A method and device for combined optimization scheduling of gas and electricity for long time domain continuous time

Abstract

The application discloses a kind of long time domain continuous time-oriented gas-electricity combined optimization scheduling method and device, belong to electrical engineering technical field, the method is by the time domain H of original solving problem of gas-electricity combined optimization scheduling model is divided into N sub time domains and obtains equivalent solving problem under N sub time domains;Equivalent solving problem of original solving problem is decomposed into N sub solving problem;Respectively solve sub solving problem and judge whether all current solution meets convergence condition;If not satisfied, update and re-solve N sub solving problem until the current solution obtained converges;This way relieves the calculation burden caused by long time domain continuous time gas-electricity combined system model, realizes high-quality solution in a short time, gives full play to the advantage of long time domain continuous time scheduling to gas-electricity combined system;Finally, the convergence solution of N sub solving problem is used to plan gas-electricity combined optimization scheduling, which provides a basis for accurately and quickly making scheduling plan.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of electrical engineering, and more particularly relates to a gas-electricity combined optimization scheduling method and system for long-time-domain continuous time. BACKGROUND

[0002] Increasing climate events have attracted worldwide attention to carbon neutrality, greatly promoting the development of renewable energy. Unfortunately, a high proportion of renewable energy greatly increases the difficulty for the power system to cope with various extreme weather. Long-time-domain continuous-time scheduling of a gas-electricity combined system can fully consider the energy adequacy in a long time window and the power balance in a time period, accurately coordinate the scheduling of pipeline gas storage and battery energy storage in a long time domain, and thus better adapt to possible long-term extreme weather.

[0003] However, the above scheduling model faces two solving difficulties: on the one hand, the integer variables (such as unit state variables and battery state variables) in the power system are large in scale in a long time domain, greatly increasing the time for solving linear integer programming problems by existing branch and bound algorithms; on the other hand, modeling the gas network in a long time domain by using continuous time theory will greatly increase the number of constraints in the optimization model, greatly increasing the difficulty of solving. The existing widely used solving algorithms include a rolling time domain solving strategy and a time decomposition algorithm based on a Lagrangian relaxation. The former sequentially solves scheduling problems in a short time domain to realize the solving of the complete time domain, but cannot guarantee the optimality of the solving result. The latter decomposes the original time domain into several equal-length sub-domains and relaxes the time coupling constraints between the sub-domains, and completes the solving of the original problem through several iterations, which can realize high-quality solving in a short time.

[0004] In summary, the existing time decomposition algorithms are all based on a discrete time solving framework and cannot be directly applied to a continuous time model, which puts forward a demand for a time decomposition algorithm applicable to a continuous time model. SUMMARY

[0005] In view of the above defects or improvement needs of the prior art, the present application provides a gas-electricity combined optimization scheduling method and system for long-time-domain continuous time, which aims to solve the technical problem that the prior art cannot solve the long-time-domain continuous-time gas-electricity combined optimization scheduling model and thus implement a scheduling plan.

[0006] To achieve the above-mentioned purpose, according to one aspect of the present application, a gas-electricity combined optimization scheduling method for long-time-domain continuous time is provided, comprising:

[0007] S1: representing a gas-electricity combined optimization scheduling model for long-time-domain continuous time in a finite-dimensional algebraic space as follows: c kx k A k b

[0008] S2: dividing the time domain H of the original solving problem of the gas-electricity combined optimization scheduling model into N sub time domains to obtain an equivalent solving problem of the original solving problem under N sub time domains:

[0009]

[0010] wherein, x k , and are link variables, x k , x k , and are empty, b n are corresponding coefficients, and ε is a parameter in the range of (0, 1);

[0011] S3: decomposing the equivalent solving problem of the original solving problem into N sub solving problems;

[0012] S4: solving each sub solving problem to obtain a current solution of each sub solving problem;

[0013] S5: judging whether the current solutions of all sub solving problems satisfy a convergence condition; if not, updating the N sub solving problems and returning to S4; if the convergence condition is satisfied, taking the current solutions of the N sub solving problems as target solutions;

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

[0015] In one embodiment, S3 includes:

[0016] S31: introducing an auxiliary variable y n reconstructing the constraint into

[0017] S32: converting the equivalent solving problem under N sub time domains into the N sub solving problems.

