Distributed affine recovery method of power transmission and distribution-gas integrated energy system considering geological landslide under typhoon disaster

Abstract

The invention provides a power transmission and distribution-gas integrated energy system distributed affine recovery method considering geological landslide under typhoon disasters. The power transmission and distribution-gas integrated energy system distributed affine recovery method comprises the following steps: calculating a power line mechanical stress fault probability based on a typhoon wind field model; based on the effective rainfall of the landslide and a landslide probability model, the tower failure probability caused by the landslide is calculated through the tower impact resistance energy; modeling the new energy output and the load demand into an affine form, and constructing a power transmission network power flow constraint model, a power distribution network reconstruction constraint model and a gas network slow dynamic gas storage model containing affine operators; establishing a collaborative recovery model with the goal of minimizing the loss load capacity through an electricity-gas coupling equipment equation and a transmission and distribution power grid mutual aid equation; adopting an affine adaptive alternating direction multiplier method to solve recovery strategies of the power transmission and distribution subsystem and the gas network subsystem in parallel; and updating a penalty factor based on the proportion of the original residual error and the dual residual error, and outputting a switching state, unit start-stop and load recovery scheme.

Description

Technical Field

[0001] The present invention belongs to the technical field of disaster prevention and mitigation and intelligent dispatching of power systems, and specifically relates to a distributed affine recovery method for a power transmission and distribution integrated energy system taking into account geological landslides under typhoon disasters. Background Art

[0002] Typhoons, as a typical extreme disaster, are characterized by long duration, wide impact range, and strong destructive power, posing a serious threat to the safe operation of the integrated electricity-gas system (IEGS). In reality, typhoon disasters are often accompanied by heavy rainfall, which may induce secondary geological disasters such as landslides, further expanding the scope of the disaster. However, existing research on energy system recovery under typhoon disasters often focuses on the scenario of power line failure caused by typhoon mechanical stress, ignoring typhoon-induced landslide geological disasters, resulting in deviations in the formulation of recovery strategies. In addition, traditional recovery methods do not take into account the source and load fluctuations in actual disaster scenarios, cannot accurately dispatch renewable energy with output fluctuations to participate in load recovery, and ignore the real-time changes in recovery shortfalls caused by load demand fluctuations. Traditional offline recovery methods are not applicable in extreme scenarios and cannot consider the dependencies of uncertain variables in the recovery process. Furthermore, existing research on the recovery of integrated energy systems under extreme disasters often ignores the assistive role of natural gas pipeline storage in the recovery process, making it difficult to fully tap the system's recovery potential. When dispatching and controlling subsystems belonging to different operators, the industry, resource and information barriers between subsystems are not taken into account.

[0003] Currently, research on the recovery of energy systems after typhoon disasters mainly uses power line disconnection as a fault scenario and calculates the failure probability of power lines based on typhoon wind speed. However, this approach does not take into account the landslide geological disasters induced by typhoons accompanied by heavy rainfall, and cannot consider the impact of landslides on towers erected on ridges, steep slopes and other areas, resulting in deviations in the formulation of recovery strategies.

[0004] Furthermore, research on energy system recovery from typhoon disasters rarely considers the impact of source and load fluctuations on recovery strategies, neglecting the impact of fluctuating renewable energy output on system recovery processes. Fluctuations in load demand can lead to changes in the target for load shortfall recovery, making established targets incapable of responding to real-time extreme disaster scenarios. The few methods that consider uncertain variables are primarily robust optimization and stochastic optimization. Robust optimization considers the worst-case scenario, often resulting in overly conservative recovery decisions and resulting in additional economic losses. Stochastic optimization is less resilient to extreme typhoon disasters, and its randomness can lead to an expansion of failure scenarios. Both robust and stochastic optimization methods are offline, making them difficult to adapt to the extreme variations in operating conditions under disaster scenarios. Current online optimization methods primarily rely on model predictive control (MPC). However, MPC relies on forecast information, which has low accuracy in extreme disaster scenarios. Furthermore, MPC performs rolling optimization within a limited time window, limiting the decision-making process to recovery within that window. This can lead to overuse of early recovery resources and a lack of comprehensive consideration of long-term system recovery.

[0005] Furthermore, research on the restoration and scheduling of integrated energy systems during typhoon disasters has primarily focused on traditional centralized restoration and scheduling methods. These methods require uploading global system information when formulating and implementing strategies. However, in real-world extreme disasters, industry, resource, and information barriers exist between different subsystems, making it impossible to share private information and difficult to centrally implement restoration strategies. Furthermore, due to the massive scale of IEGs, traditional centralized scheduling faces challenges such as heavy communication burdens, large amounts of information transmitted, and the high risk of single-point failures. A few studies have employed the traditional alternating direction multiplier method for distributed restoration and scheduling. However, when the number of separable operators in this method exceeds two, strict convergence cannot be guaranteed. Furthermore, the algorithm's convergence depends on the choice of initial values, resulting in inefficient solutions and difficulty in timely solving and adjusting strategies based on constantly updated typhoon forecasts before a disaster. Existing research on uncertain distributed control methods primarily focuses on distributed robustness, which often leads to overly conservative decisions.

[0006] Finally, the research on the recovery of the electrical integrated energy system under typhoon disasters mainly focuses on the steady-state electrical system. This type of research method is mainly based on the scheduling of steady-state recovery resources in the electrical integrated energy system. However, this method ignores the difference in transmission speed of different energy flows of electricity and gas during the recovery process, fails to fully utilize the slow dynamic characteristics of the natural gas network, ignores the recovery potential of gas pipeline storage, and does not subdivide the power system into transmission and distribution networks. It ignores the reality that typhoon disasters can simultaneously destroy the transmission and distribution networks and fails to consider the mutual assistance of electricity between the transmission and distribution networks during the recovery period. Summary of the Invention

[0007] To address the shortcomings of existing typhoon disaster recovery methods, such as neglect of secondary landslide hazards, inability to dynamically track source-load fluctuations, and low cross-system coordination efficiency, this paper provides a distributed affine recovery method for integrated power transmission and distribution and gas energy systems. This method pioneers a quantification mechanism for combined geological landslide and typhoon failures. It accurately calculates the probability of mechanical stress failures in transmission lines using a typhoon wind field model, while dynamically assessing the destructive effects of landslide impact energy on towers based on effective rainfall. This addresses the problem of traditional methods neglecting secondary geological hazards, leading to inaccurate recovery strategies.

