Distributed scheduling method, device, equipment and medium for electricity-gas integrated energy system

Abstract

The invention discloses an electricity-gas integrated energy system distributed scheduling method, device and equipment and a medium, and the method comprises the steps: constructing a centralized electricity-gas integrated energy system scheduling model according to preset parameters of an electricity-gas integrated energy system, the preset power-gas integrated energy system parameters comprise power system parameters, natural gas system parameters and power-gas coupling equipment parameters, and the power-gas integrated energy system scheduling model comprises a Weymoth equation high-precision linearization model based on state space conversion and a golden section method; carrying out distributed solution on the power-gas integrated energy system scheduling model by adopting a Benders decomposition algorithm to obtain a power-gas joint optimization scheduling result; the power-gas joint optimization scheduling result comprises the output of a thermal power generating unit, the output of a gas generating unit, the gas production rate of a natural gas well, the scheduling cost of a power system, the scheduling cost of a natural gas system and the scheduling cost of a power-gas comprehensive energy system. The technical problems of privacy disclosure, incompatibility of excitation and the like can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of joint dispatching of an electricity-gas integrated energy system, and in particular to a distributed dispatching method, device, equipment and medium for an electricity-gas integrated energy system under the condition of limited information exchange between an electric power system and a natural gas system. Background Art

[0002] The integrated energy system can break down the barriers between different energy sources, improve the flexibility of the power system, and promote the consumption of renewable energy. With the widespread use of gas-fired units (GFU), the coupling between the power system and the natural gas system has gradually deepened, thus forming an electricity-gas integrated energy system. Electricity-gas joint optimization scheduling can improve the economic benefits of the electricity-gas integrated energy system and improve energy utilization. Electricity-gas joint optimization scheduling can be divided into centralized optimization scheduling and distributed optimization scheduling. However, centralized optimization scheduling is incompatible with the distributed structure of the integrated energy system. Distributed optimization scheduling can fully protect the privacy of different subjects.

[0003] Due to the nonlinearity and non-convexity of the Weymouth equation, the modeling of the natural gas system is very complex even under steady-state operating conditions, resulting in a heavy computational burden. There are several methods that can linearize the Weymouth equation. Piecewise Linearization (PWL) is a common method, but since this method adds more auxiliary binary variables, the computational cost is high. The Taylor Expansion Method (TEM) is also a good method, but it is difficult to choose the right expansion point because the expansion point must be close to the optimal solution to ensure its accuracy. Therefore, its accuracy needs to be further verified.

[0004] The electricity system and the natural gas system are operated by different operators and there is no central organization to supervise the two operators. In addition, the operators of the two systems are reluctant to share their private information, including details such as topology, network parameters and power generation plans. This makes it impossible to use a centralized approach to dispatch the electricity-gas integrated energy system. A more effective approach is for each system to solve its own problems independently while exchanging information for potential coordination.

[0005] Existing research on electricity-gas joint optimization dispatch is usually based on collective rationality, that is, maximizing the total utility of the electricity-gas integrated energy system. Compared with the optimization dispatch of electricity and gas separately, although the electricity-gas joint optimization dispatch will reduce the total cost, it will also damage the individual interests of the power system. Therefore, the electricity-gas joint optimization dispatch based on collective rationality is not incentive compatible.

[0006] In order to achieve incentive compatibility, existing research is divided into two categories: market game method and transfer payment method. The market game method may not maximize the overall benefit. The transfer payment method is that the natural gas system shares some cooperative surplus with the power system, so that the total operating costs of both parties are reduced. However, the calculation of the optimal allocation ratio of cooperative surplus is relatively complex and difficult to operate. Summary of the invention

[0007] The present application provides a method, device, equipment and medium for dispatching an electricity-gas integrated energy system based on a distributed cooperation mechanism, which is used to solve technical problems such as non-convex nonlinearity, privacy protection and incentive compatibility of the Weymouth equation under the joint dispatch of the existing electricity-gas integrated energy system.

[0008] In view of this, the first aspect of the present application provides a distributed scheduling method for an electric-gas integrated energy system, comprising: Step 101: constructing a centralized electricity-gas integrated energy system dispatching model according to preset electricity-gas integrated energy system parameters, wherein the preset electricity-gas integrated energy system parameters include power system parameters, natural gas system parameters, and electricity-gas coupling equipment parameters, and the electricity-gas integrated energy system dispatching model includes a high-precision linearization model of the Weymouth equation based on state space transformation and golden section method; Step 102: using the Bendes decomposition algorithm to perform distributed solution on the electricity-gas integrated energy system scheduling model to obtain an electricity-gas joint optimization scheduling result; The electricity-gas joint optimization scheduling results include the output of thermal power units, the output of gas units, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system and the scheduling cost of the electricity-gas integrated energy system.

