Multi-time scale scheduling method and system for integrated energy system

Abstract

The invention provides a multi-time-scale scheduling method and system for an integrated energy system, and belongs to the technical field of power-gas integrated energy system scheduling, and the method comprises the steps: building a multi-time-scale decision model of a natural gas network based on the network dynamic characteristics of the natural gas network, and enabling the multi-time-scale decision model to be used for describing the pressure and pipe storage characteristics of the natural gas network; based on the natural gas network multi-time scale decision model, constructing a day-ahead-intra-day multi-time scale optimization scheduling model by taking the minimum operation cost of the integrated energy system as an objective function; and performing second-order cone relaxation on a weymoth equation in a natural gas network in the day-ahead-day multi-time scale optimization scheduling model, so that the optimization scheduling model can be directly solved by a solver to obtain a multi-time scale optimization scheduling scheme of the electricity-gas integrated energy system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power-gas integrated energy system scheduling, and in particular relates to a multi-time scale scheduling method and system for an integrated energy system. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] In recent years, the large-scale grid connection of renewable energy has increased the uncertain factors in the power system, and the flexible and economic operation of the system faces great risks. However, the flexible resources of the power system itself only include various types of units and energy storage devices, and are affected by line power flow and network topology, so the flexible regulation ability of the system is limited. Under this background, with the construction and improvement of the energy network and the enhancement of its coupling and interactivity, the integrated energy system has emerged.

[0004] The integrated energy system couples heterogeneous energies such as electricity, gas, heat, and cold, and has a certain regulation potential, which can improve the flexible operation level of the system. Among them, natural gas has slow dynamic characteristics during the flow process, endowing it with a certain regulation potential, and the integrated energy system with electrical-gas coupling has become a current research hotspot. However, in the establishment and solution of the scheduling model, there are significant differences in the time scale of the transmission between the natural gas system and the power system, and the nonlinear characteristics of the gas network make the model difficult to solve, and the existing scheduling and solution models need to be optimized.

[0005] In the prior art, "Tang Xiangying, Hu Yan, Geng Qi, etc. Multi-time scale optimal scheduling of integrated energy system considering multi-energy flexibility [J]. Automation of Electric Power Systems, 2021, 45(04): 81-90" and "Jiang Kai. Multi-time scale coordinated scheduling method for integrated intelligent zero-carbon power system [J]. Electric Engineering Technology, 2023(24): 97-99+103" considered the modeling and application of various energy coupling devices and energy storage in the integrated energy system, and proposed an optimal scheduling method for the integrated energy system on a multi-time scale. Both analyzed and optimized the integrated energy system. However, with the increase in the scale of new energy grid connection and the gradual improvement of prediction accuracy, it is difficult to meet the increasingly urgent flexible operation requirements of the power grid by only deciding the operation state of the natural gas system on the day-ahead time scale. The traditional linearization method introduces a large number of 0-1 variables and nonlinear constraints, resulting in a reduction in the model solution speed and being unfavorable for the improvement of the system's flexible regulation ability.

[0006] After retrieval, in the prior art, there is CN113141005B, a multi-time scale scheduling method for an integrated energy system for new energy consumption, which specifically includes the following steps: S1. Input the basic data of each energy device required for the optimal scheduling of the electrical-thermal integrated energy system and the output of wind, light, and load; S2. Construct an objective function for minimizing the operating cost of the electrical-thermal integrated energy system in the day-ahead stage; S3. Construct an objective function for minimizing the deviation in the intraday rolling stage of the electrical-thermal integrated energy system; S4. Construct a mathematical model for the coordinated operation of the electrical-thermal integrated energy system with source-network-load-storage interaction; S5. Based on the second-order cone relaxation and incremental piecewise linearization theory, perform linear transformation on the model constructed in step S4 to obtain the optimal solution for the coordinated operation of the electrical-thermal integrated energy system. It does not simultaneously consider the accuracy of source-load prediction information and the economy of the operation of the electrical-thermal integrated energy system, and cannot formulate a two-stage scheduling plan for day-ahead and intraday rolling, and cannot provide guidance for the actual operation of the electric-gas-thermal integrated energy system. Summary of the Invention

[0007] To overcome the deficiencies of the above prior art, the present invention provides a multi-time scale scheduling method for an integrated energy system. The Weymouth equation in the natural gas network is subjected to second-order cone relaxation, so that the optimization model can be directly solved by a solver, and a multi-time scale optimal scheduling plan for the electric-gas integrated energy system is obtained.

