Low-carbon demand response method considering uncertainty of marginal carbon emission factors

Abstract

The invention relates to a low-carbon demand response method considering the uncertainty of a marginal carbon emission factor. The method comprises the following steps: calculating the overall carbon emission information of a power system based on a power load prediction value, and taking the overall carbon emission information as the reference state carbon emission information of the power system; calculating the overall carbon emission information of the power system considering the new energy uncertain factors, and calculating a marginal carbon emission factor of each node of the power system based on the overall carbon emission information of the power system considering the new energy uncertain factors and the reference state carbon emission information of the power system; and issuing the marginal carbon emission factors of the corresponding nodes of the power system in different time periods to the user sides corresponding to the nodes, and optimizing the power utilization strategy by the user sides according to the received marginal carbon emission factors in each time period.

Description

Technical Field

[0001] The invention relates to a low-carbon demand response method considering the uncertainty of marginal carbon emission factors, and belongs to the field of electric power technology. Background Art

[0002] By providing carbon emission factor signals to the user side, users are encouraged to change their electricity consumption behavior, thereby affecting the carbon emission level of power generation on the source side, thereby achieving carbon emission reduction in the power system. This technology is called "low-carbon demand response technology." Existing low-carbon demand response technology calculates the user's dynamic carbon emission factor based on the composition of the user's electricity sources consumed in different time periods and the carbon emission information of the corresponding electricity sources, and publishes the predicted carbon emission factor curve to the user. The user can know his low-carbon electricity consumption period and high-carbon electricity consumption period according to the dynamic carbon emission factor, and then take electricity transfer measures to arrange carbon reduction plans by himself to change the carbon emission level of electricity consumption. This mechanism is different from the traditional unified management of user-side resources. It enables users to clearly perceive their own carbon emission levels before and after carbon reduction, so it can be promoted and applied in actual operations;

[0003] However, the low-carbon demand response technology currently under study still has problems to be solved, that is, although the predicted dynamic carbon emission factor curve can provide users with more electricity consumption time periods and less electricity consumption time periods, the impact of changes in user electricity consumption behavior on system / source-side carbon emissions is not reflected. Therefore, users can only perceive the actual carbon emission factor after the electricity consumption behavior occurs, and then compare the level of carbon emissions of electricity consumption in different time periods, but cannot clearly know the changes in system carbon emissions caused by changes in unit electricity consumption in each time period, nor can they reasonably evaluate their own carbon reduction before implementing carbon reduction measures. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a low-carbon demand response method that takes into account the uncertainty of marginal carbon emission factors.

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

[0006] In one aspect, the present invention provides a low-carbon demand response method considering the uncertainty of marginal carbon emission factors, comprising the following steps:

[0007] Calculate the overall carbon emission information of the power system based on the power load forecast value as the carbon emission information of the power system benchmark state;

[0008] Calculate the overall carbon emission information of the power system considering the uncertainty factors of new energy, and calculate the marginal carbon emission factor of each node in the power system based on the overall carbon emission information of the power system considering the uncertainty factors of new energy and the carbon emission information of the power system in the baseline state;

[0009] The marginal carbon emission factors of the nodes corresponding to the power system in different time periods are sent to the user side corresponding to the node, and the user side optimizes the electricity consumption strategy according to the received marginal carbon emission factors for each time period.

[0010] As a preferred embodiment of the present invention, the steps for calculating the overall carbon emission information of the power system are:

[0011] Through the simulation operation of power load forecast value, the power data of each unit in the power system under the optimal economic model is calculated, and then the overall carbon emission information of the power system is calculated based on the power data of each unit.

[0012] As a preferred embodiment of the present invention, the optimal economic model is specifically:

[0013] Taking minimizing the power system operation cost as the objective function, the power system optimization operation model is constructed:

[0014]

[0015] Where: c G represents the vector of the operating cost coefficients of conventional units; P G,t For YuanP G,k,t The column vector, P G,k,t represents the output of the kth conventional unit at time t; P G,min With P G,max Respectively represent the minimum output and maximum output of conventional units; e G represents the unit vector of the conventional unit; e R Represents the unit vector of the new energy unit; e D represents the load unit vector; P R,t represents the new energy output matrix; represents the new energy curtailment matrix; P D,t represents the load vector; F represents the power transfer factor matrix; J G,t The matrix representing the node position index of the conventional unit in the power system; J R,t The matrix representing the node position index of the new energy unit in the power system; P line,max Indicates the maximum power allowed to be transmitted on the line; R G Indicates the unit climbing rate.

