Distributed affine restoration method for power transmission and distribution integrated energy system considering geological landslides under typhoon disasters

Abstract

This invention provides a distributed affine recovery method for an integrated power transmission and distribution-gas energy system under typhoon disasters, taking into account landslides. It calculates the probability of mechanical stress failure in power lines based on a typhoon wind field model; calculates the probability of tower failure due to landslides based on effective rainfall and landslide probability models, using tower impact resistance energy; models renewable energy output and load demand in an affine form, constructing power flow constraints for the power transmission network, reconfiguration constraints for the distribution network, and a slow dynamic gas storage model for the gas network, all containing affine operators; establishes a collaborative recovery model with the goal of minimizing load loss through equations for coupled power and gas equipment and mutual assistance equations for the power transmission and distribution network; employs an affine adaptive alternating direction multiplier method to solve the recovery strategies of the power transmission and distribution subsystem and the gas network subsystem in parallel; and updates the penalty factor based on the ratio of the original residual and the dual residual, outputting the switch status, unit start-up / shutdown, and load recovery scheme.

Description

Technical Field

[0001] This invention belongs to the field of power system disaster prevention, mitigation and intelligent dispatching technology, specifically involving a distributed affine restoration method for a power transmission and distribution-gas integrated energy system under typhoon disasters and geological landslides. Background Technology

[0002] Typhoons, as a typical extreme disaster, are characterized by their long duration, wide impact, and strong destructive power, seriously threatening the safe operation of integrated electricity-gas systems (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 scenarios of power line faults caused by typhoon mechanical stress, neglecting typhoon-induced landslide geological disasters, leading to biases in the formulation of recovery strategies. In addition, traditional recovery methods do not consider the source and load fluctuations in actual disaster scenarios, cannot accurately dispatch renewable energy sources with fluctuating output to participate in load recovery, and ignore the real-time changes in recovery gaps caused by load demand fluctuations. Furthermore, traditional offline recovery methods are not sufficiently 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 overlooks the assisting role of natural gas pipeline storage in the recovery process, making it difficult to fully tap the system's recovery potential. Moreover, when dispatch and control are divided among subsystems of different operators, the industry, resource, and information barriers between subsystems are not considered.

[0003] Currently, research on energy system recovery under typhoon disasters mainly sets up power line interruption 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 heavy rainfall during typhoons, and cannot consider the impact of landslides on towers erected on ridges, steep slopes, and other areas, leading to deviations in the formulation of recovery strategies.

[0004] Furthermore, research on energy system recovery under typhoon disasters rarely considers the impact of source and load fluctuations on recovery strategies, neglecting the influence of fluctuating new energy output on the system recovery process. Fluctuations in load demand will lead to changes in the target amount of load shortfall recovery, rendering the established target inadequate for real-time extreme disaster situations. Among the few methods that consider uncertainties, robust optimization and stochastic optimization are the main approaches. Robust optimization considers the worst-case scenario, often resulting in overly conservative recovery decisions and additional economic losses. Stochastic optimization is less resistant to extreme typhoon disasters, and its randomness may lead to an expansion of failure scenarios. Moreover, both robust and stochastic optimization are offline methods, making them unsuitable for extreme changes in operating conditions under disaster scenarios. Current online optimization methods mainly rely on model predictive control (MMC), but MMC depends on predictive information, which has low accuracy under extreme disasters. Furthermore, MMC performs rolling optimization within a limited time window, and the decision-making process only considers recovery within that limited window, potentially leading to overuse of early recovery resources and a lack of overall consideration for long-term system recovery.

[0005] Furthermore, research on the recovery and scheduling of integrated energy systems under typhoon disasters mainly revolves around traditional centralized recovery and scheduling methods. These methods require uploading global system information when formulating and implementing strategies. However, in actual extreme disasters, different subsystems have industry, resource, and information barriers, making it impossible to share private information and centrally implement recovery strategies. Simultaneously, due to the massive scale of IEGS (Integrated Energy Systems), traditional centralized scheduling faces problems such as heavy communication burdens, large data transmission volumes, and high risks associated with single-point failures. A few studies employ the traditional alternating direction multiplier method for distributed recovery and scheduling; however, when the number of separable operators in the alternating direction multiplier method exceeds two, strict convergence cannot be guaranteed. Moreover, the convergence effect of this algorithm depends on the selection of initial values, resulting in low solution efficiency and difficulty in timely solving and adjusting strategies based on continuously updated typhoon forecast information before the disaster. Existing research on uncertain distributed control methods mainly revolves around distributed bar structures, often leading to overly conservative decision-making.

[0006] Finally, research on the recovery of integrated electrical energy systems under typhoon disasters mainly focuses on steady-state electrical systems. This type of research method is mainly based on the scheduling of steady-state recovery resources in integrated electrical energy systems. However, this method ignores the differences in transmission speed between electricity and gas energy flows during the recovery process, fails to fully utilize the slow dynamic characteristics of natural gas networks, ignores the recovery potential of gas storage in gas pipelines, and does not subdivide the power system into transmission and distribution networks. It also ignores the reality that typhoon disasters can simultaneously damage transmission and distribution networks and fails to consider the mutual assistance of power between 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 neglecting secondary landslide disasters, failing to dynamically track source load fluctuations, and exhibiting low cross-system coordination efficiency, this invention provides a distributed affine recovery method for integrated power transmission and distribution-gas energy systems. This method pioneers a hybrid landslide-typhoon fault quantification mechanism, accurately calculating the probability of mechanical stress faults in power lines using a typhoon wind field model, and dynamically assessing the destructive effect of landslide impact energy on towers based on effective rainfall data. This solves the problem of inaccurate recovery strategies caused by neglecting secondary geological disasters in traditional methods.

