A method for determining the optimal installation spacing of a photovoltaic array combined with geographical parameters

Abstract

The present invention provides a method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters, which relates to the technical field of photovoltaic power station measurement and calculation, and includes: obtaining the installation location of the photovoltaic array, collecting comprehensive parameters of the installation location; determining the corresponding geographical spacing coefficient; calculating the solar position parameters according to the geographical parameters; obtaining the optimal inclination angle of the photovoltaic module according to the terrain parameters; calculating the basic installation spacing, calculating the comprehensive correction coefficient according to the comprehensive parameters, and calculating the optimized installation spacing according to the basic installation spacing and the comprehensive correction coefficient; arranging the photovoltaic array according to the optimized installation spacing, performing shadow distribution simulation and power generation analysis in the simulation software, and finely adjusting the optimized installation spacing according to the simulation results; until the simulation results meet the requirements, and outputting the obtained optimal installation spacing. The present invention can achieve accurate calculation and optimized design of the installation spacing of the photovoltaic array, thereby improving the land utilization rate and power generation efficiency of the photovoltaic power station, and ultimately realizing the maximization of economic benefits.

Description

Technical Field

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

[0002] A photovoltaic array is the core component of a solar power generation system, which is composed of multiple photovoltaic modules arranged in a specific manner. Its installation layout directly affects the power generation efficiency and economy of the system, and the installation spacing is a key parameter for the layout of the photovoltaic array. A reasonable installation spacing can not only ensure that the photovoltaic modules obtain sufficient solar radiation, but also improve the land utilization rate, which has an important impact on the overall benefit of the photovoltaic power station.

[0003] In the prior art, the methods for determining the installation spacing of a photovoltaic array mainly include: the empirical formula method, the minimum shadow occlusion method, and the power generation optimization method. Among them, the empirical formula method mainly calculates the installation spacing based on the latitude and the module size; the minimum shadow occlusion method determines the installation spacing by calculating the minimum solar altitude angle on the winter solstice; the power generation optimization method is to optimize the calculation by combining the power generation amount and the economy on the basis of considering the shadow occlusion. These methods have been applied to a certain extent in actual projects.

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

[0005] Therefore, a new method for determining the installation spacing of a photovoltaic array 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 a photovoltaic array 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 optimized design of the installation spacing of the photovoltaic array are realized, so as to improve the land utilization rate and power generation efficiency of the photovoltaic power station and finally maximize the economic benefit.

[0007] The technical solution of the present invention is realized as follows:

[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, then take K1 = 1.0; if the calculated K1 > 2.0, then take K1 = 2.0.

[0024] On the basis of the above scheme, preferably, the calculation formula for the basic installation spacing is:

[0025]

[0026] In the formula, 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 geographical spacing coefficient.

[0027] On the basis of the above scheme, preferably, the calculation formula for the comprehensive correction coefficient is:

[0028] K = K GSI × K MCI × K env

[0029] In the formula, K GSI is the geological correction coefficient, K MCI is the microclimate correction coefficient, and K env is the environmental correction coefficient. On the basis of the above scheme, preferably, in the comprehensive correction coefficient, the calculation formulas for each coefficient are 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] In the formula, S is the geological stability factor; R is the soil type factor; E is the groundwater level influence factor; w1, w2, w3 are the corresponding weight coefficients, and w1 + w2 + w3 = 1; T is the temperature influence factor, T = (T max - T min ) / T0, T max 、T minLet \(T_{max}\) and \(T_{min}\) be the annual maximum temperature and annual minimum temperature respectively, \(T_0\) be the reference temperature difference; \(H\) be the humidity influence factor, \(H = RH / RH_0\), where \(RH\) is the annual average relative humidity and \(RH_0\) is the reference relative humidity; \(V\) be the wind speed influence factor, \(V=(v / v_0)\) 2 , where \(v\) is the maximum wind speed at the installation site and \(v_0\) is the reference wind speed; \(w\) T 、\(w\) H 、\(w\) V are the corresponding weight coefficients, and \(w\) T +\(w\) H +\(w\) V = 1; \(h\) is the altitude at the installation site; \(h_0\) is the reference altitude; \(\rho\) is the ground reflectivity; \(\rho_0\) is the reference reflectivity; \(\mu_1\), \(\mu_2\) are adaptive coefficients; \(\Delta P / P_0\) is the power generation efficiency improvement ratio; \(\Delta V / V_0\) is the wind speed change ratio.

