A demand response adjustment method and system for composite carbon emission quota sharing

Abstract

A demand response regulation method for composite carbon emission quota sharing includes: based on carbon emission flow theory, applying the node carbon intensity assessment method, constructing a countercurrent distribution matrix that considers network losses and the carbon flow injection column vector of power generation nodes, and calculating the system's carbon emission quota results and the network's node carbon footprint intensity in combination with the tidal current results; referring to historical data, allocation theory and node-related data, introducing the concept of a balance interval applicable to the power system, and forming a carbon quota constraint interval; establishing a demand response low-carbon regulation cost minimization model based on carbon quota review allocation, taking carbon constraints and load regulation capabilities into account in relevant nodes, using a particle swarm algorithm to solve the optimal value, and verifying the optimal solution. The present invention is aimed at the actual carbon emissions and demand response business, and reviews and considers factors such as historical carbon emissions, node average carbon emission principles and node load adjustable capacity to achieve a reduction in the system's total carbon emissions.

Description

Technical Field

[0001] The present invention relates to the field of carbon management, and in particular to a demand response adjustment method and system for composite carbon emission quota sharing. Background Art

[0002] Reasonable demand-side management is an important means of achieving a balance between electricity supply and demand, reducing fluctuations in electricity demand, and shaving peak loads. Because demand response can alter carbon emissions, traditional response strategies fail to consider carbon emission indicators, making it difficult to assess the impact on customers' carbon quotas. As carbon markets mature, changes in carbon emission costs caused by changes in user electricity usage behavior can also be leveraged. Therefore, exploring improved demand response strategies within carbon market environments can better address these challenges, reduce carbon emissions, and promote the development of clean energy. While some progress has been made in linking carbon emission allocations and demand response, effectively connecting the two and achieving efficient, low-carbon demand-side response remains a pressing challenge. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a demand response regulation method and system for composite carbon emission quota sharing that overcomes the above problems or at least partially solves the above problems.

[0004] In order to solve the above technical problems, the embodiments of the present application disclose the following technical solutions:

[0005] A demand response regulation method for composite carbon emission quota sharing, comprising:

[0006] S100. Based on carbon emission flow theory and applying a node carbon intensity assessment method, we constructed a countercurrent distribution matrix that considers network losses and the carbon flow injection column vectors of power generation nodes. Combined with the power flow results, we calculated the system's carbon emission quota and the network's node carbon footprint intensity.

[0007] S200. Referring to historical data, allocation theory, and node-related data, introduce the concept of a balance interval applicable to the power system to form a carbon quota constraint interval;

[0008] S300. Establish a demand response low-carbon regulation cost minimization model based on carbon quota review and allocation, take carbon constraints and load regulation capabilities into account in relevant nodes, use particle swarm algorithm to solve the optimal value, and verify the optimal solution.

[0009] Furthermore, in S100, based on the carbon emission flow theory and applying the node carbon intensity assessment method, a reverse flow distribution matrix is ​​constructed that takes into account network losses and the column vector of carbon flow injection of power generation nodes. The specific method includes: obtaining the total carbon flow vector C passing through the node and the column vector C1 of the carbon flow injection of each generation node, and calculating the reverse flow distribution matrix A that takes into account network losses; the specific calculation formula is: C = A -1 C1.

[0010] Furthermore, the reverse traffic distribution matrix A considering network loss is expressed as:

[0011]

[0012] Among them, P ab is the first-end active power entering branch ab and flowing out from node a, P b is the power flowing through node b, Γ - (a) is the incoming set of node a.

[0013] Furthermore, the system's carbon emission quota and the network's node carbon footprint intensity are calculated in combination with the power flow results. The specific method includes: first, by considering the reverse flow distribution matrix A of network loss, the system's carbon footprint intensity vector F is obtained as:

[0014]

[0015] F is the carbon footprint intensity vector of the system node, C is the total carbon flow vector passing through the node, P a is the power flowing through node a;

[0016] Then calculate the carbon emission quota results of each load node:

[0017] R i =P Di F f(i)

[0018] Among them, R i is the carbon emission responsibility share, P Di is the active power consumed by load node i; F f(i) is the carbon footprint intensity value of load node i.