[0008] To achieve the above object, one or more embodiments of the present invention provide the following technical solutions:

[0009] In the first aspect, a multi-time scale scheduling method for an integrated energy system is disclosed, including:

[0010] Based on the network dynamic characteristics of the natural gas network, establish a multi-time scale decision model for the natural gas network to describe the pressure and pipe storage characteristics of the natural gas network;

[0011] Based on the multi-time scale decision model of the natural gas network, construct a multi-time scale optimal scheduling model for day-ahead and intraday with the minimum operating cost of the integrated energy system as the objective function;

[0012] Perform second-order cone relaxation on the Weymouth equation in the natural gas network in the multi-time scale optimal scheduling model for day-ahead and intraday, so that the optimization scheduling model can be directly solved by a solver, and a multi-time scale optimal scheduling plan for the electric-gas integrated energy system is obtained.

[0013] As a further technical solution, the volume of natural gas stored in the pipeline is called pipe storage, and the gas pipe storage must be restored to the initial value at the end of the scheduling period.

[0014] As a further technical solution, the size of the pipeline inventory in the multi-time scale decision-making model of the natural gas network is related to the length of the gas network pipeline, the diameter of the gas network pipeline, the gas constant of natural gas, the gas temperature, the gas density, and the average pipeline pressure.

[0015] As a further technical solution, the day-ahead - intra-day multi-time scale optimal scheduling model includes:

[0016] The day-ahead optimal multi-time scale optimal scheduling model and the intra-day multi-time scale optimal scheduling model;

[0017] Based on the day-ahead optimal multi-time scale optimal scheduling model, generate power according to the day-ahead predicted value, and decide the start-stop of thermal power units and gas turbines and the direction of gas network flow;

[0018] During the real-time operation stage of the electrical energy system, based on the intra-day multi-time scale optimal scheduling model, decide the output adjustment amount of conventional units and gas turbines, the gas network flow, and the wind power acceptance value.

[0019] As a further technical solution, the day-ahead optimal multi-time scale optimal scheduling model includes a first objective function and day-ahead constraint conditions;

[0020] The day-ahead constraint conditions include: power system constraints, gas network constraints, and coupling element constraint conditions;

[0021] The power system constraints include node power balance constraints, unit output constraints, unit start-stop constraints, and branch power flow constraints;

[0022] The gas network constraints include gas network flow balance constraints, gas source output constraints, upper and lower limits of lost gas load constraints, and the Weymouth equation.

[0023] As a further technical solution, the Weymouth equation is used to describe the relationship between node pressure and flow in the gas network, and it is a non-linear equation; relax it to convert the mixed-integer programming model into a mixed-integer second-order cone model.

[0024] As a further technical solution, the intra-day multi-time scale optimal scheduling model includes:

[0025] A second objective function and real-time constraint conditions;

[0026] The real-time constraint conditions include: power grid constraints and gas network constraints;

[0027] The power grid constraints include: power balance constraints, conventional unit constraints, wind turbine output constraints, branch power flow constraints, and energy storage constraints;

[0028] The gas network constraints include: node gas pressure constraints, mass conservation law constraints, natural gas system power flow constraints, and gas storage tank constraints.

