Wind power plant full life cycle greenhouse gas emission intensity calculation method considering land utilization change

Abstract

The invention provides a method for calculating the full-life-cycle greenhouse gas emission intensity of a wind power plant in consideration of land utilization change, and the method comprises the steps: constructing a full-life-cycle greenhouse gas emission total amount model which comprises wind power turbine part and material emission, vegetation and soil carbon library loss emission and carbon sink loss emission; three types of land utilization indirect emission including vegetation and soil carbon library loss and potential carbon sink loss are taken into accounting, the limitation that only direct emission is concerned is broken through, emission underestimation in scenes such as a forest type wind power plant is avoided, and data support is provided for high-precision carbon footprint evaluation; power generation performance nonlinear degradation caused by equipment aging is accurately captured through staged dynamic attenuation capacity factors, and the power generation capacity and the carbon emission intensity accounting precision are improved; according to a full-life-cycle greenhouse gas emission total amount model and the power generation total amount, the full-life-cycle greenhouse gas emission intensity is obtained, and data support is provided for accounting of the wind power emission reduction potential and sustainable development of wind power.

Description

Technical Field

[0001] This invention relates to the field of carbon emission accounting, and more specifically to a method for calculating the greenhouse gas emission intensity of a wind farm throughout its entire life cycle, taking into account land use change. Background Technology

[0003] Life cycle assessment (LCA) is a common method for estimating the emission intensity of renewable energy. Numerous LCA studies have analyzed and estimated greenhouse gas emissions from wind energy in China. An overview of wind energy assessment in China is provided below. Figure 1 Wind greenhouse gas emissions are shown in the figures and in other countries (e.g., Alsaleh and Sattler, 2019; Seresová et al., 2020; Teffera et al., 2021). Figure 1 It is known that, using existing methods, the carbon dioxide emission equivalent of wind farms deployed on grasslands is underestimated by 3%–77%, while that of wind farms deployed in forests is underestimated by more than 11.5%. However, most studies focus on specific wind farms or wind turbines, resulting in significant discrepancies in the results. This is primarily due to differences in assumptions regarding turbine size, design, and the specific wind energy potential of the site. Previous research has shown that ignoring carbon emissions caused by land-use change significantly underestimates the life-cycle environmental impact of various renewable energy projects.

[0004] In the wind power sector, traditional accounting methods fail to consider vegetation carbon loss, soil organic carbon loss, and ecosystem service function decline caused by land occupation. This could lead to an underestimation of emissions from wind farms located in forest ecosystems by 37.9%. Ignoring the impact of land-use changes associated with wind farm deployment could result in a significant overestimation of greenhouse gas emission reduction potential and provide misleading information for energy structure optimization policies. Furthermore, existing life-cycle accounting models have another significant flaw: they fail to adequately incorporate the dynamic changes in the actual operating performance of wind turbines. Specifically, the actual wind speed characteristics at wind turbine sites, and the nonlinear degradation of power generation performance due to equipment aging and failures over an operating cycle of up to 20 years (or longer), are often simplified or ignored. This approach causes discrepancies between the theoretical or expected power generation calculations and the actual values, ultimately leading to inaccurate calculations of greenhouse gas emission intensity (carbon emission intensity) per unit of power generation.

[0005] Wind farms generally have higher land use intensity than other renewable energy and fossil fuel power plants. Land use change caused by large-scale deployment will have a huge impact on heat and water fluxes and global carbon cycle. However, traditional life cycle assessment methods ignore the indirect greenhouse gas emissions caused by land use change. The removal of vegetation reduces the storage of biomass, resulting in the loss of vegetation carbon pool. In addition, the on-site construction of impervious surfaces such as turbine foundations and roads will inhibit carbon input, further leading to the loss of soil carbon pool and carbon sink. Ignoring the carbon loss caused by land use change leads to incomplete and incorrect estimation of life cycle greenhouse gas emissions and overestimation of the potential for mitigating climate change. Correct accounting of land use is crucial to improve the accuracy of life cycle assessment and support the sustainable development of wind power. SUMMARY

[0006] To solve the problem of ignoring the carbon loss caused by land use change, which leads to incomplete and incorrect estimation of life cycle greenhouse gas emissions and overestimation of the potential for mitigating climate change, the present application proposes a method for calculating the life cycle greenhouse gas emission intensity of wind farms considering land use change.