[0019] Furthermore, in S200, the concept of balance interval applicable to the power system is introduced to form a carbon quota constraint interval. The specific method includes: first, according to the historical carbon emissions of each node on the demand side, the carbon emissions C allocated to node i under the historical responsibility principle is calculated. hi Then, according to the power demand forecast value of each load node, the carbon emission responsibility C allocated to load node i under the principle of individual equality is calculated. fi ; Finally, according to the calculated carbon emissions Chi and carbon emission responsibility C fi , and obtain the upper limit C of the carbon emission responsibility sharing of the load node iu and the lower limit C il , forming a carbon quota constraint range.

[0020] Furthermore, the carbon emissions C allocated to node i under the historical responsibility principle are first calculated based on the historical carbon emissions of each node on the demand side. hi , carbon emissions C hi The calculation formula is:

[0021]

[0022] Where C hi is the carbon emissions allocated to node i under the historical responsibility principle; C ti The total amount of carbon emission responsibility that should be shared by each node under the principle of historical responsibility can be calculated by combining it with the current carbon emission responsibility in proportion; HC ei is the historical carbon emissions of node i under the historical responsibility principle, which comes from the historical statistical inventory of the region; N is the total number of load nodes participating in the allocation under the historical responsibility principle.

[0023] Furthermore, the carbon emission responsibility C allocated to load node i under the principle of individual equality is calculated based on the power demand forecast value of each load node. fi , carbon emission responsibility C fi The calculation formula is:

[0024]

[0025] Among them, C fi is the carbon emission responsibility allocated to load node i under the principle of individual equality; P i It is the calculation period under the principle of equal distribution among nodes; C ri It is the total emissions under the principle of equal carbon emissions.

[0026] Furthermore, finally according to the calculated carbon emissions C hi and carbon emission responsibility C fi , and obtain the upper limit C of the carbon emission responsibility sharing of the load node iu and the lower limit C il , forming a carbon quota constraint range, the upper limit of the load node carbon emission responsibility sharing C iu and the lower limit C il The expression is:

[0027] C iu =Max(C hi ,C fi )

[0028] Cil =Min(C hi ,C fi )

[0029] Among them, C iu is the upper limit of the allocated carbon emissions of load node i, C il is the lower limit of the allocated carbon emissions of load node i.

[0030] Furthermore, in S300, the demand response low-carbon regulation cost minimization model based on the consideration of carbon quota review allocation is to minimize the sum of the load-side carbon cost and the demand response cost, and the expression is:

[0031]

[0032] Among them, N T and N N is the total time period and total number of grid nodes; C it is the unit carbon emission of node i at time t, c CE is the unit cost of carbon emissions, c DR is the unit cost of demand response; D it Load demand after the response at time t;

[0033] The demand response model constraints are:

[0034] D itmin ≤λD ita

[0035] D itmax ≤λD ita

[0036] D it =D ita +D itmax -D itmin

[0037] Among them, D ita are the load demands of node i before the response at time t; λ is the adjustable ratio of node i; D itmax and D itmin are the upward and downward adjustable powers of node i at time t, respectively;

[0038] Finally, C it and D it As a variable, C iu and C il is the carbon emission constraint condition of node i, D itmax and D itmin As the demand response constraint condition of node i, the particle swarm optimization algorithm is used to solve the demand response low-carbon scheduling model to adjust the carbon demand response.

