A method for operating an integrated electricity-gas energy system based on multi-stage stochastic optimization

Abstract

The present invention discloses an operation method of an electric-gas integrated energy system based on multi-stage random optimization, including: generating a series of typical wind power output scenarios in 24 periods based on historical cross-section data of wind farms; constructing a day-ahead stage scheduling model for the electric power system and the natural gas system; constructing an intraday stage scheduling model for the electric power system; constructing a real-time stage scheduling model for the electric power system; constructing unexpected constraints; performing second-order cone relaxation on the gas grid model to obtain a target model; and obtaining a multi-stage optimization scheduling result of the electric interconnected integrated energy system according to the target model. The present invention takes into account the coordinated operation of a three-stage electric power system and a single-stage natural gas system on the basis of a high proportion of new energy penetration. The operation fluctuations of the natural gas system caused by the output changes of the coupling element gas unit in the three stages are smoothed by the natural gas pipeline. The simulation results of the optimization scheduling model are expected to provide a reference for the scheduling strategy of the integrated energy system under a high proportion of new energy penetration.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy system optimization and scheduling, and specifically relates to an operation method of an electric-gas integrated energy system based on multi-stage stochastic optimization. Background Art

[0002] As intermittent renewable energy generation accounts for an increasing proportion, the safety of grid operation is greatly challenged. Traditional units (such as coal-fired units and nuclear power units) are constrained by ramp limits and minimum start-stop times, and cannot provide sufficient flexibility to meet the intermittent fluctuations of new energy units. In contrast, natural gas units have excellent ramp characteristics, which are crucial for smoothing intermittent renewable energy generation.

[0003] It is worth noting that in the current construction of the electricity-gas interconnected integrated energy system, the power grid is mostly based on the two-stage operation model of day-ahead-real-time. The two-stage model cannot give the power grid enough margin to deal with the errors caused by the real-time and prediction of wind power, which often easily leads to the waste of wind power resources. For the power system, it is necessary to consider more complex operation modes, such as day-ahead-intraday-real-time three-stage operation, to smooth the intermittent injection of renewable energy. At the same time, the natural gas system generally adopts a single-stage day-ahead operation model. Therefore, the coordination of the three-stage power system operation and the single-stage natural gas system operation deserves further study. Especially under the current strategic policies of my country's integrated energy system and new power system, solving the above problems is of great significance for the construction of my country's new generation of low-carbon energy system. Summary of the invention

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, a method for operating an integrated electricity-gas energy system based on multi-stage stochastic optimization is provided, which takes into account the coordinated operation of a three-stage power system and a single-stage natural gas system on the basis of a high proportion of new energy penetration. The operating fluctuations of the natural gas system caused by the output changes of the coupling element gas units in the three stages are smoothed by the natural gas pipeline. The simulation results of the optimized scheduling model are expected to provide a reference for the scheduling strategy of the integrated energy system under a high proportion of new energy penetration.

[0005] Technical solution: To achieve the above-mentioned purpose, the present invention provides an operation method of an electric-gas integrated energy system based on multi-stage stochastic optimization, comprising the following steps:

[0006] S1: Generate a series of typical wind power output scenarios in 24 periods based on historical cross-section data of wind farms;

[0007] S2: Construct the day-ahead dispatch model for the power system and natural gas system;

[0008] S3: Based on the day-ahead stage dispatch model of the power system, a power system intraday stage dispatch model is constructed;

[0009] S4: Based on the intraday stage dispatch model of the power system, a real-time stage dispatch model of the power system is constructed;

[0010] S5: Construct unexpected constraints for the real-time stage dispatch model of the power system and the day-ahead stage dispatch model of the natural gas system;

[0011] S6: Perform second-order cone relaxation on the day-ahead scheduling model of the natural gas system (gas grid model) to obtain the target model;

[0012] This step mainly converts the entire model from nonlinear programming to second-order cone programming. Since the nonlinearity of the entire model is reflected in the constraint part of the gas network model, the gas network model is relaxed by second-order cone.

[0013] S7: Obtain the multi-stage optimization scheduling results of the electrical interconnected integrated energy system based on the target model.

