CN117371590B - Carbon management optimization method and system for synergistic emission reduction of micro-unit multi-production industry - Google Patents
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Abstract
The invention discloses a carbon management optimization method and system for synergetic emission reduction of micro-unit multi-production industry. The invention establishes the interactive relation between the energy and the carbon emission in the multi-production cooperation emission reduction of the agricultural operation area, the service operation area and the industrial operation area, establishes the multi-objective optimization scheme of the industrial economic benefit and the carbon transaction benefit of the multi-production industry, and effectively improves the comprehensive benefit of the multi-production cooperation emission reduction through the optimization operation of the carbon asset management.
Description
Technical Field
The invention relates to the technical field of carbon asset operation optimization, in particular to a carbon management optimization method and system for synergistic emission reduction of micro-unit multi-production industry.
Background
Implementing the carbon trade market is an important means to achieve the national "two carbon" goal, through carbon management optimization, to achieve the inventory and value-raising of carbon assets. In the carbon trade market, including quota carbon assets and emission reduction carbon assets, the national evidence voluntary emission reduction (CHINESE CERTIFIED Emission Reduction, CCER) is an important emission reduction carbon asset, and the development of carbon dioxide emission reduction projects is a key link of the national evidence voluntary emission reduction carbon asset. In the future, CCER is restarted to generate a new requirement of multi-industry collaborative emission reduction, however, in the development of carbon dioxide emission reduction projects, carbon asset management is only performed for a single industry, such as a thermal power enterprise, a steel production enterprise performs carbon asset management, the prior art scheme can realize carbon asset management of the single industry, but cannot solve the technical problems of low efficiency, poor compatibility and low benefit of multi-industry whole society carbon asset management, the prior art has the technical problem of low income caused by inaccurate carbon asset management, and in a carbon emission right trade market, the carbon emission quota trade price (hereinafter referred to as carbon price) has the defects of inaccurate production investment decision and inaccurate carbon price prediction of the carbon dioxide emission reduction projects. In view of the new huge market demand caused by CCER about to restart, a carbon management optimization method for collaborative emission reduction of multiple industries is needed, and a 'zero-carbon micro-unit' community or park mode which is convenient for marketing and replication needs to be constructed, so that a carbon management optimization method and a carbon management optimization system for collaborative emission reduction of multiple industries of micro-units are needed.
Disclosure of Invention
In view of the above, in the market of trading of carbon emission rights, the invention provides a carbon management optimization method and system for synergetic emission reduction of micro-unit multi-production industries, which can effectively overcome the defects of inaccurate production investment decision and inaccurate carbon price prediction and solve the technical problem of low benefit caused by inaccurate carbon asset management.
According to the carbon management optimization method for the synergistic emission reduction of the micro-unit multi-industry, an agricultural operation area, a service industry operation area and an industrial operation area are set up in the multi-industry synergistic emission reduction park; the agricultural operation area adopts clean energy to supply power, and the service operation area adopts clean energy to supply power; the industrial operation area is powered by the service operation area and the outside of the park;
Wherein the net carbon dioxide absorption of the agricultural work area is determined by the planting area a of the agricultural work area; the energy consumption, carbon dioxide emission and energy supplied to the industrial operation area are determined by the reception amount c of the service operation area; the energy consumption and carbon dioxide emission of the industrial operation area are determined by the yield b of the industrial operation area;
Optimizing and solving a carbon asset management scheme { planting area a of an agricultural operation area, yield b of an industrial operation area and reception c of a service operation area } by taking the weighted sum of industrial economic benefits and carbon transaction benefits of a park as a maximum objective function; wherein the industrial economic benefits of the campus include economic benefits of agricultural, service and industrial operations areas; the carbon trade income is the product of the carbon dioxide emission reduction amount of the park and the expected value of the carbon price, wherein the carbon dioxide emission reduction amount is the net carbon dioxide absorption amount of the agricultural operation area minus the carbon dioxide emission amount of the service operation area and the industrial operation area; the planting area a of the agricultural operation area, the yield b of the industrial operation area and the reception quantity c of the service operation area meet the bearing constraint of the park, and the carbon dioxide emission reduction capacity meets the rated responsibility emission reduction constraint.
