Distributed photovoltaic access capacity evaluation method considering extreme weather failure risk

Abstract

The application discloses a kind of distributed photovoltaic access capacity evaluation methods considering extreme weather failure risk, comprising;Establish the dynamic coupling model of meteorological intensity and distribution network equipment failure rate, generate the failure probability distribution evolving with time and space;Typical failure scenario set under extreme disaster is constructed;Establish the double-layer robust optimization model with the maximum of distributed photovoltaic access capacity as upper goal, with the minimum of system active defense cost under extreme scenario as lower goal;Introduce intelligent soft switch, energy storage fast response and network reconfiguration as active defense means in the lower model of double-layer robust optimization model;The double-layer robust optimization model is iteratively solved, and the limit access capacity distribution scheme of each node distributed photovoltaic is output.The application accurately measures and calculates photovoltaic limit access capacity by coupling meteorological and equipment failure relationship, constructing double-layer robust model and introducing multiple defense means, balances photovoltaic consumption and system resilience, and guarantees the safe and stable operation of distribution network.

Description

Technical Field

[0001] This invention belongs to the field of power distribution networks and relates to power system planning and operation technology. Specifically, it relates to a method for assessing the access capacity of distributed photovoltaic power generation that takes into account the risk of failure due to extreme weather. Background Technology

[0002] With the acceleration of the global energy transition, distributed photovoltaic (PV) power, with its advantages of being clean, low-carbon, and capable of local consumption, has seen a continuous increase in its penetration rate in distribution networks, becoming a key path to alleviate energy shortages and environmental pressures. However, the random fluctuations and intermittent output of distributed PV power, coupled with the limitations of the original distribution network topology, make large-scale integration prone to problems such as voltage exceeding limits and power flow disturbances, thus restricting the full release of PV's consumption potential.

[0003] More significantly, frequent extreme weather events lead to a surge in the probability of distribution network failures, and initial faults can easily trigger Nk-level cascading trips through topological correlations, resulting in decreased system resilience and large-scale power outages. Traditional photovoltaic (PV) capacity assessment methods are mostly based on normal operating conditions, failing to fully consider the dynamic coupling relationship between extreme weather and equipment failure rates, and lacking accurate simulation of the cascading failure evolution process. This leads to overly optimistic calculation results, and in practical applications, they are prone to safety risks due to PV grid disconnection and insufficient system capacity under extreme scenarios.

[0004] Existing proactive defense measures are slow to respond and lack flexibility, making it difficult to cope with complex fault situations under extreme disasters. At the same time, conventional assessment models are mostly single-layer optimization structures, which fail to balance the dual requirements of maximum photovoltaic grid connection and system resilience assurance, and cannot provide a scientific basis for distribution network planning that takes into account both economy and safety. Summary of the Invention

[0005] Purpose of the invention: In order to address the problem that traditional photovoltaic access capacity assessment does not fully consider the risk of cascading failures due to extreme weather and lacks proactive defense, this invention provides a distributed photovoltaic access capacity assessment method that takes into account the risk of failures due to extreme weather. By coupling the relationship between meteorology and equipment failure, constructing a two-layer robust model and introducing multiple defense measures, this method can accurately calculate the photovoltaic limit access capacity, balance photovoltaic absorption and system resilience, and ensure the safe and stable operation of the distribution network.

[0006] Technical solution: To achieve the above objectives, the present invention provides a method for assessing the access capacity of distributed photovoltaic power generation considering the risk of failure due to extreme weather, comprising the following steps;

[0007] S1: Obtain historical extreme weather data and power grid geographic information for the target area, establish a dynamic coupling model between weather intensity and power grid equipment failure rate, and generate a failure probability distribution that evolves over time and space.

[0008] S2: Based on Monte Carlo sampling and topological connectivity analysis, simulate the Nk cascade tripping process caused by the initial fault under extreme weather conditions, and construct a set of typical fault scenarios under extreme disasters;

[0009] S3: Establish a two-layer robust optimization model with the goal of maximizing the access capacity of distributed photovoltaic power as the upper-layer objective and minimizing the cost of active defense of the system under extreme scenarios as the lower-layer objective;

[0010] S4: In the lower layer of the two-layer robust optimization model, intelligent soft switching, fast energy storage response and network reconfiguration are introduced as active defense measures to explore the extreme survivability of the system by relaxing the operating constraints.

