Evaluation method for ecological system service function influence of photovoltaic power station in arid region

Abstract

The invention discloses an arid region photovoltaic power station ecosystem service function influence evaluation method, and the method comprises the steps: carrying out the quantitative evaluation of the influence of the construction and operation of a photovoltaic power station on the ecosystem service function, and obtaining the spatial difference and time difference caused by the photovoltaic power station, the spatial difference comprises ecological system service function difference between the photovoltaic power station area and the contrast area and also comprises ecological system service function difference between the influence area and the contrast area, and the time difference comprises ecological system service function change between the photovoltaic power station and the contrast area and between the photovoltaic area and the influence area before and after photovoltaic construction; and further obtaining a space-time difference index, and distinguishing areas with different influence degrees for classified management and control.

Description

Technical Field

[0001] The invention relates to an impact assessment method for a photovoltaic power station in an arid area, and in particular to an impact assessment method for an ecosystem service function of a photovoltaic power station in an arid area. Background Art

[0002] Photovoltaic power generation, as a clean energy technology, is a key technical means to address climate change. Its impact on ecosystem structure and processes is multifaceted, which in turn affects the corresponding ecosystem service functions. First, photovoltaic power generation can reduce dependence on fossil fuels and reduce greenhouse gas emissions, thereby mitigating the negative impact of climate change on the ecosystem. Secondly, the construction and operation of photovoltaic projects need to consider land use. Changes in the types of land occupied by the site and surrounding areas will have an impact on different types of ecosystem service functions. In addition, large-scale photovoltaic arrays may change the microclimate of the surface and affect the habitat of local vegetation and wildlife. Therefore, to evaluate the impact of photovoltaic areas on ecosystem services, it is necessary to comprehensively consider their contributions to ecosystem services such as climate regulation, biodiversity protection, and wind and sand fixation. Combined with future climate scenarios, the potential impact of photovoltaic construction can be predicted. Through scientific planning and management, the environmental benefits of photovoltaic power generation can be maximized while minimizing its negative impact on the ecosystem.

[0003] Based on the above issues, the impact assessment and scenario prediction of the ecosystem service function in the photovoltaic power station area can provide strong support for identifying the impact of photovoltaic construction on the ecological environment, improving the ability of photovoltaic power stations to cope with climate change, optimizing the planning and construction of photovoltaic power stations, and achieving a balance between energy development and ecological protection. Summary of the invention

[0004] In order to overcome the above problems, the inventors have conducted intensive research and designed a method for evaluating the impact of photovoltaic power stations on ecosystem service functions in arid areas. In this method, the impact of photovoltaic power station construction and its operation on ecosystem service functions is quantitatively evaluated, and the spatial differences and temporal differences caused by photovoltaic power stations are obtained, wherein the spatial differences include the differences in ecosystem service functions between the photovoltaic power station area and the control area, and also include the differences in ecosystem service functions between the impact area and the control area. The temporal differences include the changes in ecosystem service functions between the photovoltaic power station and the control area, and between the photovoltaic area and the impact area before and after the photovoltaic construction; further, the spatiotemporal difference index is obtained to distinguish areas with different degrees of impact for classified management and control, thereby completing the present invention.

[0005] Specifically, the purpose of the present invention is to provide a method for evaluating the impact of the service function of the ecosystem of a photovoltaic power station in an arid area, the method comprising the following steps:

[0006] Step 1: Obtain the location, installed capacity and land use type of the PV power station.

[0007] Step 2: Divide the impact area and control area of ​​the photovoltaic power station,

[0008] Step 3: Evaluate the ecosystem service functions of the PV power station area, impact area and control area in different years;

[0009] Step 4, obtaining the spatial difference and temporal difference caused by the photovoltaic power station, and further obtaining a temporal and spatial difference index; preferably, introducing a corresponding management and control strategy according to the temporal and spatial difference index.

[0010] The beneficial effects of the present invention include:

[0011] (1) The method for evaluating the impact of photovoltaic power stations on ecosystem service functions in arid areas provided by the present invention can analyze the temporal trend of changes in ecosystem service functions under each type to identify the positive or negative impacts that may be brought about by the construction and long-term operation of photovoltaic power stations, and evaluate the potential future impact of photovoltaic power stations on the ecosystem service functions in the studied area, thereby providing a scientific basis for environmental impact assessment and management decision-making of photovoltaic projects;

[0012] (2) According to the method for evaluating the impact of photovoltaic power stations on ecosystem service functions in arid areas provided by the present invention, the method evaluates the impact of photovoltaic power generation projects on the ecological environment at a regional scale. In terms of evaluation indicators, multiple ecological factors such as atmosphere, soil, vegetation and water resources are integrated to construct a multi-dimensional evaluation system to more comprehensively reveal the overall impact of photovoltaic power stations in arid areas on ecosystem service functions;

[0013] (3) According to the method for evaluating the impact of the service function of the photovoltaic power station ecosystem in arid areas provided by the present invention, a flexible identification strategy is adopted in the method to dynamically determine the impact range according to the characteristics of environmental changes, which significantly enhances the accuracy and credibility of the assessment;

[0014] (4) According to the method for evaluating the impact of the service function of the photovoltaic power station ecosystem in arid areas provided by the present invention, the method combines regional characteristics and focuses on key ecological service functions such as climate regulation, biodiversity protection, and windbreak and sand fixation. By identifying the impact area and the control area, it emphasizes the classification of regions with different spatiotemporal variation characteristics of ecosystem service functions and proposes adaptive response strategies, providing scientific support for the long-term strategic planning and layout of photovoltaic projects, and promoting the coordinated advancement of ecological protection and energy development. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The overall logic diagram of the method for evaluating the impact of the ecosystem service function of photovoltaic power stations in arid areas of the present application is shown;

[0016] Figure 2A schematic diagram showing the division of the photovoltaic power station area, the impact area and the control area in the method for evaluating the impact of the ecosystem service function of the photovoltaic power station in arid areas of the present application is shown. DETAILED DESCRIPTION

[0017] The present invention will be further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present invention will become more clear and distinct.