[0018] In one of the embodiments, the S32 comprises: transforming the equivalent solving problem in N sub-time domains into the N sub-solving problem expression as follows:

[0019]

[0020] wherein, and ρ is a parameter in the step-by-step hedging algorithm.

[0021] In one of the embodiments, the updating the N sub-solving problem if the condition is not satisfied comprises: if the condition is not satisfied, updating the N sub-solving problem according to

[0022] In one of the embodiments, the convergence condition is as follows: wherein, subscript b corresponds to a specific time coupling constraint, N b is the number of elements in the block b, s is the element index in the block b, is the s-th element of is the s-th element of is the s-th element of is the maximum value that can be reached in the feasible region thereof, δ b is a sufficiently small parameter corresponding to the block b.

[0023] In one of the embodiments, before the S1, further comprising: representing 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 by using the interpolation coefficient vector, so as to transform into the long-time-domain continuous-time gas-electricity joint optimization scheduling model in the finite-dimensional algebraic space.

[0024] In one of the embodiments, the linking variable comprises: a continuous variable satisfying a zero-order continuity constraint, a continuous variable satisfying a first-order continuity constraint, and all integer variables in the overlapping period.

[0025] According to another aspect of the present application, there is provided a long-time-domain continuous-time gas-electricity joint optimization scheduling method, comprising:

[0026] a representation module, configured to represent the long-time-domain continuous-time gas-electricity joint optimization scheduling model in the finite-dimensional algebraic space as follows: c k represents the k-th coefficient of the objective function, x k represents the k-th control point, A k represents the k-th coefficient of the left side of the constraint, and b represents the coefficient of the right side of the constraint.

[0027] ​An equivalent module is configured to divide the time domain H of the original solving problem of the gas-electricity combined optimization scheduling model into N sub time domains, so as to obtain equivalent solving problems of the original solving problem in the N sub time domains: wherein, x k , and are all link variables, x k , x k , and are empty, b n are corresponding coefficients, and ε is a parameter in the range of (0, 1);

[0028] A decomposition module is configured to decompose the equivalent solving problems of the original solving problem into N sub solving problems.

[0029] A solving module is configured to solve each of the sub solving problems to obtain a current solution of each of the sub solving problems.

[0030] A judging module is configured to judge whether the current solutions of all the sub solving problems satisfy a convergence condition. If not, the N sub solving problems are updated, and the updated N sub solving problems are transmitted to the solving module. If the convergence condition is satisfied, the current solutions of the N sub solving problems are taken as target solutions.

[0031] A planning module is configured to splice the target solutions of the N sub solving problems to obtain a solution of the original solving problem, and to plan gas-electricity combined optimization scheduling by using the solution of the original solving problem.

[0032] According to another aspect of the present application, there is provided a gas-electricity combined optimization scheduling system for a long time domain continuous time, which comprises 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.

[0033] According to another aspect of the present application, there is provided a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps of the method.

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

[0035] (1) The present invention provides a method for gas-electricity joint optimization scheduling for long-time continuous time. By dividing the time domain H of the original solution problem of the gas-electricity joint optimization scheduling model into N sub-time domains, equivalent solution problems in the N sub-time domains corresponding to the original solution problem are obtained; the equivalent solution problem of the original solution problem is decomposed into N sub-solution problems; each sub-solution problem is solved separately and it is judged whether the current solution of all sub-solution problems meets the convergence condition; if not, the N sub-solution problems are updated and re-solved until the current solution is converged; this method alleviates the computational burden caused by the gas-electricity joint system model with long-time continuous time, realizes high-quality solution in a relatively short time, and gives full play to the advantage of long-time 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 Flowchart of the long-term continuous-time gas-electricity joint optimization scheduling method provided in Example 1 of the present invention;

