Carbon emission reduction accounting and carbon income distribution method for micro-grid type source network load storage

Abstract

The invention relates to the technical field of carbon revenue distribution, and particularly discloses a micro-grid type source network load storage carbon emission reduction accounting and carbon revenue distribution method, which comprises the following steps: constructing a coordinated optimization scheduling model of a source network load storage integrated project according to the actual operation condition of a source network load storage project, the method aims at minimizing the external electricity purchase cost of a factory, and is used for minimizing external electricity purchase and maximizing wind and light consumption. Constructing a constraint function, wherein a power balance constraint, an electricity purchasing and selling constraint, a wind and light output constraint, an energy storage output constraint and a transferable load constraint are taken as constraint conditions; and based on the operation strategy of each main body of the source network load storage project and an improved Shapley algorithm, constructing a carbon income redistribution model of the source network load storage integrated project.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon revenue distribution, and in particular to a carbon emission reduction accounting and carbon revenue distribution method for a microgrid source-grid-load-storage system. Background Art

[0002] In January 2024, the national voluntary greenhouse gas emission reduction trading market was officially launched, marking the official start of the CCER registration and trading system. As one of the main sources of carbon emissions in my country, the power industry is an inevitable choice to promote the development of CCER methodology for power emission reduction projects in order to achieve carbon peak, carbon neutrality, and promote the low-carbonization of the power system. As an important component of the new power system, the microgrid-type "source-grid-load-storage" integrated project optimizes and integrates resources on the power supply side, the grid side, the load side, and the energy storage side. It is a technical means to achieve efficient interaction with external power grids and users, reduce the cost of electricity consumption, and has the characteristics of large-scale development and promotion.

[0003] However, the carbon emission reduction contribution of the integrated power generation, grid, load and storage project has not yet been scientifically and effectively certified, and the green carbon benefits cannot be reasonably reflected. Based on the above background, the CCER carbon emission reduction methodology of the "integrated power generation, grid, load and storage project" has been formed on the basis of existing policies, which has improved the carbon emission reduction accounting system of such projects, increased the economic benefits of the projects, and broadened the cost recovery methods of the projects.

[0004] Therefore, how to provide a method for carbon emission reduction accounting and carbon benefit distribution for a microgrid source-grid-load-storage is a technical problem that needs to be urgently solved by those skilled in the art. Summary of the invention

[0005] To achieve the purpose of the present invention, the present application provides a method for calculating carbon emission reduction and allocating carbon benefits for a microgrid source-grid-load-storage system, including:

[0006] Step S1: According to the actual operation of the source-grid-load-storage project, a coordinated optimization scheduling model for the source-grid-load-storage integrated project is constructed, including: constructing an objective function with the goal of minimizing the cost of purchasing electricity from outside the factory, which is used to minimize the purchase of electricity from outside and maximize the consumption of wind and solar power; and constructing a constraint function with power balance constraints, power purchase and sales constraints, wind and solar power output constraints, energy storage output constraints, and transferable load constraints as constraints;

[0007] Step S3: Based on the operation strategies of various entities of the source-grid-load-storage project and the improved Shapley algorithm, a carbon revenue redistribution model of the source-grid-load-storage integrated project is constructed.

[0008] In some specific embodiments, step S2 includes:

[0009] Step S21: construct an objective function with the goal of minimizing the cost of purchasing electricity from outside the factory, so as to minimize the purchase of electricity from outside and maximize the consumption of wind and solar power;

[0010] Step S22: construct a constraint function, with power balance constraint, power purchase and sale constraint, wind and solar output constraint, energy storage output constraint, and transferable load constraint as constraint conditions;

[0011] In some specific embodiments, the power balance constraint is determined according to the following formula:

[0012] P wind,t +P PV,t +P buy,t +P Dis,t =L base+other,t +L EV,t +P Ch,t +P sell,tt

[0013] Where: P wind,t is the wind power at time t; P PV,t is the photovoltaic power at time t; P Dis,t is the energy storage discharge power at time t; L base+other,t is the reference load and other translatable and transferable loads at time t; L EV,t is the EV load at time t; P Ch,t is the energy storage charging power at time t.

