User-side dynamic carbon responsibility accounting method considering regional fairness and demand response

Abstract

The invention discloses a user-side dynamic carbon responsibility accounting method considering regional fairness and demand response, and the method comprises the steps: building a user-side demand response double-layer optimization model, determining various basic parameters of the model, including a user-side carbon responsibility proportion parameter, a unit cost parameter, a power grid topological parameter, a load initial amount, and a carbon market carbon price, and calculating the demand response of the user-side demand response. Calculating a starting marginal carbon emission factor as an average marginal carbon emission rate; in consideration of demand response, each load considers maximization of power consumption cost, carbon transaction cost and power consumption income, a user demand response model is solved, and a load side demand response plan under the current average marginal carbon emission rate is output and calculated; and setting a convergence condition, if not, solving the operator clearing model by taking the minimum power generation cost as a constraint, obtaining a new unit system carbon emission, updating an average marginal carbon emission rate, outputting a new demand response plan, if the convergence condition is met, outputting a final load demand response plan, and checking a user carbon emission responsibility value.

Description

Technical Field

[0001] This invention relates to the field of information technology, and more specifically, to a user-side dynamic carbon responsibility accounting method that takes into account regional equity and demand response. Background Technology

[0002] Under the global consensus on climate issues, the power industry, as the main battleground for energy transition, faces the severe challenge of global warming. my country will build a clean and low-carbon new power system, which not only requires deep decarbonization of the power generation side but also necessitates reshaping the carbon emission paradigm of the entire power industry through energy structure optimization and technological innovation. Although carbon emissions from the power system mainly originate directly from the power generation stage, end-user demand (the user side) is the fundamental driving force. Therefore, the responsibility for carbon emissions in the power sector should be shared by both the generation and user sides. However, the current carbon management system in the power industry primarily focuses on the generation side, while the user side emphasizes encouraging participation in demand response and has proposed relevant user-side carbon responsibility sharing technologies.

[0003] Current carbon emission allocation methods based on carbon flow theory require further investigation regarding accounting and incentive fairness. Across different regions, nodes closer to low-emission generating units consistently maintain a lower carbon potential, while nodes near high-emission generating units are more likely to consume high-carbon electricity, resulting in a higher carbon potential. However, user loads cannot choose their connection nodes. Although some studies have introduced generalized nodes, treating a region as an equivalent node with identical carbon emission factors for users within the region, this method fails to accurately quantify the changes in carbon emission factors after users participate in demand response. Users who do not participate in demand response also experience a decrease in their carbon emission factors, leading to an unclear carbon responsibility allocation mechanism for user-side demand response participation. Summary of the Invention

[0004] To address the problem that existing technologies for carbon liability accounting on the user side of power systems do not consider regional equity and insufficient incentives for demand response, this invention provides a dynamic carbon liability accounting method on the user side that considers regional equity and demand response.

[0005] The following is the technical solution of the present invention:

[0006] A user-side dynamic carbon liability accounting method that considers regional equity and demand response includes the following steps:

[0007] Establish a two-level optimization model for user-side demand response, determine the basic parameters of the model, including user-side carbon responsibility ratio parameters, unit cost parameters, grid topology parameters, initial load, carbon market carbon price, and calculate the initial marginal carbon emission factor as the average marginal carbon emission rate;

[0008] Considering the maximization of electricity costs, carbon trading costs, and electricity revenue for each load in the demand response, solve the user demand response model and output the load-side demand response plan calculated under the current average marginal carbon emission rate.

[0009] If the convergence condition is not met, the operator clearing model is solved with the minimum power generation cost as the constraint to obtain the new unit system carbon emissions, update the average marginal carbon emission rate, and output a new demand response plan; if the condition is met, proceed to the next step.

[0010] Output the final load demand response plan, obtain the final average marginal carbon emission rate, and calculate the user's carbon emission responsibility value.

[0011] As a preferred embodiment, the user-side demand response two-layer optimization model is specifically as follows:

[0012] The upper layer is a load-side demand response model that considers marginal carbon emissions. The objective function of the load-side demand response model is:

[0013] Based on the pre-clearing results, each load considers electricity costs, carbon trading costs, and electricity revenue, and uses the maximum increase in revenue after demand response as the objective function for demand response decision analysis.

