Distributed energy station carbon emission intensity optimization planning method, system, device and medium

Abstract

The application discloses a distributed energy station carbon emission intensity optimization planning method, system, device and medium, belongs to the technical field of comprehensive energy system planning, and comprises the following steps: establishing an energy flow model and a carbon flow model of each energy conversion device integrated in the distributed energy station; establishing a distributed energy station energy flow coupling matrix and a distributed energy station carbon flow coupling matrix to form a distributed energy station energy flow-carbon flow coupling analysis framework; establishing a traditional distributed energy station planning economic objective function and constraint condition; based on the energy flow-carbon flow coupling analysis framework, proposing the concept of energy carbon emission intensity as a quantitative index of distributed energy station energy supply cleanliness, constructing a load energy carbon emission intensity constraint, forming a distributed energy station low-carbon planning method based on energy carbon emission intensity optimization, guiding the distributed energy station system to realize low-carbon economic design of energy structure and operation strategy in the planning stage, and promoting the transformation of the distributed energy station system to energy supply clean and low carbon.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated energy system planning, and particularly relates to a distributed energy station carbon emission intensity optimization planning method, system, device and medium. BACKGROUND

[0002] The prior art focuses on the economy and energy efficiency optimization of the energy station, and lacks fine analysis of carbon emission distribution. For example, the traditional planning model regards the energy station as a "black box", only takes the total carbon emission as a constraint, and ignores the allocation of "virtual carbon loss" in the energy conversion process and the cleanliness difference of different energy form output loads. This leads to the difficulty of accurately guiding the energy station to develop in the direction of low carbonization.

[0003] In addition, there is no index in the prior art to quantify the cleanliness of energy output, which cannot effectively measure the environmental benefits of the energy station. Therefore, an innovative method is needed to finely divide carbon emissions, quantify energy cleanliness and guide low-carbon planning. Based on this, the present application quantitatively analyzes the carbon flow distribution in the energy station system, proposes an energy carbon emission intensity evaluation index, and defines the carbon emission intensity of output energy as the load energy carbon emission intensity. This index can accurately represent the cleanliness level of DES output energy, and its value is negatively correlated with energy cleanliness, which can guide the DES system to achieve the dual goals of energy economy and energy cleanliness in the energy conversion process. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is to break through the traditional DES black box processing mode by constructing an energy flow-carbon flow coupling analysis framework, and to realize the responsibility allocation of carbon emissions in the energy conversion link. Then, a load energy carbon emission intensity constraint is constructed to form a distributed energy station low-carbon planning method based on energy carbon emission intensity optimization.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a distributed energy station carbon emission intensity optimization planning method, comprising, establishing an energy flow model and a carbon flow model of integrated energy conversion in the distributed energy station; establishing an energy flow coupling matrix and a carbon flow coupling matrix based on the energy hub model to form a distributed energy station energy flow-carbon flow coupling analysis framework; On the basis of the traditional economic objective function and constraint condition, a load energy carbon emission intensity constraint is constructed based on the energy flow-carbon flow coupling framework analysis framework.

[0007] As a preferred solution of the distributed energy station carbon emission intensity optimization planning method described in the present application, wherein: the energy flow model is represented as: , wherein, , , respectively represent the CHP electric and heat output power and the input power of fuel gas, , , respectively represent the CHP electric energy conversion efficiency and the heat energy conversion rate, is the fuel gas distribution coefficient of the CHP.

[0008] As a preferred scheme of the distributed energy station carbon emission intensity optimization planning method, the carbon flow model is represented as: , , wherein, , and respectively represent the input carbon flow rate of the EB, the output heat load terminal carbon flow rate and the loss carbon flow rate, , respectively represent the node carbon potential of the input and output ports of the EB, is the loss power of the EB, is the input power of the electric boiler, is the output power of the electric boiler.

