Resilience evaluation method for integrated energy system considering source-grid-load multi-type faults

Abstract

The application discloses a comprehensive energy system resilience evaluation method considering source-network-load multi-type faults, comprising the following steps: constructing a typhoon wind field and a rainstorm-ponding equivalent model, a vulnerability model and an available capacity coefficient model; screening to obtain typical extreme fault scenarios; according to an optimal load reduction model of an electric network, a gas network and a heat network subsystem in the comprehensive energy system, the optimal load reduction results under each typical extreme fault scenario are calculated based on the available capacity coefficient model; it is judged whether the coupling device can support operation, if not, the output upper limit of the coupling device is lowered to recalculate the optimal load reduction result, if yes, the current optimal load reduction result is output for the next step; a resilience evaluation model is constructed to calculate the system resilience index and complete the resilience evaluation.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically to a comprehensive energy system resilience assessment method that considers multiple types of faults at the source, grid, and load levels. Background Technology

[0002] In recent years, my country's installed capacity of non-fossil energy has continued to climb. The high proportion of wind and solar power connected to the grid has significantly increased the randomness and volatility of system output, placing higher demands on the power system's regulation capabilities and the absorption of clean energy. To improve cross-period regulation and multi-energy mutual support capabilities, engineering practice has gradually introduced coupled equipment such as combined heat and power (CHP) units and gas turbines (GT) to coordinate and operate multiple energy carriers such as electricity, gas, and heat, forming an integrated energy system (IES), which plays an increasingly important role in the energy supply of industrial parks and regions.

[0003] However, while deep coupling of multiple energy sources enhances flexibility, it also makes the system more susceptible to disturbances in the multi-link coupling of "source-grid-load" under extreme conditions such as typhoons and heavy rains: On the source side, renewable energy output is reduced or even shut down due to wind speed, rainfall, and icing; GT and CHP units experience reduced available capacity due to fuel supply, station flooding, and safety constraints; on the grid side, transmission and distribution channels and key sites are vulnerable to combined damage such as pole collapses and line breaks caused by strong winds and flooding caused by torrential rains; on the load side, issues such as user-side equipment failures, power outages in distribution branches leading to unreachable loads, or the need to prioritize critical loads may arise; simultaneously, the constraints on coupling equipment, which transfers energy between electricity, gas, and heat, further amplify the cascading effects across the system. These factors collectively constitute the typical source-grid-load multi-type fault problems of integrated energy systems under extreme disaster conditions.

[0004] Recent events have further demonstrated that under extreme disasters, multiple types of failures often occur in a cumulative manner, rather than isolated damage to single components. For example, the extreme cold wave in Texas in 2021 resulted in frozen natural gas wells and icing pipelines, leading to a cumulative output loss of over 26GW for gas turbine units. Simultaneously, wind and solar power output declined significantly due to low temperatures and icing, with multiple disturbances on both the source and grid sides triggering widespread blackouts. Another example is Typhoon Mangkhut in 2024, which, combined with strong winds and torrential rain, caused widespread shutdowns of 35kV and above substations and 10kV lines, and triggered localized flooding. This resulted in water accumulation around some substations, heat exchange stations, gas stations, and gas turbine buildings, damaging source-side units, grid-side access routes, and some load nodes simultaneously. Meanwhile, during project operation, dispatching agencies typically implement preventative shutdowns and power throttling measures based on monitoring data such as wind speed and water depth. Even without significant physical damage to equipment, its usable capacity can still decrease significantly. It is evident that under extreme disasters, integrated energy systems face a combined state of multiple types of failures at the source, grid, and load levels, along with degradation of available capacity. This constitutes a key challenge in related resilience assessments and decision-making.

[0005] Existing technologies have shortcomings in the following two aspects: First, under extreme disaster scenarios, most studies still use typical grid failures or uniform NK line faults to represent the disaster impact, lacking differentiated vulnerability models and available capacity characterization for multiple links of source-grid-load such as wind power, photovoltaics, coupling equipment, overhead lines and load nodes, making it difficult to truly reflect the co-evolution characteristics of multiple types of faults in the time dimension; Second, in terms of characterizing resilience assessment results, existing studies lack a unified and comparable assessment caliber for multi-energy coupled systems, making it difficult to uniformly characterize dynamic characteristics such as recovery time and recovery rate in the recovery phase, which limits the quantitative verification of the strategy effects between fault combinations under different disaster scenarios.

[0006] To address the aforementioned shortcomings, this invention proposes a comprehensive energy system resilience assessment method that considers multiple types of faults across the source, grid, and load systems. Under a unified modeling framework, it achieves equivalent modeling of typhoon and rainstorm disaster intensity, probability modeling of multiple types of faults and scenario generation and selection, multi-energy flow calculation and analysis, and system resilience assessment, providing technical support for the safe operation and rapid recovery of comprehensive energy systems under extreme disaster conditions. Summary of the Invention

[0007] In view of this, the present invention provides a comprehensive energy system resilience assessment method that considers multiple types of source-grid-load faults, in order to at least solve the problem that the existing technology uses typical grid failure or uniform NK fault to approximate the disaster impact, which makes it difficult to characterize the combined effects of multiple types of faults and the differentiated degradation of multiple links, resulting in inaccurate quantitative assessment of resilience.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults includes the following steps:

[0010] S1: Obtain historical meteorological data and spatial location information of the integrated energy system architecture, and then construct typhoon wind field and rainstorm-water accumulation equivalent model;

[0011] S2: Based on the typhoon wind field and rainstorm-water accumulation equivalent model, vulnerability models and available capacity coefficient models related to disaster intensity indicators are constructed for the source-grid-load side and coupled equipment, respectively.

[0012] S3: Sample and generate a set of multiple types of failure scenarios, and select typical extreme failure scenarios from them based on the vulnerability model and system information entropy index;

[0013] S4: Based on the optimal load reduction models of the power grid subsystem, gas network subsystem, and heating network subsystem in the integrated energy system, calculate the optimal load reduction results under each typical extreme fault scenario based on the available capacity coefficient model.

[0014] S5: Based on the optimal load reduction result obtained in S4, determine whether the coupling equipment can support operation by measuring the gas consumption of the coupling equipment and the gas supply of the connected gas network node. If not, reduce the upper limit of the output of the coupling equipment and re-execute S4. If it can, output the current optimal load reduction result and proceed to S6.

[0015] S6: Based on the output of S5, construct a resilience assessment model to calculate the system resilience index and complete the resilience assessment.

[0016] Preferably, the specific content of constructing the typhoon wind field and rainstorm-water accumulation equivalent model in S1 includes:

[0017] Let the hourly rainfall intensity during the typhoon disaster be... The equivalent model of the typhoon wind field considering wind and rain loads is as follows:

[0018] ;

[0019] ;

[0020] In the formula, To account for the equivalent wind speed affected by rainfall, It is a rainstorm type. This is the peak of rainfall. Let be the wind speed at any component in the typhoon wind field;

[0021] Rainstorm-Floodwater Equivalent Model:

[0022] ;

[0023] In the formula, Due to the depth of the accumulated water, Reflecting the extreme water depth under extreme rainfall conditions, Control the speed as the water depth approaches saturation. This refers to the cumulative effective rainfall.

[0024] Preferably, the Batts model is used to simulate wind speed changes during the passage of a typhoon, assuming the radius of the typhoon's maximum wind speed is... The average wind speed at the radius of maximum wind speed is The distance between the position of any component and the center of the typhoon is The wind speed at any component in the typhoon wind field is... for:

[0025] ;

[0026] In the formula, This is the radial attenuation coefficient of typhoon intensity;

[0027] The difference between the outer pressure of the typhoon cyclone and the pressure at the center of the typhoon. Impact, after the typhoon makes landfall The size of the typhoon decreases as its intensity diminishes. From the maximum wind speed gradient and typhoon movement speed Joint decision:

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] In the formula, This is the atmospheric pressure value under standard conditions; This refers to the central air pressure value of the typhoon. The radius of maximum wind speed; The parameters for the Coriolis force during Earth's rotation; This is the Earth's rotational angular velocity; The latitude of the typhoon.

