Gas-electricity joint optimization scheduling method and device for long-time-domain continuous time

Abstract

The invention discloses a gas-electricity joint optimization scheduling method and device for long-time-domain continuous time, and belongs to the technical field of electrical engineering, and the method comprises the steps: dividing a time domain H of an original solving problem of a gas-electricity joint optimization scheduling model into N sub-time domains, and obtaining an equivalent solving problem under the N sub-time domains; decomposing an equivalent solving problem of the original solving problem into N sub solving problems; respectively solving the sub-solving problems and judging whether all the current solutions meet a convergence condition or not; if not, updating the N sub solving problems and solving again until the obtained current solution is converged; according to the method, the calculation burden caused by a long-time-domain continuous-time gas-electric combined system model is relieved, high-quality solution is completed in a short time, and the advantage of long-time-domain continuous-time scheduling of the gas-electric combined system is fully exerted; and finally, planning the gas-electricity joint optimization scheduling by using the convergence solution of the N sub-solution problems, thereby providing a basis for accurately and quickly formulating a scheduling plan.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering technology, and more specifically, relates to a gas-electricity joint optimization scheduling method and system for long-term continuous time. Background Art

[0002] The increasing number of climate events has attracted worldwide attention to carbon neutrality and greatly promoted the development of renewable energy. Unfortunately, the high proportion of renewable energy has greatly increased the difficulty of the power system in dealing with various extreme weather conditions. Continuous time scheduling of the gas-electricity combined system over a long time domain can fully consider the energy abundance in a longer time window and the power balance within the time period, and realize the precise coordinated scheduling of pipeline gas storage and battery energy storage over a long time domain, so as to better adapt to the long-term extreme weather that may be faced.

[0003] However, the above dispatch model faces two difficulties in solving: on the one hand, the integer variables in the power system (such as unit state variables, battery state variables, etc.) are huge in scale in the long time domain, which greatly increases the time of the existing branch and bound algorithm to solve the linear integer programming problem; on the other hand, the use of continuous time theory to model the gas grid in the long time domain will greatly increase the number of constraints in the optimization model, greatly increasing the difficulty of solving. The current more widely used solution algorithms include the rolling time domain solution strategy and the time decomposition algorithm based on pull relaxation. The former solves the scheduling problems in the shorter time domain sequentially to achieve the solution of the complete time domain, but it cannot guarantee the optimality of the solution result; the latter decomposes the original time domain into several equal-length sub-time domains and relaxes the time coupling constraints between the sub-time domains. The solution to the original problem is completed through several iterations, and a high-quality solution can be achieved in a shorter time.

[0004] In summary, all existing time decomposition algorithms are based on the discrete time solution framework and cannot be directly applied to continuous time models. This puts forward the demand for time decomposition algorithms suitable for continuous time models. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a method and system for gas-electricity joint optimization scheduling for long-term continuous time, which aims to solve the technical problem that the prior art is unable to solve the gas-electricity joint optimization scheduling model for long-term continuous time and then implement the scheduling plan.

[0006] To achieve the above object, according to one aspect of the present invention, a method for optimizing the joint dispatch of gas and electricity for a long time domain and continuous time is provided, comprising:

[0007] S1: The gas-electricity joint optimization scheduling model for long-term continuous time in finite-dimensional algebraic space is expressed as: c krepresents the kth coefficient of the objective function, x k represents the kth control point, A k represents the kth coefficient on the left side of the constraint, and b represents the coefficient on the right side of the constraint;

[0008] S2: Divide the time domain H of the original solution problem of the gas-electricity joint optimization scheduling model into N sub-time domains to obtain an equivalent solution problem in the N sub-time domains corresponding to the original solution problem:

[0009]

[0010] in, Include x only in sub-time domain n k , and are all bridging variables, Includes x in the overlapping period between sub-time domains n-1 and n k , Includes x in the overlap period between sub-time domains n and n+1 k , and is empty, b n is the corresponding coefficient, ε is a parameter in the range of (0,1);

[0011] S3: Decomposing the equivalent solution problem of the original solution problem into N sub-solution problems;

[0012] S4: Solve each of the sub-problems separately to obtain a current solution to each of the sub-problems;

[0013] S5: Determine whether the current solutions of all the sub-problems meet the convergence condition; if not, update the N sub-problems and return to S4; if the convergence condition is met, use the current solutions of the N sub-problems as the target solution;

[0014] S6: splicing the target solutions of the N sub-problems to obtain a solution to the original problem, and using the solution to the original problem to plan the gas-electricity joint optimization scheduling.