[0008] In the recovery strategy generation stage, an innovative affine fluctuation tracking technique is introduced to model renewable energy output and load demand as affine forms containing noisy elements, capturing uncertainties in real time. By constructing affine equations for electro-gas coupling equipment, the dynamic transmission and reverse support of power subsystem fluctuations to the gas network are realized, and the slow-dynamic gas storage characteristics of natural gas pipelines are utilized to explore the system's recovery potential. This affine collaborative mechanism overcomes the conservative limitations of traditional robust optimization in extreme disaster scenarios, and experimentally improves load recovery capabilities.

[0009] To address the challenge of distributed solutions across systems, this proposal develops an improved AAC-ADMM algorithm. It reduces computational complexity by performing Taylor expansion at the central values ​​of affine variables and introduces a variance compensation term based on the uniform distribution of noise elements, significantly improving solution accuracy. Combined with an adaptive penalty factor update mechanism, parameters are dynamically adjusted according to the ratio of the original residual to the dual residual, resulting in a significant improvement in convergence speed and effectively overcoming the convergence barrier of traditional methods in multi-subsystem scenarios.

[0010] The entire recovery process forms a closed-loop control: based on the typhoon's movement path, the fault scenario is updated online, and distributed parallel solutions are used to generate switching operation commands, unit scheduling plans, and load recovery schemes, enabling dynamic strategy adjustments under disaster conditions and providing reliable technical support for typhoon-accompanied heavy rainfall scenarios.

[0011] The specific technical solution adopted by this invention to solve its technical problem is as follows:

[0012] A distributed affine restoration method for integrated power transmission and distribution-gas energy systems considering landslides during typhoon disasters:

[0013] Calculation of power line mechanical stress failure probability based on typhoon wind field model;

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

[0015] The output of new energy sources and load demand are modeled as affine forms, and a power flow constraint, distribution network reconfiguration constraint and gas storage slow dynamic gas network model containing affine operators are constructed.

[0016] A collaborative recovery model with the goal of minimizing load loss is established by using the equations of electro-pneumatic coupling equipment and the mutual assistance equations of transmission and distribution networks.

[0017] An affine adaptive alternating direction multiplier method is used to solve the recovery strategy of the power transmission and distribution system and the gas network 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-up and shutdown and load recovery scheme 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 tower's maximum impact resistance energy. If the impact energy exceeds the impact resistance energy, the failure probability is 1.

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

[0022] Modeling of electro-pneumatic coupling devices:

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

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

[0025] By dynamically tracking the uncertainties in the energy conversion process using affine noise elements, cross-subsystem fluctuation coordination can be achieved.

[0026] In addition, slow dynamic gas storage modeling of gas network: describing the dynamics of pipeline gas storage 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 using the envelope method;

[0028] The Weymouth equation for natural gas is transformed into a second-order cone constraint.

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

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

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

[0032] The quadratic accuracy of the affine operation is maintained by the compensation term, thus ensuring the convergence of the distributed solution.

[0033] Furthermore, the update of the penalty factor satisfies the following: 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 multiplied; 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 multiplied.

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

[0035] The load recovery target is dynamically adjusted through a distributed affine recovery strategy.

[0036] Furthermore, the mutual assistance between 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 restoration system for a power transmission and distribution-gas integrated energy system that takes into account landslides during typhoon disasters, comprising:

[0038] Hybrid Fault Modeling Module:

[0039] The typhoon wind field calculation unit is used to calculate the probability of mechanical stress failure in power lines.

[0040] The landslide impact analysis unit calculates the failure probability of towers based on effective rainfall and a landslide probability model.

[0041] Affine Co-recovery Modeling Module:

[0042] The source-load affine modeling unit expresses the power output of new energy sources and load demand in an affine form.

[0043] Constraint generation unit, constructing power flow, distribution network reconfiguration, and gas storage constraints for power transmission networks containing affine operators;

[0044] The mutual assistance and coordination unit establishes a target model for minimizing load loss through electro-pneumatic coupling equations and transmission and distribution mutual assistance equations;

[0045] Distributed solver execution module:

[0046] A solution for the affine adaptive alternating direction multiplier method, with a parallel optimization subsystem and handling of affine square terms;

[0047] The adaptive coordination unit updates the penalty factor based on the residual ratio;

[0048] The strategy output interface generates switch operation commands and load recovery schemes.

[0049] And a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described above.

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

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

[0052] 1. Significantly improved adaptability to disaster scenarios

[0053] By integrating dual-path fault modeling based on typhoon mechanical stress and landslide impact energy, this method achieves, for the first time, accurate quantification of the failure probability of power poles under typhoon-accompanied heavy rainfall scenarios, effectively solving the problem of inaccurate recovery strategies caused by neglecting secondary geological disasters in traditional methods. Field tests verify that this method significantly reduces fault location deviation in power transmission and distribution systems, providing a more reliable basis for fault diagnosis in disaster environments.

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

[0055] This innovative approach employs affine arithmetic to dynamically track fluctuations in renewable energy output and changes in load demand, overcoming the conservative limitations of traditional robust optimization and stochastic programming. By using electro-gas coupled affine equations to achieve cross-system fluctuation propagation, and combining this with the slow-dynamic gas storage characteristics of the gas network, the system's load recovery capability under extreme uncertainty scenarios is verifiablely improved.

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

[0057] The proposed improved AAC-ADMM algorithm overcomes the convergence barrier of traditional distributed algorithms in the collaborative solution of multi-subsystems through Taylor expansion and a variance compensation mechanism based on noise distribution. The adaptive penalty factor update strategy significantly improves the solution efficiency, reducing the strategy generation time by more than 50% in actual tests, thus meeting the timeliness requirements of disaster response.

