Power-gas integrated energy system scheduling method based on global linear gas network model

Abstract

The invention discloses an electricity-gas integrated energy system scheduling method based on a global linearization gas network model, and the method comprises the steps: generating a gas network pipeline operation data set, and building a global linearization gas network model based on a Koopman operator theory; then, a Koopman operator is approximated, and an integrated energy system scheduling model is constructed; and finally solving the model to obtain an optimal scheduling result. According to the method, an original nonlinear partial differential equation of the gas network can be converted into a linear algebraic equation, the complex nonlinear problem in traditional gas network modeling is simplified, errors caused by local linearization and spatial difference are avoided, the linear algebraic equation can be easily integrated into a scheduling model of a comprehensive energy system, and the method is suitable for large-scale popularization and application. And the dynamic behavior of the natural gas system can be accurately captured, and the scheduling accuracy and reliability are remarkably improved. In addition, according to the method, errors possibly generated by a traditional model in a complex gas dynamics scene are effectively reduced, and system potential safety hazards caused by inaccurate models are avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of integrated energy system operation, and mainly relates to an electricity-gas integrated energy system scheduling method based on a global linearized gas network model. Background Art

[0002] With the continuous growth of global energy demand and the strict control of carbon emissions, the power-gas integrated energy system has gradually become a research hotspot for energy system optimization and scheduling. Through the coordinated scheduling of power systems and natural gas systems, multi-energy complementarity and efficient utilization of resources are achieved. However, the operating characteristics of the natural gas system are constrained by complex nonlinear partial differential equations, and the nonlinearity and cross-time scale changes of gas dynamics make its accurate modeling and optimal scheduling a very challenging task.

[0003] Electricity joint dispatch can not only improve energy efficiency, but also increase system flexibility and reduce carbon emissions. Traditional gas grid dispatch methods mostly use local linearization and spatial discretization to simplify the gas grid model, but these methods often cannot accurately capture the dynamic behavior of the natural gas system and may bring potential safety hazards to the operation of the system. Therefore, how to improve the accuracy of the gas grid model, accurately capture the dynamic changes of the gas grid, realize the global linear modeling of the natural gas system, and take into account the dynamic characteristics of the gas in the dispatch model has become a key issue to ensure the safety and efficiency of EG-IES operation. Summary of the invention

[0004] The present invention is aimed at the problems existing in the prior art, and proposes a method for dispatching an electricity-gas integrated energy system based on a global linearized gas grid model. First, a gas grid pipeline operation data set is generated, and then a global linearized gas grid model is established based on the Koopman operator theory; then the Koopman operator is approximated to construct an integrated energy system dispatching model; finally, the model is solved to obtain the optimal dispatching result. The method of the present invention can transform the original nonlinear partial differential equation of the gas grid into a linear algebraic equation, simplify the complex nonlinear problems in traditional gas grid modeling, avoid the errors caused by local linearization and spatial differentiation, and can not only be easily integrated into the dispatching model of the integrated energy system, but also accurately capture the dynamic behavior of the natural gas system, significantly improving the accuracy and reliability of the dispatch. In addition, the method of this case also effectively reduces the errors that may be generated by traditional models in complex gas dynamics scenarios, avoiding system safety hazards caused by inaccurate models.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: a method for dispatching an electricity-gas integrated energy system based on a global linearized gas grid model, comprising the following steps:

[0006] S1. Generate a gas network pipeline operation data set: establish a nonlinear PDE model for each pipeline in the gas network, and generate operation data for each pipeline in the gas network after solving the model; the operation data at least includes the head end air pressure, the end end air pressure, the head end air flow and the end end air flow of each pipeline;

[0007] S2. Establishing a global linearized gas network model: The model upgrades the system from the original space to the Koopman space, and implements global linearized modeling of the natural gas system dynamics through a data-driven Koopman operator approximation method;

[0008] S3, Approximate Koopman Operator: Using the running data set, the Koopman operator is approximated by the least squares method; at the same time, physically inspired stability constraints are embedded to improve the generalization ability and accuracy of the model;

[0009] S4. Constructing a comprehensive energy system dispatching model: The mathematical form of the model is as follows:

[0010]

[0011] sth e (x e )≤0

[0012] h g (x g )≤0

[0013] h c (x e ,x g )=0

[0014] Among them, x e represents the decision variables of the power system; x g represents the decision variables of the natural gas system; f(·) represents the objective function; h e (·) is the power system constraint; h g (·) is the natural gas system constraint; h c (·) is the coupling constraint of electrical energy conversion;

[0015] S5. Solve the model to obtain the optimal scheduling result.

