Multi-microgrid multi-energy carbon emission reduction optimization method and system based on standard digitization

Abstract

The invention relates to a standard digitization-based multi-microgrid multi-energy carbon emission reduction optimization method and system. The method comprises the following steps of: constructing output models of wind power, photovoltaic, fuel gas and carbon capture systems and power-to-gas equipment in an energy system; calculating the carbon emission quota and the carbon emission according to the output model, and constructing a target function by taking the minimum comprehensive carbon cost and the minimum operation cost as targets; and optimizing the scheduling strategy of each energy system by taking the power output by the equipment as a constraint condition and utilizing the solution of the target function. The economical efficiency of a multi-microgrid multi-energy system is organically combined with a low-carbon target, and a powerful technical support is provided for carbon emission reduction and a multi-carbon monitoring scene.

Description

Technical Field

[0001] The present invention relates to the field of energy engineering technology, and in particular to a multi-microgrid multi-energy carbon emission reduction optimization method and system based on standard digitalization. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As a highly flexible energy management technology, multi-microgrid system can integrate multiple energy forms such as wind power, solar power, gas power generation, energy storage equipment, etc., providing important support for low-carbon development. However, in related practical applications, there are the following problems:

[0004] Lack of standardized data management: The existing systems lack unified standards for data formats, models and methods in carbon emission monitoring and low-carbon optimization, resulting in poor adaptability to multi-scenario applications.

[0005] Insufficient coverage of multi-scenario carbon monitoring: Due to the large differences in carbon emission characteristics between regions and industries, existing monitoring technologies are difficult to meet diverse needs.

[0006] Insufficient integration of optimized scheduling and economic low-carbon: Current scheduling methods focus more on cost minimization, while the digital evaluation and optimization of low-carbon indicators such as carbon emission intensity and carbon efficiency grading are insufficient. Summary of the invention

[0007] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a multi-microgrid multi-energy carbon emission reduction optimization method and system based on standard digitalization, optimizes the scheduling strategy based on the constructed low-carbon digitalization results, and realizes efficient and low-carbon operation of the power system.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] The first aspect of the present invention provides a multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization, comprising the following steps:

[0010] Construct output models of wind power, photovoltaics, gas, carbon capture systems and power-to-gas equipment in the energy system;

[0011] Calculate carbon emission quotas and carbon emissions based on the output model, and construct an objective function with the goal of minimizing comprehensive carbon costs and operating costs;

[0012] Taking the power output of the equipment as the constraint condition, the dispatch strategy of each energy system is optimized using the solution of the objective function.

[0013] Furthermore, the output model of the carbon capture system is:

[0014]

[0015] In the formula, is the amount of CO2 emitted by the gas turbine in grid m during period t; is the amount of CO2 captured by the carbon capture system in the power grid m during period t; v1 is the carbon emission intensity of the gas turbine; Q gas is the calorific value of natural gas; μ CCS for carbon capture efficiency; Fixing energy consumption for carbon capture; Energy consumption for carbon capture operation; σ CCS It is the energy consumption required for carbon capture to process unit CO2.

[0016] Furthermore, the output model of the carbon storage device is:

[0017]

[0018] Where: is the amount of CO2 stored in the carbon storage device in the grid m during period t; CS is the carbon storage loss coefficient of the carbon storage system; is the CO2 output of the carbon storage device in grid m during period t.

[0019] Furthermore, the output model of the power-to-gas device is:

[0020]

[0021] In the formula, is the amount of gas synthesized by the power-to-gas equipment in microgrid m during period t; The total power consumed by the power-to-gas equipment in microgrid m during period t; er is the heat energy that can be converted into unit electrical energy; η P2G The working efficiency of the power-to-gas equipment; The amount of CO2 consumed by the power-to-gas equipment; The amount of CO2 required to generate natural gas per unit of power.

[0022] Furthermore, the microgrid carbon emission quota is:

[0023]

[0024] In the formula, Carbon allowances obtained for microgrids; is the total carbon quota for the distribution network area; The load energy consumption of the distribution network; is the energy consumption of microgrid load; T is the dispatching period; m is the microgrid identifier; M is the number of microgrids.

