Weather-based new energy system power supply capability evaluation and adjustment method

Abstract

The invention discloses a new energy system power supply capability evaluation and adjustment method based on weather, and relates to the technical field of new energy system power supply. The method comprises the following steps: acquiring meteorological data of a research area, extracting extreme meteorology, constructing extreme meteorology and an electric power system disaster scene, and recording the extreme meteorology and the electric power system disaster scene as an extreme meteorology model; and performing confluence simulation according to the extreme meteorological model to obtain ponding space distribution of the research area. Simulating a disaster scene of the deduction system according to the ponding space distribution and recording the disaster scene as a system disaster model; according to the system disaster model, in combination with breadth-first traversal, finding out a lost load node in the system; defining a risk index, and performing risk assessment on the system disaster model according to the risk index and the lost load node to obtain a system risk degree; and setting a corresponding first-aid repair adjustment scheme according to the system risk degree, and carrying out first-aid repair according to the first-aid repair adjustment scheme. The accuracy of evaluation and adjustment of the power supply capability of the new energy system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power supply of new energy systems, and in particular to a method for evaluating and adjusting the power supply capacity of new energy systems based on weather. Background Art

[0002] After entering the 21st century, the power industry has developed rapidly. The increasingly improved infrastructure construction, access to various intelligent equipment, and advanced regulation and control technologies have significantly improved the reliability of the power system. In recent years, with the development of the economy and society, the construction of distribution networks has received widespread attention. Some urban power grids have developed into large-scale urban distribution networks with complete facilities, advanced technology, and high power supply reliability. However, extreme events, represented by extreme climate, pose an increasingly severe threat to the power supply capacity of high-proportion new energy power systems. Many technologies for power supply capacity assessment and regulation do not take into account the impact of extreme weather, resulting in inaccurate power supply capacity assessment and regulation. Summary of the invention

[0003] The purpose of the present invention is to provide a weather-based new energy system power supply capacity assessment and adjustment method to solve the problems raised in the above background technology.

[0004] The present application provides a weather-based new energy system power supply capacity assessment and adjustment method, which adopts the following technical solutions:

[0005] Obtain meteorological data of the study area, extract extreme weather based on the meteorological data, construct extreme weather and power system disaster scenarios and record them as extreme weather models;

[0006] The confluence simulation is carried out according to the extreme meteorological model, and the spatial distribution of accumulated water in the study area is obtained based on the confluence simulation.

[0007] The disaster scenario of the system is simulated and deduced according to the spatial distribution of accumulated water and recorded as the system disaster model;

[0008] According to the system disaster model, combined with breadth-first traversal, the load-losing nodes in the system are found;

[0009] Define risk indicators, conduct risk assessment on the system disaster model based on risk indicators and load loss nodes, and obtain the system risk level;

[0010] According to the system risk level, set up a corresponding emergency repair adjustment plan, and carry out emergency repairs according to the emergency repair adjustment plan.

[0011] Preferably, the step of obtaining meteorological data of the study area, extracting extreme weather according to the meteorological data, constructing extreme weather and power system disaster scenarios and recording them as extreme weather models includes the following steps:

[0012] Extract the types, laws and characteristics of extreme weather and construct a meteorological model that characterizes the characteristics of extreme weather;

[0013] Extract the risk factors and disaster-causing factors of extreme weather, and analyze and extract the key parameters that can characterize extreme weather, including the degree of disaster, scope of impact, and duration;

[0014] A probability distribution model of extreme weather is established, which is converted into a dynamic disaster scenario based on key parameters. A modeling framework is formed by combining the meteorological model and the probability distribution model to obtain an extreme weather model.

[0015] Preferably, the step of performing a runoff simulation according to an extreme meteorological model and obtaining the spatial distribution of accumulated water in the study area according to the runoff simulation comprises the following steps:

[0016] Based on the extreme weather model, the rainstorm volume is designed according to the urban rainstorm intensity formula, the relationship between the urban rainstorm intensity and the return period and rainfall duration is obtained, and a rainstorm intensity-return period correlation curve and a rainstorm intensity-rainfall duration correlation curve are formed;

[0017] The single-peak Chicago rain pattern design rainfall process is selected to calculate the rainfall intensity data of each time step before and after the rain peak;

[0018] As the rainfall intensity time series under the return period scenario, the rainstorm flood disaster modeling is carried out according to the rainfall intensity time series and the rainstorm intensity-return period correlation curve and the rainstorm intensity-rainfall duration correlation curve;

[0019] The runoff generation and confluence model was selected to simulate urban waterlogging, and the net rainfall was calculated by using the runoff coefficient to combine the rainfall and the surface infiltration loss rainfall.