[0018] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.

[0019] The present invention provides a method for evaluating the impact of service functions of a photovoltaic power station ecosystem in an arid area, the method comprising the following steps:

[0020] Step 1: Obtain the location, installed capacity and land use type of the PV power station.

[0021] In step 1, the following sub-steps are specifically included:

[0022] Sub-step 1-1: Define the location of the PV power station and collect high-resolution remote sensing images over multiple time periods, such as Landsat-5, Landsat-8 and Sentinel-2 satellite data. The remote sensing images need to cover different years before and after the PV construction to ensure the continuity of the time series.

[0023] Sub-step 1-2, pre-processing the collected images, wherein the pre-processing includes atmospheric correction, geometric correction, and cloud and shadow masking processing to eliminate the influence of atmospheric conditions on the image data, ensure the spatial accuracy of the image data, and improve the data quality;

[0024] Sub-steps 1-3 are based on eCognition software. In view of the fact that the characteristics of photovoltaic power stations in the blue light band are more obvious, the spectral weight of blue light is increased, the image is segmented at multiple scales, the segmentation scale, shape parameters and compactness parameters are adjusted, and the optimal segmentation parameters are determined through visual comparison to form an accurate object reflecting the characteristics of the photovoltaic power station, ensuring a high degree of data accuracy and representativeness of the object. In this application, the photovoltaic power station appears as a gray-blue or dark brown rectangle on the remote sensing image, with a white grid inlaid in the middle, and its arrangement is in the form of an array.

[0025] Sub-steps 1-4, for each segmented object, calculate its spectral, texture and geometric features. The spectral features include mean, standard deviation, ratio index, etc.; the texture features can be calculated by gray level co-occurrence moment difference and other parameters; the geometric features include area, aspect ratio, etc.

[0026] In sub-steps 1-5, machine learning algorithms such as random forest (RF) and convolutional neural network (CNN) are used to extract the land use types of the PV power station and its surrounding areas.

[0027] The input data includes the spectral characteristics of photovoltaic power stations and different landforms, normalized vegetation index, normalized water index, ratio residential index, normalized building index, road factor, terrain factor, climate factor, as well as photovoltaic power stations and their background pictures. 70% of the data is used as a training set and 30% of the data is used as a validation set for model calculation to identify the location and area of ​​photovoltaic power stations and interpret the surrounding types including forest land, grassland, cultivated land, water body, construction land and unused land.

[0028] Using ground measured data or high-resolution images as reference, the classification results are verified by the confusion matrix method, and indicators such as the Kappa coefficient are calculated to evaluate the accuracy of the classification results.

[0029] In this application, the grid scale of the remote sensing image is 30m*30m, that is, the photovoltaic power station area, the influence area and the control area are all composed of 30m*30m divided pixels. In the subsequent settlement process, the data in each pixel is settled separately. If it is necessary to obtain data in a certain area, for example, the data in the influence area needs to be obtained, the data in multiple grids in the influence area can be averaged or summed.

[0030] In step 1, information such as the construction time, photovoltaic panel type, and installed capacity of the photovoltaic power station can be collected from government energy bureaus, industry reports, official corporate websites, and reliable energy information websites (such as Polaris Solar Photovoltaic Network, National Renewable Energy Center, International Energy Network, etc.).

[0031] When installed capacity data is missing, the area of ​​the photovoltaic power station and the coverage rate of photovoltaic modules can be determined through remote sensing images, and the installed capacity can be estimated by combining the typical installation density conversion efficiency of photovoltaic panels. Specifically, the installed capacity P is obtained through the following formula;

[0032] P=A×E×D×η

[0033] Where A represents the area of ​​the photovoltaic power station determined by remote sensing images, in m 2 ;

[0034] E represents the coverage of photovoltaic panels, expressed as a percentage;

[0035] D represents the installation density of photovoltaic panels, in kW / m 2 ,

[0036] η represents the conversion efficiency of the photovoltaic panel.

[0037] Step 2: Divide the impact area and control area of ​​the PV power station.

[0038] The area covered by the photovoltaic power station is determined as the photovoltaic power station area. The annular area obtained by expanding the photovoltaic power station area is the influence area of ​​the photovoltaic power station. The sum of the photovoltaic power station area and the influence area is the buffer zone.

[0039] In existing studies, a fixed-size buffer zone was established centered on the photovoltaic site. However, this single division method does not take into account the differences in installed capacity of different photovoltaic power stations, adjacent terrain characteristics, and external ecosystem vulnerability, and cannot well reflect the actual impact of photovoltaic power stations.

[0040] The inventors found that power stations with large installed capacity in arid areas require more complex infrastructure, such as substations and transmission lines, which may cause additional ecological impacts on the surrounding environment. The terrain where the power station is located, such as slope, will affect soil erosion, hydrological connectivity and the integrity of biological habitats, thereby causing varying degrees of impact on the ecosystem. In addition, if the ecosystem near the power station has a large distribution of vegetation, it is more sensitive to interference. Therefore, when conducting buffer zone analysis, it is necessary to refine the buffer zone range according to the ecological impact factors.

[0041] Based on this, the buffer zone described in this application is obtained by the following sub-steps:

[0042] Sub-step 2-1, through field research, thresholds of land use changes around photovoltaic power stations, and literature analysis, preliminary buffer zones corresponding to multiple photovoltaic power stations can be identified;

[0043] The land use change and field survey are to analyze the changing trends of forest and grassland areas at different distances from the photovoltaic power station through remote sensing and field measurements, and identify turning points as preliminary buffer zones.

[0044] The document may be Xu Z, Li Y, Qin Y, et al. A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing [J]. Solar Energy, 2024, 268: 112198. It records the division of a preliminary buffer zone of 1 km.