[0014]

[0015] In the formula: F i Let i be the increase in total revenue after the demand response to load i. Let i be the change in electricity cost of load i at time t. Let i be the change in carbon trading costs at time t. Let be the change in electricity revenue after the electricity load i participates in demand response at time t.

[0016] The formula for calculating the change in electricity cost of load i at time t in load node j is:

[0017]

[0018] The formula for calculating the change in carbon trading costs of load i in load node j at time t is:

[0019]

[0020] In the formula, The carbon price at time t in the national carbon emissions trading market as predicted recently.

[0021] The change in electricity revenue is calculated using the load electricity utility function, and the formula is as follows:

[0022]

[0023] In the formula, ω i and τ i Let be the marginal utility parameter of load i.

[0024] The constraints of the load-side demand response model are:

[0025] During user participation in the demand response process, the following conditions must be met:

[0026]

[0027] For a transferable load, the following conditions must be met:

[0028]

[0029] In the formula: Let L be the minimum and maximum values ​​of load i participating in the demand response at time t. p It is a set of loads that can be moved.

[0030] As a preferred embodiment, the user-side demand response two-layer optimization model is specifically as follows:

[0031] The lower layer is a pre-clearing model for solving the optimal DC power flow problem by minimizing the cost of electricity generated by the operator.

[0032] In a power system, generation is the direct source of carbon emissions, but the amount of electricity generated must meet the electricity consumption of users. Both generation and consumption contribute to carbon emissions. The total carbon emissions of the power system at time t are:

[0033]

[0034] In the formula, E t Let G be the carbon emissions of the system at time t, and G be the set of generator sets. Let e ​​be the output of generator unit i at time t. i Let represent the carbon emission intensity of generator unit i, and T represent all time periods during which emissions are cleared.

[0035] Under the principle of sharing the carbon emission responsibility of the power system between power generation and consumption, the carbon responsibility of generator unit i and load i at time t is:

[0036]

[0037] In the formula, The responsibility for carbon emissions of generator unit i at time t. Let η be the carbon emission responsibility of load i at time t, η be the carbon emission responsibility coefficient on the user side, and L be the set of loads.

[0038] Based on market quotation information, the operator aims to minimize the total daily power generation cost. The objective function is:

[0039]

[0040] The model constraints are:

[0041] The constraints on generator output and user-side load are:

[0042]

[0043] In the formula, Let be the load amount of load j at time t.

[0044] The output constraint of the generator set is:

[0045]

[0046] In the formula: P i min,g P i max,g Upper and lower limits of generator output

[0047] The constraints on line power flow are:

[0048]

[0049] In the formula: P lc,t It is the power flow of line lc at time t, μ i,lc It is the distribution factor of generator set i on line lc. R is the upper limit of line lc, and R is the set of branches.

[0050] The start-stop constraints for the generator set are:

[0051]

[0052] In the formula: Let t-1 be the time when traditional generator set i has been turned on or off. This represents the minimum start-stop time for a traditional generator set i. Let be the variable representing the start-stop of the traditional generator set i at time t, ranging from 0 to 1.

[0053] Preferably, the determination of the basic parameters of the model is specifically as follows:

[0054] The user-side carbon responsibility ratio parameter is the proportion of responsibility that the load side should bear in the total carbon emissions. The unit cost parameters include the marginal cost linear term and the marginal cost constant term. The unit performance parameters include the unit's maximum and minimum power and the unit's carbon emission factor. The grid topology parameters include parameters such as grid nodes, lines, topology, switching equipment, and transformers.

[0055] Preferably, the calculation of the starting marginal carbon emission factor, as the average marginal carbon emission rate, is specifically as follows:

[0056] The calculation of the Locational Marginal Carbon Emission Factor (LMCEF) is based on the DC optimal power flow model. The calculation process is as follows: (1) Pre-cleaning is performed with the total load of the system as input to obtain the optimal unit combination and the corresponding total carbon emissions of the system; (2) For each node in the system, a unit incremental load is injected into the node. Under the premise of keeping the load of other nodes unchanged, the power flow model is re-solved to obtain a new unit scheduling scheme and the total carbon emissions of the system; (3) The LMCEF of the node is defined as the difference between the total carbon emissions of the system obtained from the above two solutions.