[0014] Furthermore, the day-ahead dispatch model of the power system in step S2 is:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] Where: P G,v,t represents the active output of non-wind turbine v at time t, W q,t represents the active power output of wind turbine q at time t predicted in the day-ahead phase, represents the load actually supplied by each node in the day-ahead phase, θ i,t represents the phase angle of node i, x ij represents the reactance value of line ij, and Indicates the upper and lower limits of non-wind turbine output. represents the best predicted value of wind turbine output on the day before, and They represent the upper and lower limits of wind power output in the day-ahead phase, P L,d,t represents the load demand at time t, represents the upper limit of the slope of non-wind turbine v, θ REF,t Represents the reference node phase angle.

[0022] Furthermore, the day-ahead dispatch model of the natural gas system in step S2 is:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037] Among them, F S,w,t represents the gas supply at the gas source point w at time t, represents the actual node gas load supplied at time t in the day-ahead phase, F G,v,t Indicates the natural gas consumed by the gas unit, F mn,t represents the air flow rate at the head end of pipeline mn at time t, F com,k,t represents the gas flow consumed by the booster station k, F C,k,t represents the gas flow through the booster station k, represents the average flow rate in the pipeline mn, F nm,t Indicates the air flow at the end of pipe mn, W mn Weymouth constant for pipes, π m,t represents the natural gas pressure at node m at time t, and They represent the upper and lower limits of the node pressure respectively. and Respectively represent the upper and lower limits of gas supply at the gas source point at each moment, Indicates the upper limit of the gas source point climbing, FL,e,t represents the node natural gas load demand, and Respectively represent the natural gas pressure at the inlet and outlet ports of the booster station, and They respectively represent the upper and lower limits of the compression ratio of the booster station. Indicates the upper limit of the boosting flow rate at the boosting station at each moment, θ k Indicates the energy consumption ratio of the natural gas consumed by the booster station to the natural gas flowing through it, L mn,t Indicates the natural gas storage capacity of pipeline mn (pipeline storage), l mn It represents the pipe storage coefficient of pipeline mn, K1 is a fixed coefficient, L min Indicates the lower limit of natural gas pipeline storage, μ G and H G They represent the energy conversion efficiency of the gas-fired unit and the calorific value of natural gas respectively.

[0038] Furthermore, the intra-day stage dispatch model of the power system in step S3 is:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] in, and represents the upward and downward adjustment of the electric power of non-wind turbines in the intraday stage in scenario ω, represents the predicted value of wind power output in the intraday stage in scenario ω, and Indicates that the wind turbine adjusts the output power up and down in the scenario ω, represents the voltage phase angle of node i in the intraday phase in scenario ω, represents the actual electrical load supplied by the node in the scenario ω during the intraday phase, and They represent the upper and lower limits of wind power output in the day, α ΔP and α ΔWThey represent the upper limits of the intraday adjustment for non-wind turbines and wind turbines, respectively. Indicates the installed capacity of wind turbines, Represents the voltage phase angle value of the reference node in the intraday phase scenario ω.

[0048] Furthermore, in step S3, the natural gas system does not need to be dispatched in the intra-day stage, and the imbalance caused by the gas load fluctuation consumed by the gas unit is smoothed by the natural gas pipeline. The natural gas equivalently consumed by the gas unit in the intra-day stage scenario ω for:

[0049]

[0050] Furthermore, the real-time stage dispatch model of the power system in step S4 is:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059] in, and They represent the upper reserve and lower reserve of non-wind turbines respectively. represents the wind power that can be supplied in the real-time scenario ω, represents the amount of wind power abandoned in the real-time scenario ω, represents the voltage phase angle of node i in the real-time phase of scenario ω, represents the actual electrical load supplied by the node in the real-time scenario ω, and They represent the upper and lower reserve limits of non-wind turbine v, respectively. Represents the voltage phase angle value of the reference node in the real-time scenario ω.

[0060] Furthermore, in the step S4, the natural gas system does not need to be dispatched in the real-time stage. The imbalance caused by the gas load fluctuation consumed by the gas unit is smoothed by the natural gas pipeline. The natural gas equivalently consumed by the gas unit in the real-time stage scenario ω for:

[0061]

[0062] Furthermore, the unexpected constraints in step S5 are as follows:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070] in, Indicates the same intraday scenario For the real-time scenario ω, the above formulas respectively indicate that for each intraday scenario with the same wind power output scenario, the intraday output up / down adjustment of non-wind turbines, the intraday output up / down adjustment of wind turbines, the voltage phase angle of each node, the electrical load supply of each node, the gas load supply of each node, the node natural gas pressure and the gas network pipeline flow at each moment should also be the same and should not change with future scenarios.

