Distributed scheduling method and system for integrated energy system considering typhoon uncertainty

Abstract

The present disclosure provides a distributed scheduling method and system for an integrated energy system that takes into account typhoon uncertainty, relating to the technical field of coordinated operation and resilience enhancement of electricity-gas integrated energy, including: constructing an offshore wind power operation model, using the Monte Carlo simulation method to sample and generate typhoon scenarios and offshore wind power output scenarios; establishing a scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system based on the typhoon scenario and the offshore wind power output scenario; performing a solvability transformation on the scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system based on the mixed sample average approximation, converting it into a deterministic continuous linear model, and solving the deterministic continuous linear model using an efficient distributed collaborative algorithm based on multi-parameter programming theory and a critical domain accelerated search algorithm based on global optimal solution estimation to obtain a global optimal solution. The solution efficiency of the present disclosure can meet the timeliness requirements of emergency response in typhoon scenarios.

Description

Technical Field

[0001] The present disclosure relates to the technical field of coordinated operation and resilience enhancement of electricity-gas integrated energy, and in particular to a distributed scheduling method and system for an integrated energy system taking into account typhoon uncertainty. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Offshore wind power (OWP), a key renewable energy source, is experiencing rapid growth. However, the impact of extreme typhoon weather on OWP is significantly greater than onshore wind power. Although most typhoons do not make landfall, they can still force wind turbines to shut down under excessively high wind speeds, leading to wind power ramp events (WPREs). These short-term, significant power shortfalls pose a significant challenge to the power system's energy balance.

[0004] There are many solutions to the problem of safe and economic dispatch of coastal power systems under typhoon weather, but they still have the following shortcomings:

[0005] 1) Existing solutions typically rely on pre-set typhoon scenarios for modeling, failing to fully consider the uncertainties of parameters such as typhoon path, movement speed, and central pressure;

[0006] 2) Existing solutions fail to account for the risk of wind turbine damage during typhoons, which will actually increase the demand for system backup capacity;

[0007] 3) The traditional dispatching model adopted by the existing scheme is limited to the transmission network level, and relying solely on the limited regulation capacity of thermal power units to cope with the uncertainty of WPRE and wind power output is not sufficient.

[0008] 4) With the increasing prevalence of flexible resources such as distributed power sources, energy storage devices, and gas turbines on the distribution network side, as well as the addition of power-gas coupling devices, distribution networks and natural gas grids are increasingly involved in transmission network scheduling, providing a promising solution for addressing the impacts of typhoons. However, while this three-tier coordinated scheduling model for the transmission-distribution-gas integrated energy system is more flexible, it is also more complex. Because the transmission network, distribution network, and natural gas grid belong to different operating entities, centralized optimization methods cannot guarantee privacy and are computationally burdensome. Furthermore, the nested interactions between different levels of the three-tier coordinated scheduling model make traditional distributed algorithms inefficient, making it difficult to meet the timeliness requirements of emergency response in typhoon scenarios. Summary of the Invention

[0009] In order to solve the above problems, the present disclosure proposes a distributed scheduling method and system for an integrated energy system taking into account typhoon uncertainty, establishes an OWP operation model taking into account typhoon parameter uncertainty and wind turbine damage risk, so as to fully characterize the active power output characteristics of the OWP in typhoon scenarios; proposes a three-layer collaborative scheduling model for a transmission-distribution-gas integrated energy system, so as to fully utilize the flexibility resources in different networks to cope with the uncertainty of WPRE and wind power output, and introduces an efficient distributed collaborative algorithm based on multi-parameter planning theory and a critical domain acceleration search algorithm based on global optimal solution estimation to further improve its convergence speed, thereby ensuring that the solution efficiency of the above three-layer collaborative scheduling model can meet the timeliness requirements of emergency response in typhoon scenarios.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions:

[0011] A distributed scheduling method for an integrated energy system taking into account typhoon uncertainty, including:

[0012] Considering the impact of typhoon uncertainty on offshore wind power, an offshore wind power operation model is constructed. Based on the offshore wind power operation model, the Monte Carlo simulation method is used to sample and generate typhoon scenarios and offshore wind power output scenarios;

[0013] Based on typhoon scenarios and offshore wind power output scenarios, a scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system is established, taking into account the coordinated operation of the transmission grid, distribution grid, and natural gas grid.

[0014] Based on the mixed sample average approximation, the scenario-based three-layer collaborative scheduling model of the transmission-distribution-gas integrated energy system is transformed into a deterministic continuous linear model. The deterministic continuous linear model is solved using an efficient distributed collaborative algorithm based on multi-parameter programming theory and a critical domain accelerated search algorithm based on global optimal solution estimation to obtain the global optimal solution for the distributed scheduling of the integrated energy system under typhoon scenarios.

[0015] According to some embodiments, the present disclosure adopts the following technical solutions:

[0016] The distributed dispatching system for the integrated energy system taking into account the uncertainty of typhoons includes:

[0017] The scenario generation module is used to consider the impact of typhoon uncertainty on offshore wind power, build an offshore wind power operation model, and use the Monte Carlo simulation method to sample and generate typhoon scenarios and offshore wind power output scenarios based on the offshore wind power operation model;

[0018] A model building module is used to establish a scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system based on typhoon scenarios and offshore wind power output scenarios, taking into account the coordinated operation of the transmission grid, distribution grid, and natural gas grid.

[0019] The scheduling solution module is used to transform the scenario-based three-layer collaborative scheduling model of the transmission-distribution-gas integrated energy system into a deterministic continuous linear model based on the mixed sample average approximation. The deterministic continuous linear model is solved using the efficient distributed collaborative algorithm based on multi-parameter planning theory and the critical domain accelerated search algorithm based on the global optimal solution estimation to obtain the global optimal solution for the distributed scheduling of the integrated energy system under typhoon scenarios.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] A computer program product includes a computer program, which, when executed by a processor, implements the distributed scheduling method for an integrated energy system taking into account typhoon uncertainty.

[0022] According to some embodiments, the present disclosure adopts the following technical solutions:

[0023] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the distributed scheduling method of the integrated energy system taking into account typhoon uncertainty is implemented.