[0057]

[0058] In the formula: Let be the marginal carbon emission factor of node i at time t. Let t be the change in the output of generator j in the system when node i increases its unit load demand at time t.

[0059] The clearing model calculates the optimal solution of the model using Lagrange multipliers to obtain the nodal marginal electricity price.

[0060]

[0061] In the formula: λ is the marginal electricity price of node i at time t. i,t Let λ be the Lagrange multiplier corresponding to node i in the power balance constraint. lc,t It is the Lagrange multiplier corresponding to the power flow constraint of transmission line lc at time t.

[0062] The average marginal carbon emission rate reflects the carbon emissions of a user-participating unit after demand response within different time periods. The calculation formula is as follows:

[0063]

[0064] In the formula: Let E be the average marginal carbon emission rate of node i at time t. t 'This represents the carbon emissions at time t after participating in demand response and then clearing out again.' Let i be the amount of load i participating in the demand response at time t.

[0065] Preferably, the solution to the user demand response model outputs a load-side demand response plan calculated under the current average marginal carbon emission rate, specifically as follows:

[0066] The current two-layer scheduling model has relatively relaxed requirements for solution speed and adopts an iterative optimization strategy. However, changes in user demand response will lead to changes in marginal generating units and corresponding marginal carbon emission intensity, which can easily cause the solution to fail to converge. Therefore, an iterative decomposition and coordination method is applied to solve this two-layer model. The user demand response plan solved by the upper-layer model is coupled to the lower-layer model. Based on the user demand response plan passed from the upper layer, the lower-layer model uses the minimum daily power generation cost as the objective to obtain a new clearing plan and update the average marginal carbon emission rate. Then, it returns to the upper layer to iterate again.

[0067] Preferably, the setting of convergence conditions and updating of the average marginal carbon emission rate specifically involves:

[0068] The new average marginal carbon emission rate is compared with the previously obtained number. The convergence condition is:

[0069]

[0070] Let be the average marginal carbon emission rate for the nth and (n-1)th iterations, and ε be the convergence accuracy, which is 0.001%.

[0071] The updated average marginal carbon emission rate is an update correction made by applying a certain proportion τ of the deviation cost to the average marginal carbon emission rate of the previous cycle.

[0072]

[0073] Preferably, the output of the final load demand response plan yields the final average marginal carbon emission rate, and the user's carbon emission responsibility value is calculated, characterized in that...

[0074] Users who participate in demand response receive a demand response amount, which is then cleared through electricity trading. After clearing, the carbon emission responsibilities of both the power generation and user sides are calculated. The carbon responsibility of participating users is adjusted based on the pre-clearing calculation formula:

[0075]

[0076] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-described two-layer optimization scheduling method that considers the synergy of electricity-storage-carbon and user needs when it calls the computer program in the memory.

[0077] The present invention also provides a storage medium storing computer-executable instructions, which, when loaded and executed by a processor, implement the aforementioned user-side dynamic carbon responsibility accounting method that considers regional fairness and demand response.

[0078] Compared with the prior art, the beneficial effects of the technical solution of the present invention are as follows:

[0079] This invention presents a user-side dynamic carbon responsibility accounting method that considers regional equity and demand response. The method is logically clear, introducing the average marginal carbon emission rate as the accounting benchmark. It addresses the regional unfairness issues caused by differences in user grid connection locations (e.g., proximity to renewable energy or thermal power units) in traditional methods, ensuring that users with the same electricity consumption bear consistent carbon responsibility. A two-layer optimization model is constructed: users optimize their electricity consumption behavior based on the average marginal carbon emission rate, while operators pre-clear the system with the goal of minimizing power generation costs, updating carbon emission data. An iterative decomposition and coordination method achieves two-way feedback, fairly quantifying users' actual emission reduction contributions and promoting coordinated carbon reduction on both the power generation and consumption sides. Attached Figure Description

[0080] Figure 1 Flowchart of the method provided by the present invention

[0081] Figure 2 Diagram of the improved PJM-5 node system for the embodiment.

[0082] Figure 3 The above examples show the predicted load and wind and solar power output for each time period within a 24-hour period.