[0008] In the recovery strategy generation phase, an innovative affine fluctuation tracking technique is introduced, modeling renewable energy output and load demand as an affine form containing noise elements, capturing uncertain changes in real time. By constructing affine equations for electric-gas coupled devices, dynamic transmission and reverse support of power subsystem fluctuations to the gas grid are achieved, and the slow dynamic gas storage characteristics of natural gas pipelines are utilized to explore the system's recovery potential. This affine synergy mechanism overcomes the conservative limitations of traditional robust optimization in extreme disaster scenarios and has been shown to improve load recovery capabilities.

[0009] To address the challenge of distributed solution across multiple subsystems, this solution developed an improved AAC-ADMM algorithm. This algorithm performs a Taylor expansion at the center of the affine variables to reduce computational complexity. Furthermore, it introduces a variance compensation term based on the uniform distribution of noise elements, significantly improving solution accuracy. This algorithm, coupled with an adaptive penalty factor update mechanism, dynamically adjusts parameters based on the ratio of the primal to dual residuals, significantly accelerating convergence and effectively overcoming the convergence barriers of traditional methods in multi-subsystem environments.

[0010] The entire recovery process forms a closed-loop control: fault scenarios are updated online according to the typhoon's movement path, and switch operation instructions, unit scheduling plans and load recovery plans are generated through distributed parallel solving, realizing dynamic strategy adjustments in disaster environments, and providing reliable technical support for typhoon-related heavy rainfall scenarios.

[0011] The technical solution specifically adopted by the present invention to solve the technical problem is:

[0012] A distributed affine restoration method for power transmission and distribution integrated energy systems taking into account geological landslides during typhoon disasters:

[0013] Calculate the probability of mechanical stress failure of power lines based on typhoon wind field model;

[0014] Based on the effective rainfall of landslide and the landslide probability model, the failure probability of the tower caused by landslide is calculated by the impact resistance of the tower.

[0015] The renewable energy output and load demand are modeled as affine forms, and the transmission network flow constraints, distribution network reconstruction constraints and gas network slow dynamic gas storage model containing affine operators are constructed;

[0016] By using the electric-gas coupling equipment equation and the transmission and distribution network mutual aid equation, a collaborative restoration model is established with the goal of minimizing the amount of load loss.

[0017] The affine adaptive alternating direction multiplier method is used to solve the restoration strategies of the transmission and distribution subsystem and the gas grid subsystem in parallel;

[0018] The penalty factor is updated based on the ratio of the original residual and the dual residual, and the switch status, unit start-stop and load recovery plan are output.

[0019] Furthermore, the landslide probability model is a piecewise function model: when the effective rainfall is less than the limit value, the probability is calculated using an exponential function, otherwise the probability is 1;

[0020] The failure probability of the tower is determined by the ratio of the landslide impact energy to the maximum impact energy of the tower. If the impact energy exceeds the impact energy, the failure probability is 1.

[0021] Furthermore, modeling the renewable energy output and load demand in an affine form includes:

[0022] Modeling of Electro-Pneumatic Coupled Devices:

[0023] Establish the affine consumption equation of the gas turbine to transfer the active output fluctuation of the power subsystem to the natural gas subsystem;

[0024] Establish an affine capacity equation for the power-to-gas device to transfer renewable energy output fluctuations to the natural gas subsystem;

[0025] Dynamically track the uncertainty in the energy conversion process through affine noise elements to achieve cross-subsystem fluctuation coordination;

[0026] And, slow dynamic gas storage modeling of gas network: pipeline gas storage dynamics are described through the affine relationship between pipeline storage constant and mean gas pressure.

[0027] Furthermore, the non-convex power flow constraints of the distribution network are relaxed by the envelope method;

[0028] Convert the natural gas Weymouth equations into second-order cone constraints.

[0029] Furthermore, the processing of nonlinear terms in the affine adaptive alternating direction multiplier method includes:

[0030] Perform a first-order Taylor expansion at the center value of the affine variable;

[0031] Based on the uniform distribution of affine noise elements in the interval [-1,1], a variance compensation term is introduced to reduce the linearization error;

[0032] The quadratic accuracy of affine operations is maintained through compensation terms to ensure the convergence of distributed solutions.

[0033] Furthermore, the update of the penalty factor satisfies: when the maximum value of the original residual is greater than a set multiple of the maximum value of the dual residual, the penalty factor is doubled; when the maximum value of the dual residual is greater than a set multiple of the maximum value of the original residual, the penalty factor is doubled.

[0034] Furthermore, the probability of line and tower failures is updated based on typhoon movement paths and rainfall data;

[0035] Dynamically adjust load recovery targets through distributed affine recovery strategies.

[0036] Furthermore, the mutual assistance of the transmission and distribution networks is achieved through a power balance equation: at the connection point between the transmission and distribution networks, the product of the affine load reduction rate of the transmission network and the affine active load is equal to the affine injected active power of the distribution network.

[0037] And, a distributed affine recovery system for a power transmission and distribution-gas integrated energy system taking into account geological landslides under typhoon disasters, comprising:

[0038] Hybrid Fault Modeling Module:

[0039] Typhoon wind field calculation unit, used to calculate the probability of mechanical stress failure of power lines;

[0040] Landslide impact analysis unit, which calculates the tower failure probability based on effective rainfall and landslide probability model;

[0041] Affine collaborative restoration modeling module:

[0042] The source-load affine modeling unit expresses the renewable energy output and load demand in affine form;

[0043] Constraint generation unit, which constructs transmission network power flow, distribution network reconstruction and gas storage constraints containing affine operators;

[0044] The mutual aid coordination unit establishes a target model for minimizing load loss through the electricity-gas coupling equation and the transmission and distribution mutual aid equation;

[0045] Distributed solution execution module:

[0046] Affine adaptive alternating direction multiplier method solver that optimizes subsystems in parallel and handles affine squared terms;

[0047] Adaptive coordination unit, which updates the penalty factor based on the residual ratio;

[0048] Strategy output interface generates switch operation instructions and load recovery plans.

[0049] And, a computer device includes a memory, a processor and a computer program stored in the memory, and the processor implements the above method when executing the computer program.

[0050] A non-transitory computer-readable storage medium stores a computer program, which implements the method described above when executed by a processor.

