A spatiotemporal dynamic method for evaluating photovoltaic power generation emission reduction potential under multiple development paths

Abstract

The present invention provides a spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths, belonging to the field of photovoltaic power generation technology. The method comprises: constructing a historical dynamic function for a target region in different years; obtaining the incremental photovoltaic power generation installed capacity for all regions in each future year based on a global change assessment model and in combination with a linear interpolation method, and constructing a future photovoltaic power generation development path matrix based on the regional attributes of each target region; and determining the photovoltaic power generation emissions and emission reduction potential under each development path scenario based on the historical dynamic function, the future photovoltaic power generation development path matrix, and the carbon pollution reduction calculation method. The method solves the problems of existing assessment methods such as a small temporal assessment scope, a single assessment technology, and a lack of dynamic characterization of differences between different years and regions.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power generation technology, and in particular to a method for evaluating the spatiotemporal dynamic photovoltaic power generation emission reduction potential under multiple development paths. Background Art

[0002] Although renewable energy sources such as photovoltaic power generation can be considered zero-emission during the operation phase, the manufacturing process requires a large amount of fossil energy, non-renewable electricity and industrial product raw materials. The carbon emissions in the industrial chain caused by the large-scale manufacturing and deployment of photovoltaic power generation under the background of the "doubling" development of photovoltaic power generation will create a certain degree of uncertainty about its emission reduction potential; secondly, China has a vast territory, and the cleanliness of electricity varies among provinces, resulting in differences in emissions during the manufacturing process of photovoltaic modules. Under different photovoltaic development paths, the penetration of photovoltaic power generation in the power grid will in turn affect the emission factors of the power grids in various provinces, thereby affecting the emissions of its production process; thirdly, China's photovoltaic power generation potential resources are unevenly distributed. Deploying photovoltaic modules in different photovoltaic resource areas will cause significant differences in their power generation during operation, thereby affecting their unit power generation emissions and corresponding emission reduction potential throughout their life cycle.

[0003] Existing technologies have built a full life cycle assessment framework for the current emerging photovoltaic power generation technology - perovskite photovoltaic cells, but there is still a single assessment technology, which does not include multiple mainstream photovoltaic power generation technologies and cannot reflect the differences between technologies.

[0004] Existing technology has also constructed a full life cycle assessment framework for three different solar energy utilization technologies (one of which is photovoltaic power generation), but it does not incorporate future development into the assessment framework, making it impossible to further explore the differences in technology combinations under different development scenarios.

[0005] Existing technologies have also constructed full life cycle assessment schemes for renewable energy (including photovoltaic power generation) based on input and output, and include development scenarios. However, the temporal and spatial granularity of their assessments is relatively coarse, and they cannot achieve the requirements for detailed assessment of carbon emissions throughout the life cycle of photovoltaic power generation at high temporal and spatial resolutions.

[0006] Therefore, it is necessary to develop a spatiotemporal dynamic assessment method that can effectively quantify the carbon footprint of the entire photovoltaic power generation industry chain, analyze the spatiotemporal characteristics of greenhouse gas and air pollutant emissions throughout the life cycle of photovoltaic power generation under different future production and deployment schemes, identify key emission links, clarify their effective emission reduction potential, and provide a reference for low-carbon production and deployment of photovoltaic power generation.

[0007] To this end, the present invention aims to provide a spatiotemporal dynamic method for assessing the emission reduction potential of photovoltaic power generation under multiple development paths, providing a basis for accurately assessing the carbon pollution emissions of photovoltaic power generation over its entire life cycle under different years and different development path combinations. In combination with a high-precision photovoltaic power generation resource potential assessment method, the emissions over the entire life cycle of photovoltaic power generation are spatially characterized, and the emission reduction potential of China's photovoltaic power generation industry in the future development is accurately quantified. Summary of the Invention

[0008] The present invention provides a spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths, which is used to solve the problems existing in existing assessment methods, such as small temporal assessment scope, single assessment technology, and lack of dynamic characterization of differences between different years and regions.

[0009] The present invention provides a spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths, comprising:

[0010] Step 1: Construct a historical dynamic function of the target area in different years ,in, represents the set of greenhouse gas emissions of different photovoltaic power generation technologies manufactured in different regions in the corresponding target area in year t; represents the set of pollutant emissions corresponding to the target area in year t based on different photovoltaic power generation technologies manufactured in different regions;

[0011] Step 2: Based on the global change assessment model and combined with linear interpolation, obtain the annual photovoltaic power generation capacity increment for all regions in the future. Combined with the regional attributes of each target region, a future photovoltaic power generation development path matrix is ​​constructed.