[0009] As a preferred scheme of the distributed energy station carbon emission intensity optimization planning method, the energy flow coupling matrix adopts an energy hub model to construct a multi-source coupling architecture, including an input energy matrix, a conversion efficiency matrix and an output load matrix, and is represented as: , wherein, represents the output load matrix of the distributed energy station; represents the energy conversion efficiency coupling matrix of the distributed energy station; represents the input energy matrix of the distributed energy station.

[0010] As a preferred scheme of the distributed energy station carbon emission intensity optimization planning method, the carbon flow coupling matrix includes the carbon flow transmission relationship of different energy conversion paths of the energy coupling device, and is unified in a carbon flow hub model, and is represented as: , wherein, and respectively represent the input and output carbon potential vectors of the distributed energy station; represents the carbon flow rate coupling matrix.

[0011] As a preferred scheme of the distributed energy station carbon intensity optimization planning method, wherein: the load energy carbon emission intensity includes coupling the obtained carbon flow matrix, and the input side carbon emission is decoupled into process carbon loss and load terminal carbon emission; the carbon emission amount borne in unit energy is defined as the core index of energy supply cleanliness, and is expressed as: , Wherein, is the load energy carbon emission intensity at the moment; is the load terminal carbon emission of the distributed energy station at the moment; is the load terminal energy of the distributed energy station at the moment; Wherein, the process carbon loss includes the carbon emission generated by the efficiency loss of each energy coupling device in operation, and the carbon emission responsibility borne by the energy station itself; The load terminal carbon emission includes the carbon emission amount transferred to the user side through the output load of the energy station according to the carbon flow coupling matrix.

[0012] As a preferred scheme of the distributed energy station carbon intensity optimization planning method, wherein: the load energy carbon emission intensity constraint includes taking the annual average load energy carbon emission intensity as the core parameter of the low-carbon planning of the distributed energy station, and satisfying that the planning value is less than or equal to the set threshold value, which is expressed as: , Wherein, is the set annual average load energy carbon emission intensity threshold value, is the device , type of load terminal carbon emission at the moment, is the load terminal energy of the type at the moment , , respectively represent different energy coupling devices in the distributed energy station and different planning types of the same energy coupling device, represents the planning days, represents the planning hours, represents different device sets, represents the set of different types of the same device, represents the total amount of energy of different forms of load terminal energy; When the set annual average load energy carbon emission intensity threshold value is lower than the theoretical minimum value, the planning model has no feasible solution, and the threshold value satisfying the condition is expressed as: ​​​, wherein, is the full-year average load energy carbon emission intensity value, represents the full-year average load energy carbon emission intensity threshold value of the distributed energy station set artificially; wherein, the full-year average load energy carbon emission intensity is represented as: , wherein, is the full-year load energy carbon emission intensity of the distributed energy station.

[0013] The present application provides a distributed energy station carbon emission intensity optimization planning system, which accurately quantifies the energy flow transmission process and carbon flow distribution relationship of fuel to load in the energy conversion device through the device modeling module, integrates the multi-device energy conversion path and carbon flow transmission link by using the coupling framework module, constructs the energy flow-carbon flow coupling analysis structure of the system level of the distributed energy station, and embeds the load energy carbon emission intensity as the core index of clean energy supply based on the carbon intensity constraint module. The planning model realizes the low-carbon collaborative decision of the energy station equipment selection and capacity configuration, and promotes the transformation of the distributed energy system to the clean energy supply direction.

[0014] To solve the above technical problems, the present application provides the following technical scheme: a distributed energy station carbon emission intensity optimization planning system, comprising: a device modeling module, which establishes an energy flow model and a carbon flow model of integrated energy conversion in the distributed energy station; a coupling framework module, which establishes an energy flow coupling matrix and a carbon flow coupling matrix based on an energy hub model, and forms a distributed energy station energy flow-carbon flow coupling analysis framework; a carbon flow intensity constraint module, which constructs a load energy carbon emission intensity constraint based on the traditional economic objective function and constraint condition and the energy flow-carbon flow coupling framework analysis framework.

[0015] The present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the distributed energy station carbon emission intensity optimization planning method when executing the computer program.