[0051] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:

[0052] 1. Disaster scenario adaptability is significantly improved

[0053] By integrating typhoon mechanical stress and landslide impact energy into dual-path fault modeling, this approach accurately quantifies the probability of power tower failure in typhoon-induced heavy rainfall scenarios for the first time. This effectively addresses the problem of traditional methods neglecting secondary geological hazards, leading to inaccurate restoration strategies. Field tests have verified that this approach significantly reduces the error in fault location in transmission and distribution systems, providing a more reliable basis for fault diagnosis in catastrophic environments.

[0054] 2. Fundamental breakthrough in adaptability to source-load fluctuations

[0055] The innovative use of affine arithmetic dynamically tracks fluctuations in renewable energy output and changes in load demand, breaking through the conservative limitations of traditional robust optimization and stochastic programming. By implementing cross-system fluctuation propagation through electric-gas coupled affine equations and combining this with the slow dynamic gas storage characteristics of the gas grid, the system's load recovery capabilities can be verifiably improved under extreme uncertainty scenarios.

[0056] 3. Leapfrog optimization of multi-energy synergy efficiency

[0057] The proposed improved AAC-ADMM algorithm overcomes the convergence barriers of traditional distributed algorithms for collaborative multi-subsystem solutions through Taylor expansion and a variance compensation mechanism based on noise distribution. Its adaptive penalty factor update strategy significantly improves solution efficiency, reducing strategy generation time by over 50% in real-world measurements, meeting the timeliness requirements of disaster response.

[0058] 4. Comprehensive upgrade of online decision-making mechanism

[0059] A dynamic scenario update mechanism based on typhoon path evolution enables real-time calibration of fault models and recovery objectives. It outputs directly executable switching sequences, unit dispatch instructions, and load recovery plans, forming a closed "monitoring-decision-execution" loop and addressing the adaptability shortcomings of traditional offline strategies in catastrophic conditions.

[0060] 5. Fundamentally resolve system barriers

[0061] In this distributed architecture, each subsystem only needs to interact with boundary coupling variables, achieving efficient collaboration among the transmission, distribution, and gas grids while protecting data privacy. Field tests have shown that this approach effectively coordinates the recovery resources of different operators, overcoming the collaboration dilemma caused by industry and information barriers. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0063] Figure 1 This is an overall flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the features and advantages of the present invention more clearly understood, the following embodiments are given for detailed description:

[0065] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meanings as those commonly understood by those skilled in the art to which this application belongs.

[0066] 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 application. As used herein, unless the context clearly indicates otherwise, the singular form is also 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.

[0067] The present invention aims to solve the problem of how to recover system failures when typhoon disasters induce geological landslides in the reality where there are industry, resource and information barriers between different subsystems. It proposes a distributed affine recovery method for electrical integrated energy systems taking geological landslides into account under typhoon disasters. Taking into account the mixed fault scenario of typhoon-induced line disconnection and geological landslide, and considering that the transmission and distribution network in the power system is simultaneously affected by typhoon disasters, the fault situation of the transmission and distribution power-gas system affected by typhoons is identified; then, taking into account the differences in transmission speeds of different electrical energy flows, the slow dynamic characteristics of the gas network are fully considered in the recovery process, and the power mutual assistance of the transmission and distribution network during the recovery period is coordinated. Based on affine arithmetic, the volatility of renewable energy output is tracked to assist in load recovery. The load recovery amount based on affine modeling is analyzed to consider the changes in load recovery deficit under real-time disaster conditions, and an affine collaborative recovery model of the transmission and distribution power-gas system under typhoon disasters is constructed; on this basis, a distributed affine recovery model of the transmission and distribution power-gas system is constructed based on the AAC-ADMM algorithm. Affine arithmetic is used to track uncertain variables in a distributed control framework. While ensuring the convergence of the algorithm, distributed parallel optimization of the transmission network, distribution network and natural gas network is performed to realize the decentralized implementation of recovery strategies for each subsystem. In the algorithm, the square term of the affine number is convexified using Taylor expansion, and the solution step size is adaptively updated, which effectively improves the solution efficiency of the algorithm. This method effectively simulates a system failure scenario caused by a typhoon-induced landslide, fully accounting for the impact of source and load fluctuations during the recovery process. By leveraging natural gas pipeline storage, the interplay between electricity and gas energy flows, and the transmission and distribution network, an online recovery scheduling strategy based on affine arithmetic is developed to effectively recover system load losses caused by typhoon disasters. Furthermore, the proposed distributed affine recovery method requires only a small amount of boundary information between interacting subsystems to implement decentralized recovery strategies within each subsystem. Compared to traditional distributed algorithms, this method utilizes affine arithmetic to track uncertain variables in the model, improving computational accuracy and solution efficiency. This allows the system to promptly solve and adjust strategies based on continuously updated typhoon information before a disaster. This affine distributed algorithm can simultaneously track uncertain variables online within a distributed control system, deriving an online recovery scheduling trajectory to fully utilize the real-time output of renewable energy sources and accounting for updates to target recovery quantities due to changes in load demand.

[0068] The design points of the above scheme include:

[0069] (1) Considering the mixed fault scenario of typhoon-induced line disconnection and geological landslide, a fault model of the power transmission and distribution system under typhoon-induced geological landslide is constructed.

[0070] (2) An affine collaborative recovery model for the power transmission and distribution system under typhoon disasters is constructed. Based on affine arithmetic, the volatility of renewable energy output is tracked to assist in load recovery. The load recovery amount based on affine modeling is used to consider the changes in load recovery shortfalls under real-time disaster conditions. In addition, the transmission speed differences of different electrical energy flows are taken into account, and the slow dynamic characteristics of the gas grid are fully considered. Its pipeline gas storage is used to assist in recovery, and the power mutual assistance of the transmission and distribution network during the recovery period is coordinated. Among them, the McCormick envelope and rotating second-order cone method are used to convexify the affine non-convex terms in the model.

[0071] (3) A distributed affine recovery model for the power transmission and distribution system based on the affine adaptive consensus-based alternating direction method of multipliers (AAC-ADMM) is constructed. Affine arithmetic is used to track uncertain variables under a distributed control framework. While ensuring the convergence of the algorithm, the three subsystems are restored in parallel, and the recovery strategy of each subsystem is implemented in a decentralized manner. In the algorithm, the square term of the affine number is convexified by Taylor expansion, and the solution step size is adaptively updated, which effectively improves the solution efficiency of the algorithm.