[0012] Step 3: Dynamic function based on the history , the future photovoltaic power generation development path matrix and carbon pollution reduction calculation method to determine the photovoltaic power generation emissions and emission reduction potential under the development path plan.

[0013] Preferably, before determining the photovoltaic power generation emissions and emission reduction potential under the development path plan, it also includes: based on the historical dynamic function Determine the historical potential of the target area, including:

[0014] Construct a historical greenhouse gas reference sequence for the target area in year t based on the greenhouse gas emission set in, represents the greenhouse gas emissions from the i-th region excluding the target region to the target region based on the j-th photovoltaic power generation technology in year t; represents the total greenhouse gas emissions from region i to non-regions based on the j-th photovoltaic power generation technology in year t; represents the greenhouse gas emissions allocated to the target region by the i-th region in year t; ln represents the sign of the logarithmic function; Indicates the total number of regions; A unit setting amount representing greenhouse gas emissions; = represents the historical greenhouse reference coefficient of the i-th region other than the target region based on the j-th photovoltaic power generation technology in the target region in year t; n2 represents the total number of photovoltaic power generation technologies;

[0015] Inputting the historical greenhouse reference sequence into a first sequence analysis model to obtain a first potential;

[0016] Construct a historical pollutant reference sequence for the target area in year t based on the pollutant emission set in, represents the amount of pollutant emissions from the i-th region excluding the target region to the target region based on the j-th photovoltaic power generation technology in year t; It represents the total pollutant emissions from the i-th region to non-self regions based on the j-th photovoltaic power generation technology in year t; represents the set pollutant allocation emission amount of the ith region to the target region in year t; represents the emission adjustment function from the i-th region excluding the target region to the target region based on the j-th photovoltaic power generation technology in year t; Indicates the unit set amount of pollutant emissions; It indicates the historical pollutant reference coefficient of the target area in year t when the target area takes over the i-th area other than the target area based on the j-th photovoltaic power generation technology; represents the uniform radius of pollutant particles emitted by the j-th photovoltaic power generation technology in the i-th region; They represent the minimum radius and maximum radius of pollutant particles emitted by the j-th photovoltaic power generation technology;

[0017] Inputting the historical pollutant reference sequence into a second sequence analysis model to obtain a second potential;

[0018] Based on obtaining the first potentials and the second potentials of different historical years, determining the first variance for all the first potentials and the second variance for all the second potentials;

[0019] The historical potential of the target area is obtained according to the first variance and the second variance.

[0020] Preferably, obtaining the historical potential of the target area based on the first variance and the second variance includes:

[0021]

[0022] in, Indicate the historical potential of the target area; represents the adjustment coefficient; represent the average value of the first potential and the average value of the second potential respectively; represent the first variance and the second variance respectively; Indicates n1 middle The number of Indicates the actual types and quantities of pollutants produced in the target area; Indicates the amount of pollutants allowed to be produced in the target area.

[0023] Preferably, based on the global change assessment model and in combination with a linear interpolation method, the photovoltaic power generation installed capacity increment for all regions in each future year is obtained, including:

[0024] The first curve is drawn based on the total greenhouse gas amount of all regions in each year of statistical history. At the same time, the total greenhouse gas amount in adjacent historical years is subtracted to construct a first difference vector;

[0025] The second curve is drawn based on the total pollutant amount of the entire region each year of statistical history. At the same time, the total pollutant amounts in adjacent historical years are subtracted to construct a second difference vector;

[0026] Based on the linear interpolation method, the curve is linearly interpolated and linearly fitted to determine the fitting coefficient, and the corresponding difference vector is analyzed to obtain the increased variable range;

[0027] The increase in photovoltaic power generation capacity in the next year is estimated based on the variable range of greenhouse gas increases, the variable range of pollutant increases, and the power supply demand determined by the global change assessment model.

[0028] Preferably, the variable range is increased, including:

[0029] Calculate the left range bound ;

[0030]

[0031] in, represents the fitting coefficient; represents the average value of all quantities in the corresponding curve; Indicates the number of differences in the corresponding difference vector that are consistent with the sign of r1; Indicates based on The variance of the differences; Represents The average of the absolute values ​​corresponding to the differences; Indicates the total number of years involved;

[0032] Calculate the right range bounds ;

[0033]

[0034] in, Representation and Remainder The average of the absolute values ​​corresponding to the differences;

[0035] Based on the left range boundary , right range boundary , which increases the variable range.