[0034] Preferably, the period of typhoon impact is set as follows: Rainstorm pattern Triangular rain type adopted:

[0035] ;

[0036] In the formula, This refers to the moment when the peak of the rainstorm occurs; The half-duration of the triangular rain pattern represents the extension forward and backward from the peak time. Significant rainfall occurred within the timeframe.

[0037] Preferred, The calculation methods include:

[0038] The total cumulative rainfall over the entire process meets the specified extreme rainfall total constraint. for:

[0039] ;

[0040] Considering that the distribution network nodes are distributed in different areas, and each zone corresponds to different terrain elevations and drainage capacities; define the effective catchment coefficient for each zone. The equivalent rainfall intensity during the surface water accumulation process is... for:

[0041] ;

[0042] Corresponding cumulative effective rainfall for:

[0043] .

[0044] Preferably, the specific content of S2, which constructs vulnerability models associated with disaster intensity indicators for the source side, grid side, load side, and coupled equipment, includes: for the source side, the vulnerability model includes failure probability models for wind turbines and photovoltaic power plants; for the grid side, the vulnerability model includes failure probability models for distribution lines; for the load side, the vulnerability model includes failure probability models for load nodes; and for coupled equipment, the vulnerability model includes failure probability models for the coupled equipment; wherein:

[0045] Wind turbine: (The rest of the text appears to be a list of components and their functions.) The equivalent wind speed at the typhoon generator is The median wind speed at which severe damage is The logarithmic standard deviation is Then the first Typhoon machines at all times Failure probability model for:

[0046] ;

[0047] In the formula, It is the standard normal distribution function;

[0048] Photovoltaic power plants include two categories: rooftop photovoltaics and ground-mounted photovoltaics; among them,

[0049] For rooftop photovoltaics, let the equivalent wind speed of the rooftop photovoltaic array be... , and Given the median wind speed and logarithmic standard deviation corresponding to severe damage to the rooftop photovoltaic (PV) support structure, then the failure probability model for rooftop PV systems is... for:

[0050] ;

[0051] For ground-mounted photovoltaic (PV) systems, let the equivalent wind speed at the PV power station be... The water depth at the location of the station's key electrical equipment is [missing information]. Then the wind-induced damage probability model With flood failure probability model They are respectively:

[0052] ;

[0053] ;

[0054] In the formula, and The median wind speed and logarithmic standard deviation corresponding to severe damage to ground-mounted photovoltaic support structures; The water depth corresponding to a 50% probability of ground-mounted photovoltaic power station shutdown; The slope parameter of the S-shaped function for photovoltaic power plants;

[0055] The total failure probability model of ground-mounted photovoltaic power for:

[0056] ;

[0057] Power distribution lines include lines and towers; among them, lines The The conductor and the first Failure probability model of tower and They are respectively:

[0058] ;

[0059] ;

[0060] In the formula, and These represent the failure probabilities under normal operating conditions for the lines and towers, respectively. For the line Equivalent wind speed at the location; and These are the influence coefficients of wind direction on line and tower faults, respectively; and These refer to the design wind speed and the maximum wind speed that the line can withstand, respectively. and The design of the tower should be based on the wind speed it can withstand and the maximum wind speed it can withstand. and These are the sensitivity coefficients of the lines and towers to wind speed, respectively, and are negatively correlated with the strength of the line and tower components.

[0061] Comprehensive Fault Probability Model for Power Distribution Lines for:

[0062] ;

[0063] In the formula, and These refer to the number of conductor spans and the number of towers, respectively.

[0064] Failure probability model of load nodes for:

[0065] ;

[0066] In the formula, For load nodes The depth of the accumulated water; This is the slope parameter of the S-shaped function for the load node; The water depth corresponding to a 50% probability of failure for this load node;

[0067] Failure probability model of coupling device for:

[0068] ;

[0069] In the formula, The slope parameter of the S-curve function for this coupling device; For coupling devices The depth of the accumulated water; The water depth corresponding to a 50% failure probability for this coupling device;

[0070] Preferably, the available capacity factor model in S2 is:

[0071] ;

[0072] ;

[0073] In the formula, Indicates source side and coupling device In failure scenarios ,time The percentage of capacity that can still be put into operation; when components When it is a wind turbine or a photovoltaic power station, it is equivalently divided into Sub-units For the first The rated capacity of each sub-unit For the first Each subunit in the scene Down to the moment The failure state; If and only if the subunit is in the scene The value is 1 when the first fault occurs, and 0 at other times. This is the preventative operation restriction factor on the operation side, used to reflect situations where power is limited or operations are suspended due to wind speed or water depth warnings without physical damage. If preventative operation restrictions are not considered, then... .

[0074] Preferably, the specific content of S3 includes:

[0075] Assuming the integrated energy system has a total of The components are divided into elements, where wind farms and photovoltaic power plants are equivalently divided into sub-units, and each sub-unit is included in the component set; the typhoon disaster process is discretized into... At that moment; recording the components in the integrated energy system exist The probability of failure at time is ;

[0076] First, determine the scene sampling scale. Construction for each component A uniform random variable over an interval is sampled stratified in a multidimensional space using the Latin hypercube method to obtain... Each set of samples corresponds to a multi-type fault scenario.

[0077] For the Each scene, note the components. At any moment The fault state is a 0-1 variable. If and only if the element is in the scene Below the time The value is 1 when the first fault occurs, and 0 at all other times to ensure the uniqueness of the initial fault time. Information entropy of a scenario system for:

[0078] ;

[0079] In obtaining Information entropy set corresponding to each scenario Then, the kernel density estimation method is used to fit its probability density distribution, and then the upper quantile of the information entropy is obtained. and lower quantile The middle interval is denoted as In this context, the system information entropy value corresponding to the global maximum point of the probability density function obtained by kernel density estimation is taken as the typical information entropy value. Then, the extreme failure scenario with information entropy value equal to or closest to the typical information entropy value is selected from the scenario set as the typical extreme failure scenario. The typical extreme failure scenario satisfies the constraint that its information entropy does not consider repeated failures in the middle interval and during the disaster.

[0080] .

[0081] Preferably, in S4, the objective functions in the optimal load reduction calculation models for the power grid subsystem, gas network subsystem, and heating network subsystem are as follows:

[0082] ;

[0083] ;

[0084] ;

[0085] In the formula, For nodes in the power grid subsystem The amount of power load reduction; For nodes in the gas network subsystem The reduction in gas load; For nodes in the heating network subsystem Heat load reduction; The set of nodes in the power grid subsystem; The set of nodes in the gas network subsystem; The set of nodes in the heating network subsystem;

[0086] The constraints of the optimal load reduction calculation model for the power grid subsystem are:

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] ;

[0098] In the formula, and The end nodes are respectively The initial node set and initial node of the branch are The set of end nodes of the branch; and branch road and branch roads The flow of active power; and branch road and branch roads The flow of reactive power; and branch road and branch roads Current; and branch road and branch roads resistance; and branch road and branch roads Reactance; and They are nodes The active and reactive power of the gas turbine; and They are nodes Active and reactive power of the combined heat and power unit; and They are nodes The active and reactive power of the wind turbine unit; and They are nodes The active and reactive power of the photovoltaic power station; and They are nodes The active and reactive power required by the load; For nodes Reactive power at load reduction; and They are nodes and Node voltage; It is a very large positive real number; To characterize the branch Binary variables representing the running state; To represent nodes The binary variable representing the working status; Square the voltage value; Square the current value; and These are the available capacity factor and maximum output power of the gas turbine, respectively. and These are the available capacity factor and rated power of the wind turbine, respectively. and These are the available capacity factor and rated power of the photovoltaic power station, respectively.