[0071] Furthermore, in step S6, since the pipeline flow direction can generally be determined in the day-ahead scheduling, the nonlinear constraint can be converted into:

[0072]

[0073] Before the introduction of second-order cone relaxation, the above model is a nonlinear programming model. The specific nonlinearity is reflected in the gas network constraint (A-9). At this time, the model is non-convex and it is difficult to find the optimal solution. Using second-order cone relaxation to convert the equality constraint (A-9) into an inequality constraint (A-46), although it reduces the strictness of the model, it can convexify the model. The effectiveness of this method has been verified in many existing studies. Then, the second-order cone programming model can be solved with common solvers (such as GUROBI, MOSEK, CPLEX, etc.), and the optimal solution of the model can be found in a relatively short time, effectively improving the efficiency of model solving.

[0074] Furthermore, the objective function of the target model in step S7 is:

[0075]

[0076] The present invention first proposes a multi-stage coordinated operation model of an electric-gas integrated energy system based on stochastic optimization. The model takes into account the three-stage operation model of the electric power system and the single-stage operation model of the natural gas system, and smoothes the fluctuation of the amount of natural gas consumed by the multi-stage gas turbine units through the gas network management. Then, a stochastic optimization model considering the uncertainty of wind power output is proposed. The model characterizes the randomness of wind power output through a sufficient number of scenarios. At the same time, the present invention takes into account unexpected constraints to ensure that the realization of uncertainty is based on current known information and is independent of future uncertainties. The non-convex model is convexified by second-order cone relaxation, and load shedding is considered to ensure that the problem is solvable. The present invention is in line with the current operation practices of the electric power system and the natural gas system, and realizes the electricity-gas coordination of intraday operation and real-time operation (not just day-ahead operation).

[0077] Beneficial effects: Compared with the prior art, the three-stage electrical collaborative operation model of the present invention takes into account three-stage operation in the power system, and uses non-expected constraints to constrain the decision variables in the intraday stage to ensure that decisions are made only based on the known information of the current stage. The natural gas system is a single-stage operation model. The uncertainty of natural gas demand related to power generation in intraday and real-time operation is balanced by pipeline storage. Compared with the two-stage model, the present invention can reduce the operating costs of the power system and the natural gas system, and with the increase in the penetration rate of new energy, the advantages of the present invention over the two-stage scheduling are more obvious. The present invention can provide a reference for the formulation of optimized scheduling decisions for the electric-gas integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 is a flow chart of the method of the present invention;

[0079] Figure 2 It is a schematic diagram of the three stages of wind power scenario construction;

[0080] Figure 3 This is a chart showing the output share of gas turbines at different wind power penetration rates. DETAILED DESCRIPTION

[0081] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0082] The present invention provides an operation method of an electric-gas integrated energy system based on multi-stage stochastic optimization, such as Figure 1 As shown, it includes the following steps:

[0083] S1: Generate a series of typical wind power output scenarios in 24 periods based on historical cross-section data of wind farms;

[0084] S2: Reference Figure 2 , build the day-ahead dispatch model of the power system and natural gas system:

[0085] The day-ahead dispatch model of the power system is:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] Where: P G,v,t represents the active output of non-wind turbine v at time t, W q,t represents the active power output of wind turbine q at time t predicted in the day-ahead phase, represents the load actually supplied by each node in the day-ahead phase, θ i,t represents the phase angle of node i, x ij represents the reactance value of line ij, and Indicates the upper and lower limits of non-wind turbine output. represents the best predicted value of wind turbine output on the day before, and They represent the upper and lower limits of wind power output in the day-ahead phase, P L,d,t represents the load demand at time t, represents the upper limit of the climbing slope of non-wind turbine v, θ REF,t Represents the reference node phase angle.