[0036] Preferably, the estimated increase in photovoltaic power generation capacity for the next year includes:

[0037] Get the first increment based on the left range boundary and the right range from the greenhouse boundary-increment comparison table , second increment ;

[0038] Obtain the third increment based on the left range boundary and the right range from the pollutant boundary-increment comparison table , the fourth increment ;

[0039] Determine the future power required based on the power supply demand , and combined with the current amount of electricity generated , first increment , second increment , the third increment and the fourth increment , estimate the photovoltaic power generation installed capacity increment NL in the next year;

[0040]

[0041] in, Indicates the floor symbol; Indicates the capacity of electricity generated by each photovoltaic power generation installation; express The variance of .

[0042] Preferably, the regional attributes include: a clean power attribute and a rich resource attribute.

[0043] Preferably, determine the PV power generation emissions and emission reduction potential under the development path plan, including:

[0044] Based on the historical dynamic function Determine the historical potential of the target area;

[0045] Determine the production and installation scale and technology combination path of photovoltaic modules in the target area in the future based on the future photovoltaic power generation development path matrix;

[0046] Determine future potential based on production installation size and technology mix path;

[0047] The historical potential and future potential are analyzed according to the carbon pollution reduction calculation method to determine the photovoltaic power generation emissions and emission reduction potential.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] It provides a basis for accurately evaluating the carbon pollution emissions of photovoltaic power generation throughout its life cycle in different years and under different development path combinations, and combines high-precision photovoltaic power generation resource potential assessment methods to spatially characterize the emissions of photovoltaic power generation throughout its life cycle, and accurately quantify the emission reduction potential of China's future photovoltaic power generation industry development.

[0050] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 This is a flow chart of a method for evaluating the spatiotemporal dynamic photovoltaic power generation emission reduction potential under multiple development paths in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0055] The present invention provides a method for evaluating the potential of photovoltaic power generation emission reduction under multiple development paths in terms of spatiotemporal dynamics. Figure 1 As shown, including:

[0056] Step 1: Construct a historical dynamic function of the target area in different years ,in, represents the set of greenhouse gas emissions of different photovoltaic power generation technologies manufactured in different regions in the corresponding target area in year t; represents the set of pollutant emissions corresponding to the target area in year t based on different photovoltaic power generation technologies manufactured in different regions;

[0057] Step 2: Based on the global change assessment model and combined with linear interpolation, obtain the annual photovoltaic power generation capacity increment for all regions in the future. Combined with the regional attributes of each target region, a future photovoltaic power generation development path matrix is ​​constructed.

[0058] Step 3: Dynamic function based on the history , the future photovoltaic power generation development path matrix and carbon pollution reduction calculation method to determine the photovoltaic power generation emissions and emission reduction potential under the development path plan.

[0059] Preferably, the regional attributes include: a clean power attribute and a rich resource attribute.

[0060] In this embodiment, the target area refers to each province, and by determining the historical dynamic function of each province, a dynamic assessment of carbon emissions from photovoltaic power generation can be performed in time and space.

[0061] In this embodiment, each parameter in the greenhouse gas emission set is based on Calculated, where Indicates that the product manufactured in the nth province in the tth year The greenhouse gas emissions of the mth photovoltaic technology over its entire life cycle; For deployment in the i-th grid The total electricity generated by photovoltaic power generation over its entire life cycle (25 years).

[0062] Each parameter in the pollutant emission set is based on Calculated, Indicates that the product manufactured in the nth province in the tth year Air pollutant emissions over the entire life cycle of the mth photovoltaic technology, the types of pollutants mainly include , NOx and primary PM2.5.

[0063] In this embodiment, the future photovoltaic power generation development path matrix is ​​as follows:

[0064] .

[0065] The matrix represents different future combinations of PV module production, deployment, and technology in China. The business-as-usual scenario refers to a scenario in which China's PV industry develops in accordance with historical trends; the average scenario refers to a scenario in which the production and deployment of PV modules are distributed according to the same proportion among provinces, while various PV technologies develop in a balanced manner.

[0066] For example, assuming that China needs to add AkW of new photovoltaic capacity in a certain year, the photovoltaic production capacity allocated to a certain province X in that year according to the business-as-usual plan will be ,in represents the market share of photovoltaic modules produced in Province X obtained from public data; according to the business-as-usual plan, the installed capacity obtained by Province X in that year can be expressed as ,in = represents the share of the historical photovoltaic installed capacity of Province X obtained from public data in the total photovoltaic installed capacity of the country. If the average allocation is followed, the photovoltaic capacity that Province X needs to produce and deploy in that year is , where n represents the total number of provinces. It is important to note that when allocating installed capacity shares, the installed capacity potential of each province must be taken into account. If a province's planned PV capacity exceeds its maximum PV capacity potential, the excess will continue to be deployed in other provinces according to the corresponding allocation principles.