[0016] The present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the distributed energy station carbon emission intensity optimization planning method when executed by a processor.

[0017] The application has the beneficial effects that: the application solves the problem of fuzzy responsibility attribution in traditional planning by decoupling the process carbon loss and the load terminal carbon emission, accurately dividing the carbon emission responsibility boundary of the energy station and the user side; the application breaks the limitation of total carbon emission control by creating the load energy carbon emission intensity index, converting the energy supply cleanliness into quantifiable constraints, and guiding the equipment selection to tilt towards clean and low-carbon technology; the application realizes the coordination of the energy station investment operation cost optimization and the low-carbon energy supply target by embedding the constraint condition in the traditional economic planning model, and promotes the transformation of the distributed energy system structure to the environment-friendly type. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the premise of the drawings.

[0019] Figure 1 The overall flowchart of the distributed energy station carbon emission intensity optimization planning method provided by an embodiment of the application.

[0020] Figure 2 The typical distributed energy station schematic diagram of the distributed energy station carbon emission intensity optimization planning method provided by an embodiment of the application.

[0021] Figure 3 The load energy carbon emission intensity conceptual diagram of the distributed energy station carbon emission intensity optimization planning method provided by an embodiment of the application.

[0022] Figure 4 The park different season cold and heat load curve diagram of the distributed energy station carbon emission intensity optimization planning method provided by an embodiment of the application.

[0023] Figure 5 The topological structure diagram of the distributed energy station carbon emission intensity optimization planning method provided by an embodiment of the application. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor should be within the protection scope of the application.

[0025] Embodiment 1, refer to Figures 1-3For an embodiment of the present application, the embodiment provides a distributed energy station carbon intensity optimization planning method, comprising: S1: Establish the energy flow model and carbon flow model of the integrated energy conversion in the distributed energy station.

[0026] It should be noted that the distributed energy station usually integrates various energy coupling devices to realize the multi-energy coupling of the distributed energy station. Figure 2 As an example of a typical distributed energy station shown in the figure, it includes a combined heat and power unit (CHP) and an electric boiler (EB), and the energy flow model is represented as: The combined heat and power system usually uses a micro gas turbine and an internal combustion engine as the energy conversion hub, realizes electric and heat dual supply through the cascade utilization of fuel chemical energy, and serves as a multi-energy collaborative hub of the distributed energy station, represented as: , wherein, , , are the CHP electric and heat output power and the input power of the gas at the moment, , , are the CHP electric energy conversion efficiency and heat energy conversion rate, is the gas distribution coefficient of the CHP; The electric boiler converts heat energy by consuming electric energy, has higher environmental benefits compared to traditional coal-fired heating, especially in scenarios with a high proportion of clean electricity; in the distributed energy station, the electric boiler is configured to use surplus electric energy to cooperate with the CHP unit for heating during low electricity price periods or when there is excess electricity supply, and the energy flow relationship is represented as: , wherein, is the output power of the electric boiler, is the energy conversion efficiency of the electric boiler; is the input power of the electric boiler.

[0027] Further, the carbon flow model of the electric boiler is constructed and represented as: , , wherein, , and are the input carbon flow rate of the EB, the output heat load terminal carbon flow rate and the loss carbon flow rate, , are the node carbon potentials of the input and output ports of the EB, is the loss power of the EB.

[0028] The carbon flow model of the conversion device EB is represented as: , The carbon flow model of the cogeneration unit is represented as: , , wherein, is the input carbon flow rate of the CHP; is the output electric load terminal carbon flow rate of the CHP; is the output thermal load terminal carbon flow rate of the CHP; is the carbon flow rate loss of the CHP; , and are the node carbon potentials of the input and output electric / thermal ports of the CHP, respectively; is the loss power of the CHP.

[0029] Further, the carbon flow model of the CHP is represented as: , wherein, , S2: based on the energy hub model, an energy flow coupling matrix and a carbon flow coupling matrix are established to form an energy flow-carbon flow coupling analysis framework of the distributed energy station.