[0099] The constraints of the optimal load reduction calculation model for the gas network subsystem are:

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] In the formula, As a node of the gas network subsystem The gas source output; For pipelines Traffic; For nodes The gas consumption of the gas turbine; For nodes Gas consumption of combined heat and power units; For nodes The load demand; For pipelines Maximum flow rate; and These represent the positive and negative flow directions of pipeline natural gas, respectively. and They are respectively The maximum and minimum values; and For nodes and nodes The square of the air pressure at that location; and They are respectively The maximum and minimum values; and compressors The squares of the inlet and outlet node pressures; and compressors The maximum and minimum compression ratio; It is a very large positive real number; The compressor is in operating condition.

[0110] The constraints of the optimal load reduction calculation model for the heating network subsystem are:

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] In the formula, This is the node-branch correlation matrix of the heating network; Given the branch flow matrix; for Middle element; For node traffic matrix; The loop incidence matrix; This is the impedance coefficient matrix in the pipeline, which is related to the pipeline parameters; For heating network subsystem nodes The load demand; This is the specific heat capacity of water; for Elements in; For nodes The heating temperature; For nodes The regeneration temperature; Temperature at the end of the pipe; Temperature at the beginning of the pipe; Ambient temperature; This refers to the length of the pipe. The heat conversion coefficient per unit length of the pipe; and These represent the flow rates in each pipe before and after mixing, respectively. and These are the pipe fluid temperatures before and after mixing, respectively; the heating network subsystem is supplied with heat by a combined heat and power unit. and These are the actual and maximum thermal power of the combined heat and power unit, respectively. This is the available capacity factor for combined heat and power (CHP) units.

[0119] Preferably, the specific content of determining whether the optimal load reduction result can support the operation of the coupled equipment in S5 includes:

[0120] The optimal load reduction calculation for the integrated energy system adopts a decoupled approach. The optimal load reduction models of the heating network subsystem, the power grid subsystem, and the gas network subsystem are solved sequentially to obtain the output of the coupled equipment. The coupled equipment includes cogeneration units and gas turbines, all of which are powered by the access nodes in the gas network subsystem. The adjustment is made by judging whether the gas supply of the gas network subsystem nodes can support the gas consumption of the coupled equipment. If the gas supply is lower than the gas consumption, it means that the operation cannot be supported. The upper limit of the output of the coupled equipment is then lowered, and the S4 calculation is re-executed to obtain new reduction results for each subsystem. If the results are higher, the next step can be performed.

[0121] The energy conversion relationship of the coupling device is as follows:

[0122] ;

[0123] ;

[0124] ;

[0125] In the formula, This refers to the actual power generation capacity of the combined heat and power unit; This represents the actual thermal power of the combined heat and power unit. The heat-to-power ratio of a combined heat and power (CHP) unit; For the gas consumption of a combined heat and power unit; The calorific value of natural gas; and These are the heat generation efficiency and power generation efficiency of a combined heat and power (CHP) unit, respectively. This refers to the power generation capacity of the gas turbine. The power generation efficiency of the gas turbine; This refers to the gas consumption of the gas turbine.

[0126] Preferably, the toughness assessment model in S6 includes:

[0127] Demand guarantee of each subsystem Comprehensive demand protection :

[0128] ;

[0129] ;

[0130] In the formula, For any subsystem in an integrated energy system The set of load nodes; For subsystem Load Node The weight of the ranking; and For subsystem node Load demand and reduction; For each subsystem The weight values, where , , They are respectively power grid subsystems Gas network subsystem Heating network subsystem The weights are summed to 1.

[0131] Recovery time With recovery rate indicators :

[0132] ;

[0133] ;

[0134] In the formula, This serves as the starting point for the recovery assessment. The earliest time when the system reaches the recovery target; For a period of time Changes in the overall domestic demand guarantee level;

[0135] The cumulative unmet demand during the recovery phase is calculated, including subsystem levels. With comprehensive level :

[0136] ;

[0137] ;

[0138] In the formula, the cumulative unmet demand is... The smaller the value, the lower the degree of supply loss during the recovery phase and the better the recovery effect;

[0139] Constructing a cumulative indicator for comprehensive service gaps This is used to quantify the cumulative gap between the overall energy supply service level and the ideal protection level throughout the entire disaster process.

[0140] ;

[0141] In the formula, This is the moment the first fault occurred; Index for discrete time periods; The duration of this segment is approximately equivalent to the integral form when using an event-driven discrete time axis. The smaller the value, the smaller the cumulative gap in comprehensive services during the system's resilience and recovery phase under disaster, and the better the recovery effect.

[0142] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a comprehensive energy system resilience assessment method that considers multiple types of source-grid-load faults, which has the following beneficial effects:

[0143] Driven by extreme typhoon and rainstorm scenarios, this invention constructs vulnerability models corresponding to disaster intensity indicators such as passing wind speed and water depth for wind turbines, photovoltaic arrays, coupling equipment buildings, overhead lines, and load nodes, and provides a quantitative characterization of available capacity coefficients. Under a unified assessment framework, it quantitatively characterizes the impact of source-side output derating, grid-side component failure, and load-side serviceability reduction on the overall energy system's energy supply level, thereby forming a multi-energy flow analysis and resilience assessment method for multiple fault combinations of source-grid-load. Attached Figure Description

[0144] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0145] Figure 1 Flowchart of the integrated energy system resilience assessment method considering multiple types of source-grid-load faults provided by the present invention;

[0146] Figure 2 The structural diagram of the E33-G12-H6 integrated energy system provided in the embodiments of the present invention;

[0147] Figure 3 This is a time-varying diagram of the failure rate of disaster-affected components in the system provided in an embodiment of the present invention;

[0148] Figure 4 This is a probability distribution diagram of the system's comprehensive information entropy provided in an embodiment of the present invention;

[0149] Figure 5 A comparison chart of the overall system requirements assurance provided in the embodiments of the present invention;

[0150] Figure 6 A comparison chart of the cumulative deviation of system service levels provided in the embodiments of the present invention. Detailed Implementation

[0151] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0152] This invention provides a comprehensive energy system resilience assessment method that considers multiple types of source-grid-load faults, such as... Figure 1 As shown, it includes the following steps:

[0153] S1: Obtain historical meteorological data and spatial location information of the integrated energy system architecture, and then construct typhoon wind field and rainstorm-water accumulation equivalent model;

[0154] S2: Based on the typhoon wind field and rainstorm-water accumulation equivalent model, vulnerability models and available capacity coefficient models related to disaster intensity indicators are constructed for the source-grid-load side and coupled equipment, respectively.

[0155] S3: Sample and generate a set of multiple types of failure scenarios, and select typical extreme failure scenarios from them based on the vulnerability model and system information entropy index;

[0156] S4: Based on the optimal load reduction models of the power grid subsystem, gas network subsystem, and heating network subsystem in the integrated energy system, calculate the optimal load reduction results under each typical extreme fault scenario based on the available capacity coefficient model.

[0157] S5: Based on the optimal load reduction result obtained in S4, determine whether the coupling equipment can support operation by measuring the gas consumption of the coupling equipment and the gas supply of the connected gas network node. If not, reduce the upper limit of the output of the coupling equipment and re-execute S4. If it can, output the current optimal load reduction result and proceed to S6.

[0158] S6: Based on the output of S5, construct a resilience assessment model to calculate the system resilience index and complete the resilience assessment.