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

[0035]

[0036] In the formula, \(D\) base is the basic installation spacing, \(K\) 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 accumulation thickness.

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

[0038] \(f(K\) GSI , \(K\) MCI ) = 1+\(\lambda\times(K\) GSI - 1)\(\times(K\) MCI - 1)

[0039] In the formula, \(K\) GSI is the geological correction coefficient, \(K\) MCI is the microclimate correction coefficient, \(\lambda\) is the coupling coefficient, and its value range is [0.1, 0.3], and \(f(K\) GSI , \(K\) MCI ) satisfies \(0.9\leq f(K\) GSI , \(K\) MCI )\(\leq1.1\).

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

[0041]

[0042] In the formula, \(k_0\) is the basic environmental index, \(T\) max 、\(T\)min Let \(T_{max}\) and \(T_{min}\) be the annual maximum temperature and annual minimum temperature respectively, \(RH\) be the annual average relative humidity, \(RH_0\) be the reference relative humidity, \(UV\) be the annual average ultraviolet intensity, \(UV_0\) be the reference ultraviolet intensity, \(\omega_1\), \(\omega_2\), \(\omega_3\) be dynamic weight coefficients, and \(\omega_1+\omega_2+\omega_3 = 1\). Among them, the dynamic weight coefficients are adjusted dynamically according to seasons.

[0043] On the basis of the above solution, preferably, step S6 includes:

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

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

[0046] S63. Perform system power generation analysis, including calculating the theoretical power generation based on irradiance data, predicting the actual power generation considering system loss factors, and calculating the system performance ratio \(PR\) value;

[0047] S64. Set the optimization target indicators: the shadow loss rate is not greater than \(\tau_1\); the area utilization rate is not less than \(\tau_2\); the performance ratio \(PR\) value is not less than \(\tau_3\);

[0048] S65. Conduct spacing optimization 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 on the premise of meeting the shadow loss rate; when the performance ratio \(PR\) value does not meet the standard, analyze various losses and adjust the layout parameters accordingly.

[0049] The present invention has the following beneficial effects compared with the prior art:

[0050] (1) By establishing a complete technical system of multi-source data acquisition, geographical zoning calculation, multi-level correction, and dynamic optimization, the present invention realizes the accurate calculation and dynamic optimization of the installation spacing of the photovoltaic array. This method systematically integrates geographical parameters, terrain parameters, geological parameters, and meteorological parameters, and adopts an iterative optimization method to ensure that the installation spacing can not only ensure the best light-receiving area of the photovoltaic modules but also adapt to complex installation environments, improve the area utilization rate of the photovoltaic power station, effectively control the shadow loss rate, and enhance the overall power generation efficiency of the system;

[0051] (2) The present invention proposes a geographical spacing coefficient calculation method based on latitude zoning. By dividing the installation area into three types of latitude regions: low, medium, and high, and setting characteristic parameters \(a\) i and \(b\) i, a quantitative relationship with the latitude value is established. This zoning calculation method avoids the limitations of traditional empirical formulas and makes the calculation of the basic installation spacing more universal;

[0052] (3) The present invention establishes a multi-level correction system including a geological correction coefficient, a microclimate correction coefficient, and an environmental correction coefficient, and quantifies various influencing factors through a mathematical model. This systematic correction method enables 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 ), quantitatively describes the interactive influence between geological conditions and microclimate through the coupling coefficient λ, and introduces the coupling effect into the optimization calculation of the installation spacing. This coupling mechanism makes the calculation of the installation spacing more in line with the actual engineering situation, especially under complex geological and extreme climate conditions, which can effectively improve the system stability and reduce the maintenance cost;