[0024] According to some embodiments, the present disclosure adopts the following technical solutions:

[0025] An electronic device comprises: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the distributed scheduling method for the integrated energy system taking into account typhoon uncertainty.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The disclosed distributed scheduling method for an integrated energy system taking into account typhoon uncertainty establishes an OWP operation model that takes into account typhoon parameter uncertainty and wind turbine damage risk, so as to fully characterize the active power output characteristics of the OWP in typhoon scenarios; proposes a scenario-based three-layer coordinated scheduling model for a transmission-distribution-gas integrated energy system, thereby making full use of the flexibility resources in different networks to cope with the uncertainty of WPRE and wind power output; proposes an efficient distributed collaborative algorithm based on multi-parameter planning theory, and introduces a critical domain acceleration search algorithm based on global optimal solution estimation to further improve its convergence speed, thereby ensuring that the solution efficiency of the above three-layer coordinated scheduling model can meet the timeliness requirements of emergency response in typhoon scenarios.

[0028] The present invention discloses a distributed scheduling method for an integrated energy system that takes into account typhoon uncertainties. The scenario-based three-layer coordinated scheduling model of the transmission-distribution-gas integrated energy system includes a transmission network-side coordinated scheduling model, a distribution network-side coordinated scheduling model, and a natural gas network-side coordinated scheduling model. It can realize distributed coordinated scheduling of the transmission network, distribution network, and natural gas network, and at the same time make full use of the flexibility resources in different networks to ensure the safe operation of the system in typhoon scenarios.

[0029] The distributed scheduling method for an integrated energy system disclosed in the present invention, which takes into account typhoon uncertainty, is based on an efficient distributed collaborative algorithm based on multi-parameter planning theory, and introduces a critical domain acceleration search algorithm based on global optimal solution estimation to solve the scenario-based three-layer collaborative scheduling model of the transmission-distribution-gas integrated energy system. Compared with traditional distributed algorithms, such as the alternating direction multiplier method and the Benders decomposition method, it has a significant advantage in solution speed, and the calculation time can be shortened by more than 2-3 times. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0031] Figure 1 This is a flowchart of the application implementation of the distributed scheduling method for an integrated energy system taking into account typhoon uncertainty according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0035] Example 1

[0036] In one embodiment of the present disclosure, a distributed scheduling method for an integrated energy system taking into account typhoon uncertainty is provided. The method can achieve distributed coordinated scheduling of transmission networks, distribution networks, and natural gas networks, while fully utilizing the flexibility resources in different networks to ensure safe operation of the system in typhoon scenarios. The method includes the following steps:

[0037] Step 1: Considering the impact of typhoon uncertainty on offshore wind power, an offshore wind power operation model is constructed. Based on the offshore wind power operation model, the Monte Carlo simulation method is used to sample and generate typhoon scenarios and offshore wind power output scenarios;

[0038] Step 2: Based on typhoon scenarios and offshore wind power output scenarios, consider the coordinated operation of the transmission network, distribution network, and natural gas network, and establish a scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system;

[0039] Step 3: Based on the mixed sample average approximation, the scenario-based transmission-distribution-gas integrated energy system three-layer coordinated scheduling model is transformed into a deterministic continuous linear model;

[0040] Step 4: Use the efficient distributed collaborative algorithm of multi-parameter planning theory and the critical domain accelerated search algorithm based on global optimal solution estimation to solve the determined continuous linear model and obtain the global optimal solution for the distributed scheduling of the integrated energy system under typhoon scenarios.

[0041] As an embodiment, the implementation process of the distributed scheduling method of the integrated energy system taking into account typhoon uncertainty disclosed in the present invention is as follows: Figure 1 The specific implementation process is as follows:

[0042] Step 1: Construct an offshore wind power operation model that considers the uncertainty of typhoon parameters and the risk of wind turbine damage. Use the Monte Carlo simulation method to sample and generate typhoon scenarios and offshore wind power output scenarios, fully characterize the active output characteristics of OWP under typhoon scenarios, and lay the foundation for the subsequent construction of a three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system. This includes: considering the impact of typhoon uncertainty on offshore wind power, using the Holland vortex model to model the typhoon wind farm and the uncertainty of typhoon parameters. When the probability distribution satisfied by the typhoon's uncertain parameters is determined, a typhoon scenario is generated using a Monte Carlo simulation method; for any offshore wind farm, under a specific typhoon scenario, a wind speed-power active output model is constructed. According to the relative distance between the typhoon and the offshore wind farm, the entire process of the offshore wind farm encountering a typhoon event is divided into three stages: the typhoon approach stage, the typhoon transit stage, and the typhoon departure stage. The wind power output vectors of the three stages are integrated to obtain the complete output of the offshore wind farm under the typhoon scenario. The specific implementation is as follows:

[0043] Step 1-1: Typhoon wind field modeling, as follows:

[0044] Step 1-1-1: Use the Holland vortex model to model the typhoon wind field. Its mathematical form can be expressed as follows:

[0045] (1)

[0046] (2)

[0047] (3)

[0048] in, is the set of all scheduling periods; For a specific scheduling period; The straight-line distance from the typhoon center Gradient wind speed value at the measuring point; is the Coriolis force parameter; is the typhoon center pressure value, Represents the pressure difference between the typhoon center pressure and the ambient atmospheric pressure; is the air density parameter; is the latitude of the typhoon eye; is the correction factor, and its typical value range is between 0 and 0.4. is the parameter of Holland radial pressure curve; The maximum wind speed radius of the typhoon.

[0049] Step 1-2: Modeling of typhoon parameter uncertainty, as follows:

[0050] Step 1-2-1: The uncertainty of the typhoon path comes from the direction angle and translation speed The prediction errors are denoted as and . Forecast error and It can be considered to satisfy the following normal distribution:

[0051] (4)

[0052] in, and Respectively represent the mean parameter and variance parameter of the normal distribution, both of which can be estimated through historical data; * is used to refer to and , is a commonly used mathematical representation.

[0053] Step 1-2-2: Typhoon center pressure The uncertainty comes from its own prediction error. Here, the probability distribution satisfied by the typhoon central pressure is directly modeled. This probability distribution can be modeled as the following lognormal distribution:

[0054] (5)

[0055] in, Represents the predicted value of the typhoon's central pressure, and They respectively characterize the mean parameter and variance parameter of the lognormal distribution, both of which can be estimated through historical data.

