CN117575633A - Full life cycle carbon emission calculation method of photovoltaic module - Google Patents
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Abstract
The invention provides a full life cycle carbon emission calculation method of a photovoltaic module, and relates to the technical field of carbon emission calculation. Firstly, acquiring energy data of each stage in the whole life cycle of a photovoltaic module; calculating the carbon emission in the unit product silica exploitation process, the carbon emission in the industrial silicon production process, the carbon emission in the polysilicon production process, the carbon emission in the silicon wafer production process, the carbon emission in the cell production process, the carbon emission of the photovoltaic module in power generation operation and the carbon emission of the photovoltaic module out-of-service rejection; and further calculating the total carbon emission amount of the photovoltaic module of the unit product in the whole life cycle. According to the method, the electric quantity loss of the photovoltaic module in the using process is fully considered, the initial weight and the threshold value after the optimization of the improved thought evolution algorithm are utilized by combining the neural network model, systematic errors caused by the influence of the model weight can be effectively avoided, calculation errors are reduced, and the calculation accuracy of carbon emission is improved.
Description
Technical Field
The invention relates to the technical field of carbon emission calculation, in particular to a full life cycle carbon emission calculation method of a photovoltaic module.
Background
The main measure of the electric power energy source comprises the implementation of renewable energy source substitution actions, deepening the reform of an electric power system and constructing a novel electric power system taking new energy sources as main bodies. Renewable energy sources represented by wind power and photovoltaics have the characteristics of cleanness, safety, reliability and no limitation of resource distribution regions. The traditional meaning considers that the new energy sources such as photovoltaic and the like are completely clean, and no carbon emission is generated. However, the production and maintenance process of the photovoltaic module involves additional links such as production and processing of a large amount of raw materials, energy and manpower use, and carbon emission is inevitably generated.
The existing carbon emission calculation method mainly comprises a material balance method, an actual measurement method and a carbon emission factor method. The material balance method requires that the industry has complete statistics and calculation of input and output, and no clear record of input and output energy consumption, so the method is not suitable for carbon emission calculation of the full life cycle of the photovoltaic module. The actual measurement method adopts a direct measurement mode to measure and calculate the carbon emission of the product, and has great difficulty in realizing the full life cycle of the photovoltaic module. The carbon emission factor is mainly used for calculating the carbon emission of the product according to the carbon emission coefficient of combustion of different fossil energy sources, is suitable for industries mainly comprising energy consumption, and is not limited in a clear way.
The prior art aims at the defects of a carbon emission calculation method in the whole life cycle of the photovoltaic module, firstly, the prior art aims at estimating the carbon emission of the photovoltaic module in the production stage, but in practice, the calculation of the carbon emission of the photovoltaic module does not fully consider the electric quantity loss of the photovoltaic module in the use process, so that the calculation error of the equivalent carbon emission of the photovoltaic module is caused; secondly, in the prior art, a carbon emission factor method is adopted to estimate the carbon emission of corresponding energy sources, but the adopted carbon emission factors are unified carbon emission factors of corresponding provinces, and the carbon emission factors of all areas in the provinces are different due to the difference of energy source consumption in time and space, so that the error of the carbon emission estimation result of the photovoltaic module is increased to a certain extent.
For example, chinese patent grant publication No.: CN111369114B discloses a method for obtaining carbon emission of photovoltaic power generation industry based on full life cycle, which divides total carbon emission of photovoltaic power generation industry into four stages: carbon emission in the production stage of the photovoltaic power generation system, carbon emission in the operation and maintenance stage of the photovoltaic power generation system, carbon emission in the power transmission loss stage of the photovoltaic power generation system and carbon emission in the retirement stage of the photovoltaic power generation system. Although the patent calculates carbon emission at different photovoltaic production stages so as to obtain the effective carbon emission of the photovoltaic power generation industry, the estimation of the carbon emission of the photovoltaic module does not consider the carbon emission of the electric quantity loss of the photovoltaic module in the use process aiming at the carbon emission estimation of the photovoltaic module, so that the estimation result of the carbon emission has errors.
Disclosure of Invention
The invention aims to solve the technical problem of providing a full life cycle carbon emission calculation method of a photovoltaic module to realize calculation of full life cycle carbon emission of the photovoltaic module aiming at the defects of the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme: a full life cycle carbon emission calculation method for a photovoltaic module, comprising:
acquiring energy data of each stage in the whole life cycle of the photovoltaic module;
calculation of carbon emissions C per unit product during silica mining 1 Carbon emission C in industrial silicon production process 2 Carbon emission C in the production of polycrystalline silicon 3 Carbon emission C in silicon wafer production process 4 Carbon emission C in the production of battery pieces 5 Carbon emission C of photovoltaic module 6 Carbon emission C of photovoltaic module power generation operation 7 Carbon emission C of retired photovoltaic module 8 ；
Calculating total carbon emission amount C of whole life cycle of photovoltaic module of unit product Total (S) ＝C 1 +C 2 +C 3 +C 4 +C 5 +C 6 +C 7 +C 8 。
Preferably, the energy data of each stage in the whole life cycle of the photovoltaic module is collected by a monitoring unit, specifically:
identifying mine conditions in the silica mining area, including mine size, mine type and transportation conditions by utilizing a satellite remote sensing intelligent identification technology; acquiring the transportation energy consumption and the transportation distance of each batch of silica in each time range; acquiring energy data of mine enterprises in time intervals in an internet of things transmission mode; acquiring silica yield data; taking factories of industrial silicon, polysilicon, silicon chips, battery pieces and assembly production enterprises as boundaries, acquiring time-division energy data of the production enterprises in an Internet of things transmission mode, and acquiring industrial silicon yield data; the method comprises the steps of capturing energy consumption and carbon emission data of unit products of steel scrap and aluminum scrap recovery treatment in the market, and acquiring energy data of mining enterprises in time intervals in an Internet of things transmission mode.
Preferably, the carbon emission amount calculation method of the unit product silica mining process is as follows:
s1, collecting all electric oil information data in the silica exploitation process to form a basic database;
s2, constructing a carbon emission calculation model under active/reactive power in the silica exploitation process, calculating the carbon emission under active/reactive power in the silica exploitation process by utilizing the electric oil information data acquired in the S1, updating a basic database to form an electric oil information database, and dividing a training set and a test set;
the carbon emission calculation model under active/reactive power of the silica mining process is shown as the following formula:
wherein C is L Representing the carbon emissions at active/reactive power of the silica mining process;representing the consumption coefficient of the i-th material; q (Q) M，i Represents the consumption of the i-th material used for 1 year; CF (compact flash) M，i Representing the carbon emission coefficient using the i-th material;
s3, constructing a BP neural network, sending electric oil information data in sample data to an input layer of the BP neural network model, then calculating an error between an output result of the output layer of the BP neural network model and unit carbon discharge of the sample data, training and testing by utilizing a corresponding training set and a corresponding testing set in S2 to obtain a carbon emission factor model under active/reactive power in a silica exploitation process, and giving corresponding carbon emission factors under the unit active/reactive power in the silica exploitation process when different quality oils are used according to the model;
S4, constructing an online calculation model of the power generation and carbon emission in the silica exploitation process, wherein the model is shown in the following formula:
C s ＝E×CF
wherein C is s Representing the carbon emission of the generated power in the silica exploitation process; e represents the total electrical energy consumed during the production phase of the silica mining process; CF represents the electrical carbon emission coefficient of the silica mining process production phase;
s5, collecting power generation data in the silica exploitation process on line, and distinguishing active power from reactive power; combining the corresponding carbon emission factors obtained in the step S3, and dynamically calculating the corresponding carbon emission amount in the silica exploitation process on line according to an on-line calculation model of the power generation carbon emission in the silica exploitation process;
the carbon emission amount calculation model of the silica mining process is:
wherein C is 1 Is the carbon emission of the silica mining process.