[0009] Preferably, the centralized electricity-gas integrated energy system scheduling model is constructed according to the preset electricity-gas integrated energy system parameters, and the electricity-gas integrated energy system scheduling model includes a high-precision linearization model of the Weymouth equation based on state space transformation and golden section method, and also includes: Acquire preset electric-gas integrated energy system parameters, wherein the preset electric-gas integrated energy system parameters include electric power system parameters, natural gas system parameters, and electric-gas coupling equipment parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters and electric load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters and gas load parameters; The electric-gas coupling equipment parameters include operating parameters of the gas unit.

[0010] Preferably, the distributed solution of the electricity-gas integrated energy system scheduling model using the Bendes decomposition algorithm to obtain the electricity-gas joint optimization scheduling result includes: S21: based on the Bendes decomposition algorithm, the problem of solving the electricity-gas integrated energy system scheduling model is converted into a main problem of power grid scheduling and a sub-problem of gas grid scheduling, and the sub-problem of gas grid scheduling is divided into a feasible sub-problem and an infeasible sub-problem; S22: According to the feasibility of the gas network scheduling sub-problem, a corresponding gas network scheduling sub-problem result is obtained, including: S221: If the gas grid scheduling sub-problem is not feasible, the corresponding Lagrangian function is iteratively solved to obtain a feasible cutting plane; S222: If the gas grid scheduling sub-problem is feasible, the corresponding Lagrangian function is iteratively solved to obtain the optimal cutting plane; S23: Obtaining the main problem of augmented grid scheduling based on the feasible cutting plane and the optimal cutting plane of the grid-collecting gas grid, and solving the main problem of augmented grid scheduling to obtain a scheduling solution.

[0011] Preferably, the method further comprises: using the Bendes decomposition algorithm to perform distributed solution on the electricity-gas integrated energy system scheduling model to obtain the joint optimization scheduling result of the electricity-gas integrated energy system; and then further comprising: Based on the dispatching cost of the electric power system, the dispatching cost of the natural gas system and the dispatching cost of the electric-gas integrated energy system, a cooperative game calculation and analysis is performed to obtain the Shapley value of the power grid dispatching and the Shapley value of the gas grid dispatching; The grid dispatching cost and the gas grid dispatching cost based on the grid dispatching Shapley value and the gas grid dispatching Shapley value are calculated respectively.

[0012] A second aspect of the present application provides a distributed scheduling device for an electric-gas energy system, comprising: A model building unit, used to build a centralized electric-gas integrated energy system dispatching model according to preset electric-gas integrated energy system parameters, wherein the preset electric-gas integrated energy system parameters include electric power system parameters, natural gas system parameters and electric-gas coupling equipment parameters, and the electric-gas integrated energy system dispatching model includes a high-precision linearization model of the Weymouth equation based on state space transformation and golden section method; A model solving unit connected to the model building unit is used to perform distributed solving of the electricity-gas integrated energy system scheduling model by using the Bendes decomposition algorithm to obtain an electricity-gas joint optimization scheduling result; The electricity-gas joint optimization scheduling results include the output of thermal power units, the output of gas units, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system and the scheduling cost of the electricity-gas integrated energy system.

[0013] Preferably, the distributed dispatching device of the electricity-gas integrated energy system further comprises: A parameter acquisition unit, used to acquire preset electric-gas integrated energy system parameters and transmit them to the model building unit, wherein the preset electric-gas integrated energy system parameters include electric power system parameters, natural gas system parameters and electric-gas coupling equipment parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters and electric load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters and gas load parameters; The electric-gas coupling equipment parameters include operating parameters of the gas unit.

[0014] Preferably, the model solving unit comprises: The problem conversion subunit is used to convert the problem of solving the electricity-gas integrated energy system scheduling model into a main problem of power grid scheduling and a sub-problem of gas grid scheduling based on the Bendes decomposition algorithm. The sub-problem of gas grid scheduling is divided into feasible sub-problems and infeasible sub-problems.

[0015] The iterative solution subunit connected to the problem conversion subunit is used to obtain the corresponding gas network scheduling subproblem result according to the feasibility of the gas network scheduling subproblem, including: If the gas grid scheduling subproblem is infeasible, the corresponding Lagrangian function is iteratively solved to obtain a feasible cutting plane.

[0016] If the gas grid scheduling subproblem is feasible, the corresponding Lagrangian function is iteratively solved to obtain the optimal cutting plane; and an iterative solution augmentation subunit connected to the iterative solution subunit, for obtaining the augmented grid dispatching main problem based on the feasible cutting plane and the optimal cutting plane of the grid collection gas grid, and solving the augmented grid dispatching main problem to obtain a dispatching solution; Preferably, it also includes: A game analysis unit connected to the model solving unit is used to perform cooperative game calculation and analysis based on the power system dispatching cost, the natural gas system dispatching cost and the electricity-gas integrated energy system dispatching cost to obtain the power grid dispatching Shapley value and the gas grid dispatching Shapley value; A cost calculation unit is used to respectively calculate the power grid dispatching cost and the gas grid dispatching cost based on the power grid dispatching Shapley value and the gas grid dispatching Shapley value, and the cost calculation unit is connected to the game analysis unit.