[0014] In some specific embodiments, the power purchase and sale constraints are determined according to the following formula:

[0015] 0≤P buy,t ≤P buy,max ×U buy

[0016] 0≤P sell,t ≤P sell,max ×U sell

[0017] U buy +U sell ≤1

[0018] Where: P buy,max is the maximum power purchase; P sell,max is the maximum power sold; U buy is the state variable of purchasing electricity from the power grid; U sell is the state variable for selling electricity to the grid.

[0019] In some specific embodiments, the wind and solar output constraint is determined according to the following formula:

[0020] P min，w ≤P wind,t ≤Pmax,w

[0021] P min,p ≤P PV,t ≤P max,p

[0022] Where: P min，w is the minimum wind power; P max,w is the maximum wind power; P min，，p is the minimum photovoltaic power; P max,p is the maximum photovoltaic power.

[0023] In some specific embodiments, the energy storage output constraint is determined according to the following formula:

[0024] 0≤P Dis,t ≤P Dis,max ×U Dis

[0025] 0≤P Ch,t ≤P Ch,max ×U Ch

[0026] U Dis +U Ch ≤1

[0027] S OC,start,t＝0 =S OC,end,t＝T

[0028] S OC,min,t ≤S OC,t ≤S OC,max,t

[0029] S OC,t+1 =S OC,t ×γ+P Ch,t ×η Ch -P Dis,t / η Dis

[0030] Where: P Dis,max is the upper limit of energy storage discharge power; P Ch,max U is the upper limit of energy storage charging power; Dis is the energy storage discharge state variable; U Ch is the energy storage charging state variable; S OC,min,t S is the minimum storage capacity at time t; OC,max,t is the maximum SOC of energy storage at time t; γ is the self-discharge rate of energy storage; η Ch is the charging efficiency of energy storage; η Dis is the discharge efficiency of energy storage.

[0031] In some specific embodiments, the transferable load constraint is determined according to the following formula:

[0032] 0≤ΔP EV,t ≤ΔP maxEV,t

[0033] Where: ΔP EV,t is the transfer power of the electric vehicle at time t; ΔP maxEV,t is the maximum transfer power of the electric vehicle at time t.

[0034] In some specific embodiments, step S3 includes: improving the classic Shapley algorithm according to the improvement factor, and improving the benefit distribution of the Shapley value to optimize the distribution result of the carbon benefit redistribution model.

[0035] In some specific embodiments, the improvement factor is the contribution of each subject to the security of the energy system. Suppose the contribution of each subject to the security of the energy system is {α 1 ,…,α k},in,

[0036] In some specific embodiments, the allocation result of the carbon benefit redistribution model is determined according to the following formula:

[0037]

[0038] Where: r k is the carbon benefit of the kth member under the original Shapley algorithm; is the carbon benefit of the kth member after improvement; s is the carbon benefit; a k is the contribution of each entity to the security of the energy system; v(n) is the total carbon revenue of the alliance.

[0039] Beneficial effects of the above technical solution:

[0040] The present application provides a method for carbon emission reduction accounting and carbon benefit distribution for a microgrid source-grid-load-storage, which improves the carbon emission reduction accounting mechanism for microgrid source-grid-load-storage projects, promotes the economic efficiency of park-level source-grid-load-storage projects, improves the green nature of electricity supply, and provides a solution for carbon emission reduction accounting and carbon benefit quantitative distribution for microgrid park-level source-grid-load-storage projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1A flow chart of a method for calculating carbon emission reduction and allocating carbon benefits for a microgrid source-grid-load-storage system according to an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of a project boundary of a method for carbon emission reduction accounting and carbon benefit allocation for a microgrid source-grid-load-storage provided by an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the time-of-use electricity price and the wind-solar grid-connected electricity price in Ningxia region for a carbon emission reduction accounting and carbon benefit allocation method for a microgrid source-grid-load-storage provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of a steel plant source-load data curve for a carbon emission reduction accounting and carbon benefit allocation method for a microgrid source-grid-load-storage provided by an embodiment of the present invention;