[0159] It should be noted that:

[0160] In this embodiment, meteorological data such as historical typhoon trajectories, central pressure, maximum wind speed, movement speed, and cumulative rainfall in the target area are obtained, along with spatial location information of power distribution lines, wind farms, photovoltaic power stations, coupling equipment, and load nodes. Then, a typhoon wind field and rainstorm-water accumulation equivalent model based on the Batts model are constructed. The Batts model is used to simulate the wind speed changes during the passage of a typhoon.

[0161] After obtaining the disaster intensity time series such as typhoon wind speed and water depth, this embodiment constructs vulnerability models and available capacity coefficient models related to disaster intensity indicators for source-side wind turbines and photovoltaic arrays, grid-side overhead lines and towers, load-side load nodes, and coupled equipment buildings, so as to achieve a unified characterization of multiple types of faults from source to grid to load.

[0162] After obtaining the failure probabilities of various components over time, this invention introduces the Latin hypercube sampling method to generate a set of multiple types of failure scenarios, and uses the system information entropy index to measure the uncertainty level of the scenarios to achieve the screening of typical scenarios.

[0163] To more accurately assess the resilience of integrated energy systems under various types of faults, this invention introduces Latin hypercube sampling to generate fault scenario sets, based on the obtained fault probabilities of various components. Compared with traditional Monte Carlo random sampling, Latin hypercube sampling can achieve more uniform coverage in each dimension of the probability space with the same sample size, thereby improving the representativeness and efficiency of scenario generation.

[0164] After obtaining the various fault states of the integrated energy system under typical disasters and the optimal load reduction results for each time period, in order to objectively reflect the system's resilience and post-disaster recovery capabilities during extreme disasters, this invention constructs a resilience index system for multiple types of faults, which is used to uniformly evaluate disaster scenarios and fault combinations.

[0165] To further implement the above technical solutions and to characterize the amplification effect of heavy rainfall on wind loads, an equivalent wind speed model is introduced based on the Batts wind field. The specific content of constructing the typhoon wind field and the equivalent model of heavy rainfall and water accumulation in S1 includes:

[0166] Let the hourly rainfall intensity during the typhoon disaster be... The equivalent model of the typhoon wind field considering wind and rain loads is as follows:

[0167] ;

[0168] ;

[0169] In the formula, To account for the equivalent wind speed affected by rainfall, It is a rainstorm type. This is the peak of rainfall. Let be the wind speed at any component in the typhoon wind field;

[0170] To characterize the nonlinear relationship between rainstorm and urban flooding without introducing a complex two-dimensional hydrodynamic model, this invention refers to the exponential saturation relationship of "water content – ​​runoff coefficient" in the water storage function method and the improved Tank model. It uses an exponential saturation function to equate cumulative rainfall to the representative water depth of a region. The rainstorm-flooding equivalent model is as follows:

[0171] ;

[0172] In the formula, Due to the depth of the accumulated water, Reflecting the extreme water depth under extreme rainfall conditions, Control the speed as the water depth approaches saturation. This refers to the cumulative effective rainfall.

[0173] To further implement the above technical solution, the Batts model is used to simulate the wind speed changes during the passage of a typhoon. The radius of the typhoon's maximum wind speed is assumed to be... The average wind speed at the radius of maximum wind speed is The distance between the position of any component and the center of the typhoon is The wind speed at any component in the typhoon wind field is... for:

[0174] ;

[0175] In the formula, This is the radial attenuation coefficient of typhoon intensity;

[0176] The difference between the outer pressure of the typhoon cyclone and the pressure at the center of the typhoon. Impact, after the typhoon makes landfall The size of the typhoon decreases as its intensity diminishes. From the maximum wind speed gradient and typhoon movement speed Joint decision:

[0177] ;

[0178] ;

[0179] ;

[0180] ;

[0181] ;

[0182] In the formula, This is the atmospheric pressure value under standard conditions; This refers to the central air pressure value of the typhoon. The radius of maximum wind speed; The parameters for the Coriolis force during Earth's rotation; This is the Earth's rotational angular velocity; The latitude of the typhoon.

[0183] To further implement the above technical solutions, the heavy rainfall brought by typhoons will cause water accumulation in low-lying areas or areas with poor drainage. Based on the typical typhoon process, a rainstorm pattern is designed, and the typhoon impact period is set as follows: Rainstorm pattern Triangular rain type adopted:

[0184] ;

[0185] In the formula, This refers to the moment when the peak of the rainstorm occurs; The half-duration of the triangular rain pattern represents the extension forward and backward from the peak time. Significant rainfall occurred within the timeframe.

[0186] In order to further implement the above technical solution, The calculation methods include:

[0187] The total cumulative rainfall over the entire process meets the specified extreme rainfall total constraint. for:

[0188] ;

[0189] Considering that the distribution network nodes are distributed in different areas, and each zone corresponds to different terrain elevations and drainage capacities; define the effective catchment coefficient for each zone. The equivalent rainfall intensity during the surface water accumulation process is... for:

[0190] ;

[0191] Corresponding cumulative effective rainfall for:

[0192] .

[0193] To further implement the above technical solution, S2 includes the following specific content for constructing vulnerability models related to disaster intensity indicators for the source side, grid side, load side, and coupling equipment: For the source side, the vulnerability model includes failure probability models for wind turbines and photovoltaic power plants; for the grid side, the vulnerability model includes failure probability models for distribution lines; for the load side, the vulnerability model includes failure probability models for load nodes; and for coupling equipment, the vulnerability model includes failure probability models for the coupling equipment. Wherein:

[0194] Wind turbine generators: Source-side wind turbine generators primarily bear strong wind loads. This invention uses a log-normal vulnerability curve to describe the probability of wind-induced damage. Let the... The equivalent wind speed at the typhoon generator is The median wind speed at which severe damage is The logarithmic standard deviation is Then the first Typhoon machines at all times Failure probability model for:

[0195] ;

[0196] In the formula, It is the standard normal distribution function;

[0197] Photovoltaic power plants include two categories: rooftop photovoltaics and ground-mounted photovoltaics; among them,

[0198] For rooftop photovoltaics, since the modules are installed on the building roof, they are basically not directly submerged by groundwater. Only wind-induced damage is considered, and the failure probability is described using log-normal vulnerability. Let the equivalent wind speed of the rooftop photovoltaic array be... , and Given the median wind speed and logarithmic standard deviation corresponding to severe damage to the rooftop photovoltaic (PV) support structure, then the failure probability model for rooftop PV systems is... for:

[0199] ;

[0200] For ground-mounted photovoltaic (PV) systems, both wind-induced damage and flooding of the plant area can lead to power generation interruptions. This invention uses log-normal fragility and an sigmoid function to characterize two mechanisms respectively; let the equivalent wind speed at the ground-mounted PV station be... The water depth at the location of the station's key electrical equipment is [missing information]. Then the wind-induced damage probability model With flood failure probability model They are respectively:

[0201] ;

[0202] ;

[0203] In the formula, and The median wind speed and logarithmic standard deviation corresponding to severe damage to ground-mounted photovoltaic support structures; The water depth corresponding to a 50% probability of ground-mounted photovoltaic power station shutdown; The slope parameter of the S-shaped function for photovoltaic power plants;

[0204] Wind-induced damage and flooding failure can be approximated as independent events in engineering, so the total failure probability model of ground-mounted photovoltaic systems... for:

[0205] ;

[0206] Power distribution lines, including lines and towers, have a failure rate that is related to the wind and rain loads they experience. This failure rate is simplified and represented by a vulnerability function under an equivalent wind speed model. Among these, the line... The The conductor and the first Failure probability model of tower and They are respectively:

[0207] ;

[0208] ;

[0209] In the formula, and These represent the failure probabilities under normal operating conditions for the lines and towers, respectively. For the line Equivalent wind speed at the location; and These are the influence coefficients of wind direction on line and tower faults, respectively; and These refer to the design wind speed and the maximum wind speed that the line can withstand, respectively. and The design of the tower should be based on the wind speed it can withstand and the maximum wind speed it can withstand. and These are the sensitivity coefficients of the lines and towers to wind speed, respectively, and are negatively correlated with the strength of the line and tower components.