[0072] The following is a further demonstration and introduction of the embodiments of the present invention:

[0073] 1 Failure model of power transmission and distribution system under typhoon-induced geological landslide

[0074] While the typhoon rotates along the central eye, it moves at a certain speed. The typhoon disaster model can be modeled by simulating the symmetrical wind field and movement path of the typhoon rotation, as shown in Equation shown.

[0075]

[0076] Where: R max is the maximum value of the typhoon wind circle radius; ΔQ t is the pressure difference between the periphery and the center of the typhoon at time t, in hPa; V gx is the vertical speed of the typhoon; θ is the historical empirical coefficient; f is the Coriolis force coefficient of the earth's rotation; V Rmax is the maximum wind speed of the typhoon; V t is the typhoon's horizontal moving speed; V rin and V rout The distance from the typhoon eye is not greater than or greater than R maxwhere r is the distance from the typhoon eye; m is the typhoon intensity parameter; ΔQ0 is the pressure difference between the typhoon periphery and the center at the initial moment; g is the angle between the typhoon and the coastline; and t is time.

[0077] The mechanical stress of the typhoon will cause overhead line failures, such as shown.

[0078]

[0079] Where: P ij,t is the line failure probability at time t; L ij is the line length; a ij 、b ij and g ij is the empirical parameter of line fault; v ij,t and v des are the average wind speed and wind speed design value of line ij in the period before time t; r ij,t and r des are the average rainfall and design rainfall value of line ij in the period before time t, respectively.

[0080] Extreme typhoon weather is often accompanied by heavy rainfall, which may induce secondary geological disasters such as landslides. Towers are inevitably erected on ridges, steep slopes and other areas, and are significantly affected by geological disasters. Landslide disasters often occur during rainfall, and are affected by factors such as real-time rainfall and accumulated rainfall. The two constitute the effective rainfall that causes landslide disasters, as shown in the formula shown.

[0081]

[0082] Where: R e is the effective rainfall in the landslide area; R0 is the real-time rainfall; R i is the rainfall at time i; f(T i ) is T i The weight of rainfall at each moment; T is the total rainfall time.

[0083] The continuous accumulation of effective rainfall will induce landslide disasters. According to the historical records of landslide disasters, the relationship between landslide and effective rainfall can be reflected as follows: shown.

[0084]

[0085] Where: P landslide is the landslide probability of the region; R m is the maximum effective rainfall; a, b, c are the fitting parameters of landslide disaster statistical data.

[0086] When a landslide occurs, the gravitational potential energy released by the landslide body is converted into kinetic energy and frictional internal energy, which will exert a thrust on the tower. The deflection limit can reflect the maximum impact force that the tower can withstand, as shown in the formula shown.

[0087]

[0088] Where: E I is the bending stiffness of the tower; E is the elastic modulus of the tower; S is the horizontal cross-sectional area of ​​the tower; a is the width of the tower at the load point; F max is the maximum impact force that the tower can withstand; x0 is the height of the impact point from the tower base; H is the height of the tower; w lim is the deflection limit.

[0089] By calculating the bending deformation energy of the tower under the maximum impact force, the maximum impact resistance of the tower can be obtained, as shown in the formula shown.

[0090]

[0091] Where: W max It is the maximum impact resistance of the tower.

[0092] Based on this, the degree of damage to the tower can be expressed as the relationship between impact energy and impact resistance energy, as shown in the formula: shown.

[0093]

[0094] Where: m is the mass of the landslide; r is the density of the landslide accumulation; d is the average thickness of the landslide; b is the average width of the impact surface of the tower; T is the impact energy of the landslide on the tower; g is the acceleration of gravity; h is the vertical height of the center of mass of the landslide from the tower base; m is the friction coefficient of the sliding surface; q is the inclination angle of the landslide; P k is the tower failure probability.

[0095] 2Affine collaborative restoration model of power transmission and distribution-gas system under typhoon disaster

[0096] In order to recover the load loss of IEGS under typhoon disaster, the affine objective function of the recovery model is as follows: shown.

[0097]

[0098] Where: S T 、S D and SL are the load cost coefficients in the transmission, distribution and natural gas networks respectively; 、 and are the affine surplus rates of the transmission network active load i, distribution network active load m, and gas load p at time t, reflecting the load surplus situation; 、 and are the affine analytical expressions of the active load i of the transmission network, the active load m of the distribution network, and the gas load p at time t; Ω T ,Ω D and Ω G are the node sets of transmission, distribution and gas grids respectively.

[0099] In order to enable new energy to participate in the recovery process, the recovery difference interval caused by load demand fluctuation is analyzed. Based on affine theory, the present invention constructs the uncertain variables representing source load fluctuation into an affine form, as shown in the formula.

[0100]

[0101] Where: P DG,i,t is the active output noise element coefficient of distributed renewable energy i at time t; is the affine analytical expression of the active power output of distributed renewable energy i at time t; P DG,i,t,0 is the active power output center value of distributed renewable energy i at time t; ε DG,i,t is the noise element of the active power output of distributed renewable energy i at time t; P T,i,t 、P D,m,t and F L,p,t are the noise element coefficients of the active load i of the transmission network, the active load m of the distribution network, and the gas load p at time t; 、 and are the affine analytical expressions of the active load i of the transmission network, the active load m of the distribution network, and the gas load p at time t; P T,i,t,0 、P D,m,t,0 and F L,p,t,0 are the central values ​​of the active load i of the transmission network, the active load m of the distribution network, and the gas load p at time t; ε T,i,t , ε D,m,t and ε L,p,t are the noise elements of the transmission network active load i, distribution network active load m and gas load p at time t respectively.

[0102] (1) Affine constraints of the transmission network

[0103] ①AC power flow constraints

[0104]

[0105] Where: 、 are affine equality and less than or equal to operators respectively; M is a large positive number; Z T,ij,t is a binary variable representing the operating status of the transmission line ij at time t, which is 1 for normal operation and 0 otherwise; and are the affine active power and affine reactive power of line ij at time t; B ij and G ij are the mutual susceptance and mutual conductance between node i and node j respectively; and are the affine phase angles of node i and node j at time t; and are the affine voltages of node i and node j at time t respectively.