[0015] In one embodiment, the S3 includes:

[0016] S31: Introducing auxiliary variable y n Constrain Refactored to

[0017] S32: Convert the equivalent problem to be solved in the N sub-time domains into the N sub-problems to be solved.

[0018] In one embodiment, the S32 includes: converting the equivalent solution problems in N sub-time domains into the N sub-solution problems expressed as:

[0019]

[0020] in, and ρ are parameters in the stepwise hedging algorithm.

[0021] In one embodiment, if the condition is not satisfied, then the N sub-problems are updated, including: if the condition is not satisfied, then the N sub-problems are updated according to The N sub-problems are updated.

[0022] In one embodiment, the convergence condition is: Where subscript b corresponds to a specific time coupling constraint, N b is the number of elements in block b, s is the index of the element in block b, for The sth element of for The maximum value that can be achieved in its feasible region, δ b is a sufficiently small parameter corresponding to block b.

[0023] In one of the embodiments, before S1, it also includes: expressing the time variable P(t) and the space-time variable M(x, t) in the continuous time function in the gas-electricity joint optimization scheduling model in the high-dimensional time function space in the form of an interpolation coefficient vector, thereby converting it into a gas-electricity joint optimization scheduling model for long-term continuous time in a finite-dimensional algebraic space.

[0024] In one embodiment, the connection variables include: continuous variables satisfying zero-order continuity constraints, continuous variables satisfying first-order continuity constraints, and all integer variables in the overlapping period.

[0025] According to another aspect of the present invention, a method for optimizing the joint dispatch of gas and electricity for a long time domain is provided, comprising:

[0026] The representation module is used to represent the gas-electricity joint optimization scheduling model for long-term continuous time in finite-dimensional algebraic space as follows: c k represents the kth coefficient of the objective function, x k represents the kth control point, A k represents the kth coefficient on the left side of the constraint, and b represents the coefficient on the right side of the constraint;

[0027] The equivalent module is used to divide the time domain H of the original solution problem of the gas-electricity joint optimization scheduling model into N sub-time domains to obtain the equivalent solution problem in the N sub-time domains corresponding to the original solution problem: in, Include x only in sub-time domain n k , and are all bridging variables, Includes x in the overlapping period between sub-time domains n-1 and n k , Includes x in the overlap period between sub-time domains n and n+1 k , and is empty, b n is the corresponding coefficient, ε is a parameter in the range of (0,1);

[0028] A decomposition module, used for decomposing the equivalent solution problem of the original solution problem into N sub-solution problems;

[0029] A solution module, used for solving each of the sub-problems to obtain a current solution of each of the sub-problems;

[0030] A judgment module, used to judge whether the current solutions of all the sub-solutions meet the convergence condition; if not, the N sub-solutions are updated and the updated N sub-solutions are transmitted to the solution module; if the convergence condition is met, the current solutions of the N sub-solutions are used as the target solution;

[0031] The planning module is used to splice the target solutions of the N sub-problems to obtain the solution of the original problem, and use the solution of the original problem to plan the gas-electricity joint optimization scheduling.

[0032] According to another aspect of the present invention, a gas-electricity joint optimization scheduling system for long-term continuous time is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.

[0033] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0034] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0035] (1) The present invention provides a method for long-term continuous-time gas-electricity joint optimization scheduling. The method divides the time domain H of the original solution problem of the gas-electricity joint optimization scheduling model into N sub-time domains to obtain an equivalent solution problem in the N sub-time domains corresponding to the original solution problem; decomposes the equivalent solution problem of the original solution problem into N sub-solution problems; solves each sub-solution problem separately and determines whether the current solutions of all sub-solution problems meet the convergence condition; if not, updates the N sub-solution problems and solves them again until the current solution converges; this method alleviates the computational burden caused by the long-term continuous-time gas-electricity joint system model, achieves high-quality solution in a relatively short time, and gives full play to the advantages of long-term continuous-time scheduling of the gas-electricity joint system; finally, the converged solutions of the N sub-solution problems are used to plan the gas-electricity joint optimization scheduling, providing a basis for accurately and quickly formulating scheduling plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart of a method for optimizing the joint dispatch of gas and electricity for a long-term continuous period provided in Example 1 of the present invention;