[0210] Comprehensive Fault Probability Model for Power Distribution Lines for:

[0211] ;

[0212] In the formula, and These refer to the number of conductor spans and the number of towers, respectively.

[0213] Load-side failures mainly manifest as building flooding or damage to internal energy facilities, leading to the failure of existing load services. Using an S-shaped function to describe the probability of damage to buildings or energy facilities, the failure probability model of load nodes is then developed. for:

[0214] ;

[0215] In the formula, For load nodes The depth of the accumulated water; This is the slope parameter of the S-shaped function for the load node; The water depth corresponding to a 50% probability of failure for this load node;

[0216] For coupled equipment such as gas turbines and combined heat and power units, the main risk is that water accumulation in the plant area can cause primary and electrical equipment to malfunction. This invention uses an S-shaped vulnerability function to describe the vulnerability of the plant in water. Given the downtime probability, the failure probability model of the coupled equipment is... for:

[0217] ;

[0218] In the formula, The slope parameter of the S-curve function for this coupling device; For coupling devices The depth of the accumulated water; The water depth corresponding to a 50% failure probability for this coupling device;

[0219] To further implement the above technical solution, and to further apply the aforementioned time-varying fault probability to the constraints of the upper limit of source-side output and the upper limit of coupling equipment capacity, the source side and coupling equipment are defined. At any moment The available capacity factor is This represents the percentage of the component's capacity that can still be put into operation under disaster conditions; that is, the available capacity factor model in S2 is:

[0220] ;

[0221] ;

[0222] In the formula, Indicates source side and coupling device In failure scenarios ,time The percentage of capacity that can still be put into operation; when components When it is a wind turbine or a photovoltaic power station, it is equivalently divided into Sub-units For the first The rated capacity of each sub-unit For the first Each subunit in the scene Down to the moment The failure state; If and only if the subunit is in the scene The value is 1 when the first fault occurs, and 0 at other times. This is the preventative operation restriction factor on the operation side, used to reflect situations where power is limited or operations are suspended due to wind speed or water depth warnings without physical damage. If preventative operation restrictions are not considered, then... .

[0223] By modeling the vulnerabilities of the source side, network side, load side, and coupled devices, the disaster intensity index can be uniformly mapped to time-varying failure probability and available capacity coefficient, providing a foundation for the construction of subsequent multi-type failure scenarios.

[0224] To further implement the above technical solution, the specific content of S3 includes:

[0225] Assuming the integrated energy system has a total of Each component (including each line branch, wind farm, photovoltaic power station, coupling equipment, and load node) is represented by an equivalent sub-unit, with each sub-unit included in the component set; the typhoon disaster process is discretized into... At that moment; recording the components in the integrated energy system exist The probability of failure at time is ;

[0226] First, determine the scene sampling scale. Construction for each component A uniform random variable over an interval is sampled stratified in a multidimensional space using the Latin hypercube method to obtain... Each set of samples corresponds to a multi-type fault scenario.

[0227] For the Each scene, note the components. At any moment The fault state is a 0-1 variable. If and only if the element is in the scene Below the time The value is 1 when the first fault occurs, and 0 at all other times to ensure the uniqueness of the initial fault time. Information entropy of a scenario system for:

[0228] ;

[0229] In obtaining Information entropy set corresponding to each scenario Then, the kernel density estimation method is used to fit its probability density distribution, and then the upper quantile of the information entropy is obtained. and lower quantile The middle interval is denoted as In this context, the system information entropy value corresponding to the global maximum point of the probability density function obtained by kernel density estimation is taken as the typical information entropy value. Then, the extreme failure scenario with information entropy value equal to or closest to the typical information entropy value is selected from the scenario set as the typical extreme failure scenario. The typical extreme failure scenario satisfies the constraint that its information entropy does not consider repeated failures in the middle interval and during the disaster.

[0230] .

[0231] It should be noted that:

[0232] In this embodiment, the upper quantile of information entropy is used. and lower quantile Set as 5% quantile and 95% quantile respectively, and denote the middle 90% interval as .

[0233] Through the above steps, this invention extracts representative typical scenarios from a large number of multi-type fault scenarios generated by Latin hypercubes without significantly increasing the computational scale, for use in subsequent multi-energy flow calculations and resilience assessments.

[0234] To further implement the above technical solutions, in S4, each subsystem aims to minimize load reduction within its own system. The objective functions in the optimal load reduction calculation models for the power grid subsystem, gas network subsystem, and heating network subsystem are as follows:

[0235] ;

[0236] ;

[0237] ;

[0238] In the formula, For nodes in the power grid subsystem The amount of power load reduction; For nodes in the gas network subsystem The reduction in gas load; For nodes in the heating network subsystem Heat load reduction; The set of nodes in the power grid subsystem; The set of nodes in the gas network subsystem; The set of nodes in the heating network subsystem;

[0239] The optimal load reduction calculation for the power grid subsystem is based on the Distflow power flow model. Therefore, the constraints of this model are:

[0240] ;

[0241] ;

[0242] ;

[0243] ;

[0244] ;

[0245] ;

[0246] ;

[0247] ;

[0248] ;

[0249] ;

[0250] ;

[0251] In the formula, and The end nodes are respectively The initial node set and initial node of the branch are The set of end nodes of the branch; and branch road and branch roads The flow of active power; and branch road and branch roads The flow of reactive power; and branch road and branch roads Current; and branch road and branch roads resistance; and branch road and branch roads Reactance; and They are nodes The active and reactive power of the gas turbine; and They are nodes Active and reactive power of the combined heat and power unit; and They are nodes The active and reactive power of the wind turbine unit; and They are nodes The active and reactive power of the photovoltaic power station; and They are nodes The active and reactive power required by the load; For nodes Reactive power at load reduction; and They are nodes and Node voltage; It is a very large positive real number; To characterize the branch Binary variables representing the running state; To represent nodes The binary variable representing the working status; Square the voltage value; Square the current value; and These are the available capacity factor and maximum output power of the gas turbine, respectively. and These are the available capacity factor and rated power of the wind turbine, respectively. and These are the available capacity factor and rated power of the photovoltaic power station, respectively.

[0252] The optimal load reduction calculation for the gas network subsystem is based on the Weymouth equation. Therefore, the constraints of the optimal load reduction calculation model for the gas network subsystem are:

[0253] ;

[0254] ;

[0255] ;

[0256] ;

[0257] ;

[0258] ;

[0259] ;

[0260] ;

[0261] ;

[0262] In the formula, As a node of the gas network subsystem The gas source output; For pipelines Traffic; For nodes The gas consumption of the gas turbine; For nodes Gas consumption of combined heat and power units; For nodes The load demand; For pipelines Maximum flow rate; and These represent the positive and negative flow directions of pipeline natural gas, respectively. and They are respectively The maximum and minimum values; and For nodes and nodes The square of the air pressure at that location; and They are respectively The maximum and minimum values; and compressors The squares of the inlet and outlet node pressures; and compressors The maximum and minimum compression ratio; It is a very large positive real number; The compressor is in operating condition.

