A method for simulating the microclimate impact of a photovoltaic installation based on computational fluid dynamics

Abstract

The application belongs to the technical field of climate effect simulation, and particularly relates to a photovoltaic facility microclimate influence simulation method based on computational fluid dynamics, which comprises the following steps: collecting photovoltaic facility parameters of a simulation experiment site, importing the parameters into drawing software to establish a photovoltaic facility main frame diagram; importing the main frame diagram into computational fluid dynamics simulation software to create a simulation calculation domain; creating a photovoltaic facility microclimate influence solver; collecting a plurality of parameter schemes and running the solver to obtain corresponding simulation data sets; collecting actual observation information of the source of the parameter schemes to verify the simulation data sets; based on the verified simulation data sets, quantitatively analyzing energy and water vapor transport changes of the photovoltaic facility of the simulation experiment site to surrounding areas, elucidating climate environment dynamic change rules and influence mechanisms of the photovoltaic facility, and predicting recovery and promotion effects of the photovoltaic facility on surrounding area vegetation, thereby providing a theoretical basis for photovoltaic array arrangement schemes in actual applications.

Description

Technical Field

[0001] This application belongs to the field of climate effect simulation technology, specifically relating to a method for simulating the microclimate impact of photovoltaic facilities based on computational fluid dynamics. Background Technology

[0002] Besides generating significant energy benefits, photovoltaic (PV) power generation facilities also produce substantial ecological and environmental benefits during their construction and operation phases. For example, research on PV facilities shows that the reflection of solar radiation by photovoltaic panels affects local radiation balance and atmospheric circulation, converting solar radiation, regulating the thermal balance of deserts and Gobi, and thus causing changes in regional microclimates (including increased temperature and precipitation), which contributes to the restoration and growth of vegetation in local areas. Due to the inherent engineering structure of PV facilities, they also function as windbreaks and sand barriers while collecting solar energy and generating electricity. Related research indicates that the deployment of PV facilities effectively reduces local wind speeds, thereby weakening the driving force behind sandstorms and wind-blown sand flows, providing favorable conditions for the restoration and growth of vegetation beneath the panels while also preventing wind erosion and sand fixation. Furthermore, in actual operation and maintenance, large-scale PV power plants can alter the effective precipitation beneath the panels due to the interception of rainwater by the PV panels during rainfall, redistributing precipitation and thus affecting shallow soil water and its infiltration. Especially in arid and semi-arid regions, the slab-shaped shadows and surface runoff created by photovoltaic power generation facilities have become an internal increment in the surface water cycle, promoting the growth of vegetation under the slabs.

[0003] Previous studies on the climate effects of photovoltaic (PV) facilities have primarily included on-site observations and numerical simulations. On-site meteorological observations provide continuous data on wind speed and direction, air temperature and humidity, and solar radiation, enabling long-term environmental impact analyses of PV facilities. However, current observational data for PV sites is insufficient, observation systems are incomplete, and remote sensing data is limited in terms of measurement accuracy and resolution. Therefore, model building and numerical simulation studies are effective means to understand the microclimate impact mechanisms of PV facilities and determine mitigation solutions. Among these, computational fluid dynamics (CFD) models have been widely used in PV facility numerical simulations due to their high computational accuracy and low experimental costs. For example, by using the shear stress transfer (SST) k–ω model combined with a fluid-structure interaction model, the distribution of wind speed and wind pressure at different locations within a PV facility site can be simulated, while the renormalization group (RNG) k-ε turbulence model has achieved good simulation results in analyzing the impact of PV panel tilt angle on wind speed and wind pressure.