Preferably, the net Carbon dioxide uptake of the agricultural work area carbon_a=a×α_a; where α_a is the net carbon dioxide absorption coefficient per unit area planted in the agricultural work area.
Preferably, the Energy consumption energy_c=c×β_c of the service operation area, and β_c is an Energy consumption coefficient of unit reception amount of the service operation area; carbon dioxide emission capacity of service work area carbon_c=c×α_c, α_c is Carbon dioxide emission coefficient of unit reception capacity of service work area.
Preferably, the Energy consumption energy_b=b×β_b of the industrial operation area, wherein β_b is a unit yield Energy consumption coefficient of the industrial operation area; carbon dioxide emission capacity carbon_b= (energy_b-energy_b_in) ×α_b of the industrial operation area, wherein α_b is a Carbon dioxide emission coefficient of power supply of a power grid outside a park of the industrial operation area; energy_b_in is the Energy provided by the service area to the industrial area, energy_b_in=energy_c_max-energy_c, energy_c_max is the Energy supplied by the service area, and energy_c is the Energy consumption of the service area.
Preferably, the industrial economic benefit Revenue _product=a_r_a+b_r_b+c_r_c; wherein R_a is the unit planting area benefit of the agricultural operation area; r_b is the unit yield benefit of the industrial work area; r_c is the unit reception yield of the service area.
Preferably, the calculation method of the expected carbon value P is as follows:
S1, acquiring m carbon price predicted value sets, wherein a carbon price predicted value set P i＝(xi,1,xi,2,...,xi,j,...,xi,n),i＝1,2,…,m;xi,j of an ith carbon price predicted model is a j-th carbon price predicted value in the carbon price predicted value set, and j=1, 2, … and n;
S2, establishing a set pair D (S, P i), wherein S is an actual value set of carbon values, and S= (x 0,1,x0,2,...,x0,j,...,x0,n);
S3, calculating the relative error of the carbon price predicted value set P i relative to the carbon price actual value set S:
S4, calculating the identity h i of the actual carbon value set S and the predicted carbon value set P i: when ε i,j∈[0,δ1), the first indicator function ζ i,j =1; When, a first indicator function ζ i,j =0; determining identity of actual set of carbon values S and predicted set of carbon values P i
S5, calculating the difference degree k i between the actual carbon value set S and the predicted carbon value set P i: when (when)When, a second indicator function Φ i,j =1; When, the second indicator function Φ i,j =0; determining the degree of difference between the actual set of carbon values S and the predicted set of carbon values P i
S6, calculating the oppositivity r i of the actual carbon value set S and the predicted carbon value set P i: when epsilon i,j∈(δ2 is reached, ++ infinity in the time-course of which the first and second contact surfaces, a third indicator function λ i,j =1; at this time, the third indicator function λ i,j =0 determines the oppositivity of the actual set of carbon values S and the predicted set of carbon values P i
S7, establishing the contact degree of the collection pair D (S, P i)Wherein, L= -1,
S8, establishing the relative membership degree of P i to SCalculating the weight of the ith carbon price prediction model
S9, determining the expected value of the carbon price
Preferably, the agricultural operation area is built on top platforms of the industrial operation area and the service operation area; the agricultural operation area is powered by photovoltaic power, and the service operation area is powered by photovoltaic power and wind energy.
The invention also provides a carbon management optimization system for the synergistic emission reduction of the micro-unit multi-production industry, which comprises the following steps: the system comprises a carbon dioxide emission accounting module, an objective function and constraint condition construction module and an optimization decision module;
Wherein, the multi-production industry collaborative emission reduction park establishes an agricultural operation area, a service industry operation area and an industrial operation area; the agricultural operation area adopts clean energy to supply power, and the service operation area adopts clean energy to supply power; the industrial operation area is powered by the service operation area and the outside of the park;
The carbon dioxide emission accounting module is used for calculating the net carbon dioxide absorption amount of the agricultural operation area in the park, the energy consumption and the carbon dioxide emission amount of the service operation area and the energy source amount provided for the industrial operation area, and the energy consumption and the carbon dioxide emission amount of the industrial operation area;
The objective function and constraint condition construction module is used for establishing an objective function and constraint conditions; the objective function is the weighted sum of the industry economic benefit and the carbon trade benefit of the park to be maximum; the constraint conditions are as follows: the planting area of the agricultural operation area, the yield of the industrial operation area and the park scale constraint of the reception amount of the service operation area, and the carbon dioxide emission reduction amount is not less than the rated responsibility emission reduction amount;
The optimization decision module is used for establishing an objective function for optimization solution on the basis of the data of the carbon dioxide emission accounting module and the constraint conditions of the objective function and constraint condition construction module, so as to obtain an optimal carbon asset management scheme { the planting area of the agricultural operation area, the yield of the industrial operation area and the reception amount of the service operation area }.