[0017] A third aspect of the present application provides a distributed scheduling device for an electric-gas integrated energy system, the device comprising a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the distributed scheduling method of the electric-gas integrated energy system described in the first aspect according to the instructions in the program code.

[0018] The fourth aspect of the present application provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the distributed scheduling method of the electric-gas integrated energy system described in the first aspect.

[0019] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages: In the present application, a method for dispatching an electricity-gas integrated energy system based on a distributed cooperation mechanism is provided, including: constructing a centralized electricity-gas integrated energy system dispatching model according to preset electricity-gas integrated energy system parameters, the preset electricity-gas integrated energy system parameters include power system parameters, natural gas system parameters and electricity-gas coupling equipment parameters, the electricity-gas integrated energy system dispatching model includes a high-precision linearization model of the Weymouth equation based on state space transformation and golden section method; using the Bendes decomposition algorithm to perform distributed solution on the electricity-gas integrated energy system dispatching model to obtain an electricity-gas joint optimization dispatching result; the electricity-gas joint optimization dispatching result includes the output of thermal power units, the output of gas units, the gas production of natural gas wells, the power system dispatching cost, the natural gas system dispatching cost and the electricity-gas integrated energy system dispatching cost.

[0020] The electric-gas integrated energy system dispatching method based on the distributed cooperation mechanism provided in this application uses the Bendes decomposition algorithm to perform distributed optimization solution on the constructed electric-gas integrated energy system dispatching model. In this process, the Shapley value is calculated according to the marginal contribution of the electric power system and the natural gas system, and a cooperative surplus allocation mechanism based on the Shapley value is established, so that the incentives of the electric power system and the natural gas system are consistent with the expected results, thereby overcoming the problem of incompatible incentives in the joint dispatching of the electric-gas integrated energy system and ensuring the compatibility of the incentive mechanism. Therefore, this application can solve the technical problems such as privacy leakage and incentive incompatibility caused by the fact that the power grid and the gas grid belong to different operators under the existing electricity-carbon market mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for dispatching an electricity-gas integrated energy system based on a distributed cooperation mechanism provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an electric-gas integrated energy system dispatching device based on a distributed cooperation mechanism provided in an embodiment of the present application; Figure 3 A schematic diagram of a dispatching framework of an electric-gas integrated energy system based on a distributed cooperation mechanism provided in an embodiment of the present application; Figure 4 A schematic diagram of a scheduling framework for an electric-gas integrated energy system based on a Weymouth linearization model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] For easier understanding, see Figure 1 , the embodiment of the electric-gas integrated energy system scheduling method based on the distributed cooperation mechanism provided by the present application includes: Step 101: A centralized electricity-gas integrated energy system scheduling model is constructed according to preset electricity-gas integrated energy system parameters. The preset electricity-gas integrated energy system parameters include power system parameters, natural gas system parameters and electricity-gas coupling equipment parameters. The electricity-gas integrated energy system scheduling model includes a high-precision linearization model of the Weymouth equation based on state space transformation and the golden section method.

[0024] Furthermore, step 101, before that, also includes: Obtaining preset electric-gas integrated energy system parameters, which include electric power system parameters, natural gas system parameters, and electric-gas coupling equipment parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters and electric load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters and gas load parameters; The electric-gas coupling equipment parameters include operating parameters of the gas unit.

[0025] It should be noted that the power system parameters, natural gas system parameters and electric-gas coupling equipment parameters include but are not limited to the above parameters, and other related parameters can also be obtained as needed. This embodiment only gives a few examples and is not specifically limited.

[0026] The dispatch model of the electric-gas integrated energy system including the high-precision linearization model of the Weymouth equation based on state space transformation and the golden section method is expressed as: , , , , Among them, the first formula is the objective function of the electricity-gas integrated energy system scheduling model, which is to minimize the scheduling cost of thermal power units and the gas production cost of natural gas wells; It is a quadratic function, including the dispatching cost of thermal power units and the dispatching cost of gas units; It is a linear function, including the gas production cost of natural gas wells and the dispatching income of gas units; represents the internal variables of the power grid, and ; is the electrical output of a conventional thermal power unit. is the electrical output of the gas unit; is the phase angle of the busbar; represents the internal variables of the gas network, and ; Indicates the gas production of natural gas wells. Indicates the gas consumption of the gas unit; Yes Connect p and q From the node in the node pipeline p Flow to Node q The air flow rate; represents the air flow from the node into the compressor, It represents the air flow from the compressor to the node; Is a node q The air pressure, Is a node q The square of the air pressure; Is a node p With Node q The square difference of the air pressure; is the coefficient matrix of the power grid inequality constraints, is the coefficient vector of the grid inequality constraints, is the coefficient matrix of the grid equality constraints, is the coefficient vector of the grid equality constraints, is the coefficient matrix of the gas network inequality constraints, is the coefficient vector of the gas network inequality constraints, is the coefficient matrix of the gas network equality constraints, is the coefficient vector of the gas network equality constraints, is the grid coefficient matrix of coupling constraints, is the gas grid coefficient matrix of the coupling constraint, is the coefficient vector of the coupling constraints.