[0004] Currently, the Northwest region of my country, where photovoltaic (PV) infrastructure development is relatively complete, has an arid climate and complex terrestrial water and energy cycles. The series of PV-climate-vegetation ecological effects generated by PV facility deployment remain unclear. Therefore, constructing a microclimate impact model of PV facilities based on computational fluid dynamics to simulate and analyze the series of climate and environmental effects caused by PV facilities, and revealing the promoting effect of PV power plants on the restoration and growth of vegetation beneath and around the panels, is of great practical significance. This will provide a data foundation and scientific basis for future policy formulation and the selection and layout of PV power plants. Summary of the Invention

[0005] To address at least one technical problem existing in the prior art, this application provides a method for simulating the microclimate impact of photovoltaic facilities based on computational fluid dynamics.

[0006] This application discloses a method for simulating the microclimate impact of photovoltaic facilities based on computational fluid dynamics, including the following steps: Step S1: Establish a model test site, collect photovoltaic facility basic structure parameters, photovoltaic facility construction parameters and photovoltaic facility material parameters, import the photovoltaic facility basic structure parameters and photovoltaic facility construction parameters into drawing software to create a main frame diagram of the photovoltaic facility, which serves as the model reference input structure; Step S2: Import the main framework diagram into the computational fluid dynamics simulation software, create a simulation domain, set the domain boundary, and define the boundary parameters; Step S3: Based on the simulation computing domain, create a solver for the microclimate impact of photovoltaic facilities; Step S4: Set different parameter schemes according to the photovoltaic facility foundation structure parameters and photovoltaic facility construction parameters, and use the solver to solve the different parameter schemes to obtain multiple simulation datasets under the corresponding photovoltaic facility foundation structure parameters and photovoltaic facility construction parameters; The parameter scheme is a combination of parameters for the basic structural parameters and construction parameters of photovoltaic facilities; The simulation dataset includes: wind speed, temperature, and humidity flow fields corresponding to the parameter scheme; Step S5: Collect actual observation information from the source location of the parameter scheme and verify the simulation dataset; Step S6: Based on the validated simulation dataset, quantitatively analyze the relationship between the photovoltaic facilities at the simulated target site and the surrounding environment and climate, and derive the corresponding results; The methods used in the quantitative analysis include: Based on time series trend analysis of temperature and humidity during the growing season and non-growing season; Comparative analysis of temperature and humidity under photovoltaic panels, between panels, and in areas without photovoltaic panel coverage; Comparative analysis of wind speeds at different locations of the photovoltaic array from the wind speed inlet.

[0007] In some embodiments, the basic structural parameters of the photovoltaic facility include: photovoltaic panel column height, horizontal axis span, and main axis intercept; The construction parameters for the photovoltaic facilities include: photovoltaic panel tilt angle, support column spacing, and support row spacing; The photovoltaic facility material parameters include: the refractive index of the photovoltaic panel.

[0008] In some embodiments, the drawing software in step S1 is AutoCAD, and the computational fluid dynamics simulation software in step S2 is OpenFOAM; Step S2 further includes: Step S20: Generate a 3D model of the main frame diagram of the photovoltaic facility in AutoCAD, export it in STL format and set it as a positive coordinate system, and embed it into the OpenFOAM case folder.

[0009] In some embodiments, in step S2: The simulation computing domain was established by creating a structured hexahedral mesh using the blockMesh function in OpenFOAM; The boundary parameters of the computational domain boundary are set according to the coordinate range of the STL file; Step S2 also includes: Step S21: Process the STL file based on the snappyHexMesh function in OpenFOAM. The processing refers to converting the input basic shape into a three-dimensional hexadecimal master mesh.

[0010] In some embodiments, the solver in step S3 includes: a continuity equation solver, a momentum conservation equation solver, a shear stress transfer k–ω model solver, and a humidity-temperature equation solver.

[0011] In some embodiments, the continuity equation for the fluid in the continuity equation solver is:

[0012] in, (i = 1, 2, 3, x1 = x, x2 = y, x3 = z) represent the horizontal and vertical directions respectively; (u1 = u, u2 = v, u3 = w) represents the wind speed components in the x, y, and z axes.

