Network load collaborative low-carbon scheduling method and device, terminal equipment and storage medium

Abstract

The invention discloses a network load collaborative low-carbon scheduling method and device, terminal equipment and a storage medium. The method comprises the following steps: acquiring basic data of a power system; according to the basic data, by taking the minimum carbon emission of the power generation side of the power distribution network as a target, constructing a target function of network load collaborative low-carbon scheduling; constructing a system load balance constraint, a line power flow constraint, a user load all-day total power invariant constraint, a load adjustment constraint and a switch on-off constraint of the target function based on the basic data; solving the target function under the constraints of the system load balance constraint, the line power flow constraint, the user load all-day total power invariant constraint, the load adjustment constraint and the switch on-off constraint, and generating a user load curve and a power distribution network side switch on-off state when the power distribution network power generation side carbon emission is lowest; and performing network load collaborative low-carbon scheduling according to the user load curve and the on-off state of the power distribution network side switch. According to the invention, collaborative low-carbon scheduling of the power grid side and the load side can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power carbon emission, and in particular to a grid-load collaborative low-carbon scheduling method, device, terminal equipment and storage medium. Background Art

[0002] The power industry is one of the key industries for carbon emission control and needs to assume the responsibility of emission reduction. As part of the low-carbon transformation of the power industry, low-carbon dispatching needs to take into account the low-carbon characteristics of the power system, and reduce fossil energy consumption and greenhouse gas emissions by optimizing the configuration of power generation resources and dispatching strategies to reduce carbon emissions. Most of the existing low-carbon dispatching technologies achieve low-carbon dispatching by optimizing power supply, energy storage power or load demand response; for example, Chinese patent CN103544656A, an active distribution network operation control method for minimum carbon emissions, achieves low-carbon dispatching by optimizing the processing power of thermal power units, fuel cell units, photovoltaic power generation units, wind turbines and energy storage equipment, that is, low-carbon dispatching is achieved through the power supply side and the load side. However, there is a lack of consideration of the coordinated dispatching of the grid side and the load side to achieve low-carbon dispatching. Therefore, there is an urgent need for a method that considers the coordinated dispatching of the grid side and the load side to achieve low-carbon dispatching to fill the gap in the existing technology, promote the consumption of system energy, and realize the coordinated low-carbon dispatching of the distribution network area based on the grid side and the load side. Summary of the invention

[0003] The embodiments of the present invention provide a grid-load collaborative low-carbon scheduling method, apparatus, terminal equipment and storage medium, which can make up for the technical gaps in the prior art in considering the collaborative scheduling of the grid side and the load side to achieve low-carbon scheduling, and realize the collaborative low-carbon scheduling of the distribution network area based on the grid side and the load side.

[0004] An embodiment of the present invention provides a grid-load coordinated low-carbon scheduling method, including:

[0005] Obtaining basic data of the power system; wherein the basic data includes: power system load, power flow transmission limit value of each line, number of power system nodes, power transfer distribution factor, bus load value, power flow relaxation variable, maximum load adjustment ratio, active power flow data of each branch, branch power flow distribution matrix, node active flux matrix, unit injection distribution matrix and unit carbon potential vector;

[0006] According to the basic data, an objective function of grid-load coordinated low-carbon dispatch is constructed with the goal of minimizing carbon emissions on the power generation side of the distribution network; and based on the basic data, system load balance constraints, line flow constraints, user load total power unchanged throughout the day constraints, load adjustment constraints and switch breaking constraints of the objective function are constructed;

[0007] Under the constraints of the system load balance constraint, line flow constraint, user load total power unchanged constraint throughout the day, load adjustment constraint and switch disconnection constraint, the objective function is solved to generate the user load curve and the switch disconnection state of the distribution network side when the carbon emissions on the power generation side of the distribution network are the lowest;

[0008] Grid-load coordinated low-carbon dispatch is carried out according to the user load curve and the switch on-off status of the distribution network side.

[0009] Furthermore, the objective function is specifically:

[0010]

[0011] Among them, E N,i,t is the carbon potential of power supply node i in the distribution network area at time t; P i,t is the power output of node i supplying power to the distribution network area at time t; E j,t is the carbon emission intensity of the distributed energy connected to node j within the distribution network area at time t; T j,t It is the output prediction of the distributed energy connected to the j node within the distribution network area at time t.