[0037] Figure 2 A schematic diagram of a power curve in the sub-time domain of 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 This is a schematic diagram of the historical wind power output curve provided by the example of the present invention. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining 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 may 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 long-term continuous-time gas-electricity joint optimization scheduling model in finite-dimensional algebraic space is expressed as: c k represents the kth coefficient of the objective function, xk denotes the kth control point, A k denotes the kth coefficient on the left side of the constraint, b denotes the coefficient on the right side of the constraint. Wherein, x contains all control points in the model, for example, thermal power unit output Battery charge / discharge power Gas turbine unit output Power-to-gas (P2G) consumption power Natural gas flow rate Natural gas pressure

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

[0045]

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

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

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

[0049] S5: Determine whether the current solutions of all sub-solving problems meet the convergence condition; if not, update the N sub-solving problems and return to S4; if the convergence condition is met, take the current solutions of the N sub-solving problems as the target solution.

[0050] S6: Splice the target solutions of the N sub-solving problems to obtain the solution of the original solving problem, and use the solution of the original solving problem to plan the combined gas and electricity optimization scheduling.

[0051] As an optional implementation, before S1, further comprising: expressing the time variable P(t) in the continuous time function and the space-time variable M(x, t) in the gas-electricity combined optimization scheduling model in a high-dimensional time function space by using an interpolation coefficient vector, so as to convert the gas-electricity combined optimization scheduling model into a continuous time gas-electricity combined optimization scheduling model in a finite-dimensional algebraic space.

[0052] wherein the continuous time function in the original problem is expressed by using an interpolation coefficient vector by a polynomial interpolation method as follows:

[0053]

[0054] wherein P τ (t) and M τ (x, t) are piecewise functions of the continuous time functions P(t) and M(x, t) in the time period τ, wherein t ranges from 0 to 1. and are interpolation coefficients (also referred to as control points) of the piecewise continuous time function. A vector composed of and is denoted as In the above formula, the cubic BP (Bernstein Spline) spline is defined as:

[0055]

[0056] Further, the following rules are introduced to realize the solution space transformation of the continuous time model. The objective function and the constraint condition contained in the original continuous time model are divided into the following forms, and the respective transformation rules are established:

[0057] (1) Equation: based on the above BP spline interpolation basic principle, the following rule is established:

[0058]

[0059] (2) Inequality equation: based on the convex hull property of the BP spline, the following rule is established:

[0060]

[0061] (3) Integral and difference term: based on the BP spline definition, the following rule is established:

[0062]

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

[0064] Based on the above rules, the original continuous-time gas-electricity combined dispatching problem can be converted from function space to algebraic space, that is, the continuous-time function in the original problem can be represented by its spline interpolation coefficient vector. The method principle of the gas-electricity combined optimal dispatching method is further introduced below.

[0065] As an optional implementation, S3 comprises: S31: introducing auxiliary variables y n The constraint is reconstructed as S32: converting the equivalent solving problem in N sub-time domains into N sub-solving problems.

[0066] As an optional implementation, Figure 2 The power curve in the sub-time domain in the gas-electricity combined optimal dispatching method based on the improved step-by-step hedging comprises (a), (b), and (c) three parts, (a) is a discrete-time curve schematic diagram, (b) is a continuous-time curve schematic diagram, and (c) is a control curve schematic diagram; S32 comprises: converting the equivalent solving problem in N sub-time domains into N sub-solving problems, which is represented as:

[0067]

[0068] Wherein, and ρ are parameters in the step-by-step hedging algorithm.

[0069] As an optional implementation, if the condition is not met, the N sub-solving problems are updated, comprising: if the condition is not met, the above unintended constraint is updated through multiple iterations. In each iteration, L1, L n , L N and the corresponding and are solved. After that, y and ω are updated in the following way (step-by-step hedging):

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

[0071]

[0072] Wherein, the subscript b corresponds to a specific time coupling constraint, N b is the number of elements in the block b, s is the element index in the block b, is the s-th element of , is the s-th element of , is the maximum value that can reach in its feasible region, and δ b is a sufficiently small parameter corresponding to the block b.