[0083] Figure 4 Points 4 and 18 in the example are the results of user carbon responsibility. Detailed Implementation

[0084] The present invention will be further described below with reference to specific embodiments. These embodiments are only used to more clearly illustrate the technical solutions of this application and should not be construed as limiting the scope of protection of this application.

[0085] It should be understood that, in the various embodiments of the present invention, the order of the sequence numbers in each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0086] Example: A user-side dynamic carbon liability accounting method that considers regional equity and demand response, such as... Figure 1 As shown, it includes the following steps:

[0087] Step 1: Establish a two-level optimization model for user-side demand response, determine the basic parameters of the model, including user-side carbon responsibility ratio parameters, unit cost parameters, grid topology parameters, initial load, carbon market carbon price, and calculate the initial marginal carbon emission factor as the average marginal carbon emission rate.

[0088] The upper layer is a load-side demand response model that considers marginal carbon emissions. The objective function of the load-side demand response model is:

[0089] Based on the pre-clearing results, each load considers electricity costs, carbon trading costs, and electricity revenue, and uses the maximum increase in revenue after demand response as the objective function for demand response decision analysis.

[0090]

[0091] In the formula: F i Let i be the increase in total revenue after the demand response to load i. Let i be the change in electricity cost of load i at time t. Let i be the change in carbon trading costs at time t. Let be the change in electricity revenue after the electricity load i participates in demand response at time t.

[0092] The formula for calculating the change in electricity cost of load i at time t in load node j is:

[0093]

[0094] The formula for calculating the change in carbon trading costs of load i in load node j at time t is:

[0095]

[0096] In the formula, The carbon price at time t in the national carbon emissions trading market as predicted recently.

[0097] The change in electricity revenue is calculated using the load electricity utility function, and the formula is as follows:

[0098]

[0099] In the formula, ω i and τ i Let be the marginal utility parameter of load i.

[0100] The constraints of the load-side demand response model are:

[0101] During user participation in the demand response process, the following conditions must be met:

[0102]

[0103] For a transferable load, the following conditions must be met:

[0104]

[0105] In the formula: Let L be the minimum and maximum values ​​of load i participating in the demand response at time t. p It is a set of loads that can be moved.

[0106] The lower layer is a pre-clearing model for solving the optimal DC power flow problem by minimizing the cost of electricity generated by the operator.

[0107] In a power system, generation is the direct source of carbon emissions, but the amount of electricity generated must meet the electricity consumption of users. Both generation and consumption contribute to carbon emissions. The total carbon emissions of the power system at time t are:

[0108]

[0109] In the formula, E t Let G be the carbon emissions of the system at time t, and G be the set of generator sets. Let e ​​be the output of generator unit i at time t. i Let represent the carbon emission intensity of generator unit i, and T represent all time periods during which emissions are cleared.

[0110] Under the principle of sharing the carbon emission responsibility of the power system between power generation and consumption, the carbon responsibility of generator unit i and load i at time t is:

[0111]

[0112] In the formula, The responsibility for carbon emissions of generator unit i at time t. Let η be the carbon emission responsibility of load i at time t, η be the carbon emission responsibility coefficient on the user side, and L be the set of loads.

[0113] Based on market quotation information, the operator aims to minimize the total daily power generation cost. The objective function is:

[0114]

[0115] The model constraints are:

[0116] The constraints on generator output and user-side load are:

[0117]

[0118] In the formula, Let be the load amount of load j at time t.

[0119] The output constraint of the generator set is:

[0120]

[0121] In the formula: P i min,g P i max,g Upper and lower limits of generator output

[0122] The constraints on line power flow are:

[0123]

[0124] In the formula: P lc,t It is the power flow of line lc at time t, μ i,lc It is the distribution factor of generator set i on line lc. R is the upper limit of line lc, and R is the set of branches.

[0125] The start-stop constraints for the generator set are:

[0126]

[0127] In the formula: Let t-1 be the time when traditional generator set i has been turned on or off. This represents the minimum start-stop time for a traditional generator set i. Let be the variable representing the start-stop of the traditional generator set i at time t, ranging from 0 to 1.