[0012] Furthermore, the system load balancing constraint is specifically:

[0013]

[0014] Among them, P i,t is the power output of power supply node i in the distribution network area at time t; T j,t D is the output prediction of the distributed energy connected to the j node within the distribution network area at time t; t is the power system load at time t; N is the number of power supply nodes in the power system; NT is the number of all nodes containing distributed energy in the distribution network.

[0015] Furthermore, the line power flow constraint is specifically:

[0016]

[0017] in, is the power flow transmission limit value of line l; N is the number of power supply nodes in the power system; G l-i P is the power transfer distribution factor of the generator output power of the power supply node i connected to the distribution network of the power system main grid to the line l; i,t is the power output of power supply node i at time t; NT is the number of all nodes containing distributed energy in the distribution network; G l-j is the power transfer distribution factor of the generator output from node j to line l within the distribution network area; T j,tis the output forecast of the distributed energy connected to node j in the distribution network area at time t; K is the number of all nodes in the power system; G l-k is the generator output power transfer distribution factor of node k for line l; D k,t is the bus load value of node k at time t; is the forward power flow relaxation variable of line l; is the reverse power flow slack variable of line l.

[0018] Furthermore, the total power of the user load remains unchanged throughout the day, specifically:

[0019]

[0020] Among them, D q,t is the original load of user q at time t; D q ′ ,t It is the optimized load of user q at time t; the load of user q is connected to any node of the power system.

[0021] Furthermore, the load adjustment constraint is specifically:

[0022]

[0023] Among them, D q,t is the original load of user q at time t; D q ′ ,t is the optimized load of user q at time t; S q,t is the maximum proportion of load adjustment for user q at time t.

[0024] Furthermore, after obtaining the basic data of the power system, it also includes:

[0025] The carbon potential of each node in the distribution network is calculated based on the tributary flow distribution matrix, node active flux matrix, unit injection distribution matrix and unit carbon potential vector.

[0026] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0027] An embodiment of the present invention provides a network-load coordinated low-carbon dispatching device, including: a data acquisition module, a model and constraint condition construction module, a model solving module and a dispatching module;

[0028] The data acquisition module is used to acquire basic data of the power system; wherein the basic data includes: power system load, power flow transmission limit value of each line, number of power system nodes, power transfer distribution factor, bus load value, power flow relaxation variable, maximum load adjustment ratio, active power flow data of each branch, branch power flow distribution matrix, node active flux matrix, unit injection distribution matrix and unit carbon potential vector;

[0029] The model and constraint condition construction module is used to construct an objective function of grid-load coordinated low-carbon dispatch based on the basic data with the goal of minimizing carbon emissions on the generation side of the distribution network; and to construct system load balance constraints, line flow constraints, user load total power unchanged throughout the day constraints, load adjustment constraints and switch breaking constraints of the objective function based on the basic data;

[0030] The model solving module is used to solve the objective function under the constraints of the system load balance constraint, the line flow constraint, the user load total power unchanged constraint throughout the day, the load adjustment constraint and the switch disconnection constraint, and generate the user load curve and the switch disconnection state of the distribution network side when the carbon emissions on the power generation side of the distribution network are the lowest;

[0031] The dispatching module is used to perform grid-load coordinated low-carbon dispatching according to the user load curve and the switch disconnection status on the distribution network side.

[0032] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, a grid-load collaborative low-carbon scheduling method described in the above-mentioned embodiment of the invention is implemented.

[0033] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a grid-load coordinated low-carbon scheduling method described in the above-mentioned embodiment of the invention.

[0034] The following beneficial effects are achieved by implementing the present invention:

[0035] The present invention provides a method, device, terminal equipment and storage medium for coordinated low-carbon dispatching of grid and load. The method acquires basic data of the power system, and according to the basic data, takes the lowest carbon emission on the generation side of the distribution network as the goal, constructs the objective function of coordinated low-carbon dispatching of grid and load, and the system load balance constraint, line flow constraint, user load total power unchanged constraint throughout the day, load adjustment constraint and switch opening constraint of the objective function; then solves the objective function under the constraints of system load balance constraint, line flow constraint, user load total power unchanged constraint throughout the day, load adjustment constraint and switch opening constraint, and obtains the user load curve and the switch opening and closing state on the distribution network side when the carbon emission on the generation side of the distribution network is the lowest. By obtaining the user load curve and the switch opening and closing state on the distribution network side, and then adjusting the switch opening and closing state on the distribution network side to reduce carbon emissions, and by adjusting the user power consumption on the user load curve to reduce carbon emissions, the coordinated low-carbon dispatching of the distribution network area based on the grid side and the load side is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of a grid-load collaborative low-carbon scheduling method provided by an embodiment of the present invention.