[0020] The confluence simulation was completed based on the net rainfall using the local equal volume method, and the spatial distribution of accumulated water in the study area was summarized.

[0021] Preferably, the step of simulating and deducing the disaster scenario of the system according to the spatial distribution of accumulated water and recording it as a system disaster model comprises the following steps:

[0022] According to the spatial distribution of water accumulation, the spatial position of the components of the new energy power system with a high proportion is matched, the depth of water accumulation in the area where the components are located is determined, and the fault status of the components is obtained by combining the vulnerability curve;

[0023] The mixed integer linear programming model is used to analyze the multi-stage response characteristics of the high-proportion new energy power system under urban rainstorm waterlogging, and the multi-stage response characteristics include fault isolation, load transfer, emergency repair and power restoration.

[0024] Obtain the fault locations of line faults at different stages, repartition the network according to the fault locations, and simulate fault isolation;

[0025] The disaster scenarios of the system are simulated and deduced by combining the fault status, response characteristics and fault isolation of components to form a system disaster model.

[0026] Preferably, the step of finding the load-losing node in the system according to the system disaster model in combination with breadth-first traversal includes the following steps:

[0027] Determine whether the high-proportion new energy power system is topologically connected. If the high-proportion new energy power system is topologically connected, determine that the high-proportion new energy power system is in reconfiguration operation;

[0028] If the high-proportion new energy power system is in reconstruction operation, a reconstruction plan for the high-proportion new energy power system is formed by connecting lines formed by non-faulty lines.

[0029] According to the reconstruction plan, combined with breadth-first traversal, the load-losing nodes in the system are found.

[0030] Preferably, the step of defining risk indicators, performing risk assessment on the system disaster model according to the risk indicators and the load loss nodes, and obtaining the system risk level comprises the following steps:

[0031] Construct a multi-dimensional and multi-level evaluation indicator system from three different aspects: status dimension, time dimension and architecture dimension;

[0032] Use mathematical models to simulate load transfer during disasters and emergency power restoration after disasters, and determine whether any components have been returned due to failures;

[0033] If a component is withdrawn due to a fault, the minimum load reserve of the distribution network is calculated based on the load-lost node;

[0034] According to the minimum load holding, combined with the greedy algorithm that ensures single-step optimality, the repair team is deployed to calculate the time when all loads of the distribution network resume power supply;

[0035] According to the evaluation index system and the time of power restoration, the equipment failure risk index and user power outage risk index are calculated to obtain the system risk level.

[0036] Preferably, the step of constructing a multi-dimensional and multi-level evaluation index system from three different aspects of state dimension, time dimension and architecture dimension includes the following steps:

[0037] The architecture dimensions include grid data, device data, user data, environment data and elastic resources;

[0038] The state dimensions include pre-disaster indicators, mid-disaster indicators, and post-disaster indicators;

[0039] The evaluation index system is divided into static indicators and dynamic indicators, the architecture dimension is a static indicator, and the state dimension is a dynamic indicator.

[0040] Preferably, it is characterized in that the step of setting a corresponding emergency repair adjustment plan according to the system risk level and performing emergency repair according to the emergency repair adjustment plan comprises the following steps:

[0041] Rank the damaged lines that have not yet been repaired according to their importance and form a repair priority;

[0042] Determine whether there is idle time after a repair team completes the repair of a component;

[0043] If there is idle time, the repair team is judged to be an idle team;

[0044] Assign emergency repair work to idle teams according to the emergency repair sequence and formulate an emergency repair adjustment plan.

[0045] Preferably, the step of ranking the damaged lines that have not yet been repaired by importance to form a repair ranking comprises the following steps:

[0046] Obtain unrepaired damaged lines and record them as damaged lines;

[0047] Collect the real-time load of the entire line, estimate the load of the damaged line after repair through breadth-first traversal and record it as the repaired load;

[0048] The difference between the real-time load and the repaired load is calculated and recorded as the load recovery amount;

[0049] The damaged lines are sorted from large to small according to the load recovery amount to obtain the emergency repair sorting.