[0128] The user-side carbon responsibility ratio parameter is the proportion of responsibility that the load side should bear in the total carbon emissions. The unit cost parameters include the marginal cost linear term and the marginal cost constant term. The unit performance parameters include the unit's maximum and minimum power and the unit's carbon emission factor. The grid topology parameters include parameters such as grid nodes, lines, topology, switching equipment, and transformers.

[0129] In this embodiment, an improved PJM-5 node system is used for simulation. The system structure diagram is as follows. Figure 2 As shown, the system includes 1 wind turbine, 1 photovoltaic turbine, 2 coal-fired turbines, and 1 gas turbine. Parameters for each unit are shown in Tables 1 and 2. The predicted load and wind and photovoltaic output for each time period of the 24-hour period are shown in... Figure 3 Node 2 is connected to the large load 1, with ω1 = 17$ / (MW·h) and τ1 = -0.017$ / (MW·h). 2 The adjustable load is set to not exceed 15% of the initial load. Node 3 is connected to the large load 2, with a self-contained energy storage system capacity of 110 MW·h, an energy storage charge-discharge efficiency of 0.92, a maximum power of 25 MW, and a maximum transferable load of 10% of the load. Node 4 is connected to a fixed load that does not participate in demand response. The carbon price is set at $15 / t, and the carbon responsibility ratio coefficient after considering network loss allocation is 0.45.

[0130] Table 1 Unit Cost Parameters

[0131]

[0132] Table 2 Unit Performance Parameters

[0133]

[0134] The calculation of the node marginal carbon emission factor is based on the DC optimal power flow model. The calculation process is as follows: (1) Pre-cleaning is performed with the total load of the system as input to obtain the optimal unit combination and the corresponding total system carbon emissions; (2) For each node in the system, a unit incremental load is injected into the node. Under the premise of keeping the load of other nodes unchanged, the power flow model is solved again to obtain a new unit scheduling scheme and the total system carbon emissions; (3) The LMCEF of the node is defined as the difference between the total system carbon emissions obtained from the above two solutions.

[0135]

[0136] In the formula: Let be the marginal carbon emission factor of node i at time t. Let t be the change in the output of generator j in the system when node i increases its unit load demand at time t.

[0137] The clearing model calculates the optimal solution of the model using Lagrange multipliers to obtain the nodal marginal electricity price.

[0138]

[0139] In the formula: λ is the marginal electricity price of node i at time t. i,t Let λ be the Lagrange multiplier corresponding to node i in the power balance constraint. lc,t It is the Lagrange multiplier corresponding to the power flow constraint of transmission line lc at time t.

[0140] The average marginal carbon emission rate reflects the carbon emissions of a user-participating unit after demand response within different time periods. The calculation formula is as follows:

[0141]

[0142] In the formula: Let E be the average marginal carbon emission rate of node i at time t. t 'This represents the carbon emissions at time t after participating in demand response and then clearing out again.' Let i be the amount of load i participating in the demand response at time t.

[0143] Step 2: Considering the demand response of each load, maximizing electricity costs, carbon trading costs, and electricity revenue, solve the user demand response model and output the load-side demand response plan calculated under the current average marginal carbon emission rate.

[0144] The current two-layer scheduling model has relatively relaxed requirements for solution speed and adopts an iterative optimization strategy. However, changes in user demand response will lead to changes in marginal generating units and corresponding marginal carbon emission intensity, which can easily cause the solution to fail to converge. Therefore, an iterative decomposition and coordination method is applied to solve this two-layer model. The user demand response plan solved by the upper-layer model is coupled to the lower-layer model. Based on the user demand response plan passed from the upper layer, the lower-layer model uses the minimum daily power generation cost as the objective to obtain a new clearing plan and update the average marginal carbon emission rate. Then, it returns to the upper layer to iterate again.

[0145] Step 3: Set convergence conditions. If not met, solve the operator clearing model with the minimum power generation cost as the constraint to obtain the new unit system carbon emissions, update the average marginal carbon emission rate, and output a new demand response plan; if satisfied, proceed to the next step.

[0146] The new average marginal carbon emission rate is compared with the previously obtained number. The convergence condition is:

[0147]

[0148] Let be the average marginal carbon emission rate for the nth and (n-1)th iterations, and ε be the convergence accuracy, which is 0.001%.

[0149] The updated average marginal carbon emission rate is an update correction made by applying a certain proportion τ of the deviation cost to the average marginal carbon emission rate of the previous cycle.