[0037] Figure 2 It is a structural schematic diagram of a grid-load coordinated low-carbon scheduling device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] 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.

[0039] like Figure 1 As shown, a grid-load coordinated low-carbon scheduling method provided by an embodiment of the present invention includes:

[0040] Step S1: Obtain basic data of the power system; wherein the basic data includes: power system load, power flow transmission limit value of each line, number of power system nodes, power transfer distribution factor, bus load value, power flow relaxation variable, maximum load adjustment ratio, active power flow data of each branch, branch power flow distribution matrix, node active flux matrix, unit injection distribution matrix and unit carbon potential vector;

[0041] Step S2: Based on the basic data, with the goal of minimizing carbon emissions on the power generation side of the distribution network, construct an objective function for grid-load coordinated low-carbon dispatch; and based on the basic data, construct system load balance constraints, line flow constraints, user load total power unchanged throughout the day constraints, load adjustment constraints, and switch breaking constraints of the objective function;

[0042] Step S3: Under the constraints of the system load balance constraint, line flow constraint, user load total power unchanged constraint throughout the day, load adjustment constraint and switch disconnection constraint, the objective function is solved to generate the user load curve and the switch disconnection state of the distribution network side when the carbon emissions on the power generation side of the distribution network are the lowest;

[0043] Step S4: Perform grid-load coordinated low-carbon dispatching according to the user load curve and the switch disconnection status on the distribution network side.

[0044] For step S1, basic data of the power system is obtained; wherein, the basic data includes: power system load, power flow transmission limit value of each line, number of power system nodes, power transfer distribution factor, bus load value, power flow slack variable, maximum load adjustment ratio, active power flow data of each branch, branch power flow distribution matrix, node active flux matrix, unit injection distribution matrix and unit carbon potential vector. Preferably, the above basic data can be obtained from the provincial power spot market by obtaining the day-ahead clearing result data and power flow data. Obtaining data from the provincial power spot market mainly refers to the power system obtaining the day-ahead clearing result data of the provincial power spot market through database data synchronization, including the unit type and output arrangement and tie line plan arrangement of all generator sets. Among them, the types of generator sets include all types such as coal-fired, oil-fired, gas-fired, nuclear power, hydropower, wind power, photovoltaic, energy storage, nuclear power, biomass, etc. The day-ahead clearing results will cover all types of units, including market-oriented units and non-market-oriented units; the tie line plan is the market boundary of the day-ahead clearing results. Power flow data mainly refers to the active power flow data of each branch in the basic data.

[0045] In a preferred embodiment, after obtaining the basic data of the power system, it also includes: calculating the carbon potential of each node of the distribution network according to the branch flow distribution matrix, the node active flux matrix, the unit injection distribution matrix and the unit carbon potential vector.

[0046] Specifically, after obtaining the basic data, it is also necessary to combine the carbon emission flow theory to calculate the carbon flow index of the main nodes that supply power to the distribution network; in the present invention, the carbon flow index mainly refers to the node carbon potential vector calculated based on the tributary flow distribution matrix, the node active flux matrix, the unit injection distribution matrix and the unit carbon potential vector. The carbon flow index of the main nodes that supply power to the distribution network specifically refers to the node carbon potential vector of the node, and the calculation of the node carbon potential vector is as follows:

[0047]

[0048] Among them, E K is the node carbon potential vector; P B is the tributary tidal flow distribution matrix; P K is the node active flux matrix; P G Inject distribution matrix into the unit; E G is the unit carbon potential vector; the superscript T is the transpose of the matrix;

[0049] Tributary flow distribution matrix P B Obtained through:

[0050] P B =(P Bij,t ) K×K ;

[0051] Where K is the number of nodes in the power system; P Bij,t is the branch power flow distribution matrix element, P Bij,t The value of can be obtained from the active power flow data of each branch. If P is defined ij,t represents the positive active power flow from node i to node j, then P Bij,t =P ij,t , P Bij,t = 0, if the active current P flowing through the branch ij,t For reverse flow, P Bij,t =0,P Bij,t =P ij,t , in other cases P Bij,t =P ij,t ,t=0.