[0011] S5: The column and constraint generation algorithm is used to iteratively solve the two-layer robust optimization model. The system power supply retention rate under extreme weather cascading failure scenarios is not lower than the preset minimum threshold as an extreme resilience index constraint. The output is a distributed photovoltaic limit access capacity distribution scheme that satisfies the extreme resilience index constraint for each node.

[0012] Furthermore, the dynamic coupling model in step S1 is expressed as follows:

[0013]

[0014] in, For the line exist Real-time failure rate at any given moment; This represents the baseline failure rate under normal weather conditions. For the line The geographical location is Real-time weather intensity at any given moment; The critical threshold for meteorological intensity that leads to a surge in failure rate; and These are the meteorological vulnerability coefficients, used to characterize the sensitivity of the line to extreme weather.

[0015] Furthermore, the construction of the typical fault scenario set in step S2 includes:

[0016] For each time step Based on real-time failure rate Perform state sampling to determine the initial set of faulty lines;

[0017] Perform topology connectivity verification. If system disconnection occurs, calculate power flow distribution. If there are lines exceeding power flow limits, disconnect the lines exceeding the limits according to overload protection logic and update the topology.

[0018] Repeat the above process until the system power flow converges and no new lines trip, and treat the final stable failure state as a fault scenario. Add it to the typical fault scenario set Ω.

[0019] Furthermore, the mathematical expression of the upper-level model of the two-layer robust optimization model in step S3 is as follows:

[0020]

[0021] in, This refers to the set of nodes in a distribution network that are permitted to install distributed photovoltaic (PV) systems. For decision variables, represent nodes. The photovoltaic access capacity;

[0022] The constraints include:

[0023]

[0024] in, This is the maximum physical installation limit, which is restricted by the node installation area or transformer capacity.

[0025] Furthermore, in step S3, the lower-level model of the two-layer robust optimization model is based on a given upper-level capacity scheme. and worst-case failure scenarios To minimize the overall operational losses of the system:

[0026]

[0027] in, For nodes exist Active load shedding at any given moment; The unit load shedding penalty cost represents the resilience loss weight. For intelligent soft switches Operating losses; These are, respectively, a set of binary variables containing network reconfiguration states and a set of continuous variables containing voltage and current.

[0028] Furthermore, the two-layer robust optimization model in step S3 satisfies the system extreme limit survival rate constraint:

[0029]

[0030] in, For nodes The original load demand; This is the minimum power supply retention rate threshold allowed for the distribution network under extreme disasters.

[0031] Furthermore, step S4 introduces an active defense constraint of intelligent soft switching to provide emergency power support after a fault, the model of which is as follows:

[0032]

[0033] in, and SOP access nodes The active and reactive power injected from the side; For the first The rated apparent capacity of each SOP unit; This refers to the power loss within the SOP device.

[0034] Furthermore, the methods for introducing energy storage rapid response as an active defense measure in step S4 include:

[0035] The net output power of the energy storage system is expressed as:

[0036]

[0037] Energy storage charging and discharging power meets:

[0038]

[0039]

[0040] Energy storage state of charge satisfies:

[0041]

[0042]

[0043] in, For nodes Energy storage system at all times Net output power; and These are the energy storage discharge power and the charging power, respectively. and These are the maximum discharge power and the maximum charging power of energy storage, respectively. It is in a state of energy storage charge; and These are the lower and upper limits of the energy storage state of charge, respectively; and These are the energy storage charging efficiency and discharging efficiency, respectively. For time intervals.

[0044] Furthermore, the methods for introducing network reconfiguration as an active defense mechanism in step S4 include:

[0045] Let the state variables of the line switch be:

[0046]

[0047] in, Indicates the line Put into operation Indicates the line disconnect;

[0048] Line power and switch status satisfy:

[0049]

[0050] in, For the line At any moment Transmission power, This represents the maximum allowable transmission power of the line.