[0030] It should be noted that, in view of the differences in energy conversion characteristics between various energy coupling devices, the energy hub model is used to construct a multi-source coupling architecture, including an input energy matrix, a conversion efficiency matrix and an output load matrix, represented as: , wherein, represents the output load matrix of the distributed energy station; represents the energy conversion efficiency coupling matrix of the distributed energy station; represents the input energy matrix of the distributed energy station; As shown in the distributed energy station energy flow coupling matrix: Figure 2 , wherein, , , are the input electric power and gas power of the DES (distributed energy system), respectively; , are the output electric load terminal power and thermal load terminal power of the DES, respectively; is the energy conversion efficiency of the electric boiler.

[0031] Further, referring to the energy hub model, a distributed energy station carbon flow coupling matrix is established, and the carbon flow transmission relationship of different energy conversion paths of the energy coupling device is unified in the carbon flow hub model and expressed as: , wherein, and represent the input and output carbon potential vectors of the distributed energy station, respectively; represents the carbon flow rate coupling matrix; The carbon potential of the output port and the carbon potential of the input port in each energy coupling device exist corresponding relationship, which is expressed by the carbon potential conversion coefficient, and the general carbon flow model of each energy coupling device is expressed as: , wherein, is the node carbon potential of the device , type , output port ; is the node carbon potential of the device , type , input port ; is the carbon potential conversion coefficient of the energy coupling device , type , output port ; Figure 2 The carbon flow coupling matrix of the distributed energy station is expressed as shown in , wherein: , wherein, , are the carbon potential of the DES output electric load port and the carbon potential of the heat load port, respectively; , are the carbon potential of the DES input electric energy node and the carbon potential of the gas node, respectively; is the carbon potential conversion coefficient of the EB unit type ; , are the carbon potential conversion coefficients of the CHP type , output electric load port and heat load port, respectively, represents the relationship coefficient between the input power and the output power of the first energy conversion device, represents the relationship coefficient between the output power of the first energy conversion device and the input power of the second energy conversion device, represents the relationship coefficient between the input power of the second energy conversion device and the output power of the first energy conversion device, The coefficient representing the relationship between the input power and output power of the second energy conversion device.

[0032] S3: Based on the traditional economic objective function and constraints, load energy carbon emission intensity constraints are constructed using an energy-carbon flow coupling framework.

[0033] It should be noted that minimizing the annualized total cost of a distributed energy station system plan and comprehensively optimizing the economic objectives of equipment investment and operation and maintenance includes constructing a comprehensive cost model covering both planned capital expenditures and operating expenditures as a quantitative indicator to achieve synergistic optimization of initial investment and ongoing operating costs during the planning period. Mathematically, this means, under the premise of meeting technical constraints, achieving a globally minimum annualized total cost through joint optimization of equipment selection, capacity configuration, and operating strategies, expressed as: , in, Plan the annualized total cost for DES; Plan the annualized investment cost for DES; Plan the annualized operating costs for DES; This indicates that the sum of the annualized investment cost and the annualized operating cost of the DES plan is minimized. The annualized investment cost of DES planning can be expressed as the equivalent annual investment cost of different internally integrated devices: , in, For equipment ,type The proposed planned capacity; For equipment ,type The cost required per unit of planned capacity; The depreciation rate of the equipment; For equipment ,type Service life; Represents a collection of different devices; This represents a collection of different types of the same device; This represents the sum of the equivalent annual investment costs of the different devices integrated within the DES. DES planning annualized operating cost Including annual energy purchase costs Annual operation and maintenance costs Annual carbon tax costs , is represented as: , The annual energy purchase cost of DES is expressed as:

[0034] ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ The device planning capacity constraint is set to ensure that the real-time output of the device is limited to the rated configuration of the device planning capacity, which is represented as: , wherein, is the upper limit of the real-time output of the device , type ; The DES operating power balance constraint indicates that the power output of the DES should be equal to the electrical load and the thermal load of the DES terminal, as shown in the following formula: Figure 3 For example, for a DES, it is represented as: , To achieve reliable operation control of the DES multi-energy flow network, the upper limit of the DES operating energy network power is set, which is represented as: , wherein, is the upper limit of the energy input power of the th form; The device operating real-time output constraint represents the upper and lower limit constraints of the output of each energy-coupled device, which is represented as: , wherein, , , are the upper and lower limits of the operating real-time output of the generated th energy of the device , type , respectively.