[0045] Sub-step 2-2, obtaining the installed capacity, slope and vegetation index of the photovoltaic power stations in each preliminary buffer zone,

[0046] Sub-step 2-3 compares the three variables of installed capacity, slope and vegetation index within the preliminary buffer zone of each photovoltaic power station. For each variable, the minimum value is selected as the benchmark value with a coefficient of 1; wherein the minimum values ​​of the three variables can come from different buffer zones.

[0047] Sub-step 2-4, compare the installed capacity, slope and vegetation index in the preliminary buffer zone of the photovoltaic power station with the corresponding benchmark values ​​and perform range standardization. The specific calculation formula is as follows:

[0048]

[0049] x C ′ represents the installed capacity of the photovoltaic power station,

[0050] min(x C ′) represents the minimum installed capacity of all photovoltaic power stations,

[0051] max(x C ′) represents the maximum installed capacity of all photovoltaic power stations,

[0052] x T ′ represents the slope of the photovoltaic power station,

[0053] min(x T ′) represents the minimum slope value among all photovoltaic power stations,

[0054] max(x T ′) represents the maximum slope of all photovoltaic power stations,

[0055] x E ′ represents the vegetation index of photovoltaic power station,

[0056] min(x E ′) represents the minimum value of vegetation index among all photovoltaic power stations,

[0057] max(x E ′) represents the maximum value of vegetation index among all photovoltaic power stations,

[0058] The standardized value is between 1 and 2, so that the minimum value, that is, the reference value, is 1, and the rest of the values ​​are greater than 1;

[0059] Sub-step 2-5, according to the ratio of the standardized values ​​of the three variables to the reference values, obtain the correction coefficient of each variable through the following formula (1);

[0060]

[0061] Among them, C j represents the correction coefficient of the installed capacity in the preliminary buffer zone of the jth PV power station;

[0062] x C represents the standardized value of installed capacity,

[0063] min(x C ) represents the base value of installed capacity,

[0064] max(x C ) represents the maximum installed capacity, reflecting the impact of photovoltaic construction;

[0065] T j Represents the correction coefficient of the slope in the preliminary buffer zone of the jth PV plant;

[0066] x T represents the normalized value of the slope,

[0067] min(x T ) represents the base value of the slope,

[0068] max(x T ) represents the maximum value of the slope, reflecting the fragility of the ecosystem;

[0069] E j Represents the correction coefficient of the vegetation index in the preliminary buffer zone of the jth PV plant;

[0070] x E represents the standardized value of vegetation index,

[0071] min(x E ) represents the base value of vegetation index,

[0072] max(x E ) represents the maximum value of the vegetation index, which reflects the growth status of vegetation.

[0073] Sub-step 2-6, obtaining the width of the buffer zone of each photovoltaic power station by the following formula (II);

[0074]

[0075] Among them, W j represents the buffer width of the j-th PV plant,

[0076] B represents the width of the benchmark buffer zone. When identifying the width of the impact domain, the baseline width of the buffer zone can be identified through field research, the threshold of land use change around the photovoltaic power station, or literature analysis. Combined with the results of remote sensing image recognition, a benchmark buffer zone can be obtained by analyzing the trend of land use change.

[0077] The value of i is 1, 2, or 3. When i = 1, it represents the installed capacity; when i = 2, it represents the slope; when i = 3, it represents the vegetation index.

[0078] In this application, the three correction factors in the above formula (II) are regarded as equally important, and synthesizing each index in a non-linear manner can effectively reduce the influence of extreme high or low values, thereby providing a more balanced evaluation of the buffer width.

[0079] The present invention creates the method for demarcating the above buffer zone. According to the location and scale of the photovoltaic power station, and combining factors such as terrain and vegetation ecological sensitivity, different buffer zone ranges in the ecological impact area are distinguished.

[0080] Preferably, in step 2, outside the influence area of the photovoltaic power station, a circular area surrounding the influence area is demarcated as the control area, and the width of the control area is the same as the width of the influence area. As shown in Figure 2 This width is the Figure 2 b value in

[0081] Step 3: Evaluate the ecosystem service functions of the photovoltaic power station area, the influence area, and the control area in different years.

[0082] In step 3, the ecosystem service functions include climate regulation ability, biodiversity protection ability, and windbreak and sand fixation amount.

[0083] Preferably, the climate regulation ability is the sum of the vegetation carbon sequestration amount, the soil carbon sequestration amount, and the carbon emission reduction amount of the photovoltaic power station;

[0084] Preferably, the vegetation carbon sequestration amount is obtained by the following formula (III):

[0085] NPP(x,t) = APAR(x,t) × ε(x,t) (III)

[0086] Wherein, NPP(x,t) represents the vegetation carbon sequestration amount of the xth pixel in the tth month, and the unit is MJ·m -2 ·m -1 ,

[0087] ε(x,t) represents the actual light energy utilization rate of the xth pixel in the tth month, and the unit is gC·MJ -1 ,

[0088] APAR(x,t) represents the photosynthetically active radiation absorbed by the xth pixel in the tth month, and the unit is MJ·m -2 ·m -1 , and is obtained by the following formula (IV):

[0089] APAR(x,t) = SOL(x,t) × FPAR(x,t) × 0.5 (IV)

[0090] Among them, SOL(x,t) represents the total solar radiation of the x-th pixel in month t, and the unit is MJ·m -2 ·m -1 ,

[0091] FPAR(x,t) represents the proportion of photosynthetically active radiation absorbed by plants in the xth pixel in month t;

[0092] The 0.5 in formula 4 represents the proportion of photosynthetically active radiation that can be absorbed by plants to the total solar radiation;

[0093] Preferably, the soil carbon sequestration amount is obtained by multiplying the soil carbon density SOCd by the area, and the soil carbon density is obtained by the following formula (V):

[0094]

[0095] Ci represents the average content of soil organic carbon, in g kg -1 ,

[0096] θi represents the soil bulk density, in g·cm -3 ,

[0097] Di represents the thickness of the soil layer, in cm.

[0098] δi represents the volume percentage of gravel with a diameter greater than 2 mm.