[0051] For faulty lines, the following should be met:

[0052]

[0053] in, This is a collection of lines that experienced failures due to extreme weather conditions.

[0054] To avoid frequent switching actions, the number of switching actions must meet the following requirement:

[0055]

[0056] in, A collection of distribution network lines; This is the initial switching state of the line; The maximum number of switching actions allowed.

[0057] Furthermore, in step S5, a column and constraint generation algorithm is used to decompose the two-layer robust optimization model into a main problem and sub-problems for alternating iterative solution, wherein:

[0058] Main problem: Optimize photovoltaic capacity under a known subset of adverse scenarios. This provides a lower bound for capacity planning schemes;

[0059] Subproblems: those passed in by the fixed main problem Find the worst-case scenario in the fault scenario set Ω that maximizes the system load shedding. *, Verify whether the current capacity scheme meets the resilience constraints;

[0060] Iterative logic: If the adverse scenario found by the subproblem causes the resilience index to be violated, then the variables and constraints corresponding to the scenario are added back to the main problem and solved again until the convergence gap between the upper and lower bounds is less than the preset error.

[0061] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0062] 1. Improved accuracy and security of assessment results. Unlike traditional static assessment methods that ignore extreme weather conditions, this invention establishes a dynamic coupling model between weather intensity and equipment failure rate, and combines Monte Carlo sampling to simulate the evolution of Nk cascading trips triggered by initial faults. This method can accurately characterize the vulnerable links of the distribution network under extreme disasters, effectively avoiding the problem of overly optimistic estimation of photovoltaic access capacity due to ignoring the risk of cascading failures, thereby reducing the risk of large-scale power outages and photovoltaic grid disconnection in extreme scenarios.

[0063] 2. Significantly enhances the extreme survivability of the distribution network. This invention introduces smart soft switching (SOP), rapid energy storage response, and network reconfiguration as proactive defense measures in the evaluation model. Through the flexible power transfer of SOP and the flexible reconfiguration of network topology, the operating potential of the system under fault conditions is fully explored, ensuring that the system can still maintain a minimum retainability ratio (RSR) in the event of extreme faults, thus preventing system collapse.

[0064] 3. A scientific balance is achieved between photovoltaic (PV) grid integration benefits and system operational resilience. This invention constructs a two-layer robust programming model. While the upper layer aims to maximize the access capacity of distributed PV, the lower layer is constrained by minimizing the cost of active defense. Through iterative solution using column and constraint generation algorithms, this invention not only provides a scientific basis for the site selection and capacity determination of distributed PV, but also effectively balances the grid's safety and economic needs in extreme environments. Attached Figure Description

[0065] Figure 1 This is a flowchart of the method of the present invention;

[0066] Figure 2 This is a diagram showing the dynamic coupling relationship between meteorological intensity and equipment failure rate.

[0067] Figure 3 Evolution diagram of distribution network cascading fault resilience under different strategies;

[0068] Figure 4 A comparison chart of photovoltaic access capacity before and after considering extreme risks;

[0069] Figure 5 Sensitivity analysis of active defense capabilities to photovoltaic absorption potential. Detailed Implementation

[0070] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0071] Example 1:

[0072] like Figure 1 As shown in the figure, this embodiment provides a method for assessing the access capacity of distributed photovoltaic power generation that considers the risk of cascading failures due to extreme weather and proactive defense strategies, including the following steps;

[0073] S1: Obtain historical extreme weather data (typhoons, rainstorms) and power grid geographic information of the target area, establish a dynamic coupling model between meteorological intensity and power distribution network equipment failure rate, and generate a failure probability distribution that evolves over time and space;

[0074] The dynamic coupling model is expressed as follows:

[0075]

[0076] in, For the line exist Real-time failure rate at any given moment; This represents the baseline failure rate under normal weather conditions. For the line The geographical location is Real-time weather intensity at any given moment; The critical threshold for meteorological intensity that leads to a surge in failure rate; and These are the meteorological vulnerability coefficients, used to characterize the sensitivity of the line to extreme weather.