[0016] As a preferred embodiment of the present invention, the calculation formula for the overall carbon emission information of the power system is:

[0017] E sys,t =ε G (P G, mar ,t -P G0,t )

[0018] Where: G represents the row vector of fuel emission factors for conventional units; P G, mar ,t P represents the power generation power of the power system unit in the simulated operation state at time t; G0,t It represents the power generation capacity of the power system unit under the reference state at time t.

[0019] As a preferred embodiment of the present invention, the calculation steps of the marginal carbon emission factor of each node in the power system are:

[0020] When simulating operation through power load forecast value, additional scenarios of uncertainty in new energy output are introduced to perform multi-scenario simulation operation, as shown in the following formula:

[0021] P W,ω,t =P W,pre,t +x W,ω,t

[0022] P PV,ξ,t =P PV,pre,t +x PV,ξ,t

[0023] Where: P W,ω,t represents the output value of wind power generation under the scenario ω at time t; P W,pre,t represents the predicted output value of wind power generation at time t; x W,ω,t represents the sample value of the prediction error random variable of wind power generation under the scenario ω at time t; P PV,ξ,t represents the output value of photovoltaic power generation under the scenario ξ at time t; P PV,pre,t represents the predicted output value of photovoltaic power generation at time t; x PV,ξ,t represents the sample value of the prediction error random variable of photovoltaic power generation under the scenario ξ at time t;

[0024] For any scenario, randomly increase the load unit power of any node in the scenario, calculate the power data of each unit of the power system under the optimal economic model at this time, and calculate the overall carbon emission information of the power system at this time based on the power data of each unit, and then calculate the marginal carbon emission factor of the increased load of the node, traverse all nodes, and repeat the above operation to obtain the marginal carbon emission factor of the increased load of all nodes in the scenario;

[0025] At the same time, in this scenario, the load unit power of any node is randomly reduced, and the power data of each unit in the power system under the optimal economic model is calculated at this time. The overall carbon emission information of the power system at this time is calculated based on the power data of each unit, and then the marginal carbon emission factor of load reduction of the node is calculated. All nodes are traversed and the above operations are repeated to obtain the marginal carbon emission factor of load reduction of all nodes in this scenario.

[0026] As a preferred embodiment of the present invention, the calculation formula of the marginal carbon emission factor of a node is specifically as follows:

[0027]

[0028] Where: i,t represents the marginal carbon emission factor of node i at time t; E sys,t represents the carbon emission information of the power system at time t; P L,i,t Represents the load change of node i at time t.

[0029] As a preferred embodiment of the present invention, the user side optimizes the electricity consumption strategy according to the received marginal carbon emission factors of each time period, and the strategy includes the low-carbon energy consumption optimization strategy of the power user load and the energy storage carbon reduction optimization operation strategy;

[0030] The power user load low-carbon energy optimization strategy divides the power users' participation in low-carbon demand response into ordinary life production electricity and electric vehicle electricity;

[0031] The following model is constructed for ordinary life and production electricity:

[0032]

[0033] in: and They represent the marginal carbon emission factor of load increase and load reduction of node i at time t respectively; and They represent the load increase and load decrease of node i at time t respectively; D represents the adjustment coefficient;

[0034] The following model is constructed for electric vehicle electricity consumption:

[0035]

[0036]

[0037] The energy storage carbon reduction optimization operation strategy is optimized through the following model:

[0038]

[0039] 0≤P ES,ch,t ≤(1-M t ) ESmax

[0040] 0≤P ES,disch,t ≤M t P ESmax

[0041]

[0042] S ES,min ≤E ES,t ≤S ES,max

[0043] E ES,0 =E ES,24

[0044] Where: P ES,disch,t and P ES,ch,t They represent the discharge power and charging power of the energy storage at time t respectively; P ESmax Indicates the maximum charging and discharging power of energy storage; M t A binary variable representing the restriction of energy storage charging and discharging; E ES,t Represents the energy storage capacity at time t; S ES,min Indicates the minimum allowable value of energy storage capacity; S ES,max Indicates the maximum allowable energy storage capacity.