[0025] Furthermore, the microgrid carbon emission calculation formula is:

[0026]

[0027] In the formula, is the actual net CO2 emissions emitted to the atmosphere by microgrid m; is the amount of CO2 emitted by the gas turbine in the microgrid during period t; E m,t is the node carbon potential of the microgrid; It is the interaction power between the microgrid and the distribution network. Since the carbon potential represents the carbon emission of energy consumption, the interaction power is only calculated when it is positive, that is, the distribution network transmits power to the microgrid.

[0028] Furthermore, the operating cost F1 includes the operation and maintenance cost f1 1 , through a variety of energy communication channels and external electricity, heat, gas network interaction regulation benefits f1 2 、Energy purchase cost f1 3 , Environmental costs of pollutant control f1 4 , load loss cost f1 5 ;

[0029]

[0030] Where: c om is the unit operation and maintenance cost of the microgrid system; P t om It is the cumulative value of multiple energy supplies up to time t in a single maintenance cycle; They are the unit income of multiple microgrids providing energy regulation for external electricity, heat and gas grids; are the electric power, thermal power and natural gas power regulated by the multi-microgrid for the external electric, thermal and gas grids at time t, respectively; are the unit costs of purchasing electricity, heat, hydrogen, and natural gas, respectively; are the purchased electricity power, thermal power, hydrogen power, and natural gas power of the system at time t; B is the total amount of pollutant types; c is the total amount of pollutant types; b , α b are the unit treatment cost and emission coefficient of Class B pollutants respectively; are the power of electricity, heat, hydrogen and natural gas generated by the system at time t to produce type b pollutants; c pw 、c pv 、c pgt are the unit electricity, heat and natural gas load costs lost respectively.

[0031] The second aspect of the present invention provides a multi-microgrid multi-energy carbon emission reduction optimization system based on standard digitalization, including.

[0032] The data acquisition module is configured to: construct output models of wind power, photovoltaic, gas, carbon capture system and power-to-gas equipment in the energy system;

[0033] The objective function module is configured to: calculate the carbon emission quota and carbon emission amount according to the output model, and construct the objective function with the goal of minimizing the comprehensive carbon cost and the minimum operating cost;

[0034] The adjustment module is configured to: use the power output of the equipment as a constraint condition;

[0035] The scheduling module is configured to optimize the scheduling strategy of each energy system using the solution of the objective function.

[0036] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-mentioned standard digital-based multi-microgrid multi-energy carbon emission reduction optimization method.

[0037] The fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the above-mentioned standard digital-based multi-microgrid multi-energy carbon emission reduction optimization method are implemented.

[0038] Compared with the prior art, one or more of the above technical solutions have the following beneficial effects:

[0039] The economic efficiency and low-carbon goals of multi-microgrid and multi-energy systems are organically combined to provide strong technical support for carbon emission reduction and multi-carbon monitoring scenarios. In view of the different energy costs and carbon emission reduction intensities in multi-microgrid and multi-energy systems, a dispatch optimization method with low carbon as the goal is proposed for the first time, and a low-carbon optimization model that combines economic efficiency and low-carbon goals under output conditions is constructed to provide technical support for the development strategy of power grid security, economy, and low carbon. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0041] Figure 1 It is a schematic diagram of a dispatch optimization process of a multi-microgrid multi-energy system provided by one or more embodiments of the present invention;

[0042] Figure 2 is a schematic diagram of a dispatch optimization system for a multi-microgrid multi-energy system provided by one or more embodiments of the present invention;

[0043] Figure 3It is a schematic diagram of the structure of an electronic device for executing a scheduling method for a multi-microgrid multi-energy system provided by one or more embodiments of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0045] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0046] Multi-microgrid systems can integrate multiple energy forms such as wind power, solar power, gas power generation, and energy storage equipment, providing important support for low-carbon development. However, in related practical applications, there are the following problems:

[0047] Lack of standardized data management: The existing systems lack unified standards for data formats, models and methods in carbon emission monitoring and low-carbon optimization, resulting in poor adaptability to multi-scenario applications.

[0048] Insufficient coverage of multi-scenario carbon monitoring: Due to the large differences in carbon emission characteristics between regions and industries, existing monitoring technologies are difficult to meet diverse needs.