[0067] In the "Clean Power Provinces Leading" production scenario, PV module production will primarily be carried out in provinces with cleaner electricity. It is assumed that 70% of China's future PV module production will be produced in the top 30% of provinces with the lowest grid emission factors. In the "Resource-Rich Provinces Leading" deployment scenario, PV power generation equipment will primarily be deployed in resource-rich provinces. Specifically, it is assumed that 70% of China's future PV power generation equipment will be deployed in the top 30% of provinces with the highest average PV capacity factors. Similarly, in both deployment scenarios, the maximum deployment potential of each province must be considered. That is, when the planned PV installed capacity of a province exceeds its maximum PV installed capacity potential, the excess will be deployed in other provinces. Furthermore, in all deployment scenarios, PV power generation will be deployed preferentially in grid points with better resources. When the cumulative deployed capacity exceeds the maximum deployment potential of that grid point, it will be deployed in the next best grid point, and so on. Finally, in the technology combination scheme, the “Silicon photovoltaic panel dominant scheme” means that the new photovoltaic installed capacity in the future will still be mainly composed of silicon-based photovoltaic modules. For example, if it is still assumed that the new photovoltaic installed capacity required in a certain year in the future is AkW, then the amount of silicon-based photovoltaic modules is , that is, assuming that the market share of silicon-based photovoltaic modules will be 70% in the future, and the remaining 30% will be thin-film photovoltaic technology; in the "average scenario", the amount of each photovoltaic technology newly added in that year is , where m represents the type of photovoltaic technology; in the "thin-film photovoltaic dominant solution", the technology choice for future photovoltaic power generation will shift from silicon-based photovoltaic power generation technology to the new generation of thin-film photovoltaic power generation technology. Assuming that 70% of new photovoltaic power generation technologies in the future are the new generation of thin-film photovoltaic power generation technology, that is, , and the remaining 30% is silicon-based photovoltaic power generation technology.

[0068] Through the combination matrix of photovoltaic production, deployment and technology combination paths, a total of 27 future photovoltaic development paths can be combined, namely: business as usual - deployment as usual - silicon photovoltaic technology-dominated path, business as usual - deployment as usual - balanced development path of various photovoltaic technologies, business as usual - deployment as usual - thin-film photovoltaic technology-dominated path, business as usual - balanced deployment across provinces - silicon photovoltaic technology-dominated path, business as usual - balanced deployment across provinces - balanced development path of various photovoltaic technologies, business as usual - balanced deployment across provinces - thin-film photovoltaic technology-dominated path, business as usual - resource-rich provinces-dominated deployment - silicon photovoltaic technology-dominated path, business as usual - resource-rich provinces-dominated deployment - balanced development path of various photovoltaic technologies, business as usual - resource-rich provinces-dominated deployment - thin-film photovoltaic technology-dominated path, balanced production across provinces - deployment as usual - silicon photovoltaic technology-dominated path, balanced production across provinces - deployment as usual - balanced development path of various photovoltaic technologies, balanced production across provinces - deployment as usual - thin-film photovoltaic technology-dominated path, balanced production across provinces - balanced deployment across provinces - silicon photovoltaic technology-dominated path, balanced production across provinces - balanced deployment across provinces - balanced development path of various photovoltaic technologies, and Balanced production - balanced deployment across provinces - thin-film photovoltaic technology as the dominant path, balanced production across provinces - resource-rich provinces leading deployment - silicon photovoltaic technology as the dominant path, balanced production across provinces - resource-rich provinces leading deployment - balanced development path of various photovoltaic technologies, balanced production across provinces - resource-rich provinces leading deployment - thin-film photovoltaic technology as the dominant path, clean electricity provinces leading production - business as usual deployment - silicon photovoltaic technology as the dominant path, clean electricity provinces leading production - business as usual deployment - balanced development path of various photovoltaic technologies, clean electricity provinces leading production - business as usual deployment - thin-film photovoltaic technology as the dominant path, clean electricity provinces leading production - balanced deployment across provinces - silicon photovoltaic technology as the dominant path, clean electricity provinces leading production - balanced deployment across provinces - balanced development path of various photovoltaic technologies, clean electricity provinces leading production - balanced deployment across provinces - thin-film photovoltaic technology as the dominant path, clean electricity provinces leading production - resource-rich provinces leading deployment - silicon photovoltaic technology as the dominant path, clean electricity provinces leading production - resource-rich provinces leading deployment - balanced development path of various photovoltaic technologies, clean electricity provinces leading production - resource-rich provinces leading deployment - thin-film photovoltaic technology as the dominant path.

