Carbon emission accounting method and system based on park demand response economic dispatch

Abstract

The application provides a carbon emission accounting method and system based on park demand response economic scheduling, comprising: constructing component models in the park to obtain a park demand response economic scheduling model and its power flow results; combining a power grid carbon emission coefficient of an area where the park is located to calculate power distribution network input carbon emission; based on the electric-thermal power characteristics of combined heat and power units, combining a combined heat and power unit model to calculate combined heat and power carbon emission; based on a battery energy storage device model, combining the carbon storage and carbon transfer characteristics of the battery energy storage device to calculate the battery energy storage device carbon emission; based on the power flow results of the park demand response economic scheduling model, the power distribution network input carbon emission, the combined heat and power carbon emission and the battery energy storage device carbon emission, calculating the net carbon intensity of each node in the park, and accounting for the carbon emission of the demand side main body. The application fully considers and calculates the carbon emission of each component in the park, and provides data support for promoting carbon reduction development in the park.

Description

Technical Field

[0001] This invention relates to the field of power control technology, specifically to a carbon emission accounting method and system based on demand response economic dispatch in industrial parks, and also provides a corresponding terminal and medium. Background Technology

[0002] Multi-energy parks represent the most immediate and direct manifestation of the energy internet, coupling multiple energy sources and coordinating diverse energy supply systems to improve energy utilization and reduce the economic costs of the energy system. With the worsening global energy crisis and greenhouse effect, carbon emissions from multi-energy parks have gradually become a research hotspot. As multi-energy parks develop, the proportion of renewable energy sources such as wind power (WT) and photovoltaic (VP) is continuously increasing, providing possibilities for parks to participate in carbon emission reduction. If the park's optimized scheduling capabilities can be fully utilized, and the enormous potential for carbon emission reduction through the park's integrated energy system under dual carbon targets can be explored, it can provide support for the low-carbon development of the park.

[0003] Although my country has initially established carbon emission accounting methods, several practical problems remain, including an imperfect working mechanism, a relatively outdated methodology, significant biases in the statistical basis of energy consumption and some fossil fuel carbon emission factors, and a lack of annual continuity in carbon emission calculation results. Furthermore, carbon accounting and evaluation analyses vary among different enterprises. Currently, many studies on low-carbon economic dispatching of multi-energy systems in industrial parks are underway, but these studies generally focus on the power generation side, with relatively few studies on demand-side carbon emission accounting. With the development of low-carbon operation in power systems, demand-side carbon accounting based on carbon emission flow theory has seen some application. This method offers significant advantages for low-carbon economic dispatching in industrial parks.

[0004] Chinese invention patent application CN114611823A, entitled "Optimization Scheduling Method and System for Typical Multi-Energy Demand Parks (Electricity-Cooling-Heating-Gas)," discloses an optimization scheduling method for multi-energy systems (electricity-cooling-heating-gas) that considers cost and carbon emissions as objective functions. This method uses reinforcement learning to optimize the cost, carbon emissions, and energy demand of electricity and gas, achieving real-time scheduling optimization of the multi-energy system. However, this method only attributes carbon emissions to the power generation side and cannot account for carbon emissions on the load side, thus failing to motivate load-side participation in emission reduction.

[0005] Chinese invention patent application CN115241931A, entitled "Dispatching Method for Integrated Energy System in Industrial Parks Based on Time-Varying Electric Carbon Factor Curves," discloses a low-carbon economic dispatching method for integrated energy systems in industrial parks based on modeling time-varying electric carbon factors under the influence of conventional generating units. This method models the effect of changes in unit carbon emissions on the carbon emission factor per kilowatt-hour, enabling load-side dispatchable resources to participate in low-carbon economic coordinated optimization. However, this method uses a uniform carbon emission factor within the industrial park and does not consider the impact of microgrid structures on the carbon emission factor.

[0006] Chinese invention patent application CN115238597A, entitled "Construction Method of Carbon Emission Model for Source-Grid-Load of Integrated Energy System at Park Level," discloses a method for allocating grid losses to electricity consumption terminals using the Empire Competition Algorithm. This method utilizes a DBN neural network to calculate the carbon emissions of coal-fired power plants and employs carbon emission flow theory to allocate carbon emissions, including grid losses, to electricity consumption terminals, thus achieving the calculation of carbon emissions from the source-grid-load of an integrated energy system at the park level. However, this method does not provide detailed modeling of the components within the park and cannot reflect the carbon emission characteristics of each component.

[0007] Therefore, there is an urgent need in this field to find methods that consider microgrid structures, refine the carbon emission model of demand-side components, and calculate carbon emissions. Currently, no descriptions or reports of technologies similar to this invention have been found, nor have similar domestic or international materials been collected. Summary of the Invention

[0008] To address the aforementioned shortcomings in the prior art, this invention provides a carbon emission accounting method and system based on demand response economic scheduling in industrial parks, along with a corresponding terminal and medium.

[0009] According to one aspect of the present invention, a carbon emission accounting method based on demand response economic scheduling of industrial parks is provided, comprising:

[0010] An economic dispatch model for demand response in the industrial park is constructed to obtain the models of each component in the park and their power flow results; wherein, each component in the park includes: combined heat and power units, battery energy storage devices, wind turbine units, photovoltaic units and charging piles;

[0011] Calculate the carbon emissions input to the distribution network based on the power grid carbon emission coefficient of the area where the park is located;

[0012] Based on the electrothermal power characteristics of cogeneration, and combined with the cogeneration unit model, the carbon emissions of cogeneration are calculated.

[0013] Based on the battery energy storage device model, and combined with the carbon storage and carbon transfer characteristics of the battery energy storage device, the carbon emissions of the battery energy storage device are calculated.

[0014] Based on the power flow results of the demand response economic dispatch model of the park, the carbon emissions input to the distribution network, the carbon emissions from the combined heat and power generation, and the carbon emissions from the battery energy storage device, the net carbon intensity of each node in the park is calculated, and the carbon emissions of the main demand-side entities are accounted for.

[0015] Optionally, the construction of the park demand response economic scheduling model to obtain the models of each component in the park and their power flow results includes:

[0016] The power generation, grid, load, and storage resources of the industrial park's interconnected distribution network are analyzed, and the distribution network nodes are classified and defined. This is a collection of feeder lines within the park. For the set of distribution network nodes, For the collection of power nodes in the park, For the set of load nodes in the park;

[0017] Based on the Distflow model, a second-order cone relaxation method is used to establish the... The second-order cone power flow constraints of the park's microgrid for a given time period are:

[0018] (39)

[0019] (40)

[0020] (41)

[0021] (42)

[0022] (43)

[0023] (44)

[0024] Where: Equations (39) and (40) are the active and reactive power balance constraints of the park nodes, respectively. and They represent time periods respectively. feeder Active power and reactive power variables, and They represent time periods respectively. feeder Active power and reactive power variables, and feeders The resistance and reactance parameters, For time period feeder The square variable of the current, and Time periods node The active and reactive power variables of the generating unit, and Time periods The active and reactive power variables of the load at the node; Equation (41) is the constraint on the relationship between node voltage, active power, reactive power and current on the feeder of the park, where, For time period node The square of the voltage change. For time period node The voltage square variable is given by equation (42), which is the feeder capacity constraint after second-order cone relaxation. Equations (43) and (44) are the upper and lower limits of the square voltage and square current of the park, respectively. and These are the upper and lower limits of the voltage, respectively. This is the upper limit of the current.