[0049] Insufficient integration of optimized scheduling and economic low-carbon: Current scheduling methods focus more on cost minimization, while the digital evaluation and optimization of low-carbon indicators such as carbon emission intensity and carbon efficiency grading are insufficient.

[0050] Therefore, the following embodiments provide a multi-microgrid multi-energy carbon emission reduction optimization method and system based on standard digitalization, optimize the scheduling strategy based on the constructed low-carbon digitalization results, and realize efficient and low-carbon operation of the power system.

[0051] Embodiment 1:

[0052] like Figure 1 As shown, the multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization includes the following steps:

[0053] Build a multi-scenario output model to support the coordinated dispatch of multiple energy sources such as wind power, photovoltaic power, gas, carbon capture system, and power-to-gas equipment in the power system; the output model reflects the power generation, and the carbon emissions can be calculated using the power generation;

[0054] Digitally process and integrate carbon emission monitoring, carbon emission reduction potential and carbon trading mechanisms in multi-energy systems to provide data support for optimized scheduling. Mainly includes carbon emission quota calculation, carbon emission calculation and formation of carbon trading model;

[0055] Formulate low-carbon scheduling strategies with the goal of minimizing economic costs and comprehensive carbon costs (giving priority to the option with the lowest carbon cost);

[0056] By combining multiple energy forms such as wind power, photovoltaics, gas-fired power generation and carbon capture, internal multi-energy constraints are established to constrain processing power and ensure the security of grid supply.

[0057] In this embodiment, the energy source with the smallest sum of system operation cost and comprehensive carbon cost is selected for priority.

[0058] Technical principle:

[0059] 1. Energy and carbon flow modeling: Model various energy equipment in the system (such as photovoltaics, wind power, cogeneration, gas boilers, electric boilers, electric energy storage, carbon capture equipment, etc.) to describe their power and carbon emissions. By establishing a mathematical model, parameters such as the operating status of energy equipment, energy conversion efficiency, and carbon emissions are taken into consideration to provide a basis for optimized scheduling.

[0060] 2. Application of optimization algorithm: Use appropriate optimization algorithm to solve the low-carbon scheduling problem, and use formula 1.12 to minimize the system operating cost.

[0061] Based on the low-carbon digital achievements, the dispatch strategy is optimized by giving priority to the method with the lowest economic cost while meeting the output conditions, ensuring the efficient and low-carbon operation of the power system.

[0062] In some embodiments, the multiple energy forms of the multi-microgrid multi-energy system include: wind turbines, photovoltaic units, gas turbines, carbon capture systems, carbon storage systems and power-to-gas equipment.

[0063] In some embodiments, the wind turbine output model is:

[0064]

[0065] Where f(v) is the power generated by the wind turbine, v is the real-time wind speed, k is the shape parameter, and c is the scale parameter. ci and cut-out wind speed v co Divide equally between n , that is, Δv=(v co -v ci ) / W n .

[0066] The output power of the wind turbine is:

[0067]

[0068] Where P w,N is the rated power of the unit; vN is the rated wind speed. This formula can calculate the output power of the wind farm under different wind speeds, dividing the wind farm in the cycle into W n status.

[0069] In some embodiments, the output model of the photovoltaic unit is:

[0070]

[0071] Where f(γ) is the power generation of the photovoltaic unit, α and β are shape parameters, and Γ(·) is the Gamma function.

[0072] The relationship between photovoltaic output power and light intensity γ is:

[0073]

[0074] Where P PV is the photovoltaic output power; P PV,N is the photovoltaic rated power, γ N is the rated light intensity.

[0075] In some embodiments, the output model of the gas turbine is:

[0076]

[0077] In the formula, Indicates the output power of the gas turbine; L gas The power generated by natural gas consumption is generally 9.7 kWh / m 3 ; is the amount of gas consumed by the gas turbine in the power grid m during period t; η GT The power generation efficiency of the gas turbine.