[0263] The optimal load reduction calculation for the heating network subsystem is based on a thermodynamic and hydraulic model. Therefore, the constraints of this model are:

[0264] ;

[0265] ;

[0266] ;

[0267] ;

[0268] ;

[0269] ;

[0270] ;

[0271] In the formula, This is the node-branch correlation matrix of the heating network; Given the branch flow matrix; for Middle element; For node traffic matrix; The loop incidence matrix; This is the impedance coefficient matrix in the pipeline, which is related to the pipeline parameters; For heating network subsystem nodes The load demand; This is the specific heat capacity of water; for Elements in; For nodes The heating temperature; For nodes The regeneration temperature; Temperature at the end of the pipe; Temperature at the beginning of the pipe; Ambient temperature; This refers to the length of the pipe. The heat conversion coefficient per unit length of the pipe; and These represent the flow rates in each pipe before and after mixing, respectively. and These are the pipe fluid temperatures before and after mixing, respectively; the heating network subsystem is supplied with heat by a combined heat and power unit. and These are the actual and maximum thermal power of the combined heat and power unit, respectively. This is the available capacity factor for combined heat and power (CHP) units.

[0272] To further implement the above technical solution, the specific content of S5 in determining whether the optimal load reduction result can support the operation of the coupled equipment includes:

[0273] The optimal load reduction calculation for the integrated energy system adopts a decoupled approach. The optimal load reduction models of the heating network subsystem, the power grid subsystem, and the gas network subsystem are solved sequentially to obtain the output of the coupled equipment. The coupled equipment includes cogeneration units and gas turbines, all of which are powered by the access nodes in the gas network subsystem. The adjustment is made by judging whether the gas supply of the gas network subsystem nodes can support the gas consumption of the coupled equipment. If the gas supply is lower than the gas consumption, it means that the operation cannot be supported. The upper limit of the output of the coupled equipment is then lowered, and the S4 calculation is re-executed to obtain new reduction results for each subsystem. If the results are higher, the next step can be performed.

[0274] The energy conversion relationship of the coupling device is as follows:

[0275] ;

[0276] ;

[0277] ;

[0278] In the formula, This refers to the actual power generation capacity of the combined heat and power unit; This represents the actual thermal power of the combined heat and power unit. The heat-to-power ratio of a combined heat and power (CHP) unit; For the gas consumption of a combined heat and power unit; The calorific value of natural gas; and These are the heat generation efficiency and power generation efficiency of a combined heat and power (CHP) unit, respectively. This refers to the power generation capacity of the gas turbine. The power generation efficiency of the gas turbine; This refers to the gas consumption of the gas turbine.

[0279] To further implement the above technical solutions and to describe the system's ability to maintain energy supply service levels during a disaster, this invention uses demand assurance as a basic service indicator. The resilience assessment model in S6 includes:

[0280] Demand guarantee of each subsystem Comprehensive demand protection :

[0281] ;

[0282] ;

[0283] In the formula, For any subsystem in an integrated energy system The set of load nodes; For subsystem Load Node The weight of the ranking; and For subsystem node Load demand and reduction; For each subsystem The weight values, where , , They are respectively power grid subsystems Gas network subsystem Heating network subsystem The weights are summed to 1.

[0284] To quantify the speed and phased recovery rate of a system after a disaster, this invention proposes recovery time and recovery rate indices. Recovery time... With recovery rate indicators :

[0285] ;

[0286] ;

[0287] In the formula, This serves as the starting point for the recovery assessment. The earliest time when the system reaches the recovery target; For a period of time Changes in the overall domestic demand guarantee level;

[0288] The cumulative unmet demand during the recovery phase is calculated, including subsystem levels. With comprehensive level :

[0289] ;

[0290] ;

[0291] In the formula, the cumulative unmet demand is... The smaller the value, the lower the degree of supply loss during the recovery phase and the better the recovery effect;

[0292] To further characterize the cumulative deviation of the service level of an integrated energy system under extreme disaster disturbances, this invention utilizes the integrated demand guarantee curve. Based on this, construct a cumulative indicator for comprehensive service gaps. This is used to quantify the cumulative gap between the overall energy supply service level and the ideal protection level throughout the entire disaster process.

[0293] ;

[0294] In the formula, This is the moment the first fault occurred; Index for discrete time periods; The duration of this segment is approximately equivalent to the integral form when using an event-driven discrete time axis. The smaller the value, the smaller the cumulative gap in comprehensive services during the system's resilience and recovery phase under disaster, and the better the recovery effect.

[0295] The invention will be further illustrated below through simulation examples:

[0296] To verify the effectiveness of the method of this invention, a comprehensive energy system including a power grid, gas network, and heating network was selected as a case study for simulation evaluation. The power grid subsystem adopted an IEEE 33-node distribution network structure, the gas network subsystem adopted a 12-node natural gas network structure, and the heating network subsystem adopted a 6-node heating network structure. The energy conversion and combined supply of electricity, gas, and heat are achieved through coupling equipment such as combined heat and power units and gas turbines. The structural diagram is shown below. Figure 2 Typhoon disaster parameters and rainfall were obtained from historical statistical data. Based on the equivalent intensity of the disaster, the time-varying failure probabilities of wind power, photovoltaic, power lines, load nodes, and coupling equipment were calculated as follows: Figure 3 As shown, based on this, Latin hypercube sampling is used to generate a set of multiple types of fault scenarios. Typical scenarios are selected for evaluation through the information entropy probability density distribution, resulting in the system's comprehensive information entropy probability distribution diagram, as shown below. Figure 4 As shown, an optimal load reduction model for the integrated energy system is then established to obtain the load reduction amount and energy supply level for each time period. Based on the above results, the set resilience-related indicators are further calculated, thereby achieving unified quantification and comparable analysis of the resilience level of the integrated energy system under multiple types of fault scenarios.

[0297] This embodiment sets up two different cases to compare the effectiveness of the solution, as follows:

[0298] Case 1: Considering only the impact of NK line faults;

[0299] Case 2: Consider the impact of multiple types of source-grid-load faults.

[0300] The results are shown in Table 1. Figure 5 and Figure 6 As shown.

[0301] Table 1 shows the repair time, repair rate, and cumulative unmet demand index results for the two comparative schemes. It can be seen that the recovery time to the threshold is basically the same for both schemes. This is because, in this example, the system recovery process is simultaneously constrained by the timing of gradually closing the power supply channels according to the predetermined repair step size, as well as by the combined constraints of the adjustable unit output limit, gas supply constraints, and power flow accessibility constraints. When the main power supply channel has not yet recovered to the accessibility level that meets the threshold, relying solely on the island's adjustable resources is insufficient to further improve the overall service level. Therefore, the key bottleneck to reaching the threshold is dominated by the network channel recovery rhythm, rather than by the details of the source-side output. On the other hand, considering multiple types of faults, the source-side renewable energy undergoes a de-ramp recovery process in the initial stage of repair, leading to a decrease in available power supply capacity in the initial stage of repair under the same repair sequence. This significantly increases the cumulative service gap during the recovery phase. Figure 5 It can be seen that the recovery rate index differs significantly when the first remedial measures are implemented.

[0302] Depend on Figure 5 It can be concluded that during the fault occurrence phase, the overall demand guarantee level of both schemes decreases in a stepwise manner. This is because this example assumes that during the fault occurrence phase, the renewable energy source on the source side shuts down due to safety protection strategies (consistent in both schemes). Therefore, the difference at this stage is mainly caused by the limited network power supply channels due to line disconnection, which in turn significantly reduces the overall system service level. From the end of the fault to the beginning of the repair, the overall demand guarantee level remains at a low level, indicating that the system's resilience under disaster disturbances mainly depends on the supply support of existing available channels and adjustable resources. After entering the repair phase, Scheme 1 only considers line faults and assumes that renewable energy equipment can directly participate in power supply according to the daily characteristic curve after the start of repair, resulting in a more rapid recovery of the overall demand guarantee level. Scheme 2 further considers the damage to the renewable energy source on the source side and the derating-recovery process after repair, which limits the available output in the early stage of repair. The overall demand guarantee level shows a more obvious lag in recovery under the same repair time sequence, thus more realistically reflecting the impact mechanism of multiple types of faults on the recovery process.