[0039] The present invention also discloses a demand response adjustment system for composite carbon emission quota sharing, comprising: a carbon footprint intensity calculation unit, a carbon quota constraint interval formation unit and a carbon demand response adjustment unit; wherein:

[0040] The carbon footprint intensity calculation unit is used to apply the node carbon intensity assessment method based on the carbon emission flow theory, construct the reverse flow distribution matrix that takes into account the network loss and the carbon flow injection column vector of the power generation node, and calculate the system's carbon emission quota results and the network's node carbon footprint intensity in combination with the power flow results;

[0041] A carbon quota constraint interval forming unit is used to introduce the concept of balance interval applicable to the power system based on reference historical data, allocation theory and node-related data to form a carbon quota constraint interval;

[0042] The carbon demand response regulation unit is used to establish a demand response low-carbon regulation cost minimization model based on the carbon quota review and allocation, taking carbon constraints and load regulation capabilities into account in relevant nodes, using the particle swarm algorithm to solve the optimal value, and verifying the optimal solution.

[0043] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0044] The present invention discloses a demand response regulation method for composite carbon emission quota sharing, comprising: S100. Based on the carbon emission flow theory, the node carbon intensity assessment method is applied to construct a countercurrent distribution matrix that considers network losses and carbon flow injection column vectors of power generation nodes, and the carbon emission quota results of the system and the node carbon footprint intensity of the network are calculated in combination with the tidal flow results; S200. With reference to historical data, allocation theory and node-related data, the concept of a balance interval applicable to the power system is introduced to form a carbon quota constraint interval; S300. A demand response low-carbon regulation cost minimization model based on carbon quota review allocation is established, carbon constraints and load regulation capabilities are taken into account in relevant nodes, the particle swarm algorithm is used to solve the optimal value, and the optimal solution is verified.

[0045] In view of the actual carbon emissions and demand response business, the present invention reviews and considers factors such as historical carbon emissions, node average carbon emissions principle and node load adjustable capacity, and proposes a demand response adjustment method for composite carbon emission quota sharing in accordance with the principle of minimum total cost. By comparing with the data not considering this, it is further demonstrated that the proposed method can achieve a reduction in the total carbon emissions of the system while meeting the requirements for safe and stable operation of the power system, which demonstrates the effectiveness and practicality of the technical solution disclosed in the present invention.

[0046] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 This is a flow chart of a demand response adjustment method for composite carbon emission quota sharing in Example 1 of the present invention. DETAILED DESCRIPTION

[0049] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0050] In order to solve the problems existing in the prior art, an embodiment of the present invention provides a demand response adjustment method and system for composite carbon emission quota sharing.

[0051] Example 1

[0052] This embodiment discloses a demand response adjustment method for sharing composite carbon emission quotas, such as Figure 1 ,include:

[0053] S100. Based on carbon emission flow theory and applying a node carbon intensity assessment method, we constructed a countercurrent distribution matrix that considers network losses and the carbon flow injection column vectors of power generation nodes. Combined with the power flow results, we calculated the system's carbon emission quota and the network's node carbon footprint intensity.

[0054] Specifically, in this embodiment S100, based on the carbon emission flow theory, the node carbon intensity assessment method is applied to construct a reverse flow distribution matrix that takes into account network losses and the column vector of carbon flow injection of power generation nodes. The specific method includes: obtaining the total carbon flow vector C passing through the node and the column vector C1 of the carbon flow injection of each generation node, and calculating the reverse flow distribution matrix A that takes into account network losses; the specific calculation formula is: C = A -1 C1(1).

[0055] Among them, the reverse traffic distribution matrix A considering network loss is expressed as:

[0056]

[0057] Among them, P ab is the first-end active power entering branch ab and flowing out from node a, P b is the power flowing through node b, Γ - (a) is the incoming set of node a.

[0058] Based on the obtained reverse traffic distribution matrix considering network loss, the system's carbon footprint intensity vector is:

[0059]

[0060] F is the carbon footprint intensity vector of the system node, C is the total carbon flow vector passing through the node, P a is the power flowing through node a;

[0061] Then calculate the carbon emission responsibility share of each load node on the demand side:

[0062] R i =P Di F f(i) (4)

[0063] Among them, R i is the carbon emission responsibility share, P Di is the active power consumed by load node i; F f(i) is the carbon footprint intensity value of load node i.