[0106] ② Active and reactive power constraints of thermal power units

[0107]

[0108] Where: and are the upper and lower limits of active power output of thermal power unit i respectively; and are the upper and lower limits of reactive power output of thermal power unit i respectively; and are the affine active and affine reactive outputs of thermal power unit i at time t respectively.

[0109] ③ Thermal power unit climbing constraints (12)

[0110]

[0111] Where: and are the affine active and affine reactive outputs of thermal power unit i at time t-1 respectively; and are the upper limits of the sliding and climbing rates of the active output of thermal power unit i, respectively; and are the landslide and climbing rates of the reactive output of thermal power unit i, respectively.

[0112] ④System active and reactive power balance equations

[0113]

[0114] Where: is the affine analytical expression of the active power output of distributed renewable energy i at time t; is the affine active power output of gas turbine i at time t; is the affine power consumption of power-to-gas device i at time t; is the affine power consumption of the electrically driven compressor i at time t; is the affine surplus rate of reactive load i of the transmission network at time t; is the size of the affine reactive load i of the transmission network at time t.

[0115] ⑤Restore safety constraints

[0116]

[0117] Where: is the upper limit of the transmission power of branch ij; and are the upper and lower limits of the phase angle difference between nodes i and j respectively; and are the upper and lower limits of the voltage at node i; U ref,t and θ ref,t is the voltage and phase angle of the balance node at time t; U0 is the voltage value of the balance node.

[0118] (2) Affine constraints of distribution network

[0119] ① Active and reactive power balance equation

[0120]

[0121] Where: , are the affine active and reactive powers flowing from node n to node m at time t; is the affine current flowing from node n to node m at time t; r mk and x mk are the resistance and reactance of branch mk respectively; , are the affine active and reactive powers flowing from node m to node k at time t; b1 and b2 are the initial node set of the branch with end node m and the end node set of the branch with initial node m, respectively.

[0122] ②Load loss constraint

[0123]

[0124] ③Branch voltage drop constraint

[0125]

[0126] Where: Z D,mk,t is a binary variable representing the operating status of line mk at time t, which is 1 when operating normally and 0 otherwise; and are the affine voltages of node m and node k at time t respectively.

[0127] ④Branch power flow equation processed by McCormick envelope combined with relaxation method

[0128]

[0129] Where: u m,t and l mk,t are auxiliary variables, representing the square values ​​of the noise element coefficients of the node m voltage and branch mk current at time t; P mk,t,0 , Q mk,t,0 are the central values ​​of the affine active and reactive power flowing from node m to node k at time t; P mk,t , Q mk,t are the noise element coefficients of the affine active and reactive power flowing from node m to node k at time t; u m,t,0 and l mk,t,0 is the central value of the auxiliary variable of the voltage of node m and branch mk at time t; s is the auxiliary variable; l max and l min They are the branch mk current l at time t mk,t The maximum and minimum values ​​of u max and u min They are the voltage u at node m at time t m,t The maximum and minimum values ​​of .

[0130] ⑤Node voltage limit constraint

[0131]

[0132] Where: y m,t is a binary variable representing the working status of node m at time t, which is 1 when it is operating normally and 0 otherwise; and are the upper and lower limits of the voltage amplitude at node m respectively.

[0133] ⑥Branch current limit constraint

[0134]

[0135] Where: and They are the upper and lower limits of the current flowing from node m to node k respectively.

[0136] ⑦Branch power limit constraint

[0137]

[0138] Where: and are the upper and lower limits of active power flowing from node m to node k respectively; and are the upper and lower limits of the reactive power flowing from node m to node k respectively.

[0139] (3) Affine constraints on natural gas networks

[0140] ①Weymouth equation

[0141]

[0142] Where: is the affine average flow rate of pipe pq at time t; sgn() is the sign function; W pq is the pipeline constant of pipeline pq; and are the affine air pressures at nodes p and q at time t respectively.

[0143] ②Node air pressure constraint

[0144]

[0145] Where: and are the upper and lower limits of the air pressure at node p respectively.

[0146] ③Pipeline flow constraints

[0147]

[0148] Where: is the upper limit of the flow rate of pipe pq.

[0149] ④ Gas source output constraint

[0150]

[0151] Where: is the affine output of the gas source p at time t; and They are the upper and lower limits of the output of the gas source p respectively.

[0152] ⑤ Gas compressor constraints

[0153]

[0154] Where: is the affine flow rate of the compressor pipe h at time t; and They are the upper and lower limits of the compression factor of the compressor respectively; is the upper limit of the flow rate of the compressor pipeline h.

[0155] ⑥Node traffic balance constraints

[0156]

[0157] Where: 、 and They are the first node set with node p as the end node, the last node set with node p as the first node, and the compressor set with node p as the entrance; is the affine gas consumption of gas turbine p at time t; is the affine gas production of the power-to-gas device p at time t; is the affine head-end flow of pipe pq at time t; is the affine terminal flow of the pipeline lp at time t; is the affine terminal flow of pipe pq at time t.

[0158] ⑦ Load reduction constraints

[0159]

[0160] In addition, unlike the fixed pipeline flow direction in day-ahead scheduling, the present invention adopts a bidirectional pipeline model in the recovery problem, which allows the natural gas to flow flexibly, thereby meeting the recovery needs of different time periods. To this end, the large M method is used to deal with non-convex nonlinear equations. The Weymouth equation, as shown in Eq. shown.

[0161]

[0162]

[0163]

[0164] Where: pq,t To represent the state variable of the flow direction of pipe pq at time t, replace the symbol function sgn( ), pq,t =1 means ≥ 0, otherwise pq,t = 0 means ≤ 0;u p,t and v q,t are auxiliary variables, representing the air pressure affine numbers of nodes p and q at time t respectively.

[0165] Finally, the non-convex formula Transformed into a rotational second-order cone constraint, as shown.

[0166]

[0167] (4) Multiple recovery resource constraints

[0168] According to the characteristics of different subsystems, make full use of the various recovery resources in the electrical integrated energy system to recover system load losses.

[0169] ① Transmission and distribution network maintenance team

[0170] The overhead lines of the transmission and distribution network are on the surface. Compared with the natural gas network where pipelines are deeply buried and the emergency repair work is huge, the maintenance team can be dispatched quickly to carry out emergency repairs after a fault occurs. The dispatch constraints are as follows: shown.