[0303] Depend on Figure 6 It can be concluded that there are significant differences in the overall service gap between the two schemes during the repair phase. Because Scheme 2 has limited available renewable energy output reduction in the initial repair phase compared to Scheme 1, even if the lines gradually recover at the same pace, the system still needs to maintain the service level through more intensive load reduction or greater compensation from adjustable units. Therefore, its cumulative deviation increment during the recovery phase is larger, and the final cumulative value is higher. Furthermore, combined with the results in Table 1, it can be concluded that measuring the impact of multiple types of faults solely by the time to recovery to the threshold easily underestimates the impact. The proposed method, by introducing source-side damage and recovery process modeling, makes the overall demand assurance curve and its cumulative deviation index more discriminative of different fault scenarios, providing a more engineering-interpretive quantitative basis for subsequent resilience assessment and strategy verification.

[0304] Table 1 Comparison of resilience indices under different cases

[0305] ;

[0306] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults, characterized in that, Includes the following steps: S1: Obtain historical meteorological data and spatial location information of the integrated energy system architecture, and then construct typhoon wind field and rainstorm-water accumulation equivalent model; S2: Based on the typhoon wind field and rainstorm-water accumulation equivalent model, vulnerability models and available capacity coefficient models related to disaster intensity indicators are constructed for the source-grid-load side and coupled equipment, respectively. S3: Sample and generate a set of multiple types of failure scenarios, and select typical extreme failure scenarios from them based on the vulnerability model and system information entropy index; S4: Based on the optimal load reduction models of the power grid subsystem, gas network subsystem, and heating network subsystem in the integrated energy system, calculate the optimal load reduction results under each typical extreme fault scenario based on the available capacity coefficient model. S5: Based on the optimal load reduction result obtained in S4, determine whether the coupling equipment can support operation by measuring the gas consumption of the coupling equipment and the gas supply of the connected gas network node. If not, reduce the upper limit of the output of the coupling equipment and re-execute S4. If it can, output the current optimal load reduction result and proceed to S6. S6: Based on the output of S5, construct a resilience assessment model to calculate the system resilience index and complete the resilience assessment.

2. The comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 1, characterized in that, The specific content of constructing the typhoon wind field and rainstorm-water accumulation equivalent model in S1 includes: Let the hourly rainfall intensity during the typhoon disaster be... The equivalent model of the typhoon wind field considering wind and rain loads is as follows: ； ； In the formula, To account for the equivalent wind speed affected by rainfall, It is a rainstorm type. This is the peak of rainfall. Let be the wind speed at any component in the typhoon wind field; Rainstorm-Floodwater Equivalent Model: ； In the formula, Due to the depth of the accumulated water, Reflecting the extreme water depth under extreme rainfall conditions, Control the speed at which the water depth approaches saturation. This refers to the cumulative effective rainfall.

3. The comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 2, characterized in that, The Batts model was used to simulate wind speed changes during the passage of a typhoon. The radius of the typhoon's maximum wind speed was assumed to be... The average wind speed at the radius of maximum wind speed is The distance between the position of any component and the center of the typhoon is The wind speed at any component in the typhoon wind field is... for: ； In the formula, This is the radial attenuation coefficient of typhoon intensity; The difference between the outer pressure of the typhoon cyclone and the pressure at the center of the typhoon. Impact, after the typhoon makes landfall The size of the typhoon decreases as its intensity diminishes. From the maximum wind speed gradient and typhoon movement speed Joint decision: ； ； ； ； ； In the formula, This is the atmospheric pressure value under standard conditions; This refers to the central air pressure value of the typhoon. The radius of maximum wind speed; The parameters for the Coriolis force during Earth's rotation; This is the Earth's rotational angular velocity; The latitude of the typhoon.

4. The comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 2, characterized in that, Let the period of typhoon impact be Rainstorm type Triangular rain type adopted: ； In the formula, This refers to the moment when the peak of the rainstorm occurs; The half-duration of the triangular rain pattern represents the extension forward and backward from the peak time. Significant rainfall occurred within the timeframe.

5. The comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 2, characterized in that, The calculation methods include: Typhoon Affected Period The total cumulative rainfall over the entire process meets the specified extreme rainfall total constraint. for: ； Considering that the distribution network nodes are distributed in different areas, and each zone corresponds to different terrain elevations and drainage capacities; define the effective catchment coefficient for each zone. The equivalent rainfall intensity during the surface water accumulation process is... for: ； Corresponding cumulative effective rainfall for: 。 6. The comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 1, characterized in that, S2 details the construction of vulnerability models related to disaster intensity indicators for the source side, grid side, load side, and coupled equipment. These models include: for the source side, failure probability models for wind turbines and photovoltaic power plants; for the grid side, failure probability models for distribution lines; for the load side, failure probability models for load nodes; and for coupled equipment, failure probability models for the coupled equipment itself. Wind turbine: (The rest of the text appears to be a list of components and their functions.) The equivalent wind speed at the typhoon generator is The median wind speed at which severe damage is The logarithmic standard deviation is Then the first Typhoon machines at all times Failure probability model for: ； In the formula, It is the standard normal distribution function; Photovoltaic power plants include two categories: rooftop photovoltaics and ground-mounted photovoltaics; among them, For rooftop photovoltaics, let the equivalent wind speed of the rooftop photovoltaic array be... , and Given the median wind speed and logarithmic standard deviation corresponding to severe damage to the rooftop photovoltaic (PV) support structure, then the failure probability model for rooftop PV systems is... for: ； For ground-mounted photovoltaic (PV) systems, let the equivalent wind speed at the PV power station be... The water depth at the location of the station's key electrical equipment is [missing information]. Then the wind-induced damage probability model With flood failure probability model They are respectively: ； ； In the formula, and The median wind speed and logarithmic standard deviation corresponding to severe damage to ground-mounted photovoltaic support structures; The water depth corresponding to a 50% probability of ground-mounted photovoltaic power station shutdown; The slope parameter of the S-shaped function for photovoltaic power plants; The total failure probability model of ground-mounted photovoltaic power for: ； Power distribution lines include lines and towers; among them, lines The The conductor and the first Failure probability model of tower and They are respectively: ； ； In the formula, and These represent the failure probabilities under normal operating conditions for the lines and towers, respectively. For the line Equivalent wind speed at the location; and These are the influence coefficients of wind direction on line and tower faults, respectively; and These refer to the design wind speed and the maximum wind speed that the line can withstand, respectively. and The design of the tower should be based on the wind speed it can withstand and the maximum wind speed it can withstand. and These are the sensitivity coefficients of the lines and towers to wind speed, respectively, and are negatively correlated with the strength of the line and tower components. Comprehensive Fault Probability Model for Power Distribution Lines for: ； In the formula, and These refer to the number of conductor spans and the number of towers, respectively. Failure probability model of load nodes for: ； In the formula, For load nodes The depth of the accumulated water; This is the slope parameter of the S-shaped function for the load node; The water depth corresponding to a 50% probability of failure for this load node; Failure probability model of coupling device for: ； In the formula, The slope parameter of the S-curve function for this coupling device; For coupling devices The depth of the accumulated water; The water depth at which the failure probability of the coupling device is 50%.

7. The comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 1, characterized in that, The available capacity factor model in S2 is: ； ； In the formula, Indicates source side and coupling device In failure scenarios ,time The percentage of capacity that can still be put into operation; when components When it is a wind turbine or a photovoltaic power station, it is equivalently divided into Sub-units For the first The rated capacity of each sub-unit For the first Each subunit in the scene Down to the moment The failure state; If and only if the subunit is in the scene The value is 1 when the first fault occurs, and 0 at other times. This is the preventative operation restriction factor on the operation side, used to reflect situations where power is limited or operations are suspended due to wind speed or water depth warnings, even without physical damage. If preventative operation restrictions are not considered, then... .