The beneficial effects are that:
(1) According to the invention, the production investment of the multi-industry collaborative emission reduction park is optimized, and the decision accuracy is improved, so that the comprehensive benefit of the multi-industry collaborative operation is effectively improved. The invention establishes a multi-objective optimization scheme of industrial economic benefits and carbon transaction benefits of multiple industries, establishes an operation interaction flow of input and output of energy and carbon dioxide emission existing between the multiple industries, and effectively improves the comprehensive benefits of the multiple industries for collaborative emission reduction by optimizing the operation of carbon asset management.
(2) According to the invention, the information of m carbon price prediction value sets is integrated through multi-set information integration optimization, so that the prediction deviation is effectively reduced, the carbon price prediction accuracy is improved, and the accuracy of carbon asset management optimization is further enhanced. In addition, the existing carbon price predicted value set can be directly used, so that not only can the prediction accuracy be improved, but also the time and cost of data collection and data analysis are greatly reduced.
(3) The method is simple and quick in calculation and less in time for optimizing decision.
Drawings
FIG. 1 is a schematic diagram of a multi-industry collaborative emission abatement park of the present invention;
FIG. 2 is a flow chart of operations for a multi-industry collaborative emission abatement park of the present invention;
FIG. 3 is a schematic flow chart of a carbon management optimization method for multi-industry collaborative emission reduction according to the present invention;
FIG. 4 is a schematic flow chart of determining the expected value of carbon number according to the present invention;
FIG. 5 is a schematic diagram of a carbon management system for collaborative emission reduction in multiple industries according to the present invention.
Detailed Description
The invention will now be described in detail by way of example with reference to the accompanying drawings.
The invention provides a carbon management optimization method and system for synergetic emission reduction of micro-unit multi-production industry.
The present invention establishes an agricultural work area, a service work area and an industrial work area in a multi-production industrial park, and the multi-production industries cooperatively work as shown in fig. 1 and 2.
The agricultural operation area adopts clean energy to supply power automatically, so that the energy consumption of the agricultural operation area is met, and additional external power supply is not needed. The Carbon dioxide emission amount of the agricultural operation area is not only 0, but also a certain Carbon dioxide net absorption amount carbon_a=a×α_a is generated; where a is the crop planting area of the agricultural operation area and α_a is the net carbon dioxide absorption coefficient per unit planting area of the agricultural operation area.
The service operation area adopts clean energy to supply power automatically, and the power can supply power to the industrial operation area besides meeting the energy consumption of the service operation area; the Energy energy_b_in=energy_c_max-energy_c which can be provided to the industrial operation area by the service operation area, wherein energy_c_max is the clean Energy supply capacity of the service operation area; energy_c is the Energy consumption of the service area, energy_c=c×β_c, c is the reception capacity of the service area, and β_c is the Energy consumption coefficient of the unit reception capacity of the service area. Carbon dioxide emission capacity of service work area carbon_c=c×α_c, where α_c is a Carbon dioxide emission coefficient per unit reception capacity of service work area.
The industrial operation area adopts the energy provided by the service operation area and the energy provided outside the park (when needed); energy consumption of industrial work area energy_b=b×β_b, where b is the yield of industrial work area and β_b is the unit yield Energy consumption coefficient of industrial work area. Carbon dioxide emissions of industrial operation area carbon_b= (energy_b-energy_b_in) ×α_b, where α_b is the Carbon dioxide emission coefficient of the industrial operation area for the power supply of the grid outside the campus.
The agricultural operation area, the service operation area and the industrial operation area in the park are used for multi-production collaborative operation, the overall industrial economic benefit of the multi-production park and the total benefit of carbon transaction benefits are used as optimization targets, management optimization of each industry in the park is coordinated and managed, and carbon asset management optimization of multi-production collaborative emission reduction is achieved.