Preferably, the method for calculating the carbon emission in the industrial silicon production process comprises the following steps:
acquiring an operation total load section of industrial silicon production in a section of operation time; determining all characteristic load points of the industrial silicon production in the operation total load section based on the operation total load section of the industrial silicon production and a preset base load point; determining the actual carbon emission of each characteristic load point based on the actual burnout carbon content of the coal in the furnace produced by the industrial silicon and the actual characteristic energy consumption of each characteristic load point; based on the actual carbon emissions of all the characteristic load points, determining the actual total carbon emissions of the industrial silicon production over a period of operation time, as shown in the following formula:
Wherein C is p The actual carbon emissions for all characteristic load points are shown in the following equation:
C p ＝B′ p ×CF b
wherein B' p The actual characteristic energy consumption under the characteristic load point is shown in the following formula:
B′ p ＝B P +B P ×Ω P +B P ×σ P +B P ×R P +B P ×τ P
wherein CF is as follows b The actual carbon emission coefficient is the characteristic point; b (B) P The power supply coal consumption of the characteristic load points; omega shape P Is the influence coefficient of heat consumption on energy consumption; sigma (sigma) P Is the influence coefficient of combustion efficiency on energy consumption; r is R P The influence coefficient of the utilization rate of the furnace coal on the energy consumption is used; τ P Is an influence coefficient of plant power consumption.
Preferably, the method for calculating the carbon emission in the production process of the polysilicon comprises the following steps:
firstly, determining the total consumption of raw materials and carbon dioxide equivalent emission factors per unit production in the production process of the polycrystalline silicon, determining the carbon dioxide equivalent emission factors of various energy sources, and further determining the carbon emission equivalent coefficient of 1 degree electricity in the production link of the polycrystalline silicon and the carbon emission equivalent coefficient of 1 degree electricity in the transportation link of the polycrystalline silicon corresponding to the energy source obtaining stage; further calculating carbon emission in the production stage of the polysilicon and carbon emission in the transportation stage of the polysilicon; the total carbon emission in the polysilicon production process is the sum of carbon emission in two stages of polysilicon production and polysilicon transportation, and the following formula is shown:
C 3 ＝∑EC m1s ×k 1s +∑ET 1s ×k 2s
wherein s=0, 1,2, 3..n, N represents a plurality of The number of crystalline silicon species; EC (EC) m1s The energy consumption value is obtained for the polysilicon; k (k) 1s The carbon emission equivalent coefficient of 1 DEG electricity corresponding to the polysilicon production link in the energy acquisition stage; ET (electric T) 1s Energy-saving consumption values of the transport ring in the polysilicon obtaining stage; k (k) 2s The carbon emission equivalent coefficient is 1 degree electricity in the polysilicon transportation link.
Preferably, the method for calculating the carbon emission in the silicon wafer production process comprises the following steps:
determining the total consumption of each raw material and the equivalent carbon dioxide emission factor per unit production in the production process of the silicon wafer; determining carbon dioxide equivalent emission factors of various energy sources, and establishing a digital twin model database; establishing a carbon factor library for accounting carbon emission in silicon wafer production; generating a life cycle model tree of silicon wafer production based on the digital twin model database and the carbon factor library; establishing a carbon emission data calculation model based on each stage of the silicon wafer production; calculating the carbon emission of each stage of silicon wafer production based on a carbon emission data calculation model and a life cycle model tree of the silicon wafer production, wherein the carbon emission is calculated according to the following formula:
C 4 ＝(∑ED m2e +∑EF m2 +∑EL m2 +∑EM m2 +∑EN m2 )×k 3 +T m2 ×k 4
wherein C is 4 ED is the carbon emission in the silicon wafer production process m2e The energy consumption value when the slicing is performed for the silicon wafer production of the e-th silicon wafer raw material, e=1, 2,3. EF (electric F) m2 To execute the energy consumption value at the time of assembling the segments; EL (electro luminescence) m2 To perform the energy consumption value when the composition segment is formed; EM (effective microorganisms) m2 Energy consumption values for executing the group segments; EN (EN) m2 To execute the energy consumption value of the integration section; k (k) 3 The equivalent coefficient of carbon emission of 1 degree electricity is used in the production of silicon wafers; t (T) m2 The energy consumption value is the energy consumption value in the silicon wafer transportation process; k (k) 4 A carbon emission equivalent factor of 1 degree electricity was used for the wafer transportation process.
Preferably, the method for calculating the carbon emission in the production process of the battery piece comprises the following steps:
adopting an analytic hierarchy process to construct an energy electric power carbon emission index system for measuring the carbon emission level in the production process of the battery piece; constructing a carbon emission model of a battery piece production process, a carbon emission model of a power transmission system and a carbon emission model of a power utilization side, and calculating carbon emission data in the battery piece production process, the power transmission process and the power generation process; calculating historical carbon emission data of the production process of the battery piece; acquiring time sequence characteristics of historical carbon emission data by an EMD empirical mode decomposition method, training an LSTM long-term and short-term memory network by using the historical carbon emission data, and calculating carbon emission data of power generation and power transmission in the production process of the battery piece, wherein the carbon emission data is shown in the following formula:
C 5 ＝f θ (P 1 ，P 2 ，P 3 ，P 4 ，X)
Wherein C is 5 Is the carbon emission in the production process of the battery piece, f θ For constructing the LSTM model, θ is a network structure parameter of the LSTM model, including a memorized time period; x is historical carbon emission data; p (P) 1 ，P 2 ，P 3 ，P 4 The characteristic components of high-frequency, medium-frequency, low-frequency and extremely-low-frequency power generated and transmitted in the production process of the battery piece are respectively.