[0013] In some embodiments, the momentum conservation equation in the momentum conservation equation solver is:

[0014] in, t is the fluid density; p is the time; t is the static pressure. It is dynamic viscosity; It is turbulent viscosity.

[0015] In some embodiments, the turbulent transport equations in the k–ω model solver for shear stress transmission are:

[0016] in, It is turbulent kinetic energy; It is the specific dissipation rate; and These are the generating terms of turbulent kinetic energy and specific dissipation rate, respectively; and These are the effective diffusion rate and specific dissipation rate of turbulent kinetic energy, respectively. and These are the dissipation of turbulent kinetic energy and the specific dissipation rate, respectively. It is a cross-diffusion term; In the k–ω model of shear stress transmission, the shear stress is proportional to k, therefore the turbulent viscosity ( The formula is defined as follows:

[0017] in, It is an invariant measure of the rate of change of turbulence. It is an empirical coefficient. It is the distance from the board surface to the ground. =0.09.

[0018] In some embodiments, the humidity-temperature equation solver includes an energy equation (9) and a water vapor equation (10), specifically:

[0019]

[0020] in, , , and These are Prandtl number, turbulent Prandtl number, Schmidt number, and turbulent Schmidt number, respectively. , , These are the source terms of the equations for temperature, plant humidity, and soil moisture, respectively. , The calculation formula is as follows:

[0021] in, It is the ratio of the area to the volume density of the photovoltaic panel for heat exchange with the surrounding air; It is the leaf area volume density of the vegetation under the board that participates in the exchange of water vapor with the surrounding air. , , and These are the temperature of the photovoltaic panel surface, the air temperature, the humidity of the vegetation canopy surface, and the air humidity, respectively. and These are the heat absorption coefficient of the photovoltaic module and the stomatal conductance of plant transpiration, respectively. The volumetric density of the soil and surrounding air under photovoltaic panels is the density through which moisture is exchanged between them. This refers to the temperature of the soil surface layer; It is the average relative soil moisture content under soil porosity s.

[0022] In some embodiments, step S4 further includes determining the inlet and outlet of the flow field and setting corresponding boundary conditions and boundary types for different boundaries before the solver solves different parameter schemes.

[0023] This application has at least the following beneficial technical effects: 1. The computational fluid dynamics-based method for simulating the microclimate impact of photovoltaic facilities in this application collects photovoltaic facility parameters through experimental sites, then establishes a computational domain and constructs a solver. Compared with conventional methods, it has lower requirements for data acquisition, and the whole method can be applied to areas with little or no observation data, and has a wide range of regional adaptability. 2. The computational fluid dynamics-based method for simulating the microclimate impact of photovoltaic facilities in this application obtains the simulation dataset by acquiring parameters of domestic photovoltaic facilities through big data and other means and inputting them into the solver. This dataset has universality and is applicable to most types / configurations of photovoltaic power plants. It can provide parameterized schemes for optimizing the layout design of photovoltaic power plants. 3. The computational fluid dynamics-based method for simulating the microclimate impact of photovoltaic facilities in this application incorporates source terms related to surface vegetation and soil into the energy equation and water vapor equation in the humidity and temperature equation solver. This approach integrates the influence of soil, vegetation, and other factors on the microclimate around photovoltaic facilities, making the simulation scenario more consistent with actual scenarios. Attached Figure Description

[0024] Figure 1 This is a flowchart of the soil-vegetation-photovoltaic-atmosphere continuum model according to an embodiment of this application; Figure 2This is a structural diagram of the soil-vegetation-photovoltaic-atmosphere continuum model according to an embodiment of this application; Figure 3 This is a comparison of wind speed curves from fluid dynamics simulation and field observation in a specific example of this application; Figure 4 This is a three-dimensional wind speed field streamline diagram in a specific example of this application; Figure 5 This is a simulation result of temperature and humidity at different locations of the photovoltaic site selected during the growing season in a specific example of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. The described embodiments are only some, not all, of the embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0026] The following is a detailed description of the computational fluid dynamics-based method for simulating the microclimate impact of photovoltaic facilities, with reference to the accompanying drawings. All steps of this method can be verified in practice.