[0037] Figure 2 A schematic diagram of a power curve in the sub-time domain in the gas-electricity joint optimization scheduling method based on improved step-by-step hedging provided in Example 1 of the present invention;

[0038] Figure 3 Schematic diagram of a modified IEEE 6-node power system and 5-node gas grid system provided for an example of the present invention;

[0039] Figure 4 A schematic diagram of a historical wind power output curve provided for an example of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0041] Example 1

[0042] like Figure 1 As shown, this embodiment provides a gas-electricity joint optimization scheduling method for long-term continuous time, including: S1-S6.

[0043] S1: The gas-electricity joint optimization scheduling model for long-term continuous time in finite-dimensional algebraic space is expressed as: c k represents the kth coefficient of the objective function, xk represents the kth control point, A k represents the kth coefficient on the left side of the constraint, and b represents the coefficient on the right side of the constraint. Among them, x contains all the control points in the model, such as the output of the thermal power unit Battery charge / discharge power Gas unit output Power to Gas (P2G) power consumption Natural gas flow rate Natural gas pressure

[0044] S2: Divide the time domain H of the original solution problem of the gas-electricity joint optimization scheduling model into N sub-time domains, and obtain the equivalent solution problem in the N sub-time domains corresponding to the original solution problem:

[0045]

[0046] in, Include x only in sub-time domain n k , and are all bridging variables, Includes x in the overlapping period between sub-time domains n-1 and n k , Includes x in the overlap period between sub-time domains n and n+1 k , and is empty, b n is the corresponding coefficient, and ε is a parameter in the range of (0,1).

[0047] S3: Decompose the equivalent solution problem of the original solution problem into N sub-solution problems.

[0048] S4: Solve each sub-problem separately to obtain the current solution of each sub-problem.

[0049] S5: Determine whether the current solutions of all sub-problems meet the convergence conditions; if not, update the N sub-problems and return to S4; if the convergence conditions are met, use the current solutions of the N sub-problems as the target solutions.

[0050] S6: The target solutions of the N sub-problems are concatenated to obtain the solution of the original problem, and the solution of the original problem is used to plan the gas-electricity joint optimization dispatch.

[0051] As an optional implementation, before S1, it also includes: expressing the time variable P(t) and the space-time variable M(x, t) in the continuous time function in the gas-electricity joint optimization scheduling model in the high-dimensional time function space in the form of an interpolation coefficient vector, thereby converting it into a gas-electricity joint optimization scheduling model for long-term continuous time in a finite-dimensional algebraic space.

[0052] Among them, the continuous time function in the original problem is expressed in the form of an interpolation coefficient vector through the polynomial interpolation method as follows:

[0053]

[0054] Where P τ (t), M τ (x, t) are the continuous time functions P(t) and M(x, t) in the piecewise function of the time period τ, where t ranges from 0 to 1. and is the interpolation coefficient of the piecewise continuous time function (also called control point). and The vectors formed are recorded as In the above formula, the cubic BP (Bernstein Spline) spline Defined as:

[0055]

[0056] Furthermore, the following rules are introduced to achieve the solution space transformation of the continuous-time model. The objective function and constraints contained in the original continuous-time model are divided into the following forms, and their respective transformation rules are established:

[0057] (1) Equation: Based on the above basic principle of BP spline interpolation, the following rules are established:

[0058]

[0059] (2) Inequality equation: Based on the convex hull property of BP spline, the following rules are established:

[0060]

[0061] (3) Integral and differential terms: Based on the BP spline definition, the following rules are established:

[0062]

[0063] Where D, I, K are known operation matrices.

[0064] Based on the above rules, the original continuous-time gas-electricity joint scheduling problem can be transformed from the function space to the algebraic space, that is, the continuous-time functions in the original problem can be represented by their spline interpolation coefficient vectors. The following further introduces the principle of the gas-electricity joint optimization scheduling method.

[0065] As an optional implementation, S3 includes: S31: introducing an auxiliary variable y n Constrain Refactored to S32: Convert the equivalent solution problems in N sub-time domains into N sub-solution problems.