[0058] 4. The online decision-making mechanism has been fully upgraded.

[0059] Based on a dynamic scenario update mechanism for typhoon path evolution, the fault model and recovery target are calibrated in real time. It outputs directly executable switching operation sequences, unit dispatching instructions, and load recovery plans, forming a closed loop of "monitoring-decision-execution," thus addressing the adaptability deficiencies of traditional offline strategies under disaster conditions.

[0060] 5. Fundamentally resolve the systemic barrier problem

[0061] In a distributed architecture, each subsystem only needs to interact with boundary coupling variables, achieving efficient collaboration between the transmission, distribution, and gas networks while ensuring data privacy. Field tests demonstrate that this method effectively coordinates recovery resources from different operators, overcoming collaboration challenges caused by industry and information barriers. Attached Figure Description

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

[0063] Figure 1 This is a flowchart illustrating the overall process of an embodiment of the present invention. Detailed Implementation

[0064] To make the features and advantages of the present invention more apparent and understandable, specific embodiments are described below in detail:

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

[0066] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0067] This invention addresses the problem of how to recover from system failures when geological landslides are induced by typhoons, given the existence of industry, resource, and information barriers between different subsystems. It proposes a distributed affine recovery method for an integrated electrical energy system that takes into account geological landslides during typhoons. This paper considers a mixed fault scenario involving typhoon-induced line breaks and landslides, and identifies fault conditions in the power transmission and distribution network affected by typhoons. Then, taking into account the differences in transmission speeds of different electrical energy flows, the paper fully considers the slow dynamic characteristics of the gas network during the recovery process, and coordinates the power supply and distribution network's mutual power replenishment during recovery. It uses affine arithmetic to track the fluctuations in renewable energy output to assist load recovery, and analyzes the load recovery amount based on affine modeling to account for changes in load recovery deficit under real-time disaster conditions, constructing an affine collaborative recovery model for the power transmission and distribution network under typhoon disasters. Based on this, a distributed affine recovery model for the power transmission and distribution network under the AAC-ADMM algorithm is constructed. Affine arithmetic is used to track uncertain variables within a distributed control framework, ensuring algorithm convergence while performing distributed parallel optimization of the transmission network, distribution network, and natural gas network. This allows for the decentralized implementation of recovery strategies in each subsystem. Furthermore, the algorithm utilizes Taylor expansion to square the convex affine number and adaptively updates the solution step size, effectively improving the algorithm's solution efficiency. This method effectively simulates system failure scenarios under geological landslides induced by typhoons and heavy rainfall, and fully considers the impact of source and load fluctuations during the recovery process. Utilizing natural gas pipeline storage, the mutual assistance between different energy flows (electricity and gas) and the transmission and distribution network, an online recovery scheduling strategy based on affine arithmetic is formulated 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 interactive subsystems to achieve decentralized implementation of recovery strategies in each subsystem. Compared to traditional distributed algorithms, it uses affine arithmetic to track uncertain variables in the model, improving computational accuracy and solution efficiency. This allows the system to solve and adjust strategies in a timely manner based on continuously updated typhoon information before the disaster. The affine distributed algorithm can track uncertain variables online while maintaining a distributed control system, obtaining an online recovery scheduling trajectory to fully utilize the real-time output of new energy sources and considering updates to the target recovery amount due to changes in load demand.

[0068] The key design considerations for the above schemes include:

[0069] (1) Taking into account the mixed fault scenarios of typhoon-induced line breakage and geological landslide, a fault model of power transmission and distribution system under typhoon-induced geological landslide is constructed.

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

[0071] (3) A distributed affine recovery model for power transmission and distribution systems 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, three subsystems are recovered in parallel, and the recovery strategy is implemented in a decentralized manner for each subsystem. In addition, Taylor expansion is used in the algorithm to convexize the square term of the affine number and adaptively update the solution step size, which effectively improves the solution efficiency of the algorithm.

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

[0073] 1. Fault Model of Power Transmission and Distribution System under Typhoon-Induced Landslide

[0074] As a typhoon rotates along its central eye and moves at a certain speed, typhoon disaster models can be created by simulating the symmetrical wind field and movement path of the typhoon's rotation, as shown in the equation. As shown.

[0075]

[0076] In the formula: R max ΔQ represents the maximum radius of the typhoon's wind circle. t V represents the pressure difference between the outer periphery and center of the typhoon at time t, expressed in hPa. gx V is the vertical movement speed of the typhoon; θ is a historical empirical coefficient; f is the Coriolis force coefficient of Earth's rotation; V Rmax V represents the maximum wind speed of a typhoon. t V represents the horizontal speed at which the typhoon moves; rin and V rout The distances from the eye of the typhoon are no greater than and greater than R, respectively. maxThe wind speed at time; r is the distance from the eye of the typhoon; m is the typhoon intensity parameter; ΔQ0 is the pressure difference between the outer periphery and center of the typhoon at the initial moment; g is the angle between the typhoon and the coastline where it makes landfall; t is the time.

[0077] The mechanical stress from a typhoon can cause overhead power line failures, such as... As shown.

[0078]

[0079] In the formula: P ij,t Let L be the line fault probability at time t; ij The length of the line; a ij 、b ij and g ij Empirical parameters for line faults; v ij,t and v des These represent the average wind speed and the design wind speed of line ij during the period preceding time t, respectively; r ij,t and r des These represent the average rainfall and the design rainfall value for line ij during the period preceding time t, respectively.