[0078] In some embodiments, the output model of the carbon capture system is:

[0079]

[0080] In the formula, is the amount of CO2 emitted by the gas turbine in grid m during period t; is the amount of CO2 captured by the carbon capture system in the power grid m during period t; v1 is the carbon emission intensity of the gas turbine; Q gas is the calorific value of natural gas; μ CCS for carbon capture efficiency; Fixing energy consumption for carbon capture; Energy consumption for carbon capture operation; σ CCS It is the energy consumption required for carbon capture to process unit CO2.

[0081] In some embodiments, the output model of the carbon storage device is:

[0082]

[0083] Where: is the amount of CO2 stored in the carbon storage device in the grid m during period t; CS is the carbon storage loss coefficient of the carbon storage system; is the CO2 output of the carbon storage device in grid m during period t.

[0084] In some embodiments, the output model of the power-to-gas device is:

[0085]

[0086] In the formula, is the amount of gas synthesized by the power-to-gas equipment in microgrid m during period t; The total power consumed by the power-to-gas equipment in microgrid m during period t; er is the heat energy that can be converted into unit electrical energy; η P2G The working efficiency of the power-to-gas equipment; The amount of CO2 consumed by the power-to-gas equipment; The amount of CO2 required to generate natural gas per unit of power.

[0087] In some embodiments, carbon emission monitoring, carbon emission reduction potential and carbon trading mechanism in a multi-energy system are digitized and integrated, specifically including carbon emission quota calculation, carbon emission calculation and carbon trading model:

[0088] In some embodiments, the microgrid carbon emission quota is:

[0089]

[0090] In the formula, Carbon allowances obtained for microgrids; is the total carbon quota for the distribution network area; The load energy consumption of the distribution network; is the energy consumption of microgrid load; T is the dispatching period; m is the microgrid identifier; M is the number of microgrids.

[0091] In some embodiments, the microgrid carbon emissions calculation formula is:

[0092]

[0093] In the formula, is the actual net CO2 emissions emitted to the atmosphere by microgrid m; is the amount of CO2 emitted by the gas turbine in the microgrid during period t; E m,tis the node carbon potential of the microgrid; It is the interaction power between the microgrid and the distribution network. Since the carbon potential represents the carbon emission of energy consumption, the interaction power is only calculated when it is positive, that is, the distribution network transmits power to the microgrid.

[0094] In some embodiments, the multi-microgrid carbon trading model is:

[0095]

[0096] In the formula, C Trade,MMG is the carbon trading cost of multiple microgrids, c Trade The carbon trading price.

[0097] In some embodiments, a low-carbon dispatch strategy is formulated with the goal of minimizing economic cost and comprehensive carbon cost, mainly with the goal of minimizing the sum of system operation cost and comprehensive carbon cost.

[0098] In some embodiments, the operating cost F1 includes the operation and maintenance cost f1 1 , through a variety of energy communication channels and external electricity, heat, gas network interaction regulation benefits f1 2 、Energy purchase cost f1 3 , Environmental costs of pollutant control f1 4 , load loss cost f1 5 .

[0099]

[0100] Where: c om is the unit operation and maintenance cost of the microgrid system; P t om It is the cumulative value of multiple energy supplies up to time t in a single maintenance cycle; They are the unit income of multiple microgrids providing energy regulation for external electricity, heat and gas grids; are the electric power, thermal power and natural gas power regulated by the multi-microgrid for the external electric, thermal and gas grids at time t, respectively; are the unit costs of purchasing electricity, heat, hydrogen, and natural gas, respectively; are the purchased electricity power, thermal power, hydrogen power, and natural gas power of the system at time t; B is the total amount of pollutant types; c is the total amount of pollutant types; b , α b are the unit treatment cost and emission coefficient of Class B pollutants respectively; are the power of electricity, heat, hydrogen and natural gas generated by the system at time t to produce type b pollutants; c pw 、c pv 、c pgt are the unit electricity, heat and natural gas load costs lost respectively.

[0101] In some embodiments, the comprehensive carbon cost F2 includes the carbon transaction cost f2 under the carbon flow cycle 1 and carbon storage cost f2 2 :

[0102] minF2=f2 1 +f2 2 (1.13)

[0103]

[0104] In some embodiments, the constraints of internal multi-energy sources mainly include output constraints of different units and power balance constraints.