[0073] As an optional implementation, the interfacing variables include: continuous variables satisfying zero-order continuity constraints, continuous variables satisfying first-order continuity constraints and all integer variables in the overlap period.

[0074] To realize the solution of the proposed algorithm in the gas network model, the interfacing variables in the overlap period are discussed in the following. To reduce the variables that need to be hedged, only one control point is set for each interfacing variable in the overlap period, and the specific blocks included in the interfacing variables in the continuous-time gas-electricity combined system are discussed below.

[0075] Block one: including all continuous variables in the overlap period that need to satisfy zero-order continuity constraints

[0076]

[0077] where k represents the sequence number of the control point, are the interpolation control points of the abandoned wind power / cut load power / battery energy, and other variables are described above.

[0078] Block two: including all continuous variables in the overlap period that need to satisfy first-order continuity constraints including the ramp consistency constraint and the SOC trajectory consistency constraint. Here, the first-order change quantity is defined as then the variables in block two include:

[0079]

[0080] Block three: including all integer variables in the overlap 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 state duration of the unit at the end of the previous sub-time domain must be equal to the state duration of the unit at the initial segment of the next sub-time domain. To this end, an auxiliary variable is introduced to represent the start-up / shut-down duration of the interfacing period τ. The following constraints are added to the optimization model to realize the calculation of

[0081]

[0082] where M off : = [1, 2,..., T off ] T , M off : = [1, 2,..., T off ] T ​Therefore, the unit start-stop actions in several periods after the switching 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 the third block are:

[0085]

[0086] The simulation data of the first example is provided below, which selects a modified IEEE 6-node power system and a 5-node gas network system as shown in Figure 3 The maximum load of the power system is 330 MW. Three thermal power units are connected to nodes 1 / 2 / 3 respectively, and a wind farm is connected to node 3. The specific parameters are shown in Table 1, and the wind power curve is shown in Figure 4 A storage power station is connected to node 1, with a rated charge and discharge power of 120 MW and a rated continuous charge and discharge time of 4 h. A gas turbine 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 curtailment and load shedding penalty coefficients are 200 $ / MW and 1000 $ / MW respectively. The total scheduling duration considered in this problem is 14 days. The commercial solver used is Gurobi 10.0.

[0087] Table 1 Thermal and wind power unit parameters

[0088]

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

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

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

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

[0093] The simulation data of the second example is provided below, and the standard IEEE 118-node power system and the 27-node Belgian gas network system are selected. One gas turbine and one P2G device are connected to node 69 and node 13, respectively, with a rated power of 1000 MW, one wind farm and one energy storage power station are connected to node 38, the rated installed capacity of the wind farm is 4000 MW, and the rated charge and discharge power of the energy storage power station is 1000 MW, and the rated continuous charge and discharge time is 4h. Other parameters remain the same as in the previous embodiment.

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

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

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

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

[0098] Embodiment 2

[0099] The embodiment provides a gas-electricity combined optimization scheduling method for long-time-domain continuous time, which comprises a representation module, an equivalent module, a decomposition module, a solving module, a judgment module and a planning module.

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

[0101] An equivalent module is configured to divide the time domain H of the original solving problem of the gas-electricity combined optimization scheduling model into N sub time domains, to obtain N equivalent solving problems of the original solving problem in the N sub time domains.

[0102]

[0103] wherein, x k , and are link variables, x k , x k , and are empty, b n are corresponding coefficients, and ε is a parameter in the range of (0, 1).

[0104] A decomposition module is configured to decompose the equivalent solving problem of the original solving problem into N sub solving problems.

[0105] A solving module is configured to solve each sub solving problem to obtain a current solution of each sub solving problem.

[0106] A judging module is configured to judge whether the current solutions of all sub solving problems satisfy a convergence condition, and if not, to update the N sub solving problems and transmit the updated N sub solving problems to the solving module, and if yes, to take the current solutions of the N sub solving problems as target solutions.