[0080] Extreme typhoon weather is often accompanied by heavy rainfall, which may trigger secondary geological disasters such as landslides. Power poles are inevitably erected on ridges and steep slopes, making them significantly susceptible to geological hazards. Landslides often occur during rainfall periods, influenced by factors such as real-time and cumulative rainfall. These two factors constitute the effective rainfall amount that leads to landslides, as shown in the formula... As shown.

[0081]

[0082] In the formula: R e R0 is the effective rainfall in the landslide area; R0 is the real-time rainfall; R0 is the effective rainfall in the landslide area. i Let f(T) be the rainfall at time i; i ) is T i The weight of rainfall at any given moment; T is the total rainfall time.

[0083] The continuous accumulation of effective rainfall will trigger landslide disasters. Historical records of landslide disasters can reflect the relationship between landslides and effective rainfall, as shown in the formula. As shown.

[0084]

[0085] In the formula: P landslide R represents the probability of a landslide in the region. m denoted as the maximum effective rainfall; a, b, and c are fitting parameters for landslide disaster statistics.

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

[0087]

[0088] In the formula: E I denoted as , where 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 point of load application; F max x0 is the maximum impact force that the tower can withstand; H is the height of the impact point from the tower base; w is the height of the tower. lim This is the deflection limit.

[0089] Calculating the bending deformation energy of the tower under the maximum impact force yields the tower's maximum impact resistance energy, as shown in the formula. As shown.

[0090]

[0091] In the formula: W max This represents the maximum impact resistance of the tower.

[0092] Therefore, the degree of damage to the tower can be expressed as the relationship between impact energy and impact resistance energy, as shown in the equation. As shown.

[0093]

[0094] Where: m is the mass of the landslide body; r is the density of the landslide deposit; d is the average thickness of the landslide body; b is the average width of the impact surface on the tower; T is the landslide impact energy on the tower; g is the gravitational acceleration; h is the vertical height of the landslide's center of mass from the tower base; m is the coefficient of friction of the sliding surface; q is the tilt angle of the landslide body; P k This represents the probability of tower failure.

[0095] Affine Cooperative Recovery Model of Power Transmission and Distribution System under Typhoon Disaster

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

[0097]

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

[0099] To enable new energy sources to participate in the recovery process, this invention analyzes the recovery difference range caused by load demand fluctuations. Based on affine theory, this invention constructs the uncertain variables characterizing source-load fluctuations into affine form, as shown in the equation.

[0100]

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

[0102] (1) Affine constraints of power transmission networks

[0103] ① Constraints of the flow of communication

[0104]

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

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

[0107]

[0108] In the formula: and These are the upper and lower limits of the active power output of thermal power unit i, respectively. and These are the upper and lower limits of reactive power output of thermal power unit i, respectively. and Let be the affine active and affine reactive power outputs of thermal power unit i at time t, respectively.

[0109] ③Climbing constraints of thermal power units (12)

[0110]

[0111] In the formula: and These are the affine active and affine reactive power outputs of thermal power unit i at time t-1, respectively. and These are the maximum slope and ramp rate limits for the active power output of thermal power unit i, respectively. and These represent the landslide and ramp rates of reactive power output of thermal power unit i, respectively.

[0112] ④ The system's active and reactive power balance equations

[0113]

[0114] In the formula: Let be the affine analytical expression for the active power output of distributed new energy source i at time t; Let be the affine active power output of gas turbine i at time t; Let be the affine power consumption of the electro-pneumatic conversion device i at time t; Let be the affine power consumption of the electrically driven compressor i at time t; Let i be the affine surplus rate of the reactive load i in the transmission network at time t; Let i be the magnitude of the affine reactive load i of the power grid at time t.

[0115] ⑤ Restore safety constraints

[0116]

[0117] In the formula: This represents the upper limit of transmission power for branch ij; and These are the upper and lower limits of the phase angle difference between nodes i and j, respectively; and These are the upper and lower limits of the voltage at node i, respectively; U ref,t and θ ref,t Let t be the voltage and phase angle of the balancing node at time t; U0 is the voltage value of the balancing node.

[0118] (2) Affine constraints of distribution network

[0119] ① Active and reactive power balance equation

[0120]

[0121] In the formula: , Let be the affine active and reactive power flowing from node n to node m at time t, respectively. Let r be the affine current flowing from node n to node m at time t; mk and x mk These are the resistance and reactance of branch mk, respectively; , Let b1 and b2 be the affine active and reactive power flowing from node m to node k at time t, respectively; b1 and b2 are the initial node set of the branch with terminal node m and the terminal node set of the branch with initial node m, respectively.

[0122] ② Load loss constraints

[0123]

[0124] ③ Branch voltage drop constraint

[0125]

[0126] In the formula: Z D,mk,t Let t be a binary variable representing the operating state of line mk at time t, 1 for normal operation and 0 for abnormal operation; and Let be the affine voltages of nodes m and k at time t, respectively.

[0127] ④ Branch power flow equations treated by McCormick envelope combined with relaxation method

[0128]

[0129] In the formula: u m,t and l mk,t As auxiliary variables, P represents the squared values ​​of the noise element coefficients of node m voltage and branch mk current at time t, respectively. mk,t,0 Q mk,t,0 P represents the center values ​​of the affine active and reactive power flowing from node m to node k at time t; mk,t Q mk,t Let u be the noise element coefficients of the affine active and reactive power flowing from node m to node k at time t; m,t,0 and l mk,t,0 Let be the center values ​​of auxiliary variables for the voltage at node m and branch mk at time t; s is an auxiliary variable; l max and l min Let mk be the branch current l at time t. mk,t Maximum and minimum values; u max and u min The voltage u at node m at time t are respectively m,t The maximum and minimum values.