[0025] The following models are used to model each component in the park:

[0026] The model for a combined heat and power (CHP) unit is constructed as follows:

[0027] (45)

[0028] Equation (45) represents the time period of a combined heat and power unit. The upper and lower limits of output are constrained, where, For 0-1 variables, For cogeneration units The power variable; and These are the minimum and maximum power of the combined heat and power unit, respectively.

[0029] The battery energy storage device model is constructed as follows:

[0030] (46)

[0031] (47)

[0032] (48)

[0033] (49)

[0034] (50)

[0035] Among them, equations (46) and (47) represent the battery energy storage device during the time period. The charging and discharging power constraint, where, and These are the charging and discharging power variables, respectively. The variable is 0-1, constraining that battery storage devices cannot be charged and discharged simultaneously. and These are the upper and lower limits of the charging power for battery energy storage devices. and These are the upper and lower limits of the discharge power of the battery energy storage device, respectively; Equation (48) represents the discharge power of the battery energy storage device during the time period. The energy change constraint, in the formula, For a moment Energy storage status of battery energy storage devices For a moment Energy storage status of battery energy storage devices and These refer to the charging efficiency and discharging efficiency of the battery energy storage device, respectively. The discrete time step is given by equation (49); Equation (49) represents the time step of the battery energy storage device during the specified time period. The energy upper and lower limits are constrained, and in the formula, and The upper and lower limits of energy storage; Equation (50) states that the energy stored at the end and beginning of the battery energy storage device are equal, where, This represents the initial energy storage status of the battery energy storage device. This indicates the energy storage status of the battery energy storage device at the end of the process.

[0036] The models for photovoltaic units and wind turbine units are constructed as follows:

[0037] (51)

[0038] (52)

[0039] Among them, equations (51) and (52) represent the time periods of the photovoltaic unit and the wind turbine unit, respectively. The upper and lower limits of output are constrained, where, and For photovoltaic units and wind turbine units, respectively, during the time period The power variable, and These are the maximum power outputs of the photovoltaic unit and the wind turbine unit, respectively.

[0040] The charging pile model is constructed as follows:

[0041] (53)

[0042] (54)

[0043] (55)

[0044] (56)

[0045] (57)

[0046] (58)

[0047] (59)

[0048] (60)

[0049] Wherein, equations (53) and (54) represent the electric vehicle's time period. The following are the charging power constraints for fast charging piles that use DC power and slow charging piles that use AC power in conjunction with an onboard charger: where, and Electric vehicles in different time periods The charging power variables of fast charging stations and slow charging stations are as follows. and For 0-1 variables, and These represent the minimum and maximum charging power of electric vehicles at fast charging stations, respectively. and Equation (55) represents the minimum and maximum charging power of an electric vehicle at a slow charging station, respectively; Equation (56) constrains an electric vehicle to charge at only one charging station at a time; Equations (57) and (58) represent the constraints on the number of fast charging stations and slow charging stations operating simultaneously, respectively. and These represent the number of fast charging stations and slow charging stations, respectively; Equations (58) to (60) are the constraints related to the charging state of electric vehicles, where, For a moment Electric vehicle charging status The expected state of charging for electric vehicles at the end of the charging process. For a moment Electric vehicle charging status The charging efficiency of electric vehicles at fast charging stations. For discrete time steps, The charging efficiency of electric vehicles at slow charging stations. and Upper and lower limits for the charging status of electric vehicles. The charging status of the electric vehicle at the end of the charging process;

[0050] The park's scheduling time is With the goal of minimizing the total cost of electricity purchase in the industrial park, the objective function of the economic dispatch model for demand response in the industrial park is as follows:

[0051] (61)

[0052] In equation (61), for The active power input to the park from the power distribution network during the time period. Park time period Internal time-of-use electricity price; taking equation (61) as the objective function and equations (39) to (60) as constraints, the demand response economic dispatch model of the park is obtained; the objective of the park demand response economic dispatch model is optimized to obtain the second-order cone power flow constraints of the park microgrid and the state of each component in the park, that is, the power flow result of the park demand response economic dispatch model is obtained.

[0053] Optionally, the calculation of the carbon emissions input to the distribution network, based on the power grid carbon emission coefficient of the area where the park is located, includes:

[0054] The carbon emission level of the power distribution network corresponding to the target industrial park was analyzed, and the carbon emission coefficient of the power distribution network in this region was obtained using historical data. This will be used as the carbon emission level of the electricity input into the park;

[0055] Based on the carbon emission coefficient of the power distribution network Based on the electricity purchase results of the industrial park, the carbon emissions input to the distribution network are calculated as follows:

[0056] (62)

[0057] in, For time period Carbon emissions from the power distribution network input into the industrial park. The carbon emission coefficient of the power distribution network in the region. for The active power input to the park from the power distribution network during the time period. is the discrete time step.

[0058] Optionally, the calculation of carbon emissions from cogeneration based on the electrothermal power characteristics of the cogeneration unit, combined with the cogeneration unit model, includes:

[0059] The electrothermal coefficient of a combined heat and power (CHP) unit is defined as the coefficient of electrical power equivalent to thermal power. The carbon emissions from combined heat and power (CHP) power generation are calculated as follows:

[0060] (63)

[0061] in, Carbon emissions from the electrical power of combined heat and power (CHP) units. The carbon emission coefficient of thermal power of a combined heat and power unit. The electrical power equivalent heat power coefficient of a combined heat and power (CHP) unit. For cogeneration units The power variable, The discrete time step is given; Equation (63) converts the electrical power of the cogeneration unit into thermal power through electro-carbon conversion, and calculates carbon emissions according to the thermal power carbon emission coefficient;

[0062] The carbon emission coefficient of the loss portion of the cogeneration unit is calculated by substituting the carbon emission coefficient of the electrothermal power equivalent carbon emission coefficient for the loss portion. for:

[0063] (64)

[0064] in, For the thermal efficiency of combined heat and power units, The electrical efficiency of the cogeneration unit is calculated using equation (64). The equivalent carbon emission coefficient of the total electrical and thermal power is used as the carbon emission coefficient of the output power of the cogeneration unit.