[0077] S2: Based on Monte Carlo sampling and topological connectivity analysis, simulate the Nk cascade tripping process caused by the initial fault under extreme weather conditions, and construct a set of typical fault scenarios under extreme disasters;

[0078] The construction of a typical failure scenario set includes:

[0079] For each time step Based on real-time failure rate Perform state sampling to determine the initial set of faulty lines;

[0080] Perform topology connectivity verification. If system disconnection occurs, calculate power flow distribution. If there are lines exceeding power flow limits, disconnect the lines exceeding the limits according to overload protection logic and update the topology.

[0081] Repeat the above process until the system power flow converges and no new lines trip, and treat the final stable failure state as a fault scenario. Add it to the typical fault scenario set Ω.

[0082] S3: Establish a two-layer robust optimization model with the goal of maximizing the access capacity of distributed photovoltaic power as the upper-layer objective and minimizing the cost of active defense of the system (load shedding and operating costs) under extreme scenarios as the lower-layer objective;

[0083] The mathematical expression of the upper-level model of the two-layer robust optimization model is as follows:

[0084]

[0085] in, This refers to the set of nodes in a distribution network that are permitted to install distributed photovoltaic (PV) systems. For decision variables, represent nodes. The photovoltaic access capacity;

[0086] The constraints include:

[0087]

[0088] in, This is the maximum physical installation limit, which is restricted by the node installation area or transformer capacity.

[0089] The lower-level model of the two-level robust optimization model is based on the upper-level capacity scheme. and worst-case failure scenarios To minimize the overall operational losses of the system:

[0090]

[0091] in, For nodes exist Active load shedding at any given moment; The unit load shedding penalty cost represents the resilience loss weight. For intelligent soft switches Operating losses; These are, respectively, a set of binary variables containing network reconfiguration states and a set of continuous variables containing voltage and current.

[0092] The two-layer robust optimization model satisfies the system's extreme limit survival rate (RSR) constraint:

[0093]

[0094] in, For nodes The original load demand; This is the minimum allowable power retention rate threshold for the distribution network under extreme disasters; this constraint ensures the calculated photovoltaic access capacity. The system's resilience in extreme scenarios will not fall below the safety threshold due to the photovoltaic grid disconnection effect.

[0095] S4: Introduce smart soft switching (SOP), fast energy storage response, and network reconfiguration as active defense measures in the lower layer of the two-layer robust optimization model, and explore the extreme survivability of the system by relaxing operational constraints.

[0096] An active defense constraint, SOP, is introduced to provide emergency power support after a failure. Its model is as follows:

[0097]

[0098] in, and SOP access nodes The active and reactive power injected from the side; For the first The rated apparent capacity of each SOP unit; This constraint limits the power loss within the SOP device; it ensures that when a fault causes a partial line disconnection, the SOP can achieve flexible power transfer across the tie switch, thereby improving the system's survivability.

[0099] Energy storage systems are used to rapidly provide active power support after extreme weather failures, alleviating power shortages in affected areas and reducing the amount of active load shedding. The net output power of an energy storage system is expressed as:

[0100]

[0101] Energy storage charging and discharging power meets:

[0102]

[0103]

[0104] Energy storage state of charge satisfies:

[0105]

[0106]

[0107] in, For nodes Energy storage system at all times Net output power; and These are the energy storage discharge power and the charging power, respectively. and These are the maximum discharge power and the maximum charging power of energy storage, respectively. It is in a state of energy storage charge; and These are the lower and upper limits of the energy storage state of charge, respectively; and These are the energy storage charging efficiency and discharging efficiency, respectively. For time intervals.

[0108] Network reconfiguration is used to isolate faulty lines and re-establish power supply paths after extreme weather causes partial line outages. This is achieved by changing the states of sectionalizing switches and tie switches, thereby improving the power supply capacity to loads in non-faulty areas. Let the line switch state variables be:

[0109]

[0110] in, Indicates the line Put into operation Indicates the line disconnect;

[0111] Line power and switch status satisfy:

[0112]

[0113] in, For the line At any moment Transmission power, This represents the maximum allowable transmission power of the line.