[0016] As an improvement of the present invention, in step S1, a nonlinear PDE model of each pipeline of the gas network is established, which specifically includes a mass conservation equation and a momentum conservation equation, and the specific mathematical form is:

[0017]

[0018] Where p is the pipeline pressure, c is the speed of light, M is the natural gas mass flow rate, A is the pipeline cross-sectional area, λ is the friction coefficient, and d is the pipeline diameter. Solve to obtain the head end pressure of each pipeline k at each time Terminal air pressure First end airflow and terminal airflow

[0019] As an improvement of the present invention, in step S1, the operation data is preprocessed, specifically:

[0020]

[0021] in, and are the head-end pressure, end-end pressure, head-end airflow and end-end airflow of the untreated pipeline k at time t, is the standardized head pressure, is the standardized terminal pressure, To standardize the head end airflow, is the standardized terminal airflow, P base and M base are the base values ​​of air pressure and airflow, respectively.

[0022] As an improvement of the present invention, step S2 specifically includes the following steps:

[0023] S21: original state space definition, definition of state quantity Control volume The dynamic state of the natural gas system evolves as follows:

[0024]

[0025] in, represents the rate of change of the state of pipeline k in the system over time, x k is the state variable of pipeline k in the system, u k is the control input of pipeline k, function g(x k ,u k ) describes the effects of state variables and control inputs on system dynamics;

[0026] S22: Select the observer ψ to map the state x to the Koopman high-dimensional space;

[0027] S23: Construct Koopman operator:

[0028]

[0029] in, represents a combination operation, and It is the linear Koopman operator that describes the dynamic changes of pipeline k;

[0030] S24: Select the delayed embedding dimension:

[0031]

[0032] Among them, D x is the delayed embedding dimension of the state variable, D u is the delayed embedding dimension of the input variables;

[0033] S25: Map the calculation results back to the original low-dimensional state space, select the state variable x as the first observer, and obtain the original state variable:

[0034]

[0035] in,

[0036] As another improvement of the present invention, in step S23, the observation sub ψ M is the airflow-related observer, ψp 是 Observers related to air pressure, The original space is elevated to a high-dimensional space through the observer, and the total dimension of the high-dimensional space is N = N m +N p +2.

[0037] As another improvement of the present invention, step S3 specifically includes the following steps:

[0038] S31: Establish a least squares model, based on the assumption that the error follows a Gaussian distribution, and apply the least squares method to solve the Koopman operator The mathematical expression is as follows:

[0039]

[0040] S32: Establish stability constraints;

[0041] S33: Use the Mosek toolbox to solve the model and obtain the Koopman operator and

[0042] As another improvement of the present invention, the step S32 stability constraint specifically includes the following steps:

[0043] S321: Constructing system matrix A sys :

[0044]

[0045] S322: Establish stability constraints. The specific mathematical form is as follows:

[0046] norm 2 (A sys )≤1-∈i=1,2,...,D x

[0047] Among them, ∈ is a small value to ensure that A sys The spectral norm of is strictly less than 1.

[0048] As a further improvement of the present invention, step S4 specifically includes the following steps:

[0049] S41: Establishment of the DC power flow model of the power grid:

[0050]

[0051] Among them, P i is the active power injected into node i, B ij is the conductance between node i and node j, θ i and θ j are the phase angles of node i and node j, n e is the total number of nodes in the power grid;

[0052] S42: Establishing a global linearized gas network model;

[0053] S421: Establish a linearized gas network pipeline model, the specific form is:

[0054]

[0055] S422: Establish gas network node model:

[0056] For the internal node n, the node pressure conservation constraint needs to be satisfied:

[0057]

[0058] in, represents the set of pipes flowing into n, represents the set of pipes flowing out of node n; if node n has a compressor, then α n，t is the compressor pressure ratio. If there is no compressor, then α n，t =1;

[0059] For the internal nodes and load nodes n, the mass flow conservation condition needs to be met:

[0060]

[0061] in, represents the load of node n at time t.