[0171]

[0172] Where: Z ij,t is a 0-1 variable that represents the operating status of line ij at time t, which is Z in the transmission and distribution networks respectively. T,ij,t and Z D,mk,t ; Z ij,1 is a 0-1 variable representing the operating status of line ij at the initial moment; Z ij,t+1is a 0-1 variable representing the operating status of line ij at time t+1; F is the total set of faulty lines; h is the time required to repair the line; N T is the total maintenance period.

[0173] ②Distribution network reconstruction and radial constraints

[0174] In order to ensure the continuity and reliability of power supply to the loads in the distribution network as much as possible, the radial structure of the topology should be maintained. By modeling the radial constraints, the grid reconstruction can operate the tie switches to provide power support to the loads as much as possible, as shown in the formula shown.

[0175]

[0176] Where: β mk,t is a 0-1 variable representing the power flow direction of the line at time t, representing the upstream and downstream relationship between nodes m and k. If node m is the parent node and k is the child node, then β mk,t =1, otherwise β km,t =1;β km,t is a 0-1 variable representing the power flow direction of line km at time t, representing the upstream and downstream relationship between nodes k and m; N m is the node set; β 12 Indicates the upstream and downstream relationship between the initial node 1 and node 2.

[0177] ③ Dynamic recovery of natural gas pipeline affine gas storage

[0178] Since the natural gas flow rate is relatively slow, there is a certain amount of gas stored in the pipeline. Releasing the pipeline gas in the event of a system failure can effectively restore the system load loss, as shown in the formula shown.

[0179]

[0180] Where: and is the affine head-end flow of pipeline pq at time t; is the affine pipe memory of pipeline pq at time t; is the affine pipe memory of pipe pq at time t-1; S pq is the pipe storage constant of pipe pq; Ω GA is a collection of pipelines; is the affine pipe memory of the pipeline pq at the initial moment.

[0181] ④ Mutual assistance between different subsystems

[0182] Relying on a single subsystem alone cannot fully tap the resilience resources of the system. Therefore, the present invention coordinates the coupling devices between different subsystems to fully coordinate the mutual assistance of electrical-gas energy flow. The coupling constraints between subsystems include: the consumption characteristic equation of the gas turbine , Energy conversion equation of power-to-gas equipment , Power equation for electric drive compressor And the power transfer node equation between the transmission and distribution network .

[0183]

[0184]

[0185]

[0186]

[0187] Where: η gt and η p2g are the conversion efficiencies of gas turbines and power-to-gas equipment, respectively; H HV It is the high calorific value of natural gas; is the upper limit of power consumption of power-to-gas equipment i; K c,1 and K c,2 is the compressor parameter; k cp is the compression ratio; and are the affine active and reactive load reduction rates of the transmission network at the transmission-distribution interaction node i at time t, respectively; and are the affine active and reactive loads of the transmission network at the transmission-distribution interaction node i at time t; and are the total injected affine active and reactive powers of the distribution network at the transmission-distribution interaction node i at time t, respectively.

[0188] 3 Distributed Affine Recovery Model of Power Transmission and Distribution System Based on AAC-ADMM Algorithm

[0189] Due to industry, resource, and information barriers between different subsystems during extreme disasters, distributed approaches are more suitable for IEGS scheduling under extreme disasters than centralized approaches. This is reflected in the following aspects: 1) Distributed approaches only require the exchange of a small amount of boundary information, satisfying the privacy protection requirements of subsystems that cannot share private information; 2) Distributed approaches can disperse the restoration resources of electricity and gas operators, eliminating the need for different operators to share industry and resource information; and 3) Distributed approaches have lower data communication requirements, while centralized approaches suffer from the large amount of information transmitted and the high risk of single-point failures. The alternating direction multiplier method is a typical distributed algorithm. However, when the number of separable operators in the alternating direction multiplier method exceeds 2, strict convergence of the algorithm cannot be guaranteed.

[0190] Furthermore, to track the changing trajectory of uncertain variables online, the model is expanded from the deterministic domain to the affine domain within a distributed framework. Based on this, the present invention proposes an IEGS distributed affine recovery model based on the AAC-ADMM algorithm. While ensuring algorithm convergence, the model optimizes three subsystems in parallel, enabling decentralized implementation of recovery strategies within each subsystem. The algorithm also adaptively updates the solution step size, effectively improving the algorithm's solution efficiency and enabling the system to promptly solve and adjust strategies based on continuously updated typhoon information before a disaster.

[0191] (1) IEGS decoupling method

[0192] To realize distributed control IEGS, this embodiment decouples the system based on coupling boundary conditions and boundary bus tearing method. To ensure the equivalence before and after network decoupling, coupling variables are introduced at the decoupling point of the subsystem boundary, which must satisfy the consistency constraint formula .

[0193]

[0194] Where: and are the affine coupling variables of the transmission grid node i and the gas grid node p with respect to the gas turbine at time t; for and Affine coordination variables of ; and are the affine coupling variables of the transmission grid node i and the gas grid node p with respect to the power-to-gas equipment at time t; for and Affine coordination variables of ; and are the affine coupling variables of the transmission grid node i and the gas network pipeline h with respect to the electric drive compressor at time t; for and Affine coordination variables of ; and are the transmission-distribution interaction affine active and reactive powers of the transmission grid at time t; and are the affine injected active and reactive powers of the distribution network at time t, respectively; for and Affine coordination variables of ; for and Affine coordination variables of .

[0195] (2) Distributed scheduling control framework based on AAC-ADMM algorithm

[0196] After decoupling the IEGS, the recovery model of each subsystem is constructed separately to perform distributed scheduling control.

[0197] ① Transmission network affine recovery model

[0198]

[0199] Where: 、 and is the dual multiplier of the transmission grid node i at time t with respect to the gas turbine, power-to-gas device and electric drive compressor; and are the dual multipliers of the transmission-distribution interaction active and reactive power of the transmission network at time t; gt ,λ p2g and λ c are the penalty factors for gas turbines, power-to-gas equipment, and electric-driven compressors, respectively; dcc is the transmission-distribution interaction power penalty factor.

[0200] ② Distribution network affine recovery model

[0201]

[0202] Where: and are the dual multipliers of the interactive active and reactive powers of the distribution network at the transmission-distribution boundary at time t.