[0069] In this example, the emission reductions brought about by the deployment of photovoltaics come from the characterization of the electricity replaced by them, as follows:

[0070]

[0071] in, represents the total greenhouse gas emission reduction of photovoltaic power generation equipment deployed in province p; represents the carbon emission factor of the power grid in province p; Represents the PV capacity factor of the i-th grid. Similarly, replacing the grid emission factor with the grid pollutant emission factor can obtain the pollutant emission reduction achieved by deploying PV in the corresponding province.

[0072] By combining the full life cycle emissions caused by PV module production with the carbon pollution reduction brought about by PV deployment, the net reduction in greenhouse gas and pollutant emissions achieved by PV deployment can be obtained as follows (taking greenhouse gases as an example):

[0073]

[0074] in, represents the net greenhouse gas emission reductions achieved by deploying PV in province p. Accordingly, the total greenhouse gas emission reductions and emissions are replaced by pollutant emission reductions and emissions to obtain the net pollutant emission reductions achieved by deploying PV in the corresponding province.

[0075] As the deployment of photovoltaic power generation in various provinces progresses, the corresponding grid emission factors are also changing. Therefore, it is necessary to further dynamically characterize the grid emission factors of each province, as follows:

[0076]

[0077] in, represents the emission factor of the fth fossil fuel power generation in year t; It represents the proportion of the fth type of fossil fuel power generation in the pth province power grid in year t. This parameter can be obtained by sorting out the output data of the GCAM model.

[0078] In this embodiment, by constructing a spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths, the photovoltaic power generation emission reduction potential at the national and provincial levels in China is accurately and dynamically assessed, effectively improving the comprehensiveness of the assessment. At the same time, the present invention improves the temporal resolution of the full life cycle assessment through a time-dynamic full life cycle inventory; improves the spatial resolution of the photovoltaic power generation full life cycle assessment by coupling a high-precision photovoltaic power generation resource assessment method; and improves the integrity of the photovoltaic power generation full life cycle assessment technology by comprehensively considering a variety of photovoltaic power generation technologies. In addition, the present invention makes up for the deficiency of existing technologies in being unable to determine future trends by spatiotemporally quantifying the full life cycle emissions of photovoltaic power generation under different technology combinations and market shares in future years, and provides a reference for the coordinated governance of carbon pollution in the context of the future doubling of photovoltaic power generation.

[0079] The beneficial effects of the above technical solution are: providing a basis for accurately evaluating the carbon pollution emissions of photovoltaic power generation throughout its life cycle under different years and different development path combinations, and combining it with a high-precision photovoltaic power generation resource potential assessment method to spatially characterize the emissions of photovoltaic power generation throughout its life cycle, and accurately quantify the emission reduction potential of China's future photovoltaic power generation industry development.

[0080] The present invention provides a method for evaluating the potential of photovoltaic power generation emission reduction under multiple development paths in spatiotemporal dynamics. Before determining the photovoltaic power generation emissions and emission reduction potential under the development path scheme, the method further includes: based on the historical dynamic function Determine the historical potential of the target area, including:

[0081] Construct a historical greenhouse gas reference sequence for the target area in year t based on the greenhouse gas emission set in, represents the greenhouse gas emissions from the i-th region excluding the target region to the target region based on the j-th photovoltaic power generation technology in year t; represents the total greenhouse gas emissions from region i to non-regions based on the j-th photovoltaic power generation technology in year t; represents the greenhouse gas emissions allocated to the target region by the i-th region in year t; ln represents the sign of the logarithmic function; Indicates the total number of regions; A unit setting amount representing greenhouse gas emissions; = represents the historical greenhouse reference coefficient of the i-th region other than the target region based on the j-th photovoltaic power generation technology in the target region in year t; n2 represents the total number of photovoltaic power generation technologies;

[0082] Inputting the historical greenhouse reference sequence into a first sequence analysis model to obtain a first potential;

[0083] Construct a historical pollutant reference sequence for the target area in year t based on the pollutant emission set in, represents the amount of pollutant emissions from the i-th region excluding the target region to the target region based on the j-th photovoltaic power generation technology in year t; It represents the total pollutant emissions from the i-th region to non-self regions based on the j-th photovoltaic power generation technology in year t; represents the set pollutant allocation emission amount of the ith region to the target region in year t; represents the emission adjustment function from the i-th region excluding the target region to the target region based on the j-th photovoltaic power generation technology in year t; Indicates the unit set amount of pollutant emissions; It indicates the historical pollutant reference coefficient of the target area in year t when the target area takes over the i-th area other than the target area based on the j-th photovoltaic power generation technology; represents the uniform radius of pollutant particles emitted by the j-th photovoltaic power generation technology in the i-th region; They represent the minimum radius and maximum radius of pollutant particles emitted by the j-th photovoltaic power generation technology;