8. The integrated energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 1, characterized in that, The specific content of S3 includes: Assuming the integrated energy system has a total of The components are divided into elements, where wind farms and photovoltaic power stations are equivalently divided into sub-units, and each sub-unit is included in the component set; the typhoon disaster process is discretized into... At that moment; recording the components in the integrated energy system exist The probability of failure at time is ; First, determine the scene sampling scale. Construction for each component A uniform random variable over an interval is sampled stratified in a multidimensional space using the Latin hypercube method to obtain... Each set of samples corresponds to a multi-type fault scenario. For the Each scene, note the components. At any moment The fault state is a 0-1 variable. If and only if the element is in the scene Below the time The value is 1 when the first fault occurs, and 0 at all other times to ensure the uniqueness of the initial fault time. Information entropy of a scenario system for: ； In obtaining Information entropy set corresponding to each scenario Then, the kernel density estimation method is used to fit its probability density distribution, and then the upper quantile of the information entropy is obtained. and lower quantile The middle interval is denoted as In this context, the system information entropy value corresponding to the global maximum point of the probability density function obtained by kernel density estimation is taken as the typical information entropy value. Then, the extreme failure scenario with information entropy value equal to or closest to the typical information entropy value is selected from the scenario set as the typical extreme failure scenario. The typical extreme failure scenario satisfies the constraint that its information entropy does not consider repeated failures in the middle interval and during the disaster. 。 9. The comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 1, characterized in that, In S4, the objective functions in the optimal load reduction calculation models for the power grid subsystem, gas network subsystem, and heating network subsystem are, in order: ； ； ； In the formula, For nodes in the power grid subsystem The amount of power load reduction; For nodes in the gas network subsystem The reduction in gas load; For nodes in the heating network subsystem Heat load reduction; The set of nodes in the power grid subsystem; The set of nodes in the gas network subsystem; The set of nodes in the heating network subsystem; The constraints of the optimal load reduction calculation model for the power grid subsystem are: ； ； ； ； ； ； ； ； ； ； ； In the formula, and The end nodes are respectively The initial node set and initial node of the branch are The set of end nodes of the branch; and branch road and branch roads The flow of active power; and branch road and branch roads The flow of reactive power; and branch road and branch roads Current; and branch road and branch roads resistance; and branch road and branch roads Reactance; and They are nodes The active and reactive power of the gas turbine; and They are nodes Active and reactive power of the combined heat and power unit; and They are nodes The active and reactive power of the wind turbine unit; and They are nodes The active and reactive power of the photovoltaic power station; and They are nodes The active and reactive power required by the load; For nodes Reactive power at load reduction; and They are nodes and Node voltage; It is a very large positive real number; To characterize the branch Binary variables representing the running state; To represent nodes The binary variable representing the working status; Square the voltage value; Square the current value; and These are the available capacity factor and maximum output power of the gas turbine, respectively. and These are the available capacity factor and rated power of the wind turbine, respectively. and These are the available capacity factor and rated power of the photovoltaic power station, respectively. The constraints of the optimal load reduction calculation model for the gas network subsystem are: ； ； ； ； ； ； ； ； ； In the formula, As a node of the gas network subsystem The gas source output; For pipelines Traffic; For nodes The gas consumption of the gas turbine; For nodes Gas consumption of combined heat and power units; For nodes The load demand; For pipelines Maximum flow rate; and These represent the positive and negative flow directions of pipeline natural gas, respectively. and They are respectively The maximum and minimum values; and For nodes and nodes The square of the air pressure at that location; and They are respectively The maximum and minimum values; and compressors The squares of the inlet and outlet node pressures; and compressors The maximum and minimum compression ratio; It is a very large positive real number; The compressor is in operating condition. The constraints of the optimal load reduction calculation model for the heating network subsystem are: ； ； ； ； ； ； ； In the formula, This is the node-branch correlation matrix of the heating network; Given the branch flow matrix; for Middle element; For node traffic matrix; The loop incidence matrix; This is the impedance coefficient matrix in the pipeline, which is related to the pipeline parameters; For heating network subsystem nodes The load demand; This is the specific heat capacity of water; for Elements in; For nodes The heating temperature; For nodes The regeneration temperature; Temperature at the end of the pipe; Temperature at the beginning of the pipe; The ambient temperature; This refers to the length of the pipe. The heat conversion coefficient per unit length of the pipe; and These represent the flow rates in each pipe before and after mixing, respectively. and These are the pipe fluid temperatures before and after mixing, respectively; the heating network subsystem is supplied with heat by a combined heat and power unit. and These are the actual and maximum thermal power of the combined heat and power unit, respectively. This is the available capacity factor for combined heat and power (CHP) units.

10. The comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 1, characterized in that, The specific steps in S5 to determine whether the optimal load reduction result can support the operation of the coupled equipment include: The optimal load reduction calculation for the integrated energy system adopts a decoupled approach. The optimal load reduction models of the heating network subsystem, the power grid subsystem, and the gas network subsystem are solved sequentially to obtain the output of the coupled equipment. The coupled equipment includes cogeneration units and gas turbines, all of which are powered by the access nodes in the gas network subsystem. The adjustment is made by judging whether the gas supply of the gas network subsystem nodes can support the gas consumption of the coupled equipment. If the gas supply is lower than the gas consumption, it means that the operation cannot be supported. The upper limit of the output of the coupled equipment is then lowered, and the S4 calculation is re-executed to obtain new reduction results for each subsystem. If the results are higher, the next step can be performed. The energy conversion relationship of the coupling device is as follows: ； ； ； In the formula, This refers to the actual power generation capacity of the combined heat and power unit; This represents the actual thermal power of the combined heat and power unit. The heat-to-power ratio of a combined heat and power (CHP) unit; For the gas consumption of a combined heat and power unit; The calorific value of natural gas; and These are the heat generation efficiency and power generation efficiency of a combined heat and power (CHP) unit, respectively. This refers to the power generation capacity of the gas turbine. The power generation efficiency of the gas turbine; This refers to the gas consumption of the gas turbine.

11. The comprehensive energy system resilience assessment method considering multiple types of source-grid-load faults according to claim 1, characterized in that, The resilience assessment model in S6 includes: Demand guarantee of each subsystem Comprehensive demand protection : ； ； In the formula, For any subsystem in an integrated energy system The set of load nodes; For subsystem Load Node The weight of the ranking; and For subsystem node Load demand and reduction; For each subsystem The weight values, where , , They are respectively power grid subsystems Gas network subsystem Heating network subsystem The weights are summed to 1. Recovery time With recovery rate indicators : ； ； In the formula, This serves as the starting point for the recovery assessment; The earliest time when the system reaches the recovery target; For a period of time Changes in the overall domestic demand guarantee level; The cumulative unmet demand during the recovery phase is calculated, including subsystem levels. With comprehensive level : ； ； In the formula, the cumulative unmet demand is... The smaller the value, the lower the degree of supply loss during the recovery phase and the better the recovery effect; Constructing a cumulative indicator for comprehensive service gaps This is used to quantify the cumulative gap between the overall energy supply service level and the ideal protection level throughout the entire disaster process. ； In the formula, This is the moment the first fault occurred; Index for discrete time periods; For the first The duration of a discrete time period can be approximately equivalent to the integral form when using an event-driven discrete time axis. The smaller the value, the smaller the cumulative gap in comprehensive services during the system's resilience and recovery phase under disaster, and the better the recovery effect.

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