Method for determining optimal installation spacing of photovoltaic array in combination with geographic parameters

Abstract

The invention provides a method for determining the optimal installation spacing of a photovoltaic array in combination with geographic parameters, and relates to the technical field of photovoltaic power station measurement and calculation, and the method comprises the steps: obtaining the installation place of the photovoltaic array, and collecting the comprehensive parameters of the installation place; determining a corresponding geographical spacing coefficient; calculating sun position parameters according to the geographic parameters; obtaining an optimal inclination angle of the photovoltaic module according to the terrain parameters; calculating a basic installation interval, calculating a comprehensive correction coefficient according to the comprehensive parameters, and calculating an optimized installation interval according to the basic installation interval and the comprehensive correction coefficient; arranging the photovoltaic array according to the optimized installation spacing, performing shadow distribution simulation and generating capacity analysis in simulation software, and performing fine adjustment on the optimized installation spacing according to a simulation result; and outputting and obtaining the optimal installation spacing until the simulation result meets the requirement. According to the method, accurate calculation and optimal design of the photovoltaic array installation spacing can be realized, so that the land utilization rate and the power generation efficiency of a photovoltaic power station are improved, and finally the maximization of economic benefits is realized.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power station measurement technology, and in particular to a method for determining optimal installation spacing of photovoltaic arrays in combination with geographical parameters. Background Art

[0002] Photovoltaic array is the core component of solar power generation system, which is composed of multiple photovoltaic modules arranged in a specific way. Its installation layout directly affects the power generation efficiency and economy of the system, among which the installation spacing is the key parameter of photovoltaic array layout. Reasonable installation spacing can not only ensure that photovoltaic modules receive sufficient solar radiation, but also improve land utilization, which has an important impact on the overall benefits of photovoltaic power stations.

[0003] In the prior art, the methods for determining the installation spacing of photovoltaic arrays mainly include: empirical formula method, minimum shadow shielding method and power generation optimization method. Among them, the empirical formula method mainly calculates the installation spacing based on latitude and component size; the minimum shadow shielding method determines the installation spacing by calculating the minimum solar altitude angle on the winter solstice; the power generation optimization method optimizes the calculation based on the consideration of shadow shielding, combined with power generation and economy. These methods have been applied to a certain extent in actual engineering.

[0004] The invention patent with Chinese application number 201811569149.6 discloses a spacing optimization method for bifacial photovoltaic modules. By establishing a calculation model for the radiation of the front and back sides of bifacial photovoltaic modules and combining it with PVsyst software for verification calculation, the radiation gain, total power generation of the power station and static unit electricity cost under different installation spacings are calculated, and finally the optimal installation spacing is determined to maximize the economic benefits of the power station. However, this method has the following technical defects: first, its calculation model is too simplified and does not fully consider the influence of environmental factors such as terrain and meteorology; second, the optimization process only considers the static unit electricity cost, ignoring dynamic factors such as operation and maintenance costs and component attenuation.

[0005] Therefore, a new method for determining the installation spacing of photovoltaic arrays is needed to solve the above problems. Summary of the invention

[0006] In view of this, the present invention proposes a method for determining the optimal installation spacing of photovoltaic arrays in combination with geographical parameters. By establishing a complete multi-source parameter acquisition system, constructing a scientific calculation model and correction mechanism, and combining simulation analysis and dynamic optimization adjustment, the accurate calculation and optimal design of the installation spacing of photovoltaic arrays can be achieved, thereby improving the land utilization rate and power generation efficiency of photovoltaic power stations, and ultimately achieving maximum economic benefits.

[0007] The technical solution of the present invention is achieved in this way:

[0008] The present invention provides a method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters, comprising:

[0009] S1. Obtain the installation location of the photovoltaic array and use a multi-source data acquisition system to collect comprehensive parameters of the installation location, including geographical parameters, topographic parameters, geological parameters and meteorological parameters;

[0010] S2. Determine the corresponding geographic spacing coefficient based on the geographic parameters of the installation site;

[0011] S3. Calculate the solar position parameters according to the geographical parameters, including the minimum solar altitude angle and the solar azimuth angle;

[0012] S4, adjusting the inclination angle of the photovoltaic module based on the optimal light receiving area according to the terrain parameters to obtain the optimal inclination angle of the photovoltaic module;

[0013] S5. Calculate the basic installation spacing based on the optimal inclination angle, the minimum solar altitude angle and the geographic spacing coefficient, calculate the comprehensive correction coefficient based on the comprehensive parameters, and calculate the optimized installation spacing based on the basic installation spacing and the comprehensive correction coefficient;

[0014] S6. Arrange the photovoltaic array according to the optimized installation spacing, perform shadow distribution simulation and power generation analysis in the simulation software, and fine-tune the optimized installation spacing according to the simulation results;

[0015] S7. Repeat step S6 until the simulation result meets the requirements and output the optimal installation spacing.

[0016] Based on the above scheme, preferably, the geographical parameters include longitude, latitude and altitude; the terrain parameters include terrain slope and terrain orientation; the geological parameters include soil type, geological stability and groundwater level; the meteorological parameters include maximum wind speed, maximum snow thickness, annual maximum temperature and annual minimum temperature.