[0056] Steps 1-3: Typhoon scenario generation. When the probability distribution satisfied by the typhoon's uncertain parameters is determined, a Monte Carlo simulation-based approach can be used to generate the typhoon scenario. The details are as follows:

[0057] Step 1-3-1: Get the typhoon eye position for the current period , predicted position for the next period And the predicted typhoon center pressure value for the next period .

[0058] Step 1-3-2: Based on the current typhoon eye position and the predicted position for the next period , calculate the predicted azimuth change value and predicted translational velocity .

[0059] Step 1-3-3: Based on the Monte Carlo simulation method, the typhoon path prediction error is calculated according to formula (4). and Sampling is performed, and the possible typhoon center pressure value in the next period is calculated based on formula (5). Conduct sampling.

[0060] Step 1-3-4: Calculate the azimuth change value of the simulated sampling and the translation speed of the simulated sampling , and then calculate the possible typhoon eye position in the next period .in, and The azimuth and translation speed of the current period

[0061] Step 1-3-5: Return to step 1-3-1 and repeat steps 1-3-1 to 1-3-4 until the last period, and finally obtain a complete simulated typhoon scenario.

[0062] Step 1-3-6: Assume the number of scenes to be generated is , repeat steps 1-3-1 to 1-3-5 until you get simulated typhoon scenes. The index of each typhoon scene is defined as , the set of all scenes is defined as .

[0063] Step 1-4: Generate offshore wind farm output scenario, define the set of offshore wind farms as , as follows:

[0064] Step 1-4-1: For any offshore wind farm , in a specific typhoon scenario Under this condition, its active output can be described by the following wind speed-power model:

[0065] (6)

[0066] in, 、 and Respectively represent the rated wind speed, cut-in wind speed and cut-out wind speed; is the rated active power of the wind turbine, For offshore wind farms The wind speed at the location.

[0067] Step 1-4-2: According to the relative distance between the typhoon and the offshore wind farm, the whole process of the offshore wind farm encountering a typhoon event can be divided into three stages, namely, the approach stage , transit stage and the departure stage .in, 、 、 as well as They are the time when the typhoon encounters, the time when offshore wind power is shut down, the time when offshore wind power resumes power generation, and the time when the typhoon impact disappears.

[0068] Step 1-4-3: In the approach phase , formula (6) can be used to directly calculate the offshore wind farm The active output of .

[0069] Step 1-4-4: During the transit phase , offshore wind farms The wind speed is always greater than the cut-out wind speed of the wind turbines inside it, that is, At this time, according to formula (6), the offshore wind farm The active output is .

[0070] Steps 1-4-5: During the transit phase If the wind turbine is within the critical damage radius of the typhoon Middle, that is, the distance between the wind turbine and the typhoon center , then the wind turbine may be damaged. Its damage probability can be described by the following probability distribution function:

[0071] (7)

[0072] in, For a single Damage probability of wind turbine at the moment; represents the standard normal cumulative distribution function; Characterizes the standard deviation of the natural logarithm of the wind speed when the wind turbine reaches the damage threshold; is the median value of the design wind speed; and They are the moments of entering and leaving the typhoon's critical destructive radius, respectively.

[0073] Steps 1-4-6: During the departure phase As the typhoon center gradually moves away, the offshore wind farm The wind speed at the location gradually decreases to below the cut-out wind speed. At this time, the wind turbines that were not damaged during the transit phase will resume generating electricity, and their active power output can be calculated by the following formula:

[0074] (8)

[0075] in, It can be calculated by formula (6).

[0076] Step 1-4-7: Vectorize the wind power output of the three phases 、 、 Put together, you get an offshore wind farm In typhoon scene The complete output vector under:

[0077] (9)

[0078] Step 1-4-8: For offshore wind farms ,right All scenarios in the above example are executed by executing steps 1-4-1 to 1-4-7 to obtain the offshore wind farm. The number of complete output vectors in all scenarios should be equal to the total number of scenarios .

[0079] Step 1-4-9: Define the offshore wind farm The complete output vector in the prediction scenario is , then the offshore wind farm In any scene The complete output vector under the prediction scenario can be expressed as the complete output vector under the prediction scenario With the scene The deviation value of the complete output vector compared to the predicted scenario The summation form:

[0080] (10)

[0081] Step 1-4-10: For all For all offshore wind farms, perform steps 1-4-1 to 1-4-9 to obtain the complete output vectors of all offshore wind farms under all typhoon scenarios, as shown below:

[0082] (11)

[0083] Step 2: Based on the stochastic optimization theory and the typhoon scenario and offshore output scenario generated in step 1, a three-layer coordinated dispatching model of the transmission-distribution-gas integrated energy system is constructed, including: establishing a scenario-based three-layer coordinated dispatching model of the transmission-distribution-gas integrated energy system, wherein the scenario-based three-layer coordinated dispatching model of the transmission-distribution-gas integrated energy system includes a transmission network side coordinated dispatching model, a distribution network side coordinated dispatching model, and a natural gas network side coordinated dispatching model; wherein, the objective function of the transmission network side coordinated dispatching model is to minimize the transmission network side operating cost, the objective function of the distribution network side coordinated dispatching model is to minimize the distribution network side operating cost, and the objective function of the natural gas network side coordinated dispatching model is to minimize the natural gas network side operating cost, and the constraints of each coordinated dispatching model are set separately. Specifically as follows:

[0084] Step 2-1: Establish a transmission network-side coordinated dispatch model, which includes the objective function and constraints, as follows:

[0085] Step 2-1-1: The objective function of the transmission network side coordinated dispatch model is to minimize the transmission network side operating cost, and its mathematical form is as follows:

[0086] (12)

[0087] in, is the set of all scheduling moments; It is the set of all thermal power generating units on the transmission grid side; is the power generation cost coefficient of thermal power units; It is the benchmark output of thermal power units; is the transmission network side reserve cost coefficient; and They are the benchmark upward spin reserve capacity and benchmark downward spin reserve capacity on the transmission network side respectively.