[0093] The day-ahead dispatch model of the natural gas system is:

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] Among them, F S,w,t represents the gas supply at the gas source point w at time t, represents the actual node gas load supplied at time t in the day-ahead phase, F G,v,t Indicates the natural gas consumed by the gas unit, F mn,t represents the air flow rate at the head end of pipeline mn at time t, F com,k,t represents the gas flow consumed by the booster station k, F C,k,t represents the gas flow through the booster station k, represents the average flow rate in the pipeline mn, F nm,t Indicates the air flow at the end of pipe mn, W mn Weymouth constant for pipes, π m,t represents the natural gas pressure at node m at time t, and They represent the upper and lower limits of the node pressure respectively. and Respectively represent the upper and lower limits of gas supply at the gas source point at each moment, Indicates the upper limit of the gas source point climbing, F L,e,t represents the node natural gas load demand, and Respectively represent the natural gas pressure at the inlet and outlet ports of the booster station, and They respectively represent the upper and lower limits of the compression ratio of the booster station. Indicates the upper limit of the boosting flow rate at the boosting station at each moment, θ k Indicates the energy consumption ratio of the natural gas consumed by the booster station to the natural gas flowing through it, L mn,t Indicates the natural gas storage capacity of pipeline mn (pipeline storage), l mn It represents the pipe storage coefficient of pipeline mn, K1 is a fixed coefficient, L minIndicates the lower limit of natural gas pipeline storage, μ G and H G They represent the energy conversion efficiency of the gas-fired unit and the calorific value of natural gas respectively.

[0109] S3: Constructing a daily phase dispatch model for the power system:

[0110] The intraday dispatch model of the power system is:

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119] in, and represents the upward and downward adjustment of the electric power of non-wind turbines in the intraday stage in scenario ω, represents the predicted value of wind power output in the intraday stage in scenario ω, and Indicates that the wind turbine adjusts the output power up and down in the scenario ω, represents the voltage phase angle of node i in the intraday phase in scenario ω, represents the actual electrical load supplied by the node in the scenario ω during the intraday phase, and They represent the upper and lower limits of wind power output in the day, α ΔP and α ΔW They represent the upper limits of the intraday adjustment for non-wind turbines and wind turbines, respectively. Indicates the installed capacity of wind turbines, Represents the voltage phase angle value of the reference node in the intraday phase scenario ω.

[0120] The natural gas system does not need to be dispatched during the day. The imbalance caused by the fluctuation of gas load consumed by the gas unit is smoothed by the natural gas pipeline. The natural gas equivalently consumed by the gas unit in the day phase scenario is for:

[0121]

[0122] S4: Constructing a real-time stage dispatch model for the power system:

[0123] The real-time dispatch model of the power system is:

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] in, and They represent the upper and lower reserves of non-wind turbines, represents the wind power that can be supplied in the real-time scenario ω, represents the amount of wind power abandoned in the real-time scenario ω, represents the voltage phase angle of node i in the real-time phase of scenario ω, represents the actual electrical load supplied by the node in the real-time scenario ω, and They represent the upper and lower reserve limits of non-wind turbine v, respectively. Represents the voltage phase angle value of the reference node in the real-time scenario ω.

[0133] The natural gas system also does not need to be dispatched in the real-time stage. The imbalance caused by the fluctuation of gas load consumed by the gas unit is smoothed by the natural gas pipeline. The natural gas equivalently consumed by the gas unit in the real-time scene is for:

[0134]

[0135] S5: Constructing unexpected constraints:

[0136] The unexpected constraints are as follows:

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144] in, Indicates the same intraday scenario For the real-time scenario ω, the above formulas respectively indicate that for each intraday scenario with the same wind power output scenario, the intraday output up / down adjustment of non-wind turbines, the intraday output up / down adjustment of wind turbines, the voltage phase angle of each node, the electrical load supply of each node, the gas load supply of each node, the node natural gas pressure and the gas network pipeline flow at each moment should also be the same and should not change with future scenarios.

[0145] S6: Perform second-order cone relaxation on the day-ahead scheduling model of the natural gas system (gas grid model) to obtain the target model:

[0146] Since the pipeline flow direction can generally be determined in day-ahead scheduling, the nonlinear constraint can be converted into the following using second-order cone relaxation:

[0147]

[0148] Before the introduction of second-order cone relaxation, the above model is a nonlinear programming model. The specific nonlinearity is reflected in the gas network constraint (A-9). At this time, the model is non-convex and it is difficult to find the optimal solution. Using second-order cone relaxation to convert the equality constraint (A-9) into an inequality constraint (A-46), although it reduces the strictness of the model, it can convexify the model. The effectiveness of this method has been verified in many existing studies. Then, the second-order cone programming model can be solved with common solvers (such as GUROBI, MOSEK, CPLEX, etc.), and the optimal solution of the model can be found in a relatively short time, effectively improving the efficiency of model solving.

[0149] S7: Obtain the multi-stage optimization scheduling results of the electrical interconnected integrated energy system based on the target model.