[0017] Based on the above solution, preferably, step S2 includes:

[0018] The latitude is divided into three regions according to the range, namely low-latitude region, mid-latitude region and high-latitude region;

[0019] According to the latitude of the installation location Determine the region to which it belongs and its characteristic parameters a i and b i , i = 1, 2, 3, where i = 1 represents the low-latitude region, i = 2 represents the mid-latitude region, and i = 3 represents the high-latitude region;

[0020] According to the characteristic parameters of the region, calculate the geographical spacing coefficient K1:

[0021]

[0022] In the formula, is the absolute value of the latitude of the installation location, a1 <a2<a3,b1<b2<b3;

[0023] Among them, the value range of the geographical spacing coefficient K1 is 1.0-2.0. If the calculated K1<1.0, K1=1.0 is taken; if the calculated K1>2.0, K1=2.0 is taken.

[0024] Based on the above solution, preferably, the calculation formula for the foundation installation spacing is:

[0025]

[0026] Where D base is the basic installation spacing, L is the length of the photovoltaic module, θ is the optimal inclination angle, β is the terrain slope, α min is the minimum solar altitude angle, and K1 is the geographic spacing coefficient.

[0027] Based on the above scheme, preferably, the calculation formula of the comprehensive correction coefficient is:

[0028] K=K GSI ×K MCI ×K env

[0029] In the formula, K GSI is the geological correction factor, K MCI is the microclimate correction coefficient, K env Is the environmental correction coefficient. Based on the above scheme, preferably, in the comprehensive correction coefficient, the calculation formula of each coefficient is as follows:

[0030] K GSI =w1×S+w2×R+w3×E

[0031] K MCI =T×w T +H×w H +V×w V

[0032]

[0033] Where S is the geological stability factor; R is the soil type factor; E is the groundwater level factor; w1, w2, w3 are the corresponding weight coefficients, and w1+w2+w3=1; T is the temperature factor, T=(T max -T min ) / T0,T max , T minis the annual maximum temperature and annual minimum temperature, T0 is the reference temperature difference; H is the humidity influence factor, H = RH / RH0, RH is the annual average relative humidity, RH0 is the reference relative humidity; V is the wind speed influence factor, V = (v / v0) 2 , v is the maximum wind speed at the installation site, v0 is the reference wind speed; w T 、w H 、w V is the corresponding weight coefficient, and w T +w H +w V =1; h is the altitude of the installation site; h0 is the reference altitude; ρ is the ground reflectivity; ρ0 is the reference reflectivity; μ1 and μ2 are adaptive coefficients; ΔP / P0 is the power generation efficiency improvement ratio; ΔV / V0 is the wind speed change ratio.

[0034] Based on the above solution, preferably, in step S5, the calculation formula for optimizing the installation spacing is as follows:

[0035]

[0036] Where D base is the basic installation distance, K is the comprehensive correction coefficient, f(K GSI ,K MCI ) is the geological-microclimate coupling function, k is the environmental impact index, v is the maximum wind speed at the installation site, and s is the maximum snow thickness.

[0037] Based on the above scheme, preferably, the expression of the geology-microclimate coupling function is as follows:

[0038] f(K GSI ,K MCI )=1+λ×(K GSI -1)×(K MCI -1)

[0039] In the formula, K GSI is the geological correction factor, K MCI is the microclimate correction coefficient, λ is the coupling coefficient, and its value range is [0.1, 0.3], f(K GSI ,K MCI ) satisfies 0.9≤f(K GSI ,K MCI )≤1.1.

[0040] Based on the above scheme, preferably, the environmental impact index k adopts a dynamic weight calculation method:

[0041]

[0042] Where k0 is the basic environmental index, T max , Tmin are the annual maximum temperature and the annual minimum temperature, RH is the annual average relative humidity, RH0 is the benchmark relative humidity, UV is the annual average ultraviolet intensity, UV0 is the benchmark ultraviolet intensity, ω1, ω2, ω3 are dynamic weight coefficients, and ω1+ω2+ω3=1, where the dynamic weight coefficient is adjusted dynamically according to the season.

[0043] Based on the above solution, preferably, step S6 includes:

[0044] S61, establish a three-dimensional model of the photovoltaic array, import the geographical location, meteorological data, component parameters and photovoltaic array layout plan;

[0045] S62. Perform hourly shadow distribution simulation throughout the year in the simulation software, including calculating static shadow coverage area and occlusion rate, simulating dynamic shadow change trajectory throughout the year, and analyzing shadow maps during key periods;

[0046] S63, performing system power generation analysis, including calculating theoretical power generation based on irradiance data, predicting actual power generation considering system loss factors, and calculating system performance ratio PR value;

[0047] S64, set optimization target indicators: shadow loss rate is not greater than τ1; area utilization is not less than τ2; performance ratio PR value is not less than τ3;