[0084] Inputting the historical pollutant reference sequence into a second sequence analysis model to obtain a second potential;

[0085] Based on obtaining the first potentials and the second potentials of different historical years, determining the first variance for all the first potentials and the second variance for all the second potentials;

[0086] The historical potential of the target area is obtained according to the first variance and the second variance.

[0087] Preferably, obtaining the historical potential of the target area based on the first variance and the second variance includes:

[0088]

[0089] in, Indicate the historical potential of the target area; represents the adjustment coefficient; represent the average value of the first potential and the average value of the second potential respectively; represent the first variance and the second variance respectively; Indicates n1 The number of Indicates the actual types and quantities of pollutants produced in the target area; Indicates the amount of pollutants allowed to be produced in the target area.

[0090] In this embodiment, the first sequence analysis model is obtained by training a neural network model based on different combinations of greenhouse reference sequences and potential evaluation results of the greenhouse reference sequences as samples. Therefore, the first potential can be directly obtained.

[0091] The second sequence analysis model is obtained by training a neural network model based on samples of different combinations of pollutant reference sequences and potential evaluation results for the pollutant reference sequences. Therefore, the second potential can be directly obtained.

[0092] In this embodiment, Ny is less than or equal to Sy.

[0093] The beneficial effect of the above technical solution is: by obtaining the historical temperature reference coefficient based on different years for each region, it is convenient to obtain the first potential, and by combining the pollutant reference coefficient based on different years, it is convenient to obtain the second potential, thereby ensuring the accuracy of the historical potential for the region and providing an accurate basis for subsequent evaluation.

[0094] The present invention provides a spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths. Based on a global change assessment model and combined with a linear interpolation method, the method obtains the photovoltaic power generation installed capacity increment for all regions in each future year, including:

[0095] The first curve is drawn based on the total greenhouse gas amount of all regions in each year of statistical history. At the same time, the total greenhouse gas amount in adjacent historical years is subtracted to construct a first difference vector;

[0096] The second curve is drawn based on the total pollutant amount of the entire region each year of statistical history. At the same time, the total pollutant amounts in adjacent historical years are subtracted to construct a second difference vector;

[0097] Based on the linear interpolation method, the curve is linearly interpolated and linearly fitted to determine the fitting coefficient, and the corresponding difference vector is analyzed to obtain the increased variable range;

[0098] The increase in photovoltaic power generation capacity in the next year is estimated based on the variable range of greenhouse gas increases, the variable range of pollutant increases, and the power supply demand determined by the global change assessment model.

[0099] Preferably, the variable range is increased, including:

[0100] Calculate the left range bound ;

[0101]

[0102] in, represents the fitting coefficient; represents the average value of all quantities in the corresponding curve; Indicates the number of differences in the corresponding difference vector that are consistent with the sign of r1; Indicates based on The variance of the differences; Represents The average of the absolute values ​​corresponding to the differences; Indicates the total number of years involved;

[0103] Calculate the right range bounds ;

[0104]

[0105] in, Representation and Remainder The average of the absolute values ​​corresponding to the differences;

[0106] Based on the left range boundary , right range boundary , which increases the variable range.

[0107] In this example, the variable range is increased by , right range boundary ).

[0108] In this embodiment, the first curve is obtained by plotting the year as the horizontal axis and the total amount of greenhouse gases generated each year as the vertical axis, and the first difference vector = They represent the total greenhouse gas amounts corresponding to the 2nd year, the 1st year, the tth year, and the t-1th year, respectively.

[0109] In this embodiment, the second curve is drawn with the year as the horizontal axis and the total amount of pollutants generated each year as the vertical axis, and the second difference vector They represent the total greenhouse gas amounts corresponding to the 2nd year, the 1st year, the tth year, and the t-1th year, respectively.

[0110] In this embodiment, the linear interpolation method is used to fill in missing values ​​in the curve, which is a prior art. Then, the filled curve is linearly fitted, which is also a prior art. Then, the fitting coefficient can be directly obtained. When the fitting coefficient is greater than or equal to 0, it is regarded as a positive symbol. When the fitting coefficient is less than 0, it is regarded as a negative symbol.