[0114] For faulty lines, the following should be met:

[0115]

[0116] in, This is a collection of lines that experienced failures due to extreme weather conditions.

[0117] To avoid frequent switching actions, the number of switching actions must meet the following requirement:

[0118]

[0119] in, A collection of distribution network lines; This is the initial switching state of the line; The maximum number of switching actions allowed.

[0120] Therefore, rapid energy storage response is reflected through constraints on energy storage charging and discharging power and state of charge, while network reconfiguration is reflected through constraints on line switch state variables, fault line isolation, and the number of switch actions. Together with intelligent soft switching, these two serve as active defense mechanisms in the lower-level model, used to reduce load shedding in extreme weather cascading failure scenarios and improve system power supply retention.

[0121] S5: The column and constraint generation algorithm is used to iteratively solve the two-layer robust optimization model. The system power supply retention rate under extreme weather cascading failure scenarios is not lower than the preset minimum threshold as an extreme resilience index constraint. The output is a distributed photovoltaic limit access capacity distribution scheme that satisfies the constraint for each node.

[0122] In this embodiment, a column and constraint generation algorithm is used to decompose the two-layer robust optimization model into a main problem and sub-problems, which are then solved iteratively and alternately.

[0123] Main problem: Optimize photovoltaic capacity under a known subset of adverse scenarios. This provides a lower bound for capacity planning schemes;

[0124] Subproblems: those passed in by the fixed main problem Find the worst-case scenario in the fault scenario set Ω that maximizes the system load shedding. * Verify whether the current capacity scheme meets the resilience constraints;

[0125] Iterative logic: If the adverse scenario found by the subproblem causes the resilience index to be violated, then the variables and constraints corresponding to the scenario are added back to the main problem and solved again until the convergence gap between the upper and lower bounds is less than the preset error.

[0126] Example 2:

[0127] To verify the effectiveness of the method of the present invention, the following standard calculation examples are used for testing in this embodiment:

[0128] Test system: Improved IEEE 33-node distribution network.

[0129] Meteorological scenario: Simulates a typical typhoon passage process, lasting 24 hours.

[0130] Wind speed model: The Rankine vortex model was used to simulate the typhoon wind field. The wind center passed through the power grid area in the 12th hour, with the maximum wind speed reaching 35 m / s.

[0131] Comparison of options:

[0132] Option 1 (Deterministic Planning): Ignoring extreme weather and cascading failures, only considering the N-1 safety criterion.

[0133] Option 2 (Traditional Robust Planning): Considers extreme weather risks, but lacks proactive defense measures and relies solely on load shedding.

[0134] Option 3 (the method of this invention): Considering the cascading failures caused by extreme weather + SOP / reconfiguration / energy storage active defense strategy.

[0135] Parameter settings:

[0136] SOP access location: between node 18 and node 33, with a capacity of 0.5 MW.

[0137] Minimum Survival Rate Threshold (RSR): 0.85.

[0138] Figure 2 This demonstrates the spatiotemporal evolution of wind speed and line failure rate during typhoon passage, from... Figure 2 It can be seen that during the period from T=8h to T=16h, as the wind speed exceeds the critical threshold of 20m / s, the failure probability of the power distribution network equipment exhibits a non-linear exponential increase, and reaches its highest point at the peak wind speed at T=12h.

[0139] Conclusion: This study verifies the necessity of the dynamic coupling model proposed in this invention. Traditional static failure rate models cannot capture this risk abrupt change process, which can easily lead to system collapse caused by photovoltaic grid disconnection at extreme moments.

[0140] Figure 3 The load supply capacity of the system under different strategies when subjected to Nk cascading failures was compared, among which:

[0141] Option 2 (orange line): It adopts traditional robust planning but lacks active defense. After the fault occurs, T=10h, in order to prevent cascading tripping, the system is forced to cut off about 40% of the load, and the fault recovery is slow.