[0088] Step 2-1-2: Affine decision criterion constraint. In step 1, it was derived that the impact of typhoon parameter uncertainty on offshore wind farms can be converted into a deviation from the offshore wind farm's output under the forecast scenario. Assume that the transmission grid operator will allocate the adjusted power required by the transmission-side thermal power units and the distribution network interconnected with the transmission side according to the following affine decision criterion:

[0089] (13)

[0090] in, is the set of all thermal power generating units on the transmission grid side, It is the set of all distribution network tie lines interconnected with the transmission network; The actual output of the thermal power unit; It is the actual interactive active power for transmission and distribution; Predict interactive active power for transmission and distribution; The actual output of the thermal power unit; is the power adjustment factor of the thermal power unit; is the power adjustment factor of the distribution network; is a random variable that characterizes the output deviation of offshore wind farms.

[0091] Step 2-1-3: Transmission grid power balance constraints:

[0092] (14)

[0093] in, is the set of all nodes in the transmission network; It is the set of all distribution network tie lines interconnected with the transmission network; Contribute to offshore wind farm forecasting; is the load on the transmission grid side; is the benchmark interaction power of the distribution network tie line.

[0094] Step 2-1-4: Thermal power unit operation constraints:

[0095] (15)

[0096] (16)

[0097] in, is the probability that the inequality in the brackets holds true; is a risk factor; and They are the upward spin reserve capacity and downward spin reserve capacity on the transmission grid side respectively; and The upper limit of the upward and downward reserve capacity on the transmission network side; and are the upper and lower bounds of the thermal power unit output respectively.

[0098] Step 2-1-5: Thermal power unit ramp constraints:

[0099] (17)

[0100] In the above formula, It is the upper limit of the climbing capacity of thermal power units.

[0101] Step 2-1-6: Transmission and distribution tie line exchange power constraints:

[0102] (18)

[0103] In the above formula, is the upper bound of the tie line exchange power.

[0104] Step 2-1-7: Transmission grid side line power flow constraints:

[0105] (19)

[0106] in, It is the set of all lines on the transmission network side; is the upper limit of the power flow on the transmission network side; 、 、 、 They are the power transmission distribution factors of offshore wind farms, transmission grid side loads, thermal power units, and transmission and distribution interconnection lines.

[0107] Step 2-2: Define the set of all distribution networks interconnected with the upper transmission network as , then for any distribution network , a coordinated dispatch model for the distribution network side can be established, which includes the objective function and constraints; the details are as follows:

[0108] Step 2-2-1: The objective function of the coordinated dispatching model on the distribution network side is to minimize the operating cost on the distribution network side. Its mathematical form is as follows:

[0109] (20)

[0110] in, It is the set of all distributed power sources in the distribution network; It is the set of all gas generating units in the distribution network; It is the collection of all power-to-gas devices in the distribution network; It is the collection of all energy storage devices in the distribution network; is the cost coefficient of distributed generation; is the reserve capacity cost coefficient on the distribution network side; is the cost coefficient of the power-to-gas device; and are the charging and discharging cost coefficients of the energy storage device respectively; is the power generation cost coefficient of the gas-fired unit; It is the benchmark active power output of distributed power sources; is the benchmark active power of the power-to-gas device; and are the benchmark charging and discharging active powers of the energy storage device respectively; It is the benchmark active power output of the gas generator set; and They are the benchmark upward spin reserve capacity and benchmark downward spin reserve capacity on the distribution network side respectively.

[0111] Step 2-2-2: Affine decision criterion constraint. The distribution network operator will redistribute the adjusted power allocated in the transmission network dispatch to the distributed generation, gas units, energy storage equipment, and power-to-gas devices in the distribution network according to the affine decision criterion. Its mathematical form is as follows:

[0112] (twenty one)

[0113] (twenty two)

[0114] in, Represents the adjustment power allocated to the distribution network in the transmission network side dispatch; 、 、 、 、 They are the adjustment factors for distributed power sources, gas-fired units, energy storage equipment discharge, energy storage equipment charging, and power-to-gas devices.

[0115] Step 2-2-3: Distribution network flow constraints:

[0116] (twenty three)

[0117] in, For distribution network nodes The set of all child nodes of ; is the set of all lines in the distribution network; It is the actual active power output of distributed power source; It is the actual active power output of the gas generator set; is the actual active power of the power-to-gas device; and are the actual charging and discharging active powers of the energy storage device respectively; is the active load on the distribution network side; Flow to the distribution network node Active power; For distribution network nodes Flow to its child nodes Active power; It is the active power injected into the root node of the distribution network by the transmission and distribution tie line; exchanging power for the tie lines between the transmission grid and the distribution grid; Flow to the distribution network node Reactive power; For distribution network nodes Flow to its child nodes Reactive power; Provides distributed electric reactive power; Provide reactive power for the gas generator set; is the reactive load on the distribution network side; For distribution network nodes The voltage amplitude; For distribution network nodes The voltage amplitude of the parent node; is the voltage amplitude of the root node of the distribution network; and Distribution network lines resistance and reactance.

[0118] Step 2-2-4: Distribution network node voltage constraints:

[0119] (twenty four)

[0120] in, and are the upper and lower bounds of the voltage amplitude of the distribution network nodes, respectively.

[0121] Step 2-2-5: Power-to-gas unit operation constraints:

[0122] (25)

[0123] in, It is the maximum operating active power of the power-to-gas device.

[0124] Step 2-2-6: Distribution network side reserve capacity constraint:

[0125] (26)

[0126] (27)

[0127] In the above formula, and They are the upward spin reserve capacity and downward spin reserve capacity on the distribution network side respectively; and The upper limit of the upward and downward reserve capacity on the distribution network side; and are the upper and lower bounds of the distributed generation output respectively; and They are the upper and lower bounds of the gas turbine output respectively.

[0128] Step 2-2-7: Energy storage equipment operation constraints:

[0129] (28)

[0130] (29)

[0131] In the above formula, and are the upper bounds of the charge and discharge power of the energy storage device respectively; The amount of electricity stored for the energy storage device; The upper limit of the capacity of the energy storage device; is the conversion efficiency of the energy storage device.

[0132] Step 2-3: Establish a natural gas grid-side coordinated dispatch model, which includes an objective function and constraints; the details are as follows:

[0133] Step 2-3-1: The objective function of the natural gas grid-side coordinated scheduling model is to minimize the natural gas grid-side operating cost. Its mathematical form is as follows:

[0134] (30)

[0135] In the above formula, A collection of gas wells in a natural gas grid; It is the set of all nodes in the natural gas grid; is the gas production cost coefficient of the natural gas well; is the gas production of the natural gas well; is the gas consumption of the gas unit.