[0130] ⑤ Node voltage limit constraints

[0131]

[0132] In the formula: y m,t Let t be a binary variable representing the working state of node m at time t, 1 for normal operation and 0 otherwise; and These are the upper and lower limits of the voltage amplitude at node m, respectively.

[0133] ⑥ Branch current limit constraints

[0134]

[0135] In the formula: and These are the upper and lower limits of the current flowing from node m to node k, respectively.

[0136] ⑦ Branch power limit constraints

[0137]

[0138] In the formula: and These are the upper and lower limits of the active power flowing from node m to node k, respectively. and These are the upper and lower limits of reactive power flowing from node m to node k, respectively.

[0139] (3) Affine constraints of natural gas network

[0140] ①Weymouth equation

[0141]

[0142] In the formula: Let be the affine average flow rate of pipe pq at time t; sgn() is the sign function; W pq Let pq be the pipe constant; and Let be the affine pressures at nodes p and q at time t, respectively.

[0143] ②Nodal pressure constraints

[0144]

[0145] In the formula: and These are the upper and lower pressure limits of node p, respectively.

[0146] ③ Pipeline flow constraints

[0147]

[0148] In the formula: This is the maximum flow rate of pipe pq.

[0149] ④ Gas source output constraint

[0150]

[0151] In the formula: Let p be the affine output force of the gas source p at time t; and These are the upper and lower limits of the output power of the gas source p, respectively.

[0152] ⑤ Gas compressor constraint

[0153]

[0154] In the formula: Let h be the affine flow rate of the compressor pipe at time t; and These are the upper and lower limits of the compressor's compression factor, respectively; This represents the upper limit of the flow rate in the compressor pipe h.

[0155] ⑥ Node flow balancing constraints

[0156]

[0157] In the formula: , and These are the set of first nodes with node p as the last node, the set of last nodes with node p as the first node, and the set of compressors with node p as the entry point, respectively. Let be the affine gas consumption of gas turbine p at time t; Let be the affine gas production rate of the electro-gas conversion device p at time t; Let be the affine flow rate at the beginning of pipe pq at time t; Let lp be the affine flow rate at the end of pipe at time t. Let be the affine flow rate at the end of pipe pq at time t.

[0158] ⑦ Load reduction constraints

[0159]

[0160] Furthermore, unlike the fixed pipeline flow direction in day-ahead scheduling, this invention employs a bidirectional pipeline model in the recovery problem, allowing for flexible natural gas flow to meet recovery needs at different times. To this end, the large M method is used to handle non-convex and nonlinear equations. Weymouth equation, as shown in the formula As shown.

[0161]

[0162]

[0163]

[0164] In the formula: pq,t To represent the state variable characterizing the flow direction of pipe pq at time t, replacing the symbolic 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 pressure affine numbers of nodes p and q at time t, respectively.

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

[0166]

[0167] (4) Multiple recovery resource constraints

[0168] Based on the characteristics of different subsystems, make full use of various recovery resources in the integrated electrical energy system to restore system load loss.

[0169] ① Transmission and distribution network maintenance team

[0170] The overhead transmission and distribution lines are located on the ground, unlike the natural gas network where pipelines are buried deep underground and require massive repair work. This allows for the rapid dispatch of repair teams after a fault occurs. The dispatch constraints are as follows: As shown.

[0171]

[0172] In the formula: Z ij,t The 0-1 variables characterizing the operating state of line ij at time t are Zij and Zij respectively in the transmission and distribution networks. T,ij,t and Z D,mk,t Z ij,1 Z is a 0-1 variable characterizing the initial operating state of line ij. ij,t+1Let F be a 0-1 variable representing the operating state of line ij at time t+1; let F be the total set of faulty lines; let h be the time spent on line maintenance; and let N be the total number of faulty lines. T This refers to the total maintenance period.

[0173] ② Distribution network reconfiguration and radial constraints

[0174] To ensure the continuity and reliability of power supply to loads in the distribution network, the radial structure of the topology should be maintained. By modeling the radial constraints, network reconfiguration can activate tie switches to provide power support to loads as much as possible, as shown in equation [equation missing]. As shown.

[0175]

[0176] Where: β mk,t Let β be a 0-1 variable representing the power flow direction at time t, characterizing 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 Let N be a 0-1 variable representing the power flow direction of line km at time t, characterizing the upstream and downstream relationship between nodes k and m; m For the set of nodes; β 12 This 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] Because natural gas flows relatively slowly, pipelines contain a certain amount of stored gas. Releasing this stored gas during system failures can effectively restore system load losses, as shown in the formula... As shown.

[0179]

[0180] In the formula: and Let be the affine flow rate at the beginning and end of pipe pq at time t; The affine tube storage of pipe pq at time t; For the affine tube storage of pipe pq at time t-1; S pq Ω is the pipe storage constant of pipe pq; GA For a collection of pipes; The affine tube storage of pipe pq at the initial moment.

[0181] ④ Mutual support between different subsystems

[0182] Relying solely on a single subsystem cannot fully exploit the resilience resources of a system. Therefore, this invention coordinates the coupling devices between different subsystems to fully leverage the synergistic effect of electro-gas energy flow. The coupling constraints between subsystems include: the gas turbine's consumption characteristic equations. Energy conversion equations for electro-gas conversion equipment Power equation of an electrically driven compressor and the power transmission nodal equations between transmission and distribution networks .