[0050] In summary, the present application includes at least one of the following beneficial technical effects:

[0051] 1. In extremely severe weather conditions, by establishing a model of disaster scenarios, it can guide the power system to improve its disaster prevention and recovery capabilities through the coordination of various resources such as new energy after a disaster, and improve the accuracy of the assessment and adjustment of the power supply capacity of the new energy system.

[0052] 2. By establishing a model of the disaster scenario and assessing the risks of the power system, appropriate emergency repair and adjustment plans can be set up to provide an effective reference for the power supply and optimized operation of the power system under disasters, thereby improving the intelligence of the evaluation and adjustment of the power supply capacity of the new energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the specific steps of an embodiment of a method for evaluating and adjusting the power supply capacity of a new energy system based on weather of the present invention. DETAILED DESCRIPTION

[0054] Below is a combination of the embodiments and Figure 1 The present invention is further described in detail, but the embodiments of the present invention are not limited thereto.

[0055] The present invention discloses a method for evaluating and adjusting the power supply capacity of a new energy system based on weather, which specifically comprises the following steps:

[0056] Step S1, obtain meteorological data of the study area, extract extreme weather according to the meteorological data, construct extreme weather and power system disaster scenarios and record them as extreme weather models.

[0057] Step S2, performing runoff simulation according to the extreme weather model, and obtaining the spatial distribution of accumulated water in the study area according to the runoff simulation.

[0058] Step S3, simulating and deducing the disaster scenario of the system according to the spatial distribution of accumulated water and recording it as the system disaster model.

[0059] Step S4, according to the system disaster model, combined with breadth-first traversal, find the load-losing node in the system.

[0060] Step S5, defining risk indicators, and conducting risk assessment on the system disaster model according to the risk indicators and load loss nodes to obtain the system risk level.

[0061] Step S6, setting a corresponding emergency repair adjustment plan according to the system risk level, and performing emergency repair according to the emergency repair adjustment plan.

[0062] In practical application, combined with existing theoretical research, simulation technology and actual needs, we focus on overcoming the key technical difficulties of power supply capacity assessment and regulation of high-proportion renewable energy power systems under extreme climate conditions. In response to the problems of drastic changes in renewable energy output, abnormal nuclear power shutdown, power grid equipment failure and power supply risks caused by extreme climate conditions, we study the complex nonlinear correlation between multi-source heterogeneous meteorological information and new power system components, and carry out key technology research on power supply capacity assessment and optimal dispatch of high-proportion renewable energy power systems, clarify the impact of extreme climate events on renewable energy output and stable operation of nuclear power, propose a system power supply capacity assessment method under high renewable energy proportion and a high-proportion renewable energy emergency regulation strategy under extreme events, guide the power system to achieve disaster prevention and recovery capabilities through the coordination of multiple resources such as renewable energy after disasters, and provide an effective reference for power supply and optimized operation of the power system under disasters.

[0063] Clarify the mechanism and transmission mechanism of extreme weather affecting power supply and demand, reveal the key influencing factors, and propose an analysis method for new energy power generation and load demand under extreme weather conditions, providing a basis for assessing the safety risks of high-proportion new energy power systems under extreme events. Propose a system comprehensive regulation capability assessment method for flexible resource access of "source-load-storage", and propose an assessment method for the power supply guarantee capability of high-proportion new energy power systems under extreme conditions, build a balance scenario that takes into account the characteristics of new energy and loads, accurately judge the safety risks faced by high-proportion new energy power systems under extreme events, and guide the power system to conduct risk assessment and differentiated disaster prevention design guidance before disasters. Construct a multi-dimensional and multi-level assessment indicator system for risk assessment.

[0064] The steps of obtaining meteorological data of the study area, extracting extreme weather according to the meteorological data, constructing extreme weather and power system disaster scenarios and recording them as extreme weather models include the following steps:

[0065] Step S11, extracting the types, laws and characteristics of extreme weather, and constructing a meteorological model that characterizes the characteristics of extreme weather.

[0066] Step S12, extracting risk factors and disaster-causing factors of extreme weather, analyzing and extracting key parameters that can characterize extreme weather, the key parameters include the degree of disaster, the scope of impact, and the duration.

[0067] Step S13, establish a probability distribution model of extreme weather, convert it into a dynamic disaster scenario based on key parameters, combine the meteorological model and the probability distribution model to form a modeling framework, and obtain an extreme weather model.