[0048] S65. Optimize the spacing based on the simulation results: when the shadow loss rate exceeds the standard, gradually increase the row spacing and record the data; when the area utilization is insufficient, reduce the row spacing while meeting the shadow loss rate; when the performance ratio PR value does not meet the standard, analyze the various losses and adjust the layout parameters accordingly.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] (1) The present invention realizes the accurate calculation and dynamic optimization of the installation spacing of photovoltaic arrays by establishing a complete technical system of multi-source data collection, geographical partition calculation, multi-level correction and dynamic optimization. The method integrates geographical parameters, terrain parameters, geological parameters and meteorological parameters in a systematic way, and adopts an iterative optimization method to ensure that the installation spacing can not only ensure that the photovoltaic components obtain the best light-receiving area, but also adapt to the complex installation environment, thereby improving the area utilization rate of the photovoltaic power station, effectively controlling the shadow loss rate, and improving the overall power generation efficiency of the system;

[0051] (2) The present invention proposes a method for calculating the geographic spacing coefficient based on latitude zoning, by dividing the installation area into three types of latitude areas: low, medium, and high, and setting characteristic parameters a for each type of area. i and b i, a quantitative relationship with the latitude value is established. This partition calculation method avoids the limitations of traditional empirical formulas and makes the calculation of foundation installation spacing more universal;

[0052] (3) The present invention establishes a multi-level correction system including geological correction coefficients, microclimate correction coefficients, and environmental correction coefficients, and quantifies various influencing factors through mathematical models. This systematic correction method allows the calculation of the installation spacing to fully consider the influence of actual environmental conditions. Compared with the traditional single correction method, the present invention can improve the environmental adaptability of the system and the safety and reliability of photovoltaic modules;

[0053] (4) The present invention proposes a geological-microclimate coupling function f(K GSI ,K MCI ), the coupling coefficient λ is used to quantitatively describe the interaction between geological conditions and microclimate, and the coupling effect is introduced into the installation spacing optimization calculation. This coupling mechanism makes the calculation of installation spacing more in line with actual engineering conditions, especially under complex geological and extreme climatic conditions, which can effectively improve system stability and reduce maintenance costs;

[0054] (5) The present invention adopts a dynamic weight adjustment mechanism based on seasonal changes, and adjusts the weight coefficients of the three environmental factors of temperature ω1, humidity ω2 and ultraviolet rays ω3, so that the installation spacing can adapt to seasonal environmental changes. This dynamic adjustment method can improve the power generation efficiency of the photovoltaic array throughout the year, keep the system performance ratio PR value at a high level, and effectively improve the economic benefits of the photovoltaic power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] like Figure 1As shown, the present invention provides a method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters, comprising:

[0059] S1. Obtain the installation location of the photovoltaic array and use a multi-source data acquisition system to collect comprehensive parameters of the installation location, including geographical parameters, topographic parameters, geological parameters and meteorological parameters;

[0060] S2. Determine the corresponding geographic spacing coefficient based on the geographic parameters of the installation site;

[0061] S3. Calculate the solar position parameters according to the geographical parameters, including the minimum solar altitude angle and the solar azimuth angle;

[0062] S4, adjusting the inclination angle of the photovoltaic module based on the optimal light receiving area according to the terrain parameters to obtain the optimal inclination angle of the photovoltaic module;

[0063] S5. Calculate the basic installation spacing based on the optimal inclination angle, the minimum solar altitude angle and the geographic spacing coefficient, calculate the comprehensive correction coefficient based on the comprehensive parameters, and calculate the optimized installation spacing based on the basic installation spacing and the comprehensive correction coefficient;

[0064] S6. Arrange the photovoltaic array according to the optimized installation spacing, perform shadow distribution simulation and power generation analysis in the simulation software, and fine-tune the optimized installation spacing according to the simulation results;

[0065] S7. Repeat step S6 until the simulation result meets the requirements and output the optimal installation spacing.

[0066] Specifically, in one embodiment of the present invention, step S1 includes:

[0067] Collection of geographical parameters: Use GPS positioning system to obtain the longitude and latitude information of the installation site; use digital elevation meter (DEM) to measure the altitude of the installation site. The measurement accuracy of longitude and latitude should not be less than 0.0001 degrees, and the measurement accuracy of altitude should not be less than 1 meter.

[0068] Collection of terrain parameters: Use a 3D laser scanner to map the installation area and obtain terrain slope data; use an electronic compass combined with a GIS system to measure the terrain orientation. The measurement accuracy of the terrain slope should not be less than 0.1 degrees, and the measurement accuracy of the terrain orientation should not be less than 1 degree.

[0069] Collection of geological parameters: Determine soil type through on-site geological survey and soil sampling analysis; use geological radar detector to measure groundwater level; use geological stability assessment system and drilling data to assess geological stability. Among them, the measurement depth of groundwater level shall not be less than 10 meters, and the measurement accuracy shall not be less than 0.1 meter.

[0070] Meteorological parameter collection: Set up an automatic weather station at the installation site to continuously collect meteorological data for no less than one year, including wind speed, temperature, snowfall and other parameters; or directly call the historical database of the local weather station to obtain meteorological data in recent years, including maximum wind speed, maximum snow thickness, annual maximum temperature and annual minimum temperature. Among them, the wind speed measurement accuracy should be no less than 0.1 m / s, the temperature measurement accuracy should be no less than 0.1℃, and the snow thickness measurement accuracy should be no less than 1 cm.