[0046] FIG5(a) is a schematic diagram of a baseline scenario scheduling scheme of a carbon emission reduction accounting and carbon benefit allocation method for a microgrid source-grid-load-storage provided by an embodiment of the present invention;

[0047] FIG5( b ) is a schematic diagram of a method for calculating carbon emission reduction and allocating carbon benefits for a microgrid source-grid-load-storage system according to an embodiment of the present invention;

[0048] FIG5( c ) is a schematic diagram of a second scheduling scheme of a carbon emission reduction accounting and carbon benefit allocation method for a microgrid source-grid-load-storage provided by an embodiment of the present invention;

[0049] FIG5( d ) is a schematic diagram of a third scheduling scheme of a carbon emission reduction accounting and carbon benefit allocation method for a microgrid source-grid-load-storage provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0051] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0052] Embodiment 1

[0053] An embodiment of the present invention provides a method for calculating carbon emission reduction and allocating carbon benefits for a microgrid source-grid-load-storage system. Figure 1 As shown, including:

[0054] According to relevant regulations, the CCER methodology for source-grid-load-storage projects is formulated, including: project boundaries, baseline emissions, project emissions, project emission reductions, and data detection and measurement plans. Among them, in the carbon emission reduction methodology of the source-grid-load-storage integrated project, the specific process is: Project boundaries: The spatial scope includes project facilities, including distributed photovoltaics, distributed wind power, energy storage units, load-side resources, and regional power plants connected to the power grid as defined by the "Power System Emission Factor Calculation Tool". Project boundaries are as follows: Figure 2 shown.

[0055] The greenhouse gas emissions within the project boundary are shown in Table 1.

[0056] Table 1 Greenhouse gas emissions

[0057]

[0058]

[0059] Baseline emissions: In the baseline scenario, the baseline emissions (BE y ) includes CO generated by the original electricity purchase in the integrated power generation, grid-load and storage project. 2 Emissions are determined by the baseline emission factor The original amount of electricity purchased by the project (EG y ), which is calculated as follows:

[0060]

[0061] Where, BE y is the baseline emissions of the integrated project in year y, EG y is the original electricity purchase amount of the integrated project in the baseline scenario in year y, It is the regional power grid emission factor calculated using the “Power System Emission Factor Calculation Tool” in year y.

[0062] Project emissions: In the project scenario, distributed wind power, distributed photovoltaic power, and energy storage can all be used as auxiliary power sources to provide power support for the park, reducing the amount of electricity purchased from power companies, thereby achieving greenhouse gas emissions reduction. y ) is the regional power system emission factor and the effective power purchase amount of the project activity (EM y ), which is calculated as follows:

[0063]

[0064] Where, PE y is the project emissions of the integrated project in year y, EM yis the effective electricity purchase amount of the integrated project in the yth year in the project scenario.

[0065] Project emission reduction: In the power industry, the carbon leakage of the project is mainly reflected in the construction of power plants and the processing of power generation fuels, which is not considered in this study. Referring to the emission reduction mechanism of the methodology, the project activities mainly reduce the high-carbon power transmission of regional power plants through the coordination of multiple sides of the source, grid, load and storage, thereby achieving CO emission reduction. 2 Without considering the leakage, the carbon emission reduction RE generated by the project activities y Baseline carbon emissions BE y Minus the project carbon emissions PE y The emission reduction of the project in year y can be expressed as follows:

[0066] RE y =BE y -PE y

[0067] In the formula, RE y is the carbon emission reduction of the integrated project in the yth year.

[0068] Data monitoring and calculation plan: Before this project adopts CCER methodology to calculate the carbon emission reduction of the park, it is necessary to formulate relevant data monitoring and calculation plans. The data that need to be monitored mainly refers to the online and offline power of the integrated project in the yth year. The data that needs to be calculated refers to the regional power grid power emission factor in the yth year of the project area.