[0203] ③ Natural gas network affine recovery model

[0204]

[0205] Where: 、 and are the dual multipliers of the gas grid node i at time t regarding the gas turbine, power-to-gas equipment and electric-driven compressor, respectively.

[0206] Since non-convex square terms of affine number subtraction appear in each subsystem, in order to reduce the computational complexity, the terms in each model are processed to For example, the specific processing is as follows:

[0207] Expand this term at the central value to .

[0208]

[0209] Where: is the affine coupling variable of the transmission grid node i with respect to the gas turbine at time t; for and Affine coordination variables of ; and They are and The central value of an affine number; and They are and Noise element coefficients of affine numbers; and They are and Affine number noise element.

[0210] In addition, since the variance of the noise when it is uniformly distributed in the interval [-1,1] is one-third, in order to reduce the error of Taylor expansion, a penalty term is introduced into the model , and will eventually Processing .

[0211]

[0212] Finally, the subsystem models of the transmission network, distribution network and natural gas network are processed according to the above method.

[0213] (3) Solution process of AAC-ADMM algorithm

[0214] The proposed model is solved in parallel based on the AAC-ADMM algorithm, and the penalty factor in the algorithm is adaptively updated to improve the convergence performance of the algorithm. The specific steps are as follows:

[0215] Step 1: Set the number of iterations k = 0; initialize the dual multiplier, coordination variable and penalty factor; set the original residual threshold φ priand the threshold φ of the dual residual dual . .

[0216] Step 2: The transmission grid, distribution grid, and natural gas network each independently and in parallel solve their own sub-affine optimization problems to obtain the values ​​of the coupled variables.

[0217] Step 3: Update the coordination variables according to the latest values ​​of the coupling variables, as shown in Eq. The k+1 in the superscript of the above variables indicates the variables at the k+1th iteration.

[0218]

[0219] Where: and are the affine coupling variables of the transmission grid node i and the gas grid node p with respect to the gas turbine at time t at the k+1th iteration; for and Affine coordination variables of ; and are the affine coupling variables of the transmission grid node i and the gas grid node p with respect to the power-to-gas device at time t at the k+1th iteration; for and Affine coordination variables of ; and are the affine coupling variables of the transmission network node i and the gas network pipeline h with respect to the electric drive compressor at time t at the k+1th iteration; for and Affine coordination variables of ; and are the transmission-distribution interaction affine active and reactive powers of the transmission network at time t at the k+1th iteration; and are the affine injected active and reactive powers of the distribution network at time t during the k+1th iteration; for and Affine coordination variables of ; for and Affine coordination variables of .

[0220] Step 4: Determine the original residual and the dual residual Whether the algorithm convergence condition is met If it is satisfied, the iteration stops and the result is output; otherwise, continue to step 5. The calculation of the original residual and the dual residual is as follows: - shown.

[0221]

[0222] Where: is the original residual of gas turbine i at time t at the k+1th iteration; is the original residual of the power-to-gas device i at time t during the k+1th iteration; are the original residuals of the electrically driven compressor i at time t at the k+1th iteration; and are the original residuals of the transmission-distribution interaction power at time t at the k+1th iteration.

[0223]

[0224] Where: is the dual residual of the gas turbine ip at time t at the k+1th iteration; is the dual residual of the power-to-gas device ip at time t during the k+1th iteration; are the dual residuals of the electrically driven compressor ip at time t at the k+1th iteration; and are the dual residuals of the transmission-distribution interaction power at time t at the k+1th iteration.

[0225]

[0226] Where: and is the maximum primal residual and dual residual at the k+1th iteration.

[0227] Step 5: Update the penalty factor according to the values ​​of the original residual and the dual residual to speed up the convergence. shown.

[0228]

[0229] Where: is the penalty factor at the k+1th iteration; is the penalty factor at the kth iteration; is the original residual at the k+1th iteration; is the dual residual at the k+1th iteration.

[0230] Step 6: Based on the latest values ​​of coupling variables and coordination variables, use the formula Update the dual multiplier; set the number of iterations k = k + 1 and proceed to step 2.

[0231]

[0232] Where: is the dual multiplier of the transmission grid node i with respect to the gas turbine at time t during the k+1th iteration; is the dual multiplier of the natural gas network node p with respect to the gas turbine at time t during the k+1th iteration; is the dual multiplier of the transmission network node i with respect to the power-to-gas device at time t at the k+1th iteration; is the dual multiplier of the natural gas network node p with respect to the power-to-gas device at time t during the k+1th iteration; is the dual multiplier of the transmission network node i with respect to the electric drive compressor at time t during the k+1th iteration; is the dual multiplier of the natural gas network node p with respect to the electric drive compressor at time t at the k+1th iteration; is the dual multiplier of the transmission network with respect to the transmission-distribution interaction power at time t during the k+1th iteration; is the dual multiplier of the distribution network with respect to the transmission-distribution interaction power at time t during the k+1th iteration; is the dual multiplier of the transmission grid node i with respect to the gas turbine at time t during the k-th iteration; is the dual multiplier of the natural gas network node p with respect to the gas turbine at time t during the k-th iteration; is the dual multiplier of the transmission network node i with respect to the power-to-gas device at time t during the k-th iteration; is the dual multiplier of the natural gas network node p with respect to the power-to-gas device at time t during the k-th iteration; is the dual multiplier of the transmission network node i with respect to the electric drive compressor at time t at the k-th iteration; is the dual multiplier of the natural gas network node p with respect to the electric drive compressor at time t at the k-th iteration; is the dual multiplier of the transmission network with respect to the transmission-distribution interaction power at time t during the k-th iteration; is the dual multiplier of the distribution network with respect to the transmission-distribution interaction power at time t during the k-th iteration.

[0233] Note: The k in the superscript of the above variables refers to the variable at the kth iteration; the k+1 in the superscript of the above variables refers to the variable at the k+1th iteration.

[0234] Based on the above design, Figure 1 As shown, the typical implementation steps of the distributed affine restoration method for the power transmission and distribution integrated energy system taking into account geological landslides under typhoon disasters proposed by the present invention are as follows:

[0235] Step 1: Input the parameters of the power transmission and distribution-gas integrated energy system and typhoon forecast data;

[0236] Step 2: Based on the proposed power transmission and distribution system failure model under typhoon-induced geological landslide, according to Eq. - The failure probability of power lines affected by typhoons is calculated. Based on this, the failure probability of the towers in the typhoon-induced geological landslide and the landslide body is calculated according to equations (3)-(7). The failure scenarios of the electrical integrated energy system caused by typhoon disasters are selected by sampling method.