[0107] A planning module is configured to splice the target solutions of the N sub solving problems to obtain a solution of the original solving problem, and to plan gas-electricity combined optimization scheduling by using the solution of the original solving problem.

[0108] Embodiment 3

[0109] The embodiment provides a gas-electricity combined optimization scheduling system for a long time domain continuous time, which comprises 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] Embodiment 4

[0111] The embodiment provides a computer readable storage medium, which stores a computer program, and the steps of the method are implemented when the computer program is executed by a processor.

[0112] Those skilled in the art can easily understand that the above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing the joint dispatch of gas and electricity for long-term continuous time, characterized in that: include: S1: The long-term continuous-time gas-electricity joint optimization scheduling model 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, and obtain the equivalent solution problem in the N sub-time domains corresponding to the original solution problem: ； in, Including only in the sub-time domain n , and are all bridging variables. Including the overlapping period between sub-time domains n-1 and n , Including the overlapping period between sub-time domains n and n+1 , and It's empty. 、 、 is the corresponding coefficient, is a parameter in the range of (0,1); S3: Decompose 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: combining 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 gas-electricity joint optimization scheduling; The S3 includes: S31: Introducing auxiliary variables y n Constrain Refactored to ; S32: converting the equivalent problem to be solved in the N sub-time domains into the N sub-problems to be solved; The S32 includes: converting the equivalent solution problems in the N sub-time domains into the N sub-solution problems expressed as: ； ； ； in, and are parameters in the stepwise hedging algorithm.

2. The method for long-term continuous-time gas-electricity joint optimization scheduling according to claim 1, characterized in that: If the above is not satisfied, the N sub-problems are updated, including: if the above is not satisfied, the N sub-problems are updated according to The N sub-problems are updated.

3. The method for long-term continuous-time gas-electricity joint optimization scheduling according to claim 2, characterized in that: The convergence condition is: ; Among them, the subscript b Corresponding to a specific type of time coupling constraint, For block b The number of elements in , s For block b The element index in , For the middle No. s elements, for The maximum value that can be achieved within its feasible region is For the corresponding block b A sufficiently small parameter of .

4. The method for long-term continuous-time gas-electricity joint optimization scheduling according to claim 1, characterized in that: Before S1, it also includes: converting the time variables in the continuous time function in the gas-electricity joint optimization scheduling model under high-dimensional time function space and spatiotemporal variables It is expressed in the form of an interpolation coefficient vector, thus being transformed into a long-term continuous-time gas-power joint optimization scheduling model in a finite-dimensional algebraic space.

5. The method for long-term continuous-time gas-electricity joint optimization scheduling according to any one of claims 1 to 4, characterized in that: The connection variables include: continuous variables that meet the zero-order continuity constraint, continuous variables that meet the first-order continuity constraint, and all integer variables in the overlapping period.

6. A gas-electricity joint optimization scheduling device for long-term continuous time, characterized in that: The method for executing the gas-electricity combined optimization scheduling method according to any one of claims 1 to 5 comprises: The representation module is used to express the long-term continuous-time gas-electricity joint optimization scheduling model in a 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, Including only in the sub-time domain n , and are all bridging variables. Including the overlapping period between sub-time domains n-1 and n , Including the overlapping period between sub-time domains n and n+1 , and It's empty. 、 、 is the corresponding coefficient, is a parameter in the range of (0,1); A decomposition module, configured to decompose the equivalent solution of the original solution into N sub-solutions; A solution module, configured to solve each of the sub-problems separately to obtain a current solution to each of the sub-problems; A judgment module, configured to judge whether the current solutions of all the sub-problems satisfy a convergence condition; if not, updating the N sub-problems and transmitting the updated N sub-problems to the solution module; and if the convergence condition is satisfied, using the current solutions of the N sub-problems as target solutions; A 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.

7. A long-term continuous-time gas-electricity joint optimization scheduling system, 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 5 are implemented.

8. 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 5 are implemented.

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