[0062] S43: Integrated energy system equipment operation constraints;

[0063] S431: Establish gas source output constraints:

[0064]

[0065] in, and are respectively the air flow mass flow and air pressure of the air source node n at time t, and M s are the upper and lower limits of the gas source airflow, and p s are the upper and lower limits of the gas source pressure, Ω s is the set of gas source nodes;

[0066] S432: Establish renewable energy constraints:

[0067]

[0068] in, For the predicted output of wind turbines, is the output of the fan at time t;

[0069] S433: Establish coupling device model:

[0070] P2G equipment can convert electricity into natural gas. The specific model is:

[0071]

[0072] in, is the natural gas mass flow rate generated by P2G at time t, is the power consumed by P2G at time t, η P2G is the power-to-gas factor of the P2G system;

[0073] Gas turbines can convert natural gas into electricity. The specific model is:

[0074]

[0075] in, is the mass flow rate of natural gas consumed by the gas turbine at time t, is the electrical power generated by the gas turbine at time t, η GT is the gas-to-electricity factor of the gas turbine system;

[0076] S44: Establish the objective function, the specific form is:

[0077] Power system generation cost C e :

[0078]

[0079] in, is the power generated by generator i at time t, a i , b i , c i is the cost coefficient of the generator, Δt elec is the time interval of the power system, N gen is the total number of generators, T is the total number of time periods in the dispatch cycle;

[0080] Total cost of natural gas system C g :

[0081]

[0082] Among them, c gas is the unit cost of natural gas, N g is the total number of nodes in the gas network, Δt ngs is the time interval of the natural gas system, η cov is the natural gas unit conversion factor.

[0083] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides an optimal scheduling method for an electric-gas integrated energy system based on a global linearized gas grid model of the Koopman operator theory. First, the system is upgraded from the original space to the Koopman space, and then the global linearized modeling of the natural gas system dynamics is realized through the data-driven Koopman operator approximation method; secondly, a collaborative optimization scheduling model that simultaneously considers the nonlinear dynamic characteristics of the power system and the natural gas system is constructed, and physically inspired stability constraints are introduced to improve the generalization ability and scheduling accuracy of the model. Through the proposed method, the dynamic characteristics of the natural gas system can be effectively captured, and the potential safety hazards caused by the inability of the local linearized model to accurately describe the dynamic behavior of the system can be avoided, which significantly improves the scheduling reliability and operation safety of the electric-gas integrated energy system. At the same time, the present invention can also improve the scheduling efficiency of the integrated energy system, and provide strong technical support for ensuring the stable operation and sustainable development of the energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is a structural diagram of the comprehensive energy system of the present invention;

[0085] Figure 2 It is a flowchart of the steps of the method for dispatching an electricity-gas integrated energy system based on a global linearized gas network model of the present invention;

[0086] Figure 3 is a grid-gas grid structure diagram of the integrated energy system in Example 2 of the present invention;

[0087] Figure 4 is a schematic diagram of standard error of training data of air pressure in Example 2 of the present invention;

[0088] Figure 5 is a schematic diagram of standard error of airflow training data in Example 2 of the present invention;

[0089] Figure 6 It is a schematic diagram of the standard error of air pressure and airflow in the scheduling results in Example 2 of the present invention. DETAILED DESCRIPTION

[0090] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0091] Example 1

[0092] A dispatching method for an electricity-gas integrated energy system based on a global linearized gas grid model is applied to the integrated energy system. Figure 1 The system includes two energy forms, electricity and natural gas. The two systems can be coupled and converted through P2G equipment and gas turbines. The process of this method is as follows Figure 2 As shown, the following steps are included:

[0093] Step S1, generating a gas network pipeline operation data set;

[0094] S11. Establish a nonlinear PDE model for each pipeline in the gas network, which includes the mass conservation equation and momentum conservation equation. The specific mathematical form is:

[0095]

[0096] Where p is the pipeline pressure, c is the speed of light, M is the natural gas mass flow rate, A is the pipeline cross-sectional area, λ is the friction coefficient, and d is the pipeline diameter. Solve to obtain the head end pressure of each pipeline k at each time Terminal air pressure First end airflow and terminal airflow

[0097] S12. Solve the model and generate the operating data of each pipeline in the gas network:

[0098] Each set of data contains the gas pressure P at the head end of each pipeline in the gas network. in , terminal pressure P out , head end airflow M in and terminal airflow M out .

[0099] S13. Data preprocessing:

[0100]

[0101] in, and are the head-end pressure, end-end pressure, head-end airflow and end-end airflow of the untreated pipeline k at time t, is the standardized head pressure, is the standardized terminal pressure, To standardize the head end airflow, is the standardized terminal airflow, P base and M base are the base values ​​of air pressure and airflow, respectively.