[0111] In this embodiment, the global change assessment model is obtained by training a neural network model based on annual electricity consumption and annual electricity consumption increase as samples, so the power supply demand can be directly obtained.

[0112] The beneficial effect of the above technical solution is: by drawing curves for temperature and pollutants and constructing differential vectors, the range of variables to be increased is determined to ensure the accuracy of the estimated increase in installed power generation capacity. Among them, the integrity of the numerical values ​​in the curve is guaranteed by linear interpolation of the curve, and the rationality of the increase is indirectly guaranteed by subsequently calculating the range boundaries on the left and right sides.

[0113] The present invention provides a spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths, which estimates the photovoltaic power generation installed capacity increment for the next year, including:

[0114] Get the first increment based on the left range boundary and the right range from the greenhouse boundary-increment comparison table , second increment ;

[0115] Obtain the third increment based on the left range boundary and the right range from the pollutant boundary-increment comparison table , the fourth increment ;

[0116] Determine the future power required based on the power supply demand , and combined with the current amount of electricity generated , first increment , second increment , the third increment and the fourth increment , estimate the photovoltaic power generation installed capacity increment NL in the next year;

[0117]

[0118] in, Indicates the floor symbol; Indicates the capacity of electricity generated by each photovoltaic power generation installation; express The variance of .

[0119] In this embodiment, the greenhouse boundary-increment comparison table contains increments that match the left and right temperature boundaries, and the first increment and the second increment can be directly matched.

[0120] In this embodiment, the pollutant boundary-increment comparison table contains increments that match the left and right boundaries of the pollutant, and the third increment and the fourth increment can be directly matched.

[0121] The beneficial effect of the above technical solution is: by determining the increment based on greenhouse and pollutants, it is convenient to effectively estimate the increment of photovoltaic power generation installed capacity, and provide a data analysis basis for subsequent front-line potential assessment.

[0122] The present invention provides a spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths, which determines photovoltaic power generation emissions and emission reduction potential under development path scenarios, including:

[0123] Based on the historical dynamic function Determine the historical potential of the target area;

[0124] Determine the production and installation scale and technology combination path of photovoltaic modules in the target area in the future based on the future photovoltaic power generation development path matrix;

[0125] Determine future potential based on production installation size and technology mix path;

[0126] The historical potential and future potential are analyzed according to the carbon pollution reduction calculation method to determine the photovoltaic power generation emissions and emission reduction potential.

[0127] In this embodiment, the production installation scale and technology combination path are 27 results generated based on the matrix.

[0128] In this embodiment, the future potential is calculated by averaging each potential under the 27 outcomes.

[0129] In this embodiment, power generation emissions and emission reduction potential are obtained based on the average of historical potential and future potential.

[0130] The beneficial effect of the above technical solution is that by determining the historical potential and future potential, the emission and emission reduction potential of photovoltaic power generation can be effectively determined.

[0131] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths, characterized by: include: Step 1: Construct a historical dynamic function of the target area in different years ,in, represents the set of greenhouse gas emissions of different photovoltaic power generation technologies manufactured in different regions in the corresponding target area in year t; represents the set of pollutant emissions corresponding to the target area in year t based on different photovoltaic power generation technologies manufactured in different regions; Step 2: Based on the global change assessment model and combined with linear interpolation, obtain the annual photovoltaic power generation capacity increment for all regions in the future. Combined with the regional attributes of each target region, a future photovoltaic power generation development path matrix is ​​constructed. Step 3: Dynamic function based on the history , the future photovoltaic power generation development path matrix and carbon pollution reduction calculation method to determine the photovoltaic power generation emissions and emission reduction potential under the development path plan; Among them, the photovoltaic power generation emissions and emission reduction potential under the development path plan are determined, including: Based on the historical dynamic function Determine the historical potential of the target area; Determine the production and installation scale and technology combination path of photovoltaic modules in the target area in the future based on the future photovoltaic power generation development path matrix; Determine future potential based on production installation size and technology mix path; The historical potential and future potential are analyzed according to the carbon pollution reduction calculation method to determine the photovoltaic power generation emissions and emission reduction potential.