[0045] As a preferred embodiment of the present invention, the actual power system load after the user side uses the optimized power consumption strategy is collected, and the actual carbon emission information of the power system is calculated based on the actual power system load;

[0046] The carbon reduction amount after the power system implements the marginal carbon emission factor of the power system is calculated based on the carbon emission information of the power system's baseline state and the power system's actual carbon emission information.

[0047] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any embodiment of the present invention is implemented.

[0048] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0049] The present invention has the following beneficial effects:

[0050] 1. The present invention uses marginal carbon emission factor technology to characterize the impact of power fluctuations at each load node on the overall carbon emissions of the system, further constructs a load-side low-carbon energy consumption optimization model, minimizes system carbon emissions, and takes into account the impact of new energy prediction errors on marginal carbon emission factors. It can guide user-side loads to respond to low-carbon demand, actively absorb zero-carbon and low-carbon energy, and achieve low-carbon optimization operation of the power system under the user-side active participation mode; it can more accurately quantify carbon emission changes and achieve better carbon reduction effects; it can more comprehensively control the uncertainties faced by system carbon reduction, and by providing the probability distribution of demand response measures under random scenarios, it can provide users with targeted suggestions for taking carbon reduction measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0054] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0055] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0056] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0057] Embodiment 1:

[0058] See also Figure 1 , a low-carbon demand response method considering the uncertainty of marginal carbon emission factors, comprising the following steps:

[0059] Calculate the overall carbon emission information of the power system based on the power load forecast value as the carbon emission information of the power system benchmark state;

[0060] Calculate the overall carbon emission information of the power system considering the uncertainty factors of new energy, and calculate the marginal carbon emission factor of each node in the power system based on the overall carbon emission information of the power system considering the uncertainty factors of new energy and the carbon emission information of the power system in the baseline state;

[0061] The marginal carbon emission factors of the nodes corresponding to the power system in different time periods are sent to the user side corresponding to the node, and the user side optimizes the electricity consumption strategy according to the received marginal carbon emission factors for each time period.

[0062] As a preferred implementation of this embodiment, the steps for calculating the overall carbon emission information of the power system are:

[0063] Through the simulation operation of power load forecast value, the power data of each unit in the power system under the optimal economic model is calculated, and then the overall carbon emission information of the power system is calculated based on the power data of each unit.

[0064] As a preferred implementation of this embodiment, the optimal economic model is specifically:

[0065] Taking minimizing the power system operation cost as the objective function, the power system optimization operation model is constructed:

[0066]

[0067] Where: c G represents the vector of the operating cost coefficients of conventional units; P G,t For YuanP G,k,t The column vector, P G,k,t represents the output of the kth conventional unit at time t; P G,min With P G,max Respectively represent the minimum output and maximum output of conventional units; e G represents the unit vector of conventional units (the number of elements is the number of conventional units); e R Represents the unit vector of new energy units (the number of elements is the number of new energy units); e D represents the load unit vector (the number of elements is the load quantity); P R,t represents the new energy output matrix; represents the new energy curtailment matrix; P D,t represents the load vector; F represents the power transfer factor matrix; J G,t The matrix representing the node position index of the conventional unit in the power system; J R,t The matrix representing the node position index of the new energy unit in the power system; P line,max Indicates the maximum power allowed to be transmitted on the line; R G represents the unit ramp rate; the first row of formulas indicates that the objective function of the entire power system operation is economic optimization; the second row of formulas indicates that the output of the generator unit must be between the maximum and minimum outputs allowed; the third row of formulas is the source-load balance constraint; the fourth row of formulas is the line transmission power constraint, which aims to distribute the injected power of each node on the branch; the fifth row of formulas limits the power change of the conventional unit in adjacent time periods to within the ramp rate.

[0068] As a preferred implementation of this embodiment, the calculation formula for the overall carbon emission information of the power system is:

[0069] Esys,t =ε G (P G, mar ,t -P G0,t )

[0070] Where: G represents the row vector of fuel emission factors for conventional units; P G, mar ,t P represents the power generation power of the power system unit in the simulated operation state at time t; G0,t It represents the power generation capacity of the power system unit under the reference state at time t.