[0150] The objective function of the target model is:

[0151]

[0152] Based on the above scheme, it can be seen that the scheme of the present invention aims at the problem that the safety and stability of the electricity-gas integrated energy system under the intermittent high-proportion new energy penetration is challenged, and proposes an electricity-gas three-stage random optimization collaborative scheduling model, that is, the power system adopts the three-stage optimization scheduling of day-ahead-intraday-real-time, while the natural gas system only adopts a single-stage scheduling strategy that is more in line with engineering practice. The imbalance caused by the gas unit in the natural gas system is smoothed by the natural gas pipeline; at the same time, the unexpected constraints are used to make the scheduling strategy of the intraday stage based only on the current known information and unrelated to future uncertainties. The present invention aims to realize the simulation of the integrated energy system through a three-stage random optimization scheduling model, and realize the coordinated operation of the power system and the natural gas system under the high proportion of new energy penetration.

[0153] In order to verify the actual effect of the three-stage model provided by the present invention, the three-stage model provided by the present invention is compared with the existing two-stage model, and the superiority value VMS is used as the measurement standard. The expression of the superiority value VMS is as follows:

[0154]

[0155] Among them, z TS represents the objective function of the two-stage scheduling, z MS Represents the objective function of the three-stage scheduling.

[0156] In this embodiment, the IEEE-118-node power system and the 48-node natural gas system are taken as examples for explanation as follows:

[0157] This embodiment takes into account 50 real-time scenarios, and every 5 real-time scenarios constitute 1 intraday scenario, that is, there are 10 intraday scenarios in total. The natural gas system includes 9 gas sources, 43 pipelines, 22 load nodes and 8 booster stations; the generators connected to the power buses 10, 12, 15, 25, 26, 61, 65, 66 and 69 are gas turbine units, and are connected to the natural gas nodes 45, 33, 36, 31, 30, 40, 39, 38 and 25.

[0158] This embodiment designs three comparative cases, in which the proportion of wind power installed capacity is 20%, 30% and 40% respectively. In the 20% wind power case, there are two wind farms connected to power bus 49 and 116 respectively, with capacities of 1400MW and 1500MW respectively; compared with the 20% wind power case, the 30% wind power case adds a wind farm with a capacity of 1400MW at power bus 59; compared with the 30% wind power case, the 40% wind power case adds a wind farm with a capacity of 1400MW at power bus 94. The comparative analysis results of the three cases are shown in Table 1. It can be seen that with the increase of wind power penetration rate, the superiority (VMS) of the present invention will become more obvious. The output proportion of gas turbines increases with the increase of wind power penetration rate as shown in Table 1. Figure 3 As shown in the figure, it can be seen from the results that with the increase in wind power penetration, the power system needs to dispatch a higher proportion of gas units to participate in load supply, which increases the degree of coupling between the power system and the natural gas system. The above integrated energy system dispatch simulation results provide a decision-making basis for dispatchers of the electricity-gas interconnected integrated energy system.

[0159] Table 1 Comparison of model operation objective functions under different wind power penetration rates

[0160] Percentage of wind power installed capacity 20％ 30％ 40％ Two-stage / M$ 43.707 45.326 45.899 This invention / M$ 43.337 44.656 44.580 VMS / % 0.85 1.50 2.96