[0035] Further, on the basis of the obtained distributed energy station carbon flow coupling matrix, the system input side carbon emissions are decoupled into process carbon loss and load terminal carbon emissions. wherein, the process carbon loss refers to the carbon emissions generated by the energy-coupled devices in operation due to efficiency loss, and the energy station itself assumes the responsibility of carbon emissions; the load terminal carbon emissions refer to the carbon emissions transferred to the user side through the output load of the energy station according to the carbon flow coupling matrix; the energy carbon emission intensity is defined as the carbon emissions assumed in unit energy, and the load energy carbon emission intensity is referred to as the load energy carbon emission intensity, which is taken as the core index of energy supply cleanliness, and is represented as: , wherein, is the load energy carbon emission intensity at time; is the load terminal carbon emissions of the distributed energy station at time; is the load terminal energy of the distributed energy station at time; The time scale of the distributed energy station planning is equivalent year planning, and the annual average load energy carbon emission intensity is taken as a core index in the planning, which is represented as: , Among them, is the annual load energy carbon emission intensity of the distributed energy station; represents different device sets; represents the same device of different types; is the device , type , momentary load terminal carbon emission; is the momentary load terminal energy of the th form; represents the total amount of energy of different forms of load terminal energy; The annual average load energy carbon emission intensity constraint is established to realize the clean energy supply quantitative control of the distributed energy station. The annual average load energy carbon emission intensity is taken as the core parameter of the low-carbon planning solution of the distributed energy station, and needs to satisfy that its value is less than or equal to the set threshold value, which is represented as: , Among them, is the set annual average load energy carbon emission intensity threshold; Considering that when the set annual average load energy carbon emission intensity threshold is lower than the theoretical minimum value, the planning model has no feasible solution, therefore the set threshold value is represented as: , Among them, is all possible annual average load energy carbon emission intensity values.

[0036] Embodiment 2, referring to Figure 4 and Figure 5 , an embodiment of the present application provides a carbon emission intensity optimization planning method of a distributed energy station. In order to verify the beneficial effects of the present application, scientific demonstration is carried out through experiments.

[0037] The system load mainly includes cold load and heat load, wherein the cold load is divided into summer cold load and transition season cold load, and the heat load is divided into winter heat load and transition season heat load. The park load curve is as shown in Figure 4 The energy conversion devices include electric refrigerating unit, air-cooled heat pump and electric boiler, wherein the electric refrigerating unit is used for summer cooling, the air-cooled heat pump is used for transition season cooling and heating, and the electric boiler is used for winter heating. At the same time, the park constructs a thermal energy storage device for coordinating the supply of heat load of the heating device. The device parameters are shown in Tables 1-4. The power source used by the park comes from the superior power grid, and the energy station topological structure is asFigure 5 As shown, the selection and capacity planning design are carried out for the energy supply characteristics and energy demand.

[0038] Table 1: Parameters of each type of electric refrigeration unit to be planned Table 2: Parameters of each type of air-cooled heat pump unit to be planned Table 3: Parameters of each type of electric boiler to be planned Table 4: Parameters of thermal energy storage equipment For the required parameters: peak, flat, and valley electricity prices are , the carbon tax cost coefficient is , the upper grid emission coefficient is ; To analyze the benefits of the low-carbon planning method of the distributed energy station based on energy carbon emission intensity optimization proposed in the present application, two planning schemes are set: scheme one is the low-carbon planning of the distributed energy station without considering energy carbon emission intensity optimization, i.e., without considering the load energy carbon emission intensity constraint in the planning; scheme two is the low-carbon planning of the distributed energy station considering energy carbon emission intensity optimization, i.e., considering the load energy carbon emission intensity constraint in the planning; the planning and operation optimization results can be obtained by planning and solving the scene without considering the energy carbon density constraint, as shown in Tables 5-10.