[0071] In this embodiment, the multi-source data acquisition system includes: a GPS positioning module, a digital elevation measurement module, a three-dimensional laser scanning module, a geological survey equipment, an automatic weather station, and a data integration processing unit. The data integration processing unit connects each acquisition module through a wired or wireless network, receives and processes the collected data in real time, and stores the processed data in the system database. During the data acquisition process, the system will verify and screen the collected data in real time, eliminate outliers, and ensure the accuracy and reliability of the data. For data that is missing or cannot be directly measured, the system will perform interpolation calculations based on adjacent areas or historical data to ensure the integrity of the data. All collected data are stored and managed in a unified format to provide data support for subsequent calculations and analysis.

[0072] Specifically, in one embodiment of the present invention, step S2 includes:

[0073] The latitude is divided into three regions according to the range, namely low-latitude region, mid-latitude region and high-latitude region;

[0074] According to the latitude of the installation location Determine the region to which it belongs and its characteristic parameters a i and b i , i = 1, 2, 3, where i = 1 represents the low-latitude region, i = 2 represents the mid-latitude region, and i = 3 represents the high-latitude region;

[0075] According to the characteristic parameters of the region, calculate the geographical spacing coefficient K1:

[0076]

[0077] In the formula, is the absolute value of the latitude of the installation location, a1 <a2<a3,b1<b2<b3;

[0078] Among them, the value range of the geographical spacing coefficient K1 is 1.0-2.0. If the calculated K1<1.0, K1=1.0 is taken; if the calculated K1>2.0, K1=2.0 is taken.

[0079] In this embodiment, the characteristic parameter a i and b iThe specific value of needs to take into account local lighting conditions and climate characteristics, and can be fine-tuned and optimized through field test data.

[0080] Specifically, this embodiment provides a value reference, as shown in Table 1.

[0081] Table 1 Latitude region division and corresponding characteristic parameter values

[0082]

[0083] In this embodiment, the characteristic parameter a is introduced i and b i , a quantitative relationship between latitude value and spacing coefficient is established, so that the calculation results can accurately reflect the impact of different latitudes on the installation spacing of photovoltaic arrays. At the same time, by setting the value range of the coefficient, the rationality and practicality of the calculation results are ensured.

[0084] Specifically, in one embodiment of the present invention, step S3 includes:

[0085] First, define the sun position parameters: solar altitude angle α: the angle between the sun's rays and the horizontal plane; solar azimuth angle γ: the angle between the projection of the sun's rays on the horizontal plane and the south direction, which is negative in the east and positive in the west.

[0086] Calculate the solar declination:

[0087]

[0088] Where n is the nth day of the year (January 1 is the 1st day); 23.45° is the inclination of the Earth.

[0089] Calculate the local hour angle:

[0090] ω=15°×(t-12)

[0091] Where t is solar time (hours).

[0092] Calculate the sun's altitude angle:

[0093]

[0094] in, is the latitude of the installation location, δ is the solar declination angle, and ω is the local hour angle.

[0095] During the period from 8:00 to 16:00 on the winter solstice (n=355), the solar altitude angle is calculated every 30 minutes, and the minimum value is taken as α min . Usually, to ensure the calculation accuracy, α min Should not be less than 10°.

[0096] The calculation formula for the solar azimuth is:

[0097]

[0098] When the sun is in the Eastern Hemisphere, γ is negative; when it is in the Western Hemisphere, γ is positive.

[0099] The actual solar altitude angle needs to take into account the influence of atmospheric refraction, and the correction formula is:

[0100]

[0101] Corrected solar altitude angle: α′=α+Δα.

[0102] When the installation site is located in a mountainous area or has obstructions, it is necessary to consider the impact of terrain obstruction on the sun's position parameters: use a digital terrain model (DTM) to analyze the obstruction angle; calculate the effective sunshine time; and correct the minimum sun altitude angle.

[0103] Verify the accuracy of the calculation results through solar position calculation software, such as SPA, and compare the calculation results with the measured data to ensure that the error is within the allowable range: the solar altitude angle error should be less than 0.5°; the solar azimuth angle error should be less than 1.0°.

[0104] The calculated minimum solar altitude angle α min The solar azimuth angle γ is stored in the system database as an important parameter for the subsequent calculation of the basic installation spacing. At the same time, a solar trajectory diagram is generated to intuitively display the changes in the sun's position throughout the year, providing a reference for the layout of the photovoltaic array.

[0105] Specifically, in one embodiment of the present invention, step S4 includes:

[0106] The theoretical optimal inclination angle is calculated using the empirical formula:

[0107] Calculation of the light receiving area per unit area of ​​photovoltaic modules:

[0108] A=cos(θ-α)

[0109] Wherein, A is the effective light receiving area per unit area, and θ is the component inclination angle.

[0110] Calculation of annual average light intensity:

[0111] G=∑[l i ×cos(θ-α i )×Δt]

[0112] Among them, G is the annual total light intensity, l i is the solar radiation intensity at the i-th moment, α i is the solar altitude angle at the i-th moment, and Δt is the time interval.