[0071] As a preferred implementation of this embodiment, the calculation steps of the marginal carbon emission factor of each node in the power system are:

[0072] When simulating operation through power load forecast value, additional scenarios of uncertainty in new energy output are introduced to perform multi-scenario simulation operation, as shown in the following formula:

[0073] P W,ω,t =P W,pre,t +x W,ω,t

[0074] P PV,ξ,t =P PV,pre,t +x PV,ξ,t

[0075] Where: P W,ω,t represents the output value of wind power generation under the scenario ω at time t; P W,pre,t represents the predicted output value of wind power generation at time t; x W,ω,t represents the sample value of the prediction error random variable of wind power generation under the scenario ω at time t; P PV,ξ,t represents the output value of photovoltaic power generation under the scenario ξ at time t; P PV,pre,t represents the predicted output value of photovoltaic power generation at time t; x PV,ξ,t represents the sample value of the prediction error random variable of photovoltaic power generation under the scenario ξ at time t;

[0076] If there are n wind power generation scenarios in the power system W There are n photovoltaic power generation scenarios in total. PV The total number of scenarios of the power system is the combination of the two, that is: n W ×n PV .

[0077] For any scenario, randomly increase the load unit power of any node in the scenario, calculate the power data of each unit of the power system under the optimal economic model at this time, and calculate the overall carbon emission information of the power system at this time based on the power data of each unit, and then calculate the marginal carbon emission factor of the increased load of the node, traverse all nodes, and repeat the above operation to obtain the marginal carbon emission factor of the increased load of all nodes in the scenario;

[0078] At the same time, in this scenario, the load unit power of any node is randomly reduced, and the power data of each unit in the power system under the optimal economic model is calculated at this time. The overall carbon emission information of the power system at this time is calculated based on the power data of each unit, and then the marginal carbon emission factor of load reduction of the node is calculated. All nodes are traversed and the above operations are repeated to obtain the marginal carbon emission factor of load reduction of all nodes in this scenario.

[0079] As a preferred implementation of this embodiment, the calculation formula of the marginal carbon emission factor of the node is specifically:

[0080]

[0081] Where: i,t represents the marginal carbon emission factor of node i at time t; E sys,t represents the carbon emission information of the power system at time t; P L,i,t Represents the load change of node i at time t.

[0082] As a preferred implementation of this embodiment, the user side optimizes the electricity consumption strategy according to the received marginal carbon emission factors of each time period, and the strategy includes the low-carbon energy consumption optimization strategy of the power user load and the energy storage carbon reduction optimization operation strategy;

[0083] The power user load low-carbon energy optimization strategy divides the power users' participation in low-carbon demand response into ordinary life production electricity and electric vehicle electricity;

[0084] The characteristic of the change of electricity consumption in ordinary life and production is that the use of electricity is closely related to production and life. Therefore, in the case of considering the comfort of electricity consumption, the load response can only be carried out in adjacent time periods, that is, the electricity can only be transferred in adjacent time periods. The following model is constructed:

[0085]

[0086] in: and They represent the marginal carbon emission factor of load increase and load reduction of node i at time t. In order to distinguish them, the load changes under load increase and load reduction are all taken as positive numbers. Therefore The value is positive, The value is negative; and They represent the load increase and load decrease of node i at time t respectively; D It represents the adjustment coefficient, indicating the percentage of the adjustable load to the current load; the first row of formulas represents the maximum carbon reduction of the system; the second row of formulas constrains the load reduction; the third row of formulas indicates that the load added in a certain period cannot exceed the sum of the loads transferred from the two adjacent periods; the fourth row of formulas indicates that the load in the same period cannot both increase and decrease; the fifth row of formulas indicates that the total load remains unchanged within the same day.