Claims

1. A method for operating an electric-gas integrated energy system based on multi-stage stochastic optimization, characterized in that: The following steps are involved: S1: Generate a series of typical wind power output scenarios based on historical cross-section data of wind farms; S2: Construct the day-ahead dispatch model for the power system and natural gas system; S3: Based on the day-ahead stage dispatch model of the power system, a power system intraday stage dispatch model is constructed; S4: Based on the intraday stage dispatch model of the power system, a real-time stage dispatch model of the power system is constructed; S5: Construct unexpected constraints for the real-time stage dispatch model of the power system and the day-ahead stage dispatch model of the natural gas system; S6: Perform second-order cone relaxation on the day-ahead scheduling model of the natural gas system to obtain the target model; S7: Obtain the multi-stage optimization dispatch results of the electrical interconnected integrated energy system based on the target model; The day-ahead dispatch model of the power system in step S2 is: Where: P G,v,t represents the active output of non-wind turbine v at time t, W q,t represents the active power output of wind turbine q at time t predicted in the day-ahead phase, represents the load actually supplied by each node in the day-ahead phase, θ i,t represents the phase angle of node i, x ij represents the reactance value of line ij, and Indicates the upper and lower limits of non-wind turbine output. represents the best predicted value of wind turbine output on the day before, and They represent the upper and lower limits of wind power output in the day-ahead phase, P L,d,t represents the load demand at time t, represents the upper limit of the slope of non-wind turbine v, θ REF,t represents the reference node phase angle; The day-ahead dispatch model of the natural gas system in step S2 is: Among them, F S,w,t represents the gas supply at the gas source point w at time t, represents the actual node gas load supplied at time t in the day-ahead phase, F G,v,t Indicates the natural gas consumed by the gas unit, F mn,t represents the air flow rate at the head end of pipeline mn at time t, F com,k,t represents the gas flow consumed by the booster station k, F C,k,t represents the gas flow through the booster station k, represents the average flow rate in the pipeline mn, F nm,t Indicates the air flow at the end of pipe mn, W mn Weymouth constant for pipes, π m,t represents the natural gas pressure at node m at time t, and They represent the upper and lower limits of the node pressure respectively. and Respectively represent the upper and lower limits of gas supply at the gas source point at each moment, Indicates the upper limit of the gas source point climbing, F L,e,t represents the node natural gas load demand, and Respectively represent the natural gas pressure at the inlet and outlet ports of the booster station, and They represent the upper and lower limits of the compression ratio of the booster station respectively. Indicates the upper limit of the boosting flow rate at the boosting station at each moment, θ k Indicates the energy consumption ratio of the natural gas consumed by the booster station to the natural gas flowing through it, L mn,t represents the natural gas storage capacity of the pipeline mn, l mn It represents the pipe storage coefficient of pipeline mn, K1 is a fixed coefficient, L min Indicates the lower limit of natural gas pipeline storage, μ G and H G They represent the energy conversion efficiency of gas-fired units and the calorific value of natural gas respectively; The intra-day phase dispatch model of the power system in step S3 is: in, and represents the upward and downward adjustment of the electric power of non-wind turbines in the intraday stage in scenario ω, represents the predicted value of wind power output in the intraday stage in scenario ω, and Indicates that the wind turbine adjusts the output power up and down in the scenario ω, represents the voltage phase angle of node i in the intraday phase in scenario ω, represents the actual electrical load supplied by the node in the scenario ω during the intraday phase, and They represent the upper and lower limits of wind power output in the day, α ΔP and α ΔW They represent the upper limits of the intraday adjustment for non-wind turbines and wind turbines, respectively. Indicates the installed capacity of wind turbines, represents the voltage phase angle value of the reference node in the intraday phase scenario ω; In step S3, the natural gas system does not need to be dispatched in the intra-day stage, and the imbalance caused by the gas load fluctuation consumed by the gas unit is smoothed by the natural gas pipeline. The natural gas equivalently consumed by the gas unit in the intra-day stage scenario ω is for: The real-time dispatch model of the power system in step S4 is: in, and They represent the upper and lower reserves of non-wind turbines, represents the wind power that can be supplied in the real-time scenario ω, represents the amount of wind power abandoned in the real-time scenario ω, represents the voltage phase angle of node i in the real-time phase of scenario ω, represents the actual electrical load supplied by the node in the real-time scenario ω, and They represent the upper and lower reserve limits of non-wind turbine v, respectively. represents the voltage phase angle value of the reference node in the real-time scenario ω; In the step S4, the natural gas system also does not need to be dispatched in the real-time stage. The imbalance caused by the fluctuation of the gas load consumed by the gas unit is smoothed by the natural gas pipeline. The natural gas equivalently consumed by the gas unit in the real-time stage scenario ω is for: The unexpected constraints in step S5 are as follows: in, Indicates the same intraday scenario The above formula (A-45) indicates that for each wind power output scenario with the same intraday scenario, the intraday output up / down adjustment of non-wind turbines, the intraday output up / down adjustment of wind turbines, the voltage phase angle of each node, the electrical load supply of each node, the gas load supply of each node, the node natural gas pressure and the gas network pipeline flow at each time should also be the same and should not change with future scenarios; The second-order cone relaxation in step S6 is specifically as follows: Use second-order cone relaxation to convert the equality constraint (A-9) into an inequality constraint (A-46).

2. The method for operating an electric-gas integrated energy system based on multi-stage stochastic optimization according to claim 1, characterized in that: The objective function of the target model in step S7 is:

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