Determining a Carbon dioxide emission reduction capacity of the multi-industrial park carbon_ reduct =carbon_in-carbon_ou, wherein the Carbon dioxide absorption capacity of the multi-industrial park carbon_in=carbon_a; carbon dioxide emissions from multiple industrial parks carbon_out=carbon_c+carbon_b.
Determining economic benefits of the multi-production park: the economic benefits of the multi-production industrial park include the industrial economic benefits of the park and the carbon trade benefits; wherein, industrial economic benefit Revenue _product=a_r_a+b_r_b+c_r_c, r_a is the benefit of unit planting area of the agricultural operation area; r_b is the unit yield benefit of the industrial work area; r_c is unit reception quantity benefit of the service industry operation area; carbon trade benefit Revenue _carbon=carbon_ reduct ×p, P being the desired value of Carbon price.
And optimizing and solving a carbon asset management scheme { a planting area a of an agricultural operation area, a yield b of an industrial operation area and a reception c of a service operation area } by taking the weighted sum of industrial economic benefits and carbon trade benefits of the park as a maximum objective function. Namely: the objective functions max=w 1×Revenue_product+w2×Revenue_carbon,w1 and w 2 are weights, which can be set by the decision maker themselves, w 1+w2 =1. The following constraints should also be satisfied during optimization: (1) the planting area a of the agricultural operation area satisfies: a is more than 0 and less than or equal to the maximum planting area A; (2) the production b of the industrial work area satisfies: b is more than 0 and less than or equal to the maximum productivity B; (3) the reception amount c of the service work area satisfies: c is more than 0 and less than or equal to the maximum reception amount C; (4) The Energy energy_b_in which can be provided for the industrial operation area by the service operation area is more than or equal to 0; (5) Carbon dioxide emission reduction carbon_ reduct is more than or equal to rated responsibility emission reduction Q.
The flow of the carbon management optimization method for the synergistic emission reduction of the micro-unit multi-production industry is shown in fig. 3.
In addition, the invention also provides a method for determining the expected value P of the carbon price, which integrates the information of a plurality of prediction data sets according to the analysis principle of the set, and obtains the weight of the plurality of prediction data sets by determining the contact degree and the relative membership degree of the plurality of prediction data sets so as to realize the combined prediction of the expected value P of the carbon price. The method for determining the expected value P of the carbon number can effectively fuse the multi-prediction model information, reduce the uncertainty of a prediction result and improve the prediction accuracy.
The specific flow is shown in fig. 4, and specifically is as follows:
S1, acquiring m carbon price predicted value sets, wherein a carbon price predicted value set P i＝(xi,1,xi,2,...,xi,j,...,xi,n),i＝1,2,…,m;xi,j of an ith carbon price predicted model is a j-th carbon price predicted value in the carbon price predicted value set, and j=1, 2, … and n; according to the invention, through multi-set information integration optimization, the information of m carbon price prediction value sets is integrated, the prediction deviation is effectively reduced, the carbon price prediction accuracy is improved, and the accuracy of carbon asset management optimization is further enhanced;
S2, establishing a set pair D (S, P i), wherein S is an actual value set of carbon values, and S= (x 0,1,x0,2,...,x0,j,...,x0,n);
S3, calculating the relative error of the carbon price predicted value set P i relative to the carbon price actual value set S:
S4, calculating the identity h i of the actual carbon value set S and the predicted carbon value set P i: when epsilon i,j∈[0,δ1), a first indicator function When, a first indicator function ζ i,j =0; determining identity of actual set of carbon values S and predicted set of carbon values P i When the data volume of the carbon valence number set is large, the technical problem of slow identity judgment exists, and the simple and quick calculation at the same time is realized by constructing a first indication function, so that the decision optimization time is saved;
S5, calculating the difference degree k i between the actual carbon value set S and the predicted carbon value set P i: when ε i,j∈[δ1,δ2 ], the second indicator function When, the second indicator function Φ i,j =0; determining the degree of difference between the actual set of carbon values S and the predicted set of carbon values P i When the data volume of the carbon valence number set is larger, the technical problem of slow difference judgment exists, and the simple and quick calculation of the difference degree is realized by constructing a second indication function, so that the decision optimization time is saved;