[0099] i represents each land use type, and n represents the total number of land use types;

[0100] Preferably, the carbon emission reduction R of the photovoltaic power station is c Obtained by the following formula (VI):

[0101] R c =E×EP(vi)

[0102] Among them, E represents the annual power generation of the photovoltaic power station;

[0103] EP stands for emission factor.

[0104] The annual power generation E of the photovoltaic power station can be obtained by solving the following formula:

[0105] E=C×CF

[0106] C represents the installed capacity of the photovoltaic power station,

[0107] CF represents the annual average power generation efficiency.

[0108] In a preferred embodiment, the habitat quality is assessed by the Habitat Quality module of the InVEST model as an indicator of biodiversity conservation capacity;

[0109] The input data of this module include land use type, stress factor raster dataset, impact distance and spatial weight of stress factor;

[0110] Among them, the stress factors include photovoltaic power stations, access roads, farmland, and construction land, but do not include solar photovoltaic panels.

[0111] Preferably, the biodiversity conservation capacity index Q of the j-th habitat type on the x-th grid is xj Obtained by the following formula:

[0112]

[0113] Among them, H j represents the habitat suitability score of the j-th landscape type, D xj represents the total threat level of habitat type j on grid x, i.e., habitat degradation index; K represents the half-saturation constant, i.e., half of the maximum degradation degree; Z represents the normalization constant, and the preferred value is 2.5;

[0114]

[0115] Where R represents the total amount of stress factor category; W r represents the weight; Yr represents the total number of grid cells of the stress factor; B x represents the accessibility level of grid x; r y Represents the number of stress factors on the grid cell; S jr represents the sensitivity of landscape j to the stress factor; i rxy Indicates the impact distance of the stress factor, which is usually divided into exponential and linear. The solution is:

[0116]

[0117] Among them, d xy is the linear distance between grids x and y; d rmax is the maximum impact range of the threat factor r.

[0118] In a preferred embodiment, the windbreak and sand fixation capacity SF is expressed in kg / m 2 , obtained by the following formula (VII):

[0119] SF=SL r -SL(VII)

[0120] Among them, SL rIndicates the potential amount of wind erosion, in kg / m 2 ,

[0121]

[0122] SL represents the actual amount of wind erosion, in kg / m 2 ,

[0123]

[0124] z represents the distance of maximum wind erosion in the downwind direction, which is 50m;

[0125] S r represents the potential key plot length,

[0126] S r =150.71×(WF×EF×SCF×K′) -0.3711

[0127] S represents the actual key plot length,

[0128] S=150.71×(WF×EF×SCF×K′×C) -0.3711

[0129] Q rmax represents the potential transfer volume,

[0130] Q rmax =109.8×(WF×EF×SCF×K′)

[0131] Q max Indicates the actual transfer volume.

[0132] Q max =109.8×(WF×EF×SCF×K′×C)

[0133] WF represents the climate factor,

[0134] EF stands for soil erodibility factor,

[0135] SCF stands for soil crust factor,

[0136] K′ represents the surface roughness factor,

[0137] C represents the vegetation cover factor.

[0138] Step 4, obtaining the spatial difference and temporal difference caused by the photovoltaic power station, and further obtaining a temporal and spatial difference index; preferably, introducing corresponding governance measures according to the temporal and spatial difference index.

[0139] In step 4, the spatial differences include the differences in ecosystem service functions between the PV power station area and the control area, so as to reveal the ecological changes directly caused by PV construction, and also include the differences in ecosystem service functions between the impact area and the control area, so as to reflect the environmental effects that may spread around the PV power station, thus providing a comprehensive assessment perspective;

[0140] Preferably, the spatial difference ΔS(t) in a specific year is obtained by the following formula (VIII):

[0141]

[0142] in, represents the mean value of the ith ecosystem service function in the photovoltaic power station area in year t,

[0143]

[0144] represents the mean value of the ith ecosystem service function in the impact area in year t,

[0145]

[0146] represents the mean value of the ith ecosystem service function in the control area in year t,

[0147]

[0148] Among them, α represents the weight coefficient of the average, 1-α represents the weight coefficient of the median. If the data distribution is symmetrical and not obviously skewed, its value is 0.5. When the data distribution has many extreme values, the median weight is increased to make full use of the values ​​of each grid.

[0149] N represents the total number of grids in the region,

[0150] represents the ith ecosystem service function of the jth grid in the PV power station area in year t;

[0151] represents the ith ecosystem service function of the jth grid in the impact area in year t;

[0152] represents the ith ecosystem service function of the jth grid in the control area in year t.

[0153] In this application, the above solution combines the calculation method of the mean and the median, and assigns different weights, which can be applied to data distribution in various situations. For example, when the data distribution is relatively uniform, a higher weight can be assigned to the mean calculation, and when the data deviation is large, a higher weight can be assigned to the median. This application does not distinguish between the odd and even cases of the total number of grids.

[0154] In a preferred embodiment, in step 4, the time difference includes the changes in ecosystem service functions between the photovoltaic power station area and the control area before and after the construction of the photovoltaic power station, and between the photovoltaic area and the impact area; the difference of the differences (i.e., the second-order difference) is calculated to evaluate the impact of time changes on service functions.

[0155] Preferably, the time difference ΔF(t) is obtained by the following formula (IX):

[0156]

[0157] Among them, F PV (t) represents the mean value of ecological service function in the photovoltaic power station area in year t,

[0158] F PV (t0) represents the mean value of ecological service function in the photovoltaic power station area in the year t0 before construction,

[0159] F C (t) represents the mean value of ecological service function in the impact area in year t,

[0160] F C (t0) represents the mean value of ecological service function in the affected area in the year t0 before construction, F I (t) represents the mean value of ecological service function in the control area in year t,

[0161] F I (t0) represents the mean value of ecological service function in the year t0 before the construction of the control area.