[0054] (5) The present invention adopts a dynamic weight adjustment mechanism based on seasonal changes. By adjusting the weight coefficients of three environmental factors, namely temperature ω1, humidity ω2, and ultraviolet ray ω3, the installation spacing can adapt to seasonal environmental changes. This dynamic adjustment method can improve the annual power generation efficiency of the photovoltaic array, 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 technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0056] Figure 1 is the method flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

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

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

[0060] S2. Based on the geographical parameters of the installation location, determine the corresponding geographical spacing coefficient;

[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. According to the terrain parameters, adjust the inclination angle of the photovoltaic module based on the best light-receiving area to obtain the best inclination angle of the photovoltaic module;

[0063] S5. Calculate the basic installation spacing based on the best inclination angle, the minimum solar altitude angle, and the geographical spacing coefficient, calculate the comprehensive correction coefficient according to the comprehensive parameters, and calculate the optimized installation spacing according to 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 results meet the requirements, and output the optimal installation spacing.

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

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

[0068] Collection of terrain parameters: Use a three-dimensional laser scanner to conduct topographic mapping of the installation area to obtain terrain slope data; use an electronic compass combined with a GIS system to measure the terrain orientation. Among them, the measurement accuracy of the terrain slope should not be lower than 0.1 degrees, and the measurement accuracy of the terrain orientation should not be lower than 1 degree.

[0069] Collection of geological parameters: Determine the soil type through on-site geological exploration and soil sampling analysis; use a geological radar detector to measure the groundwater level; use a geological stability assessment system to evaluate the geological stability in combination with drilling data. Among them, the measurement depth of the groundwater level should be no less than 10 meters, and the measurement accuracy should not be lower 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 needs to consider the local lighting conditions and climatic characteristics, and can be fine-tuned and optimized through field test data.

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

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

[0082]

[0083] This embodiment establishes a quantitative relationship between the latitude value and the spacing coefficient by introducing characteristic parameters a i and b i , enabling the calculation results to accurately reflect the influence of different latitude regions on the installation spacing of the photovoltaic array. 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 an embodiment of the present invention, step S3 includes:

[0085] First, define the solar position parameters. Solar altitude angle α: The angle between the solar rays and the horizontal plane; Solar azimuth angle γ: The angle between the projection of the solar rays on the horizontal plane and the due south direction, negative for the east direction and positive for the west direction.

[0086] Calculate the solar declination angle:

[0087]

[0088] In the formula, n is the nth day of the current year (January 1st is the 1st day); 23.45° is the Earth's inclination.

[0089] Calculate the local hour angle:

[0090] ω = 15°×(t - 12)

[0091] where t is the solar time (hour).

[0092] Calculate the solar altitude angle:

[0093]

[0094] where 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), calculate the solar altitude angle every 30 minutes, and take the minimum value 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 angle is as follows:

[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 consider the influence of atmospheric refraction, and the correction formula is:

[0100]

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

[0102] When the installation site is located in a mountainous area or there are obstacles, it is necessary to consider the influence of terrain occlusion on the solar position parameters: analyze the occlusion angle using a digital terrain model (DTM); calculate the effective sunshine time; correct the minimum solar 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 error of the solar altitude angle should be less than 0.5°; the error of the solar azimuth angle should be less than 1.0°.

[0104] The calculated minimum solar altitude angle α min and the solar azimuth angle γ are stored in the system database as important parameters for calculating the basic installation spacing subsequently. At the same time, a solar trajectory diagram is generated to visually display the annual solar position changes and provide a reference for the layout of the photovoltaic array.

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

[0106] Use an empirical formula to calculate the theoretical optimal tilt angle:

[0107] Calculate the light-receiving area of the photovoltaic module per unit area:

[0108] A = cos(θ - α)

[0109] where A is the effective light-receiving area per unit area, and θ is the tilt angle of the module.