[0039] Table 5: Annual total carbon emission amount division results of the distributed energy station in scheme one Table 6: Energy conversion equipment planning results of the distributed energy station in scheme one Table 7: Annual planning and operation comprehensive cost of the distributed energy station in scheme one Table 8: Annual total carbon emission amount division results of the distributed energy station in scheme two Table 9: Energy conversion equipment planning results of the distributed energy station in scheme two Table 10: Annual planning and operation comprehensive cost of the distributed energy station in scheme two ​​​​​​​​​​​​From the comparison of scheme one and scheme two, it can be seen from the comparison of the two distributed energy station planning schemes that scheme two has significant advantages in low-carbon benefits and system optimization. The scheme adjusts the energy carbon emission intensity optimization strategy, reduces the total annual carbon emission, the load terminal carbon emission, and the average load energy carbon emission intensity by 0.75%, 3.27%, and 3.27% respectively, verifying the effectiveness of the clean load output improvement. In terms of equipment configuration, scheme two uses electric chiller unit 3 instead of electric chiller unit 2 in scheme one, and realizes system reconstruction by reducing the capacity of the heat storage tank and increasing the capacity of the electric boiler, which reflects the synergistic optimization of equipment selection and energy storage strategy. In terms of economy, although the total cost increases by 2.07% due to the increase of 4.1% in energy purchase cost, the investment cost is reduced by 8.1%, the operation and maintenance cost is reduced by 0.88%, and the carbon tax cost is reduced by 0.75%, effectively alleviating the pressure of cost increase.

[0040] In summary, scheme two optimizes the equipment configuration and fine-tunes the carbon emission intensity control, and builds a more environmentally friendly distributed energy station system, providing a practical path for low-carbon energy planning of distributed energy stations with emission reduction benefits and economic benefits.

[0041] Embodiment 3 is an embodiment of the present application, which provides a distributed energy station carbon emission intensity optimization planning system, comprising: a device modeling module for establishing an energy flow model and a carbon flow model of integrated energy conversion in a distributed energy station; a coupling framework module for establishing an energy flow coupling matrix and a carbon flow coupling matrix based on an energy hub model to form a distributed energy station energy flow-carbon flow coupling analysis framework; a carbon flow intensity constraint module for constructing a load energy carbon emission intensity constraint based on the traditional economic objective function and constraint condition and the energy flow-carbon flow coupling analysis framework.

[0042] The embodiment also provides an electronic device suitable for the distributed energy station carbon emission intensity optimization planning method, comprising a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the distributed energy station carbon emission intensity optimization planning method proposed in the above embodiment.

[0043] The embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to realize the distributed energy station carbon emission intensity optimization planning method proposed in the above embodiment.

[0044] The storage medium proposed in the embodiment belongs to the same inventive concept as the distributed energy station carbon emission intensity optimization planning method proposed in the above embodiment, and the technical details not described in the embodiment can be seen from the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0045] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, etc., including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0046] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limited, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for optimizing carbon emission intensity planning of distributed energy stations, characterized in that: include, Establish energy flow and carbon flow models for integrated energy conversion in distributed energy stations; Based on the energy hub model, energy flow coupling matrix and carbon flow coupling matrix are established to form an energy flow-carbon flow coupling analysis framework for distributed energy stations. Based on the traditional economic objective function and constraints, a load energy carbon emission intensity constraint is constructed using an energy-carbon flow coupling framework.

2. The carbon emission intensity optimization planning method for distributed energy stations as described in claim 1, characterized in that: The energy flow model is expressed as follows: ， in, , , They are respectively The CHP's electrical and thermal output power and gas input power at all times. , These are the electrical energy conversion efficiency and thermal energy conversion rate of CHP, respectively. This is the gas distribution coefficient for CHP.