[0162] In a preferred embodiment, the spatiotemporal difference index ΔSF(Δt) is obtained by the following formula (10):

[0163]

[0164] Among them, ΔS(t2) represents the spatial difference ΔS(t) when the year is t=t2,

[0165] ΔS(t1) represents the spatial difference ΔS(t) when year t=t1,

[0166] ΔF(t2) represents the time difference ΔF(t) when the year is t=t2,

[0167] ΔF(t1) represents the time difference ΔF(t) when the year is t=t1.

[0168] In this application, two different years t1 and t2 are selected; the spatiotemporal characteristics of ecosystem service functions are revealed by obtaining the spatiotemporal difference index ΔSF(Δt), and the spatiotemporal change of different time periods can reflect the spatiotemporal change trend from t1 to t2 by combining temporal change with spatial change. Both t1 and t2 are the years after the construction of the photovoltaic power station. This formula indicates that a certain area may show significant ecological service degradation after the construction of the photovoltaic power station, and this degradation may gradually increase or decrease over time.

[0169] Preferably, in order to accurately assess the temporal and spatial differences in ecological services, an appropriate time window should be selected for calculation. It is usually recommended that the time span from t1 to t2 be no less than 3 years. If the time interval is a multiple of 3, it is recommended to calculate it in segments and average the results of each time period to obtain a more accurate assessment. For example, when the time interval is not a multiple of 3, such as 5 years, the changes in ecological services in the first three years and the last three years should be calculated separately. Note that the data for the third year in the middle will be used overlappingly in the two stages. Through this method, the changing trend of ecological service functions over time can be more accurately analyzed, thereby providing a scientific basis for ecological management and policy making.

[0170] In a preferred embodiment, corresponding control strategies are introduced according to different values ​​of the spatiotemporal difference index ΔSF(Δt); specifically,

[0171] (1) When ΔSF(Δt)>1, it indicates that the ecosystem service function of the buffer zone has significantly improved relative to that of the control zone over time, and has shown an increasing trend over time, which may be due to effective environmental management measures, such as restoring vegetation and improving soil quality.

[0172] Control strategy: Continuously monitor and maintain implemented measures and consider applying them to other similar environments.

[0173] (2) When 0<ΔSF(Δt)<1, it means that the ecosystem service function has improved over time, but the magnitude of the improvement has decreased over time; this may be because ecological restoration or protection measures have had a certain effect, but have reached the threshold of ecological restoration.

[0174] Management strategies: Strengthen ecological restoration efforts or introduce more innovative and effective environmental management technologies.

[0175] (3) When ΔSF(Δt) = 0, it indicates that the ecosystem service function is relatively stable and does not show significant improvement or degradation.

[0176] Management Strategy: Maintain current measures and conduct regular ecological monitoring to ensure continued stability and prevent possible future degradation.

[0177] (4) When -1<ΔSF(Δt)<0, it means that the ecosystem service function shows a slight degradation trend, but the degree of degradation gradually weakens over time, which may be because the environmental pressure may be easing or the restoration measures taken are beginning to take effect.

[0178] Management and control strategies: Analyze the causes of degradation and strengthen remediation measures.

[0179] (5) When ΔSF(Δt) < -1, it indicates that the ecosystem service function is degrading, and the degree of degradation gradually increases over time, which may be due to improper management of photovoltaic power plants, environmental stress, etc.

[0180] Management and control strategies: Take strong environmental restoration and protection measures, such as increasing ecological corridors.

[0181] The above zoning method in this application can analyze the temporal trend of changes in ecosystem service functions under each type to identify the positive or negative impacts that may be brought about by the construction and long-term operation of photovoltaic power plants. Combined with the obtained results, the future impact potential of photovoltaic power plants on the ecosystem service functions of the studied area is evaluated, thereby providing a scientific basis for environmental impact assessment and management decision-making of photovoltaic projects.

[0182] Example

[0183] The impact of the Huaneng Gonghe 25MWp grid-connected photovoltaic power generation project in the Gonghe Basin of Qinghai Province on its ecosystem service function was evaluated. The specific evaluation method includes the following steps:

[0184] Step 1: Obtain the location, installed capacity and land use type of the PV power station.

[0185] The photovoltaic power station is located at N 36 05'24.2"~N 3606'6.32", E10033'8.26"~E 100°33'24.13', with an installed capacity of 25 MW. The land use type is wasteland or semi-degraded grassland, with a total land area of ​​0.534km 2 Step 2: Divide the impact area and control area of ​​the PV power station.

[0186] The area covered by the photovoltaic power station is determined as the photovoltaic power station area. The annular area obtained by expanding the photovoltaic power station area is the influence area of ​​the photovoltaic power station. The sum of the photovoltaic power station area and the influence area is the buffer zone.

[0187] The buffer zone is obtained by the following sub-steps:

[0188] Sub-step 2-1, through field research, thresholds of land use change around photovoltaic power stations, and literature analysis, preliminary buffer zones corresponding to multiple photovoltaic power stations can be identified.

[0189] Sub-step 2-2, obtaining the installed capacity, slope and vegetation index of the photovoltaic power stations in each preliminary buffer zone,

[0190] Sub-step 2-3, comparing the three variables of installed capacity, slope and vegetation index within the preliminary buffer zone of each PV power station, and for each variable, selecting the minimum value as the benchmark value with a coefficient of 1;

[0191] Sub-step 2-4, comparing the installed capacity, slope and vegetation index in the preliminary buffer zone of the PV power station with the corresponding benchmark values, and performing range normalization processing;

[0192] The standardized value is between 1 and 2, so that the minimum value, that is, the reference value, is 1, and the rest of the values ​​are greater than 1;

[0193] Sub-step 2-5, according to the ratio of the standardized values ​​of the three variables to the reference values, obtain the correction coefficient of each variable through the following formula (1);

[0194]

[0195] Among them, C j represents the correction coefficient of the installed capacity in the preliminary buffer zone of the jth PV power station;

[0196] x C represents the standardized value of installed capacity,

[0197] min(x C ) represents the base value of installed capacity,

[0198] max(x C ) represents the maximum installed capacity,

[0199] T j Represents the correction coefficient of the slope in the preliminary buffer zone of the jth PV plant;

[0200] x T represents the normalized value of the slope,

[0201] min(x T ) represents the base value of the slope,

[0202] max(x T ) represents the maximum value of the slope,

[0203] E jRepresents the correction coefficient of the vegetation index in the preliminary buffer zone of the jth PV plant;

[0204] x E represents the standardized value of vegetation index,

[0205] min(x E ) represents the base value of vegetation index,

[0206] max(x E ) represents the maximum value of the vegetation index.