[0105] In some embodiments, the power constraints of the multiple device outputs are:

[0106]

[0107] In some embodiments, the power balance constraint of multiple device outputs is:

[0108]

[0109] represents the output power of the gas turbine of device m at time t;

[0110] represents the power consumption of a certain type of load (may be a motor or other type of load) of device m at time t;

[0111] represents the output power of the photovoltaic power generation system and the micro gas turbine of device m working together at time t;

[0112] represents the output power of the wind turbine and micro gas turbine of device m working together at time t;

[0113] represents the output power of the distributed generation equipment of device m at time t;

[0114] represents the charging power of device m at time t, which is usually related to the energy storage device; represents the output power of the micro gas turbine of device m at time t;

[0115] represents the power transmission loss or power conversion loss of device m at time t; represents the output power of device m at time t, that is, the power supplied to the external system;

[0116] P t inter represents the non-dispatchable input power at time t, which may include some fixed-output power generation equipment or natural energy input;

[0117] represents the dispatchable power output of device m at time t;

[0118] represents the power consumption of the carbon collection and storage system of device m at time t;

[0119] In the above equation, the left side of the equation is the sum of the power input of all devices, including the output of the power generation equipment, the power consumption of the load, the charging power, etc. The right side of the equation is the power output of all devices, including the output of the micro gas turbine, power transmission loss, output power, non-dispatchable input power, dispatchable power generation output and the power consumption of the carbon collection system. This balance constraint ensures that at any time t, the power input and output in the system are equal, thereby maintaining the stability of the system.

[0120] Embodiment 2:

[0121] like Figure 2 As shown in the figure, the multi-microgrid multi-energy carbon emission reduction optimization system based on standard digitalization includes:

[0122] Determine the modules and build a multi-scenario electricity-carbon standard model to support the coordinated dispatch of multiple energy forms such as wind power, photovoltaic power, gas, carbon capture system, and power-to-gas equipment in the power system;

[0123] Establish modules to digitally process and integrate carbon emission monitoring, carbon reduction potential and carbon trading mechanisms in multi-energy systems to provide data support for optimized scheduling;

[0124] The optimization module formulates a low-carbon scheduling strategy with the goal of minimizing economic cost and comprehensive carbon cost.

[0125] Adjust the module, combine multiple energy forms such as wind power, photovoltaics, gas power generation and carbon capture, and establish internal multi-energy constraints.

[0126] The dispatching module optimizes the dispatching strategy based on the constructed low-carbon digital achievements to achieve efficient and low-carbon operation of the power system.

[0127] Embodiment three:

[0128] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization as described in the above-mentioned embodiment 2 are implemented.

[0129] Embodiment 4:

[0130] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization as described in the above-mentioned embodiment 2 are implemented.

[0131] The computer device in this embodiment may be an electronic device 300, such as Figure 3 As shown, electronic device 300 includes one or more processors 310 and memory 320 .

[0132] The processor 310 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0133] The memory 320 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 310 may run the program instructions to implement the multi-microgrid multi-energy system carbon emission reduction monitoring and optimization scheduling method based on standard digitization of the various embodiments of the present application described above and / or other desired functions. Various contents such as category correspondences may also be stored in the computer-readable storage medium.

[0134] In one example, the electronic device 300 may further include: an input device 330 and an output device 340 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0135] In addition, the input device 330 may also include, for example, a keyboard, a mouse, an interface, etc. The output device 340 may output various information to the outside, including analysis results, etc. The output device 340 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0136] Of course, to simplify, Figure 3 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0137] The steps involved in the above embodiments 2 to 4 correspond to those in embodiment 1. For the specific implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization, characterized in that: The following steps are involved: Construct output models for wind power, photovoltaics, gas, carbon capture systems and power-to-gas equipment in the energy system; Calculate carbon emission quotas and carbon emissions based on the output model, and construct an objective function with the goal of minimizing comprehensive carbon costs and operating costs; Taking the power output of the equipment as the constraint condition, the dispatch strategy of each energy system is optimized using the solution of the objective function.