[0065] Analyzing the carbon emissions from the loss portion of a combined heat and power (CHP) unit, where the loss portion represents the carbon emissions that the CHP unit should bear, the carbon emissions from CHP are calculated as follows:

[0066] (65)

[0067] in, The carbon emissions from the loss portion of the cogeneration unit; Equation (65) uses the equivalent carbon emission coefficient to calculate the carbon emission coefficient caused by the loss portion, forming the carbon emissions that the cogeneration unit itself should bear.

[0068] Optionally, calculating the carbon emissions of the battery energy storage device based on the battery energy storage device model and considering the carbon storage and carbon transfer characteristics of the battery energy storage device includes:

[0069] Based on the carbon storage level of the battery energy storage device itself, the carbon emission coefficient of the battery energy storage device in the state of discharge is calculated as follows:

[0070] (66)

[0071] in, This represents the carbon emission factor of a battery energy storage device under discharge conditions. For the previous moment The carbon emissions stored by battery energy storage devices. For the previous moment Energy storage status of battery energy storage devices;

[0072] The carbon emissions of battery energy storage devices are analyzed, and the carbon emissions are calculated based on the charging and discharging states of the battery energy storage device:

[0073] (67)

[0074] (68)

[0075] in, for The carbon emissions stored by the battery energy storage device itself at all times. for The carbon emissions stored by the battery energy storage device itself at all times. and These refer to the charging efficiency and discharging efficiency of the battery energy storage device, respectively. and Time periods Battery energy storage device charging and discharging power variables, for The carbon emissions from the charging and discharging power of the battery energy storage device at any given time; Equations (67) and (68) are respectively used to calculate the carbon emissions from the storage of the battery energy storage device and the carbon emissions it bears itself. Indicates time period Net carbon intensity at the location of the battery energy storage device.

[0076] Optionally, the calculation of net carbon intensity at each node in the park based on the power flow results of the park's demand response economic dispatch model, the carbon emissions from the distribution network input, the carbon emissions from the combined heat and power (CHP) system, and the carbon emissions from the battery energy storage device, and the accounting of the carbon emissions of the main demand-side entities, includes:

[0077] Based on the power flow results of the demand response economic dispatch model of the park, the carbon emissions input to the distribution network, the carbon emissions from the combined heat and power generation, and the carbon emissions from the battery energy storage device, the net carbon intensity and network carbon loss of each node in the park within a certain time period are calculated according to the carbon emission flow analysis method.

[0078] Based on the calculation results of the net carbon intensity, carbon accounting is performed on electric vehicles and conventional loads;

[0079] Based on the carbon accounting results, the carbon emissions of demand-side entities are calculated.

[0080] Optionally, the calculation of net carbon intensity and network carbon loss at each node in the park within a certain time period, based on the power flow results of the park's demand response economic dispatch model, the carbon emissions from the distribution network, the carbon emissions from the combined heat and power (CHP) system, and the carbon emissions from the battery energy storage device, according to carbon emission flow analysis methods, includes:

[0081] Using the carbon emissions from the power distribution network, the combined heat and power (CHP) system, and the battery energy storage device as the carbon emissions from each node, the initial input power and initial input carbon emission coefficient of each node are calculated as follows:

[0082] (69)

[0083] (70)

[0084] (71)

[0085] in, Inject power into the units at each node of the park's network. This refers to the number of units at each node of the park. For nodes No. Taiwan unit injected power, Unit time node Input carbon emissions. For the first Carbon emission coefficient of the Taiwanese unit For a unit of time, the first The carbon emissions of each energy storage input node. For the first The power of each energy storage input node. For the first The carbon emission coefficient of energy storage;

[0086] Based on the power flow results of the aforementioned demand response economic scheduling model for the park, the set of outflow nodes for each node is obtained. and inflow node set and the corresponding quantity and ;

[0087] The search satisfies nodes and its Set to -1;

[0088] compute nodes Net carbon intensity is:

[0089] (72)

[0090] If for any node , If the result is -1, the calculation ends; otherwise, proceed to the next step.

[0091] For nodes All outflow nodes Carbon flow is passed through the feeder Transfer to node and nodes Decrease the number of inflow nodes by one, and simultaneously calculate the feeder. Carbon loss is:

[0092] (73)

[0093] The search process is repeated until the net carbon intensity and carbon loss of all nodes are calculated.

[0094] The carbon accounting for electric vehicles and conventional loads, based on the calculation results of the net carbon intensity, includes:

[0095] (74)

[0096] (75)

[0097] (76)

[0098] in, and Electric vehicles in different time periods The carbon emissions from fast charging stations and slow charging stations. and Electric vehicles in different time periods The charging power variables of fast charging stations and slow charging stations are as follows. Indicates time period Net carbon intensity at the location of the battery storage device For regular load during the time period Carbon emissions below For regular load during the time period The power under the condition; Equations (74)-(76) respectively calculate the carbon responsibility of electric vehicles for fast charging, slow charging and conventional load.

[0099] According to another aspect of the present invention, a carbon emission accounting system based on demand response economic scheduling in industrial parks is provided, comprising:

[0100] The module for constructing the economic dispatch model for park demand response is used to construct models of various components in the park and obtain the economic dispatch model for park demand response and its power flow results. The components in the park include: cogeneration units, battery energy storage devices, wind turbines, photovoltaic units, and charging piles.

[0101] The module for calculating carbon emissions from the power distribution network input combines the carbon emission coefficient of the power grid in the area where the park is located to calculate the carbon emissions from the power distribution network input.

[0102] A combined heat and power (CHP) carbon emission calculation module. This module calculates the CHP carbon emissions based on the CHP electrothermal power characteristics and the CHP unit model.

[0103] A module for calculating carbon emissions from a battery energy storage device. This module calculates the carbon emissions from the battery energy storage device based on the battery energy storage device model and in combination with the carbon storage and carbon transfer characteristics of the battery energy storage device.

[0104] The carbon emission accounting module calculates the net carbon intensity of each node in the park based on the power flow results of the park's demand response economic dispatch model, the carbon emissions input to the distribution network, the carbon emissions from the combined heat and power generation, and the carbon emissions from the battery energy storage device, and performs accounting for the main carbon emissions on the demand side.

[0105] According to a third aspect of the present invention, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, can be used to perform the method described in any one of the preceding descriptions, or to run the system described in any one of the preceding descriptions.

[0106] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, can be used to perform the method described in any one of the preceding descriptions, or to run the system described in any one of the preceding descriptions.

[0107] By adopting the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0108] The carbon emission accounting method and system based on demand response economic scheduling of industrial parks provided by this invention proposes a carbon emission calculation model for cogeneration units. This model provides a detailed model of the carbon emissions from the power output of cogeneration units and the carbon emissions borne by their own losses, which helps to analyze the composition of carbon emissions from cogeneration and clarify the carbon emission responsibilities that should be borne.