[0087] Electric vehicles have a greater degree of flexibility in electricity consumption within a cycle, and are not subject to the constraints of load transfer in adjacent time periods. The adjustable load ratio is also large. The corresponding model is constructed as follows:

[0088]

[0089]

[0090] Among them: the first row of formulas represents the maximum carbon reduction of the system; the second row of formulas represents that the load cannot increase and decrease at the same time; the third row of formulas represents that the total load on the same day remains unchanged; the fourth row of formulas represents the power regulation constraint;

[0091] Energy storage can increase the overall system load's absorption of clean energy through its own charging and discharging behavior, thereby achieving carbon reduction and emission reduction. The energy storage carbon reduction optimization operation strategy is optimized through the following model:

[0092]

[0093] 0≤P ES,ch,t ≤(1-M t ) ESmax

[0094] 0≤P ES,disch,t ≤M t P ESmax

[0095]

[0096] S ES,min ≤E ES,t ≤S ES,max

[0097] E ES,0 =E ES,24

[0098] Where: P ES,disch,t and P ES,ch,t They represent the discharge power and charging power of the energy storage at time t respectively; P ESmax Indicates the maximum charging and discharging power of energy storage; M tA binary variable representing the restriction of energy storage charging and discharging; E ES,t Represents the energy storage capacity at time t; S ES,min Indicates the minimum allowable value of energy storage capacity; S ES,max It represents the maximum allowable value of energy storage capacity; the first row shows that the objective function of energy storage carbon reduction optimization operation is to minimize the carbon emissions added to the system during the charging process and to increase the carbon emissions reduced for the system during the discharging process as much as possible; the second and third row formulas show that the storage device cannot be in the discharging state and the charging state at the same time; the fourth row formula is the energy storage charge state constraint in adjacent time periods; the fifth row formula constrains the energy storage capacity to be between the maximum and minimum allowable values; the sixth row formula shows that after a charging and discharging cycle, the energy storage charge state remains unchanged.

[0099] As a preferred implementation of this embodiment, the actual power system load after the user side uses the optimized power consumption strategy is collected, and the actual carbon emission information of the power system is calculated based on the actual power system load;

[0100] The carbon reduction amount after the power system implements the marginal carbon emission factor of the power system is calculated based on the carbon emission information of the power system's baseline state and the power system's actual carbon emission information.

[0101] Embodiment 2:

[0102] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0103] Embodiment three:

[0104] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0105] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0106] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0107] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0108] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0109] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A low-carbon demand response method considering the uncertainty of marginal carbon emission factors, characterized in that: The following steps are involved: Calculate the overall carbon emission information of the power system based on the power load forecast value as the carbon emission information of the power system benchmark state; Calculate the overall carbon emission information of the power system considering the uncertainty factors of new energy, and calculate the marginal carbon emission factor of each node in the power system based on the overall carbon emission information of the power system considering the uncertainty factors of new energy and the carbon emission information of the power system in the baseline state; The marginal carbon emission factors of the nodes corresponding to the power system in different time periods are sent to the user side corresponding to the node, and the user side optimizes the electricity consumption strategy according to the received marginal carbon emission factors of each time period.

2. A low-carbon demand response method considering the uncertainty of marginal carbon emission factors according to claim 1, characterized in that: The calculation steps of the overall carbon emission information of the power system are as follows: Through the simulation operation of power load forecast value, the power data of each unit in the power system under the optimal economic model is calculated, and then the overall carbon emission information of the power system is calculated based on the power data of each unit.

3. A low-carbon demand response method considering the uncertainty of marginal carbon emission factors according to claim 2, characterized in that: The optimal economic model is specifically: Taking minimizing the power system operation cost as the objective function, the power system optimization operation model is constructed: P G,min ≤P G,t ≤P G,max -R G ≤P G，t -P G，t-1 ≤R G Where: c G represents the vector of the operating cost coefficients of conventional units; P G，t For YuanP G，k，t The column vector, P G，k，t represents the output of the kth conventional unit at time t; P G，min With P G，max Respectively represent the minimum output and maximum output of conventional units; e G represents the unit vector of the conventional unit; e R Represents the unit vector of the new energy unit; e D represents the load unit vector; P R，t represents the new energy output matrix; represents the new energy curtailment matrix; P D，t represents the load vector; F represents the power transfer factor matrix; J G，t The matrix representing the node position index of the conventional unit in the power system; J R，t The matrix representing the node position index of the new energy unit in the power system; P line，max Indicates the maximum power allowed to be transmitted on the line; R G Indicates the unit climbing rate.