[0052] The node active flux matrix is ​​obtained by:

[0053] P K =(P Kij,t ) K×K ;

[0054]

[0055] Where K is the number of nodes in the power system; P Kij,t is the element in the node active flux matrix; P s,t is the active power of branch S at time t; is the set of branches with flow flowing into node i; P G,i,t is the active output of the unit connected to node i;

[0056] The unit injection distribution matrix is ​​obtained by:

[0057] P G =(P Gmj,t) M×K ;

[0058] Where K is the number of nodes in the power system; M is the number of generator sets; P Gmj,t The elements of the distribution matrix for the unit injection, P Gmj,t It represents the active power flow injected into node j by unit m (m=1,2,…,M) at time t.

[0059] The unit carbon potential vector is obtained by:

[0060] E G =[e G,1 ,e G,2 ,…,e G,M ] T ;

[0061] Among them, e G,1 ,e G,2 ,…,e G,M is the carbon emission intensity of units 1 to M. The unit carbon emission intensity data is taken from the IPCC carbon emission factor database. It should be noted that the unit carbon emission intensity is essentially the carbon potential.

[0062] For step S2, based on the above basic data, an objective function for grid-load coordinated low-carbon scheduling is constructed with the goal of minimizing carbon emissions on the generation side of the distribution network.

[0063] In a preferred embodiment, the objective function is specifically:

[0064]

[0065] Among them, E N,i,t is the carbon potential of the power supply node i in the distribution network area at time t, and the actual connection point of the distribution network after the switch disconnection state of the distribution network side is adjusted is the node i; P i,t is the power output of power supply node i in the distribution network area at time t; E j,t is the carbon emission intensity of the distributed energy connected to node j within the distribution network area at time t; T j,t It is the output prediction of the distributed energy connected to the j node within the distribution network area at time t.

[0066] The objective function is constructed based on the basic data to include system load balance constraints, line flow constraints, user load total power unchanged throughout the day constraints, load adjustment constraints and switch breaking constraints.

[0067] In a preferred embodiment, the system load balancing constraint is specifically:

[0068]

[0069] Among them, Pi,t is the power output of power supply node i in the distribution network area at time t, P i,t When power is supplied to the distribution network area, it is positive, P i,t It is negative when power is delivered to the distribution area; T j,t D is the output prediction of the distributed energy connected to the j node within the distribution network area at time t; t is the power system load at time t; N is the number of power supply nodes in the power system; NT is the number of all nodes containing distributed energy in the distribution network.

[0070] In a preferred embodiment, the line power flow constraint is specifically:

[0071]

[0072] in, is the power flow transmission limit value of line l; N is the number of power supply nodes in the power system; G l-i P is the power transfer distribution factor of the generator output power of the power supply node i connected to the distribution network of the power system main grid to the line l; i,t is the power output of power supply node i at time t; NT is the number of all nodes containing distributed energy in the distribution network; G l-j is the power transfer distribution factor of the generator output from node j to line l within the distribution network area; T j,t is the output forecast of the distributed energy connected to node j in the distribution network area at time t; K is the number of all nodes in the power system; G l-k is the generator output power transfer distribution factor of node k for line l; D k,t is the bus load value of node k at time t; is the forward power flow relaxation variable of line l; is the reverse power flow slack variable of line l.

[0073] The constraint that the total power of the user load remains unchanged throughout the day is mainly used to constrain the total power of the user load throughout the day after optimization and the original total power demand throughout the day to remain unchanged. In a preferred embodiment, the constraint that the total power of the user load remains unchanged throughout the day is specifically:

[0074]

[0075] Among them, D q,t is the original load of user q at time t; D q ′ ,t It is the optimized load of user q at time t; the load of user q is connected to a node among all nodes K in the power system.