[0007] In order to achieve the above technical effects, the technical scheme of the present application is as follows: A method for calculating the life cycle greenhouse gas emission intensity of wind farms considering land use change, comprising the following steps: Constructing a total life cycle greenhouse gas emission model GHG total : GHG total Including wind turbine component and material emissions GHG M , vegetation and soil carbon pool loss emissions GHG CS , carbon sink loss emissions GHG LASC ; its expression is: GHG total = GHG M + GHG CS + GHG LASC wherein, GHG total represents the total life cycle greenhouse gas emission, GHG M represents the life cycle greenhouse gas emission of different components and materials of wind turbine, GHG CS represents the indirect greenhouse gas emissions caused by the loss of vegetation and soil carbon pool, GHGLASC represents the indirect greenhouse gas emissions caused by the loss of carbon sink; total power generation PG ; GHG of the whole life cycle int GHG of the whole life cycle total and total power generation PG ; the expression is:

[0008] wherein, GHG of the whole life cycle, GHG of the whole life cycle, total power generation.

[0009] Compared with the prior art, the beneficial effects of the technical scheme of the present application are: The present application provides a more accurate calculation method of wind farm life cycle greenhouse gas emission intensity considering land use change, which builds a whole life cycle greenhouse gas emission total amount model including wind turbine component and material emission, vegetation and soil carbon pool loss emission, and carbon sink loss emission, and includes three types of land use indirect emissions of vegetation and soil carbon pool loss and potential carbon sink loss in accounting, breaks through the limitation of traditional methods only focusing on direct emissions, avoids underestimation of emissions in forest wind farm and other scenarios, provides data support for high-precision carbon footprint assessment, and accurately captures the nonlinear decline of power generation performance caused by equipment aging through the capacity factor of dynamic attenuation in stages, improves the accuracy of power generation and carbon emission intensity accounting, and obtains the whole life cycle greenhouse gas emission intensity according to the whole life cycle greenhouse gas emission total amount model and the total power generation, provides data support for the accounting of wind power emission reduction potential and the sustainable development of wind power, so as to effectively avoid the underestimation of carbon dioxide emission equivalent. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 The present application is shown in the Chinese wind energy assessment overview diagram.

[0011] Figure 2 The present application is shown in the Chinese wind energy assessment overview diagram.

[0012] Figure 3 The present application is shown in the Chinese wind energy assessment overview diagram.

[0013] Figure 4 The present application is shown in the Chinese wind energy assessment overview diagram. Detailed Implementation

[0014] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0015] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0016] It should be understood that although the terms first, second, third, etc., may be used in this invention to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of this invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0017] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0018] Example 1 This embodiment proposes a method for calculating the greenhouse gas emission intensity of a wind farm throughout its entire life cycle, taking into account land use change. The process is as follows: Figure 2 As shown, it includes the following steps: Constructing a life-cycle greenhouse gas emissions model GHG total : GHG total Emissions from wind turbine components and materials GHG M vegetation and soil carbon pool loss emissions GHG CS Carbon sink loss emissions GHG LASC Its expression is: GHG total = GHG M + GHG CS+ GHG LASC wherein, GHG total GHGtotalrepresents the total amount of life cycle greenhouse gas emissions, GHG M GHGpartrepresents the total amount of life cycle greenhouse gas emissions of different parts and materials of wind turbine, GHG CS GHGindirectrepresents the indirect greenhouse gas emissions caused by the loss of vegetation and soil carbon pool, GHG LASC GHGindirectsinkrepresents the indirect greenhouse gas emissions caused by the loss of carbon sink; GHGtotalrepresents the total amount of life cycle greenhouse gas emissions, PG ; GHGintensity represents the life cycle greenhouse gas emission intensity, int GHGtotalrepresents the total amount of life cycle greenhouse gas emissions, total GHGtotalrepresents the total amount of life cycle greenhouse gas emissions, PG ; the expression is:

[0019] wherein, GHGintensity represents the life cycle greenhouse gas emission intensity, GHGtotalrepresents the total amount of life cycle greenhouse gas emissions, GHGtotalrepresents the total amount of life cycle greenhouse gas emissions.