[0109] The carbon emission accounting method and system based on demand response economic scheduling of industrial parks provided by this invention proposes a carbon emission calculation model for battery energy storage devices. This model provides a refined model of the carbon transfer characteristics of battery energy storage devices, which helps industrial parks achieve carbon reduction effects over a longer time scale by leveraging the carbon transfer characteristics of battery energy storage devices.

[0110] The carbon emission accounting method and system based on demand response economic scheduling of industrial parks provided by this invention can fully consider and calculate the carbon emissions of each component in the park, which helps to clarify the carbon emission responsibilities of each component and provides data reference support for promoting carbon reduction in industrial parks. Attached Figure Description

[0111] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0112] Figure 1 This is a flowchart illustrating the carbon emission accounting method based on demand response economic scheduling in a preferred embodiment of the present invention.

[0113] Figure 2This is a schematic diagram of the carbon accounting process in a preferred embodiment of the present invention.

[0114] Figure 3 This is a schematic diagram of the constituent modules of a carbon emission accounting system based on demand response economic scheduling in a preferred embodiment of the present invention. Detailed Implementation

[0115] The embodiments of the present invention are described in detail below: These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

[0116] One embodiment of the present invention provides a carbon emission accounting method based on demand response economic scheduling of industrial parks. This method can calculate the carbon emissions of each component in the industrial park while optimizing scheduling.

[0117] According to one aspect of the present invention, a carbon emission accounting method based on demand response economic scheduling of industrial parks is provided, which may include:

[0118] S1, construct models of each component in the park, and obtain the park's demand response economic dispatch model and its power flow results; among which, each component in the park includes: cogeneration units, battery energy storage devices, wind turbines, photovoltaic units and charging piles;

[0119] S2, combined with the power grid carbon emission coefficient of the area where the park is located, establish a calculation model for the input carbon emissions of the distribution network and calculate the input carbon emissions of the distribution network;

[0120] S3. Based on the electrothermal power characteristics of cogeneration and combined with the cogeneration unit model, a cogeneration carbon emission calculation model is established to calculate the carbon emissions of cogeneration.

[0121] S4. Based on the battery energy storage device model, and combining the carbon storage and carbon transfer characteristics of the battery energy storage device, establish a carbon emission calculation model for the battery energy storage device to calculate the carbon emissions of the battery energy storage device.

[0122] S5 calculates the net carbon intensity of each node in the park based on the power flow results of the park's demand response economic dispatch model, the carbon emissions from the distribution network, the carbon emissions from combined heat and power, and the carbon emissions from battery energy storage devices, and performs accounting for the main carbon emissions on the demand side.

[0123] In a preferred embodiment of S1, constructing models of each component in the park to obtain the park's demand response economic scheduling model and its power flow results may include:

[0124] S11 analyzes the power generation, grid, load, and storage resources of the industrial park's interconnected distribution network, classifies the distribution network nodes, and defines them. This is a collection of feeder lines within the park. For the set of distribution network nodes, For the collection of power nodes in the park, For the set of load nodes in the park;

[0125] S12, based on the existing Distflow model, uses the second-order cone relaxation method to establish the... The second-order cone power flow constraints of the park's microgrid for a given time period are:

[0126] (77)

[0127] (78)

[0128] (79)

[0129] (80)

[0130] (81)

[0131] (82)

[0132] Where: Equations (77) and (78) are the active and reactive power balance constraints of the park nodes, respectively. and They represent time periods respectively. feeder Active power and reactive power variables, and They represent time periods respectively. feeder Active power and reactive power variables, and feeders The resistance and reactance parameters, For time period feeder The square variable of the current, and Time periods node The active and reactive power variables of the generating unit, and Time periods The active and reactive power variables of the load at the node; Equation (79) is the constraint on the relationship between node voltage, active power, reactive power and current on the feeder of the park, where, For time period node The square of the voltage change. For time period node The voltage square variable is given by equation (80), which is the feeder capacity constraint after second-order cone relaxation. Equations (81) and (82) are the upper and lower limits of the square voltage and square current of the park, respectively. and These are the upper and lower limits of the voltage, respectively. This is the upper limit of the current.

[0133] S13, the park includes combined heat and power units, battery energy storage devices, wind turbines, photovoltaic units and charging piles. The following model is performed for each component in the park:

[0134] S131, the model for a combined heat and power (CHP) unit is constructed as follows:

[0135] (83)

[0136] Wherein, equation (83) represents the time period of the combined heat and power unit. The upper and lower limits of output are constrained, where, For 0-1 variables, For cogeneration units The power variable; and These are the minimum and maximum power of the combined heat and power unit, respectively.

[0137] S132, the battery energy storage device model is constructed as follows:

[0138] (84)

[0139] (85)

[0140] (86)

[0141] (87)

[0142] (88)

[0143] Among them, equations (84) and (85) represent the battery energy storage device during the time period. The charging and discharging power constraint, where, and These are the charging and discharging power variables, respectively. The variable is 0-1, constraining that battery storage devices cannot be charged and discharged simultaneously. and These are the upper and lower limits of the charging power for battery energy storage devices. and These are the upper and lower limits of the discharge power of the battery energy storage device, respectively; Equation (86) represents the discharge power of the battery energy storage device during the time period. The energy change constraint, in the formula, For a moment Energy storage status of battery energy storage devices For a moment Energy storage status of battery energy storage devices and These refer to the charging efficiency and discharging efficiency of the battery energy storage device, respectively. The discrete time step is given by equation (87); Equation (87) represents the time step of the battery energy storage device during the specified time period. The energy upper and lower limits are constrained, and in the formula, and The upper and lower limits of energy storage; Equation (88) states that the energy stored at the end and beginning of the battery energy storage device are equal, where, This represents the initial energy storage status of the battery energy storage device. This indicates the energy storage status of the battery energy storage device at the end of the process.

[0144] S133, construct the photovoltaic unit and wind turbine unit models as follows:

[0145] (89)

[0146] (90)

[0147] Among them, equations (89) and (90) represent the time periods for photovoltaic units and wind turbine units, respectively. The upper and lower limits of output are constrained, where, and For photovoltaic units and wind turbine units, respectively, during the time period The power variable, and These are the maximum power outputs of the photovoltaic unit and the wind turbine unit, respectively.