[0066] As an optional implementation, Figure 2 The power curve in the sub-time domain of the gas-electricity joint optimization scheduling method based on improved step-by-step hedging includes three parts: (a), (b), and (c), where (a) is a schematic diagram of a discrete time curve; (b) is a schematic diagram of a continuous time curve, and (c) is a schematic diagram of a control curve; S32 includes: converting the equivalent solution problems in N sub-time domains into N sub-solution problems as follows:

[0067]

[0068] in, and ρ are parameters in the stepwise hedging algorithm.

[0069] As an optional implementation, if the problem is not satisfied, the N sub-problems are updated, including: if the problem is not satisfied, the above unexpected constraints are updated through multiple iterations. Gradually converge. In each iteration, L1 and L n , L N and corresponding and After that, y and ω are updated as follows (stepwise hedging):

[0070] As an optional implementation, the convergence condition is:

[0071]

[0072] Where subscript b corresponds to a specific time coupling constraint, N b is the number of elements in block b, s is the index of the element in block b, for The sth element of for The maximum value that can be achieved in its feasible region, δ b is a sufficiently small parameter corresponding to block b.

[0073] As an optional implementation, the connection variables include: continuous variables satisfying a zero-order continuity constraint, continuous variables satisfying a first-order continuity constraint, and all integer variables in the overlapping period.

[0074] In order to realize the solution of the proposed algorithm in the gas grid model, the connection variables in the overlap period In order to reduce the variables that need to be hedged, only one control point is set for each bridging variable in the overlapping period. The following discusses the specific blocks contained in the bridging variables in the continuous-time gas-electricity joint system.

[0075] Block 1: includes all continuous variables in the overlapping period that need to satisfy the zero-order continuity constraint

[0076]

[0077] Where k represents the sequence number of the control point, are the interpolation control points of wind power abandonment / load shedding power / battery energy respectively, and the other variables have been explained above.

[0078] Block 2: Includes all continuous variables in the overlapping period that need to satisfy the first-order continuity constraints Including climbing consistency constraints and SOC trajectory consistency constraints. Here, the first-order variation is defined as The variables in block 2 include:

[0079]

[0080] Block 3: includes all integer variables in the overlapping period (Unit state variables, unit state duration variables, etc.). The minimum start-stop time constraint in the original model needs to be consistent between adjacent sub-time domains, that is, the unit state duration at the end of the previous sub-time domain must be equal to its state duration at the beginning of the next sub-time domain. To this end, the auxiliary variable To represent the on / off duration of the transition period τ. The following constraints are added to the optimization model to achieve Calculation:

[0081]

[0082] In the formula M off :=[1,2,...,T off ] T , M off :=[1,2,...,T off ] TTherefore, the start and stop actions of the units in several time periods after the connection period τ can be considered by adding the following constraints in the next sub-time domain:

[0083]

[0084] Where M is a sufficiently large positive number, N on :=[T on -1,T on -2,...,0] T , N off :=[T off -1,T off -2,...,0] T Combining the above two equations, the variables in block three include:

[0085]

[0086] The simulation data of the first example is provided below, and the modified IEEE 6-node power system and 5-node gas grid system are selected, such as Figure 3 The maximum load of the power system is 330MW. Three thermal power units are connected to nodes 1 / 2 / 3 respectively, and one wind farm is connected to node 3. The specific parameters are shown in Table 1. The wind power curve is shown in Figure 4 As shown. A storage power station is connected to node 1, with a rated charge and discharge power of 120MW and a rated continuous charge and discharge time of 4h. A gas unit and a P2G device are connected to nodes 2 and 4 respectively. The maintenance cost of the storage power station is 10$ / MW. The gas price is set to 0.16$ / kg. The wind abandonment and load shedding penalty coefficients are 200$ / MW and 1000$ / MW respectively. The total dispatch time considered in this problem is 14 days. The commercial solver used is Gurobi 10.0.

[0087] Table 1 Parameters of thermal power and wind turbines

[0088]

[0089] The above operations S1-S3 are used to decompose and solve the example model, and the results are compared with the direct solution results and rolling solution results using Gurobi, and the solution results are recorded in Table 2.