[0020] In this embodiment, by constructing a total life cycle greenhouse gas emission model including wind turbine component and material emissions, vegetation and soil carbon pool loss emissions, and carbon sink loss emissions, the vegetation and soil carbon pool loss, and potential carbon sink loss are included in the accounting, which breaks through the limitation of traditional methods only focusing on direct emissions, avoids the underestimation of emissions in forest wind farm and other scenarios, and provides data support for high-precision carbon footprint assessment; through the capacity factor of dynamic attenuation in stages, the nonlinear decline of power generation performance caused by equipment aging is accurately captured, and the accuracy of power generation and carbon emission intensity accounting is improved; according to the total life cycle greenhouse gas emission model and the total amount of power generation, the life cycle greenhouse gas emission intensity is obtained, which provides data support for the accounting of wind power emission reduction potential and the sustainable development of wind power.

[0021] Embodiment 2 This embodiment is further explained on the basis of embodiment 1.

[0022] For example, the total amount of life cycle greenhouse gas emissions GHGtotalof a wind farm (the life cycle of the wind farm in this embodiment is set to 20 years) total mainly includes three parts, the total amount of life cycle greenhouse gas emissions GHGpartof different parts and materials of wind turbine, MIndirect greenhouse gas emissions (GHG) from the loss of vegetation and soil carbon pools CS and indirect greenhouse gas emissions (GHG) caused by carbon sink losses LASC .

[0023] In one optional embodiment, the total life-cycle greenhouse gas emissions model, which includes emissions from wind turbine components and materials, emissions from vegetation and soil carbon pool losses, and emissions from carbon sink losses, is expressed as follows: GHG total = GHG M + GHG CS + GHG LASC in, GHG total This represents the total greenhouse gas emissions over the entire life cycle. GHG M This represents the total greenhouse gas emissions throughout the entire lifecycle of different components and materials in a wind turbine. GHG CS This represents the indirect greenhouse gas emissions resulting from the loss of carbon pools in vegetation and soil. GHG LASC This indicates the amount of indirect greenhouse gas emissions caused by carbon sink losses.

[0024] In one alternative embodiment, the emissions from wind turbine components and materials are obtained based on the amount of materials and energy used and the greenhouse gas emission activity factor of materials and energy used; the expression is:

[0025] in, This represents the total greenhouse gas emissions throughout the entire lifecycle of different components and materials in a wind turbine. Indicates materials and energy consumption Usage Indicates materials and energy consumption Greenhouse gas emission activity factors; The materials and energy consumption usage The calculation expression is:

[0026] in, This indicates the number of wind turbines in a wind farm. Indicates the first stage of the entire life cycle Raw materials and energy used in the stage The quality or quantity.

[0027] In an alternative embodiment, the vegetation and soil carbon pool loss emissions are derived from the life cycle of vegetation biomass carbon loss and the life cycle of soil organic carbon loss; the expression is:

[0028] wherein 44 / 12 represents the ratio of the molecular molar mass of carbon dioxide and carbon, represents the life cycle of vegetation biomass carbon loss, represents the life cycle of soil organic carbon loss.

[0029] The vegetation removal for wind farm construction mainly causes the loss of aboveground parts.