[0207] Sub-step 2-6, obtaining the width of the buffer zone of each photovoltaic power station by the following formula (II);

[0208]

[0209] Among them, W j represents the buffer width of the j-th PV plant,

[0210] B represents the width of the reference buffer,

[0211] The values ​​of i are 1, 2, and 3. When i is 1, it represents installed capacity; when i is 2, it represents slope; and when i is 3, it represents vegetation index.

[0212] In step 2, outside the influence area of ​​the photovoltaic power station, an annular area surrounding the influence area is defined as a control area, and the width of the control area is consistent with the width of the influence area.

[0213] Step 3: Evaluate the ecosystem service functions of the PV power station area, impact area and control area in different years.

[0214] The ecosystem service functions include climate regulation capacity, biodiversity protection capacity and wind and sand fixation capacity.

[0215] The climate regulation capacity is the sum of vegetation carbon sequestration, soil carbon sequestration and carbon emission reduction of photovoltaic power stations;

[0216] The vegetation carbon fixation amount is obtained by the following formula (III):

[0217] NPP(x,t)=APAR(x,t)×ε(x,t) (3)

[0218] Where NPP(x,t) represents the vegetation carbon sequestration of the xth pixel in month t, and the unit is MJ·m -2 ·m -1 ,

[0219] ε(x,t) represents the actual light energy utilization rate of the x-th pixel in month t, and the unit is gC·MJ -1 ,

[0220] APAR(x,t) represents the photosynthetically active radiation absorbed by the x-th pixel in month t, in MJ·m -2 ·m -1 , obtained by the following formula (IV):

[0221] APAR(x,t)=SOL(x,t)×FPAR(x,t)×0.5 (IV)

[0222] Among them, SOL(x,t) represents the total solar radiation of the x-th pixel in month t, and the unit is MJ·m -2 ·m -1 ,

[0223] FPAR(x,t) represents the proportion of photosynthetically active radiation absorbed by plants in the xth pixel in month t;

[0224] The soil carbon sequestration amount is obtained by multiplying the soil carbon density SOCd by the area, and the soil carbon density is obtained by the following formula (V):

[0225]

[0226] Ci represents the average content of soil organic carbon, in g kg -1 ,

[0227] θi represents the soil bulk density, in g·cm -3 ,

[0228] Di represents the thickness of the soil layer, in cm.

[0229] δi represents the volume percentage of gravel with a diameter greater than 2 mm.

[0230] i represents each land use type, and n represents the total number of land use types;

[0231] The carbon emission reduction of the photovoltaic power station is R c Obtained by the following formula (VI):

[0232] R c =E×EP(vi)

[0233] Among them, E represents the annual power generation of the photovoltaic power station;

[0234] EP stands for emission factor.

[0235] The annual power generation E of the photovoltaic power station can be obtained by solving the following formula:

[0236] E=C×CF

[0237] C represents the installed capacity of the photovoltaic power station,

[0238] CF represents the annual average power generation efficiency.

[0239] The Habitat Quality module of the InVEST model is used to assess habitat quality as an indicator of biodiversity conservation capacity;

[0240] The input data of this module include land use type, stress factor raster dataset, impact distance and spatial weight of stress factor;

[0241] Among them, the stress factors include photovoltaic power stations, access roads, farmland, and construction land, but do not include solar photovoltaic panels.

[0242] The windbreak and sand fixation capacity SF is in kg / m 2 , obtained by the following formula (VII):

[0243] SF=SL r -SL(VII)

[0244] Among them, SL r Indicates the potential amount of wind erosion, in kg / m 2 ,

[0245]

[0246] SL represents the actual amount of wind erosion, in kg / m 2 ,

[0247]

[0248] z represents the distance of maximum wind erosion in the downwind direction, which is 50m;

[0249] S r represents the potential key plot length,

[0250] S r =150.71×(WF×EF×SCF×K′) -0.3711

[0251] S represents the actual key plot length,

[0252] S=150.71×(WF×EF×SCF×K′×C) -0.3711

[0253] Q rmax represents the potential transfer volume,

[0254] Q rmax =109.8×(WF×EF×SCF×K′)

[0255] Q max Indicates the actual transfer volume.

[0256] Q max =109.8×(WF×EF×SCF×K′×C)

[0257] WF represents the climate factor,

[0258] EF stands for soil erodibility factor,

[0259] SCF stands for soil crust factor,

[0260] K′ represents the surface roughness factor,

[0261] C represents the vegetation cover factor.

[0262] Step 4, obtaining the spatial difference and temporal difference caused by the photovoltaic power station, and further obtaining a temporal and spatial difference index; preferably, introducing corresponding governance measures according to the temporal and spatial difference index.

[0263] The spatial differences include differences in ecosystem service functions between the photovoltaic power station area and the control area, and also include differences in ecosystem service functions between the impact area and the control area;

[0264] The spatial difference ΔS(t) of the specific year is obtained by the following formula (VIII):

[0265]

[0266] in, represents the mean value of the ith ecosystem service function in the photovoltaic power station area in year t,

[0267]

[0268] represents the mean value of the ith ecosystem service function in the impact area in year t,

[0269]

[0270] represents the mean value of the ith ecosystem service function in the control area in year t,

[0271]

[0272] Among them, α represents the weight coefficient of the mean, 1-α represents the weight coefficient of the median,

[0273] N represents the total number of grids in the region,

[0274] represents the ith ecosystem service function of the jth grid in the PV power station area in year t;

[0275] represents the ith ecosystem service function of the jth grid in the impact area in year t;

[0276] represents the ith ecosystem service function of the jth grid in the control area in year t.