2. The multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization as claimed in claim 1 is characterized in that: The output model of the carbon capture system is: In the formula, is the amount of CO2 emitted by the gas turbine in grid m during period t; is the amount of CO2 captured by the carbon capture system in the power grid m during period t; v1 is the carbon emission intensity of the gas turbine; Q gas is the calorific value of natural gas; μ CCS for carbon capture efficiency; Fixing energy consumption for carbon capture; Energy consumption for carbon capture operation; σ CCS It is the energy consumption required for carbon capture to process unit CO2.

3. The multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization according to claim 1 is characterized in that: The output model of the carbon storage device is: Where: is the amount of CO2 stored in the carbon storage device in the grid m during period t; CS is the carbon storage loss coefficient of the carbon storage system; is the CO2 output of the carbon storage device in grid m during period t.

4. The multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization according to claim 1 is characterized in that: The output model of the power-to-gas equipment is: In the formula, is the amount of gas synthesized by the power-to-gas equipment in microgrid m during period t; The total power consumed by the power-to-gas equipment in the microgrid m during the time period; er It is the heat energy that can be converted into unit electrical energy; η P2G The working efficiency of the power-to-gas equipment; The amount of CO2 consumed by the power-to-gas equipment; The amount of CO2 required to generate natural gas per unit of power.

5. The multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization according to claim 1 is characterized in that: The carbon emission quota of the microgrid is: In the formula, Carbon allowances obtained for microgrids; is the total carbon quota for the distribution network area; The load energy consumption of the distribution network; is the energy consumption of microgrid load; T is the dispatching period; m is the microgrid identifier; M is the number of microgrids.

6. The multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization according to claim 1 is characterized in that: The formula for calculating carbon emissions of microgrids is: In the formula, is the actual net CO2 emissions emitted to the atmosphere by microgrid m; is the amount of CO2 emitted by the gas turbine in the microgrid during period t; E m,t is the node carbon potential of the microgrid; It is the interaction power between the microgrid and the distribution network. Since the carbon potential represents the carbon emission of energy consumption, the interaction power is only calculated when it is positive, that is, the distribution network transmits power to the microgrid.

7. The multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization according to claim 1 is characterized in that: Operation cost F1 includes operation and maintenance cost f1 1 , through a variety of energy communication channels and external electricity, heat, gas network interaction regulation benefits f1 2 、Energy purchase cost f1 3 , Environmental costs of pollutant control f1 4 , load loss cost f1 5 ; Where: c om is the unit operation and maintenance cost of the microgrid system; P t om It is the cumulative value of multiple energy supplies up to time t in a single maintenance cycle; They are the unit income of multiple microgrids providing energy regulation for external electricity, heat and gas grids; are the electric power, thermal power and natural gas power regulated by the multi-microgrid for the external electric, thermal and gas grids at time t, respectively; are the unit costs of purchasing electricity, heat, hydrogen, and natural gas, respectively; are the purchased electricity power, thermal power, hydrogen power, and natural gas power of the system at time t; B is the total amount of pollutant types; c is the total amount of pollutant types; b , α b are the unit treatment cost and emission coefficient of Class B pollutants respectively; are the power of electricity, heat, hydrogen and natural gas generated by the system at time t to produce type b pollutants; c pw 、c pv 、c pgt are the unit electricity, heat and natural gas load costs lost respectively.

8. A multi-microgrid multi-energy carbon emission reduction optimization system based on standard digitalization, characterized by: include: The data acquisition module is configured to: construct output models of wind power, photovoltaic, gas, carbon capture system and power-to-gas equipment in the energy system; The objective function module is configured to: calculate the carbon emission quota and carbon emission amount according to the output model, and construct the objective function with the goal of minimizing the comprehensive carbon cost and the minimum operating cost; The adjustment module is configured to: use the power output of the equipment as a constraint condition; The scheduling module is configured to optimize the scheduling strategy of each energy system using the solution of the objective function.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, which, when executed by a processor, implements the steps in the standard digitalized multi-microgrid multi-energy carbon emission reduction optimization method as described in any one of claims 1 to 7.

10. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the multi-microgrid multi-energy carbon emission reduction optimization method based on standard digitalization are implemented as described in any one of claims 1 to 7.

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