[0064] S200. Referring to historical data, allocation theory, and node-related data, introduce the concept of a balance interval applicable to the power system to form a carbon quota constraint interval;

[0065] Specifically, in S200 of this embodiment, the concept of a balance interval applicable to the power system is introduced to form a carbon quota constraint interval. The specific method includes: first, according to the historical carbon emissions of each node on the demand side, the carbon emissions C allocated to node i under the historical responsibility principle is calculated. hi Then, according to the power demand forecast value of each load node, the carbon emission responsibility C allocated to load node i under the principle of individual equality is calculated. fi ; Finally, according to the calculated carbon emissions C hi and carbon emission responsibility C fi , and obtain the upper limit C of the carbon emission responsibility sharing of the load node iu and the lower limit C il , forming a carbon quota constraint range.

[0066] Among them, firstly, the carbon emissions C allocated to node i under the historical responsibility principle is calculated based on the historical carbon emissions of each node on the demand side. hi , carbon emissions C hi The calculation formula is:

[0067]

[0068] Where C hi is the carbon emissions allocated to node i under the historical responsibility principle; C tiThe total amount of carbon emission responsibility that should be shared by each node under the principle of historical responsibility can be calculated by combining it with the current carbon emission responsibility in proportion; HC ei is the historical carbon emissions of node i under the historical responsibility principle, which comes from the historical statistical inventory of the region; N is the total number of load nodes participating in the allocation under the historical responsibility principle.

[0069] Then, the carbon emission responsibility C allocated to load node i under the principle of individual equality is calculated based on the power demand forecast value of each load node. fi , carbon emission responsibility C fi The calculation formula is:

[0070]

[0071] Among them, C fi is the carbon emission responsibility allocated to load node i under the principle of individual equality; P i It is the calculation period under the principle of equal distribution among nodes; C ri It is the total emissions under the principle of equal carbon emissions.

[0072] Finally, according to the calculated carbon emissions C hi and carbon emission responsibility C fi , and obtain the upper limit C of the carbon emission responsibility sharing of the load node iu and the lower limit C il , forming a carbon quota constraint range, the upper limit of the load node carbon emission responsibility sharing C iu and the lower limit C il The expression is:

[0073] C iu =Max(C hi ,C fi ) (7)

[0074] C il =Min(C hi ,C fi ) (8)

[0075] Among them, C iu is the upper limit of the allocated carbon emissions of load node i, C il is the lower limit of the allocated carbon emissions of load node i.

[0076] S300. Establish a demand response low-carbon regulation cost minimization model based on carbon quota review and allocation, take carbon constraints and load regulation capabilities into account in relevant nodes, use particle swarm algorithm to solve the optimal value, and verify the optimal solution.

[0077] In S300 of this embodiment, the demand response low-carbon regulation cost minimization model based on carbon quota review allocation is to minimize the sum of the load-side carbon cost and the demand response cost, which is expressed as:

[0078]

[0079] Among them, N T and N N is the total time period and total number of grid nodes; C it is the unit carbon emission of node i at time t, c CE is the unit cost of carbon emissions, c DR is the unit cost of demand response; D it Load demand after the response at time t;

[0080] The demand response model constraints are:

[0081] D itmin ≤λD ita (10)

[0082] D itmax ≤λD ita (11)

[0083] D it =D ita +D itmax -D itmin (12)

[0084] Among them, D ita are the load demands of node i before the response at time t; λ is the adjustable ratio of node i; D itmax and D itmin are the upward and downward adjustable powers of node i at time t, respectively;

[0085] Finally, C it and D it As a variable, C iu and C il is the carbon emission constraint condition of node i, D itmax and D itmin As the demand response constraint condition of node i, the particle swarm optimization algorithm is used to solve the demand response low-carbon scheduling model to adjust the carbon demand response.