[0029] In a second aspect, a multi-time scale scheduling system for an integrated energy system is disclosed, including:

[0030] A natural gas network multi-time scale decision model establishment module, configured to: establish a natural gas network multi-time scale decision model based on the network dynamic characteristics of the natural gas network, for describing the pressure and pipe storage characteristics of the natural gas network;

[0031] A day-ahead to intra-day multi-time scale optimal scheduling model establishment module, configured to: construct a day-ahead to intra-day multi-time scale optimal scheduling model with the minimum operating cost of the integrated energy system as the objective function based on the natural gas network multi-time scale decision model;

[0032] A solution module, configured to: perform second-order cone relaxation on the Weymouth equation in the natural gas network in the day-ahead to intra-day multi-time scale optimal scheduling model, so that the optimal scheduling model can be directly solved by a solver, and obtain a multi-time scale optimal scheduling scheme for the electricity-gas integrated energy system.

[0033] The above one or more technical solutions have the following beneficial effects:

[0034] Based on the fact that the flow of natural gas has slow dynamic characteristics and it is difficult to change the flow direction in a short time, in order to fit the actual operating state of the gas network, the technical solution of the present invention first analyzes the slow dynamic characteristics of natural gas and models an important parameter of the pipe storage of the natural gas network. Then, in day-ahead scheduling, the start-stop of each unit and the flow direction of the gas network are decided, and in intra-day scheduling, considering the uncertainty of wind power output, intra-day scheduling decisions are made.

[0035] The start-stop state of the unit and the flow direction of the gas network pipeline decided by day-ahead scheduling are fixed, and only the flow direction of the gas network and the output of the unit are decided.

[0036] Based on the fact that the Weymouth equation of the gas network is a non-linear equation, which will lead to slow solution speed or inability to solve. For this, traditional linearization methods need to introduce a large number of 0-1 variables, and the solution efficiency is not high. Second-order cone relaxation is a model convexification method. The technical solution of the present invention performs second-order cone relaxation on the Weymouth equation of the gas network, converting the mixed integer programming model into a mixed integer second-order cone model. On the premise of ensuring the solution accuracy, the solution efficiency of the model is improved.

[0037] Advantages of additional aspects of the present invention will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0039] Figure 1 This is the flowchart of the method according to the embodiments of the present invention. Detailed implementation manners

[0040] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0041] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention.

[0042] In the case of no conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0043] Embodiment 1

[0044] See the appendix Figure 1 As shown, in view of the flexibility shortage problem caused by the dual uncertainties of the source and load in the electric-gas integrated energy system, this embodiment discloses a multi-time scale scheduling method for the integrated energy system, including:

[0045] Step 1: Construct a multi-time scale decision model for the natural gas network based on the network dynamic characteristics of the natural gas network to describe the pressure and pipeline storage characteristics of the natural gas network;

[0046] Step 2: Construct a day-ahead and intra-day multi-time scale optimal scheduling model with the minimum operating cost of the integrated energy system as the objective function;

[0047] Step 3: Perform second-order cone relaxation on the Weymouth equation in the natural gas network so that the optimization model can be directly solved by a solver to obtain a multi-time scale optimal scheduling scheme for the electric-gas integrated energy system.

[0048] In Step 1, the dynamic characteristics modeling of the natural gas network:

[0049] Flexibility refers to the ability of the power system to respond to uncertain disturbances during operation. The electric-gas integrated energy system couples heterogeneous energies, significantly improving the system's regulation ability. Natural gas has slow dynamic characteristics during transmission, which endows the pipeline with a certain energy storage capacity. The volume of natural gas stored in the pipeline is called pipeline storage, and its steady-state model is as follows:

[0050]

[0051] M ,

[0050] , gl,tT , , , gl,t0 , ,

[0052] ,

[0051] = M gl,tT (3)

[0052] Where: Mgl,t is the pipeline inventory at time period t, M gl,t-1 is the pipeline inventory at time period t-1 is the length of gas pipeline l, D gl is the diameter of gas pipeline l, R is the gas constant of natural gas, T gas is the gas temperature, ρ0 is the gas density is the average pipeline pressure is the inlet flow rate of pipeline gl at time period t is the outlet flow rate of pipeline gl at time period t, M gl,t0 is the initial gas pipeline inventory, M gl,tT is the gas pipeline inventory at the end of the last scheduling period. Equation (3) indicates that the gas pipeline inventory must be restored to the initial value at the end of the scheduling period.