Preferably, the carbon emission amount calculation method of the photovoltaic module is as follows:
dividing the photovoltaic module into a building material and component production stage, a transportation and operation stage and a component disassembly and reuse stage, defining calculation boundaries of each stage, and adding carbon emission of each stage to obtain the total carbon emission C of the module 6 The following formula is shown:
C 6 ＝EM m4 ×k 6 +ET m4 ×k 7 +T m3 ×k 8
wherein EM is m4 The energy consumption value of the building material and the component production stage of the photovoltaic module; k (k) 6 The carbon emission equivalent coefficient of 1 degree electricity is used for the building materials and the component production stage of the photovoltaic component; ET (electric T) m4 The energy consumption value is the energy consumption value of the photovoltaic module in the transportation and operation stage; k (k) 7 Using 1 degree electrical carbon for transport and run phases of photovoltaic modulesAn emission equivalent coefficient; t (T) m3 Energy consumption values at the disassembly and reuse stages of components of the photovoltaic module; k (k) 8 A carbon emission equivalent factor of 1 degree electricity was used for the component disassembly and reuse stage of the photovoltaic module.
Preferably, the carbon emission amount calculation method of the power generation operation is as follows:
firstly, acquiring carbon footprint evaluation parameters of power generation operation in a target project and power generation operation basic data related to carbon emission; calculating a data deviation value between the power generation operation basic data and the carbon emission real data by using a Bayesian network method; combining the power generation operation basic data with the upper limit and the lower limit of the deviation value to construct plan review technical distribution so as to fit the data distribution condition of the power generation operation basic data; uniformly simplifying the continuously-changed carbon footprint evaluation parameters into three-angle distribution so as to simulate the parameter distribution condition of the carbon footprint evaluation parameters; combining the data distribution condition of the basic data of the power generation operation and the parameter distribution condition of the carbon footprint evaluation parameters, and calculating by a Monte Carlo simulation method to obtain the carbon footprint distribution condition of the power generation operation in a target project; calculating the carbon emission of the power generation operation of the target project based on the carbon footprint distribution condition of the power generation operation, wherein the carbon emission is shown in the following formula:
wherein PF is u，v A carbon emission coefficient representing a v-th power generation operation of the u-th material; GWP v The carbon emission loss coefficient of the v-th power generation operation is shown.
Preferably, the calculation method of the carbon emission amount of retired photovoltaic modules comprises the following steps:
Acquiring calculation data of historical carbon emission events of the photovoltaic module; calculating data of retired scrapping based on historical carbon emission time, and updating model parameters for the constructed power grid BP neural network model until the variance E of the carbon emission obtained by the current parameters output by the model and the expected carbon emission is smaller than a set value, so as to obtain determined model parameters and a trained power grid BP neural network model; the model parameters comprise weight values of neurons and normalized carbon emission influence parameters of each group; obtaining the retired and scrapped electricity consumption at the current time point and the reference electricity consumption, inputting a trained power grid BP neural network model, and determining the retired and scrapped carbon emission of the photovoltaic module according to the output of the power grid BP neural network model, wherein the carbon emission is represented by the following formula:
wherein C is 8 AD for carbon emissions of retired scrap z Calculating the retired discard number, EF, for the z-th historical carbon emission z Calculating a retired carbon emission coefficient for the z-th historical carbon emission;
wherein the power grid BP neural network model comprises:
a carbon emission neuron computational mathematical model construction unit for constructing a carbon emission neuron computational mathematical model, wherein the parameters involved in training the neuron computational mathematical model include: weight value W of neuron p to neuron q pq Input information x from neuron p received at time t p (t) presetting a neuron transfer function f (x);
an input layer acquisition unit for taking each group of normalized carbon emission influence parameters as an input layer vector x= (x) of the BP neural network based on the BP neural network structure 1 ，x 2 ，…x p ，…x P ) Wherein P is the number of neurons of the input layer;
a hidden layer acquisition unit for calculating an input layer vector x= (x) corresponding to each group of normalized carbon emission influence parameters contained in the training sample based on the carbon emission neuron 1 ，x 2 ，…x p ，…x P ) Obtaining hidden layer vector y= (y) of BP neural network 1 ，y 2 ，...，y q ，...，y Q )×W pq Wherein y is q Is the Q-th neuron in the hidden layer Q neurons, W pq The vector is the weight value from the p-th neuron of the input layer to the q-th neuron of the hidden layer;
an output layer acquisition unit forCalculating a carbon emission budget value a output by an output layer of the BP neural network corresponding to each group of normalized carbon emission influence parameters based on the hidden layer vector and a preset neuron transfer function f (x), wherein W qk The weight value from the q-th neuron of the hidden layer to the k-th neuron of the output layer is 1;
an output error calculation unit for acquiring actual carbon emission values b corresponding to the normalized carbon emission influence parameters of each group according to the normalized carbon emission data of each group included in the training sample, and calculating corresponding output errors of the normalized carbon emission influence parameters of each group
The parameter error acquisition judging unit takes the sum of squares of output errors err corresponding to each group of normalized carbon emission influence parameters as a parameter error and judges whether the parameter error is within a preset error allowable range; if yes, outputting carbon emission data; if not, the weight values of the neurons p to q are adjusted and then recalculated.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in: according to the full life cycle carbon emission calculation method of the photovoltaic module, calculation of the full life cycle carbon emission amount of the photovoltaic module is achieved, the total life cycle carbon emission amount of the photovoltaic module is calculated by calculating the carbon emission amount of unit product silicon ore exploitation, the carbon emission amount of industrial silicon, the carbon emission amount of polycrystalline silicon, the carbon emission amount of silicon chips, the carbon emission amount of battery pieces, the carbon emission amount of the photovoltaic module, the carbon emission amount of photovoltaic module power generation operation and the carbon emission amount of retired photovoltaic module, the electric quantity loss of the photovoltaic module in the use process is fully considered, and the initial weight and the threshold value after optimization by utilizing an improved thinking evolution algorithm are combined with a neural network model, so that systematic errors caused by the influence of model weights can be effectively avoided, correction and compensation can be carried out, calculation errors are reduced, and the calculation accuracy of the carbon emission is improved.