[0027] Method Principles This application discloses a method for simulating the microclimate impact of photovoltaic facilities based on computational fluid dynamics, such as... Figure 1 and Figure 2 As shown, this method specifically includes the following steps: Step S1: Construct a photovoltaic facility model experimental site and integrate photovoltaic facility parameters, including photovoltaic facility basic structure parameters, photovoltaic facility construction parameters, and photovoltaic facility material parameters; import the collected photovoltaic facility basic structure parameters and photovoltaic facility construction parameters into drawing software and create a main framework diagram as the model's baseline input structure (all photovoltaic facility basic structure parameters are reflected in the framework diagram); the drawing software can be any of the currently known suitable software, and in this embodiment, AutoCAD is preferred.

[0028] Furthermore, the photovoltaic (PV) facility parameters used in the simulated PV facility test sites were selected through big data screening, choosing the most widely applicable and mainstream PV facility models and parameters in China. If mainstream domestic solutions were unsuitable for the test areas, the most mainstream PV facility models and parameters in the local area were selected.

[0029] Furthermore, the basic structural parameters of photovoltaic facilities include: wind speed, temperature, humidity, and flow field of photovoltaic facilities; the construction parameters of photovoltaic facilities include: tilt angle of photovoltaic panels, spacing between support columns, and spacing between support rows; and the material parameters of photovoltaic facilities include: refractive index of photovoltaic panels.

[0030] Step S2: Import the main framework diagram of the photovoltaic facility completed in step S1 into the open-source computational fluid dynamics (CFD) simulation software, create a simulation calculation domain, set the domain boundary and define the boundary parameters.

[0031] Specifically, this embodiment will use OpenFOAM as an example to briefly describe the simulation calculation process, including the following steps: Step S20: Convert the main frame diagram of the photovoltaic facility into a 3D model in AutoCAD, and set the corresponding generated STL file to a positive coordinate system and embed it into the OpenFOAM example folder.

[0032] Furthermore, in step S2, the simulation computation domain is established by creating a structured hexahedral mesh using the blockMesh function in OpenFOAM (and the simulation computation domain is created based on this mesh); in addition, the boundary parameters of the computation domain boundary are set according to the coordinate range of the Stl file. By setting a reasonable computation domain boundary and defining the boundary parameters, the complete generation of the wake and other flow fields is ensured in the subsequent calculation process.

[0033] Accordingly, step S2 also includes: Step S21: The STL file is processed using the snappyHexMesh function in OpenFOAM to create a high-quality three-dimensional hexadecimal master mesh from the input of a simple basic shape, so as to facilitate subsequent simulation calculations.

[0034] Step S3: Based on the simulation computation domain obtained in Step S2, create a solver for the microclimate impact of photovoltaic facilities, and customize the solver source code using a programming language. Customization includes: numerical methods for the simulation process, discretization schemes, and other essential functions necessary for solver operation.

[0035] Specifically, the source code compilation includes the following formulas: continuity equation, momentum conservation equation, shear stress transfer k–ω model, and humidity-temperature equation, which can also be called the continuity equation solver, momentum conservation equation solver, shear stress transfer k–ω model solver, and humidity-temperature equation solver, respectively.