[0030] Further, in an alternative embodiment, the life cycle of vegetation biomass carbon loss includes the aboveground biomass of arbor layer, shrub layer and herb layer; the expression is: CL biomass = C tree + C shrub + C grass wherein, C tree represents the aboveground biomass of arbor layer, C shrub represents the aboveground biomass of shrub layer, C grass represents the aboveground biomass of herb layer; Further, for the aboveground part of arbor layer, the expression is:

[0031] wherein, B tree, k is the average unit area aboveground biomass of tree species k in the stand, CF tree, k is the carbon content rate of tree species k , A k is the area occupied by tree species k ; B tree, k According to the standard tree species of "Standing biomass model and carbon measurement parameters", the biomass of single tree is calculated by measuring the diameter at breast height and height, and then the biomass of the tree species is calculated, or the base material density, biomass expansion factor and measured volume of tree species k provided in Appendix A.1 of "Guidelines for Measurement of Carbon Storage in Forest Ecosystems" are multiplied to obtain; CF tree, kThe results of forest ecosystem carbon pool investigation and tree species carbon content rate determination are adopted, or the values provided in Appendix A.1 of the "Guidelines for Measurement of Carbon Storage in Forest Ecosystems" are selected, and the default value is 0.5 tC / t2dm; CF For the aboveground part of the shrub layer, the calculation expression is: C shrub = B shrub × CF shrub × A shrub wherein, B shrub is the average unit area biomass of the shrub layer, CF shrub is the average carbon content rate of the shrub, A shrub is the ground area occupied by the shrub layer, CF shrub and A shrub The actual investigation and determination results are adopted, and if not, the default values 12.51 tdm / hm and 0.47 tC / t2dm are adopted; 2 For the aboveground part of the herb layer, the calculation expression is: C grass = B grass × CF grass × A grass wherein, B grass represents the average unit area biomass of the aboveground part of the herb layer, CF grass represents the average carbon content rate of the herb layer, A grass represents the ground area occupied by the herb layer; In an optional embodiment, the soil organic carbon loss in the whole life cycle is obtained according to the soil organic carbon density at the time of wind farm construction and the soil organic carbon density at the end of the whole life cycle; the expression is: CL soc = n × A × ( SOCD 0- SOCD 20 ) wherein, SOCD 0 represents the soil organic carbon density at the time of wind farm construction,​​SOCD 20 This represents the soil organic carbon density at the end of its entire life cycle.

[0032] Furthermore, SOCD 0 was obtained through actual measurement in this method. SOCD 20 The calculations were obtained by establishing a system dynamics model, and the results are as follows:

[0033]

[0034]

[0035] This method divides soil organic carbon into three independent carbon pools, among which... C f The content of active soil organic carbon, C s Organic carbon content in slow-moving soils C p Organic carbon content in inert soil. k f , k s and k p The decomposition rates of the three soil carbon pools, f ( T () is a correction for the effect of temperature on the decomposition rate. a 56 , a 57 , a 65 , a 75 and a 76 This represents the conversion ratio between different libraries. C f , C s and C p The initial values ​​are as follows: C f = 0.05 × SOCD 0 C s = 0.46 × SOCD 0 C p = 0.49 × SOCD 0 Decomposition rates of the three different soil carbon pools kf , k s and k p The values ​​are 0.14, 0.0038, and 0.00013, respectively.

[0036] Correction of the effect of temperature on decomposition rate f ( T The calculation is as follows:

[0037] in, Temp The step size is dt In this method, "time" refers to the average daily temperature over a 20-year lifespan.

[0038] The conversion ratio between different libraries a 56 , a 57 , a 65 , a 75 and a 76 The calculation is as follows: a 56 = 0.42 a 57 = 0.45 a 65 = 0.75 × Txrt – 0.004 a 75 = 0.004 a 76 = 0.03 in, Txrt This represents the proportion of clay and silt in the soil. If no actual measurement data is available, the proportion is 0.15 for sandy soil, 0.5 for loam, and 0.85 for clay.