S6, calculating the oppositivity r i of the actual carbon value set S and the predicted carbon value set P i: when epsilon i,j∈(δ2 is reached, ++ infinity in the time-course of which the first and second contact surfaces, a third indicator function λ i,j =1; at this time, the third indicator function λ i,j =0 determines the oppositivity of the actual set of carbon values S and the predicted set of carbon values P i When the data volume of the carbon valence number set is large, the technical problem of slow oppositivity judgment exists, and the third indication function is constructed to realize simple and quick calculation of oppositivity, so that decision optimization time is saved;
s7, establishing a degree of association mu S,Pi＝hi+kiF+ri L of the set pair D (S, P i), wherein, l= -1, Through constructing the degree of association, the multidimensional fusion integration of the multi-set information is realized, the identity, the variability and the oppositeness of the multi-set information are fully fused, and the uncertainty of decision making is effectively reduced;
s8, establishing the relative membership degree of P i to S Calculating the weight of the ith carbon price prediction modelThe relative importance measurement of the multi-set information is obtained by constructing the relative membership, so that the fusion of the multi-set information is realized, and the multi-set information is used for the combined prediction of the expected value of the carbon price;
s9, determining the expected value of the carbon price The method can establish a prediction model to obtain the carbon price predicted value set, and can directly use the existing carbon price predicted value set, so that not only can the prediction accuracy be improved, but also the time and cost of data collection and data analysis are greatly reduced.
The invention also provides a carbon management optimization system for the synergistic emission reduction of the micro-unit multi-production industry, which is shown in fig. 5, and comprises a carbon dioxide emission accounting module, an objective function and constraint condition construction module and an optimization decision module;
Wherein the Carbon dioxide emission accounting module is used for determining the net Carbon dioxide absorption capacity carbon_a of the agricultural operation area in the park; energy consumption energy_c of the service work area, carbon dioxide emission amount carbon_c, and Energy energy_b_in which can be provided to the industrial work area; energy consumption energy_b and Carbon dioxide emission carbon_b of the industrial operation area;
The objective function and constraint condition construction module is used for establishing an objective function and constraint conditions; the objective function is the weighted sum of the industry economic benefit and the carbon trade benefit of the park to be maximum; the constraint conditions are as follows: the planting area a of the agricultural operation area, the yield b of the industrial operation area and the reception capacity c of the service operation area are limited by the scale of the park, and the Energy energy_b_in which can be provided for the industrial operation area by the service operation area is more than or equal to 0 and the Carbon dioxide emission reduction carbon_ reduct is more than or equal to the rated responsibility emission reduction Q;
The optimization decision module is used for establishing an objective function for optimization solution on the basis of the data of the carbon dioxide emission accounting module and the constraint conditions of the objective function and constraint condition construction module, so as to obtain an optimal carbon asset management scheme { planting area a of an agricultural operation area, yield b of an industrial operation area and reception capacity c of a service operation area }.
The following description is made in connection with a specific example:
A multi-production cooperation emission reduction park is provided with an agricultural garden (agricultural operation area), a steel production area (industrial operation area) and a meeting service center (service operation area); the agricultural garden is built on a top-layer platform of a steel production area and a meeting service center, and is powered by photovoltaic power, and a completely independent self-powered system is used; the conference service center is powered by photovoltaic and wind energy, and the conference service center is a completely independent self-powered system and provides power for the steel production area; the steel production area uses the electric power provided by the conference service center, and the steel production area also uses an external power grid to supply power according to the energy consumption.
The various industrial parameters for the multi-industry park are shown in table 1.
TABLE 1 industry parameters table for a Multi-product campus
The actual carbon number value set and 3 carbon number prediction value sets using different carbon number prediction models are shown in table 2 when determining the desired carbon number value P. The calculation results of the weights of the 3 carbon number prediction models are shown in table 3.
TABLE 2 actual carbon number value set and carbon number prediction value set of 3 carbon number prediction models
TABLE 3 weight calculation results
According to the weight results in table 3, the calculation result of determining the carbon number expected value is p= 73.02.