[0069] For the data that needs to be monitored, the online and offline power consumption of the project in the yth year can be monitored and obtained through the electric energy meters at the metering points according to the verification and calibration requirements to ensure the data quality.

[0070] For the measured data, the calculation process of the regional power grid electricity emission factor in the yth year of the project area is as follows. For relevant parameters, please refer to the "China Energy Statistical Yearbook", "Public Institution Energy Consumption Statistical System" and "Electric Power Industry Statistical Data Compilation".

[0071]

[0072] In the formula, is the electricity emission factor for the output region in year y, EF grid-in,OM,y is the electricity emission factor for the input region in year y, EG y is the total power generation of the regional power system in year y, EG y,in is the input power of the regional power system in year y, FC i,y NCV is the total consumption of fuel i by the above units in year y (in units of mass or volume), i,yis the average lower calorific value of fuel i in year y (GJ / mass or volume unit), is the CO of fuel i in year y 2 Heat emission factor (tCO 2 / J), i is the type of fossil fuel consumed by the power system for electricity generation in year y.

[0073] Step S1: According to the actual operation of the source-grid-load-storage project, a coordinated optimization scheduling model of the source-grid-load-storage integrated project is constructed.

[0074] In some specific embodiments, step S2 includes:

[0075] Step S11: construct an objective function with the goal of minimizing the cost of purchasing electricity from outside the factory, so as to minimize the purchase of electricity from outside and maximize the consumption of wind and solar power;

[0076] Step S12: construct a constraint function, with power balance constraint, power purchase and sale constraint, wind and solar output constraint, energy storage output constraint, and transferable load constraint as constraint conditions;

[0077] In a specific embodiment of the present invention, the power balance constraint is determined according to the following formula:

[0078] P wind,t +P PV,t +P buy,t +P Dis,t =L base+other,t +L EV,t +P Ch,t +P sell,tt

[0079] Where: P wind,t is the wind power at time t; P PV,t is the photovoltaic power at time t; P Dis,t is the energy storage discharge power at time t; L base+other,t is the reference load and other translatable and transferable loads at time t; L EV,t is the EV load at time t; P Ch,t is the energy storage charging power at time t.

[0080] In a specific embodiment of the present invention, the power purchase and sale constraint is determined according to the following formula:

[0081] 0≤P buy,t ≤P buy,max ×U buy

[0082] 0≤P sell,t ≤P sell,max ×U sell

[0083] U buy +Usell ≤1

[0084] Where: P buy,max is the maximum power purchase; P sell,max is the maximum power sold; U buy is the state variable of purchasing electricity from the power grid; U sell is the state variable for selling electricity to the grid.

[0085] In a specific embodiment of the present invention, the wind and solar output constraint is determined according to the following formula:

[0086] P min，w ≤P wind,t ≤P max,w

[0087] P min,p ≤P PV,t ≤P max,p

[0088] Where: P min，w is the minimum wind power; P max,w is the maximum wind power; P min，，p is the minimum photovoltaic power; P max,p is the maximum photovoltaic power.

[0089] In a specific embodiment of the present invention, the energy storage output constraint is determined according to the following formula:

[0090] 0≤P Dis,t ≤P Dis,max ×U Dis

[0091] 0≤P Ch,t ≤P Ch,max ×U Ch

[0092] U Dis +U Ch ≤1

[0093] S OC,start,t＝0 =S OC,end,t＝T

[0094] S OC,min,t ≤S OC,t ≤S OC,max,t

[0095] S OC,t+1 =S OC,t ×γ+P Ch,t ×η Ch -P Dis,t / η Dis

[0096] Where: P Dis,maxis the upper limit of energy storage discharge power; P Ch,max U is the upper limit of energy storage charging power; Dis is the energy storage discharge state variable; U Ch is the energy storage charging state variable; S OC,min,t S is the minimum storage capacity at time t; OC,max,t is the maximum SOC of energy storage at time t; γ is the self-discharge rate of energy storage; η Ch is the charging efficiency of energy storage; η Dis is the discharge efficiency of energy storage.