[0148] S134, the charging pile model is constructed as follows:

[0149] (91)

[0150] (92)

[0151] (93)

[0152] (94)

[0153] (95)

[0154] (96)

[0155] (97)

[0156] (98)

[0157] Wherein, equations (91) and (92) represent the electric vehicle's time period, respectively. The charging power constraints for fast charging piles (charging piles that use DC power for fast charging) and slow charging piles (charging piles that use AC power in conjunction with an onboard charger) are given by the formula, where, and Electric vehicles in different time periods The charging power variables of fast charging stations and slow charging stations are as follows. and For 0-1 variables, and These represent the minimum and maximum charging power of electric vehicles at fast charging stations, respectively. and Equation (93) represents the minimum and maximum charging power of an electric vehicle at a slow charging station, respectively; Equation (94) constrains an electric vehicle to charge at only one charging station at a time; Equations (95) and (96) represent the constraints on the number of fast charging stations and slow charging stations operating simultaneously, respectively. and These represent the number of fast charging stations and slow charging stations, respectively; Equations (96) to (98) are the relevant constraints on the charging state of electric vehicles, where, For a moment Electric vehicle charging status The expected state of charging for electric vehicles at the end of the charging process. For a moment Electric vehicle charging status The charging efficiency of electric vehicles at fast charging stations. For discrete time steps, The charging efficiency of electric vehicles at slow charging stations. and Upper and lower limits for the charging status of electric vehicles. The charging status of the electric vehicle at the end of the charging process;

[0158] S14, the park's scheduling time is With the goal of minimizing the total cost of electricity purchase in the industrial park, the objective function of the economic dispatch model for demand response in the industrial park is as follows:

[0159] (99)

[0160] In equation (99), for The active power input to the park from the power distribution network during the time period. Park time period Time-of-use pricing; using equation (99) as the objective function and equations (77) to (98) as constraints, the demand response economic dispatch model of the park is obtained; the objective of the demand response economic dispatch model of the park is optimized to obtain the second-order cone power flow constraints of the park microgrid and the state of each component in the park (i.e., the calculation results of equations (83) to (98)), thus obtaining the power flow result of the demand response economic dispatch model of the park.

[0161] In a preferred embodiment of S2, a calculation model for the input carbon emissions of the distribution network is established by combining the power grid carbon emission coefficient of the area where the park is located, and the calculation of the input carbon emissions of the distribution network may include:

[0162] S21. Analyze the carbon emission level of the distribution network corresponding to the target industrial park, and obtain the carbon emission coefficient of the distribution network in this region using historical data. This will be used as the carbon emission level of the electricity input into the park;

[0163] S22, based on the carbon emission coefficient of the distribution network Based on the results of electricity purchases in the industrial park, a calculation model for carbon emissions input to the distribution network is constructed, and the carbon emissions input to the distribution network are calculated as follows:

[0164] (100)

[0165] in, For time period Carbon emissions from the power distribution network input into the industrial park. The carbon emission coefficient of the power distribution network in the region. for The active power input to the park from the power distribution network during the time period. is the discrete time step.

[0166] In a preferred embodiment of S3, based on the electrothermal power characteristics of cogeneration and combined with a cogeneration unit model, a cogeneration carbon emission calculation model is established to calculate the carbon emissions input to the distribution network, which may include:

[0167] S31 analyzes the carbon emissions of combined heat and power (CHP) units. The energy output of CHP units is divided into two parts: electrical energy and thermal energy. The electrothermal coefficient of CHP units is defined as the electrical power equivalent thermal power coefficient. Then, a calculation model for carbon emissions from combined heat and power (CHP) power is constructed, and the carbon emissions from CHP power are calculated as follows:

[0168] (101)

[0169] in, This refers to the carbon emissions from the losses incurred by combined heat and power (CHP) units. The carbon emission coefficient of thermal power of a combined heat and power unit. The electrical power equivalent heat power coefficient of a combined heat and power (CHP) unit. For cogeneration units The power variable, The discrete time step is given; Equation (25) converts the electrical power of the cogeneration unit into thermal power through electro-carbon conversion, and calculates carbon emissions according to the thermal power carbon emission coefficient;

[0170] S32, Calculate the carbon emission coefficient of the loss portion of the combined heat and power unit. The carbon emission coefficient is calculated as follows, based on the equivalent carbon emission coefficient of electrothermal power instead of the carbon emission coefficient of the loss portion:

[0171] (102)

[0172] in, The carbon emission coefficient for the loss portion of a combined heat and power (CHP) unit. For the thermal efficiency of combined heat and power units, The electrical efficiency of the cogeneration unit is given by equation (102). The equivalent carbon emission coefficient of the total electrical power and thermal power is calculated and used as the carbon emission coefficient of the output power of the cogeneration unit.

[0173] Analyzing the carbon emissions from the loss portion of a combined heat and power (CHP) unit, which represents the carbon emissions that the CHP unit should bear, a carbon emission calculation model for CHP is constructed to calculate the carbon emissions from CHP:

[0174] (103)

[0175] in, The carbon emissions from the loss portion of the cogeneration unit; Equation (103) uses the equivalent carbon emission coefficient to calculate the carbon emission coefficient caused by the loss portion, forming the carbon emissions that the cogeneration unit itself should bear.

[0176] In a preferred embodiment of S4, based on the battery energy storage device model and combining the carbon storage and carbon transfer characteristics of the battery energy storage device, a carbon emission calculation model for the battery energy storage device is constructed to calculate the carbon emissions of the battery energy storage device, which may include:

[0177] S41, Analyze the carbon emission coefficient of the battery energy storage device under discharge conditions. Based on the carbon storage level of the battery energy storage device itself, calculate the carbon emission coefficient of the battery energy storage device under discharge conditions as follows:

[0178] (104)

[0179] in, This represents the carbon emission factor of a battery energy storage device under discharge conditions. For the previous moment The carbon emissions stored by battery energy storage devices. For the previous moment Energy storage status of battery energy storage devices;

[0180] S42, Analyze the carbon emissions of battery energy storage devices. Based on the charging and discharging states of the battery energy storage device, construct a carbon emission calculation model for the charging and discharging states, and calculate the carbon emissions as follows:

[0181] (105)

[0182] (106)

[0183] in, for The carbon emissions stored by the battery energy storage device itself at all times. for The carbon emissions stored by the battery energy storage device itself at all times. and These refer to the charging efficiency and discharging efficiency of the battery energy storage device, respectively. and Time periods Battery energy storage device charging and discharging power variables, for The carbon emissions from the charging and discharging power of the battery energy storage device at any given time; Equations (105) and (106) are respectively used to calculate the carbon emissions from the storage of the battery energy storage device and the carbon emissions it bears itself. Indicates time period The net carbon intensity at the location of the battery energy storage device is generated during subsequent carbon accounting.