[0136] Step 2-3-2: Gas unit coupling constraints:

[0137] (31)

[0138] In the above formula, is the gas consumption of the gas unit, is the first-order term of the gas consumption coefficient of the gas generator set; is the constant term of gas consumption coefficient of gas turbine unit; It is the adjusted power allocated to the gas generator set in the worst scenario.

[0139] Step 2-3-3: Coupling constraints of the power-to-gas device:

[0140] (32)

[0141] In the above formula, Gas consumption of the power-to-gas device; is the conversion efficiency of the power-to-gas device.

[0142] Step 2-3-4: Natural gas flow balance constraints:

[0143] (33)

[0144] In the above formula, is the gas production of the natural gas well; Gas consumption of the power-to-gas device; is the gas consumption of the gas unit; and Represents pipelines and Gas flow rate; is the natural gas node load.

[0145] Step 2-3-5: Steady-state gas flow equation for natural gas grid:

[0146] (34)

[0147] In the above formula, Represents a pipeline Weymouth characteristic parameters; and is the node pressure; For pipelines The upper limit of natural gas flow.

[0148] Step 2-3-6: Natural gas well production constraints:

[0149] (35)

[0150] In the above formula, It is the upper limit of gas production of natural gas wells.

[0151] Step 2-3-7: Node pressure constraint:

[0152] (36)

[0153] In the above formula, and are the upper and lower bounds of the nodal pressure, respectively.

[0154] Step 3: Based on the mixed sample average approximation, the transmission-distribution-gas three-layer coordinated scheduling model of random variables is transformed into a solvable model, and it is rewritten into a deterministic continuous linear model to facilitate subsequent distributed solution. Including: Based on the mixed sample average approximation, the scenario-based transmission-distribution-gas integrated energy system three-layer coordinated scheduling model is transformed into a solvable model, including: performing solvability transformation on the opportunity constraints containing random variables, based on each generated scenario, using the sample average approximation to describe the opportunity constraints containing random variables, setting a binary indicator variable, if the internal constraint is established in a certain scenario, the binary indicator variable takes the value of 1, otherwise it takes the value of 0, and relaxing the binary indicator variable to be in the interval , and introduces mixed inequalities to tighten the feasible domain expanded by relaxation. The general form of the chance constraint is transformed into a set of continuous linear constraints without random variables. After the solvability transformation, the scenario-based transmission-distribution-gas integrated energy system three-layer coordinated scheduling model is expressed as a linear objective function and a set of linear constraints. The details are as follows:

[0155] Step 3-1: Perform solvability transformation on the chance constraints containing random variables, such as formulas (16)-(19), (24), (25), (27)-(29), and rewrite them into deterministic continuous linear constraints; the details are as follows:

[0156] Step 3-1-1: The general form of the chance constraint involving random variables is as follows:

[0157] (37)

[0158] In the above formula, and Respectively represent the active / reactive power decision vectors of the controllable units. and represent the decision vectors for increasing / decreasing the spare capacity respectively. Characterizes linear terms involving random variables. is the decision vector 、 、 、 The linear function corresponding to the corresponding joint opportunity constraint is constructed.

[0159] Step 3-1-2: Based on the generated in step 1 For this scenario, we can use the sample average approximation to rewrite formula (37) into the following form:

[0160] (38)

[0161] In the above formula, is the upper bound of the uncertainty term; is a non-negative parameter; For the scene The probability of occurrence, and the conditions are met ; is a slack variable; formula (a) in formula (39) is used to determine the scenario Lower internal constraints Is it true, if it is in the scene If the following holds, then the binary indicator variable The value is 1; otherwise The value is 0. Formula (b) is used to ensure that the maximum probability of the total number of scenarios that violate the internal constraints does not exceed the risk factor , that is, formula (b) can ensure At the confidence level Established.

[0162] Step 3-1-3: Binary indicator variable Relaxation is in the interval Continuous variables, and introduce mixed inequalities to tighten the relaxation For the expanded feasible domain, the general form of the chance constraint, such as formula (37), can be transformed into a set of continuous linear constraints without random variables as shown below:

[0163] (39)

[0164] In the above formula, is the mixed inequality introduced; represents the feasible region after tightening; 、 、 To follow the sequence Reordered non-negative parameters , and satisfies and ; A continuous indicator variable representing a mixed inequality.

[0165] Step 3-2: Build a compact model; details are as follows:

[0166] Step 3-2-1: The three-layer coordinated scheduling model of the transmission-distribution-gas integrated energy system after solvability transformation can be expressed as the following linear objective function (40) and a set of linear constraints (41)-(43):

[0167] (40)

[0168] (41)

[0169] (42)

[0170] (43)

[0171] In the above formula, 、 、 are the compact objective functions for the transmission grid side, distribution grid side, and natural gas grid side respectively; 、 、 represent the internal decision variables on the transmission network side, distribution network side, and natural gas network side respectively; is the transmission network and the index is Boundary variables between distribution networks, The index is The boundary variables between the distribution network and the local natural gas network. 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 They represent the coefficient matrices corresponding to the constraints.

[0172] Step 4: Use an efficient distributed collaborative algorithm based on multi-parameter planning theory and a critical domain acceleration search algorithm based on global optimal solution estimation to solve the three-layer transmission-distribution-gas collaborative scheduling model after the solvability transformation, including: using an efficient distributed collaborative algorithm based on multi-parameter planning theory and a critical domain acceleration search algorithm based on global optimal solution estimation to solve the scenario-based transmission-distribution-gas integrated energy system three-layer collaborative scheduling model after the solvability transformation, which is divided into two solution levels: the transmission level and the distribution level. The transmission level is an iterative solution between the transmission network and the distribution network, and the distribution level is an iterative solution between the distribution network and the local natural gas network. The iterative solution of the transmission level must be carried out after the distribution level converges. Then calculate the critical domain and local objective function. The calculation method of the critical domain and local objective function at the transmission level is consistent with that at the distribution level. If the iterative solution does not converge to the global optimal solution of the subproblem, the obtained local optimal solution and optimal target value are used as the data set and the optimal target value respectively. The data set is used to fit the global objective function, and the estimated value of the global optimal solution is calculated based on the fitted value. Set the step vector and search the next critical domain based on the step vector until the subproblem is infeasible or the local optimal value begins to increase. Return to the last feasible critical domain and switch to a one-by-one search method to find the global optimal solution. The details are as follows:

[0173] Step 4-1: Use an efficient distributed collaborative algorithm based on multi-parameter programming theory to solve the three-layer collaborative scheduling model of the transmission-distribution-gas integrated energy system after the solvability transformation. The solution can be divided into two levels: the transmission level and the distribution level. The transmission level is the iterative solution between the transmission network and the distribution network, and the distribution level is the iterative solution between the distribution network and the local natural gas network. The iterative solution of the transmission level must be carried out after the distribution level converges. The details are as follows:

[0174] Step 4-1-1: Distributed collaborative solution at the distribution level. Note that this distributed collaborative solution process is for all the The distribution network is carried out in parallel; the specific steps are as follows:

[0175] Step 4-1-1-1: For any Initialize the number of iterations for the indexed distribution network .

[0176] Step 4-1-1-2: Input the power distribution network Initial values ​​of boundary variables with the local natural gas grid , note that in subsequent calculations are treated as parameters rather than variables.

[0177] Step 4-1-1-3: Distribution Network The operator will bound the variable initial value Sent to the local gas grid operator.

[0178] Step 4-1-1-4: Local gas grid operator Solve the gas network side problem as shown in Equation (45), where For the The step vector for the iteration is used to remove the local gas grid operator from the distribution network. The operator receives the bound variable Move to the next critical region. Sometimes ,when hour, The calculation method of is shown in step 4-3.

[0179] (44)

[0180] in, is the cost coefficient of the decision variable on the gas grid side.

[0181] Step 4-1-1-5: If the grid-side subproblem (44) is feasible, the local gas grid operator calculates the critical region at the distribution level and the local objective function and sends it back to the distribution network operator. and the local objective function The specific calculation method is shown in step 4-2.

[0182] Step 4-1-1-6: Distribution Network The operator receives the critical domain of the distribution level and the local objective function Calculate the distribution side main problem (45) to obtain the updated boundary variables :

[0183] (45)

[0184] Step 4-1-1-7: Determine convergence, if ,in As the convergence criterion, if it is a small positive constant, the iterative solution of the distribution level converges and the iterative solution of the transmission level can be carried out; otherwise, update , distribution network The operator will Send it to the local natural gas grid operator and return to step 4-1-1-4 to continue iterating until convergence.

[0185] Step 4-1-2: Distributed collaborative solution at the transmission level; the specific steps are as follows:

[0186] Step 4-1-2-1: Initialize the number of transmission layer iterations .

[0187] Step 4-1-2-2: Input the initial values ​​of the boundary variables between the transmission network and the distribution network , note that in subsequent calculations are treated as parameters rather than variables.

[0188] Step 4-1-2-3: The transmission network operator sets the initial values ​​of the boundary variables Sent to various distribution network operators.

[0189] Step 4-1-2-4: For the All distribution networks, their respective operators Solve the distribution network side sub-problem in parallel as shown in Equation (46), where For the The step vector of the iteration is used to convert the boundary variables received by each distribution network operator from the transmission network operator into Move to the next critical region. Sometimes ,when hour, The calculation method of is shown in step 4-3.

[0190] (46)

[0191] in, and are the distribution-gas boundary variables and local objective functions that converge in the iterative solution at the distribution level, both of which are steady values.

[0192] Step 4-1-2-5: If the distribution network side sub-problem 46 is feasible, each distribution network operator calculates the critical region at the transmission level and the local objective function and sends it back to the transmission grid operator. and the local objective function The specific calculation method is shown in step 4-2.

[0193] Step 4-1-2-6: The transmission network operator receives the critical area and the local objective function Calculate the distribution side main problem (47) to obtain the updated boundary variables .

[0194] (47)

[0195] Step 4-1-2-7: Determine convergence, if , then the iterative solution at the transmission level converges, and the global optimal solution has been obtained; otherwise, update , the transmission grid operator will Send it to each distribution network operator and return to step 4-1-2-4 to continue iterating until convergence.

[0196] Step 4-2: Calculate the critical region and local objective function. Note that the calculation method of the critical region and local objective function at the transmission level is the same as that at the distribution level; the details are as follows:

[0197] Step 4-2-1: Define the Lagrangian function of the gas grid subproblem (44) at the distribution level as follows:

[0198] (48)

[0199] In the above formula, is the dual multiplier of the equality constraint, is the dual multiplier of the inequality constraint.

[0200] Step 4-2-2: After solving the gas network side sub-problem (44), according to the optimal solution of this sub-problem The active and inactive constraints can be expressed as follows:

[0201] (49)

[0202] (50)

[0203] In the above formula, and are the sets of active constraints and inequality constraints respectively.

[0204] Step 4-2-3: Solve the Karush-Kuhn-Tucker equations (49). The coefficient matrix on the left side of equation (49) can be inverted in blocks and then multiplied by the right side of the equation. The calculation results are as follows:

[0205] (51)

[0206] In the above formula, and The sub-block inverse matrix after block inversion of the coefficient matrix.

[0207] Step 4-2-4: From formula (51), we can see that the optimal solution of the sub-problem is It can be expressed as Function:

[0208] (52)

[0209] Step 4-2-5: Although It will change during the iterative solution process, that is, although It is considered as a parameter in solving the sub-problem on the gas grid side, but is considered as a variable in the complete solution of the coordinated dispatch model at the distribution level. , at this time, as long as the combination of the effective constraints and ineffective constraints in (50) remains unchanged, equation (52) always holds. The feasible region of is called the critical region, which must satisfy the following conditions: the dual multiplier remains greater than zero and the inactive constraint remains less than zero. The mathematical form of the critical region can be expressed as:

[0210] (53)

[0211] (54)

[0212] (55)

[0213] Step 4-2-6: Under the limitation of , by bringing Equation (52) into the objective function of the gas network side subproblem (44), we can get the current critical region The local objective function is:

[0214] (56)

[0215] Step 4-3: Calculate the step vector. Note that the calculation method of the step vector at the transmission level is the same as that at the distribution level; the details are as follows:

[0216] Step 4-3-1: Set the iteration threshold to , when the number of iterations hour, Can be set as the local objective function used in this iteration exist Take a small step forward in the negative gradient direction, and the mathematical form is as follows:

[0217] (57)

[0218] In the above formula, is the step length, is the local objective function exist The negative gradient unit vector of .