Drawings
Fig. 1 is a block diagram of a full life cycle carbon emission calculation method of a photovoltaic module according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for calculating carbon emissions during silica mining provided by an embodiment of the present invention;
FIG. 3 is a flow chart of a method for calculating carbon emission in an industrial silicon production process according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for calculating carbon emission in a polysilicon production process according to an embodiment of the present invention;
FIG. 5 is a flowchart of a method for calculating carbon emission in the production process of a silicon wafer according to an embodiment of the present invention;
FIG. 6 is a flowchart of a method for calculating carbon emissions in a process of producing a battery sheet according to an embodiment of the present invention;
fig. 7 is a flowchart of a carbon emission amount calculation method for power generation operation of a photovoltaic module according to an embodiment of the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In this embodiment, as shown in fig. 1, a method for calculating carbon emission in a full life cycle of a photovoltaic module is first to obtain energy data of each stage in the full life cycle of the photovoltaic module; recalculating carbon emissions C during unit product silica mining 1 Carbon emission C in industrial silicon production process 2 Carbon emission C in the production of polysilicon 3 Carbon emission C in silicon wafer production process 4 Carbon emission C in production process of battery piece 5 Carbon emission C of photovoltaic module 6 Carbon emission C of photovoltaic module power generation operation 7 Carbon emission C of retired photovoltaic module 8 The method comprises the steps of carrying out a first treatment on the surface of the Finally, calculating the total carbon emission amount C of the whole life cycle of the photovoltaic module of the unit product Total (S) ＝C 1 +C 2 +C 3 +C 4 +C 5 +C 6 +C 7 +C 8 。
1. The energy data of each stage in the whole life cycle of the photovoltaic module is collected by a monitoring unit, and specifically comprises the following steps:
identifying mine conditions in the silica mining area, including mine size, mine type and transportation conditions by utilizing a satellite remote sensing intelligent identification technology; the method comprises the steps that through the mode of additionally installing an intelligent instrument of the Internet of things and GPS positioning on a transport tool, the transport energy consumption and the transport distance of each time range and each batch of silica are obtained; installing a meter in an energy inlet or an energy storage area of a mine exploitation enterprise, and acquiring energy data of the mine enterprise in time intervals in an internet of things transmission mode; installing a meter on an ore warehouse or a transport vehicle to obtain silica yield data; taking factories of industrial silicon, polysilicon, silicon chips, battery pieces and assembly production enterprises as boundaries, installing meters in energy inlets or energy storage areas of the whole factories, acquiring time-division energy data of the production enterprises in an internet of things transmission mode, and installing meters on industrial silicon product warehouses or transport vehicles to acquire industrial silicon yield data; the method comprises the steps of grabbing energy consumption and carbon emission data of unit products of steel scraps and aluminum scraps in the market, installing a meter in an energy inlet or an energy storage area of a whole factory of waste articles, and acquiring energy data of mining enterprises in time intervals in an Internet of things transmission mode.
Carbon emissions C per unit of product silica mining 1 As shown in fig. 2, the calculation method of (a) specifically comprises:
s1, collecting all electric oil information data in the silica exploitation process to form a basic database;
s2, constructing a carbon emission calculation model under active/reactive power in the silica exploitation process, calculating the carbon emission under active/reactive power in the silica exploitation process by utilizing the electric oil information data acquired in the S1, updating a basic database to form an electric oil information database, and dividing a training set and a test set;
the carbon emission calculation model under active/reactive power of the silica mining process is shown as the following formula:
wherein C is L Representing carbon emissions at active/reactive power of silica mining processAn amount of;representing the consumption coefficient of use of the ith material, including silicon rock, iron and electrical energy, available from the CLCD database; q (Q) M，i Represents the consumption of the i-th material used for 1 year; CF (compact flash) M，i Representing the carbon emission coefficient using the i-th material;
s3, constructing a BP neural network, sending electric oil information data in sample data to an input layer of the BP neural network model, then calculating an error between an output result given by the output layer of the BP neural network model and unit carbon displacement of the sample data, training and testing by utilizing a corresponding training set and a corresponding testing set in S2 to obtain a carbon emission factor model under active/reactive power in a silica exploitation process, and giving corresponding carbon emission factors under the unit active/reactive power in the silica exploitation process when different quality oils are used according to the model;
S4, constructing an online calculation model of the power generation and carbon emission in the silica exploitation process, wherein the model is shown in the following formula:
C s ＝E×CF
wherein C is s Representing the carbon emission of the generated power in the silica exploitation process; e represents the total electrical energy consumed in the production phase of the silica mining process, available from the energy agency; CF represents the electric energy carbon emission coefficient of the production stage of the silica exploitation process and can be obtained through corresponding climate change department of China ecological environment;
s5, collecting power generation data in the silica exploitation process on line, and distinguishing active power from reactive power; combining the corresponding carbon emission factors obtained in the step S3, and dynamically calculating the corresponding carbon emission amount in the silica exploitation process on line according to an on-line calculation model of the power generation carbon emission in the silica exploitation process;
the carbon emission amount calculation model of the silica mining process is:
in this example, the material and energy consumption data for manufacturing a 1kWp photovoltaic module are shown in Table 1;
TABLE 1 Material and energy data during silicon ore mining
2. Carbon emission C in industrial silicon production process 2 As shown in fig. 3, the calculation method of (a) specifically includes:
in the industrial silicon production process, the main material consumption is raw material silica and various carbonaceous reducing agents; the main energy consumption is coal combustion and electric power; and generating wastes in different states such as gas, solid and the like. The carbon emission sources thus include: electric power (indirect carbon emissions), coal combustion (direct carbon emissions), and process emissions generated after chemical reaction of silica with carbon. Acquiring an operation total load section of industrial silicon production in a section of operation time; determining all characteristic load points of the industrial silicon production in the operation total load section based on the operation total load section of the industrial silicon production and a preset base load point; determining the actual carbon emission of each characteristic load point based on the actual burnout carbon content of the coal in the furnace produced by the industrial silicon and the actual characteristic energy consumption of each characteristic load point; based on the actual carbon emissions of all the characteristic load points, determining the actual total carbon emissions of the industrial silicon production over a period of operation time, as shown in the following formula:
Wherein C is p The actual carbon emissions for all characteristic load points are shown in the following equation:
C p ＝B′ p ×CF b
wherein B' p The actual characteristic energy consumption under the characteristic load point is shown in the following formula:
B′ p ＝B P +B P ×Ω P +B P ×σ P +B P ×R P +B P ×τ P
wherein CF is as follows b The actual carbon emission coefficient is the characteristic point; b (B) P The power supply coal consumption of the characteristic load points; omega shape P Is the influence coefficient of heat consumption on energy consumption; sigma (sigma) P Is the influence coefficient of combustion efficiency on energy consumption; r is R P The influence coefficient of the utilization rate of the furnace coal on the energy consumption is used; τ P Is an influence coefficient of plant power consumption.