[0184] In a preferred embodiment of S5, such as Figure 2 As shown, based on the power flow results of the park's demand response economic dispatch model, the carbon emissions from the distribution network input, the carbon emissions from combined heat and power (CHP), and the carbon emissions from battery energy storage devices, the net carbon intensity of each node in the park is calculated, and the carbon emissions of the main demand-side entities are accounted for. This can include:

[0185] S51, based on the power flow results of the park's demand response economic dispatch model, the carbon emissions from the distribution network, the carbon emissions from combined heat and power (CHP), and the carbon emissions from battery energy storage devices, calculates the net carbon intensity and network carbon loss of each node in the park within a certain time period using carbon emission flow analysis methods; specifically, this may include:

[0186] S511, taking the carbon emissions from the distribution network, combined heat and power (CHP), and battery energy storage as the input carbon emissions for each node, calculates the initial input power and initial input carbon emission coefficient for each node as follows:

[0187] (107)

[0188] (108)

[0189] (109)

[0190] in, Inject power into the units at each node of the park's network. This refers to the number of units at each node of the park. For nodes No. Taiwan unit injected power, Unit time node Input carbon emissions. For the first Carbon emission coefficient of the Taiwanese unit For a unit of time, the first The carbon emissions of each energy storage input node. For the first The power of each energy storage input node. For the first The carbon emission coefficient of energy storage;

[0191] S512, based on the power flow results of the park's demand response economic scheduling model, obtains the outflow node set for each node. and inflow node set and the corresponding quantity and ;

[0192] S513, search satisfies nodes and its Set to -1;

[0193] S514, Compute Node Net carbon intensity is:

[0194] (110)

[0195] S515, if for any node , If the result is -1, the calculation ends; otherwise, proceed to S516.

[0196] S516, for nodes All outflow nodes Carbon flow is passed through the feeder Transfer to node and nodes Decrease the number of inflow nodes by one, and simultaneously calculate the feeder. Carbon loss is:

[0197] (111)

[0198] Re-execute S513 until the net carbon intensity and carbon loss of all nodes are completed;

[0199] S52, combining the results of net carbon intensity calculations, performs carbon accounting for electric vehicles and conventional loads; the accounting process may include:

[0200] (112)

[0201] (113)

[0202] (114)

[0203] in, and Electric vehicles in different time periods The carbon emissions from fast charging stations and slow charging stations. and Electric vehicles in different time periods The charging power variables of fast charging stations and slow charging stations are as follows. Indicates time period Net carbon intensity at the location of the battery storage device For regular load during the time period Carbon emissions below For regular load during the time period The power under the condition; Equations (112)-(114) respectively calculate the carbon responsibility of electric vehicles for fast charging, slow charging and conventional load.

[0204] The carbon emission accounting method based on demand response economic dispatching in industrial parks provided in the above embodiments of the present invention considers the demand response characteristics of electric vehicles as movable loads, uses the Distflow model to form second-order cone power flow constraints for the industrial park microgrid, establishes a demand response economic dispatching model, and aims to minimize the total system operating cost. The model is analyzed and solved to obtain the optimal solution for optimized dispatching. Combined with the regional power grid carbon emission coefficient, a distribution network input carbon emission model is established. Considering the carbon emissions of electrical power, thermal power, and losses of cogeneration units, a cogeneration carbon emission model is established. Combining the carbon storage and carbon transfer characteristics of battery energy storage devices, the carbon emissions of battery energy storage devices are calculated. Based on the carbon emission flow analysis method, according to the power flow conditions and the carbon emissions of battery energy storage devices, the net carbon intensity of each node in the industrial park is formed, completing the main carbon emission accounting on the demand side, and realizing the demand side carbon emission accounting of the integrated energy system in the industrial park.

[0205] One embodiment of the present invention provides a carbon emission accounting system based on demand response economic scheduling in industrial parks.

[0206] like Figure 3 As shown, the carbon emission accounting system based on demand response economic scheduling in a park provided in this embodiment may include:

[0207] The module for constructing the economic dispatch model for the park's demand response is used to build models of various components in the park and obtain the economic dispatch model for the park's demand response and its power flow results. The components in the park include: combined heat and power units, battery energy storage devices, wind turbines, photovoltaic units, and charging piles.

[0208] The module for calculating carbon emissions from the power distribution network input combines the carbon emission coefficient of the power grid in the area where the park is located to establish a calculation model for carbon emissions from the power distribution network input and calculate the carbon emissions from the power distribution network input.

[0209] The combined heat and power (CHP) carbon emission calculation module is based on the electrothermal power characteristics of CHP and combined with the CHP unit model to establish a CHP carbon emission calculation model and calculate the CHP carbon emissions.

[0210] A module for calculating carbon emissions from battery energy storage devices. This module is based on a battery energy storage device model and combines the carbon storage and carbon transfer characteristics of battery energy storage devices to establish a carbon emission calculation model for battery energy storage devices and calculate the carbon emissions of battery energy storage devices.

[0211] The carbon emission accounting module calculates the net carbon intensity of each node in the park based on the power flow results of the park's demand response economic dispatch model, the carbon emissions from the distribution network, the carbon emissions from combined heat and power, and the carbon emissions from battery energy storage devices, and performs accounting for the carbon emissions of the main demand-side entities.

[0212] It should be noted that the steps in the method provided by the present invention can be implemented using corresponding modules, devices, units, etc. in the system. Those skilled in the art can refer to the technical solution of the method to realize the composition of the system. That is, the embodiments in the method can be understood as preferred examples for building the system, and will not be elaborated here.

[0213] One embodiment of the present invention provides a computer terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it can be used to perform any of the methods in the above embodiments of the present invention, or to run any of the systems in the above embodiments of the present invention.

[0214] Optionally, the memory is used to store programs; the memory may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; the memory may also include non-volatile memory, such as flash memory. The memory is used to store computer programs (such as application programs and functional modules that implement the above methods), computer instructions, etc., and the aforementioned computer programs and computer instructions can be partitioned and stored in one or more memories. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by the processor.

[0215] The aforementioned computer programs, computer instructions, etc., can be stored in partitions within one or more memory locations. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by a processor.

[0216] A processor is used to execute computer programs stored in memory to implement the various steps of the methods or various modules of the systems involved in the above embodiments. For details, please refer to the relevant descriptions in the preceding method and system embodiments.

[0217] The processor and memory can be separate structures or integrated structures. When the processor and memory are separate structures, they can be coupled together via a bus.

[0218] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, can be used to perform the method of any of the above embodiments of the present invention, or to run the system of any of the above embodiments of the present invention.

[0219] Those skilled in the art will understand that, in addition to implementing the system and its various devices provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices of this invention function as logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices provided by this invention can be considered as a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0220] Any matters not covered in the above embodiments of the present invention are well-known in the art.