[0027] The high-precision linearization model of the Weymouth equation based on state space transformation and golden section method is constructed as follows: In the natural gas system, the relationship between node gas pressure and pipeline gas flow is expressed by the Weymouth equation: , in, is a constant parameter. The Weymouth equation is nonlinear and nonconvex, so it is very time-consuming to solve and it is also very challenging to find the global optimum. To simplify the Weymouth equation, we put and Replace with and , Representation Node p The square of the air pressure, Representation Node q The square of the air pressure, in addition, by introducing a new variable Representation Node q and q The square difference of the gas pressure between , through state space transformation, the Weymouth equation is written as: , Is a connection node p and q The pipeline constant parameter of the pipeline. The first and second derivatives of the above formula are as follows: , , According to the mean value theorem and considering the concavity of the function, we get: , in, Represents a connected node p and q The pipeline v Linearization points.

[0028] According to the original formula, the second-order derivative is always less than or equal to zero, so we can conclude that: .

[0029] In the natural gas system model, there are upper and lower limit constraints on the node gas pressure. Therefore, in the linearized inequality constraint of the above Weymouth equation, There are also upper and lower limits: , in, Representation Node p The upper limit of the square of the air pressure, Representation Node qThe lower limit of the square of the air pressure, Representation Node p With Node q The upper limit of the square difference of air pressure, Representation Node p With Node q The lower limit of the square difference of air pressure, is the maximum interval of the square difference of the node gas pressure, which can be evenly divided into n+1 small intervals, expressed as , , …, ; It also has an upper and lower limit.

[0030] , in, represents the upper limit of the airflow in the pipe connecting nodes p and q, represents the lower limit of the airflow in the pipe connecting nodes p and q, is the maximum interval of pipeline natural gas flow, which can be evenly divided into n+1 small intervals, respectively expressed as , , …, .

[0031] Further, use the golden section method to select To balance calculation accuracy and solution efficiency.

[0032] , Represents a connected node p and q The pressure difference in the pipeline is v The upper limit of the linearization interval, Represents a connected node p and q The pressure difference in the pipeline is v The lower limit of the linearization interval.

[0033] The Weymouth equation is constructed into its exact linearized model through the above-mentioned state space transformation and golden section method.

[0034] Step 102: Use the Bendes decomposition algorithm to perform distributed solution on the electricity-gas integrated energy system scheduling model to obtain the electricity-gas joint optimization scheduling result.

[0035] The results of the electricity-gas joint optimization scheduling include the output of thermal power units, the output of gas units, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system and the scheduling cost of the electricity-gas integrated energy system.

[0036] Furthermore, step 102 includes: Based on the Bendes decomposition algorithm, the problem of solving the electricity-gas integrated energy system dispatch model is converted into the main problem of power grid dispatch and the sub-problem of gas grid dispatch. The sub-problem of gas grid dispatch is divided into feasible sub-problems and infeasible sub-problems. According to the feasibility of the gas network scheduling sub-problem, the corresponding gas network scheduling sub-problem results are obtained, including: If the gas grid scheduling subproblem is infeasible, the corresponding Lagrangian function is iteratively solved to obtain a feasible cutting plane.

[0037] If the gas grid scheduling subproblem is feasible, the corresponding Lagrangian function is iteratively solved to obtain the optimal cutting plane.

[0038] Obtaining the main problem of augmented grid dispatching based on the feasible cutting plane and the optimal cutting plane of the grid-collecting gas grid, and solving the main problem of augmented grid dispatching to obtain a dispatching solution; It should be noted that the number of iterations needs to be initialized before the main problem transformation based on the Bendes decomposition algorithm k =1 and optimal value y (0)=∞. Solve the main problem of power grid dispatching, and its optimal solution is recorded as : , , in, is the main problem slack variable; the power grid will be the boundary variable Transmit to the gas grid, Yes The gas network optimizes its subproblem in the first k In the iteration, the gas grid scheduling subproblem is expressed as: , in, is the Lagrange multiplier. Depending on the given boundary variables, the gas grid scheduling subproblem is either feasible or infeasible. When the gas grid scheduling subproblem is feasible, the gas grid operator generates an optimal cut plane that reflects the sensitivity of the optimal scheduling cost of the natural gas system to the boundary variables. When the gas grid scheduling subproblem is infeasible, the gas grid operator generates a feasible cut plane that represents the necessary conditions for not violating the boundary variables.