[0102] Step S2, establishing a global linearized gas network model;

[0103] S21, original state space definition:

[0104] Defining state quantities Control volume The dynamic state evolution of the natural gas system can be expressed by the following equation:

[0105]

[0106] in, represents the rate of change of the state of pipeline k in the system over time, x k is the state variable of pipeline k in the system, u k is the control input of pipeline k, function g(x k ,u k ) describes the effects of state variables and control inputs on the system dynamics.

[0107] S22. Select an observer ψ to map the state quantity x to the Koopman high-dimensional space.

[0108] S23. Construct Koopman operator:

[0109]

[0110] in, represents a combination operation, and is the linear Koopman operator that describes the dynamic changes of pipeline k, and the observer can be expressed as ψ M is the airflow-related observer, ψ p is an observable related to air pressure, The original space is elevated to a high-dimensional space through the observer, and the total dimension of the high-dimensional space is N = N m +N p +2.

[0111] S24. Select the delayed embedding dimension:

[0112]

[0113] Among them, D x is the delayed embedding dimension of the state variable, D u is the delayed embedding dimension of the input variables.

[0114] S25, map the calculation results back to the original low-dimensional state space:

[0115] Select the state variable x as the first observer, so the original state variable can be obtained, and its mathematical expression can be written as:

[0116]

[0117] in,

[0118] Step S3, approximating the Koopman operator: Using the running data set, the Koopman operator is approximated by the least squares method. At the same time, a physically inspired stability constraint is embedded to improve the generalization ability and accuracy of the model.

[0119] S31. Establish the least squares model:

[0120] Based on the assumption that the error follows a Gaussian distribution, the least squares method can be used to solve the Koopman operator The mathematical expression is as follows:

[0121]

[0122] S32, establish stability constraints;

[0123] S321, build system matrix A sys :

[0124]

[0125] S322, establish stability constraints:

[0126] Mathematically, we have ρ(A sys )<1. However, adding this constraint to the model will make the optimization problem difficult to solve. Therefore, tighten the constraints to ensure that the 2-norm of the system matrix is ​​less than 1. The specific mathematical form is as follows:

[0127] norm 2 (Asys )≤1-∈i=1,2,...,D x

[0128] Among them, ∈ is a small value to ensure that A sys The spectral norm of is strictly less than 1.

[0129] S33, use the Mosek toolbox to solve the model and obtain the Koopman operator and

[0130] Step S4: Establish a comprehensive energy system dispatch model, the mathematical form of which is as follows:

[0131]

[0132] sth e (x e )≤0

[0133] h g (x g )≤0

[0134] h c (x e ,x g )=0

[0135] Among them, x e represents the decision variables of the power system; x g represents the decision variables of the natural gas system; f(·) represents the objective function; h e (·) is the power system constraint; h g (·) is the natural gas system constraint; h c (·) is the coupling constraint for electrical energy conversion, such as gas turbines and power-to-gas (P2G) devices.

[0136] S41, establishing a DC power flow model for the power grid;

[0137]

[0138] Among them, P i is the active power injected into node i, B ij is the conductance between node i and node j, θ i and θ j are the phase angles of node i and node j respectively, n e is the total number of nodes in the power grid.

[0139] S42, establishing a global linearized gas network model;

[0140] S421. Establish a global linearized gas network model, the specific form is:

[0141]

[0142] S422. Establish gas network node model:

[0143] For the internal node n, the node pressure conservation constraint needs to be satisfied:

[0144]

[0145] in, represents the set of pipes flowing into n, represents the set of pipes that flow out of node n. If node n has a compressor, then α n，t is the compressor pressure ratio. If there is no compressor, then α n，t =1.

[0146] For the internal nodes and load nodes n, the mass flow conservation condition needs to be met:

[0147]

[0148] in, represents the load of node n at time t.

[0149] S43, integrated energy system equipment operation constraints;

[0150] S431: Establish gas source output constraints:

[0151]

[0152] in, and are respectively the air flow mass flow and air pressure of the air source node n at time t, and M s are the upper and lower limits of the gas source airflow, and p s are the upper and lower limits of the gas source pressure, Ω s is the collection of gas source nodes.

[0153] S432: Establish renewable energy constraints:

[0154]

[0155] in, For the predicted output of wind turbines, is the output of the fan at time t.

[0156] S433: Establish coupling device model:

[0157] P2G equipment can convert electricity into natural gas. The specific model is:

[0158]

[0159] in, is the natural gas mass flow rate generated by P2G at time t, is the power consumed by P2G at time t, η P2G is the power-to-gas factor of the P2G system.