[0113] When there is a terrain slope β at the installation site, the actual inclination angle θ r calculate:

[0114] θ r =θ t ±β

[0115] Among them, when the slope direction is consistent with the optimal direction, a minus sign is taken; when the slope direction is opposite to the optimal direction, a plus sign is taken.

[0116] According to the terrain orientation γ t The deviation from the ideal orientation γ0 introduces the orientation correction factor K γ :

[0117] K γ = cos(γ t -γ0)

[0118] Corrected inclination:

[0119] θ m =θ r ×K γ

[0120] This embodiment can also optimize the inclination angle, and the process is as follows:

[0121] Establish the optimization objective function: F(θ) = G(θ) × η(θ); where: G(θ) is the annual average total light intensity; η(θ) is the photoelectric conversion efficiency function. Constraints: 15°≤θ≤45°; |θ-θ m |≤10°.

[0122] Use the golden section method to search for the optimal solution within the constraints: a. Initial search interval [θ min ,θ max ]; b. Calculate the golden section points θ1, θ2; c. Compare the values ​​of F(θ1) and F(θ2); d. Narrow the search interval; e. Repeat steps bd until convergence.

[0123] Verify the calculation results through PVsyst; compare and analyze with empirical values; consider the actual engineering construction requirements and round off the calculation results to integer values.

[0124] The calculated optimal inclination angle θ is stored in the system database as an input parameter for the subsequent calculation of the foundation installation spacing. At the same time, an inclination optimization report is generated, including: theoretical calculation value; correction process; final determination value; expected power generation gain.

[0125] Specifically, in one embodiment of the present invention, step S5 includes:

[0126] Calculate the foundation installation spacing based on the optimal inclination angle, minimum sun altitude angle and geographic spacing factor:

[0127]

[0128] Where D base is the basic installation spacing, L is the length of the photovoltaic module, θ is the optimal inclination angle, β is the terrain slope, α min is the minimum solar altitude angle, and K1 is the geographic spacing coefficient.

[0129] Among them, the length of the photovoltaic module L is determined according to the actual module specifications selected; the optimal inclination angle θ is calculated through step S4; the terrain slope β is obtained through measurement or topographic map; the minimum solar altitude angle α min : Calculated through step S3; Geographic spacing coefficient K1: Calculated through step S2.

[0130] Calculate the comprehensive correction coefficient based on the comprehensive parameters:

[0131] K=K GSI ×K MCI ×K env

[0132] In the formula, K GSI is the geological correction factor, K MCI is the microclimate correction coefficient, K env is the environmental correction factor.

[0133] Specifically, the comprehensive correction factor is a basic correction factor that takes into account the linear influence of each independent factor, where K GSI Reflecting the geological conditions, K MCI Reflecting the basic impact of climate, K env Reflects the basic impact of the environment.

[0134] In the comprehensive correction coefficient, the calculation formula of each coefficient is as follows:

[0135] K GSI =w1×S+w2×R+w3×E

[0136] K MCI =T×w T +H×w H +V×w V

[0137]

[0138] Where S is the geological stability factor; R is the soil type factor; E is the groundwater level factor; w1, w2, w3 are the corresponding weight coefficients, and w1+w2+w3=1; T is the temperature factor, T=(T max-T min ) / T0,T max , T min is the annual maximum temperature and annual minimum temperature, T0 is the reference temperature difference; H is the humidity influence factor, H = RH / RH0, RH is the annual average relative humidity, RH0 is the reference relative humidity; V is the wind speed influence factor, V = (v / v0) 2 , v is the maximum wind speed at the installation site, v0 is the reference wind speed; w T 、w H 、w V is the corresponding weight coefficient, and w T +w H +w V =1; h is the installation site altitude; h0 is the reference altitude; ρ is the ground reflectivity; ρ0 is the reference reflectivity; μ1 and μ2 are adaptive coefficients.

[0139] In this embodiment, the quantitative standard of the geological stability factor S is: S = 1.0: excellent geological stability; S = 0.9: good geological stability; S = 0.8: average geological stability; S = 0.7: poor geological stability; S < 0.7: not recommended for installation. Its value is based on the comprehensive evaluation of the following indicators:

[0140] Evaluation Project Weight Scoring criteria (0-1 points) Foundation bearing capacity 0.3 <200kPa:0.3;200-300kPa:0.6;>300kPa:1.0 earthquake intensity 0.3 >7 degrees: 0.3; 6-7 degrees: 0.7; <6 degrees: 1.0 Surface stability 0.4 Soft soil / liquefaction: 0.2; General soil: 0.6; Rock: 1.0

[0141] The final score is the geological stability factor S value.

[0142] This embodiment provides a quantitative standard for soil type factor R:

[0143] Soil Type R-Value Sandy soil 0.95 Clay soil 0.85 Loam 0.90 rock 1.00 Silt soil 0.70

[0144] The quantification process of the groundwater level influencing factor E is: E = 1-0.1×e (-d / 2) ; where d is the depth of groundwater level (m).