[0150]

[0151] Step 4: Output the final load demand response plan, obtain the final average marginal carbon emission rate, and calculate the user's carbon emission responsibility value.

[0152] Users who participate in demand response receive a demand response amount, which is then cleared through electricity trading. After clearing, the carbon emission responsibilities of both the power generation and user sides are calculated. The carbon responsibility of participating users is adjusted based on the pre-clearing calculation formula:

[0153]

[0154] In this embodiment, two times, 4:00 and 18:00, when user-side electricity consumption begins to increase and decrease respectively, are selected to obtain the carbon emission responsibility results of the power generation and consumption entities, such as... Figure 4 As shown.

Claims

1. A user-side dynamic carbon responsibility accounting method considering regional equity and demand response, characterized in that, include: Establish a two-level optimization model for user-side demand response, determine the basic parameters of the model, including user-side carbon responsibility ratio parameters, unit cost parameters, grid topology parameters, initial load, carbon market carbon price, and calculate the initial marginal carbon emission factor as the average marginal carbon emission rate; Considering the maximization of electricity costs, carbon trading costs, and electricity revenue for each load in the demand response, solve the user demand response model and output the load-side demand response plan calculated under the current average marginal carbon emission rate. If the convergence condition is not met, the operator clearing model is solved with the minimum power generation cost as the constraint to obtain the new unit system carbon emissions, update the average marginal carbon emission rate, and output a new demand response plan; if the condition is met, proceed to the next step. Output the final load demand response plan, obtain the final average marginal carbon emission rate, and calculate the user's carbon emission responsibility value.

2. The user-side demand response two-layer optimization model as described in claim 1, characterized in that, The upper layer is a load-side demand response model that considers marginal carbon emissions. The objective function of the load-side demand response model is: Based on the pre-clearing results, each load considers electricity costs, carbon trading costs, and electricity revenue, and uses the maximum increase in revenue after demand response as the objective function for demand response decision analysis. In the formula: F i Let i be the increase in total revenue after the demand response to load i. Let i be the change in electricity cost of load i at time t. Let i be the change in carbon trading costs at time t. Let be the change in electricity revenue after the electricity load i participates in demand response at time t. The formula for calculating the change in electricity cost of load i at time t in load node j is: The formula for calculating the change in carbon trading costs of load i at time t in load node j is as follows: In the formula, The carbon price at time t in the national carbon emissions trading market as predicted recently. The change in electricity revenue is calculated using the load electricity utility function, and the formula is as follows: In the formula, ω i and τ i Let be the marginal utility parameter of load i. The constraints of the load-side demand response model are: During user participation in the demand response process, the following conditions must be met: For a transferable load, the following conditions must be met: In the formula: Let L be the minimum and maximum values ​​of the load i participating in the demand response at time t. p It is a set of loads that can be moved.

3. The user-side demand response two-layer optimization model as described in claim 1, characterized in that, The lower layer is a pre-clearing model for solving the optimal DC power flow problem by minimizing the cost of electricity generated by the operator. In a power system, generation is the direct source of carbon emissions, but the amount of electricity generated must meet the electricity consumption of users. Both generation and consumption contribute to carbon emissions. The total carbon emissions of the power system at time t are: In the formula, E t Let G be the carbon emissions of the system at time t, and G be the set of generator sets. Let e ​​be the output of generator unit i at time t. i Let represent the carbon emission intensity of generator unit i, and T represent all time periods during which emissions are cleared. Under the principle of sharing the carbon emission responsibility of the power system between power generation and consumption, the carbon responsibility of generator unit i and load i at time t is: In the formula, The responsibility for the carbon emissions of generator unit i at time t. Let η be the carbon emission responsibility of load i at time t, η be the carbon emission responsibility coefficient on the user side, and L be the set of loads. Based on market quotation information, the operator aims to minimize the total daily power generation cost. The objective function is: The model constraints are: The constraints on generator output and user-side load are: In the formula, Let be the load amount of load j at time t. The output constraint of the generator set is: In the formula: P i min,g P i max,g Upper and lower limits of generator output The constraints on line power flow are: In the formula: P lc,t It is the power flow of line lc at time t, μ i,lc It is the distribution factor of generator set i on line lc. R is the upper limit of line lc, and R is the set of branches. The start-stop constraints for the generator set are: In the formula: Let t-1 be the time when traditional generator set i has been turned on or off. This represents the minimum start-stop time for a traditional generator set i. Let be the variable representing the start-stop of the traditional generator set i at time t, ranging from 0 to 1.