[0219] Step 4-3-2: When the number of iterations If the iterative solution does not converge to the global optimal solution of the subproblem, the local optimal solution obtained will be and the optimal target value As data sets and save.

[0220] Step 4-3-3: Utilize the dataset and Fitting the global objective function Here we use the following convex quadratic objective function for fitting:

[0221] (58)

[0222] In the above formula, 、 、 is a constant coefficient matrix.

[0223] Step 4-3-4: Calculate the estimated value of the global optimal solution according to formula (58) At this time, the step vector can be set as shown in the following formula:

[0224] (59)

[0225] In the above formula, is an appropriate step size parameter.

[0226] Step 4-3-5: Based on step vector Search for the next critical region. If the subproblem is feasible and the local optimal value is reduced, and Updated and , and return to step 4-3-2.

[0227] Step 4-3-6: Repeat steps 4-3-2 to 4-3-5 until the subproblem is infeasible or the local optimal value begins to increase, return to the last feasible critical region, and switch to the one-by-one search method of step 4-3-1 to find the global optimal solution.

[0228] Example 2

[0229] In one embodiment of the present disclosure, a distributed scheduling system for an integrated energy system taking into account typhoon uncertainty is provided, comprising:

[0230] The scenario generation module is used to consider the impact of typhoon uncertainty on offshore wind power, build an offshore wind power operation model, and use the Monte Carlo simulation method to sample and generate typhoon scenarios and offshore wind power output scenarios based on the offshore wind power operation model;

[0231] A model building module is used to establish a scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system based on typhoon scenarios and offshore wind power output scenarios, taking into account the coordinated operation of the transmission grid, distribution grid, and natural gas grid.

[0232] The scheduling solution module is used to transform the scenario-based three-layer collaborative scheduling model of the transmission-distribution-gas integrated energy system into a deterministic continuous linear model based on the mixed sample average approximation. The deterministic continuous linear model is solved using the efficient distributed collaborative algorithm based on multi-parameter planning theory and the critical domain accelerated search algorithm based on the global optimal solution estimation to obtain the global optimal solution for the distributed scheduling of the integrated energy system under typhoon scenarios.

[0233] Example 3

[0234] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the distributed scheduling method for an integrated energy system taking into account typhoon uncertainty.

[0235] Example 4

[0236] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, which is used to store computer instructions. When the computer instructions are executed by a processor, the distributed scheduling method of the integrated energy system taking into account typhoon uncertainty is implemented.

[0237] Example 5

[0238] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the distributed scheduling method of the integrated energy system taking into account typhoon uncertainty.

[0239] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0240] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0241] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A distributed scheduling method for an integrated energy system taking into account typhoon uncertainty, characterized in that: include: Considering the impact of typhoon uncertainty on offshore wind power, an offshore wind power operation model is constructed. Based on the offshore wind power operation model, the Monte Carlo simulation method is used to sample and generate typhoon scenarios and offshore wind power output scenarios. According to the relative distance between the typhoon and the offshore wind farm, the whole process of the offshore wind farm encountering a typhoon event can be divided into three stages, namely, the approach stage , transit stage and the departure stage ;in, 、 、 as well as They are the time when the typhoon hits, the time when offshore wind power is shut down, the time when offshore wind power resumes power generation, and the time when the typhoon impact disappears; During the typhoon's approach phase, the offshore wind farm's active power output is directly calculated based on the wind speed-power model. During the typhoon's transit phase, the wind speed at the offshore wind farm exceeds the wind turbine's cut-out wind speed, and the wind speed-power model can be used to derive the offshore wind farm's active power output. At the same time, if the wind turbine is within the typhoon's critical damage radius, its damage probability is described by a probability distribution function. During the departure phase, the typhoon's center gradually moves away, and undamaged wind turbines resume power generation. Their active power output is calculated based on the wind speed-power model. Finally, the wind power output vectors of the three phases are integrated to obtain the complete output vector of the offshore wind farm in the typhoon scenario. Based on typhoon scenarios and offshore wind power output scenarios, a scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system is established, taking into account the coordinated operation of the transmission grid, distribution grid, and natural gas grid. Based on the hybrid sample average approximation, the scenario-based three-layer coordinated scheduling model of the transmission-distribution-gas integrated energy system is transformed into a deterministic continuous linear model. The deterministic continuous linear model is solved using an efficient distributed collaborative algorithm based on multi-parameter programming theory and a critical region accelerated search algorithm based on global optimal solution estimation to obtain the global optimal solution for the distributed scheduling of the integrated energy system under typhoon scenarios. Based on the hybrid sample average approximation, the scenario-based transmission-distribution-gas integrated energy system three-layer coordinated scheduling model is transformed into a solvable model, including: performing solvability transformation on the opportunity constraints containing random variables, based on each generated scenario, using the sample average approximation to describe the opportunity constraints containing random variables, setting a binary indicator variable, if the internal constraint is established in a certain scenario, the binary indicator variable takes the value of 1, otherwise it takes the value of 0, and relaxing the binary indicator variable to be in the interval The continuous variables of the transmission-distribution-gas integrated energy system are transformed into a three-layer coordinated scheduling model based on the scenario after solvability transformation, which is expressed as a linear objective function and a set of linear constraints. An efficient distributed collaborative algorithm based on multi-parameter programming theory and a critical domain accelerated search algorithm based on global optimal solution estimation are used to solve the scenario-based three-layer collaborative scheduling model of the transmission-distribution-gas integrated energy system after solvability transformation. A compact model is constructed, and the scenario-based three-layer collaborative scheduling model of the transmission-distribution-gas integrated energy system after solvability transformation is expressed as a linear objective function and a set of linear constraints. The model is divided into two solution levels: the transmission level and the distribution level. The transmission level is an iterative solution between the transmission network and the distribution network, and the distribution level is an iterative solution between the distribution network and the local natural gas network. The iterative solution of the transmission level must be carried out after the distribution level converges. Calculate the critical domain and local objective function. The calculation method of the critical domain and local objective function at the transmission level is consistent with that at the distribution level. If the iterative solution does not converge to the global optimal solution of the subproblem, the obtained local optimal solution and optimal target value are used as the data set and the optimal target value respectively. The global objective function is fitted using the data set, and the estimated value of the global optimal solution is calculated based on the fitted value. Set the step vector and search the next critical domain based on the step vector until the subproblem is infeasible or the local optimal value begins to increase. Return to the last feasible critical domain and switch to a one-by-one search method to find the global optimal solution.