[0039] When the gas grid scheduling subproblem is feasible, an optimal cutting plane is generated in each iteration. The optimal cutting plane is: , in, is the optimal cut vector, is the optimal constant term; , , are the Lagrange multipliers associated with the corresponding constraints, and are the optimal solutions of the current iteration respectively.

[0040] If the gas grid scheduling subproblem is infeasible, construct a relaxed gas grid scheduling subproblem: , in, is the subproblem slack variable, is the Lagrange multiplier of the equality constraint of the gas grid dispatch problem, is the Lagrange multiplier of the inequality constraint of the gas grid dispatch problem, is the Lagrange multiplier of the coupled constraint, is the Lagrange multiplier for the slack variable constraints.

[0041] The Lagrangian function associated with the relaxed gas grid scheduling subproblem is as follows: , The Lagrange dual function corresponding to the above equation can be written as: , The Lagrangian dual problem associated with the above equation is as follows: .

[0042] If the gas grid scheduling subproblem is infeasible, the optimal value of the relaxed gas grid scheduling subproblem is greater than zero. According to the strong duality theorem, the optimal value of the above Lagrangian dual problem is also greater than zero, and its expression is: .

[0043] If the gas grid scheduling subproblem is not feasible, the above equation is greater than zero. The necessary condition for the gas grid scheduling subproblem to be feasible is: , in, , , is the feasible cutting plane coefficient matrix, is the feasible cutting plane coefficient vector; the above formula is the feasible cutting plane, The feasible cutting plane is violated. As long as the above formula is added to the main problem of power grid dispatching, infeasible boundary variables can be avoided in subsequent optimization problems.

[0044] The grid operator collects all optimal cut planes and feasible cut planes and solves the augmented grid dispatching main problem.

[0045] , in, It is The optimal cutting plane coefficient matrix of optimal cutting plane constraints, It is The optimal cutting plane coefficient vector of optimal cutting plane constraints, It is The feasible cutting plane coefficient matrix of the feasible cutting plane constraints, It is The feasible cutting plane coefficient vector of feasible cutting plane constraints, among which, There are optimal cutting plane constraints, The grid operator uses the optimal cutting plane and feasible cutting plane generated by the gas grid scheduling subproblem to update the optimal solution of the grid scheduling main problem. The coordinated operation of the electricity-gas integrated energy system is a convex quadratic programming problem. The global optimal solution is obtained by iterating between the grid scheduling main problem and the gas grid scheduling subproblem.

[0046] For a detailed dispatch framework of the electricity-gas integrated energy system based on the distributed cooperation mechanism and the Weymouth high-precision linearization model, please refer to Figure 3 and Figure 4 .

[0047] It is understandable that if k If convergence is reached, the optimal value can be calculated; assuming is the convergence parameter, ε=1×10 -2 10 -2 ;like , the iteration is terminated; each iteration is completed, you can update , and then return to the calculation steps of the gas grid scheduling sub-problem. In addition, the results of the electricity-gas joint optimization scheduling mainly include the output of thermal power units, the output of gas units, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system, and the scheduling cost of the electricity-gas integrated energy system. Other related parameters can also be solved as needed, which are not limited here.

[0048] Furthermore, step 102 further includes: Based on the dispatching cost of the power system, the dispatching cost of the natural gas system and the dispatching cost of the electricity-gas integrated energy system, the cooperative game calculation and analysis are carried out to obtain the Shapley value of the power grid dispatching and the Shapley value of the gas grid dispatching; The grid dispatching cost and gas grid dispatching cost based on the Shapley value of grid dispatching and the Shapley value of gas grid dispatching are calculated respectively.

[0049] It should be noted that in order to make the electricity-gas integrated energy system scheduling model proposed in this embodiment achieve incentive compatibility, this embodiment introduces Shapley value calculation and allocates the cooperation surplus of the power grid and the gas grid based on the Shapley value.

[0050] Cooperative Game ,| N |= n , No. i The Shapley value of a participant is defined as: , in, For factorial calculation, To include The full set of participants, Is to exclude A subset of participants, Is a collection The number of participants in For collection Assume that the dispatch process of the electric-gas integrated energy system includes a power grid and A gas network, Represents the power grid, Indicates gas network, then the full set is , the subset includes , ,…… Etc., each subset corresponds to a characteristic function .

[0051] Taking the dispatch process of the electricity-gas integrated energy system involving only one power grid and one gas grid as an example, the full set is , the subset of power grids is and , the gas network subset is and .

