claim 1, wherein the wind turbine location determination unit determines the location candidate site based on land conditions that are land constraints on the location of the wind turbine, in addition to the wind conditions.

4. 4. The wind turbine location optimization device according to claim 1, wherein the wind turbine location determination unit determines the location candidate site based on social conditions that are social constraints on the location of the wind turbine, in addition to the wind conditions.

5. 5. The wind turbine placement optimization device according to claim 1, wherein when determining the placement candidate sites using NSGA-II, the wind turbine placement determination unit determines the cost associated with installing the wind turbines using any one of a branch and bound method, a Prim method, or an Esau-Eilliams method, with a multi-objective optimization problem of multiple wind turbine placements with trade-offs.

6. The wind turbine location optimization device according to claim 1 , wherein the determination unit determines whether or not the location candidate site is appropriate based on costs related to installation and operation of the wind turbine.

7. A simulation of wind conditions in an area where the placement of a wind turbine is being considered, the simulation comprising: simulating a wind speed that the wind turbine would experience if the wind turbine were placed in the area; creating a prediction model based on wind conditions for the arrangement of the wind turbines by regression analysis using the simulation results, calculating the amount of generated power and extreme wind speed obtained from the wind turbines based on the wind speed by regression analysis, and creating a relational expression between the position coordinates of the wind turbines and at least one of the amount of generated power and the extreme wind speed as the prediction model; determining at least one or more candidate locations for the wind turbine based on the prediction model, and determining, as the candidate location, the position coordinates at which the amount of generated power is greater than a preset value for the amount of generated power based on the prediction model; A wind turbine placement optimization method comprising: determining whether the placement candidate sites are appropriate; and determining, among the placement candidate sites, as appropriate, a site where the amount of generated power is maximum and the extreme wind speed is smaller than a threshold value.

8. A simulation of wind conditions in an area where the placement of a wind turbine is being considered, the simulation comprising: simulating a wind speed that the wind turbine would experience if the wind turbine were placed in the area; creating a prediction model based on wind conditions for the arrangement of the wind turbines by regression analysis using the simulation results, calculating the amount of generated power and extreme wind speed obtained from the wind turbines based on the wind speed by regression analysis, and creating a relational expression between the position coordinates of the wind turbines and at least one of the amount of generated power and the extreme wind speed as the prediction model; determining at least one or more candidate locations for the wind turbine based on the prediction model, and determining, as the candidate location, the position coordinates at which the amount of generated power is greater than a preset value for the amount of generated power based on the prediction model; A program for causing a computer to execute a process for determining whether the candidate placement sites are appropriate, and determining that a site among the candidate placement sites where the amount of generated power is maximum and the extreme wind speed is smaller than a threshold value is appropriate.

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