[0145] In this embodiment, the reference temperature difference T0 is set to 40°C, the annual temperature difference reference value is 25°C-35°C, the reference relative humidity RH0 is set to 65%, the suitable relative humidity range is set to 45%-75%, the reference wind speed v0 is set to 25m / s, the safe wind speed threshold is set to 30m / s, the reference altitude h0 is set to 500m, the high altitude correction starting point is 1000m, the reference reflectivity ρ0 is set to 0.2, the typical ground reflectivity range is 0.1-0.3, the altitude adaptation coefficient μ1=0.05×(1+h / 1000), where h is the altitude of the installation site (m); the reflectivity adaptation coefficient μ2=0.1×(1+ρ / 0.2), where ρ is the ground reflectivity.

[0146] In this embodiment, w1, w2, and w3 are determined as w1=0.5, w2=0.3, and w3=0.2. T 、w H 、w V Set to w T =0.4, w H =0.3, w V =0.3.

[0147] In step S5, the calculation formula for optimizing the installation spacing is as follows:

[0148]

[0149] Where D base is the basic installation distance, K is the comprehensive correction coefficient, f(K GSI ,K MCI ) is the geological-microclimate coupling function, k is the environmental impact index, v is the maximum wind speed at the installation site, and s is the maximum snow thickness.

[0150] The expression of the geological-microclimate coupling function is as follows:

[0151] f(K GSI ,K MCI )=1+λ×(K GSI -1)×(K MCI -1)

[0152] In the formula, K GSI is the geological correction factor, K MCI is the microclimate correction coefficient, λ is the coupling coefficient, and its value range is [0.1, 0.3], f(K GSI ,K MCI ) satisfies 0.9≤f(K GSI ,K MCI )≤1.1.

[0153] Specifically, this embodiment introduces a coupling function f(K GSI ,K MCI ) is used to deal with the interaction between geological conditions and microclimate, and its value range is limited to between 0.9 and 1.1, indicating that this is a fine-tuning factor. The purpose of this coupling function is to supplement the calculation of the mutual influence between the two factors.

[0154] In the formula for optimizing installation spacing, D base is the basic calculation value, K represents the comprehensive linear influence, f(K GSI ,K MCI ) represents the nonlinear interaction effect, What is considered is the influence of wind speed and snow accumulation, because wind load and snow accumulation are additional stresses on the structure, these stresses will have an exponential impact. If the installation site is in an area where it does not snow, the value of s is 0, and only the influence of wind speed is considered.

[0155] The environmental impact index k adopts a dynamic weight calculation method:

[0156]

[0157] Where k0 is the basic environmental index, T max , T min are the annual maximum temperature and the annual minimum temperature, RH is the annual average relative humidity, RH0 is the benchmark relative humidity, UV is the annual average ultraviolet intensity, UV0 is the benchmark ultraviolet intensity, ω1, ω2, ω3 are dynamic weight coefficients, and ω1+ω2+ω3=1, where the dynamic weight coefficient is adjusted dynamically according to the season.

[0158] In this embodiment, k0 takes a value of 0.001 and UV0 takes a value of 2000 W / m 2 .

[0159] The rules for dynamically adjusting the dynamic weight coefficient according to the season provided in this embodiment are as follows:

[0160] Seasonal adjustment of temperature weight (ω1), humidity weight (ω2), and ultraviolet weight (ω3):

[0161] season <![CDATA[ω1]]> <![CDATA[ω2]]> <![CDATA[ω3]]> spring 0.3 0.4 0.3 summer 0.4 0.3 0.3 autumn 0.3 0.4 0.3 winter 0.4 0.4 0.2

[0162] Specifically, in one embodiment of the present invention, step S6 includes:

[0163] S61, establish a three-dimensional model of the photovoltaic array, import the geographical location, meteorological data, component parameters and photovoltaic array layout plan;

[0164] S62. Perform hourly shadow distribution simulation throughout the year in the simulation software, including calculating static shadow coverage area and occlusion rate, simulating dynamic shadow change trajectory throughout the year, and analyzing shadow maps during key periods;

[0165] S63, performing system power generation analysis, including calculating theoretical power generation based on irradiance data, predicting actual power generation considering system loss factors, and calculating system performance ratio PR value;

[0166] S64, set optimization target indicators: shadow loss rate is not greater than τ1; area utilization is not less than τ2; performance ratio PR value is not less than τ3;

[0167] S65. Optimize the spacing based on the simulation results: when the shadow loss rate exceeds the standard, gradually increase the row spacing and record the data; when the area utilization is insufficient, reduce the row spacing while meeting the shadow loss rate; when the performance ratio PR value does not meet the standard, analyze the various losses and adjust the layout parameters accordingly.

[0168] In a specific example, PVsyst is selected as the main photovoltaic simulation software, and the geographical location data are prepared: longitude, latitude (accurate to 4 decimal places), altitude (meters), terrain slope (degrees), site topographic map (CAD format); meteorological data: Meteonorm meteorological database, NASASE data; measured data of the nearest meteorological station (≥3 years), including: hourly irradiance data, ambient temperature, wind speed data, snowfall data; component parameters: component specification, component size, temperature coefficient, power attenuation coefficient, IV characteristic curve; system layout plan: preliminary spacing calculation results, component arrangement method, bracket design parameters.