[0097] In a specific embodiment of the present invention, the transferable load constraint is determined according to the following formula:

[0098] 0≤ΔP EV,t ≤ΔP maxEV,t

[0099] Where: ΔP EV,t is the transfer power of the electric vehicle at time t; ΔP maxEV,t is the maximum transfer power of the electric vehicle at time t.

[0100] Step S3: Based on the operation strategies of various entities of the source-grid-load-storage project and the improved Shapley algorithm, a carbon revenue redistribution model of the source-grid-load-storage integrated project is constructed.

[0101] In a specific embodiment of the present invention, step S3 includes: improving the classic Shapley algorithm according to the improvement factor, and improving the benefit distribution of the Shapley value to optimize the distribution result of the carbon benefit redistribution model.

[0102] Specifically, in the integrated park of source, grid, load, storage, production and sales, the cooperation of multiple subjects meets the basic definition of cooperative game. In order to reflect the carbon emission reduction contribution of different subjects in the cooperative alliance, a carbon benefit redistribution model based on the improved Shapley value method is constructed to redistribute CCER carbon benefits to different subjects. Let the participant set be N, and the number of elements be n. The participants in N form a cooperative alliance s, and the number of participants in the alliance is s. Assuming that the participants form an alliance by themselves, the probability of the emergence of an alliance containing member k and a cooperation scale of s is as follows:

[0103]

[0104] Basic Shapley value method, cooperation scale is s and includes carbon benefits r obtained by member k k as follows:

[0105]

[0106] In the formula: v(s) is the carbon benefit obtained by alliance s based on the CCER methodology; v(s / k) is the carbon benefit obtained by the alliance excluding entity k, and its scale is s-1.

[0107] In a specific embodiment of the present invention, the improvement factor is the contribution of each subject to the security of the energy system. Suppose the contribution of each subject to the security of the energy system is {α 1 ,…,α k},in,

[0108] In a specific embodiment of the present invention, the allocation result of the carbon benefit redistribution model is determined according to the following formula:

[0109]

[0110] Where: r k is the carbon benefit of the kth member under the original Shapley algorithm; is the carbon benefit of the kth member after improvement; s is the carbon benefit; a k is the contribution of each entity to the security of the energy system; v(n) is the total carbon revenue of the alliance.

[0111] The present application provides a method for carbon emission reduction accounting and carbon benefit distribution for a microgrid source-grid-load-storage, which improves the carbon emission reduction accounting mechanism for microgrid source-grid-load-storage projects, promotes the economic efficiency of park-level source-grid-load-storage projects, improves the green nature of electricity supply, and provides a solution for carbon emission reduction accounting and carbon benefit quantitative distribution for microgrid park-level source-grid-load-storage projects.

[0112] Embodiment 2

[0113] An embodiment of the present invention provides a method for calculating carbon emission reduction and allocating carbon benefits for a microgrid source-grid-load-storage system.

[0114] The example uses a 5MW wind turbine and a 5MW photovoltaic unit, and sets the simulation step length to 1h. The power generation and power consumption data involved are all from a steel plant substation in Ningdong. The energy storage device uses commercially mature electrochemical energy storage, designed according to 10% on the source side, with a charging and discharging time of 4h, a rated capacity of 4MW.h, a rated power of 1MW, and a minimum SOC capacity of 20%. Let γ be 0.99, and η Ch and η Dis Both are 0.95, assuming that the energy storage starting SOC is 50%. The upper limit of the EV transferable load power is set to 20% of the maximum EV power. The time-of-use electricity price and the wind and solar grid-connected electricity price are based on the settlement electricity price of 35kv and above general industrial and commercial and other electricity consumption in Ningxia. Figure 3 shown.