According to the method of the invention, the constraint conditions of the table 1 are combined, and the optimal carbon asset management scheme { the planting area a=20000 of the agricultural garden, the yield b=12750 of the steel production area, and the reception capacity c=1000000 of the conference service center }.
In summary, the above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The carbon management optimization method for the synergistic emission reduction of the micro-unit multi-production industry is characterized by comprising the following steps of:
step 1, constructing a multi-industry collaborative emission reduction park model, wherein the model comprises an agricultural operation area module, a service industry operation area module and an industrial operation area module;
step 2, constructing an internal carbon cycle of the park model:
the agricultural operation area module adopts clean energy source to supply power, the energy consumption is 0, and the agricultural operation area module has Carbon dioxide net absorption capacity carbon_a=a×α_a; wherein a is the planting area of the agricultural operation area module, and alpha_a is the net carbon dioxide absorption coefficient of the unit planting area of the agricultural operation area module;
The service operation area module adopts clean Energy to supply power automatically, the Energy consumption is energy_c=c×beta_c, and the service operation area module has residual Energy energy_b_in=energy_c_max-energy_c; carbon dioxide emission is carbon_c=c×α_c; wherein c is the reception amount of the service operation area module; beta_c is the Energy consumption coefficient of the unit reception amount of the service operation area module, energy_c_max is the supply Energy of the service operation area module, and alpha_c is the carbon dioxide emission coefficient of the unit reception amount of the service operation area module;
The industrial operation area module is powered by the service operation area module and the outside of the park, wherein the Energy consumption is energy_b=b×β_b, and the Carbon dioxide emission quantity carbon_b= (energy_b-energy_b_in) ×α_b; wherein b is the output of the industrial operation area module, beta_b is the unit output energy consumption coefficient of the industrial operation area module, and alpha_b is the carbon dioxide emission coefficient of power supply of a power grid outside the park of the industrial operation area module;
step 3, optimizing and solving the production scale of each module in the park model by taking the maximum profit of the park model as an objective function;
Wherein the benefits of the campus model are a weighted sum of the industry economic benefits of the campus model and the benefits of the carbon trade; industrial economic benefit Revenue _product=a_r_a+b_r+c_r_c of the campus model; wherein R_a is the unit planting area benefit of the agricultural operation area module; r_b is the unit yield benefit of the industrial work area module; r_c is unit reception quantity benefit of the service industry operation area module; the carbon trade gain is the product of the carbon dioxide emission reduction amount of the park model and the expected value of the carbon price; the carbon dioxide emission reduction amount is the net carbon dioxide absorption amount of the agricultural operation area module minus the carbon dioxide emission amount of the service operation area module and the industrial operation area module;
and 4, simulating the multi-production collaborative emission reduction park model according to the production scale of the optimization solution in the step 3, and obtaining the maximum benefit of the park model.
2. The method according to claim 1, wherein the carbon number expected value P is calculated as follows:
S1, acquiring m carbon price predicted value sets, wherein a carbon price predicted value set P i＝(xi,1,xi,2,...,xi,j,...,xi,n),i＝1,2,…,m;xi,j of an ith carbon price predicted model is a j-th carbon price predicted value in the carbon price predicted value set, and j=1, 2, … and n;
S2, establishing a set pair D (S, P i), wherein S is an actual value set of carbon values, and S= (x 0,1,x0,2,...,x0,j,...,x0,n);
S3, calculating the relative error of the carbon price predicted value set P i relative to the carbon price actual value set S:
S4, calculating the identity h i of the actual carbon value set S and the predicted carbon value set P i: when epsilon i,j∈[0,δ1), a first indicator function When, a first indicator function ζ i,j =0; determining identity of actual set of carbon values S and predicted set of carbon values P i
S5, calculating the difference degree k i between the actual carbon value set S and the predicted carbon value set P i: when ε i,j∈[δ1,δ2 ], the second indicator function φ i,j =1; When, the second indicator function Φ i,j =0; determining the degree of difference between the actual set of carbon values S and the predicted set of carbon values P i
S6, calculating the oppositivity r i of the actual carbon value set S and the predicted carbon value set P i: when epsilon i,j∈(δ2 is reached, ++ infinity in the time-course of which the first and second contact surfaces, a third indicator function λ i,j =1; at this time, the third indicator function λ i,j =0 determines the oppositivity of the actual set of carbon values S and the predicted set of carbon values P i
S7, establishing the contact degree of the collection pair D (S, P i)Wherein, L= -1,
S8, establishing the relative membership degree of P i to SCalculating the weight of the ith carbon price prediction model
S9, determining the expected value of the carbon price
3. The method of claim 1, wherein the agricultural work area module is built on top of an industrial work area module and a service work area module; the agricultural operation area module is powered by photovoltaic power, and the service operation area module is powered by photovoltaic and wind energy.