[0237] Step 3: According to the formula - , construct the proposed affine collaborative restoration model of power transmission and distribution-gas system under typhoon disaster;

[0238] Step 4: According to the formula The affine collaborative recovery model of the power transmission and distribution system under typhoon disaster is decoupled. According to Eq. - Construct affine restoration models for different subsystems respectively.

[0239] Step 5: Based on the distributed affine recovery model of the power transmission and distribution-gas system of the AAC-ADMM algorithm, the proposed AAC-ADMM algorithm solution process is used to distributely solve the affine recovery models of different subsystems, and the distributed affine recovery strategy of the power transmission and distribution-gas system taking into account geological landslides under typhoon disasters is output.

[0240] Step 6: According to the formula The typhoon model determines whether the typhoon has passed through the system. If so, the process ends; otherwise, the process returns to step 2.

[0241] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0242] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, performs the above-described method. The storage medium may be any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0243] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0244] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

[0245] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive a distributed affine restoration method for the power transmission and distribution integrated energy system taking into account geological landslides under various other forms of typhoon disasters under the guidance of the present invention. All equal changes and modifications made within the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A distributed affine restoration method for a power transmission and distribution integrated energy system taking into account geological landslides during typhoon disasters, characterized by: Calculate the probability of mechanical stress failure of power lines based on typhoon wind field model; Based on the effective rainfall of landslide and the landslide probability model, the failure probability of the tower caused by landslide is calculated by the impact resistance of the tower. The renewable energy output and load demand are modeled as affine forms, and the transmission network flow constraints, distribution network reconstruction constraints and gas network slow dynamic gas storage model containing affine operators are constructed; By using the electric-gas coupling equipment equation and the transmission and distribution network mutual aid equation, a collaborative restoration model is established with the goal of minimizing the amount of load loss. The affine adaptive alternating direction multiplier method is used to solve the recovery strategies of the transmission and distribution subsystems and the gas grid subsystem in parallel. The penalty factor is updated based on the ratio of the original residual and the dual residual, and the switch status, unit start-stop and load recovery plan are output.

2. The distributed affine restoration method for a power transmission and distribution integrated energy system taking into account geological landslides under typhoon disasters according to claim 1 is characterized by: The landslide probability model is a piecewise function model: when the effective rainfall is less than the limit value, the probability is calculated using an exponential function, otherwise the probability is 1; The failure probability of the tower is determined by the ratio of the landslide impact energy to the maximum impact energy of the tower. If the impact energy exceeds the impact energy, the failure probability is 1.

3. The distributed affine restoration method for a power transmission and distribution integrated energy system taking into account geological landslides under typhoon disasters according to claim 1 is characterized by: The modeling of renewable energy output and load demand in an affine form includes: Modeling of Electro-Pneumatic Coupled Devices: Establish the affine consumption equation of the gas turbine to transfer the active output fluctuation of the power subsystem to the natural gas subsystem; Establish an affine capacity equation for the power-to-gas device to transfer renewable energy output fluctuations to the natural gas subsystem; Dynamically track the uncertainty in the energy conversion process through affine noise elements to achieve cross-subsystem fluctuation coordination; And, slow dynamic gas storage modeling of gas network: pipeline gas storage dynamics are described through the affine relationship between pipeline storage constant and mean gas pressure.

4. The distributed affine restoration method for a power transmission and distribution integrated energy system taking into account geological landslides under typhoon disasters according to claim 1 is characterized by: The non-convex power flow constraints of the distribution network are relaxed by the envelope method; Convert the natural gas Weymouth equations into second-order cone constraints.

5. The distributed affine restoration method for a power transmission and distribution integrated energy system taking into account geological landslides under typhoon disasters according to claim 1 is characterized by: The processing of nonlinear terms in the affine adaptive alternating direction multiplier method includes: Perform a first-order Taylor expansion at the center value of the affine variable; Based on the uniform distribution of affine noise elements in the interval [-1,1], a variance compensation term is introduced to reduce the linearization error; The quadratic accuracy of affine operations is maintained through compensation terms to ensure the convergence of distributed solutions.

6. The distributed affine restoration method for a power transmission and distribution integrated energy system taking into account geological landslides under typhoon disasters according to claim 1, characterized in that: The update of the penalty factor satisfies: when the maximum value of the original residual is greater than the set multiple of the maximum value of the dual residual, the penalty factor is doubled; when the maximum value of the dual residual is greater than the set multiple of the maximum value of the original residual, the penalty factor is doubled.

7. The distributed affine restoration method for a power transmission and distribution integrated energy system taking into account geological landslides under typhoon disasters according to claim 1 is characterized by: Update line and tower failure probabilities based on typhoon movement paths and rainfall data; Dynamically adjust load recovery targets through distributed affine recovery strategies.

8. The distributed affine restoration method for a power transmission and distribution integrated energy system taking into account geological landslides under typhoon disasters according to claim 1 is characterized by: The mutual assistance of the transmission and distribution networks is achieved through a power balance equation: at the connection point between the transmission and distribution networks, the product of the affine load reduction rate of the transmission network and the affine active load is equal to the affine injected active power of the distribution network.

9. A distributed affine recovery system for a power transmission and distribution-gas integrated energy system taking into account geological landslides during typhoon disasters, characterized by: include: Hybrid Fault Modeling Module: Typhoon wind field calculation unit, used to calculate the probability of mechanical stress failure of power lines; Landslide impact analysis unit, which calculates the tower failure probability based on effective rainfall and landslide probability model; Affine collaborative restoration modeling module: The source-load affine modeling unit expresses the renewable energy output and load demand in affine form; Constraint generation unit, which constructs transmission network power flow, distribution network reconstruction and gas storage constraints containing affine operators; The mutual aid coordination unit establishes a target model for minimizing load loss through the electricity-gas coupling equation and the transmission and distribution mutual aid equation; Distributed solution execution module: Affine adaptive alternating direction multiplier method solver that optimizes subsystems in parallel and handles affine squared terms; Adaptive coordination unit, which updates the penalty factor based on the residual ratio; Strategy output interface generates switch operation instructions and load recovery plans.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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