[0036] The continuity equation reflects the relationship between fluid motion and fluid mass distribution, and is a concrete expression of the law of conservation of mass in classical mechanics within fluid mechanics. According to the law of conservation of mass, for a fixed volume element, the mass of fluid flowing out of the fluid element per unit time should be equal to the decrease in mass within the fluid element per unit time, that is:

[0037] In formula (1), For fluid density; (i = 1, 2, 3, x1 = x, x2 = y, x3 = z) represent the horizontal and vertical directions respectively; (u1 = u, u2 = v, u3 = w) represents the wind speed components along the x, y, and z axes; t represents time. In this study, air is considered an incompressible fluid, therefore the density of each fluid particle remains constant during the flow process, and the continuity equation of the fluid is given by equation (2):

[0038] Furthermore, according to the momentum theorem, the forces acting on a fluid element mainly come from two sources: one is volume forces, which are forces acting directly on the entire volume element of the fluid element, such as gravity and electromagnetic forces; the other is surface forces, including pressure and viscous forces acting on the surface of the element. The resulting momentum conservation equation is as follows:

[0039] In formula (3), p is the static pressure; It is the dynamic viscosity (1.79×10⁻⁵ N·s·m⁻²). It is turbulent viscosity.

[0040] Furthermore, the shear stress transport (SST) k–ω model can be expressed by the following formula:

[0041] In formulas (4) and (5), It is turbulent kinetic energy. It is the specific dissipation rate; and These are the generating terms of turbulent kinetic energy and specific dissipation rate, respectively; and These are the effective diffusion rate and specific dissipation rate of turbulent kinetic energy, respectively. and These are the dissipation of turbulent kinetic energy and the specific dissipation rate, respectively. It is a cross-diffusion term.

[0042] In the SST k–ω model, since the shear stress is proportional to k, the turbulent viscosity can be expressed as... ) is defined as:

[0043] In formulas (6), (7), and (8), It is an invariant measure of the rate of change of turbulence. It is an empirical coefficient; It is the distance from the board surface to the ground. =0.09.

[0044] Furthermore, when constructing the temperature and humidity equations for the soil-vegetation-photovoltaic-atmosphere continuum model, the heat absorption and release of the photovoltaic panels themselves, as well as the influence of the vegetation beneath the panels on temperature and humidity, must be considered. Therefore, the temperature and humidity equations include: the energy equation and the water vapor equation, which are as follows:

[0045]

[0046] In formulas (9) and (10), , , and These are Prandtl number, turbulent Prandtl number, Schmidt number, and turbulent Schmidt number.

[0047] , , These are the source terms of the equations for temperature, plant humidity, and soil moisture, respectively. and, The calculation formulas are as follows:

[0048] In formulas (11), (12), and (13), It is the ratio of the area to the volume density of the photovoltaic panel for heat exchange with the surrounding air; It is the leaf area volume density of the vegetation under the board that participates in the exchange of water vapor with the surrounding air. , , and These are the temperature of the photovoltaic panel surface, the air temperature, the humidity of the vegetation canopy surface, and the air humidity, respectively. and These are the heat absorption coefficient of the photovoltaic module and the stomatal conductance of plant transpiration, respectively. The volumetric density of the soil and surrounding air under photovoltaic panels is the density through which moisture is exchanged between them. This refers to the temperature of the soil surface layer; It is the average relative soil moisture content under soil porosity s.

[0049] In this step, source terms related to surface vegetation and soil are added to the energy equation and water vapor equation in the humidity and temperature equation solver. This incorporates the influence of soil, vegetation and other factors on the microclimate around the photovoltaic facility, making the simulation scenario more consistent with the actual situation.

[0050] Step S4: Based on the photovoltaic facility foundation structure parameters and photovoltaic facility construction parameters collected in Step S1, set different parameter schemes, and use the solver to solve the different parameter schemes. Before running the solver, first determine the inlet and outlet of the flow field, and set reasonable boundary conditions and edge types for different boundaries. Then run the solver to collect and organize the simulation datasets under the corresponding photovoltaic facility foundation structure parameters and photovoltaic facility construction parameters.

[0051] Specifically, in this embodiment, the parameter scheme mainly refers to the combination of parameters for the basic structural parameters of photovoltaic facilities and the construction parameters of photovoltaic facilities; the simulation dataset includes: wind speed, temperature and temperature flow field, etc., for the corresponding scenario / parameter scheme.