[0142] Option 3 (This invention, green line): After introducing SOP and network reconfiguration, the system utilizes the flexible power transfer capability of SOP to effectively bypass the fault area. Although there is still a small amount of load shedding, it is always maintained above the minimum survival rate RSR = 0.85, and power supply is quickly restored after the wind speed decreases. The proactive defense strategy of this invention significantly improves the survivability of the distribution network under extreme disasters.

[0143] Figure 4 The photovoltaic access capacity results for key nodes are presented, including:

[0144] Option 1 (red): The result is the most aggressive and the total capacity is the largest. However, since extreme risks are not considered, there is a high probability of voltage exceeding limits and large-scale grid disconnection of photovoltaic power in the event of a typhoon.

[0145] Option 2 (yellow): To avoid risks, it is necessary to drastically reduce the amount of photovoltaic power connected, resulting in a waste of photovoltaic resources and poor economic efficiency.

[0146] Option 3 (blue): This invention taps into the network's carrying capacity potential through proactive defense measures, thereby increasing the photovoltaic access capacity by approximately 60%-80% compared to Option 2 while ensuring extreme security. This invention effectively resolves the contradiction between security and grid integration, maximizing the photovoltaic access capacity while ensuring extreme resilience.

[0147] Figure 5 This reveals a positive correlation between SOP configuration capacity and total permitted photovoltaic capacity. Figure 5 As can be seen, as the SOP capacity increases from 0 to 1.0MW, the power grid's power flow regulation capability is enhanced, and the total photovoltaic capacity that can be accommodated shows a significant upward trend and gradually approaches saturation. This proves that the model of this invention can not only evaluate photovoltaic capacity, but also provide a quantitative basis for the coordinated planning of primary and secondary equipment in the distribution network, and has significant engineering application value.

Claims

1. A method for assessing the access capacity of distributed photovoltaic power generation considering the risk of failure due to extreme weather, characterized in that, Includes the following steps; S1: Obtain historical extreme weather data and power grid geographic information for the target area, establish a dynamic coupling model between weather intensity and power grid equipment failure rate, and generate a failure probability distribution that evolves over time and space. S2: Based on Monte Carlo sampling and topological connectivity analysis, simulate the Nk cascade tripping process caused by the initial fault under extreme weather conditions, and construct a set of typical fault scenarios under extreme disasters; S3: Establish a two-layer robust optimization model with the goal of maximizing the access capacity of distributed photovoltaic power as the upper-layer objective and minimizing the cost of active defense of the system under extreme scenarios as the lower-layer objective; S4: In the lower layer of the two-layer robust optimization model, intelligent soft switching, fast energy storage response and network reconfiguration are introduced as active defense measures to explore the extreme survivability of the system by relaxing the operating constraints. S5: The column and constraint generation algorithm is used to iteratively solve the two-layer robust optimization model. The system power supply retention rate under extreme weather cascading failure scenarios is not lower than the preset minimum threshold as an extreme resilience index constraint. The output is a distributed photovoltaic limit access capacity distribution scheme that satisfies the extreme resilience index constraint for each node.

2. The distributed photovoltaic access capacity assessment method considering extreme weather failure risk according to claim 1, characterized in that, The dynamic coupling model in step S1 is expressed as follows: ； in, For the line exist Real-time failure rate at any given moment; This represents the baseline failure rate under normal weather conditions. For the line The geographical location is Real-time weather intensity at any given moment; The critical threshold for meteorological intensity that leads to a surge in failure rate; and These are the meteorological vulnerability coefficients, used to characterize the sensitivity of the line to extreme weather.

3. The distributed photovoltaic access capacity assessment method considering extreme weather failure risk according to claim 2, characterized in that, The construction of the typical fault scenario set in step S2 includes: For each time step Based on real-time failure rate Perform state sampling to determine the initial set of faulty lines; Perform topology connectivity verification. If system disconnection occurs, calculate power flow distribution. If there are lines exceeding power flow limits, disconnect the lines exceeding the limits according to overload protection logic and update the topology. Repeat the above process until the system power flow converges and no new lines trip, and treat the final stable failure state as a fault scenario. Add it to the typical fault scenario set Ω.