4. A low-carbon demand response method considering the uncertainty of marginal carbon emission factors according to claim 3, characterized in that: The calculation formula for the overall carbon emission information of the power system is: E sys，t =e G (P G，mar，t -P G0，t ) Where: G represents the row vector of fuel emission factors for conventional units; P G，mar，t P represents the power generation power of the power system unit in the simulated operation state at time t; G0，t It represents the power generation capacity of the power system unit under the reference state at time t.

5. A low-carbon demand response method considering the uncertainty of marginal carbon emission factors according to claim 4, characterized in that: The calculation steps of the marginal carbon emission factor of each node in the power system are: When simulating operation through power load forecast value, additional scenarios of uncertainty in new energy output are introduced to perform multi-scenario simulation operation, as shown in the following formula: P W，ω，t =P W，pre,t +x W,ω，t P PV,ξ，t =P PV，pre，t +x PV,ξ,t Where: P W，ω， t represents the output value of wind power generation under the scenario ω at time t; P W，pre,t represents the predicted output value of wind power generation at time t; x W，ω，t represents the sample value of the prediction error random variable of wind power generation under the scenario ω at time t; P PV，ξ，t represents the output value of photovoltaic power generation under the scenario ξ at time t; P PV，pre,t represents the predicted output value of photovoltaic power generation at time t; x PV,ξ，t represents the sample value of the prediction error random variable of photovoltaic power generation under the scenario ξ at time t; For any scenario, randomly increase the load unit power of any node in the scenario, calculate the power data of each unit of the power system under the optimal economic model at this time, and calculate the overall carbon emission information of the power system at this time based on the power data of each unit, and then calculate the marginal carbon emission factor of the increased load of the node, traverse all nodes, and repeat the above operation to obtain the marginal carbon emission factor of the increased load of all nodes in the scenario; At the same time, in this scenario, the load unit power of any node is randomly reduced, and the power data of each unit in the power system under the optimal economic model is calculated at this time. The overall carbon emission information of the power system at this time is calculated based on the power data of each unit, and then the marginal carbon emission factor of load reduction of the node is calculated. All nodes are traversed and the above operations are repeated to obtain the marginal carbon emission factor of load reduction of all nodes in this scenario.

6. A low-carbon demand response method considering the uncertainty of marginal carbon emission factors according to claim 5, characterized in that: The calculation formula of the marginal carbon emission factor of the node is as follows: Where: i，t represents the marginal carbon emission factor of node i at time t; E sys，t represents the carbon emission information of the power system at time t; P L，i，t Represents the load change of node i at time t.

7. A low-carbon demand response method considering the uncertainty of marginal carbon emission factors according to claim 6, characterized in that: The user side optimizes the electricity consumption strategy according to the received marginal carbon emission factors for each time period, wherein the strategy includes the low-carbon energy consumption optimization strategy for power user loads and the energy storage carbon reduction optimization operation strategy; The power user load low-carbon energy optimization strategy divides the power users' participation in low-carbon demand response into ordinary life production electricity and electric vehicle electricity; The following model is constructed for ordinary life and production electricity: in: and They represent the marginal carbon emission factor of load increase and load reduction of node i at time t respectively; and They represent the load increase and load decrease of node i at time t respectively; D represents the adjustment coefficient; The following model is constructed for electric vehicle electricity consumption: The energy storage carbon reduction optimization operation strategy is optimized through the following model: 0≤P ES,ch,t ≤(1-M t )P ESmax 0≤P ES,disch,t ≤M t P ESmax S ES,min ≤E ES，t ≤S ES,max AND ES,0 =And ES,24 Where: P ES，disch，t and P ES，ch,t They represent the discharge power and charging power of the energy storage at time t respectively; P ESmax Indicates the maximum charging and discharging power of energy storage; M t A binary variable representing the restriction of energy storage charging and discharging; E ES,t Represents the energy storage capacity at time t; S ES,min Indicates the minimum allowable value of energy storage capacity; S ES，max Indicates the maximum allowable energy storage capacity.

8. A low-carbon demand response method considering the uncertainty of marginal carbon emission factors according to claim 1, characterized in that: Collect the actual power system load after the user side uses the optimized power consumption strategy, and calculate the actual carbon emission information of the power system based on the actual power system load; The carbon reduction amount after the power system implements the marginal carbon emission factor of the power system is calculated based on the carbon emission information of the power system's baseline state and the power system's actual carbon emission information.

9. An electronic device 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, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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