[0053] There is a coupling relationship between the variables M and p (gas network pressure) and q (gas network flow rate). The above formulas (1)-(3) describe the inherent characteristics of the gas network.

[0054] In step two, the day-ahead and intra-day multi-time scale optimal scheduling model includes: the day-ahead optimal multi-time scale optimal scheduling model and the intra-day multi-time scale optimal scheduling model;

[0055] The day-ahead optimized wind power outputs according to the day-ahead predicted values, and the start-stop of thermal power units and gas turbines and the direction of gas network flow are determined.

[0056] Objective function

[0057]

[0058] In the formula: T is the number of time periods; N G is the number of thermal power units; P i,t is the output of thermal power unit i at time period t; a i 、b i 、c i are the operating cost coefficients of the thermal power unit; S Ti is the start-up cost of the thermal power unit, y i,t is the start-up flag variable of the thermal power unit, S Di is the shutdown cost of the thermal power unit, z i,t is the shutdown flag variable of the thermal power unit; N gt is the number of gas turbines; is the output power of gas turbine i at time period t; is the operating cost coefficient of the gas turbine; N m is the number of wind farms, is the predicted value of the wind farm power at time period t, P wt,t is the wind power output value at time period t; N eload is the number of electric load nodes, is the power loss of node i; N g is the number of gas loads, is the gas loss power; is the predicted wind power of wind farm wt in the day-ahead stage; P wt,t is the actual output of wind farm wt; C gcost,t is the penalty term introduced by the second-order cone relaxation, and GL is the set of gas network pipelines.

[0059] Day-ahead constraint conditions:

[0060] (1) Power system constraints

[0061] 1) Node power balance constraint

[0062] For each node of the power system, the constraint condition that the injected power and the output power are equal should be satisfied.

[0063]

[0064] In the formula: are the output powers of the thermal power unit and the gas turbine connected to node i at time t, respectively; P load,i,t is the power of the load connected to node i; is the power of the load loss connected to node i; P ij,t is the active power flowing from node i to node j on line ij; is the start-stop state of conventional thermal power unit i at time t, which is a 0-1 variable, 1 means starting up, and 0 means not starting up; is the start-stop state of gas turbine i at time t, which is a 0-1 variable, 1 means starting up, and 0 means not starting up.

[0065] 2) Unit output constraint

[0066]

[0067] In the formula: are the upper and lower limits of the output of the thermal power unit and the gas turbine, respectively; are the maximum values of the upward and downward ramp powers of the thermal power unit and the gas turbine, respectively.

[0068] 3) Unit start-stop constraint

[0069]

[0070] In the formula: is the startup state of conventional thermal power unit i at time t, which is a 0-1 variable, 1 means starting up, and 0 means not starting up; is the shutdown state of conventional thermal power unit i at time t, which is a 0-1 variable, 1 means shutting down, and 0 means not shutting down; Ti G,S and T i G,O are the minimum start-up and shutdown times of the conventional thermal power unit i respectively, and T i DA is the scheduling time scale.

[0071] 4) Branch power flow constraint

[0072]

[0073] In the formula: θ ij,t is the difference (rad) in the node voltage phase angle between node i and node j of the power grid at time t; x ij and are the line reactance and the transmission active power limit between nodes i and j in the power system respectively.

[0074] (2) Gas network constraint

[0075] 1) Gas network flow balance constraint

[0076]

[0077] In the formula: is the gas source output value at node g; is the gas load flow of node g, is the gas loss load of node g, in million cubic meters; η gt is the conversion efficiency of gas to electricity of the gas turbine. H GV is the calorific value of natural gas, in MJ / m³;

[0078] 2) Gas source output constraint

[0079] The natural gas well sends gas to each load through the pipeline, and the upper and lower limit constraints of the gas source output are:

[0080]

[0081] In the formula: and are the upper and lower limit values of the output of gas source w (Mm 3 / s) respectively.