[0090] Table 2 Solution results of IEEE 6-node system and 5-node gas grid system

[0091] Comparison Items Gurobi direct solution Rolling Solution Proposed decomposition algorithm Total cost(k$) 9931.96 10241.63 9947.36 Calculation time(s) 632.96 42.35 88.68

[0092] As can be seen from Table 2, compared with directly using Gurobi for solving, the proposed gas-electricity joint optimization scheduling method can reduce the solution time by 86.89%, and the total cost obtained is only 0.2% different from the optimal solution. The results in the above table prove that the proposed time decomposition method can significantly reduce the solution time while achieving higher accuracy.

[0093] The following provides simulation data for the second example, selecting the standard IEEE 118-node power system and the 27-node Belgian gas grid system. One gas turbine and one P2G device are connected to nodes 69 and 13 respectively, with a rated power of 1000MW. One wind farm and one energy storage power station are connected to node 38. The rated installed capacity of the wind farm is 4000MW, the rated charging and discharging power of the energy storage power station is 1000MW, and the rated continuous charging and discharging time is 4h. Other parameters are consistent with the previous embodiment.

[0094] The above operations S1-S3 are used to decompose and solve the example model, and the results are compared with the direct solution results and rolling solution results using Gurobi, and the solution results are recorded in Table 3.

[0095] Table 3 Solution results of IEEE 118-node system and Belgian 27-node gas grid system

[0096] Comparison Items Gurobi direct solution Rolling Solution Proposed decomposition algorithm Total cost(k$) - 220072.58 221554.76 Calculation time(s) >3 days 4608.37 5941.12

[0097] According to the results in Table 3, if direct solution is used, a feasible solution cannot be obtained within a limited time, but the proposed time decomposition method can find a high-quality feasible solution within 100 minutes. Compared with the rolling solution method, the proposed algorithm provides a higher-quality feasible solution at the cost of an acceptable increase in computing time, while reducing the total cost by 1.5M$ while the solution time is no more than 20 minutes longer.

[0098] Example 2

[0099] This embodiment provides a method for optimizing the joint scheduling of gas and electricity for long-term continuous time, including: a representation module, an equivalent module, a decomposition module, a solution module, a judgment module and a planning module.

[0100] The representation module is used to represent the gas-electricity joint optimization scheduling model for long-term continuous time in finite-dimensional algebraic space as follows: c k represents the kth coefficient of the objective function, x k represents the kth control point, A k represents the kth coefficient on the left side of the constraint, and b represents the coefficient on the right side of the constraint.

[0101] The equivalent module is used to divide the time domain H of the original solution problem of the gas-electricity joint optimization scheduling model into N sub-time domains, and obtain the equivalent solution problem in the N sub-time domains corresponding to the original solution problem:

[0102]

[0103] in, Include x only in sub-time domain n k , and are all bridging variables, Includes x in the overlapping period between sub-time domains n-1 and n k , Includes x in the overlap period between sub-time domains n and n+1 k , and is empty, b n is the corresponding coefficient, and ε is a parameter in the range of (0,1).

[0104] The decomposition module is used to decompose the equivalent solution problem of the original solution problem into N sub-solution problems.

[0105] The solution module is used to solve each sub-problem separately to obtain the current solution of each sub-problem.

[0106] The judgment module is used to judge whether the current solutions of all sub-solution problems meet the convergence conditions; if not, the N sub-solution problems are updated and the updated N sub-solution problems are transmitted to the solution module; if the convergence conditions are met, the current solutions of the N sub-solution problems are used as the target solutions.

[0107] The planning module is used to splice the target solutions of N sub-problems to obtain the solution of the original problem, and use the solution of the original problem to plan the gas-electricity joint optimization dispatch.

[0108] Example 3

[0109] This embodiment provides a gas-electricity joint optimization scheduling system for long-term continuous time, including a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.