4. The distributed photovoltaic access capacity assessment method considering extreme weather failure risk according to claim 3, characterized in that, The mathematical expression of the upper-level model of the two-layer robust optimization model in step S3 is as follows: ； in, This refers to the set of nodes in a distribution network that are permitted to install distributed photovoltaic (PV) systems. For decision variables, represent nodes. The photovoltaic access capacity; The constraints include: ； in, This is the maximum physical installation limit, which is restricted by the node installation area or transformer capacity.

5. The distributed photovoltaic access capacity assessment method considering extreme weather failure risk according to claim 4, characterized in that, In step S3, the lower-level model of the two-layer robust optimization model is based on the given upper-level capacity scheme. and worst-case failure scenarios To minimize the overall operational losses of the system: ； in, For nodes exist Active load shedding at any given moment; The unit load shedding penalty cost represents the resilience loss weight. For intelligent soft switches Operating losses; These are, respectively, a set of binary variables containing network reconfiguration states and a set of continuous variables containing voltage and current.

6. The distributed photovoltaic access capacity assessment method considering extreme weather failure risk according to claim 5, characterized in that, The two-layer robust optimization model in step S3 satisfies the system extreme limit survival rate constraint: ； in, For nodes The original load demand; This is the minimum power supply retention rate threshold allowed for the distribution network under extreme disasters.

7. The distributed photovoltaic access capacity assessment method considering extreme weather failure risk according to claim 6, characterized in that, Step S4 introduces an active defense constraint for intelligent soft switching to provide emergency power support after a fault. Its model is as follows: ； in, and SOP access nodes The active and reactive power injected from the side; For the first The rated apparent capacity of each SOP unit; This refers to the power loss within the SOP device.

8. The distributed photovoltaic access capacity assessment method considering extreme weather failure risk according to claim 7, characterized in that, The methods for introducing energy storage rapid response as an active defense measure in step S4 include: The net output power of the energy storage system is expressed as: ； Energy storage charging and discharging power meets: ； ； Energy storage state of charge satisfies: ； ； in, For nodes Energy storage system at all times Net output power; and These are the energy storage discharge power and the charging power, respectively. and These are the maximum discharge power and the maximum charging power of energy storage, respectively. It is in a state of energy storage charge; and These are the lower and upper limits of the energy storage state of charge, respectively; and These are the energy storage charging efficiency and discharging efficiency, respectively. For time intervals.

9. A method for assessing the access capacity of distributed photovoltaic power generation considering the risk of failure due to extreme weather, as described in claim 8, is characterized in that... The methods for introducing network reconfiguration as an active defense mechanism in step S4 include: Let the state variables of the line switch be: ； in, Indicates the line Put into operation Indicates the line disconnect; Line power and switch status satisfy: ； in, For the line At any moment Transmission power, This represents the maximum allowable transmission power of the line. For faulty lines, the following should be met: ； in, This is a collection of lines that experienced failures due to extreme weather conditions. To avoid frequent switching actions, the number of switching actions must meet the following requirement: ； in, A collection of distribution network lines; This is the initial switching state of the line; The maximum number of switching actions allowed.

10. A method for assessing the access capacity of distributed photovoltaic power generation considering the risk of failure due to extreme weather, as described in claim 9, is characterized in that... In step S5, a column and constraint generation algorithm is used to decompose the two-layer robust optimization model into a main problem and sub-problems, which are then solved iteratively and alternately. Main problem: Optimize photovoltaic capacity under a known subset of adverse scenarios. This provides a lower bound for capacity planning schemes; Subproblems: those passed in by the fixed main problem Find the worst-case scenario in the fault scenario set Ω that maximizes the system load shedding. *, Verify whether the current capacity scheme meets the resilience constraints; Iterative logic: If the adverse scenario found by the subproblem causes the resilience index to be violated, then the variables and constraints corresponding to the scenario are added back to the main problem and solved again until the convergence gap between the upper and lower bounds is less than the preset error.

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