[0082] 3) Upper and lower limit constraints of gas loss load

[0083]

[0084] 4) Weymouth equation

[0085] Different from the power grid, in addition to following the law of conservation of flow, the flow of gas in the natural gas network also needs to satisfy the law of conservation of mass and the law of conservation of momentum. There is a certain relationship between the pressures at both ends of each pipeline in the gas network and the roughness, friction coefficient, diameter, length, etc. of the pipeline. The Weymouth equation is used to describe the relationship between pipeline pressure and pipeline flow as follows:

[0086]

[0087] p i,t,min ≤p i,t ≤p i,t,max (21)

[0088] p j,t,min ≤p j,t ≤p j,t,max (22)

[0089] q ij,t,min ≤q ij,t ≤q ij,t,max (23)

[0090] In the formula: p i,t is the pressure of node i at time t, which needs to satisfy the upper and lower bound constraints. Equation (20) describes the relationship between pipeline flow and pressure. sign is the sign function. When sign = 1, it means that the gas flow in the pipeline flows from node i to node j; when sign = -1, it means that the gas flow in the pipeline flows from node j to node i.

[0091] (3) Coupling element constraint conditions

[0092] In the integrated system, a gas turbine unit is considered as a coupling element between the power system and the natural gas system.

[0093]

[0094] In the formula: and are the lower and upper limits of the gas consumption of the gas turbine unit respectively; η is the power generation efficiency of the gas turbine, taking 40%. The minimum start-up and shutdown time constraints of the gas turbine unit are the same as those in Equations (12) to (15).

[0095] Real-time optimization: An intraday multi-time scale optimal dispatching model is adopted;

[0096] To cope with the uncertainty of wind power output, during the real-time operation stage of the electrical energy system, the output adjustment amounts of conventional units and gas turbines, the gas network flow, and the wind power acceptance value are determined.

[0097] Objective function

[0098]

[0099] Where: ΔP i,t is the adjustment amount of the output of the thermal power unit; is the adjustment amount of the output of the gas turbine; is the predicted value of the wind power output in the real-time stage, is the actual wind power output in the real-time stage.

[0100] Real-time constraint conditions

[0101] (1) Grid constraint

[0102] 1) Power balance

[0103]

[0104] Where: is the charging power of the energy storage at time t, is the discharging power of the energy storage at time t.

[0105] 2) Conventional unit constraint

[0106] I i,t P i,min P i,t +ΔP i,t I i,t P i,max (30)

[0107]

[0108] 3) Wind turbine output constraint

[0109]

[0110] 4) Branch power flow constraint

[0111]

[0112] Where: θ ij,t is the difference in the node voltage phase angle (rad) between node i and node j of the power grid at time t; x ij and are the line reactance and the transmission active power limit between nodes i and j in the power system respectively.

[0113] 5) Energy storage constraint

[0114]

[0115] Where: S SOC,t is the state of charge of the energy storage at time t, is the charging conversion efficiency of the energy storage, is the discharging conversion efficiency of the energy storage.

[0116] (2) Gas network constraints

[0117] 1) Node air pressure constraints

[0118] p j,t,min ≤p j,t +Δp j,t ≤p j,t,max (36)

[0119] In the formula: Δp j,t is the air pressure change value of node i at time t in the k-th scenario.

[0120] 2) Mass conservation law constraints

[0121]

[0122] In the formula: and are the change amounts of the natural gas source point, gas turbine device, and the outlet and inlet flows of pipeline ij in the k-th scenario, respectively.

[0123] 3) Natural gas system power flow constraints

[0124]

[0125] 4) Gas storage tank constraints

[0126]

[0127] In the formula: Q x,t is the energy stored in the gas storage tank during the t period, σ x is the energy self-loss rate, and are the charging and discharging efficiencies of the energy storage device, respectively, and are the charging and discharging states of the energy storage device, respectively, and are the minimum and maximum charging and discharging powers of the device, Q x,1 and Q x,T are the energies stored at the initial and final moments within the period, respectively.