[0039] In one alternative embodiment, the carbon sink loss emissions are obtained based on the land area occupied by a single wind turbine and the net primary productivity of the ecosystem; its expression is:

[0040] in, This represents the land area occupied by a single wind turbine (including the area cleared of vegetation), encompassing roads, foundations, etc., obtained using drone aerial photography or high-resolution remote sensing imagery. Net primary productivity of an ecosystem, measured by eddy covariance or high-resolution global carbon assimilation data from multi-source satellite remote sensing.

[0041] In an alternative embodiment, the total power generation is calculated according to the rated power of the wind turbine and the capacity factor of the phased dynamic attenuation; the expression is: PG = 8760 × 20 × n × cf × P wherein, PG the total power generation, cf the average capacity factor of the wind turbine throughout the life cycle, P the rated power of the wind turbine.

[0042] In an alternative embodiment, the average capacity factor of the wind turbine throughout the life cycle is calculated according to the rated power of the wind turbine and the capacity factor of the phased dynamic attenuation; the expression is:

[0043] wherein, the curtailment rate, calculated by using the local wind power on-grid power divided by the theoretical power generation, the number of hours throughout the life cycle, the average capacity factor of the wind turbine throughout the life cycle at time; the average capacity factor of the wind turbine throughout the life cycle at time is obtained by the power curve of the specific type of wind turbine; the expression is: cf t = PC A ( v h,t ) wherein, PC A the integrated power curve of the wind turbine, v h,t the wind speed at t time and at a height of h , h the hub height of the wind turbine.

[0044] In an alternative embodiment, the integrated power curve of the wind turbine is calculated according to the set wind turbine power curve and the Gaussian filter function; the expression is:

[0045] wherein, represents a set wind turbine power curve, represents a Gaussian filter function, represents a function argument, represents a standard deviation.

[0046] Further, Γ n , σ is a Gaussian filter function, and its calculation formula is as follows:

[0047] wherein, n represents a function argument, σ represents a standard deviation.

[0048] The wind speed at the height h v h , and the wind shear function is used for calculation: v h / v 0= ( h / h 0) α wherein, v 0 is the measured wind speed at the height h 0, and if there is no measured data, the MERRA-2 hourly precision wind speed data is used; α is a power index of the wind shear function, and is equal to 1 / 7.

[0049] Further, considering the possible attenuation of wind turbine performance, the wind farm capacity factor calculation method provided by the present application can more accurately reflect the influence of wind turbine performance attenuation on the wind farm capacity factor by setting attenuation parameters in stages and introducing measured correction factors at key nodes. When having measured data of unit performance, the measured attenuation parameter is directly used; if there is no measured data, the capacity factor is corrected and estimated by using a staged correction model cf . For the first stage (running for 1-10 years), the capacity factor is corrected and estimated by annual attenuation of 0.2%, and an additional performance decline of 1.2% is superimposed on the 10th year to match the typical attenuation characteristics.

[0050] In an optional embodiment, the capacity factor of the staged dynamic attenuation is obtained by setting attenuation parameters in stages and introducing measured correction factors; the capacity factor calculation expression for the first 10 years is:

[0051] wherein, represents the annual attenuation rate of the first 10 years, and 0.2% is used when missing, ​​represents an additional attenuation correction factor; Further, when n = 10, δ 10,10 = (1 - Δr), where Δr is the additional attenuation rate in the 10th year, and other years δ n,10 For the nth year (11≤n≤20) that has been in operation for more than 10 years, when there is no measured attenuation data, the capacity factor annual attenuation rate r2 of the last 10 years is 0.4%.

[0052] When after 10 years, the capacity factor of the said phased dynamic attenuation is recursively calculated based on the 10th year correction value expression:

[0053] When = 10, represents the capacity factor value after correction in the 10th year.

[0054] In an optional embodiment, the total amount of greenhouse gas emissions in the whole life cycle is obtained according to the total amount of greenhouse gas emissions in the whole life cycle model and the total amount of power generation; and the expression is:

[0055] Wherein, represents the total amount of greenhouse gas emissions in the whole life cycle, represents the total amount of greenhouse gas emissions in the whole life cycle, represents the total amount of power generation.