[0068] In practical applications, we study the types, laws, and characteristics of extreme climate, build models to characterize the characteristics of extreme climate, characterize the risk factors and disaster-causing factors of extreme climate, analyze and extract key parameters that can characterize the intensity of disaster-causing factors such as the degree, scope of impact, and duration of extreme climate disasters, establish their probability distribution models or extreme value regression models, convert the extracted key parameters into dynamic disaster scenarios based on meteorological or geographical models, and propose a modeling framework suitable for various climate disasters.

[0069] The confluence simulation is carried out according to the extreme meteorological model, and the steps of obtaining the spatial distribution of water accumulation in the study area according to the confluence simulation include the following steps:

[0070] Step S21, based on the extreme weather model, the rainstorm amount is designed according to the urban rainstorm intensity formula, the relationship between the urban rainstorm intensity and the return period and rainfall duration is obtained, and the rainstorm intensity-return period correlation curve and the rainstorm intensity-rainfall duration correlation curve are formed.

[0071] The rainstorm intensity formula is a statistical formula that describes the relationship between the average rainfall intensity (q), the return period (P) and the rainfall duration (t):

[0072] Step S22, selecting a single-peak Chicago rain pattern design rainfall process, and calculating the rainfall intensity data of each time step before and after the rain peak.

[0073] Step S23, as the rainfall intensity time series under the return period scenario, rainstorm flood disaster modeling is performed according to the rainfall intensity time series and the rainstorm intensity-return period correlation curve and the rainstorm intensity-rainfall duration correlation curve.

[0074] Step S24, selecting a runoff generation and confluence model to simulate urban waterlogging, and using the runoff coefficient to calculate the rainfall and the surface infiltration loss rainfall to obtain the net rainfall.

[0075] Step S25, completing the runoff simulation using the local equal volume method according to the net rainfall, and summarizing the spatial distribution of water accumulation in the study area.

[0076] In actual application, the rainstorm volume is first designed according to the urban rainstorm intensity formula to obtain the relationship between urban rainstorm intensity and recurrence period and rainfall duration.

[0077] Then, the unimodal Chicago rainfall type was selected to design the design rainfall process based on the design rainfall volume, and the rainfall intensity data of each time step before and after the rain peak were calculated as the rainfall intensity time series under the return period scenario, which was used for subsequent rainstorm and flood disaster modeling.

[0078] Chicago Rain Pattern: Pre-Peak:

[0079] After the peak:

[0080] Rain peak coefficient r = 0.48

[0081] Finally, the urban waterlogging was simulated according to the runoff generation and confluence model. The net rainfall was calculated by deducting the surface infiltration loss from the rainfall using the runoff coefficient for the runoff generation simulation process. The confluence simulation of the study area was completed using the local equal volume method, and the spatial distribution of water accumulation in the study area was obtained by summarizing. Runoff generation model: The net rainfall was calculated by deducting the loss from the rainfall using the runoff coefficient. Confluence model: The equal volume method was used to calculate the distribution and depth of water accumulation caused by net rain.

[0082] The steps of simulating and deducing the disaster scenario of the system according to the spatial distribution of accumulated water and recording it as the system disaster model include the following steps:

[0083] Step S31, matching the spatial position of the high-proportion new energy power system components according to the spatial distribution of water accumulation, determining the water accumulation depth in the area where the components are located, and obtaining the component fault status in combination with the vulnerability curve.

[0084] Step S32, using a mixed integer linear programming model to analyze and obtain the multi-stage response characteristics of a high-proportion new energy power system under urban rainstorm and waterlogging, the multi-stages including fault isolation, load transfer, emergency repair and power restoration.

[0085] Step S33, obtaining the fault locations of line faults at different stages, repartitioning the network according to the fault locations, and simulating fault isolation.

[0086] Step S34, combining the component fault status, response characteristics and fault isolation to simulate the disaster scenario of the system and form a system disaster model.

[0087] In actual application, taking into account the fault chain problem and line fault repair time existing in the actual high-proportion renewable energy power system, that is, the line fault is considered to have a timing characteristic, and the network is repartitioned according to the location where the line fault occurs in different stages to realize fault isolation simulation.

[0088] According to the system disaster model, combined with the breadth-first traversal, the steps of finding the load-losing nodes in the system include the following steps:

[0089] Step S41, determining whether the high-proportion new energy power system is topologically connected. If the high-proportion new energy power system is topologically connected, determining that the high-proportion new energy power system is in reconfiguration operation.