[0160] Gas turbines can convert natural gas into electricity. The specific model is:

[0161]

[0162] in, is the mass flow rate of natural gas consumed by the gas turbine at time t, is the electrical power generated by the gas turbine at time t, η GTY is the gas-to-electricity factor of the gas turbine system.

[0163] S44. Establish an objective function.

[0164] Power system generation cost C e :

[0165]

[0166] in, is the power generated by generator i at time t, a i , b i , c i is the cost coefficient of the generator, Δt elec is the time interval of the power system, N gen is the total number of generators, and T is the total number of time periods in the dispatch cycle.

[0167] Total cost of natural gas system C g :

[0168]

[0169] Among them, c gas is the unit cost of natural gas, N g is the total number of nodes in the gas network, Δt ngs is the time interval of the natural gas system, η cov is the natural gas unit conversion factor.

[0170] Step S5: Solve the model to obtain the optimal scheduling result.

[0171] S51, based on MATLAB, use Mosek to solve the Koopman operator approximation model;

[0172] S52. Use CPLEX to solve the optimal scheduling model.

[0173] Example 2

[0174] The multi-energy flow system of this embodiment is composed of IEEE 30-node power grid and 20-node gas grid modified from the Belgian system, such as Figure 3 As shown in Figure 1, the power system consists of 6 generators, including 2 wind turbines, 3 coal-fired generators and 1 gas generator, with an optimization period of 24 hours and a scheduling interval of 1 hour. The power system and the natural gas system are coupled through the P2G device and the gas turbine.

[0175] According to the steps of the present invention, the optimal scheduling method for the integrated energy system is carried out. When the Koopman model is trained, the air pressure training errors of 19 pipelines are as follows: Figure 4 As shown in Figure 1, all errors can be controlled within 5e-3 p.u., and most errors can be controlled within 1e-3. Only the error of pipe 18 is large. This is because pipe 18 is long and there is error transmission in the network. The airflow error of the training data is shown in Figure 1. Figure 5 As shown in the figure, most errors can be controlled within 1e-2p.u. It can be seen that the proposed method can effectively linearize the gas network pipeline model and apply it to the electricity-gas scheduling problem. The trained global linearized gas network model is introduced into the scheduling problem of the integrated energy system, and the solver can be used directly to solve it. The scheduling results obtained by the solution are verified by network simulation. The simulation result error is shown in Figure 6 As shown in Figure 2, it can be seen that the scheduling error is maintained at a low level.

[0176] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0177] It should be noted that the above content merely illustrates the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications all fall within the protection scope of the claims of the present invention.

Claims

1. A dispatching method for an integrated electricity-gas energy system based on a global linearized gas grid model, characterized in that: The steps include: S1. Generate a gas network pipeline operation data set: establish a nonlinear PDE model for each pipeline in the gas network, generate operation data for each pipeline in the gas network after solving the model, and pre-process the operation data; the operation data at least includes the head end air pressure, the terminal air pressure, the head end air flow and the terminal air flow of each pipeline; S2. Establishing a global linearized gas network model: The model upgrades the system from the original space to the Koopman space, and implements global linearized modeling of the natural gas system dynamics through a data-driven Koopman operator approximation method; S3, Approximate Koopman operator: Use the running data set and the least squares method to approximate the Koopman operator; S4. Constructing a comprehensive energy system dispatching model: The mathematical form of the model is as follows: s.t.h e (x e )≤0 h g (x g )≤0 h c (x e ,x g )=0 Among them, x e represents the decision variables of the power system; x g represents the decision variables of the natural gas system; f(·) represents the objective function; h e (·) is the power system constraint; h g (·) is the natural gas system constraint; h c (·) is the coupling constraint of electrical energy conversion; S5. Solve the model to obtain the optimal scheduling result.

2. The method for dispatching an electricity-gas integrated energy system based on a global linearized gas grid model according to claim 1, characterized in that: The nonlinear PDE model of each pipeline in the gas network in step S1 includes a mass conservation equation and a momentum conservation equation, which are specifically: Where p is the pipeline pressure, c is the speed of light, M is the natural gas mass flow rate, A is the pipeline cross-sectional area, λ is the friction coefficient, and d is the pipeline diameter.

3. The method for dispatching an electricity-gas integrated energy system based on a global linearized gas grid model according to claim 2, characterized in that: In step S1, the operation data is preprocessed, specifically: in, and are the head-end pressure, end-end pressure, head-end airflow and end-end airflow of the untreated pipeline k at time t, is the standardized head pressure, is the standardized terminal pressure, To standardize the head end airflow, is the standardized terminal airflow, P base and M base are the base values ​​of air pressure and airflow, respectively.