[0056] In this embodiment, three types of land use indirect emissions, vegetation and soil carbon pool loss, and potential carbon sink loss, are included in the accounting, breaking through the limitation of traditional methods that only focus on direct emissions, avoiding underestimation of emissions in scenarios such as forest wind power plants, and providing data support for high-precision carbon footprint assessment; based on high-precision meteorological data and Gaussian filtering algorithm, multi-dimensional site selection indicators including carbon loss and energy efficiency are provided, the interactive influence of land use and power generation performance is quantified, and the synergistic optimization of emission reduction, land use and economic efficiency is assisted, and a decision-making tool is provided for energy policy making; a dynamic performance evaluation model is created, and 1-10 year attenuation rates (default value 0.2%) and 11-20 year attenuation rates (default value 0.4%) and 10th year attenuation correction factor (default value 1.2%) are set in stages, which accurately captures the nonlinear decline of power generation performance caused by equipment aging, solves the simplification defects of traditional methods, and improves the accuracy of power generation and carbon emission intensity accounting.

[0057] Embodiment 3 This embodiment is based on embodiments 1 and 2.

[0058] The capacity factors calculated from these geographically representative wind farms were used as inputs to the life cycle assessment model to quantify the uncertainty range for three selected wind farms. To assess the uncertainty of other factors, including material consumption, lifetime, land use area, transportation distance, and curtailment rate, ±20% variation was made to key factors and assumptions, and combined with Monte Carlo simulation for life cycle assessment modeling. The probability distribution of input parameters in Monte Carlo simulation was determined based on previous studies and databases.

[0059] (1) Inner Mongolia XX wind farm life cycle carbon emissions The wind turbine type used in the wind farm was determined to be Vestas V80-2000 kW wind turbine, with a total of 100 wind turbines in the entire wind farm, and a total installed capacity of 200 MW. The altitude of the location of the wind farm was determined to be 1650-1740 meters.

[0060] The installed power curve of the V80-2000 wind turbine was obtained from the Vestas website specification, as shown in Figure 3 .

[0061] The power curve was smoothed using a Gaussian filter function, and a power curve with an accuracy of 0.01 was established. According to the geographical coordinates of the wind farm, the hourly scale multi-year wind speed data (2000-2020) of MERRA-2 was downloaded, and the wind speed at the height of the wind turbine was calculated according to the wind shear function, which was brought into the processed power curve function to calculate the capacity factor of each hour, and the capacity factor of the entire life cycle was 41.90%. The theoretical power generation of the entire life cycle was calculated to be 13551 GWh.

[0062] Using visual interpretation method, draw a polygon on Baidu Map or Google Map to determine the land occupation area of the wind farm including roads, foundations, substations, etc. 37.17 hectares. According to the CLCD land use type data, it is determined that the land occupation area of 37.17 hectares of Inner Mongolia XX wind farm is all meadow grassland. Based on the 20-year flux data (NEE) of the local China FLUX Duolun ecological station, the average annual net ecosystem productivity value of 78.89 g C m -2 y -1 was calculated. According to the actual land occupation area, the indirect greenhouse gas emissions GHG LASC caused by the carbon sink loss caused by the land use change of the wind turbine in the entire life cycle was calculated to be 5.86 kt CO2. Using a system dynamics model, the change in soil carbon density was simulated, and the loss of soil organic carbon content in the entire life cycle of 20 years was calculated to be 5.27 kt CO2. The actual survey obtained the biomass data of 74.73 t C ha -1, the carbon loss caused by biomass removal is 9.95 kt CO2. Therefore, the emissions caused by land use change are as follows:

[0063] (2) Carbon emissions of Heilongjiang XX wind farm in the whole life cycle The average altitude of the wind turbine device is 900-1000 m. The wind farm is composed of 33 Gold Wind GW77-1500KW wind turbines, with a total installed capacity of 49.5 MW.