[0052] The power system dispatch cost , Natural gas system dispatching costs Dispatch cost of the integrated energy system of electricity and gas Substituting them into the above formulas, we can get the corresponding characteristic functions. The corresponding characteristic function is and ; Natural gas system dispatching costs The corresponding characteristic function is ; Dispatch cost of electricity-gas integrated energy system The corresponding characteristic function is the total characteristic function of the power grid Total characteristic function of Heqi network .

[0053] Based on the above, the Shapley value of power grid dispatch and the Shapley value of gas grid dispatch can be calculated; then the power grid dispatch cost based on the Shapley value of power grid dispatch and the gas grid dispatch cost based on the Shapley value of gas grid dispatch can be further calculated.

[0054] For ease of understanding, the present application also provides a large-scale electricity-gas integrated energy system to which the electricity-gas integrated energy system scheduling method based on the distributed cooperation mechanism of the present embodiment is applied. In order to further quantify the accuracy of the high-precision linearization model of the Weymouth equation based on state space conversion and the golden section method, the relative error of the pipeline natural gas flow is defined as follows: , The average relative error is defined as the average of the relative errors of all pipelines in all scheduling time periods.

[0055] The specific Weymouth linearization based on state space transformation and golden section method can be found in Table 1.

[0056] Table 1 Comparison of linearization methods of Weymouth equation for large-scale electricity-gas integrated energy system

[0057] From Table 1, in this comparison, one hour is taken as a scheduling period. In the Taylor expansion method, the average relative error of the gas flow rate of all pipelines in the large-scale electricity-gas integrated energy system within 24 hours is 2.42×10 -3 , the maximum relative error is 55%. To ensure the fairness of the comparison, both the piecewise linearization and the golden section method use three segments. In the piecewise linearization, the average relative error of the gas flow of all pipelines in the large-scale electricity-gas integrated energy system within 24 hours is 1.03×10 -4 , the maximum relative error is 8%. In the golden section method, the average relative error of all pipeline gas flows in a large-scale electricity-gas integrated energy system within 24 hours is 2.32×10 -11 , the maximum relative error is 4.15×10 -8 This further confirms that the accuracy of the golden section method is better than that of the Taylor expansion method and the piecewise linearization method. In addition, the golden section method is computationally more efficient than the piecewise linearization method. If both methods use 100 segments, the piecewise linearization method takes 143.8046 seconds, while the golden section method only takes 5.5770 seconds.

[0058] Please refer to Table 2 for a comparison of the economic benefits of large-scale electricity-gas integrated energy systems based on the Shapley value.

[0059] Table 2 Comparison of economic benefits of large-scale electricity-gas integrated energy systems

[0060] The total cost of the power-gas integrated energy system in the case of separate gas-electricity dispatch is $5,796,969. The total cost of the power-gas integrated energy system in the case of joint gas-electricity dispatch is $4,757,071. Compared with the optimal dispatch of gas-electricity alone, the cost of joint gas-electricity dispatch is $1,039,898 less than that of separate gas-electricity dispatch, and the total cost of the gas grid in joint gas-electricity dispatch is $1,257,998 lower than that of separate gas-electricity dispatch. However, the grid cost of joint gas-electricity dispatch is $218,100 higher than that of separate dispatch. Allocate cooperative surplus according to Shapley value. The cost of the gas grid based on Shapley value is $738,049 lower than that of separate dispatch; the cost of the grid based on Shapley value is $738,049 lower than that of separate dispatch. Allocating cooperative surplus according to Shapley value can promote cooperation between the grid and the gas grid.

[0061] The electric-gas integrated energy system dispatching method based on the distributed cooperation mechanism provided in the embodiment of the present application adopts the Bendes decomposition algorithm to perform distributed optimization solution on the constructed electric-gas integrated energy system dispatching model. In this process, the Shapley value is used to distribute the benefits so that the incentives of the power system and the natural gas system are consistent with the expected results, thereby overcoming the problem of incompatible incentives for the joint dispatching of the electric-gas integrated energy system and ensuring the compatibility of the incentive mechanism. Therefore, the present application can solve the technical problems such as privacy leakage and incentive incompatibility caused by the fact that the power grid and the gas grid belong to different operators under the existing electricity-carbon market mechanism.

[0062] For easier understanding, see Figure 2 The present application provides an embodiment of a distributed dispatching device for an electric-gas integrated energy system, including: The model building unit 201 is used to build a centralized electric-gas integrated energy system scheduling model according to preset electric-gas integrated energy system parameters, wherein the preset electric-gas integrated energy system parameters include electric power system parameters, natural gas system parameters and electric-gas coupling equipment parameters, and the electric-gas integrated energy system scheduling model includes a high-precision linearization model of the Weymouth equation based on state space transformation and golden section method, and further includes: Weymouth linearization subunit 2011 is used to establish a high-precision linearization model for Weymouth equations based on state space transformation and golden section method.