[0086] Among them, based on the particle swarm algorithm, the fitness function is first determined, the parameters are set, and the initialization steps are completed, specifically including: 1. Initializing a group of particles, including random positions and speeds; 2. Evaluating the fitness of each particle; 3. For each particle, its fitness value is compared with the best position it has passed through. If it is better, it is used as the current best position; 4. For each particle, its fitness value is compared with the best position it has passed through. If it is better, it is used as the current best position; 5. Adjust the particle speed and position according to formulas (2) and (3); 6. If the end condition is not met, go to step 2. The iterative termination condition is selected according to the specific problem as the maximum number of iterations or (and) the optimal position searched so far by the particle swarm meets the predetermined minimum fitness threshold.

[0087] In this embodiment, the comparison of the changes in node carbon potential and load before and after the response during the peak load regulation period shows that most nodes in the system will reduce their load after receiving the demand response regulation signal during the peak electricity consumption period. Therefore, the node load after the peak demand response is usually less than before the response. After participating in the demand response during the peak period, the load of each node will be restored. Through analysis, it is found that the node carbon potential of these nodes is often at a low level before the response. At this time, the carbon emissions of electricity consumption will be greatly reduced compared to normal times. After considering the carbon cost, the carbon dioxide emission reduction cost of their electricity consumption may be greater than the basic response benefits. Therefore, the number of load hours of these nodes during the peak period increases rather than decreases. In addition, due to the influence of load type, different nodes will produce different response results due to their own different regulation capabilities, resulting in the degree of response being not linearly related to the node's carbon potential.

[0088] In this example, a comparison chart of carbon emissions at system nodes before and after demand response is shown. The benefit evaluation of demand response with composite carbon emission quota sharing is performed by evaluating the carbon emission reductions at each node before and after participating in low-carbon demand response. Traditional demand response, on the other hand, implements load reductions at all nodes in equal proportion, and the total carbon emissions are calculated by summing the carbon emissions of all nodes in the system. The emissions of each node and the carbon emissions of the entire system before and after the response are shown in Table 1. The demand response scheduling strategy proposed in this paper introduces a carbon emission optimization target based on conventional demand response strategies and adopts a demand response adjustment method based on different user load carbon emission shares. Data analysis shows that carbon emissions before traditional demand response were 5011.47 tons. Table 1 (which compares system carbon emissions before and after demand response) shows that carbon dioxide emissions decreased from 5011.47 tons before demand response to 4243.66 tons after demand response. After applying this model, the reduction was further reduced to 4114.43 tons, a further reduction of 129.23 tons compared to traditional demand response. It was verified that the carbon footprint intensity calculation unit, carbon quota constraint interval formation unit and carbon demand response adjustment unit meet the requirements for safe and stable operation of the power system while reducing the total carbon emissions of the system.

[0089] Table 1

[0090] period Carbon emissions / tCO2 Before demand response 5011.47 Traditional demand response methods 4243.66 Optimization method of this embodiment 4114.43

[0091] The present embodiment discloses a demand response regulation method for composite carbon emission quota sharing, comprising: S100. Based on the carbon emission flow theory, the node carbon intensity assessment method is applied to construct a countercurrent distribution matrix that considers network losses and the carbon flow injection column vector of the power generation node, and the carbon emission quota results of the system and the node carbon footprint intensity of the network are calculated in combination with the flow results; S200. With reference to historical data, allocation theory and node-related data, the concept of a balance interval applicable to the power system is introduced to form a carbon quota constraint interval; S300. A demand response low-carbon regulation cost minimization model based on carbon quota review allocation is established, carbon constraints and load regulation capabilities are taken into account in relevant nodes, the particle swarm algorithm is used to solve the optimal value, and the optimal solution is verified.