[0110] Example 4

[0111] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0112] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A gas-electricity joint optimization scheduling method for long-term continuous time, characterized in that: include: S1: The gas-electricity joint optimization scheduling model for long-term continuous time in finite-dimensional algebraic space is expressed as: c k represents the kth coefficient of the objective function, x k represents the kth control point, A k represents the kth coefficient on the left side of the constraint, and b represents the coefficient on the right side of the constraint; S2: Divide the time domain H of the original solution problem of the gas-electricity joint optimization scheduling model into N sub-time domains to obtain an equivalent solution problem in the N sub-time domains corresponding to the original solution problem: in, Include x only in sub-time domain n k , and are all bridging variables, Includes x in the overlapping period between sub-time domains n-1 and n k , Includes x in the overlap period between sub-time domains n and n+1 k , and is empty, b n is the corresponding coefficient, ε is a parameter in the range of (0,1); S3: Decomposing the equivalent solution problem of the original solution problem into N sub-solution problems; S4: Solve each of the sub-problems separately to obtain a current solution to each of the sub-problems; S5: Determine whether the current solutions of all the sub-problems meet the convergence condition; if not, update the N sub-problems and return to S4; if the convergence condition is met, use the current solutions of the N sub-problems as the target solution; S6: splicing the target solutions of the N sub-problems to obtain a solution to the original problem, and using the solution to the original problem to plan the gas-electricity joint optimization scheduling.

2. The method for optimizing the joint dispatch of gas and electricity for long-term continuous time according to claim 1, characterized in that: The S3 includes: S31: Introducing auxiliary variable y n Constrain Refactored to S32: Convert the equivalent problem to be solved in the N sub-time domains into the N sub-problems to be solved.

3. The method for optimizing the joint dispatch of gas and electricity for long-term continuous time according to claim 2, characterized in that: The S32 includes: converting the equivalent solution problems in N sub-time domains into the N sub-solution problems expressed as: in, and ρ are parameters in the stepwise hedging algorithm.

4. The method for optimizing the joint dispatch of gas and electricity for long-term continuous time according to claim 2, characterized in that: If the above conditions are not met, the N sub-problems are updated, including: if the above conditions are not met, the N sub-problems are updated according to The N sub-problems are updated.

5. The method for optimizing the joint dispatch of gas and electricity for long-term continuous time according to claim 4, characterized in that: The convergence condition is: Where subscript b corresponds to a specific type of time coupling constraint, N b is the number of elements in block b, s is the index of the element in block b, For the middle The sth element of for The maximum value that can be achieved in its feasible region, δ b is a sufficiently small parameter corresponding to block b.

6. The method for optimizing the joint dispatch of gas and electricity for long-term continuous time according to claim 1, characterized in that: Before S1, it also includes: expressing the time variable P(t) and the space-time variable M(x, t) in the continuous time function in the gas-electricity joint optimization scheduling model in the high-dimensional time function space in the form of an interpolation coefficient vector, thereby converting it into a gas-electricity joint optimization scheduling model for long-term continuous time in a finite-dimensional algebraic space.

7. The method for optimizing the joint dispatch of gas and electricity for a long time domain and continuous time according to any one of claims 1 to 6, characterized in that: The connection variables include: continuous variables satisfying the zero-order continuity constraint, continuous variables satisfying the first-order continuity constraint and all integer variables in the overlapping period.

8. A method for optimizing the joint dispatch of gas and electricity for long-term continuous time, characterized in that: include: The representation module is used to represent the gas-electricity joint optimization scheduling model for long-term continuous time in finite-dimensional algebraic space as follows: c k represents the kth coefficient of the objective function, x k represents the kth control point, A k represents the kth coefficient on the left side of the constraint, and b represents the coefficient on the right side of the constraint; The equivalent module is used to divide the time domain H of the original solution problem of the gas-electricity joint optimization scheduling model into N sub-time domains to obtain the equivalent solution problem in the N sub-time domains corresponding to the original solution problem: in, Include x only in sub-time domain n k , and are all bridging variables, Includes x in the overlapping period between sub-time domains n-1 and n k , Includes x in the overlap period between sub-time domains n and n+1 k , and is empty, b n is the corresponding coefficient, ε is a parameter in the range of (0,1); A decomposition module, used for decomposing the equivalent solution problem of the original solution problem into N sub-solution problems; A solution module, used for solving each of the sub-problems to obtain a current solution of each of the sub-problems; A judgment module, used to judge whether the current solutions of all the sub-solutions meet the convergence condition; if not, the N sub-solutions are updated and the updated N sub-solutions are transmitted to the solution module; if the convergence condition is met, the current solutions of the N sub-solutions are used as the target solution; The planning module is used to splice the target solutions of the N sub-problems to obtain the solution of the original problem, and use the solution of the original problem to plan the gas-electricity joint optimization scheduling.

9. A gas-electricity joint optimization dispatching system for long-term continuous time, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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