[0090] Step S42: if the high-proportion new energy power system is in reconstruction operation, a reconstruction plan of the radial high-proportion new energy power system is formed according to the connected lines formed by the non-faulty lines.

[0091] Step S43: according to the reconstruction scheme, the load-losing node in the system is found in combination with breadth-first traversal.

[0092] In practical application, the urban waterlogging model is first used to match the spatial positions of components in a high-proportion renewable energy power system to determine the depth of water accumulation in the area where the components are located, and then the fault state of the components is obtained based on the vulnerability curve; then the mixed integer linear programming model is used to analyze and characterize the multi-stage response characteristics of fault isolation, load transfer, emergency repair and power restoration of a high-proportion renewable energy power system under urban rainstorm waterlogging.

[0093] Taking into account the fault chain problem and line fault repair time existing in the actual high-proportion renewable energy power system, that is, the line fault is considered to have a timing characteristic, and the network is repartitioned according to the location where the line fault occurs in different stages to realize fault isolation simulation.

[0094] In the simulation of load transfer during disasters, since there must be one or more minimum spanning trees in the undirected connected graph, as long as the high-proportion new energy power system is topologically connected in all distribution lines including the connecting branches, a radial high-proportion new energy power system reconstruction solution can be found. Therefore, in the simulation of load transfer during disasters, as long as the graph composed of all non-faulty lines of the high-proportion new energy power system is connected, it is considered that the high-proportion new energy power system can be reconstructed and operated, and the load loss nodes in the system can be found through breadth-first traversal.

[0095] The steps of defining risk indicators, conducting risk assessment on the system disaster model according to the risk indicators and the load loss nodes, and obtaining the system risk degree include the following steps:

[0096] Step S51, constructing a multi-dimensional and multi-level evaluation index system from three different aspects: state dimension, time dimension and architecture dimension.

[0097] Step S52, using a mathematical model to simulate load transfer during a disaster and emergency power restoration after a disaster, to determine whether any component has been returned due to a fault.

[0098] Step S53: If a component is returned due to a fault, the minimum load reserve of the distribution network is calculated according to the load-lost node.

[0099] Step S54, according to the minimum load holding amount, combined with the greedy algorithm that ensures single-step optimality, the repair team is deployed to calculate the time when all loads in the distribution network resume power supply.

[0100] Step S55, calculating the equipment failure risk index and the user power outage risk index according to the evaluation index system and the time of power restoration to obtain the system risk level.

[0101] In actual application, we first build a multi-dimensional and multi-level evaluation index system from three different aspects: state dimension, time dimension and architecture dimension.

[0102] Secondly, the rainstorm simulation generator is used to generate simulated waterlogging scenarios according to a certain probability distribution, and the rainfall information data sequence is given to realize state sampling. The water accumulation depth of distribution network components is simulated by using the urban waterlogging model, and the fault state of the components is generated according to the vulnerability curve.

[0103] Then, a mathematical model is used to simulate the load transfer during a disaster and the emergency power restoration after a disaster. When a component is withdrawn due to a fault, a breadth-first search is used to find the load-losing nodes in the distribution network and calculate the minimum load holding capacity of the distribution network. After the disaster, a greedy algorithm that guarantees single-step optimality is used to deploy the emergency repair team and calculate the time when all loads in the distribution network resume power supply.

[0104] Finally, the risk indicators are calculated. According to the simulation results of heavy rain and waterlogging, indicators such as equipment failure risk and user power outage risk are calculated to characterize the performance change process of the system.

[0105] The steps to construct a multi-dimensional and multi-level evaluation indicator system from three different aspects: state dimension, time dimension, and architecture dimension include the following steps:

[0106] Step S511, the architecture dimensions include network data, device data, user data, environment data and elastic resources.

[0107] Step S512, the status dimension includes pre-disaster indicators, mid-disaster indicators and post-disaster indicators.

[0108] Step S513: The evaluation indicator system is divided into static indicators and dynamic indicators, the architecture dimension is a static indicator, and the state dimension is a dynamic indicator.

[0109] The method is characterized in that a corresponding emergency repair adjustment plan is set according to the system risk level, and the steps of performing emergency repair according to the emergency repair adjustment plan include the following steps:

[0110] Step S61, sorting the damaged lines that have not been repaired by importance to form an emergency repair sequence.