In this example, the material and energy consumption data for manufacturing a 1kWp photovoltaic module are shown in Table 2;
TABLE 2 Material energy data in Industrial silicon production Process
3. Carbon emission C in polysilicon production process 3 As shown in fig. 4, the calculation method of (a) specifically includes:
firstly, inquiring and determining the total consumption of raw materials and unit production carbon dioxide equivalent emission factors thereof in the production process of the polycrystalline silicon from a CLCD database, determining the carbon dioxide equivalent emission factors of various energy sources, and further determining the carbon emission equivalent coefficient of 1 degree electricity corresponding to the production place of the polycrystalline silicon and the carbon emission equivalent coefficient of 1 degree electricity in the transportation link of the polycrystalline silicon in the energy source obtaining stage; due to CO 2 Emission (kg greenhouse gas) =activity E level (tai j) ×k carbon dioxide equivalent emission factor (kg greenhouse gas/taij), therefore, the carbon dioxide equivalent emission factor is the carbon emission equivalent coefficient of 1 degree electricity of the polysilicon production site corresponding to the energy source acquisition stage;
Further calculating the carbon emission in the raw material production stage, the carbon emission in the raw material transportation stage and the carbon emission in the polysilicon production stage; determining the production amount of each raw material according to the unit production amount of the polycrystalline silicon and the total production amount mixing ratio of the polycrystalline silicon enterprises, and accumulating the production carbon emission of each material to obtain the carbon emission of the raw material in the production stage; calculating the carbon emission of the raw material in the transportation stage according to the transportation energy consumption and the transportation distance of the transportation machinery; subdividing the production stage of the polycrystalline silicon into processes of mixing a mixture, reacting and processing in a reduction furnace and the like, respectively calculating, and determining the carbon emission of the production stage of the polycrystalline silicon according to the energy consumption of each process unit shift, the number of mechanical shifts, the carbon emission factor of an energy unit and the like; the total carbon emission in the polysilicon production process is the sum of the carbon emissions of the two stages of raw material production and transportation, and the following formula is shown:
C 3 ＝∑EC m1s ×k 1s +∑ET 1s ×k 2s
wherein s=0, 1,2, 3..n, N represents the number of polysilicon species; EC (EC) m1s The energy consumption value is obtained for the polysilicon; k (k) 1s The carbon emission equivalent coefficient of 1 DEG electricity corresponding to the polysilicon production link in the energy acquisition stage; ET (electric T) 1s Energy-saving consumption values of the transport ring in the polysilicon obtaining stage; k (k) 2s The carbon emission equivalent coefficient is 1 DEG electricity in the polysilicon transportation link;
Taking the manufacturing and production process of polysilicon as an example, the materials and energy consumption data of the 1kWp photovoltaic templates are shown in Table 3;
TABLE 3 Material energy data during the manufacture of polycrystalline silicon
4. Carbon emission C in silicon wafer production process 4 As shown in fig. 5, the calculation method of (a) specifically includes:
determining the total consumption of each raw material and the equivalent carbon dioxide emission factor per unit production in the production process of the silicon wafer; determining carbon dioxide equivalent emission factors of various energy sources, and establishing a digital twin model database; establishing a carbon factor library for accounting carbon emission in silicon wafer production; generating a life cycle model tree of silicon wafer production based on the digital twin model database and the carbon factor library; establishing a carbon emission data calculation model based on each stage of the silicon wafer production; calculating the carbon emission of each stage of silicon wafer production based on a carbon emission data calculation model and a life cycle model tree of the silicon wafer production, wherein the carbon emission is calculated according to the following formula:
C 4 ＝(∑ED m2e +∑EF m2 +∑EL m2 +∑EM m2 +∑EN m2 )×k 3 +T m2 ×k 4
wherein C is 4 ED is the carbon emission in the silicon wafer production process m2e The energy consumption value when the slicing is performed for the silicon wafer production of the e-th silicon wafer raw material, e=1, 2,3. EF (electric F) m2 To execute the energy consumption value at the time of assembling the segments; EL (electro luminescence) m2 To perform the energy consumption value when the composition segment is formed; EM (effective microorganisms) m2 Energy consumption values for executing the group segments; EN (EN) m2 To execute the energy consumption value of the integration section; k (k) 3 The equivalent coefficient of carbon emission of 1 degree electricity is used in the production of silicon wafers; t (T) m2 The energy consumption value is the energy consumption value in the silicon wafer transportation process; k (k) 4 A carbon emission equivalent factor of 1 degree electricity was used for the wafer transportation process.
The digital twin model database comprises an engineering construction database, an engineering material database, an energy-saving analysis database, an engineering setting database and a silicon wafer production information model database; the life cycle model tree for silicon wafer production comprises: analyzing a silicon wafer production information model database according to the production information in the silicon wafer production process and the use information in the silicon wafer production and use process; establishing a carbon emission data calculation model according to the parsed silicon wafer production information model database and each stage of splitting the silicon wafer production life cycle stage process to form each raw material; the production information comprises main products, byproducts, parameter values and transportation information of the production process of each raw material, and the use information comprises production, use process, use amount and waste amount of each raw material; the life cycle stage process of the silicon wafer production comprises a construction stage, a use stage and a dismantling stage of the silicon wafer production.
The life cycle model tree for silicon wafer production is used for summarizing and calculating data, and for avoiding data omission or repeated calculation, for example, in a carbon emission calculation formula of each stage of silicon wafer production, calculation processing divided into various energy consumption values is the embodiment of the model tree.
In this example, taking the silicon wafer manufacturing process as an example, the materials and energy consumption data for manufacturing a 1kWp photovoltaic template are shown in Table 4;
TABLE 4 Material energy data during silicon wafer fabrication
5. Carbon emission C in production process of battery piece 5 As shown in fig. 6, the calculation method of (a) specifically includes:
adopting an analytic hierarchy process to construct an energy electric power carbon emission index system for measuring the carbon emission level in the production process of the battery piece; constructing a carbon emission model of a battery piece production process, a carbon emission model of a power transmission system and a carbon emission model of a power utilization side, and calculating carbon emission data in the battery piece production process, the power transmission process and the power generation process; calculating historical carbon emission data of the production process of the battery piece; acquiring time sequence characteristics of historical carbon emission data by an EMD empirical mode decomposition method, training an LSTM long-term and short-term memory network by using the historical carbon emission data, and calculating carbon emission data of power generation and power transmission in the production process of the battery piece, wherein the carbon emission data is shown in the following formula:
C 5 ＝f θ (P 1 ，P 2 ，P 3 ，P 4 ，X)
Wherein C is 5 Is the carbon emission in the production process of the battery piece, f θ For constructing the LSTM model, θ is a network structure parameter of the LSTM model, including a memorizing time period (data of the first 11 months of model selection are used for measuring and calculating the current month carbon emission) and the like; x is historical carbon emission data; p (P) 1 ，P 2 ，P 3 ，P 4 The characteristic components of high-frequency, medium-frequency, low-frequency and extremely-low-frequency power generated and transmitted in the production process of the battery piece are respectively.
In this example, taking the manufacturing process of the battery piece as an example, the materials and the energy consumption data for manufacturing the 1kWp photovoltaic template are shown in Table 5;
table 5 data during the manufacture of the battery cells
6. Carbon emission C of photovoltaic module 6 The calculation method comprises the following steps:
dividing the photovoltaic module into a building material and component production stage, a transportation and operation stage and a component disassembly and reuse stage, defining calculation boundaries of each stage, and adding carbon emission of each stage to obtain the total carbon emission C of the module 6 The following formula is shown:
C 6 ＝EM m4 ×k 6 +ET m4 ×k 7 +T m3 ×k 8
wherein EM is m4 The energy consumption value of the building material and the component production stage of the photovoltaic module; k (k) 6 The carbon emission equivalent coefficient of 1 degree electricity is used for the building materials and the component production stage of the photovoltaic component; ET (electric T) m4 The energy consumption value is the energy consumption value of the photovoltaic module in the transportation and operation stage; k (k) 7 The carbon emission equivalent coefficient of 1 degree electricity is used for the transportation and operation stage of the photovoltaic module; t (T) m3 Energy consumption values at the disassembly and reuse stages of components of the photovoltaic module; k (k) 8 A carbon emission equivalent factor of 1 degree electricity was used for the component disassembly and reuse stage of the photovoltaic module.