[0183]

[0184]

[0185]

[0186]

[0187] In the formula: η gt and η p2g These are the conversion efficiencies of the gas turbine and the electro-gas conversion equipment, respectively; H HV It has a high calorific value for natural gas; K represents the upper limit of power consumption for the electro-gas conversion device i; c,1 and K c,2 For compressor parameters; k cp This refers to the compression ratio; and These are the affine active and reactive load reduction rates of the transmission network at transmission-distribution interaction node i at time t, respectively. and Let i be the affine active and reactive loads of the transmission network at the transmission-distribution interaction node i at time t; and Let be the total injected affine active and reactive power of the distribution network at node i of the transmission-distribution interaction at time t.

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

[0189] Under extreme disasters, industry, resource, and information barriers exist between different subsystems. Compared to centralized methods, distributed methods are more suitable for IEGS scheduling under extreme disasters, specifically in the following aspects: 1) Distributed methods only require the exchange of a small amount of boundary information to meet the privacy protection requirements of subsystems that cannot share private information; 2) Distributed methods can disperse the recovery resources of electricity and gas operators, and different operators do not need to share industry and resource information; 3) Distributed methods have lower requirements for data communication, while centralized methods suffer from problems such as large amounts of transmitted information and high single-point failure hazards. 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 changes in uncertain variables online, the model is extended from the deterministic domain to the affine domain within a distributed framework. Accordingly, this invention proposes an IEGS distributed affine recovery model based on the AAC-ADMM algorithm. While ensuring algorithm convergence, it optimizes three subsystems in parallel, enabling each subsystem to implement recovery strategies in a distributed manner. Moreover, the algorithm adaptively updates the solution step size, effectively improving its solution efficiency and allowing the system to solve and adjust strategies promptly based on continuously updated typhoon information before the disaster.

[0191] (1) Decoupling method of IEGS

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

[0193]

[0194] In the formula: and Let i and p be the affine coupling variables of the power grid node i and the gas grid node p at time t, respectively, with respect to the gas turbine. for and affine coordination variables; and Let i and p be the affine coupling variables of the power grid node i and gas grid node p at time t with respect to the power-to-gas equipment, respectively. for and affine coordination variables; and Let i and h be the affine coupling variables of the power grid node i and the gas pipeline h at time t with respect to the electric-driven compressor, respectively. for and affine coordination variables; and These represent the transmission-distribution interactive affine active and reactive power of the power grid at time t; and These are the affine injected active and reactive power of the distribution network at time t, respectively. for and affine coordination variables; for and Affine coordination variables.

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

[0196] After decoupling IEGS, recovery models for each subsystem are constructed separately for distributed scheduling and control.

[0197] ① Affine restoration model of power transmission network

[0198]

[0199] In the formula: , and Let i be the dual multiplier of the power grid node i at time t with respect to the gas turbine, the electric-to-gas conversion equipment, and the electric drive compressor; and λ represents the dual multiplier of the active and reactive power of the transmission-distribution interaction in the power grid at time t; gt , λ p2g and λ c These are penalty factors for gas turbines, electric-to-gas conversion equipment, and electric-driven compressors, respectively; λ dcc This is the power penalty factor for transmission-distribution interaction.

[0200] ② Affine restoration model of distribution network

[0201]

[0202] In the formula: and These are the dual multipliers of the active and reactive power of the distribution network at the transmission-distribution boundary at time t.

[0203] ③ Affine restoration model of natural gas network

[0204]

[0205] In the formula: , and These are the dual multipliers of gas network node i at time t with respect to the gas turbine, the electric-to-gas conversion equipment, and the electric-driven compressor, respectively.

[0206] Since each subsystem contains a non-convex squared term involving the subtraction of affine numbers, this term in each model is processed to reduce computational complexity, taking the power transmission network model as an example. For example, the specific processing is as follows:

[0207] Expand the term at the central value as follows: .

[0208]

[0209] In the formula: Let be the affine coupling variable of the transmission network node i with respect to the gas turbine at time t; for and affine coordination variables; and They are respectively and The central value of an affine number; and They are respectively and Noise element coefficients of affine numbers; and They are respectively and Noise element of affine number.

[0210] Furthermore, since the variance of noise uniformly distributed in the interval [-1, 1] is one-third, a penalty term is introduced into the model to reduce the error of the Taylor expansion. Ultimately Processed as a formula .

[0211]

[0212] Finally, the models of each subsystem of the transmission network, distribution network, and natural gas network were processed using the methods described above.

[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. The specific steps are as follows:

[0215] Step 1: Set the iteration number k=0; initialize the dual multipliers, coordination variables, and penalty factors; set the original residual threshold φ. priThreshold φ of dual residuals dual ...

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

[0217] Step 3: Update the coordinating variable based on the latest values ​​of the coupling variables, as shown in the equation. As shown above, the k+1 in the superscript of the variables all represent the variable at the (k+1)th iteration.

[0218]

[0219] In the formula: and Let i and p be the affine coupling variables of the power grid node i and the gas grid node p with respect to the gas turbine at time t during the (k+1)th iteration. for and affine coordination variables; and Let i and p be the affine coupling variables of the power grid node i and gas grid node p with respect to the power-to-gas equipment at time t during the (k+1)th iteration; for and affine coordination variables; and Let i and h be the affine coupling variables of the power grid node i and the gas pipeline h with respect to the electric drive compressor at time t during the (k+1)th iteration. for and affine coordination variables; and These represent the transmission-distribution interactive affine active and reactive power of the power grid at time t during the (k+1)th iteration; and These are the affine injected active and reactive power of the distribution network at time t during the (k+1)th iteration; for and affine coordination variables; for and Affine coordination variables.

[0220] Step 4: Determine the original residuals and dual residuals Does the algorithm meet the convergence condition? If the condition is met, 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: - As shown.