[0221] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A carbon emission accounting method based on demand response economic scheduling in industrial parks, characterized in that, include: Models of each component in the park are constructed to obtain the park's demand response economic dispatch model and its power flow results; wherein, the components in the park include: cogeneration units, battery energy storage devices, wind turbines, photovoltaic units and charging piles; Calculate the carbon emissions input to the distribution network based on the power grid carbon emission coefficient of the area where the park is located; Based on the electrothermal power characteristics of combined heat and power (CHP) and combined heat and power unit models, the carbon emissions of CHP are calculated; including: The electrothermal coefficient of a combined heat and power (CHP) unit is defined as the coefficient of electrical power equivalent to thermal power. The carbon emissions from combined heat and power (CHP) power generation are calculated as follows: （25） in, Carbon emissions from the electrical power of combined heat and power (CHP) units. The carbon emission coefficient of thermal power of a combined heat and power unit. The electrical power equivalent heat power coefficient of a combined heat and power (CHP) unit. For cogeneration units The power variable, The discrete time step is given; Equation (25) converts the electrical power of the cogeneration unit into thermal power through electro-carbon conversion, and calculates carbon emissions according to the thermal power carbon emission coefficient; The carbon emission coefficient of the loss portion of the cogeneration unit is calculated by substituting the carbon emission coefficient of the electrothermal power equivalent carbon emission coefficient for the loss portion. for: (26) in, For the thermal efficiency of combined heat and power units, The electrical efficiency of the cogeneration unit is given by equation (26). The equivalent carbon emission coefficient of the total electrical power and thermal power is calculated by equation (26) and used as the carbon emission coefficient of the output power of the cogeneration unit. Analyzing the carbon emissions from the loss portion of a combined heat and power (CHP) unit, where the loss portion represents the carbon emissions that the CHP unit should bear, the carbon emissions from CHP are calculated as follows: （27） in, The carbon emissions from the loss portion of the cogeneration unit; Equation (27) uses the equivalent carbon emission coefficient to calculate the carbon emission coefficient caused by the loss portion, forming the carbon emissions that the cogeneration unit itself should bear; Based on the battery energy storage device model, and combined with the carbon storage and carbon transfer characteristics of the battery energy storage device, the carbon emissions of the battery energy storage device are calculated. Based on the power flow results of the demand response economic dispatch model of the park, the carbon emissions input to the distribution network, the carbon emissions from the combined heat and power generation, and the carbon emissions from the battery energy storage device, the net carbon intensity of each node in the park is calculated, and the carbon emissions of the main demand-side entities are accounted for.

2. The carbon emission accounting method based on demand response economic scheduling in industrial parks according to claim 1, characterized in that, The construction of component models within the park yields the park's demand response economic scheduling model and its power flow results, including: The power generation, grid, load, and storage resources of the industrial park's interconnected distribution network are analyzed, and the distribution network nodes are classified and defined. This is a collection of feeder lines within the park. For the set of distribution network nodes, For the collection of power nodes in the park, For the set of load nodes in the park; Based on the Distflow model, a second-order cone relaxation method is used to establish the... The second-order cone power flow constraints of the park's microgrid for a given time period are: （1） （2） （3） （4） （5） （6） Where: Equations (1) and (2) are the active and reactive power balance constraints of the park nodes, respectively, and in the equations, and They represent time periods respectively. feeder Active power and reactive power variables, and They represent time periods respectively. feeder Active power and reactive power variables, and feeders Resistance and reactance parameters, For time period feeder The square variable of the current, and Time periods node The active and reactive power variables of the generating unit and Time periods The active and reactive power variables of the load at the node; Equation (3) is the constraint on the relationship between node voltage, active power, reactive power and current on the feeder of the park, where, For time period node The square of the voltage change. For time period node The voltage square variable is given by equation (4), which is the feeder capacity constraint after second-order cone relaxation. Equations (5) and (6) are the upper and lower limits of the square voltage and square current in the park, respectively. and These are the upper and lower limits of the voltage, respectively. This is the upper limit of the current. The following models are used to model each component in the park: The model for a combined heat and power (CHP) unit is constructed as follows: （7） Wherein, equation (7) represents the time period of the combined heat and power unit. The upper and lower limits of output are constrained, where, For 0-1 variables, For cogeneration units The power variable; and These are the minimum and maximum power of the combined heat and power unit, respectively. The battery energy storage device model is constructed as follows: （8） （9） （10） （11） （12） Among them, equations (8) and (9) represent the battery energy storage device during the time period. The charging and discharging power constraint, where, and These are the charging and discharging power variables, respectively. The variable is 0-1, constraining that battery storage devices cannot be charged and discharged simultaneously. and These are the upper and lower limits of the charging power for battery energy storage devices. and These are the upper and lower limits of the discharge power of the battery energy storage device, respectively; Equation (10) represents the discharge power of the battery energy storage device during the time period. The energy change constraint, in the formula, For a moment Energy storage status of battery energy storage devices For a moment Energy storage status of battery energy storage devices and These refer to the charging efficiency and discharging efficiency of the battery energy storage device, respectively. The discrete time step is given by equation (11), which represents the time step of the battery energy storage device during the specified time period. The energy upper and lower limits are constrained, and in the formula, and The upper and lower limits of energy storage; Equation (12) states that the energy stored at the end and beginning of the battery energy storage device are equal, where, This represents the initial energy storage status of the battery energy storage device. This indicates the energy storage status of the battery energy storage device at the end of the process. The photovoltaic and wind turbine models are constructed as follows: （13） （14） Equations (13) and (14) represent the time periods for photovoltaic units and wind turbine units, respectively. The upper and lower limits of output are constrained, where, and For photovoltaic units and wind turbine units, respectively, during the time period The power variable, and These are the maximum power outputs of the photovoltaic unit and the wind turbine unit, respectively. The charging pile model is constructed as follows: （15） （16） （17） （18） （19） （20） （21） Wherein, equations (15) and (16) represent the electric vehicle's time period, respectively. The following are the charging power constraints for fast charging piles that use DC power and slow charging piles that use AC power in conjunction with an onboard charger: where, and Electric vehicles in different time periods The charging power variables of fast charging stations and slow charging stations are as follows. and For 0-1 variables, and These represent the minimum and maximum charging power of electric vehicles at fast charging stations, respectively. and Equation (17) represents the minimum and maximum charging power of an electric vehicle at a slow charging station, respectively; Equation (18) constrains an electric vehicle to charge at only one charging station at a time; Equations (19) and (10) represent the constraints on the number of fast charging stations and slow charging stations operating simultaneously, respectively. and The numbers represent the number of fast charging stations and slow charging stations, respectively; Equations (20) to (22) are the constraints related to the charging state of electric vehicles, where, For a moment Electric vehicle charging status The expected state of charging for electric vehicles at the end of the time period. For a moment Electric vehicle charging status The charging efficiency of electric vehicles at fast charging stations. For discrete time steps, The charging efficiency of electric vehicles at slow charging stations. and Upper and lower limits for the charging status of electric vehicles. The charging status of the electric vehicle at the end of the charging process; The park's scheduling time is With the goal of minimizing the total cost of electricity purchase in the industrial park, the objective function of the economic dispatch model for demand response in the industrial park is as follows: （23） In equation (23), for The active power input to the park from the power distribution network during the specified time period. Park time period Internal time-of-use electricity price; taking equation (23) as the objective function and equations (1) to (22) as constraints, the demand response economic dispatch model of the park is obtained; the objective of the park demand response economic dispatch model is optimized and solved to obtain the second-order cone power flow constraints of the park microgrid and the state of each component in the park, that is, the power flow result of the park demand response economic dispatch model is obtained.