In the embodiment, taking the manufacturing and production process of the photovoltaic module as an example, the materials and the energy consumption data for manufacturing the 1kWp photovoltaic module are shown in Table 6;
TABLE 6 Material energy data during photovoltaic module manufacturing
7. Carbon emission C of photovoltaic module power generation operation 7 The calculation method is shown in fig. 7, and specifically comprises the following steps:
firstly, acquiring carbon footprint evaluation parameters of power generation operation in a target project and power generation operation basic data related to carbon emission; calculating a data deviation value between the power generation operation basic data and the carbon emission real data by using a Bayesian network method; combining the power generation operation basic data with the upper limit and the lower limit of the deviation value to construct plan review technical distribution so as to fit the data distribution condition of the power generation operation basic data; uniformly simplifying the continuously-changed carbon footprint evaluation parameters into three-angle distribution so as to simulate the parameter distribution condition of the carbon footprint evaluation parameters; combining the data distribution condition of the basic data of the power generation operation and the parameter distribution condition of the carbon footprint evaluation parameters, and calculating by a Monte Carlo simulation method to obtain the carbon footprint distribution condition of the power generation operation in a target project; calculating the carbon emission of the power generation operation of the target project based on the carbon footprint distribution condition of the power generation operation, wherein the carbon emission is shown in the following formula:
Wherein PF is u，v The carbon emission coefficient representing the v-th power generation operation of the u-th material can be obtained from the CLCD database; GWP v The carbon emission loss coefficient representing the v-th power generation operation can be obtained from the CLCD database.
Taking the photovoltaic module power generation operation process as an example, the materials for manufacturing the 1kWp photovoltaic template and the energy consumption data are shown in Table 7;
TABLE 7 Material energy data during Power Generation operation
8. Carbon emission C of retired photovoltaic module 8 The calculation method of (1) is as follows:
acquiring historical carbon emission calculation event data of a photovoltaic module; calculating retired data based on historical carbon emission calculation event data, and updating model parameters for a pre-constructed power grid BP neural network model until the variance E of the carbon emission obtained by the current parameters output by the model and the expected carbon emission is smaller than a set value, so as to obtain determined model parameters and a trained power grid BP neural network model; the model parameters comprise weight values of neurons and normalized carbon emission influence parameters of each group; obtaining the retired and scrapped electricity consumption at the current time point and the reference electricity consumption, inputting a trained power grid BP neural network model, and determining the retired and scrapped carbon emission of the photovoltaic module according to the output of the power grid BP neural network model, wherein the carbon emission is represented by the following formula:
Wherein C is 8 AD for carbon emissions of retired scrap z Calculating the retired discard number, EF, for the z-th historical carbon emission z The retired carbon emission coefficient was calculated for the z-th historical carbon emission.
The power grid BP neural network model comprises:
a carbon emission neuron computational mathematical model construction unit for constructing a carbon emission neuron computational mathematical model, wherein the parameters involved in training the neuron computational mathematical model include: weight value W of neuron p to neuron q pq Input information x from neuron p received at time t p (t) presetting a neuron transfer function f (x);
an input layer acquisition unit for taking each group of normalized carbon emission influence parameters as an input layer vector x= (x) of the BP neural network based on the BP neural network structure 1 ，x 2 ，…x p ，…x P ) Wherein P is the number of neurons of the input layer;
a hidden layer acquisition unit for calculating an input layer vector x= (x) corresponding to each group of normalized carbon emission influence parameters contained in the training sample based on the carbon emission neuron 1 ，x 2 ，…x p ，...x P ) Obtaining hidden layer vector y= (y) of BP neural network 1 ，y 2 ，...，y q ，...，y Q )×W pq Wherein y is q Is the Q-th neuron in the hidden layer Q neurons, W pq The vector is the weight value from the p-th neuron of the input layer to the q-th neuron of the hidden layer;
An output layer acquisition unit for calculating each group of normalized carbon emissions based on the hidden layer vector and a preset neuron transfer function f (x)A carbon emission budget value a output by an output layer of the BP neural network corresponding to the quantity influence parameter, wherein W qk The weight value from the q-th neuron of the hidden layer to the k-th neuron of the output layer is 1;
an output error calculation unit for acquiring actual carbon emission values b corresponding to the normalized carbon emission influence parameters of each group according to the normalized carbon emission data of each group included in the training sample, and calculating corresponding output errors of the normalized carbon emission influence parameters of each group
The parameter error acquisition judging unit takes the sum of squares of output errors err corresponding to each group of normalized carbon emission influence parameters as a parameter error and judges whether the parameter error is within a preset error allowable range; if yes, outputting carbon emission data; if not, the weight values of the neurons p to q are adjusted and then recalculated.
Taking the retired scrapping process of the photovoltaic module as an example, the material and energy consumption data for manufacturing the 1kWp photovoltaic module are shown in Table 8;
data in retired scrapping process of 8V component
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions, which are defined by the scope of the appended claims.
Claims (10)
1. A full life cycle carbon emission calculation method of a photovoltaic module is characterized in that:
acquiring energy data of each stage in the whole life cycle of the photovoltaic module;
calculation of carbon emissions C per unit product during silica mining 1 Carbon emission C in industrial silicon production process 2 Carbon emission C in the production of polycrystalline silicon 3 Carbon emission C in silicon wafer production process 4 Carbon emission C in the production of battery pieces 5 Carbon emission C of photovoltaic module 6 Carbon emission C of photovoltaic module power generation operation 7 Carbon emission C of retired photovoltaic module 8 ；
Calculating total carbon emission amount C of whole life cycle of photovoltaic module of unit product Total (S) ＝C 1 +C 2 +C 3 +C 4 +C 5 +C 6 +C 7 +C 8 。
2. The full life cycle carbon emission calculation method of a photovoltaic module according to claim 1, wherein: the energy data of each stage in the whole life cycle of the photovoltaic module is collected by a monitoring unit, and specifically comprises the following steps:
identifying mine conditions in the silica mining area, including mine size, mine type and transportation conditions by utilizing a satellite remote sensing intelligent identification technology; acquiring the transportation energy consumption and the transportation distance of each batch of silica in each time range; acquiring energy data of mine enterprises in time intervals in an internet of things transmission mode; acquiring silica yield data; taking factories of industrial silicon, polysilicon, silicon chips, battery pieces and assembly production enterprises as boundaries, acquiring time-division energy data of the production enterprises in an Internet of things transmission mode, and acquiring industrial silicon yield data; the method comprises the steps of capturing energy consumption and carbon emission data of unit products of steel scrap and aluminum scrap recovery treatment in the market, and acquiring energy data of mining enterprises in time intervals in an Internet of things transmission mode.