[0277] The temporal differences include changes in ecosystem service functions between the PV power station area and the control area before and after the construction of the PV power station, and between the PV area and the impact area;

[0278] The time difference ΔF(t) is obtained by the following formula (IX):

[0279]

[0280] Among them, F PV (t) represents the mean value of ecological service function in the photovoltaic power station area in year t,

[0281] F PV (t0) represents the mean value of ecological service function in the photovoltaic power station area in the year t0 before construction,

[0282] F C (t) represents the mean value of ecological service function in the impact area in year t,

[0283] F C (t0) represents the mean value of ecological service function in the affected area in the year t0 before construction, F I (t) represents the mean value of ecological service function in the control area in year t,

[0284] F I (t0) represents the mean value of ecological service function in the year t0 before the construction of the control area.

[0285] The spatiotemporal difference index ΔSF(Δt) is obtained by the following formula (10):

[0286]

[0287] Among them, ΔS(t2) represents the spatial difference ΔS(t) when the year is t=t2,

[0288] ΔS(t1) represents the spatial difference ΔS(t) when year t=t1,

[0289] ΔF(t2) represents the time difference ΔF(t) when the year is t=t2,

[0290] ΔF(t1) represents the time difference ΔF(t) when the year is t=t1.

[0291] Among them, t1 is 2018, which is 2 years after completion, t0 is 2015, one year before construction, and t2 is 2021, which is 5 years after construction;

[0292] The obtained spatiotemporal difference index ΔSF(Δt) is 0.86, indicating that the ecosystem service function has improved over time, but the magnitude of the improvement has decreased over time; this may be because ecological restoration or protection measures have had a certain effect, but have reached the threshold of ecological restoration. The corresponding management and control strategies are to strengthen ecological restoration efforts or introduce more innovative and effective environmental management technologies.

[0293] We further retrieved the data on the ecosystem service functions of the region before and after the construction of the Huaneng Gonghe 25MWp grid-connected photovoltaic power generation project, and evaluated specific indicators related to the ecosystem service functions of the region, forming the following table:

[0294]

[0295] According to the above data, after the construction of the photovoltaic power station, the three ecosystem service functions have been significantly improved due to the improvement of regional microclimate conditions, but the improvement of ecosystem service functions has decreased with the increase of operation time. This is basically consistent with the evaluation results of the impact evaluation method of the ecosystem service function of photovoltaic power stations in arid areas provided in this application.

[0296] It can be seen from this that the method for evaluating the impact of photovoltaic power stations on ecosystem service functions in arid areas provided by the present invention can measure the complex changes in the ecosystem functions in photovoltaic construction areas by using the spatiotemporal difference index. The evaluation results are accurate and reliable, can provide a basis for ecological management, and are worthy of large-scale promotion and application.

[0297] The present invention has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, the present invention may be subjected to a variety of substitutions and improvements, all of which fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the impact of photovoltaic power stations on ecosystem services in arid areas, characterized in that: The method comprises the following steps: Step 1: Obtain the location, installed capacity and land use type of the PV power station. Step 2: Divide the impact area and control area of ​​the photovoltaic power station, Step 3: Evaluate the ecosystem service functions of the PV power station area, impact area and control area in different years; Step 4, obtaining the spatial difference and temporal difference caused by the photovoltaic power station, and further obtaining a temporal and spatial difference index; preferably, introducing a corresponding management and control strategy according to the temporal and spatial difference index.

2. The method for evaluating the impact of the service function of the photovoltaic power station ecosystem in arid areas according to claim 1 is characterized in that: The area covered by the photovoltaic power station is determined as the photovoltaic power station area. The annular area obtained by expanding the photovoltaic power station area is the influence area of ​​the photovoltaic power station. The sum of the photovoltaic power station area and the influence area is the buffer zone. The buffer zone is obtained by the following sub-steps: Sub-step 2-1, through field research and the threshold of land use change around the photovoltaic power station, identify and obtain preliminary buffer zones corresponding to multiple photovoltaic power stations. Sub-step 2-2, obtaining the installed capacity, slope and vegetation index of the photovoltaic power stations in each preliminary buffer zone, Sub-step 2-3, comparing the three variables of installed capacity, slope and vegetation index within the preliminary buffer zone of each PV power station, and for each variable, selecting the minimum value as the benchmark value with a coefficient of 1; Sub-step 2-4, comparing the installed capacity, slope and vegetation index in the preliminary buffer zone of the PV power station with the corresponding benchmark values, and performing range normalization processing; Sub-step 2-5, according to the ratio of the standardized values ​​of the three variables to the reference values, obtain the correction coefficient of each variable by the following formula (1); Among them, C j represents the correction coefficient of the installed capacity in the preliminary buffer zone of the jth PV power station; x C represents the standardized value of installed capacity, min(x C ) represents the base value of installed capacity, max(x C ) represents the maximum installed capacity, T j Represents the correction coefficient of the slope in the preliminary buffer zone of the jth PV plant; x T represents the normalized value of the slope, min(x T ) represents the base value of the slope, max(x T ) represents the maximum value of the slope, E j Represents the correction coefficient of the vegetation index in the preliminary buffer zone of the jth PV plant; x E represents the standardized value of vegetation index, min(x E ) represents the base value of vegetation index, max(x E ) represents the maximum value of vegetation index; Sub-step 2-6, obtaining the width of the buffer zone of each photovoltaic power station by the following formula (II); Among them, W j represents the buffer width of the j-th PV plant, B represents the width of the reference buffer, The values ​​of i are 1, 2, and 3. When i is 1, it represents installed capacity; when i is 2, it represents slope; and when i is 3, it represents vegetation index.