4. The method for dispatching an electricity-gas integrated energy system based on a global linearized gas grid model according to claim 2, characterized in that: The step S2 specifically includes the following steps: S21: original state space definition, definition of state quantity Control volume The dynamic state of the natural gas system evolves as follows: in, represents the rate of change of the state of pipeline k in the system over time, x k is the state variable of pipeline k in the system, u k is the control input of pipeline k, function g(x k ,u k ) describes the effects of state variables and control inputs on system dynamics; S22: Select the observer ψ to map the state x to the Koopman high-dimensional space; S23: Construct the Koopman operator to elevate the original space to a high-dimensional space: Among them, ○ represents a combination operation, and It is the linear Koopman operator that describes the dynamic changes of pipeline k; S24: Select the delayed embedding dimension: Among them, D x is the delayed embedding dimension of the state variable, D u is the delayed embedding dimension of the input variables; S25: Map the calculation results back to the original low-dimensional state space, select the state variable x as the first observer, and obtain the original state variable: in, 5. The method for dispatching an integrated electricity-gas energy system based on a global linearized gas grid model according to claim 4, characterized in that: In step S23, the observation ψ M is the airflow-related observer, ψ p is an observable related to air pressure, The total dimension of the high-dimensional space is N = N m +N p +2.

6. The method for dispatching an electricity-gas integrated energy system based on a global linearized gas grid model according to claim 5, characterized in that: The step S3 specifically includes the following steps: S31: Establish a least squares model and apply the least squares method to solve the Koopman operator based on the assumption that the error follows a Gaussian distribution The mathematical expression is as follows: S32: Establish stability constraints; S33: Use the Mosek toolbox to solve the model and obtain the Koopman operator and 7. The method for dispatching an integrated electricity-gas energy system based on a global linearized gas grid model according to claim 6, characterized in that: The step S32 of stability constraint specifically includes the following steps: S321: Constructing system matrix A sys : S322: Establish stability constraints. The specific mathematical form is as follows: norm2(A sys )≤1-∈i=1,2,...,D x Among them, ∈ is a small value to ensure that A sys The spectral norm of is strictly less than 1.

8. The method for dispatching an electricity-gas integrated energy system based on a global linearized gas grid model according to claim 6, characterized in that: The step S4 specifically includes the following steps: S41: Establish a DC power flow model of the power grid, wherein the model is specifically: Among them, P i is the active power injected into node i, B ij is the conductance between node i and node j, θ i and θ j are the phase angles of node i and node j respectively, n e is the total number of nodes in the power grid; S42: Establishing a global linearized gas network model, the steps specifically include: S421: Establish a linearized gas network pipeline model: S422: Establish gas network node model: For an internal node n, the node pressure conservation constraint is satisfied: in, represents the set of pipes flowing into n, represents the set of pipes flowing out of node n; if node n has a compressor, then α n，t is the compressor pressure ratio. If there is no compressor, then α n，t =1; For the internal nodes and load nodes n, the mass flow conservation condition is satisfied: in, represents the load of node n at time t; S43: comprehensive energy system equipment operation constraints, wherein the equipment operation constraints at least include gas source output constraints and renewable energy constraints; S44: Establishing an objective function, wherein the objective function at least includes the power generation cost of the electric power system and the total cost of the natural gas system.

9. The method for dispatching an electricity-gas integrated energy system based on a global linearized gas grid model according to claim 8, characterized in that: The gas source output constraint in step S43 is: in, and are respectively the air flow mass flow and air pressure of the air source node n at time t, and are the upper and lower limits of the gas source airflow, and are the upper and lower limits of the gas source pressure, Ω s is the set of gas source nodes; The renewable energy constraint is: in, For the predicted output of wind turbines, is the output of the fan at time t.

10. The method for dispatching an electricity-gas integrated energy system based on a global linearized gas grid model according to claim 8, characterized in that: In step S44, the power system power generation cost C e : in, is the power generated by generator i at time t, a i , b i , c i is the cost coefficient of the generator, Δt elec is the time interval of the power system, N gen is the total number of generators, T is the total number of time periods in the dispatch cycle; The total cost of the natural gas system is C g : Among them, c gas is the unit cost of natural gas, N g is the total number of nodes in the gas network, Δt ngs is the time interval of the natural gas system, η cov is the natural gas unit conversion factor.

Images