[0052] Step S5: Obtain the actual observation data from the site selected in Step S4 (i.e., the source of the parameter scheme), and verify and analyze the simulation dataset in S4 to ensure its accuracy.

[0053] Specifically, in this embodiment, the verification items include: wind speed verification, temperature and humidity verification, and temperature-flow field verification. As for the verification methods, there are many mature calculation methods available on the market, which will not be elaborated here.

[0054] Step S6: Based on the validated simulation dataset, quantitatively analyze the relationship between the photovoltaic facilities at the simulated target site and the surrounding environment and climate, and derive the corresponding results.

[0055] The quantitative analysis methods include: time series trend analysis of temperature and humidity based on the growing season and non-growing season; comparative analysis of temperature and humidity under, between, and in areas without photovoltaic panel coverage; and comparative analysis of wind speed at different locations of the photovoltaic array from the wind speed inlet.

[0056] The following section will further illustrate the computational fluid dynamics-based method for simulating the microclimate impact of photovoltaic facilities, based on a specific application example: Example 1 The experimental site was located at the photovoltaic site in Desheng Village, Zhangjiakou City, Hebei Province.

[0057] The photovoltaic site in Desheng Village, Zhangjiakou City, Hebei Province (41°10'16"-41°10'43" N, 114°50'03"-114°50'36" E) has a temperate continental monsoon climate with an average annual temperature of 6.2 ℃ (25.7 ℃ in summer and -5.9 ℃ in winter). Built in 2016, the photovoltaic power station covers an area of ​​173 hectares and has two 500 kW photovoltaic generator units, generating 750,000 kWh of electricity annually. Within the study area, the photovoltaic panel array is arranged in an east-west direction and tilted southward at a predetermined angle.

[0058] Desheng Village has a relatively open terrain, and the photovoltaic power station is far from the city center, so the impact of the urban heat island on wind speed and temperature can be ignored. The photovoltaic facilities on the site include three types: flat single-axis, inclined single-axis, and high-support. Chinese medicinal herbs and cash crops are planted under the photovoltaic panels, including astragalus, bupleurum, saposhnikovia, and alfalfa, forming an integrated agricultural-photovoltaic economic system.

[0059] Currently, ecological data monitoring at the Desheng Village photovoltaic observation site is divided into two types: long-term fixed-location monitoring and regular field surveys. Fixed-location monitoring includes equipment deployed at typical photovoltaic sites such as the EM50 soil temperature and humidity monitoring system, the HOBO air temperature and humidity monitoring system, a small weather station, and lysimeters. Observational indicators include ground air temperature and humidity, soil temperature and humidity, soil evaporation, surface wind speed and direction, irradiance, and precipitation. Regular surveys, based on different crop types at the photovoltaic sites, monitor meteorological elements such as surface wind speed, temperature, humidity, and irradiance, as well as biological indicators such as vegetation cover, biomass, and biodiversity.

[0060] like Figure 3 As shown, a comparison of wind speed curves from CFD simulation and actual field observations is presented. Figure 3 (a) is a photovoltaic array with a tilt angle of 35°, a column spacing of 0 meters, and a row spacing of 1 meter; Figure 3 (b) is a photovoltaic array with a tilt angle of 35°, a column spacing of 0 meters, and a row spacing of 2 meters.

[0061] The model was validated by comparing wind speed profiles at five locations (30m, 40m, 50m, 60m, and 125m from the entrance), all of which had wind speeds 1m above the ground surface. The results show that the simulation and observations at the selected locations are in excellent agreement. The wind speed profile at the first row of photovoltaic panels, located 30m from the entrance, closely matches the observed data. Subsequently, the wind speed shows a clear decreasing trend. At 40m and 50m (directly behind the third and sixth rows of photovoltaic panels), the wind speed profiles deviate slightly from the observed data. The wind profile shows a clear decreasing trend as it passes through the photovoltaic panels, only beginning to rise at the next row, consistent with normal physical laws. The wind profile at 60m (5m behind the last row of photovoltaic panels) also shows a good correlation with the observed data. The wind profile at 125m (75m behind the last row of photovoltaic panels) is consistent with the observed data. Based on these validations, it can be concluded that the current CFD model and simulation can provide high-resolution and accurate airflow results.