3. The method for evaluating the impact of the service function of the photovoltaic power station ecosystem in arid areas according to claim 2 is characterized in that: In step 2, outside the influence area of ​​the photovoltaic power station, an annular area surrounding the influence area is defined as a control area, and the width of the control area is consistent with the width of the influence area.

4. The method for evaluating the impact of the service function of the photovoltaic power station ecosystem in arid areas according to claim 1 is characterized in that: In step 3, the ecosystem service functions include climate regulation capacity, biodiversity protection capacity and windbreak and sand fixation capacity.

5. The method for evaluating the impact of the service function of the photovoltaic power station ecosystem in arid areas according to claim 4 is characterized in that: The climate regulation capacity is the sum of vegetation carbon sequestration, soil carbon sequestration and carbon emission reduction of photovoltaic power stations; Preferably, the vegetation carbon fixation amount is obtained by the following formula (III): NPP(x,t)=APAR(x,t)×ε(x,t)(3) Among them, NPP(x,t) represents the vegetation carbon fixation of the x-th pixel in month t. ε(x,t) represents the actual light energy utilization rate of the x-th pixel in month t. APAR(x, t) represents the photosynthetically active radiation absorbed by the x-th pixel in month t, which is obtained by the following formula (IV): APAR(x, t) = SOL(x, t) × FPAR(x, t) × 0.5 (IV) where SOL(x, t) represents the total solar radiation of the x-th pixel in month t, FPAR(x, t) represents the proportion of photosynthetically active radiation absorbed by plants of the x-th pixel in month t; Preferably, the soil carbon sequestration amount is obtained by multiplying the soil carbon density SOCd by the area, and the soil carbon density is obtained by the following formula (V): where Ci represents the average content of soil organic carbon, θi represents the soil bulk density, Di represents the soil layer thickness, δi represents the volume percentage content of gravel with a diameter greater than 2 mm, i represents each land use type, and n represents the total number of land use types; Preferably, the carbon emission reduction R of the photovoltaic power station is c Obtained by the following formula (VI): R c =E×EP(vi) where E represents the annual power generation of the photovoltaic power station; EP represents the emission factor.

6. The method for evaluating the impact of the ecosystem service function of a photovoltaic power station in an arid area according to claim 4, characterized in that the habitat quality is evaluated through the Habitat Quality module of the InVEST model, and this is used as an indicator of the biodiversity protection ability; the input data of this module includes the land use type, the raster dataset of stress factors, the influence distance of stress factors, and the spatial weight; where the stress factors include photovoltaic power stations, access roads, farmland, and construction land, excluding solar photovoltaic panels.

7. The method for evaluating the impact of the ecosystem service function of a photovoltaic power station in an arid area according to claim 4, characterized in that the wind prevention and sand fixation amount SF is obtained by the following formula (VII): SF = SL r -SL(Seven) Among them, SL r represents the potential amount of wind erosion, SL represents the actual wind erosion amount, z represents the maximum downwind wind erosion occurrence distance; S r represents the potential key plot length, S r =150.71×(WF×EF×SCF×K′) -0.3711 S represents the actual key plot length, S=150.71×(WF×EF×SCF×K′×C) -0.3711 Q rmax represents the potential transfer volume, Q rmax =109.8×(WF×EF×SCF×K′) Q max Indicates the actual transfer volume. Q max =109.8×(WF×EF×SCF×K′×C) WF represents the climate factor, EF represents the soil erodibility factor, SCF represents the soil crust factor, K′ represents the surface roughness factor, C represents the vegetation cover factor.

8. The method for evaluating the impact of the ecosystem service function of a photovoltaic power station in an arid area according to claim 1, characterized in that in step 4, the spatial difference includes the difference in the ecosystem service function between the photovoltaic power station area and the control area, and also includes the difference in the ecosystem service function between the impact area and the control area; Preferably, the spatial difference ΔS(t) in a specific year is obtained by the following formula (VIII): in, represents the comprehensive value of the ith ecosystem service function in the photovoltaic power station area in year t, expressed as the proportional distribution of the mean and the median, represents the mean value of the ith ecosystem service function in the impact area in year t, represents the mean value of the ith ecosystem service function in the control area in year t, where α represents the weight coefficient of the mean, 1 - α represents the weight coefficient of the median. If the data distribution is symmetric and there is no obvious skew, its value is 0.

5. When there are more extreme values in the data distribution, the median weight is increased to make full use of the grid values; N represents the total number of grids in the region, represents the ith ecosystem service function of the jth grid in the PV power station area in year t; represents the ith ecosystem service function of the jth grid in the impact area in year t; represents the ith ecosystem service function of the jth grid in the control area in year t.

9. The method for evaluating the impact of the ecosystem service function of a photovoltaic power station in an arid area according to claim 1, characterized in that in step 4, the time difference includes the changes in the ecosystem service function of the photovoltaic power station area and the control area before and after the construction of the photovoltaic power station, as well as the photovoltaic area and the impact area; Preferably, the time difference ΔF(t) is obtained by the following formula (IX): Among them, F PV (t) represents the mean value of ecological service function in the photovoltaic power station area in year t, F PV (t0) represents the mean value of ecological service function in the photovoltaic power station area in the year t0 before construction, F C (t) represents the mean value of ecological service function in the impact area in year t, F C (t0) represents the mean value of ecological service function in the affected area in the year t0 before construction. F I (t) represents the mean value of ecological service function in the control area in year t, F I (t0) represents the mean value of ecological service function in the year t0 before the construction of the control area.

10. The method for evaluating the impact of the ecosystem service function of a photovoltaic power station in an arid area according to claim 1, characterized in that The spatiotemporal difference index ΔSF(Δt) is obtained by the following formula (10): Among them, ΔS(t2) represents the spatial difference ΔS(t) when the year is t=t2, ΔS(t1) represents the spatial difference ΔS(t) when year t=t1, ΔF(t2) represents the time difference ΔF(t) when the year is t=t2, ΔF(t1) represents the time difference ΔF(t) when the year is t=t1.

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