[0110] Calculate the annual average light-receiving amount:

[0111] G = ∑[l i × cos(θ - α i ) × Δt]

[0112] where G is the annual average total light-receiving amount, l i is the solar irradiance 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 Calculation:

[0114] θ r = θ t ±β

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

[0116] According to the terrain orientation γ t The deviation from the ideal orientation γ0, introduce the orientation correction coefficient K γ :

[0117] K γ = cos(γ t - γ0)

[0118] The corrected inclination angle:

[0119] θ m = θ r ×K γ

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

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

[0122] Use the golden section method to search for the optimal solution within the constraint range: 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 b - d until convergence.

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

[0124] Store the calculated optimal inclination angle θ in the system database as the input parameter for calculating the subsequent basic installation spacing. At the same time, generate an inclination angle optimization report, including: theoretical calculation value; correction process; final determined value; expected power generation gain.

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

[0126] Calculate the basic installation spacing based on the optimal inclination angle, the minimum solar altitude angle, and the geographical spacing coefficient:

[0127]

[0128] In the formula, 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 geographical spacing coefficient.

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

[0130] Calculate the comprehensive correction coefficient according to the comprehensive parameters:

[0131] K = K GSI ×K MCI ×K env

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

[0133] Specifically, the comprehensive correction coefficient is a basic correction coefficient that takes into account the linear effects of various independent factors. Among them, K GSI reflects the geological foundation conditions, K MCI reflects the basic climate impacts, K env reflects the basic environmental impacts.

[0134] In the comprehensive correction coefficient, the calculation formulas for each coefficient are 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] In the formula, S is the geological stability factor; R is the soil type factor; E is the groundwater level impact factor; w1, w2, and w3 are the corresponding weight coefficients, and w1 + w2 + w3 = 1; T is the temperature impact factor, T = (T max-T min ) / T0, T max , T min are the annual maximum temperature and the 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 are the corresponding weight coefficients, 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, μ2 are the adaptive coefficients.

[0139] In this embodiment, the quantization standard of the geological stability factor S is: S = 1.0: extremely good geological stability; S = 0.9: good geological stability; S = 0.8: general geological stability; S = 0.7: poor geological stability; S < 0.7: not recommended for installation. Its value is comprehensively evaluated based on the following indicators:

[0140] Evaluation Items Weight Scoring Criteria (0 - 1 point) Foundation Bearing Capacity 0.3 <200 kPa: 0.3; 200 - 300 kPa: 0.6; >300 kPa: 1.0 Seismic 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 value of the geological stability factor S.

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

[0143] Soil Type R Value Sandy Soil 0.95 Clayey Soil 0.85 Loam 0.90 Rock 1.00 Silty Soil 0.70

[0144] The quantization process of the groundwater level influence factor E is: E = 1 - 0.1×e (-d / 2) ; where d is the groundwater level depth (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 25 m / s, the set safety wind speed threshold is 30 m / s, the reference altitude h0 is set to 500 m, the high altitude correction starting point is 1000 m, the reference reflectivity ρ0 is set to 0.2, the typical ground reflectivity range is 0.1 - 0.3, the altitude adaptive coefficient μ1 = 0.05×(1 + h / 1000), where h is the altitude of the installation site (m); the reflectivity adaptive 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. w T , w H , w V is set as 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] In the formula, D base is the basic installation spacing, 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 depth.

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

[0153] Specifically, in this embodiment, a coupling function f(K GSI , K MCI ) is introduced to handle the interaction between geological conditions and microclimate, and its value range is limited to 0.9 - 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 the installation spacing, D base is the basic calculated value, K represents the comprehensive linear influence, and f(K GSI , K MCI ) represents the non - linear interaction influence. The influence of wind speed and snow accumulation is considered, because wind load and snow accumulation are additional stresses on the structure, and these stresses will have an exponential impact. If the installation location is in an area without snowfall, then 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] In the formula, k0 is the basic environmental index, T max 、T min are the annual highest temperature and the annual lowest temperature, RH is the annual average relative humidity, RH0 is the reference relative humidity, UV is the annual average ultraviolet intensity, UV0 is the reference ultraviolet intensity, ω1, ω2, ω3 are dynamic weight coefficients, and ω1 + ω2 + ω3 = 1. Among them, the dynamic weight coefficients are dynamically adjusted according to seasons.