4. The method for determining the basic parameters of the model as described in claim 1, characterized in that, The user-side carbon responsibility ratio parameter is the proportion of responsibility that the load side should bear in the total carbon emissions. The unit cost parameters include the marginal cost linear term and the marginal cost constant term. The unit performance parameters include the unit's maximum and minimum power and the unit's carbon emission factor. The grid topology parameters include parameters such as grid nodes, lines, topology, switching equipment, and transformers.

5. The method for calculating the initial marginal carbon emission factor as described in claim 1, as the average marginal carbon emission rate, is characterized in that... The calculation of the Locational Marginal Carbon Emission Factor (LMCEF) is based on the DC optimal power flow model. The calculation process is as follows: (1) Pre-cleaning is performed with the total system load as input, and the optimal unit combination and the corresponding total system carbon emissions are obtained by solving the problem. (2) For each node in the system, inject a unit incremental load into the node, and under the premise of keeping the load of other nodes unchanged, resolve the power flow model to obtain a new unit scheduling scheme and the total carbon emissions of the system; (3) The LMCEF of the node is defined as the difference between the total carbon emissions of the system obtained from the above two solutions. In the formula: Let be the marginal carbon emission factor of node i at time t. Let t be the change in the output of generator j in the system when node i increases its unit load demand at time t. The clearing model calculates the optimal solution of the model using Lagrange multipliers to obtain the nodal marginal electricity price. In the formula: λ is the marginal electricity price of node i at time t. i,t Let λ be the Lagrange multiplier corresponding to node i under the power balance constraint. lc,t It is the Lagrange multiplier corresponding to the power flow constraint of transmission line lc at time t. The average marginal carbon emission rate reflects the carbon emissions of a user-participating unit after demand response within different time periods. The calculation formula is as follows: In the formula: Let E be the average marginal carbon emission rate of node i at time t. t 'This represents the carbon emissions at time t after participating in demand response and then clearing out again.' Let i be the amount of load i participating in the demand response at time t.

6. The method for solving the user demand response model as described in claim 1, outputting a load-side demand response plan calculated under the current average marginal carbon emission rate, characterized in that... The current two-layer scheduling model has relatively relaxed requirements for solution speed and adopts an iterative optimization strategy. However, changes in user demand response will lead to changes in marginal generating units and corresponding marginal carbon emission intensity, which can easily cause the solution to fail to converge. Therefore, an iterative decomposition and coordination method is applied to solve this two-layer model. The user demand response plan solved by the upper-layer model is coupled to the lower-layer model. Based on the user demand response plan passed from the upper layer, the lower-layer model uses the minimum daily power generation cost as the objective to obtain a new clearing plan and update the average marginal carbon emission rate. Then, it returns to the upper layer to iterate again.

7. The method for setting convergence conditions and updating the average marginal carbon emission rate as described in claim 1, characterized in that, The new average marginal carbon emission rate is compared with the previously obtained number. The convergence condition is: Let be the average marginal carbon emission rate for the nth and (n-1)th iterations, and ε be the convergence accuracy, which is 0.001%. The updated average marginal carbon emission rate is an update correction made by applying a certain proportion τ of the deviation cost to the average marginal carbon emission rate of the previous cycle.

8. As described in claim 1, outputting the final load demand response plan, obtaining the final average marginal carbon emission rate, and calculating the user's carbon emission responsibility value, characterized in that, Users who participate in demand response receive a demand response amount, which is then cleared through electricity trading. After clearing, the carbon emission responsibilities of both the power generation and user sides are calculated. The carbon responsibility of participating users is adjusted based on the pre-clearing calculation formula:

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor, when calling the computer program in the memory, implements the steps of a user-side dynamic carbon responsibility accounting method that considers regional equity and demand response as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, The storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the steps of a user-side dynamic carbon liability accounting method that considers regional equity and demand response as described in any one of claims 1 to 8.

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