[0064] The installed power curve of GW77-1500KW wind turbine is obtained from the technical parameters of Goldwind wind turbine generator set, as shown in Figure 4 .

[0065] The power curve is smoothed using a Gaussian filter function, and a power curve with an accuracy of 0.01 is established. According to the geographical coordinates of the wind farm, download the hourly scale of MERRA-2 multi-year wind speed data (2000-2020), and calculate the wind speed at the height of the wind turbine according to the wind shear function, and bring it into the processed power curve function to calculate the capacity factor of each hour, and the capacity factor of the whole life cycle is 20.79%. The theoretical power generation of the whole life cycle is 54341 GWh.

[0066] Using visual interpretation method, draw a polygon on Baidu map or Google map to determine the land occupation area of the wind farm including roads, foundations, substations, etc. 37.75 hectares. According to the CLCD land use type data, it is determined that the land occupation area of Heilongjiang XX wind farm is 37.75 hectares of forest. Based on the measured data of local NEP (NEE), the average annual net ecosystem productivity value is 311.72 gC m-2 y-1. According to the actual land occupation area, the indirect greenhouse gas emissions (GHGLASC) caused by carbon sink loss caused by land use change in the whole life cycle is 23.54 kt CO2. Using system dynamics model, simulate and calculate the change of soil carbon density, and according to the area, calculate the loss of soil organic carbon content in the whole life cycle of 20 years is 28.01 kt CO2. The actual investigation obtains the biomass data of 74.73 t Cha-1, and the carbon loss caused by biomass removal is 243.88 kt CO2. Therefore, the emissions caused by land use change are as follows:

[0067] The various embodiments in the present application are described in a progressive manner, and the same or similar parts among the various embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, the device embodiments are described more simply because they are basically similar to the method embodiments, and the relevant parts can be referred to the description of the method embodiments. The device embodiments described above are merely exemplary, and the modules described as separate components can or can not be physically separated, and the functions of the modules can be implemented in one or more software and / or hardware in the implementation of the present application. Part or all of the modules can be selected to achieve the purpose of the present embodiment according to actual needs.

[0068] Obviously, the above-described embodiments of the present application are merely examples for clearly illustrating the present application, and are not intended to limit the implementation manner of the present application. Based on the above description, any modification, equivalent replacement and improvement within the spirit and principle of the present application should be included in the protection scope of the claims of the present application.

Claims

1. A method for calculating the wind farm life cycle greenhouse gas emission intensity considering land use change, characterized in that, The expression is: Constructing a model of total greenhouse gas emissions over the life cycle GHG total : GHG total Emissions from wind turbine components and materials GHG M Emissions from vegetation and soil carbon pool losses GHG CS Emissions from carbon sink losses GHG LASC ; The expression is: GHG total = GHG M + GHG CS + GHG LASC wherein, GHG total represents the total amount of life cycle greenhouse gas emissions, GHG M represents the amount of life cycle greenhouse gas emissions for different components and materials of a wind turbine, GHG CS represents the amount of indirect greenhouse gas emissions due to loss of vegetation and soil carbon stocks, GHG LASC represents the amount of indirect greenhouse gas emissions due to loss of carbon sinks; Calculate total power generation PG ; GHG intensity of total life cycle greenhouse gas emissions int GHG intensity of total life cycle greenhouse gas emissions total and the total amount of electricity generated PG divided by each other; the expression of which is: wherein, represents the intensity of greenhouse gas emissions for the entire life cycle, represents the total amount of greenhouse gas emissions for the entire life cycle, represents the total amount of electricity generation.

2. The method for calculating the wind farm life cycle greenhouse gas emission intensity considering land use change according to claim 1, characterized in that, The wind turbine component and material emissions are obtained according to the material and energy use amount and the material and energy greenhouse gas emission activity factor; The expression is: wherein, represents the total life cycle greenhouse gas emissions of different components and materials of a wind turbine, represents the amount of use of materials and energy , represents the greenhouse gas emission activity factor of materials and energy . The material and energy The amount of use The calculation expression is: in, This indicates the number of wind turbines in a wind farm. Indicates the first stage of the entire life cycle Raw materials and energy used in the stage The quality or quantity.