3. The full life cycle carbon emission calculation method of a photovoltaic module according to claim 2, wherein: the carbon emission amount calculation method of the unit product silica mining process comprises the following steps:
s1, collecting all electric oil information data in the silica exploitation process to form a basic database;
s2, constructing a carbon emission calculation model under active/reactive power in the silica exploitation process, calculating the carbon emission under active/reactive power in the silica exploitation process by utilizing the electric oil information data acquired in the S1, updating a basic database to form an electric oil information database, and dividing a training set and a test set;
the carbon emission calculation model under active/reactive power of the silica mining process is shown as the following formula:
wherein C is L Representing the carbon emissions at active/reactive power of the silica mining process;representing the consumption coefficient of the i-th material; q (Q) M,i Represents the consumption of the i-th material used for 1 year; CF (compact flash) M,i Representing the carbon emission coefficient using the i-th material;
s3, constructing a BP neural network, sending electric oil information data in sample data to an input layer of the BP neural network model, then calculating an error between an output result of the output layer of the BP neural network model and unit carbon discharge of the sample data, training and testing by utilizing a corresponding training set and a corresponding testing set in S2 to obtain a carbon emission factor model under active/reactive power in a silica exploitation process, and giving corresponding carbon emission factors under the unit active/reactive power in the silica exploitation process when different quality oils are used according to the model;
S4, constructing an online calculation model of the power generation and carbon emission in the silica exploitation process, wherein the model is shown in the following formula:
C s ＝E×CF
wherein C is s Representing the carbon emission of the generated power in the silica exploitation process; e represents the total electrical energy consumed during the production phase of the silica mining process; CF represents silicaThe electric energy carbon emission coefficient of the production stage of the exploitation process;
s5, collecting power generation data in the silica exploitation process on line, and distinguishing active power from reactive power; combining the corresponding carbon emission factors obtained in the step S3, and dynamically calculating the corresponding carbon emission amount in the silica exploitation process on line according to an on-line calculation model of the power generation carbon emission in the silica exploitation process;
the carbon emission amount calculation model of the silica mining process is:
wherein C is 1 Is the carbon emission of the silica mining process.
4. A full life cycle carbon emission calculation method of a photovoltaic module according to claim 3, wherein: the method for calculating the carbon emission in the industrial silicon production process comprises the following steps:
acquiring an operation total load section of industrial silicon production in a section of operation time; determining all characteristic load points of the industrial silicon production in the operation total load section based on the operation total load section of the industrial silicon production and a preset base load point; determining the actual carbon emission of each characteristic load point based on the actual burnout carbon content of the coal in the furnace produced by the industrial silicon and the actual characteristic energy consumption of each characteristic load point; based on the actual carbon emissions of all the characteristic load points, determining the actual total carbon emissions of the industrial silicon production over a period of operation time, as shown in the following formula:
Wherein C is p The actual carbon emissions for all characteristic load points are shown in the following equation:
C p ＝B′ p ×CF b
wherein B' p For actual characteristic energy consumption at characteristic load points, e.g.The following formula is shown:
B′ p ＝B P +B P ×Ω P +B P ×σ P +B P ×R P +B P ×τ P
wherein CF is as follows b The actual carbon emission coefficient is the characteristic point; b (B) P The power supply coal consumption of the characteristic load points; omega shape P Is the influence coefficient of heat consumption on energy consumption; sigma (sigma) P Is the influence coefficient of combustion efficiency on energy consumption; r is R P The influence coefficient of the utilization rate of the furnace coal on the energy consumption is used; τ P Is an influence coefficient of plant power consumption.
5. The full life cycle carbon emission calculation method of a photovoltaic module according to claim 4, wherein: the calculation method of the carbon emission in the production process of the polysilicon comprises the following steps:
firstly, determining the total consumption of raw materials and carbon dioxide equivalent emission factors per unit production in the production process of the polycrystalline silicon, determining the carbon dioxide equivalent emission factors of various energy sources, and further determining the carbon emission equivalent coefficient of 1 degree electricity in the production link of the polycrystalline silicon and the carbon emission equivalent coefficient of 1 degree electricity in the transportation link of the polycrystalline silicon corresponding to the energy source obtaining stage; further calculating carbon emission in the production stage of the polysilicon and carbon emission in the transportation stage of the polysilicon; the total carbon emission in the polysilicon production process is the sum of carbon emission in two stages of polysilicon production and polysilicon transportation, and the following formula is shown:
C 3 ＝∑EC m1s ×k 1s +∑ET 1s ×k 2s
Wherein s=0, 1,2, 3..n, N represents the number of polysilicon species; EC (EC) m1s The energy consumption value is obtained for the polysilicon; k (k) 1s The carbon emission equivalent coefficient of 1 DEG electricity corresponding to the polysilicon production link in the energy acquisition stage; ET (electric T) 1s Energy-saving consumption values of the transport ring in the polysilicon obtaining stage; k (k) 2s The carbon emission equivalent coefficient is 1 degree electricity in the polysilicon transportation link.
6. The full life cycle carbon emission calculation method of a photovoltaic module according to claim 5, wherein: the method for calculating the carbon emission in the silicon wafer production process comprises the following steps:
determining the total consumption of each raw material and the equivalent carbon dioxide emission factor per unit production in the production process of the silicon wafer; determining carbon dioxide equivalent emission factors of various energy sources, and establishing a digital twin model database; establishing a carbon factor library for accounting carbon emission in silicon wafer production; generating a life cycle model tree of silicon wafer production based on the digital twin model database and the carbon factor library; establishing a carbon emission data calculation model based on each stage of the silicon wafer production; calculating the carbon emission of each stage of silicon wafer production based on a carbon emission data calculation model and a life cycle model tree of the silicon wafer production, wherein the carbon emission is calculated according to the following formula:
c 4 ＝(ΣED m2e +∑EF m2 +∑EL m2 +∑EN m2 +∑EN m2 )×k 3 +T m2 ×k 4
Wherein C is 4 ED is the carbon emission in the silicon wafer production process m2e The energy consumption value when the slicing is performed for the silicon wafer production of the e-th silicon wafer raw material, e=1, 2,3. EF (electric F) m2 To execute the energy consumption value at the time of assembling the segments; EL (electro luminescence) m2 To perform the energy consumption value when the composition segment is formed; EM (effective microorganisms) m2 Energy consumption values for executing the group segments; EN (EN) m2 To execute the energy consumption value of the integration section; k (k) 3 The equivalent coefficient of carbon emission of 1 degree electricity is used in the production of silicon wafers; t (T) m2 The energy consumption value is the energy consumption value in the silicon wafer transportation process; k (k) 4 A carbon emission equivalent factor of 1 degree electricity was used for the wafer transportation process.