2. The distributed scheduling method for an integrated energy system taking into account typhoon uncertainty according to claim 1, characterized in that: Considering the impact of typhoon uncertainty on offshore wind power, the Holland vortex model is used to model the typhoon wind farm and the uncertainty of typhoon parameters. When the probability distribution satisfied by the typhoon's uncertain parameters is determined, a typhoon scenario is generated by Monte Carlo simulation. For any offshore wind farm, a wind speed-power active output model is constructed under a specific typhoon scenario. According to the relative distance between the typhoon and the offshore wind farm, the whole process of the offshore wind farm encountering a typhoon event is divided into three stages, namely, the typhoon approach stage, the typhoon transit stage and the typhoon departure stage. The wind power output vectors of the three stages are integrated to obtain the complete output of the offshore wind farm under the typhoon scenario.

3. The distributed scheduling method for an integrated energy system taking into account typhoon uncertainty according to claim 1, characterized in that: A scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system is established. The scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system includes a transmission network side coordinated scheduling model, a distribution network side coordinated scheduling model, and a natural gas network side coordinated scheduling model; wherein, the objective function of the transmission network side coordinated scheduling model is to minimize the transmission network side operating cost, the objective function of the distribution network side coordinated scheduling model is to minimize the distribution network side operating cost, and the objective function of the natural gas network side coordinated scheduling model is to minimize the natural gas network side operating cost, and the constraints of each coordinated scheduling model are set respectively.

4. A distributed dispatching system for an integrated energy system taking into account typhoon uncertainty, characterized by: include: The scenario generation module is used to consider the impact of typhoon uncertainty on offshore wind power, build an offshore wind power operation model, and use the Monte Carlo simulation method to sample and generate typhoon scenarios and offshore wind power output scenarios based on the offshore wind power operation model. According to the relative distance between the typhoon and the offshore wind farm, the whole process of the offshore wind farm encountering a typhoon event can be divided into three stages, namely, the approach stage , transit stage and the departure stage ;in, 、 、 as well as They are the time when the typhoon hits, the time when offshore wind power is shut down, the time when offshore wind power resumes power generation, and the time when the typhoon impact disappears; During the typhoon's approach phase, the offshore wind farm's active power output is directly calculated based on the wind speed-power model. During the typhoon's transit phase, the wind speed at the offshore wind farm exceeds the wind turbine's cut-out wind speed, and the wind speed-power model can be used to derive the offshore wind farm's active power output. At the same time, if the wind turbine is within the typhoon's critical damage radius, its damage probability is described by a probability distribution function. During the departure phase, the typhoon's center gradually moves away, and undamaged wind turbines resume power generation. Their active power output is calculated based on the wind speed-power model. Finally, the wind power output vectors of the three phases are integrated to obtain the complete output vector of the offshore wind farm in the typhoon scenario. A model building module is used to establish a scenario-based three-layer coordinated scheduling model for the transmission-distribution-gas integrated energy system based on typhoon scenarios and offshore wind power output scenarios, taking into account the coordinated operation of the transmission grid, distribution grid, and natural gas grid. The scheduling solution module is used to transform the scenario-based three-layer coordinated scheduling model of the transmission-distribution-gas integrated energy system into a deterministic continuous linear model based on the hybrid sample average approximation. The model is then solved using an efficient distributed collaborative algorithm based on multi-parameter programming theory and a critical region accelerated search algorithm based on global optimal solution estimation to obtain the global optimal solution for the distributed scheduling of the integrated energy system in typhoon scenarios. Based on the hybrid sample average approximation, the scenario-based transmission-distribution-gas integrated energy system three-layer coordinated scheduling model is transformed into a solvable model, including: performing solvability transformation on the opportunity constraints containing random variables, based on each generated scenario, using the sample average approximation to describe the opportunity constraints containing random variables, setting a binary indicator variable, if the internal constraint is established in a certain scenario, the binary indicator variable takes the value of 1, otherwise it takes the value of 0, and relaxing the binary indicator variable to be in the interval The continuous variables of the transmission-distribution-gas integrated energy system are transformed into a three-layer coordinated scheduling model based on the scenario after solvability transformation, which is expressed as a linear objective function and a set of linear constraints. An efficient distributed collaborative algorithm based on multi-parameter programming theory and a critical domain accelerated search algorithm based on global optimal solution estimation are used to solve the scenario-based three-layer collaborative scheduling model of the transmission-distribution-gas integrated energy system after solvability transformation. A compact model is constructed, and the scenario-based three-layer collaborative scheduling model of the transmission-distribution-gas integrated energy system after solvability transformation is expressed as a linear objective function and a set of linear constraints. The model is divided into two solution levels: the transmission level and the distribution level. The transmission level is an iterative solution between the transmission network and the distribution network, and the distribution level is an iterative solution between the distribution network and the local natural gas network. The iterative solution of the transmission level must be carried out after the distribution level converges. Calculate the critical domain and local objective function. The calculation method of the critical domain and local objective function at the transmission level is consistent with that at the distribution level. If the iterative solution does not converge to the global optimal solution of the subproblem, the obtained local optimal solution and optimal target value are used as the data set and the optimal target value respectively. The global objective function is fitted using the data set, and the estimated value of the global optimal solution is calculated based on the fitted value. Set the step vector and search the next critical domain based on the step vector until the subproblem is infeasible or the local optimal value begins to increase. Return to the last feasible critical domain and switch to a one-by-one search method to find the global optimal solution.

5. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the distributed scheduling method for an integrated energy system taking into account typhoon uncertainty as described in any one of claims 1 to 3 is implemented.

6. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the distributed scheduling method of the integrated energy system taking into account typhoon uncertainty as described in any one of claims 1 to 3 is implemented.

7. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the distributed scheduling method for an integrated energy system taking into account typhoon uncertainty as described in any one of claims 1 to 3.

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