[0169] Modeling is performed in a three-dimensional environment, including terrain modeling and array modeling. Terrain modeling includes: importing terrain data: importing CAD terrain maps, setting the site coordinate system, and establishing terrain surfaces; setting surface parameters: surface reflectivity, surface roughness, and obstacle information. Array modeling includes: component layout: setting component type, defining installation inclination, setting azimuth, and inputting calculated spacing; bracket modeling: defining bracket type, setting installation height, and establishing bracket structure.

[0170] The shadow distribution simulation is carried out hourly throughout the year in the simulation software. The process is as follows:

[0171] 1. Static shadow calculation: Shadow coverage = shadow area / total component area × 100%; occlusion rate = number of occluded components / total number of components × 100%.

[0172] 2. Select key time periods, such as the winter solstice, summer solstice, and spring and autumnal equinoxes, and select the time from 8:00 to 16:00 for hourly analysis to simulate shadow trajectories: generate a shadow dynamic change map, record the shadow position at key moments, and count the occlusion duration throughout the day.

[0173] 3. Generate shadow map.

[0174] Annual shadow distribution map: horizontal shadow distribution, inclined shadow distribution, and shadow overlapping area identification.

[0175] Shadow map during critical periods: generate iso-illuminance lines, mark shadow boundaries, and calculate effective area.

[0176] To perform system power generation analysis, the process is as follows:

[0177] Irradiance calculation:

[0178] B total =B direct +B diffuse +B reflected ; Among them: B total is the total radiation exposure; B direct is the direct radiation; B diffuse is the scattered radiation; B reflected is the reflected irradiance.

[0179] Theoretical power generation:

[0180] P theory =η×Area×B total ×(1-λ temp );where: η is the module efficiency; Area is the total area; λ temp is the temperature loss coefficient.

[0181] Analyze the system losses, including optical losses, component losses, and environmental losses, and adjust the system losses to obtain the actual power generation.

[0182] Calculate the performance ratio PR value:

[0183] PR=P actual / (P theory ×η STC );where: P actual is the actual power generation, η STC Efficiency under standard test conditions.

[0184] Set the optimization target indicators: the shadow loss rate is not greater than τ1; the area utilization rate is not less than τ2; the performance ratio PR value is not less than τ3; specifically, τ1 is 3%, τ2 is 85%, and τ3 is 80%.

[0185] Optimize the spacing based on the simulation results: when the shadow loss rate exceeds the standard, gradually increase the row spacing and record the data; when the area utilization is insufficient, reduce the row spacing while meeting the shadow loss rate; when the performance ratio PR value does not meet the standard, analyze the various losses and adjust the layout parameters accordingly. The specific process is as follows:

[0186] When the shadow loss rate exceeds the standard, the initial spacing is increased by 0.1m, and then the shadow loss is recalculated. The optimization formula is: Dnew=Dold+ΔD; ΔD=0.1×(actual shadow loss rate / target shadow loss rate).

[0187] When the area utilization is insufficient, the initial spacing is reduced to 0.05m, the shadow loss rate is verified, and the new area utilization is recalculated. The optimization conditions are: IF (shadow loss rate ≤ τ1) AND (area utilization < τ2) THEN execute spacing reduction ENDIF.

[0188] When the performance ratio PR value does not meet the standard, analyze the factors affecting PR: check temperature loss, estimate shadow loss, analyze system loss, and take corresponding optimization measures: adjust the bracket height, optimize the string mode, and improve ventilation conditions.

[0189] In this embodiment, the optimization process of the overall spacing is: record the initial parameters, perform simulation analysis, evaluate the optimization index, adjust the optimization parameters, repeat the simulation verification, and record the optimization results. The optimization is terminated when all the following conditions are met: shadow loss rate ≤ τ1, area utilization ≥ τ2, PR value ≥ τ3, and the change between two optimization results ≤ 0.1%. The optimal spacing value is output.

[0190] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters, characterized in that: include: S1. Obtain the installation location of the photovoltaic array and use a multi-source data acquisition system to collect comprehensive parameters of the installation location, including geographical parameters, topographic parameters, geological parameters and meteorological parameters; S2. Determine the corresponding geographic spacing coefficient based on the geographic parameters of the installation site; S3. Calculate the solar position parameters according to the geographical parameters, including the minimum solar altitude angle and the solar azimuth angle; S4, adjusting the inclination angle of the photovoltaic module based on the optimal light receiving area according to the terrain parameters to obtain the optimal inclination angle of the photovoltaic module; S5. Calculate the basic installation spacing based on the optimal inclination angle, the minimum solar altitude angle and the geographic spacing coefficient, calculate the comprehensive correction coefficient based on the comprehensive parameters, and calculate the optimized installation spacing based on the basic installation spacing and the comprehensive correction coefficient; S6. Arrange the photovoltaic array according to the optimized installation spacing, perform shadow distribution simulation and power generation analysis in the simulation software, and fine-tune the optimized installation spacing according to the simulation results; S7. Repeat steps S5 and S6 until the simulation results meet the requirements and output the optimal installation spacing.

2. A method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters as claimed in claim 1, characterized in that: Geographic parameters include longitude, latitude and altitude; terrain parameters include terrain slope and terrain orientation; geological parameters include soil type, geological stability and groundwater level; meteorological parameters include maximum wind speed, maximum snow thickness, annual maximum temperature and annual minimum temperature.