[0128] Model linearization method based on second-order cone relaxation:

[0129] The Weymouth equation describes the relationship between node pressure and flow in the gas network. It is a non-linear equation, which will cause the feasible region of the model to be non-convex, and commercial solvers cannot directly solve it. Relax it to convert the mixed-integer programming model into a mixed-integer second-order cone model:

[0130]

[0131] The mixed-integer programming model consists of constraint conditions and an objective function.

[0132] Since the gas flow direction in the pipeline is uncertain, a binary variable x is introduced for each pipeline. ij,t To distinguish the gas flow direction of pipeline ij at time period t, equations (40)-(41) are transformed into (42)-(44):

[0133] x ij,t (1 - x ij,t ) = 0 (42)

[0134]

[0135] In the formula: M is a very large number. The non-convexity of equation (44) makes the model difficult to solve. To convert the feasible region of the model into a convex set, a second-order cone transformation is adopted to convert the mixed-integer programming problem into a mixed-integer second-order cone programming, and equation (44) is transformed into:

[0136]

[0137] That is

[0138]

[0139] It is expressed in the standard second-order cone form as

[0140]

[0141] The x in equation (47) ij,t is a binary variable, which will lead to a slow model solving speed and poor convergence. Two continuous variables u ij,t and v ij,t

[0142]

[0143] are converted into the second-order cone standard form as

[0144]

[0145] To ensure that the equality is taken as much as possible, a penalty term is added to the objective function

[0146]

[0147] In the formula: λ gl,t is the penalty factor.

[0148] Solution process: First, solve the day-ahead scheduling model to obtain the unit start-stop and gas network flow directions. Then, on the premise of fixing the unit start-stop and gas network flow directions, solve the intra-day scheduling model. The output of the solution is the values of all variables. After obtaining this result, analysis can be carried out, but this patent does not analyze the results.

[0149] Example Two

[0150] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above method are implemented.

[0151] Example Three

[0152] The purpose of this embodiment is to provide a computer-readable storage medium.

[0153] A computer-readable storage medium has a computer program stored thereon. When the program is executed by a processor, the steps of the above method are executed.

[0154] Example Four

[0155] The purpose of this embodiment is to provide a multi-time scale scheduling system for an electric-gas integrated energy system, including:

[0156] A multi-time scale decision-making model establishment module for the natural gas network, configured to: establish a multi-time scale decision-making model for the natural gas network based on the network dynamic characteristics of the natural gas network, for describing the pressure and pipe storage characteristics of the natural gas network;

[0157] A day-ahead to intra-day multi-time scale optimal scheduling model establishment module, configured to: construct a day-ahead to intra-day multi-time scale optimal scheduling model with the minimum operation cost of the integrated energy system as the objective function based on the multi-time scale decision-making model of the natural gas network;

[0158] A solving module, configured to: perform second-order cone relaxation on the weymouth equation in the natural gas network in the day-ahead to intra-day multi-time scale optimal scheduling model, so that the optimal scheduling model can be directly solved by a solver, and obtain a multi-time scale optimal scheduling scheme for the electric-gas integrated energy system.

[0159] Example Five

[0160] The purpose of this embodiment is to provide a computer program product containing instructions, which, when running on a computer, enables the computer to execute the methods and functions involved in any one of the above embodiments.

[0161] The steps involved in the devices of the above embodiments correspond to those of Method Embodiment One. For specific implementation manners, reference may be made to the relevant description part of Embodiment One. The term "computer-readable storage medium" should be understood to include a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium that can store, encode, or carry an instruction set for execution by a processor and enable the processor to execute any one of the methods in the present invention.