[0062] like Figure 4 As shown, a three-dimensional wind speed field streamline diagram in a real-world application, wherein... Figure 4 (a) is a three-dimensional simulation streamline diagram and wake of wind speed with a row spacing of 1 meter for the photovoltaic array; Figure 4 (b) is a three-dimensional simulation streamline diagram and wake of wind speed for a photovoltaic array with a row spacing of 3 meters. The inlet wind speed of both arrays is the same, which is 4 m / s.

[0063] Considering different row spacing schemes, the intricate three-dimensional turbulent vortices around the photovoltaic array are visually presented. Figure 4 In both different configurations, the flow pattern around the initial row of photovoltaic panels remained largely unchanged. Notably, compared to the case with narrower row spacing, the impact wind speed was significantly reduced with wider row spacing, resulting in an expanded turbulent vortex region and a shortened weakened flow region in the leeward direction. Therefore, the photovoltaic panel array with optimal row spacing exhibited a stronger effect in mitigating wind intensity. Furthermore, in other configurations, the turbulent vortices observed in the configuration with larger column spacing and tilt angle also showed larger turbulent vortex regions and shorter weakened flow regions compared to the configuration with smaller column spacing and tilt angle. It can be seen that increasing the number of photovoltaic panel arrays improves windbreak efficiency and extends the wind speed recovery distance.

[0064] like Figure 5 As shown in the simulation data of temperature and humidity at different locations of the site during the growing season from July to September, it can be seen that: in terms of humidity, the photovoltaic power station has a significant moisturizing effect, but it increases the humidity difference between day and night; in terms of temperature, the air temperature in the area covered by the photovoltaic power station increases, with an average increase of about 1.2℃, and the photovoltaic power station has a significant buffering effect on sudden temperature changes in the surrounding area.