3. The method of claim 1, wherein, The vegetation and soil carbon pool loss emissions are obtained according to the whole life cycle vegetation biomass carbon loss and the whole life cycle soil organic carbon loss; The expression is: wherein 44 / 12 represents the ratio of the molecular molar mass of carbon dioxide to carbon, represents the carbon loss of vegetation biomass for the whole life cycle, represents the carbon loss of soil organic carbon for the whole life cycle.

4. The method for calculating the whole life cycle greenhouse gas emission intensity of a wind farm considering land use change according to claim 3, characterized in that, The whole life cycle vegetation biomass carbon loss includes the arbor layer aboveground biomass, the shrub layer aboveground biomass and the herb layer aboveground biomass; The expression is: CL biomass = C tree + C shrub + C grass wherein, C tree represents aboveground biomass of the tree layer, C shrub represents aboveground biomass of the shrub layer, C grass represents aboveground biomass of the herb layer.

5. The method for calculating the wind farm's whole life cycle greenhouse gas emission intensity considering land use change according to claim 3, characterized in that, The whole life cycle soil organic carbon loss is obtained according to the soil organic carbon density during the wind farm construction and the last whole life cycle soil organic carbon density; The expression is: CL soc = n × A × ( SOCD 0 - SOCD 20 ) wherein, SOCD 0 represents the soil organic carbon density at the time of construction of the wind farm, SOCD 20 represents the soil organic carbon density at the end of the full life cycle.

6. The method for calculating the wind farm life cycle greenhouse gas emission intensity considering land use change according to claim 1, characterized in that, The carbon sink loss emissions are obtained according to the land area occupied by a single wind turbine and the net primary productivity of the ecosystem; the expression is: wherein, represents the land area occupied by a single wind turbine, represents the net primary productivity of the ecosystem.

7. The method for calculating the wind farm life cycle greenhouse gas emission intensity considering land use change according to claim 1, characterized in that, The total power generation is calculated according to the wind turbine rated power and the phased dynamic attenuation capacity factor; The expression is: PG = 8760 × 20 × n × cf × P wherein, PG represents the total amount of electricity generated, cf represents the average capacity factor of the wind turbine over its lifetime, P represents the rated power of the wind turbine.

8. The method for calculating the wind farm life cycle greenhouse gas emission intensity considering land use change according to claim 7, characterized in that, The wind turbine whole life cycle average capacity factor; the expression is: wherein, represents the rate of rejection, represents the number of hours of the full life cycle, represents the average capacity factor of the wind turbine over the full life cycle at time. The in The average capacity factor over the lifetime of a wind turbine in time is obtained from the wind turbine power curve; its expression is: cf t = PC A ( v h,t ) wherein PC A represents the integrated power curve of the wind turbine, v h,t represents t time, height h of the wind speed at the height, h represents the hub height of the wind turbine.

9. The method for calculating the wind farm life cycle greenhouse gas emission intensity considering land use change according to claim 8, characterized in that, The integrated power curve of the wind turbine is calculated according to the set wind turbine power curve and the Gaussian filter function; The expression is: wherein represents a set wind turbine power curve, represents a Gaussian filter function, represents a function argument, represents a standard deviation.

10. The method for calculating the wind farm life cycle greenhouse gas emission intensity considering land use change according to any one of claims 1 to 9, characterized in that, The phased dynamic attenuation capacity factor is obtained by setting the attenuation parameters in stages and introducing the measured correction factor; the capacity factor calculation expression in the first-10 years is: wherein, represents the annual decay rate for the previous 10 years, represents an additional decay correction factor; When after 10 years, the phased dynamic attenuation capacity factor is recursively calculated based on the 10th year correction value expression is: When = 10, represents the 10th year revised capacity factor value.

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