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

[0159] The rules for dynamically adjusting the dynamic weight coefficients provided in this embodiment according to seasons 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 an embodiment of the present invention, step S6 includes:

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

[0164] S62. Conduct a full-year hourly shadow distribution simulation in the simulation software, including calculating the static shadow coverage area and occlusion rate, simulating the annual dynamic shadow change trajectory, and analyzing the shadow atlas at key time periods;

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

[0166] S64. Set 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;

[0167] S65. Optimize 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 on the premise of meeting the shadow loss rate; when the performance ratio (PR) value does not meet the standard, analyze various losses and adjust the layout parameters accordingly.

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

[0169] Model in a 3D environment, including terrain modeling and array modeling. Terrain modeling includes: importing terrain data: importing CAD topographic map, setting the site coordinate system, and establishing the terrain surface; setting surface parameters: surface reflectivity, surface roughness, obstacle information. Array modeling includes: component layout: setting the component type, defining the installation tilt angle, setting the azimuth angle, and inputting the calculated spacing; support modeling: defining the support type, setting the installation height, and establishing the support structure.

[0170] Conduct a full-year hourly shadow distribution simulation in the simulation software, and the process is as follows:

[0171] 1. Static shadow calculation: Shadow coverage rate = 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, spring and autumn equinoxes, and analyze hourly from 8:00 to 16:00 for shadow trajectory simulation: generate a dynamic shadow change diagram, record the shadow positions at critical moments, and count the total occlusion duration throughout the day.

[0173] 3. Generate a shadow atlas.

[0174] Annual shadow distribution map: horizontal plane shadow distribution, inclined plane shadow distribution, identification of shadow overlapping areas.

[0175] Key period shadow map: generate isophotes, mark the shadow boundaries, and calculate the effective area.

[0176] Execute system power generation analysis, and the process is as follows:

[0177] Irradiance calculation:

[0178] B total = B direct + B diffuse + B reflected ; where: B total is the total irradiation; B direct is the direct irradiation; B diffuse is the scattered irradiation; B reflected is the reflected irradiation.

[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 losses of the system, including optical losses, module losses, and environmental losses, and adjust to obtain the actual power generation considering the system losses.

[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 is the 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] Perform spacing optimization 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 on the premise of meeting the shadow loss rate; when the performance ratio PR value does not meet the standard, analyze each loss and adjust the layout parameters accordingly. The specific process is as follows:

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

[0187] When the area utilization rate is insufficient, the initial reduction in spacing is 0.05 m, verify the shadow loss rate, and recalculate the new area utilization rate. The optimization condition is: IF (shadow loss rate ≤ τ1) AND (area utilization rate < τ2) THEN execute spacing reduction ENDIF.

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

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

[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 replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining the optimal installation spacing of a photovoltaic array combined with geographical parameters, characterized in that Including: 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, terrain parameters, geological parameters, and meteorological parameters; S2. Determine the corresponding geographical spacing coefficient based on the geographical parameters of the installation location; S3. Calculate the solar position parameters according to the geographical parameters, including the minimum solar altitude angle and the solar azimuth angle; S4. Adjust the inclination angle of the photovoltaic module based on the terrain parameters and the optimal light-receiving area 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 geographical spacing coefficient, calculate the comprehensive correction coefficient according to the comprehensive parameters, and calculate the optimized installation spacing according to the basic installation spacing and the comprehensive correction coefficient; S6. Arrange the photovoltaic array according to the optimized installation spacing, conduct 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. The method for determining the optimal installation spacing of a photovoltaic array combined with geographical parameters according to claim 1, wherein 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 cover thickness, annual maximum temperature, and annual minimum temperature.