2. The spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths according to claim 1 is characterized in that: Before determining the photovoltaic power generation emissions and emission reduction potential under the development path plan, it also includes: based on the historical dynamic function Determine the historical potential of the target area, including: Construct a historical greenhouse gas reference sequence for the target area in year t based on the greenhouse gas emission set , ,in, represents the greenhouse gas emissions from the i-th region excluding the target region to the target region based on the j-th photovoltaic power generation technology in year t; represents the total greenhouse gas emissions from region i to non-regions based on the j-th photovoltaic power generation technology in year t; represents the greenhouse gas emissions allocated to the target region by the i-th region in year t; ln represents the sign of the logarithmic function; Indicates the total number of regions; A unit setting amount representing greenhouse gas emissions; = represents the historical greenhouse reference coefficient of the i-th region other than the target region based on the j-th photovoltaic power generation technology in the target region in year t; n2 represents the total number of photovoltaic power generation technologies; Inputting the historical greenhouse reference sequence into a first sequence analysis model to obtain a first potential; Construct a historical pollutant reference sequence for the target area in year t based on the pollutant emission set , ,in, represents the amount of pollutant emissions from the i-th region excluding the target region to the target region based on the j-th photovoltaic power generation technology in year t; It represents the total pollutant emissions from the i-th region to non-self regions based on the j-th photovoltaic power generation technology in year t; represents the set pollutant allocation emission amount of the ith region to the target region in year t; represents the emission adjustment function from the i-th region excluding the target region to the target region based on the j-th photovoltaic power generation technology in year t; , Indicates the unit set amount of pollutant emissions; It indicates the historical pollutant reference coefficient of the target area in year t when the target area takes over the i-th area other than the target area based on the j-th photovoltaic power generation technology; represents the uniform radius of pollutant particles emitted by the j-th photovoltaic power generation technology in the i-th region; They represent the minimum radius and maximum radius of pollutant particles emitted by the j-th photovoltaic power generation technology; Inputting the historical pollutant reference sequence into a second sequence analysis model to obtain a second potential; Based on obtaining the first potentials and the second potentials of different historical years, determining the first variance for all the first potentials and the second variance for all the second potentials; The historical potential of the target area is obtained according to the first variance and the second variance.

3. The spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths according to claim 2 is characterized in that: Based on the first variance and the second variance, the historical potential of the target area is obtained, including: ； ； ； in, Indicate the historical potential of the target area; 、 represents the adjustment coefficient; represent the average value of the first potential and the average value of the second potential respectively; represent the first variance and the second variance respectively; Indicates n1 middle The number of Indicates the actual types and quantities of pollutants produced in the target area; Indicates the amount of pollutants allowed to be produced in the target area.

4. The spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths according to claim 1 is characterized in that: Based on the global change assessment model and combined with linear interpolation, the annual photovoltaic power generation capacity increment for all regions in the future is obtained, including: The first curve is drawn based on the total greenhouse gas amount of all regions in each year of statistical history. At the same time, the total greenhouse gas amount in adjacent historical years is subtracted to construct a first difference vector; The second curve is drawn based on the total pollutant amount of the entire region each year of statistical history. At the same time, the total pollutant amounts in adjacent historical years are subtracted to construct a second difference vector; Based on the linear interpolation method, the curve is linearly interpolated and linearly fitted to determine the fitting coefficient, and the corresponding difference vector is analyzed to obtain the increased variable range; The increase in photovoltaic power generation capacity in the next year is estimated based on the variable range of greenhouse gas increases, the variable range of pollutant increases, and the power supply demand determined by the global change assessment model.

5. The spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths according to claim 4 is characterized in that: Get increased variable scope, including: Calculate the left range bound ; ； in, represents the fitting coefficient; represents the average value of all quantities in the corresponding curve; Indicates the number of differences in the corresponding difference vector that are consistent with the sign of r1; Indicates based on The variance of the differences; Represents The average of the absolute values ​​corresponding to the differences; Indicates the total number of years involved; Calculate the right range bounds ; ； in, Representation and Remainder The average of the absolute values ​​corresponding to the differences; Based on the left range boundary , right range boundary , which increases the variable range.

6. The spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths according to claim 4 is characterized in that: The estimated increase in photovoltaic power generation capacity in the next year includes: Get the first increment based on the left range boundary and the right range from the greenhouse boundary-increment comparison table , second increment ; Obtain the third increment based on the left range boundary and the right range from the pollutant boundary-increment comparison table , the fourth increment ; Determine the future power required based on the power supply demand , and combined with the current amount of electricity generated , first increment , second increment , the third increment and the fourth increment , estimate the photovoltaic power generation installed capacity increment NL in the next year; in, Indicates the floor symbol; Indicates the capacity of electricity generated by each photovoltaic power generation installation; express The variance of .

7. The spatiotemporal dynamic photovoltaic power generation emission reduction potential assessment method under multiple development paths according to claim 1 is characterized in that: The regional attributes include: clean power attribute and rich resource attribute.

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