[0162] Those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computer device. Optionally, they can be implemented by program codes executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0163] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications or deformations that can be made without creative efforts on the basis of the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A multi-time scale scheduling method for an integrated energy system, characterized in that Including: Establish a multi-time scale decision-making model for the natural gas network based on the network dynamic characteristics of the natural gas network to describe the pressure and pipeline storage characteristics of the natural gas network; Based on the multi-time scale decision-making model of the natural gas network, construct a day-ahead to intra-day multi-time scale optimal scheduling model with the minimum operating cost of the integrated energy system as the objective function; Perform second-order cone relaxation on the Weymouth equation in the natural gas network of the day-ahead to intra-day multi-time scale optimal scheduling model, so that the optimal scheduling model can be directly solved by a solver to obtain the multi-time scale optimal scheduling scheme for the electric-gas integrated energy system.

2. The multi-time scale scheduling method for an integrated energy system according to claim 1, characterized in that The volume of natural gas stored in the pipeline is called pipeline storage, and the pipeline storage of the gas network must be restored to the initial value at the end of the scheduling period.

3. The multi-time-scale scheduling method for an integrated energy system according to claim 1, characterized in that The size of the pipeline storage in the multi-time scale decision-making model of the natural gas network is related to the length of the gas network pipeline, the diameter of the gas network pipeline, the gas constant of natural gas, the gas temperature, the gas density, and the average pipeline pressure.

4. The multi-time-scale scheduling method for an integrated energy system according to claim 1, characterized in that, The day-ahead to intra-day multi-time scale optimal scheduling model includes: The day-ahead optimal multi-time scale optimal scheduling model and the intra-day multi-time scale optimal scheduling model; Based on the day-ahead optimal multi-time scale optimal scheduling model, generate power according to the day-ahead prediction value, and decide the start-stop of thermal power units and gas turbines and the direction of gas network flow; During the real-time operation stage of the electric-gas energy system, based on the intra-day multi-time scale optimal scheduling model, decide the output adjustment amount of conventional units and gas turbines, the gas network flow, and the wind power acceptance value.

5. The multi-time scale scheduling method for an integrated energy system according to claim 1, characterized in that The day-ahead optimal multi-time scale optimal scheduling model includes a first objective function and day-ahead constraint conditions; The day-ahead constraint conditions include: power system constraints, gas network constraints, and coupling element constraint conditions; The power system constraints include node power balance constraints, unit output constraints, unit start-stop constraints, and branch power flow constraints; The gas network constraints include gas network flow balance constraints, gas source output constraints, lost gas load upper and lower limit constraints, and the Weymouth equation; The Weymouth equation is used to describe the relationship between node pressure and flow in the gas network, and it is a non-linear equation; perform relaxation on it to convert the mixed integer programming model into a mixed integer second-order cone model.

6. The multi-time scale scheduling method for an integrated energy system according to claim 1, characterized in that, The intra-day multi-time scale optimal scheduling model includes: A second objective function and real-time constraint conditions; The real-time constraint conditions include: power grid constraints and gas network constraints; The power grid constraints include: power balance constraints, conventional unit constraints, wind turbine output constraints, branch power flow constraints, and energy storage constraints; The gas network constraints include: node air pressure constraints, mass conservation law constraints, natural gas system power flow constraints, and gas storage tank constraints.

7. An electric-gas integrated energy system multi-time scale scheduling system, characterized in that it includes: A natural gas network multi-time scale decision-making model establishment module, configured to: establish a natural gas network multi-time scale decision-making model based on the network dynamic characteristics of the natural gas network to describe the pressure and pipeline storage characteristics of the natural gas network; A day-ahead to intra-day multi-time scale optimal scheduling model establishment module, configured to: construct a day-ahead to intra-day multi-time scale optimal scheduling model based on the natural gas network multi-time scale decision-making model with the minimum operating cost of the integrated energy system as the objective function; A solution module, configured to: perform second-order cone relaxation on the Weymouth equation in the natural gas network in the day-ahead and intra-day multi-time scale optimal scheduling model, so that the optimal scheduling model can be directly solved by a solver to obtain a multi-time scale optimal scheduling plan for the integrated electricity-gas energy system.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 6.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of the above claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it executes the steps of the method according to any one of the above claims 1-6.

Images