[0115] By comparing and analyzing the daily wind power, photovoltaic power generation and power consumption of the small steel plant in June, the source-load curve of the steel plant shows three types: source is close to load, source is much greater than load, and source is much less than load. Figure 4 As shown in Figure 2, the ratio of the source approximate charge is much higher than that in the other two cases.

[0116] According to the results of the steel plant's source-load time series analysis, based on the source-grid-load-storage optimization scheduling model proposed by S2, three operation schemes are proposed, as shown in Table 2. The calculation and distribution of carbon benefits of each entity in various schemes are carried out on a typical day with approximate source load.

[0117] Table 2 Operation scheme design

[0118]

[0119]

[0120] The power generation capacity of wind and solar units, power purchase and sales capacity of power grid, energy storage charging and discharging capacity, and load capacity under the baseline scenario and various schemes are shown as follows: Figure 5(a)-Figure 5(d) As shown. In the baseline scenario, the steel mill purchases electricity from the power grid and does not participate in the coordination of source, grid, load and storage; in option one, the source, grid and storage are linked, and the wind and solar units can directly supply power to the steel mill. During the period of high wind and solar power generation, the surplus electricity can be stored by the energy storage unit and sold to the power grid. During the peak load, the energy storage is released, the power purchase of the power grid is reduced, thereby reducing carbon emissions; in option two, the source, grid and load are linked, the power consumption time of the transferable load of electric vehicles is shifted, the charging amount of the transferable load during the peak power consumption period is reduced, the power consumption of the transferable load during the period of high wind and solar power generation is increased, and the power purchase of the power grid is reduced, thereby reducing carbon emissions; in option three, the source, grid, load and storage are linked, during the load valley and power generation peak period, the charging load of electric vehicles is shifted, and the excess electricity is stored by the energy storage unit. During the peak load, the charging amount of electric vehicles is reduced, the energy storage is released, and the power purchase of the power grid is reduced, thereby achieving the goal of reducing carbon emissions.

[0121] Based on the power generation of each unit in the optimized scheduling scheme and the load power of each electricity user, the total carbon emissions under each scheme and the contribution rate of each subject are calculated, and the carbon emission benefits of each subject are distributed according to the carbon emission contribution rate of each subject. The total carbon emissions and the total CCER benefits under each scheme are shown in Table 3.

[0122] Table 3 Total carbon emissions and CCER benefits under each scheme

[0123]

[0124] The carbon emission contribution rate and shared benefits of each entity under each plan are shown in Table 4.

[0125] Table 4 Carbon emission contribution rate and benefit distribution of each entity under each plan

[0126]

[0127]

[0128] The present application provides a method for carbon emission reduction accounting and carbon benefit distribution for a microgrid source-grid-load-storage, which improves the carbon emission reduction accounting mechanism for microgrid source-grid-load-storage projects, promotes the economic efficiency of park-level source-grid-load-storage projects, improves the green nature of electricity supply, and provides a solution for carbon emission reduction accounting and carbon benefit quantitative distribution for microgrid park-level source-grid-load-storage projects.

[0129] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0130] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referenced to each other. The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the functions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing terminal device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention. Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0131] The method and device provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

[0132] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", "one specific embodiment" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for calculating carbon emission reduction and allocating carbon benefits for a microgrid source-grid-load-storage system, characterized in that: include: Step S1: According to the actual operation of the source-grid-load-storage project, a coordinated optimization scheduling model for the source-grid-load-storage integrated project is constructed, including: constructing an objective function with the goal of minimizing the external power purchase cost of the factory, which is used to minimize the external power purchase and maximize the wind and solar power consumption; And construct a constraint function, with power balance constraint, power purchase and sales constraint, wind and solar output constraint, energy storage output constraint, and transferable load constraint as constraint conditions; Step S2: Based on the operation strategies of various entities of the source-grid-load-storage project and the improved Shapley algorithm, a carbon revenue redistribution model of the source-grid-load-storage integrated project is constructed.