[0111] Step S62, determining whether there is idle time after a repair team completes the repair of a component.

[0112] Step S63: If there is idle time, the repair team is determined to be an idle team.

[0113] Step S64, allocate emergency repair work to idle teams according to the emergency repair sequence, and form an emergency repair adjustment plan.

[0114] In practical applications, Sequential Monte Carlo simulation is widely used in power system reliability assessment, risk assessment and other research. Its basic idea is to determine the start and stop sequence of components in a period of time based on the random failure and repair model of the components, and to test the operating status of each step in the system in turn. Sequential Monte Carlo truly simulates the action sequence of the system. It can not only simulate the failure and repair of components, but also accurately simulate the development of system behavior after system accidents, so as to fully consider the system recovery process in the state assessment. Therefore, Sequential Monte Carlo simulation can be used, combined with techniques such as subset simulation, to efficiently capture rare and severe events and realize the evaluation of power system resilience.

[0115] The steps of ranking the importance of damaged lines that have not yet been repaired to form a repair ranking include the following steps:

[0116] Step S611, obtaining unrepaired damaged lines and recording them as damaged lines.

[0117] Step S612, collect the real-time load of the entire line, estimate the load of the damaged line after repair through breadth-first traversal and record it as the repaired load.

[0118] Step S613: Calculate the difference between the real-time load and the repaired load and record it as the load recovery amount.

[0119] Step S614, sorting the damaged lines from large to small according to the load recovery amount to obtain an emergency repair sorting.

[0120] In actual application, in the post-disaster optimization scheduling, a greedy algorithm that guarantees single-step optimality is adopted. When a repair team completes the repair of a component and becomes idle, the dispatcher will sort the importance of the damaged lines that have not yet been scheduled for repair, and assign the most important repair line to the repair team. The importance of the unrepaired damaged lines is sorted according to the amount of load recovery that can be brought about after the line is repaired. The greater the amount of load recovery that can be brought about by the repair of a suspended line, the more important the damaged line is at the current moment. Among them, the amount of load recovery that can be brought about after the line is repaired can still be calculated through breadth-first traversal.

[0121] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A weather-based new energy system power supply capacity assessment and adjustment method, characterized in that: The following steps are involved: Acquire meteorological data of the study area, extract extreme weather according to the meteorological data, construct extreme weather and power system disaster scenarios and record them as extreme weather models; The spatial distribution of water accumulation in the study area was obtained by runoff simulation based on extreme meteorological models. Simulate the disaster scenario of the system according to the spatial distribution of accumulated water and form a system disaster model; Find the load-loss nodes in the system according to the system disaster model; Define risk indicators, conduct risk assessment on the system disaster model based on risk indicators and load loss nodes, and obtain the system risk level; According to the system risk level, set up a corresponding emergency repair adjustment plan, and carry out emergency repairs according to the emergency repair adjustment plan.

2. The method for evaluating and adjusting the power supply capacity of a new energy system based on weather according to claim 1, characterized in that: The steps of obtaining meteorological data of the study area, extracting extreme weather according to the meteorological data, constructing extreme weather and power system disaster scenarios and recording them as extreme weather models include the following steps: Extract the types, laws and characteristics of extreme weather and construct a meteorological model that characterizes the characteristics of extreme weather; Extract the risk factors and disaster-causing factors of extreme weather, and analyze and extract the key parameters that can characterize extreme weather, including the degree of disaster, scope of impact, and duration; A probability distribution model of extreme weather is established, which is converted into a dynamic disaster scenario based on key parameters. A modeling framework is formed by combining the meteorological model and the probability distribution model to obtain an extreme weather model.

3. The weather-based new energy system power supply capacity assessment and adjustment method according to claim 2 is characterized in that: The confluence simulation is carried out according to the extreme meteorological model, and the steps of obtaining the spatial distribution of water accumulation in the study area according to the confluence simulation include the following steps: Based on the extreme weather model, the rainstorm volume is designed according to the urban rainstorm intensity formula, the relationship between the urban rainstorm intensity and the return period and rainfall duration is obtained, and a rainstorm intensity-return period correlation curve and a rainstorm intensity-rainfall duration correlation curve are formed; The single-peak Chicago rain pattern design rainfall process is selected to calculate the rainfall intensity data of each time step before and after the rain peak; As the rainfall intensity time series under the return period scenario, the rainstorm flood disaster modeling is carried out according to the rainfall intensity time series and the rainstorm intensity-return period correlation curve and the rainstorm intensity-rainfall duration correlation curve; The runoff generation and confluence model was selected to simulate urban waterlogging, and the net rainfall was calculated by using the runoff coefficient to combine the rainfall and the surface infiltration loss rainfall. The confluence simulation was completed based on the net rainfall using the local equal volume method, and the spatial distribution of accumulated water in the study area was summarized.