[0092] This embodiment aims at the actual carbon emissions and demand response business, reviews and considers factors such as historical carbon emissions, node average carbon emissions principle and node load adjustable capacity, and proposes a demand response adjustment method for compound carbon emission quota sharing in accordance with the principle of minimum total cost. By comparing with the data without considering this, it is further demonstrated that the proposed method can achieve a reduction in the total carbon emissions of the system while meeting the requirements for safe and stable operation of the power system, which demonstrates the effectiveness and practicality of the technical solution disclosed in this invention.

[0093] Example 2

[0094] Based on the demand response adjustment method for composite carbon emission quota sharing in Example 1, this embodiment discloses a demand response adjustment system for composite carbon emission quota sharing, including: a carbon footprint intensity calculation unit, a carbon quota constraint interval formation unit, and a carbon demand response adjustment unit; wherein:

[0095] The carbon footprint intensity calculation unit is used to apply the node carbon intensity assessment method based on the carbon emission flow theory, construct the reverse flow distribution matrix that takes into account the network loss and the carbon flow injection column vector of the power generation node, and calculate the system's carbon emission quota results and the network's node carbon footprint intensity in combination with the power flow results;

[0096] A carbon quota constraint interval forming unit is used to introduce the concept of balance interval applicable to the power system based on reference historical data, allocation theory and node-related data to form a carbon quota constraint interval;

[0097] The carbon demand response regulation unit is used to establish a demand response low-carbon regulation cost minimization model based on the carbon quota review and allocation, taking carbon constraints and load regulation capabilities into account in relevant nodes, using the particle swarm algorithm to solve the optimal value, and verifying the optimal solution.

[0098] Among them, the specific working principles of the carbon footprint intensity calculation unit, the carbon quota constraint interval formation unit and the carbon demand response adjustment unit have been described in detail in Example 1, and will not be repeated in this embodiment.

[0099] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0100] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are therefore hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0101] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0102] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in a user terminal as discrete components.

[0103] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0104] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A demand response adjustment method for composite carbon emission quota sharing, characterized in that: include: S100. Based on carbon emission flow theory and applying a node carbon intensity assessment method, we constructed a countercurrent distribution matrix that considers network losses and the carbon flow injection column vectors of power generation nodes. Combined with the power flow results, we calculated the system's carbon emission quota and the network's node carbon footprint intensity. S200. Referring to historical data, allocation theory and node-related data, the concept of balance interval applicable to the power system is introduced to form a carbon quota constraint interval. In S200, the concept of balance interval applicable to the power system is introduced to form a carbon quota constraint interval. The specific method includes: first, according to the historical carbon emissions of each node on the demand side, the carbon emissions C allocated to node i under the historical responsibility principle is calculated. hi Then, according to the power demand forecast value of each load node, the carbon emission responsibility C allocated to load node i under the principle of individual equality is calculated. fi ; Finally, according to the calculated carbon emissions C hi and carbon emission responsibility C fi , and obtain the upper limit C of the carbon emission responsibility sharing of the load node iu and the lower limit C il , forming a carbon quota constraint range; First, the carbon emissions C allocated to node i under the historical responsibility principle are calculated based on the historical carbon emissions of each node on the demand side. hi , carbon emissions C hi The calculation formula is: ； Where C hi is the carbon emissions allocated to node i under the historical responsibility principle; C ti The total amount of carbon emission responsibility that should be shared by each node under the principle of historical responsibility can be calculated by combining it with the current carbon emission responsibility in proportion; HC ei is the historical carbon emissions of node i under the historical responsibility principle, which comes from the historical statistical inventory of the node area; N is the total number of load nodes participating in the allocation under the historical responsibility principle; Then, the carbon emission responsibility C allocated to load node i under the principle of individual equality is calculated based on the power demand forecast value of each load node. fi , carbon emission responsibility C fi The calculation formula is: ； Among them, C fi is the carbon emission responsibility allocated to load node i under the principle of individual equality; P i It is the calculation period under the principle of equal distribution among nodes; C ri is the total emissions under the principle of equal carbon emissions; Finally, according to the calculated carbon emissions C hi and carbon emission responsibility C fi , and obtain the upper limit C of the carbon emission responsibility sharing of the load node iu and the lower limit C il , forming a carbon quota constraint range, the upper limit of the load node carbon emission responsibility sharing C iu and the lower limit C il The expression is: ； Among them, C iu is the upper limit of the allocated carbon emissions of load node i, C il is the lower limit of the allocated carbon emissions of load node i; S300. Establish a demand response low-carbon regulation cost minimization model based on carbon quota review and allocation, take carbon constraints and load regulation capabilities into account in relevant nodes, use particle swarm algorithm to solve the optimal value, and verify the optimal solution.