3. The carbon emission accounting method based on demand response economic scheduling in industrial parks according to claim 1, characterized in that, The calculation of the carbon emissions input to the distribution network, based on the power grid carbon emission coefficient of the area where the park is located, includes: The carbon emission level of the power distribution network corresponding to the target industrial park was analyzed, and the carbon emission coefficient of the power distribution network in this region was obtained using historical data. This will be used as the carbon emission level of the electricity input into the park; Based on the carbon emission coefficient of the power distribution network Based on the electricity purchase results of the industrial park, the carbon emissions input to the distribution network are calculated as follows: （24） in, For time period Carbon emissions from the power distribution network input into the industrial park. The carbon emission coefficient of the power distribution network in the region. for The active power input to the park from the power distribution network during the specified time period. is the discrete time step.

4. The carbon emission accounting method based on demand response economic scheduling in industrial parks according to claim 1, characterized in that, The calculation of carbon emissions from the battery energy storage device based on the battery energy storage device model, combined with the carbon storage and carbon transfer characteristics of the battery energy storage device, includes: Based on the carbon storage level of the battery energy storage device itself, the carbon emission coefficient of the battery energy storage device in the state of discharge is calculated as follows: （28） in, This represents the carbon emission factor of a battery energy storage device under discharge conditions. For the previous moment The carbon emissions stored by battery energy storage devices. For the previous moment Energy storage status of battery energy storage devices; The carbon emissions of battery energy storage devices are analyzed, and the carbon emissions are calculated based on the charging and discharging states of the battery energy storage device: （29） （30） in, for The carbon emissions stored by the battery energy storage device itself at all times. for The carbon emissions stored by the battery energy storage device itself at all times. and These refer to the charging efficiency and discharging efficiency of the battery energy storage device, respectively. and Time periods Battery energy storage device charging and discharging power variables, for The carbon emissions from the charging and discharging power of the battery energy storage device at any given time; Equations (29) and (30) are respectively used to calculate the carbon emissions from the storage of the battery energy storage device and the carbon emissions it bears itself. Indicates time period Net carbon intensity at the location of the battery energy storage device.

5. The carbon emission accounting method based on demand response economic scheduling in industrial parks according to claim 1, characterized in that, The net carbon intensity of each node in the park is calculated based on the power flow results of the park's demand response economic dispatch model, the carbon emissions from the distribution network, the carbon emissions from the combined heat and power (CHP) system, and the carbon emissions from the battery energy storage device. This process is used to account for the main carbon emissions on the demand side, including: Based on the power flow results of the demand response economic dispatch model of the park, the carbon emissions input to the distribution network, the carbon emissions from the combined heat and power generation, and the carbon emissions from the battery energy storage device, the net carbon intensity and network carbon loss of each node in the park within a certain time period are calculated according to the carbon emission flow analysis method. Based on the calculation results of the net carbon intensity, carbon accounting is performed on electric vehicles and conventional loads; Based on the carbon accounting results, the carbon emissions of demand-side entities are calculated.

6. The carbon emission accounting method based on demand response economic scheduling in industrial parks according to claim 5, characterized in that, Based on the power flow results of the park's demand response economic dispatch model, the carbon emissions from the distribution network, the carbon emissions from the combined heat and power (CHP) system, and the carbon emissions from the battery energy storage device, the net carbon intensity and network carbon loss of each node in the park within a certain time period are calculated using carbon emission flow analysis methods, including: Using the carbon emissions from the power distribution network, the combined heat and power (CHP) system, and the battery energy storage device as the carbon emissions from each node, the initial input power and initial input carbon emission coefficient of each node are calculated as follows: （31） （32） （33） in, Inject power into the units at each node of the park's network. This refers to the number of units at each node of the park. For nodes No. Taiwan unit injected power, Unit time node Input carbon emissions. For the first Carbon emission coefficient of the Taiwanese unit For a unit of time, the first The carbon emissions of each energy storage input node. For the first The power of each energy storage input node. For the first The carbon emission coefficient of energy storage; Based on the power flow results of the aforementioned demand response economic scheduling model for the park, the set of outflow nodes for each node is obtained. and inflow node set and the corresponding quantity and ; The search satisfies nodes and its Set to -1; compute nodes Net carbon intensity is: （34） If for any node , If the result is -1, the calculation ends; otherwise, proceed to the next step. For nodes All outflow nodes Carbon flow is passed through the feeder Transfer to node and nodes Decrease the number of inflow nodes by one, and simultaneously calculate the feeder. Carbon loss is: （35） The search process is repeated until the net carbon intensity and carbon loss of all nodes are calculated. The carbon accounting for electric vehicles and conventional loads, based on the calculation results of the net carbon intensity, includes: （36） （37） （38） in, and Electric vehicles in different time periods The carbon emissions from fast charging stations and slow charging stations. and Electric vehicles in different time periods The charging power variables of fast charging stations and slow charging stations are as follows. Indicates time period Net carbon intensity at the location of the battery storage device For regular load during the time period Carbon emissions below For regular load during the time period The power under the condition; Equations (36)-(38) respectively calculate the carbon responsibility of electric vehicles for fast charging, slow charging and conventional load.

7. A carbon emission accounting system based on industrial park demand response economic scheduling for implementing the method of any one of claims 1-6, characterized in that, include: The module for constructing the economic dispatch model for park demand response is used to construct models of various components in the park and obtain the economic dispatch model for park demand response and its power flow results. The components in the park include: cogeneration units, battery energy storage devices, wind turbines, photovoltaic units, and charging piles. The module for calculating carbon emissions from the power distribution network input combines the carbon emission coefficient of the power grid in the area where the park is located to calculate the carbon emissions from the power distribution network input. The combined heat and power (CHP) carbon emission calculation module calculates the CHP carbon emissions based on the CHP electrothermal power characteristics and a CHP unit model. A module for calculating carbon emissions from a battery energy storage device. This module calculates the carbon emissions from the battery energy storage device based on the battery energy storage device model and in combination with the carbon storage and carbon transfer characteristics of the battery energy storage device. The carbon emission accounting module calculates the net carbon intensity of each node in the park based on the power flow results of the park's demand response economic dispatch model, the carbon emissions input to the distribution network, the carbon emissions from the combined heat and power generation, and the carbon emissions from the battery energy storage device, and performs accounting for the main carbon emissions on the demand side.

8. A computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it can be used to perform the method of any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, this program can be used to perform the method of any one of claims 1-6.

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