3. The carbon emission intensity optimization planning method for distributed energy stations as described in claim 2, characterized in that: The carbon flow model is expressed as follows: ， ， in, , and These are the input carbon flow rate, the output heat load terminal carbon flow rate, and the loss carbon flow rate of EB, respectively. , These are the nodal carbon potentials at the input and output ports of the EB, respectively. For the power loss of EB, This refers to the input power of the electric boiler. This refers to the output power of the electric boiler.

4. The carbon emission intensity optimization planning method for distributed energy stations as described in claim 3, characterized in that: The energy flow coupling matrix employs an energy hub model to construct a multi-source coupling architecture, including an input energy matrix, a conversion efficiency matrix, and an output load matrix, expressed as: ， in, This represents the output load matrix of the distributed energy station; This represents the energy conversion efficiency coupling matrix of a distributed energy station. This represents the input energy matrix of the distributed energy station.

5. The carbon emission intensity optimization planning method for distributed energy stations as described in claim 4, characterized in that: The carbon flow coupling matrix includes the carbon flow transfer relationships of different energy conversion paths of the energy coupling device, unified under the carbon flow hub model, and is expressed as: ， in, and These represent the input and output carbon potential vectors of the distributed energy station, respectively. This represents the carbon flow rate coupling matrix.

6. The carbon emission intensity optimization planning method for distributed energy stations as described in claim 4, characterized in that: The load energy carbon emission intensity includes, based on the obtained carbon flow coupling matrix, the input-side carbon emission decoupled into process carbon loss and load terminal carbon emission; defined as the amount of carbon emissions carried per unit of energy, serving as a core indicator of energy supply cleanliness, expressed as: ， in, for Momentary load energy carbon emission intensity; for Carbon emissions from distributed energy station load terminals at all times; for Energy at the load terminal of the distributed energy station at all times; Among them, process carbon loss includes carbon emissions generated by efficiency loss of various energy coupling equipment during operation, and the energy station itself shall bear the responsibility for carbon emissions. Load terminal carbon emissions include the amount of carbon emissions transmitted to the user side through the load output of the energy station, based on the carbon flow coupling matrix.

7. The carbon emission intensity optimization planning method for distributed energy stations as described in claim 4, characterized in that: The load energy carbon emission intensity constraint includes using the annual average load energy carbon emission intensity as the core parameter for solving the low-carbon planning of distributed energy stations, ensuring that the planned value is less than or equal to a set threshold, expressed as: ， in, The threshold for the annual average load energy carbon emission intensity is set. For equipment ,type of Constant load terminal carbon emissions, for Time of the first Types of load terminal energy, , These represent different energy coupling devices in a distributed energy station and different planning types within the same energy coupling device. Indicates the number of planning days. Indicates the planned hour. This represents a collection of different devices. This represents a collection of different types of the same device. This represents the total energy available at different types of load terminals. When the set annual average load energy carbon emission intensity threshold is lower than the theoretical minimum, the planning model has no feasible solution. The threshold satisfaction condition is then expressed as: ， in, The value is taken as the annual average load energy carbon emission intensity. This represents the threshold for the average annual load energy carbon emission intensity of a distributed energy station, which is set manually. The annual average load energy carbon emission intensity is expressed as: ， in, The annual load energy carbon emission intensity of distributed energy stations.

8. A distributed energy station carbon emission intensity optimization planning system, employing the distributed energy station carbon emission intensity optimization planning method as described in any one of claims 1 to 7, characterized in that, include: The equipment modeling module establishes energy flow and carbon flow models for integrated energy conversion in distributed energy stations. The coupling framework module establishes energy flow coupling matrices and carbon flow coupling matrices based on the energy hub model, forming an energy flow-carbon flow coupling analysis framework for distributed energy stations. The carbon flow intensity constraint module constructs load energy carbon emission intensity constraints based on the traditional economic objective function and constraint conditions, using an energy flow-carbon flow coupled framework analysis framework.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the distributed energy station carbon emission intensity optimization planning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed energy station carbon emission intensity optimization planning method as described in any one of claims 1 to 7.

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