3. The method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters according to claim 2, wherein, Step S2 includes: Divide the latitude into three types of regions according to the interval range, denoted as low-latitude region, mid-latitude region, and high-latitude region; Determine the region to which it belongs and its characteristic parameters a according to the latitude φ of the installation location 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; Calculate the geographical spacing coefficient K1 according to the characteristic parameters of the region; ； In the formula, is the absolute value of the latitude of the installation location, , ; Among them, the value range of the geographical spacing coefficient K1 is 1.0 - 2.

0. If the calculated is obtained, then is taken; if the calculated is obtained, then is taken.

4. The optimal installation spacing determination method of a photovoltaic array combined with geographical parameters according to claim 2, characterized in that The calculation formula for the basic installation spacing is: ； In the formula, is the basic installation spacing, L is the length of the photovoltaic module, is the optimal tilt angle, is the terrain slope, is the minimum solar altitude angle, is the geographical spacing coefficient.

5. The method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters according to claim 2, wherein The calculation formula for the comprehensive correction coefficient is: ； In the formula, is the geological correction coefficient, is the microclimate correction coefficient, is the environmental correction coefficient.

6. The method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters according to claim 5, wherein In the comprehensive correction coefficient, the calculation formula for each coefficient is as follows: ； ； ； In the formula, is the geological stability factor; R is the soil type factor; E is the groundwater level influence factor; , , are the corresponding weight coefficients, and ; T is the temperature influence factor, , are the annual maximum temperature and the annual minimum temperature, is the reference temperature difference; H is the humidity influence factor, , is the annual average relative humidity, is the reference relative humidity; V is the wind speed influence factor, , v is the maximum wind speed at the installation site, v0 is the reference wind speed; , , are the corresponding weight coefficients, and ; h is the altitude of the installation site; h0 is the reference altitude; ρ is the ground reflectivity; ρ0 is the reference reflectivity; , are the adaptive coefficients.

7. The method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters according to claim 5, wherein In step S5, the calculation formula for the optimized installation spacing is as follows: ； In the formula, is the basic installation spacing, is the comprehensive correction coefficient, is the geology - microclimate coupling function, is the environmental impact index, v is the maximum wind speed at the installation site, and s is the maximum snow accumulation thickness.

8. The method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters according to claim 7, characterized in that The expression of the geology-microclimate coupling function is as follows: ； Wherein, is the geological correction coefficient, is the microclimate correction coefficient, is the coupling coefficient, and its value range is [0.1, 0.3], satisfies .

9. The method for determining the optimal installation spacing of a photovoltaic array in combination with geographical parameters according to claim 7, wherein Environmental impact index Adopt a dynamic weight calculation method: ； In the formula, is the basic environmental index, , are the annual maximum temperature and the annual minimum temperature, is the reference temperature difference, RH is the annual average relative humidity, RH0 is the reference relative humidity, UV is the annual average ultraviolet intensity, and UV0 is the reference ultraviolet intensity. , , are dynamic weight coefficients, and , where the dynamic weight coefficients are dynamically adjusted according to seasons.

10. A method for determining the optimal installation spacing of a photovoltaic array 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, and import geographical location, meteorological data, component parameters, and the photovoltaic array layout plan; S62. Conduct hourly shadow distribution simulation throughout the year in the simulation software, including calculating the static shadow coverage area and occlusion rate, simulating the annual dynamic shadow change trajectory, and analyzing the shadow pattern during key periods; S63. Perform system power generation analysis, including calculating the theoretical power generation based on irradiance data, predicting the actual power generation considering system loss factors, and calculating the system performance ratio PR value; S64. Set optimization target indicators: the shadow loss rate is not greater than ; the area utilization rate is not less than ; the performance ratio PR value is not less than ; S65. Optimize 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 on the premise of meeting the shadow loss rate; when the performance ratio PR value does not meet the standard, analyze various losses and adjust the layout parameters accordingly.

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