[0065] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for simulating the microclimate impact of photovoltaic facilities based on computational fluid dynamics, characterized in that, Includes the following steps: Step S1: Establish a model test site, collect photovoltaic facility basic structure parameters, photovoltaic facility construction parameters and photovoltaic facility material parameters, import the photovoltaic facility basic structure parameters and photovoltaic facility construction parameters into drawing software to create a main frame diagram of the photovoltaic facility, which serves as the model reference input structure; The basic structural parameters of the photovoltaic facility include: photovoltaic panel column height, horizontal axis span, and main axis intercept. The construction parameters for the photovoltaic facilities include: photovoltaic panel tilt angle, support column spacing, and support row spacing; The photovoltaic facility material parameters include: the refractive index of the photovoltaic panel; Step S2: Import the main framework diagram into the computational fluid dynamics simulation software, create a simulation domain, set the domain boundary, and define the boundary parameters; Step S3: Based on the simulation computing domain, create a solver for the microclimate effects of photovoltaic facilities; The solver specifically includes: a continuity equation solver, a momentum conservation equation solver, a shear stress transport k–ω model solver, and a humidity-temperature equation solver. In the continuity equation solver, the continuity equation for the fluid is: （2）； in, (i = 1, 2, 3, x1 = x, x2 = y, x3 = z) represent the horizontal and vertical directions respectively; (u1 = u, u2 = v, u3 = w) represents the wind speed components in the x, y, and z axis directions; The momentum conservation equation in the momentum conservation equation solver is: （3）； in, t is the fluid density; t is time; p is the static pressure; It is dynamic viscosity; It is turbulent viscosity; The turbulent transport equation in the k–ω model solver for shear stress transmission is: （4）； （5）； in, It is turbulent kinetic energy; It is the specific dissipation rate; and These are the generating terms of turbulent kinetic energy and specific dissipation rate, respectively; and These are the effective diffusion rate and specific dissipation rate of turbulent kinetic energy, respectively. and These are the dissipation of turbulent kinetic energy and the specific dissipation rate, respectively. It is a cross-diffusion term; In the k–ω model of shear stress transmission, the shear stress is proportional to k, therefore the turbulent viscosity ( The formula is defined as follows: （6）； （7）； （8）； in, It is an invariant measure of the rate of change of turbulence. It is an empirical coefficient. It is the distance from the board surface to the ground. =0.09; The humidity-temperature equation solver includes an energy equation (9) and a water vapor equation (10), specifically: （9）； （10）； in, , , and These are Prandtl number, turbulent Prandtl number, Schmidt number, and turbulent Schmidt number, respectively. , , These are the source terms of the equations for temperature, plant humidity, and soil moisture, respectively. , The calculation formula is as follows: （11）； （12）； （13）； in, It is the ratio of the area density to the volume density of the photovoltaic panel and the surrounding air in terms of heat exchange. It is the leaf area density of the vegetation under the board that participates in the exchange of water vapor with the surrounding air. , , and These are the temperature of the photovoltaic panel surface, the air temperature, the humidity of the vegetation canopy surface, and the air humidity, respectively. and These are the heat absorption coefficient of photovoltaic modules and the stomatal conductance of plant transpiration, respectively. The volumetric density of the soil and surrounding air under photovoltaic panels is the density of moisture exchanged between them. This refers to the temperature of the soil surface layer; It is the average relative soil moisture content under soil porosity s; Step S4: Set different parameter schemes according to the photovoltaic facility foundation structure parameters and photovoltaic facility construction parameters, and use the solver to solve the different parameter schemes to obtain multiple simulation datasets under the corresponding photovoltaic facility foundation structure parameters and photovoltaic facility construction parameters; The parameter scheme is a combination of parameters for the basic structural parameters and construction parameters of photovoltaic facilities; The simulation dataset includes: wind speed, temperature, and humidity flow fields corresponding to the parameter scheme; Step S5: Collect actual observation information from the source location of the parameter scheme and verify the simulation dataset; Step S6: Based on the validated simulation dataset, quantitatively analyze the relationship between the photovoltaic facilities at the simulated target site and the surrounding environment and climate, and derive the corresponding results; The methods used in the quantitative analysis include: Based on time series trend analysis of temperature and humidity during the growing season and non-growing season; Comparative analysis of temperature and humidity under photovoltaic panels, between panels, and in areas without photovoltaic panel coverage; Comparative analysis of wind speeds at different locations of the photovoltaic array from the wind speed inlet.

2. The simulation method according to claim 1, characterized in that, The drawing software in step S1 is AutoCAD, and the computational fluid dynamics simulation software in step S2 is OpenFOAM; Step S2 further includes: Step S20: Generate a 3D model of the main frame diagram of the photovoltaic facility in AutoCAD, export it in STL format and set it as a positive coordinate system, and embed it into the OpenFOAM case folder.

3. The simulation method according to claim 2, characterized in that, In step S2: The simulation computing domain was established by creating a structured hexahedral mesh using the blockMesh function in OpenFOAM; The boundary parameters of the computational domain boundary are set according to the coordinate range of the STL file; Step S2 also includes: Step S21: Process the STL file based on the snappyHexMesh function in OpenFOAM. The processing refers to converting the input basic shape into a three-dimensional hexadecimal master mesh.

4. The simulation method according to claim 1, characterized in that, In step S4, before the solver solves different parameter schemes, it also includes: determining the inlet and outlet of the flow field, and setting corresponding boundary conditions and boundary types for different boundaries.

Images