7. The full life cycle carbon emission calculation method of a photovoltaic module according to claim 6, wherein: the method for calculating the carbon emission in the production process of the battery piece comprises the following steps:
adopting an analytic hierarchy process to construct an energy electric power carbon emission index system for measuring the carbon emission level in the production process of the battery piece; constructing a carbon emission model of a battery piece production process, a carbon emission model of a power transmission system and a carbon emission model of a power utilization side, and calculating carbon emission data in the battery piece production process, the power transmission process and the power generation process; calculating historical carbon emission data of the production process of the battery piece; acquiring time sequence characteristics of historical carbon emission data by an EMD empirical mode decomposition method, training an LSTM long-term and short-term memory network by using the historical carbon emission data, and calculating carbon emission data of power generation and power transmission in the production process of the battery piece, wherein the carbon emission data is shown in the following formula:
C 5 ＝f θ (P 1 ,P 2 ,P 3 ,P 4 ,X)
Wherein C is 5 Is the carbon emission in the production process of the battery piece, f θ For constructing the LSTM model, θ is a network structure parameter of the LSTM model, including a memorized time period; x is historical carbon emission data; p (P) 1 ,P 2 ,P 3 ,P 4 The characteristic components of high-frequency, medium-frequency, low-frequency and extremely-low-frequency power generated and transmitted in the production process of the battery piece are respectively.
8. The full life cycle carbon emission calculation method of a photovoltaic module according to claim 7, wherein: the carbon emission amount calculation method of the photovoltaic module comprises the following steps:
dividing the photovoltaic module into a building material and component production stage, a transportation and operation stage and a component disassembly and reuse stage, defining calculation boundaries of each stage, and adding carbon emission of each stage to obtain the total carbon emission C of the module 6 The following formula is shown:
C 6 ＝EM m4 ×k 6 +ET m4 ×k 7 +T m3 ×k 8
wherein EM is m4 The energy consumption value of the building material and the component production stage of the photovoltaic module; k (k) 6 The carbon emission equivalent coefficient of 1 degree electricity is used for the building materials and the component production stage of the photovoltaic component; ET (electric T) m4 The energy consumption value is the energy consumption value of the photovoltaic module in the transportation and operation stage; k (k) 7 The carbon emission equivalent coefficient of 1 degree electricity is used for the transportation and operation stage of the photovoltaic module; t (T) m3 Disassembly and reuse of components for photovoltaic modules Energy consumption value of stage; k (k) 8 A carbon emission equivalent factor of 1 degree electricity was used for the component disassembly and reuse stage of the photovoltaic module.
9. The full life cycle carbon emission calculation method of a photovoltaic module according to claim 8, wherein: the carbon emission amount calculation method for the power generation operation comprises the following steps:
firstly, acquiring carbon footprint evaluation parameters of power generation operation in a target project and power generation operation basic data related to carbon emission; calculating a data deviation value between the power generation operation basic data and the carbon emission real data by using a Bayesian network method; combining the power generation operation basic data with the upper limit and the lower limit of the deviation value to construct plan review technical distribution so as to fit the data distribution condition of the power generation operation basic data; uniformly simplifying the continuously-changed carbon footprint evaluation parameters into three-angle distribution so as to simulate the parameter distribution condition of the carbon footprint evaluation parameters; combining the data distribution condition of the basic data of the power generation operation and the parameter distribution condition of the carbon footprint evaluation parameters, and calculating by a Monte Carlo simulation method to obtain the carbon footprint distribution condition of the power generation operation in a target project; calculating the carbon emission of the power generation operation of the target project based on the carbon footprint distribution condition of the power generation operation, wherein the carbon emission is shown in the following formula:
Wherein PF is u,v A carbon emission coefficient representing a v-th power generation operation of the u-th material; GWP v The carbon emission loss coefficient of the v-th power generation operation is shown.
10. The full life cycle carbon emission calculation method of a photovoltaic module according to claim 9, wherein: the calculation method of the carbon emission amount of the retired photovoltaic module comprises the following steps:
acquiring calculation data of historical carbon emission events of the photovoltaic module; calculating data of retired scrapping based on historical carbon emission time, and updating model parameters for the constructed power grid BP neural network model until the variance E of the carbon emission obtained by the current parameters output by the model and the expected carbon emission is smaller than a set value, so as to obtain determined model parameters and a trained power grid BP neural network model; the model parameters comprise weight values of neurons and normalized carbon emission influence parameters of each group; obtaining the retired and scrapped electricity consumption at the current time point and the reference electricity consumption, inputting a trained power grid BP neural network model, and determining the retired and scrapped carbon emission of the photovoltaic module according to the output of the power grid BP neural network model, wherein the carbon emission is represented by the following formula:
wherein C is 8 AD for carbon emissions of retired scrap z Calculating the retired discard number, EF, for the z-th historical carbon emission z Calculating a retired carbon emission coefficient for the z-th historical carbon emission;
wherein the power grid BP neural network model comprises:
a carbon emission neuron computational mathematical model construction unit for constructing a carbon emission neuron computational mathematical model, wherein the parameters involved in training the neuron computational mathematical model include: weights w for neurons p through q pq Input information x from neuron p received at time t p (t) presetting a neuron transfer function f (x);
an input layer acquisition unit for taking each group of normalized carbon emission influence parameters as an input layer vector x= (x) of the BP neural network based on the BP neural network structure 1 ，x 2 ，···x p ，···x P ) Wherein P is the number of neurons of the input layer;
a hidden layer acquisition unit for calculating an input layer vector x= (x) corresponding to each group of normalized carbon emission influence parameters contained in the training sample based on the carbon emission neuron 1 ，x 2 ，···x p ，···x P ) Obtaining hidden layer vector y= (y) of BP neural network 1 ，y 2 ，…，y q ，…，y Q )×w pq Wherein y is q Is the Q-th neuron in the hidden layer Q neurons, w pq The vector is the weight value from the p-th neuron of the input layer to the q-th neuron of the hidden layer;
an output layer obtaining unit for calculating a carbon emission budget value a output by an output layer of the BP neural network corresponding to each group of normalized carbon emission influence parameters based on the hidden layer vector and a preset neuron transfer function f (x), wherein w is qk The weight value from the q-th neuron of the hidden layer to the k-th neuron of the output layer is 1;
an output error calculation unit for acquiring actual carbon emission values b corresponding to the normalized carbon emission influence parameters of each group according to the normalized carbon emission data of each group included in the training sample, and calculating corresponding output errors of the normalized carbon emission influence parameters of each group
The parameter error acquisition judging unit takes the sum of squares of output errors err corresponding to each group of normalized carbon emission influence parameters as a parameter error and judges whether the parameter error is within a preset error allowable range; if yes, outputting carbon emission data; if not, the weight values of the neurons p to q are adjusted and then recalculated.
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