3. A method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters as claimed in claim 2, characterized in that: Step S2 includes: The latitude is divided into three regions according to the range, namely low-latitude region, mid-latitude region and high-latitude region; According to the latitude of the installation location Determine the region to which it belongs and its characteristic parameters a i and b i , i = 1, 2, 3, where i = 1 represents the low-latitude region, i = 2 represents the mid-latitude region, and i = 3 represents the high-latitude region; According to the characteristic parameters of the region, calculate the geographical spacing coefficient K1: In the formula, is the absolute value of the latitude of the installation location, a1 <a2<a3,b1<b2<b3; Among them, the value range of the geographical spacing coefficient K1 is 1.0-2.

0. If the calculated K1<1.0, K1=1.0 is taken; if the calculated K1>2.0, K1=2.0 is taken.

4. The method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters as claimed in claim 2, characterized in that: The calculation formula for the foundation installation spacing is: Where D base is the basic installation spacing, L is the length of the photovoltaic module, θ is the optimal inclination angle, β is the terrain slope, α min is the minimum solar altitude angle, and K1 is the geographic spacing coefficient.

5. The method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters as claimed in claim 2, characterized in that: The calculation formula of the comprehensive correction coefficient is: K=K GSI ×K MCI ×K env In the formula, K GSI is the geological correction factor, K MCI is the microclimate correction coefficient, K env is the environmental correction factor.

6. A method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters as claimed in claim 5, characterized in that: In the comprehensive correction coefficient, the calculation formula of each coefficient is as follows: K GSI =w1×S+w2×R+w3×E K MCI =T×w T +H×w H +V×w V Where S is the geological stability factor; R is the soil type factor; E is the groundwater level factor; w1, w2, w3 are the corresponding weight coefficients, and w1+w2+w3=1; T is the temperature factor, T=(T max -T min ) / T0,T max , T min is the annual maximum temperature and annual minimum temperature, T0 is the reference temperature difference; H is the humidity influence factor, H = RH / RH0, RH is the annual average relative humidity, RH0 is the reference relative humidity; V is the wind speed influence factor, V = (v / v0) 2 , v is the maximum wind speed at the installation site, v0 is the reference wind speed; w T 、w H 、w V is the corresponding weight coefficient, and w T +w H +w V =1; h is the installation site altitude; h0 is the reference altitude; ρ is the ground reflectivity; ρ0 is the reference reflectivity; μ1 and μ2 are adaptive coefficients.

7. The method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters as claimed in claim 2, characterized in that: In step S5, the calculation formula for optimizing the installation spacing is as follows: Where D base is the basic installation distance, K is the comprehensive correction coefficient, f(K GSI ,K MCI ) is the geological-microclimate coupling function, k is the environmental impact index, v is the maximum wind speed at the installation site, and s is the maximum snow thickness.

8. The method for determining the optimal installation spacing of photovoltaic arrays in combination with geographical parameters as claimed in claim 7, characterized in that: The expression of the geological-microclimate coupling function is as follows: f(K GSI ,K MCI )=1+λ×(K GSI -1)×(K MCI -1) In the formula, K GSI is the geological correction factor, K MCI is the microclimate correction coefficient, λ is the coupling coefficient, and its value range is [0.1, 0.3], f(K GSI ,K MCI ) satisfies 0.9≤f(K GSI ,K MCI )≤1.

1.

9. The method for determining the optimal installation spacing of photovoltaic arrays in combination with geographical parameters according to claim 7, characterized in that: The environmental impact index k adopts a dynamic weight calculation method: Where k0 is the basic environmental index, T max , T min are the annual maximum temperature and the annual minimum temperature, RH is the annual average relative humidity, RH0 is the benchmark relative humidity, UV is the annual average ultraviolet intensity, UV0 is the benchmark ultraviolet intensity, ω1, ω2, ω3 are dynamic weight coefficients, and ω1+ω2+ω3=1, where the dynamic weight coefficient is adjusted dynamically according to the season.

10. The method for determining the optimal installation spacing of photovoltaic arrays in combination with geographical parameters according to claim 2, characterized in that: Step S6 includes: S61, establish a three-dimensional model of the photovoltaic array, import the geographical location, meteorological data, component parameters and photovoltaic array layout plan; S62. Perform hourly shadow distribution simulation throughout the year in the simulation software, including calculating static shadow coverage area and occlusion rate, simulating dynamic shadow change trajectory throughout the year, and analyzing shadow maps during key periods; S63, performing system power generation analysis, including calculating theoretical power generation based on irradiance data, predicting actual power generation considering system loss factors, and calculating system performance ratio PR value; S64, set optimization target indicators: shadow loss rate is not greater than τ1; area utilization is not less than τ2; performance ratio PR value is not less than τ3; S65, optimizing the spacing according to the simulation results: when the shadow loss rate exceeds the standard, gradually increase the row spacing and record the data; when the area utilization rate is insufficient, reduce the row spacing while satisfying the shadow loss rate; When the performance ratio PR value does not meet the standard, analyze the various losses and adjust the layout parameters accordingly.

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