4. The method for evaluating and adjusting the power supply capacity of a new energy system based on weather according to claim 3 is characterized in that: The steps of simulating and deducing the disaster scenario of the system according to the spatial distribution of accumulated water and recording it as the system disaster model include the following steps: According to the spatial distribution of water accumulation, the spatial position of the components of the new energy power system with a high proportion is matched, the depth of water accumulation in the area where the components are located is determined, and the fault status of the components is obtained by combining the vulnerability curve; The mixed integer linear programming model is used to analyze the multi-stage response characteristics of the high-proportion renewable energy power system under urban rainstorm waterlogging, including fault isolation, load transfer, emergency repair and power restoration; Obtain the fault locations of line faults at different stages, re-partition the network according to the fault locations, and simulate fault isolation; The disaster scenarios of the system are simulated and deduced by combining the fault status, response characteristics and fault isolation of components to form a system disaster model.

5. The weather-based new energy system power supply capacity assessment and adjustment method according to claim 4 is characterized in that: The steps of finding the load loss node in the system according to the system disaster model include the following steps: Determine whether the high-proportion new energy power system is topologically connected. If the high-proportion new energy power system is topologically connected, determine that the high-proportion new energy power system is in reconfiguration operation; If the high-proportion new energy power system is in reconstruction operation, a reconstruction plan for the high-proportion new energy power system is formed by connecting lines formed by non-faulty lines. According to the reconstruction plan, the load-losing nodes in the system are found.

6. A weather-based new energy system power supply capacity assessment and adjustment method according to claim 5, characterized in that: The steps of defining risk indicators, conducting risk assessment on the system disaster model according to the risk indicators and the load loss nodes, and obtaining the system risk degree include the following steps: Construct a multi-dimensional and multi-level evaluation indicator system from three different aspects: status dimension, time dimension and architecture dimension; Use mathematical models to simulate load transfer during disasters and emergency power restoration after disasters, and determine whether any components have been returned due to failures; If a component is withdrawn due to a fault, the minimum load reserve of the distribution network is calculated based on the load-lost node; According to the minimum load holding, combined with the greedy algorithm that ensures single-step optimality, the repair team is deployed to calculate the time when all loads of the distribution network resume power supply; According to the evaluation index system and the time of power restoration, the equipment failure risk index and user power outage risk index are calculated to obtain the system risk level.

7. A weather-based new energy system power supply capacity assessment and adjustment method according to claim 6, characterized in that: The steps to construct a multi-dimensional and multi-level evaluation indicator system from three different aspects: state dimension, time dimension, and architecture dimension include the following steps: The architecture dimensions include grid data, device data, user data, environment data and elastic resources; The state dimensions include pre-disaster indicators, mid-disaster indicators, and post-disaster indicators; The evaluation index system is divided into static indicators and dynamic indicators, the architecture dimension is a static indicator, and the state dimension is a dynamic indicator.

8. The weather-based new energy system power supply capacity assessment and adjustment method according to claim 7 is characterized in that: According to the system risk level, a corresponding emergency repair adjustment plan is set, and the steps for emergency repair according to the emergency repair adjustment plan include the following steps: Rank the damaged lines that have not yet been repaired according to their importance and form a repair priority; Determine whether there is idle time after a repair team completes the repair of a component; If there is idle time, the repair team is judged to be an idle team; Assign emergency repair work to idle teams according to the emergency repair sequence and formulate an emergency repair adjustment plan.

9. A weather-based new energy system power supply capacity assessment and adjustment method according to claim 8, characterized in that: The steps of ranking the importance of damaged lines that have not yet been repaired to form a repair ranking include the following steps: Obtain unrepaired damaged lines and record them as damaged lines; Collect the real-time load of the entire line, estimate the load of the damaged line after repair and record it as the repaired load; The difference between the real-time load and the repaired load is calculated and recorded as the load recovery amount; The damaged lines are sorted from large to small according to the load recovery amount to obtain the emergency repair sorting.

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