[0221]

[0222] In the formula: The original residual of gas turbine i at time t during the (k+1)th iteration; The original residual of the electro-gas conversion device i at time t during the (k+1)th iteration; These are the original residuals of the electrically driven compressor i at time t during the (k+1)th iteration; and These are the original residuals of the transmission-distribution interaction power at time t during the (k+1)th iteration.

[0223]

[0224] In the formula: The dual residual of the gas turbine ip at time t during the (k+1)th iteration; The dual residual of the electro-gas conversion device IP at time t during the (k+1)th iteration; These are the dual residuals of the electrically driven compressor ip at time t during the (k+1)th iteration; and These are the dual residuals of the power exchange between the power supply and distribution at time t during the (k+1)th iteration.

[0225]

[0226] In the formula: and These are the maximum original residual and dual residual at the (k+1)th iteration.

[0227] Step 5: Update the penalty factor based on the values ​​of the original residual and the dual residual to accelerate convergence. (See equation...) As shown.

[0228]

[0229] In the formula: This is the penalty factor at the (k+1)th iteration; This is the penalty factor at the k-th iteration; This represents the original residual at the (k+1)th iteration; This is the dual residual at the (k+1)th iteration.

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

[0231]

[0232] In the formula: For the (k+1)th iteration, the dual multiplier of the transmission network node i with respect to the gas turbine at time t; Let be the dual multiplier of natural gas network node p with respect to the gas turbine at time t during the (k+1)th iteration; For the (k+1)th iteration, the dual multiplier of the power grid node i with respect to the power-to-gas equipment at time t; For the (k+1)th iteration, the dual multiplier of natural gas network node p with respect to the electro-gas conversion equipment at time t; For the (k+1)th iteration, the dual multiplier of the power grid node i with respect to the electrically driven compressor at time t; For the (k+1)th iteration, the dual multiplier of natural gas network node p with respect to the electrically driven compressor at time t; For the (k+1)th iteration, the dual multiplier of the transmission network with respect to the transmission-distribution interaction power at time t; For the (k+1)th iteration, the dual multiplier of the distribution network at time t with respect to the transmission-distribution interaction power; Let i be the dual multiplier of the transmission network node i with respect to the gas turbine at time t during the k-th iteration; Let be the dual multiplier of natural gas network node p with respect to the gas turbine at time t during the k-th iteration; For the k-th iteration, at time t, the dual multiplier of the power grid node i with respect to the power-to-gas equipment; For the k-th iteration, at time t, the dual multiplier of natural gas network node p with respect to the electro-gas conversion equipment; Let i be the dual multiplier of the power grid node i with respect to the electrically driven compressor at time t during the k-th iteration; Let be the dual multiplier of natural gas network node p with respect to the electrically driven compressor at time t during the k-th iteration; For the transmission network at time t during the k-th iteration, the dual multiplier for the transmission-distribution interaction power is given by the power multiplier. Let be the dual multiplier of the power distribution network at time t during the k-th iteration with respect to the transmission-distribution interaction power.

[0233] Note: k in the superscript of the above variables all represent the variable at the k-th iteration; k+1 in the superscript of the above variables all represent the variable at the (k+1)-th iteration.

[0234] Based on the above design, such as Figure 1 As shown, the typical implementation steps of the distributed affine restoration method for the integrated power transmission and distribution energy system considering landslides under typhoon disasters proposed in this invention are as follows:

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

[0236] Step 2: Based on the proposed fault model of the power transmission and distribution system under typhoon-induced landslides, according to the formula... - Calculate the probability of power line failure caused by typhoon. Based on this, calculate the probability of typhoon-induced landslide and tower failure at the landslide site according to formulas (3)-(7). Select the electrical integrated energy system failure scenario caused by typhoon disaster by sampling method.

[0237] Step 3: According to the formula - We constructed an affine collaborative recovery model for the power transmission and distribution system under typhoon disasters.

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

[0239] Step 5: Based on the distributed affine restoration model of the power transmission and distribution system of the AAC-ADMM algorithm, use the solution process of the proposed AAC-ADMM algorithm to solve the affine restoration models of different subsystems in a distributed manner, and output the distributed affine restoration strategy of the power transmission and distribution system considering geological landslides under typhoon disaster.

[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, it returns to step 2.

[0241] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0242] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can 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 of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, 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, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can 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 this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object 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 invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0245] This invention is not limited to the above-described preferred embodiments. Anyone inspired by this invention can derive other distributed affine restoration methods for integrated power transmission and distribution energy systems that take into account landslides during typhoon disasters. All equivalent variations and modifications made within the scope of the claims of this invention should be included in the scope of this invention.