[0076] In a preferred embodiment, the load adjustment constraint is specifically:

[0077]

[0078] Among them, D q,t is the original load of user q at time t; D q ′ ,t is the optimized load of user q at time t; S q,t is the maximum proportion of load adjustment for user q at time t.

[0079] Preferably, the switch disconnection constraint is specifically:

[0080] 0≤a i,t ≤1;

[0081] Among them, a i,t is the switch state of switch a at time t.

[0082] For step S3, the objective function is solved under the constraints of the system load balance constraint, the line flow constraint, the user load total power unchanged throughout the day constraint, the load adjustment constraint and the switch opening constraint. The solution process is a linear optimization problem, which can be solved by a linear optimization solver. In this optimization problem, the optimization variables are the switch opening state on the distribution network side and the user load curve, that is, the user load curve and the switch opening state on the distribution network side are solved under the above constraints when the carbon emissions on the power generation side of the distribution network are the lowest.

[0083] For step S4, the grid-load coordinated low-carbon dispatch is performed according to the user load curve and the switch on / off status of the distribution network side. Specifically, the carbon potential of the distribution network at multiple nodes on the main grid can be compared, and the switch on / off status of the distribution network can be adjusted to select nodes with low carbon potential to reduce carbon emissions; on the basis of the user's daily total electricity consumption remaining unchanged, the user side load can be shifted to use more electricity at low carbon emission intensity moments and less electricity at high carbon emission intensity moments to reduce overall carbon emissions.

[0084] By adjusting the on-off status of switches on the grid side and regulating electricity consumption based on the user load curve on the electricity user side, coordinated low-carbon regulation on the grid side and the load side can be achieved, thereby promoting the consumption of new energy and achieving low-carbon scheduling.

[0085] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0086] like Figure 2 As shown, an embodiment of the present invention provides a network-load coordinated low-carbon scheduling device, including: a data acquisition module, a model and constraint condition construction module, a model solving module and a scheduling module;

[0087] The data acquisition module is used to acquire basic data of the power system; wherein the basic data includes: power system load, power flow transmission limit value of each line, number of power system nodes, power transfer distribution factor, bus load value, power flow relaxation variable, maximum load adjustment ratio, active power flow data of each branch, branch power flow distribution matrix, node active flux matrix, unit injection distribution matrix and unit carbon potential vector;

[0088] The model and constraint condition construction module is used to construct an objective function of grid-load coordinated low-carbon dispatch based on the basic data with the goal of minimizing carbon emissions on the generation side of the distribution network; and to construct system load balance constraints, line flow constraints, user load total power unchanged throughout the day constraints, load adjustment constraints and switch breaking constraints of the objective function based on the basic data;

[0089] The model solving module is used to solve the objective function under the constraints of the system load balance constraint, the line flow constraint, the user load total power unchanged constraint throughout the day, the load adjustment constraint and the switch disconnection constraint, and generate the user load curve and the switch disconnection state of the distribution network side when the carbon emissions on the power generation side of the distribution network are the lowest;

[0090] The dispatching module is used to perform grid-load coordinated low-carbon dispatching according to the user load curve and the switch disconnection status on the distribution network side.

[0091] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0092] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0093] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0094] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a grid-load collaborative low-carbon scheduling method described in any one of the present invention is implemented.

[0095] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0096] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0097] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0098] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0099] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a grid-load collaborative low-carbon scheduling method as described in any one of the present inventions.

[0100] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0101] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A grid-load collaborative low-carbon scheduling method, characterized in that: include: Obtaining basic data of the power system; wherein the basic data includes: power system load, power flow transmission limit value of each line, number of power system nodes, power transfer distribution factor, bus load value, power flow relaxation variable, maximum load adjustment ratio, active power flow data of each branch, branch power flow distribution matrix, node active flux matrix, unit injection distribution matrix and unit carbon potential vector; According to the basic data, an objective function of grid-load coordinated low-carbon dispatch is constructed with the goal of minimizing carbon emissions on the power generation side of the distribution network; and based on the basic data, system load balance constraints, line flow constraints, user load total power unchanged throughout the day constraints, load adjustment constraints and switch breaking constraints of the objective function are constructed; Under the constraints of the system load balance constraint, line flow constraint, user load total power unchanged constraint throughout the day, load adjustment constraint and switch disconnection constraint, the objective function is solved to generate the user load curve and the switch disconnection state of the distribution network side when the carbon emissions on the power generation side of the distribution network are the lowest; Grid-load coordinated low-carbon dispatch is carried out according to the user load curve and the switch on-off status of the distribution network side.