2. A demand response adjustment method for composite carbon emission quota sharing according to claim 1, characterized in that: In S100, based on the carbon emission flow theory, the node carbon intensity assessment method is applied to construct a countercurrent distribution matrix that considers network losses and the carbon flow injection column vector of the power generation node. The specific method includes: obtaining the total carbon flow vector passing through the node C and a column vector of carbon flux injected into each generation node C 1. Calculate the reverse traffic distribution matrix A considering network loss; the specific calculation formula is: C = A -1 C 1 。 3. A demand response adjustment method for composite carbon emission quota sharing according to claim 2, characterized in that: The reverse traffic distribution matrix A considering network loss is expressed as: ； Among them, P ab is the first-end active power entering branch ab and flowing out from node a, P b is the power flowing through node b, is the incoming set of node a.

4. A demand response adjustment method for composite carbon emission quota sharing according to claim 3, characterized in that: The carbon emission quota of the system and the carbon footprint intensity of the network nodes are calculated by combining the power flow results. The specific method includes: first, by considering the reverse flow distribution matrix A of the network loss, the carbon footprint intensity vector F of the system is obtained as follows: ； F is the carbon footprint intensity vector of the system node, C is the total carbon flow vector passing through the node, P a is the power flowing through node a; Then calculate the carbon emission quota results of each load node: ； Among them, R i is the carbon emission responsibility share, P Di is the active power consumed by load node i; F f（i） is the carbon footprint intensity value of load node i.

5. A demand response adjustment method for composite carbon emission quota sharing according to claim 1, characterized in that: In S300, the demand response low-carbon regulation cost minimization model based on the consideration of carbon quota review allocation is to minimize the sum of the load-side carbon cost and the demand response cost, and the expression is: ； in, N T and N N is the total time period and total number of grid nodes; C it is a node i In time t Unit carbon emissions, c CE is the unit cost of carbon emissions, c DR is the demand response unit cost; In time t The load demand after the response; The demand response model constraints are: ； in, Node i In time t The load demand before the response; is a node i Adjustable ratio; D itmax and D itmin Node i In time t Adjustable power up and down at Finally, C it and As a variable, C iu and C il is the carbon emission constraint condition of node i, D itmax and D itmin As the demand response constraint condition of node i, the particle swarm optimization algorithm is used to solve the demand response low-carbon scheduling model to adjust the carbon demand response.

6. A demand response regulation system for composite carbon emission quota sharing, adopting the response regulation method according to any one of claims 1 to 5 above, characterized in that: include: Carbon footprint intensity calculation unit, carbon quota constraint interval formation unit and carbon demand response adjustment unit; among which: The carbon footprint intensity calculation unit is used to apply the node carbon intensity assessment method based on the carbon emission flow theory, construct the reverse flow distribution matrix that takes into account the network loss and the carbon flow injection column vector of the power generation node, and calculate the system's carbon emission quota results and the network's node carbon footprint intensity in combination with the power flow results; A carbon quota constraint interval forming unit is used to introduce the concept of balance interval applicable to the power system based on reference historical data, allocation theory and node-related data to form a carbon quota constraint interval; The carbon demand response regulation unit is used to establish a demand response low-carbon regulation cost minimization model based on the carbon quota review and allocation, taking carbon constraints and load regulation capabilities into account in relevant nodes, using the particle swarm algorithm to solve the optimal value, and verifying the optimal solution.

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