Claims

1. A distributed affine restoration method for an integrated power transmission and distribution-gas energy system considering landslides during typhoon disasters, characterized in that: Calculation of power line mechanical stress failure probability based on typhoon wind field model; Based on the effective rainfall and landslide probability model, the probability of tower failure caused by landslide is calculated by the tower's impact resistance energy. The output of new energy sources and load demand are modeled as affine forms, and a power flow constraint, distribution network reconfiguration constraint and gas storage slow dynamic gas network model containing affine operators are constructed. A collaborative recovery model with the goal of minimizing load loss is established by using the equations of electro-pneumatic coupling equipment and the mutual assistance equations of transmission and distribution networks. A parallel solution of the recovery strategies for the power supply and distribution system and the gas network subsystem is adopted using the affine adaptive alternating direction multiplier method. The penalty factor is updated based on the ratio of the original residual and the dual residual, and the switch status, unit start-up and shutdown and load recovery scheme are output. The specific implementation process includes: Step 1: Input the parameters of the integrated power transmission and distribution-gas energy system and typhoon forecast data; Step 2: Based on the proposed fault model of power transmission and distribution system under typhoon-induced landslide, calculate the fault probability of power lines affected by typhoon. On this basis, calculate the failure probability of typhoon-induced landslide and tower at the landslide body. Select the fault scenario of electrical integrated energy system caused by typhoon disaster by sampling method. Step 3: Construct the affine collaborative recovery model of the power transmission and distribution system under typhoon disaster; Step 4: Decouple the affine collaborative recovery model of the power transmission and distribution system under typhoon disaster, and construct affine recovery models for different subsystems respectively; The coupling constraints between subsystems include: the consumption characteristic equation of the gas turbine; Energy conversion equations for electro-gas conversion equipment; Power equation for an electrically driven compressor; And the power transmission node equations between transmission and distribution networks; Furthermore, coupling variables are introduced at the subsystem boundary decoupling points to satisfy consistency constraints: In the formula: and Let i and p be the affine coupling variables of the power grid node i and the gas grid node p at time t, respectively, with respect to the gas turbine. for and affine coordination variables; and Let i and p be the affine coupling variables of the power grid node i and gas grid node p at time t with respect to the power-to-gas equipment, respectively. for and affine coordination variables; and Let i and h be the affine coupling variables of the power grid node i and the gas pipeline h at time t with respect to the electric-driven compressor, respectively. for and affine coordination variables; and These represent the transmission-distribution interactive affine active and reactive power of the power grid at time t; and These are the affine injected active and reactive power of the distribution network at time t, respectively. for and affine coordination variables; for and affine coordination variables; Step 5: Based on the distributed affine restoration model of the power transmission and distribution-gas system using the AAC-ADMM algorithm, solve the affine restoration models of different subsystems in a distributed manner using the solution process of the proposed AAC-ADMM algorithm, including: affine restoration model of the power transmission network, affine restoration model of the distribution network, and affine restoration model of the natural gas network, and output the distributed affine restoration strategy of the power transmission and distribution-gas system considering geological landslides under typhoon disasters.

2. The distributed affine restoration method for a power transmission and distribution-gas integrated energy system considering geological landslides under typhoon disasters, as described in claim 1, is characterized in that: 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 tower's maximum impact resistance energy. If the impact energy exceeds the impact resistance energy, the failure probability is 1.

3. The distributed affine restoration method for a power transmission and distribution-gas integrated energy system considering landslides under typhoon disasters, as described in claim 1, is characterized in that: The method of modeling new energy output and load demand in an affine form includes: Modeling of electro-pneumatic coupling devices: Establish an affine consumption equation for the gas turbine to transfer the active power output fluctuation of the power subsystem to the natural gas subsystem; Establish an affine capacity equation for power-to-gas conversion equipment to transmit renewable energy output fluctuations to the natural gas subsystem; By dynamically tracking the uncertainties in the energy conversion process using affine noise elements, cross-subsystem fluctuation coordination can be achieved. In addition, slow dynamic gas storage modeling of gas network: describing the dynamics of pipeline gas storage through the affine relationship between pipeline storage constant and mean gas pressure.

4. The distributed affine restoration method for a power transmission and distribution-gas integrated energy system considering landslides under typhoon disasters, as described in claim 1, is characterized in that: Relaxing non-convex power flow constraints in the distribution network using the envelope method; The Weymouth equation for natural gas is transformed into a second-order cone constraint.

5. The distributed affine restoration method for a power transmission and distribution-gas integrated energy system considering landslides under typhoon disasters, as described in claim 1, is characterized in that: The handling of nonlinear terms in the affine adaptive alternating direction multiplier method includes: Perform a first-order Taylor expansion at the central value of the affine variable; Based on the uniform distribution characteristics of affine noise elements in the [-1,1] interval, a variance compensation term is introduced to reduce linearization error. The quadratic accuracy of the affine operation is maintained by the compensation term, thus ensuring the convergence of the distributed solution.

6. The distributed affine restoration method for a power transmission and distribution-gas integrated energy system considering landslides under typhoon disasters, as described in claim 1, is characterized in that: The penalty factor is updated in such a way that: 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 multiplied; 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 multiplied.

7. The distributed affine restoration method for a power transmission and distribution-gas integrated energy system considering landslides under typhoon disasters, as described in claim 1, is characterized in that: Update the probability of line and tower failures based on typhoon movement path and rainfall data; The load recovery target is dynamically adjusted through a distributed affine recovery strategy.

8. The distributed affine restoration method for a power transmission and distribution-gas integrated energy system considering geological landslides under typhoon disasters, as described in claim 1, is characterized in that: The mutual assistance between 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 restoration system for a power transmission and distribution-gas integrated energy system considering landslides during typhoon disasters, used to implement the method as described in claim 1, characterized in that, include: Hybrid Fault Modeling Module: The typhoon wind field calculation unit is used to calculate the probability of mechanical stress failure in power lines. The landslide impact analysis unit calculates the failure probability of towers based on effective rainfall and a landslide probability model. Affine Co-recovery Modeling Module: The source-load affine modeling unit expresses the power output of new energy sources and load demand in an affine form. Constraint generation unit, constructing power flow, distribution network reconfiguration, and gas storage constraints for power transmission networks containing affine operators; The mutual assistance and coordination unit establishes a target model for minimizing load loss through electro-pneumatic coupling equations and transmission and distribution mutual assistance equations; Distributed solver execution module: A solution for the affine adaptive alternating direction multiplier method, with a parallel optimization subsystem and handling of affine square terms; The adaptive coordination unit updates the penalty factor based on the residual ratio; The strategy output interface generates switch operation commands and load recovery schemes.

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, it implements the method as described in any one of claims 1-8.

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