[0063] The model solving unit 202 connected to the model building unit 201 is used to perform distributed solving of the electricity-gas integrated energy system scheduling model by using the Bendes decomposition algorithm to obtain the electricity-gas joint optimization scheduling result; The results of the electricity-gas joint optimization scheduling include the output of thermal power units, the output of gas units, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system and the scheduling cost of the electricity-gas integrated energy system.

[0064] Furthermore, it also includes: The parameter acquisition unit 203 connected to the model building unit 201 is used to acquire preset electric-gas integrated energy system parameters, the preset electric-gas integrated energy system parameters include electric power system parameters, natural gas system parameters and electric-gas coupling equipment parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters and electric load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters and gas load parameters; The electric-gas coupling equipment parameters include operating parameters of the gas unit.

[0065] Furthermore, the model solving unit 202 includes: The problem conversion subunit 2021 is used to convert the problem of solving the electricity-gas integrated energy system dispatch model into a main problem of power grid dispatch and a sub-problem of gas grid dispatch based on the Bendes decomposition algorithm. The sub-problem of gas grid dispatch is divided into a feasible sub-problem and an infeasible sub-problem. The iterative solution subunit 222 connected to the problem conversion subunit 2021 is used to obtain the corresponding gas network scheduling subproblem result according to the feasibility of the gas network scheduling subproblem, including: If the gas grid scheduling subproblem is infeasible, the corresponding Lagrangian function is iteratively solved to obtain a feasible cutting plane; If the gas grid scheduling subproblem is feasible, the corresponding Lagrangian function is iteratively solved to obtain the optimal cutting plane; and an iterative solution augmentation subunit 2023 connected to the iterative solution subunit, for obtaining an augmented grid dispatching main problem based on the feasible cutting plane and the optimal cutting plane of the grid collection gas grid, and solving the augmented grid dispatching main problem to obtain a dispatching solution; Furthermore, it also includes: The game analysis unit 204 connected to the model solving unit 202 is used to perform cooperative game calculation and analysis based on the power system dispatching cost, the natural gas system dispatching cost and the electricity-gas integrated energy system dispatching cost to obtain the power grid dispatching Shapley value and the gas grid dispatching Shapley value; The cost calculation unit 205 is used to calculate the power grid dispatching cost and the gas grid dispatching cost based on the power grid dispatching Shapley value and the gas grid dispatching Shapley value respectively. The cost calculation unit 205 is connected to the game analysis unit 204.

[0066] The present application also provides a distributed scheduling device for an electric-gas integrated energy system, the device comprising a processor and a memory; The memory is used to store the program code and transmit the program code to the processor; The processor is used to execute the electric-gas energy system scheduling method based on the distributed cooperation mechanism in the above method embodiment according to the instructions in the program code.

[0067] The present application also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the distributed scheduling method of the electric-gas integrated energy system in the above method embodiment.

[0068] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0069] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0070] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0071] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0072] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A distributed scheduling method for an electricity-gas integrated energy system, characterized in that: include: Step 101: constructing a centralized electricity-gas integrated energy system dispatching model according to preset electricity-gas integrated energy system parameters, wherein the preset electricity-gas integrated energy system parameters include power system parameters, natural gas system parameters, and electricity-gas coupling equipment parameters, and the electricity-gas integrated energy system dispatching model includes a high-precision linearization model of the Weymouth equation based on state space transformation and golden section method; Step 102: using the Bendes decomposition algorithm to perform distributed solution on the electricity-gas integrated energy system scheduling model to obtain an electricity-gas joint optimization scheduling result; The electricity-gas joint optimization scheduling results include the output of thermal power units, the output of gas units, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system and the scheduling cost of the electricity-gas integrated energy system.

2. The distributed scheduling method of the electric-gas integrated energy system according to claim 1 is characterized in that: The centralized electricity-gas integrated energy system dispatching model is constructed according to the preset electricity-gas integrated energy system parameters, and the method also includes: Acquire preset electric-gas integrated energy system parameters, wherein the preset electric-gas integrated energy system parameters include electric power system parameters, natural gas system parameters, and electric-gas coupling equipment parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters and electric load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters and gas load parameters; The electric-gas coupling equipment parameters include the operating parameters of the gas unit; The electricity-gas integrated energy system scheduling model includes a high-precision linearization model of the Weymouth equation based on state space transformation and the golden section method.