2. The carbon emission reduction accounting and carbon benefit distribution method of microgrid source-grid-load-storage according to claim 1 is characterized in that: The power balance constraint is determined according to the following formula: P wind,t +P PV,t +P buy,t +P Dis,t =L base+other,t +L EV,t +P Ch,t +P selll,t Where: P wind,t is the wind power at time t; P PV,t is the photovoltaic power at time t; P Dis,t is the energy storage discharge power at time t; L base+other,t is the reference load and other translatable and transferable loads at time t; L EV,t is the EV load at time t; P Ch,t is the energy storage charging power at time t.

3. The carbon emission reduction accounting and carbon benefit distribution method of microgrid source-grid-load-storage according to claim 1 is characterized in that: The power purchase and sale constraints are determined according to the following formula: 0≤P buy,t ≤P buy,max ×U buy 0≤P sell,t ≤P sell,max ×U sell IN buy +U sell ≤1 Where: P buy,max is the maximum power purchase; P sell,max is the maximum power sold; U buy is the state variable of purchasing electricity from the power grid; U sell is the state variable for selling electricity to the grid.

4. The carbon emission reduction accounting and carbon benefit distribution method of microgrid source-grid-load-storage according to claim 1 is characterized in that: The wind and solar output constraint is determined according to the following formula: P min，w ≤P wind,t ≤P max,w P min,p ≤P PV,t ≤P max,p Where: P min，w is the minimum wind power; P max,w is the maximum wind power; P min，p is the minimum photovoltaic power; P max,p is the maximum photovoltaic power.

5. The carbon emission reduction accounting and carbon benefit distribution method of microgrid source-grid-load-storage according to claim 1 is characterized in that: The energy storage output constraint is determined according to the following formula: 0≤P Dis,t ≤P Dis,max ×U Dis 0≤P Ch,t ≤P Ch,max ×U Ch IN Dis +U Ch ≤1 S OC,start,t＝0 =S OC,end,t＝T S OC,min,,t ≤S OC,t ≤S OC,max,t S OC,t+1 sS OC,t ×γ+P Ch,t ×η Ch -P Dis,t / η Dis Where: P Dis,max is the upper limit of energy storage discharge power; P Ch,max U is the upper limit of energy storage charging power; Dis is the energy storage discharge state variable; U Ch is the energy storage charging state variable; S OC,min,t S is the minimum storage capacity at time t; OC,max,t is the maximum energy storage SOC at time t; γ is the self-discharge rate of energy storage; η Ch is the charging efficiency of energy storage; η Dis is the discharge efficiency of energy storage.

6. The carbon emission reduction accounting and carbon benefit distribution method of microgrid source-grid-load-storage according to claim 1 is characterized in that: The transferable load constraint is determined according to the following formula: 0≤ΔP EV,t ≤ΔP maxEV,t Where: ΔP EV,t is the transfer power of the electric vehicle at time t; ΔP maxEV,,t is the maximum transfer power of the electric vehicle at time t.

7. The carbon emission reduction accounting and carbon benefit distribution method of microgrid source-grid-load-storage according to claim 1 is characterized in that: Step S3 includes: improving the classic Shapley algorithm according to the improvement factor, and improving the benefit distribution of the Shapley value to optimize the distribution result of the carbon benefit redistribution model.

8. The carbon emission reduction accounting and carbon benefit distribution method of microgrid source-grid-load-storage according to claim 7 is characterized in that: The improvement factor is the contribution of each subject to the security of the energy system. Suppose the contribution of each subject to the security of the energy system is {α1,…,α k },in, 9. The carbon emission reduction accounting and carbon benefit distribution method of microgrid source-grid-load-storage according to claim 8 is characterized in that: The allocation result of the carbon benefit redistribution model is determined according to the following formula: Where: r k is the carbon income of the kth member under the original Shapley algorithm; r k * is the carbon benefit of the kth member after improvement; s is the carbon benefit; a k is the contribution of each entity to the security of the energy system; v(n) is the total carbon revenue of the alliance.

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