4. A carbon management optimization system for synergistic emission reduction in a microcell multiprocessing comprising: the system comprises a multi-production collaborative emission reduction park model construction module, an energy consumption and carbon dioxide emission accounting module, an objective function and constraint condition construction module and an optimization decision module;
The multi-production collaborative emission reduction park model building module is used for building an agricultural operation area module, a service industry operation area module and an industrial operation area module in the multi-production collaborative emission reduction park model; the agricultural operation area module and the service operation area module adopt clean energy source power supply, and the industrial operation area module is powered by the service operation area module and the outside of the park;
Wherein the agricultural operation area module adopts clean energy to supply power automatically, the energy consumption unit is 0, and the carbon dioxide absorption and emission unit is the net absorption amount of carbon dioxide
The service industry operation area module adopts clean energy source to supply power, and the energy consumption in the energy consumption unit is as follows; the carbon dioxide absorption and discharge unit is as follows;
The industrial operation area module is powered by the service operation area module and the outside of the park, and the energy consumption unit is
The energy consumption and carbon dioxide emission accounting module is used for calculating the energy consumption and carbon dioxide absorption/emission of each operation area module;
Wherein the energy consumption of the agricultural operation area module is 0, and the net Carbon dioxide absorption amount is carbon_a=a×α_a; wherein a is the planting area of the agricultural operation area module, and alpha_a is the net carbon dioxide absorption coefficient of the unit planting area of the agricultural operation area module;
The Energy consumption of the service operation area module is energy_c=c×β_c, and a certain residual Energy energy_b_in=energy_c_max-energy_c is also provided; carbon dioxide emissions are carbon_c=c×α_c; wherein c is the reception amount of the service operation area module; beta_c is the Energy consumption coefficient of the unit reception amount of the service operation area module, energy_c_max is the supply Energy of the service operation area module, and alpha_c is the carbon dioxide emission coefficient of the unit reception amount of the service operation area module;
the Energy consumption of the industrial operation area module is energy_b=b×β_b; carbon dioxide emission is carbon_b= (energy_b-energy_b_in) ×α_b; wherein b is the output of the industrial operation area module, beta_b is the unit output energy consumption coefficient of the industrial operation area module, and alpha_b is the carbon dioxide emission coefficient of power supply of a power grid outside the park of the industrial operation area module;
The objective function and constraint condition construction module is used for establishing an objective function and constraint conditions; the objective function is the biggest benefit of the park model; wherein the benefits of the campus model are a weighted sum of the industry economic benefits of the campus model and the benefits of the carbon trade; industrial economic benefit Revenue _product=a_r_a+b_r+c_r_c of the campus model; wherein R_a is the unit planting area benefit of the agricultural operation area module; r_b is the unit yield benefit of the industrial work area module; r_c is unit reception quantity benefit of the service industry operation area module; the carbon trade gain is the product of the carbon dioxide emission reduction amount of the park model and the expected value of the carbon price; the carbon dioxide emission reduction amount is the net carbon dioxide absorption amount of the agricultural operation area module minus the carbon dioxide emission amount of the service operation area module and the industrial operation area module;
The constraint conditions are as follows: the planting area of the agricultural operation area module, the yield of the industrial operation area module and the park scale constraint of the reception amount of the service operation area module, and the carbon dioxide emission reduction amount is not less than the rated responsibility emission reduction amount;
The optimization decision module performs optimization solution on the objective function established by the objective function and constraint condition construction module based on the data calculated by the energy consumption and carbon dioxide emission accounting module, so as to obtain the optimal park production scale { the planting area of the agricultural operation area module, the yield of the industrial operation area module, the reception amount of the service operation area module }.
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