3. The distributed scheduling method of the electric-gas integrated energy system according to claim 2 is characterized in that: The Bendes decomposition algorithm is used to perform distributed solution on the electricity-gas integrated energy system scheduling model to obtain the electricity-gas joint optimization scheduling result, including: S21: based on the Bendes decomposition algorithm, the problem of solving the electricity-gas integrated energy system scheduling model is converted into a main problem of power grid scheduling and a sub-problem of gas grid scheduling, and the sub-problem of gas grid scheduling is divided into a feasible sub-problem and an infeasible sub-problem; S22: According to the feasibility of the gas network scheduling sub-problem, a corresponding gas network scheduling sub-problem result is obtained, including: S221: If the gas grid scheduling sub-problem is not feasible, the corresponding Lagrangian function is iteratively solved to obtain a feasible cutting plane; S222: If the gas grid scheduling sub-problem is feasible, the corresponding Lagrangian function is iteratively solved to obtain the optimal cutting plane; S23: Obtaining the main problem of augmented grid scheduling based on the feasible cutting plane and the optimal cutting plane of the grid-collecting gas grid, and solving the main problem of augmented grid scheduling to obtain a scheduling solution.

4. The distributed dispatching method of the electric-gas integrated energy system according to claim 3 is characterized in that: The distributed solution of the electricity-gas integrated energy system scheduling model is performed by using the Bendes decomposition algorithm to obtain the electricity-gas joint optimization scheduling result, and then further includes: Based on the dispatching cost of the electric power system, the dispatching cost of the natural gas system and the dispatching cost of the electric-gas integrated energy system, a cooperative game calculation and analysis is performed to obtain the Shapley value of the power grid dispatching and the Shapley value of the gas grid dispatching; The grid dispatching cost and the gas grid dispatching cost based on the grid dispatching Shapley value and the gas grid dispatching Shapley value are calculated respectively.

5. A distributed dispatching device for an electric-gas integrated energy system, characterized in that: include: A model building unit, used to build a centralized electric-gas integrated energy system dispatching model according to preset electric-gas integrated energy system parameters, wherein the preset electric-gas integrated energy system parameters include electric power system parameters, natural gas system parameters and electric-gas coupling equipment parameters, and the electric-gas integrated energy system dispatching model includes a high-precision linearization model of the Weymouth equation based on state space transformation and golden section method; A model solving unit connected to the model building unit is used to perform distributed solving of the electricity-gas integrated energy system scheduling model by using the Bendes decomposition algorithm to obtain an electricity-gas joint optimization scheduling result; The electricity-gas joint optimization scheduling results include the output of thermal power units, the output of gas units, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system and the scheduling cost of the electricity-gas integrated energy system.

6. The distributed dispatching device of the electric-gas integrated energy system according to claim 5 is characterized in that: Also includes: A parameter acquisition unit, used to acquire preset electric-gas integrated energy system parameters and transmit them to the model building unit, wherein the preset electric-gas integrated energy system parameters include electric power system parameters, natural gas system parameters and electric-gas coupling equipment parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters and electric load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters and gas load parameters; The electric-gas coupling equipment parameters include operating parameters of the gas unit.

7. The distributed dispatching device of the electric-gas integrated energy system according to claim 5 is characterized in that: The model solving unit comprises: A problem conversion subunit, used for converting the problem of solving the electricity-gas integrated energy system scheduling model into a main problem of power grid scheduling and a sub-problem of gas grid scheduling based on the Bendes decomposition algorithm, wherein the sub-problem of gas grid scheduling is divided into a feasible sub-problem and an infeasible sub-problem; The iterative solution subunit connected to the problem conversion subunit is used to obtain the corresponding gas network scheduling subproblem result according to the feasibility of the gas network scheduling subproblem, including: If the gas grid scheduling subproblem is infeasible, the corresponding Lagrangian function is iteratively solved to obtain a feasible cutting plane; If the gas grid scheduling subproblem is feasible, the corresponding Lagrangian function is iteratively solved until convergence, and the optimal cutting plane and local optimal cost are obtained; And an iterative solution augmentation subunit connected to the iterative solution subunit is used to obtain the augmented power grid scheduling main problem based on the feasible cutting plane and the optimal cutting plane of the power grid collection gas network, and solve the augmented power grid scheduling main problem to obtain a scheduling solution.

8. The distributed dispatching device of the electric-gas integrated energy system according to claim 5 is characterized in that: Also includes: A game analysis unit connected to the model solving unit is used to perform cooperative game calculation and analysis based on the power system dispatching cost, the natural gas system dispatching cost and the electricity-gas integrated energy system dispatching cost to obtain the power grid dispatching Shapley value and the gas grid dispatching Shapley value; A cost calculation unit is used to respectively calculate the power grid dispatching cost and the gas grid dispatching cost based on the power grid dispatching Shapley value and the gas grid dispatching Shapley value, and the cost calculation unit is connected to the game analysis unit.

9. A distributed dispatching device for an electric-gas integrated energy system, characterized in that: The device comprises a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the distributed scheduling method of the electric-gas integrated energy system according to any one of claims 1-4 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the distributed scheduling method of the electric-gas integrated energy system described in any one of claims 1-4.

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