2. A grid-load coordinated low-carbon dispatching method as claimed in claim 1, characterized in that: The objective function is specifically: Among them, E N,i,t is the carbon potential of power supply node i in the distribution network area at time t; P i,t is the power output of power supply node i in the distribution network area at time t; E j,t is the carbon emission intensity of the distributed energy connected to node j within the distribution network area at time t; T j,t It is the output prediction of the distributed energy connected to the j node within the distribution network area at time t.

3. A grid-load coordinated low-carbon dispatching method as claimed in claim 1, characterized in that: The system load balancing constraints are specifically: Among them, P i,t is the power output of power supply node i in the distribution network area at time t; T j,t D is the output prediction of the distributed energy connected to the j node within the distribution network area at time t; t is the power system load at time t; N is the number of power supply nodes in the power system; NT is the number of all nodes containing distributed energy in the distribution network.

4. A grid-load coordinated low-carbon dispatching method as claimed in claim 1, characterized in that: The line power flow constraints are specifically: in, is the power flow transmission limit value of line l; N is the number of power supply nodes in the power system; G l-i P is the power transfer distribution factor of the generator output power of the power supply node i connected to the distribution network of the power system main grid to the line l; i,t is the power output of power supply node i at time t; NT is the number of all nodes containing distributed energy in the distribution network; G l-j is the power transfer distribution factor of the generator output from node j to line l within the distribution network area; T j,t is the output forecast of the distributed energy connected to node j in the distribution network area at time t; K is the number of all nodes in the power system; G l-k is the generator output power transfer distribution factor of node k for line l; D k,t is the bus load value of node k at time t; is the forward power flow relaxation variable of line l; is the reverse power flow slack variable of line l.

5. A grid-load coordinated low-carbon dispatching method as claimed in claim 1, characterized in that: The constraint that the total power of the user load remains unchanged throughout the day is specifically: Among them, D q,t is the original load of user q at time t; D q ′ ,t It is the optimized load of user q at time t; the load of user q is connected to any node of the power system.

6. A grid-load coordinated low-carbon dispatching method as claimed in claim 1, characterized in that: The load adjustment constraint is specifically: Among them, D q,t is the original load of user q at time t; D q ′ ,t is the optimized load of user q at time t; S q,t is the maximum proportion of load adjustment for user q at time t.

7. A grid-load coordinated low-carbon dispatching method as claimed in claim 1, characterized in that: After obtaining the basic data of the power system, it also includes: The carbon potential of each node in the distribution network is calculated based on the tributary flow distribution matrix, node active flux matrix, unit injection distribution matrix and unit carbon potential vector.

8. A grid-load coordinated low-carbon dispatching device, characterized in that: include: Data acquisition module, model and constraint building module, model solving module and scheduling module; The data acquisition module is used to acquire basic data of the power system; wherein the basic data includes: power system load, power flow transmission limit value of each line, number of power system nodes, power transfer distribution factor, bus load value, power flow relaxation variable, maximum load adjustment ratio, active power flow data of each branch, branch power flow distribution matrix, node active flux matrix, unit injection distribution matrix and unit carbon potential vector; The model and constraint condition construction module is used to construct an objective function of grid-load coordinated low-carbon dispatch based on the basic data with the goal of minimizing carbon emissions on the generation side of the distribution network; and to construct system load balance constraints, line flow constraints, user load total power unchanged throughout the day constraints, load adjustment constraints and switch breaking constraints of the objective function based on the basic data; The model solving module is used to solve the objective function under the constraints of the system load balance constraint, the line flow constraint, the user load total power unchanged constraint throughout the day, the load adjustment constraint and the switch disconnection constraint, and generate the user load curve and the switch disconnection state of the distribution network side when the carbon emissions on the power generation side of the distribution network are the lowest; The dispatching module is used to perform grid-load coordinated low-carbon dispatching according to the user load curve and the switch disconnection status on the distribution network side.

9. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a grid-load coordinated low-carbon scheduling method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a grid-load coordinated low-carbon scheduling method as described in any one of claims 1 to 7.

Images