A method and device for mitigating the risks of natural disasters in a power system

Abstract

The present invention discloses a method and device for mitigating natural disaster risks in a power system, including: constructing an hourly composite cascading event set generation module for generating an hourly data set under various natural disaster scenarios, capturing the cascading effects of different disasters, and providing data support for power system risk impact quantification and risk mitigation module strategy generation; constructing a distributed resource assessment module for evaluating the availability and power generation potential of distributed resources in a tertiary region, and analyzing their power generation potential during various natural disasters in combination with weather and disaster factors; constructing a natural disaster impact module for simulating the impact of various natural disasters on the power system based on the hourly composite cascading event set, analyzing the damage degree of disasters to loads, power generation facilities, transmission and distribution lines, etc., and quantifying the risk impact on the power system; constructing a risk mitigation module. For areas with risk impacts, a disaster emergency plan is formulated based on a three-level collaborative mechanism. Based on the power generation potential of the distributed resource assessment module, the impact of disasters is mitigated by dispatching various distributed resources, repairing the power grid, and adjusting the load, ensuring the rapid recovery and stable operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and particularly to a method and device for mitigating the risks of natural disasters in a power system. Background Art

[0002] The power system is an important part of social infrastructure, and its stability is directly related to the normal progress of economic development and social operation. However, with the intensification of global climate change, the frequency and intensity of natural disasters have gradually increased. For example, earthquakes, floods, typhoons, etc. all pose serious threats to the safe operation of the power system. In a multi-level power system including the transmission grid, medium- and high-voltage distribution grids, and low-voltage distribution grids, natural disasters may not only cause power outages in local areas, but also trigger cross-regional chain reactions, resulting in large-scale power outages. Therefore, researching and proposing effective methods for mitigating natural disaster risks, especially integrated mitigation technologies for multi-level power systems, is one of the major challenges faced by the current power industry. Therefore, the natural disaster risk mitigation technology based on multi-level power systems needs to consider the coordinated optimization of both the overall situation and the local areas.

[0003] Currently, most of the power system protection measures against natural disasters focus on single levels or local areas, lacking a systematic and coordinated overall risk mitigation plan. At the same time, traditional risk mitigation technologies often have problems such as slow response speed and low recovery efficiency when dealing with sudden disasters such as extreme weather events.

[0004] Therefore, proposing a method and device for mitigating natural disaster risks that can be based on a global perspective and combine the actual needs of each level of the power system can not only enhance the resilience of the power grid, but also more effectively coordinate the recovery processes between multi-level power grids, achieving an organic combination of the overall situation and the local areas.

[0005] References

[0006] [1]He G, Lin J, Sifuentes F, et al. Rapid cost decrease of renewables and storage accelerates the decarbonization of China’s power system[J]. Nature communications, 2020, 11(1): 2486.

[0007] [2]Staffell I, Pfenninger S, Johnson N. A global model of hourly spaceheating and cooling demand at multiple spatial scales[J]. Nature Energy, 2023, 8(12): 1328-1344.

[0008] [3]Blakers A, Stocks M, Lu B, et al. Pathway to 100%renewableelectricity[J]. IEEE Journal of Photovoltaics, 2019, 9(6): 1828-1833.

[0009] [4]Child M, Kemfert C, Bogdanov D, et al. Flexible electricitygeneration, grid exchange and storage for the transition to a 100%renewableenergy systemin Europe[J]. Renewable energy, 2019, 139: 80-101.

[0010] [5]Arif A, Wang Z, Wang J, et al. Power distribution system outagemanagement with co-optimization of repairs, reconfiguration, and DG dispatch[J]. IEEE Transactions on Smart Grid, 2017, 9(5): 4109-4118. Summary of the Invention

[0011] The present invention provides a method and device for mitigating the risk of natural disasters in a power system. The present invention improves the overall resistance of the power system to natural disasters at multiple levels, as described in detail below:

[0012] In a first aspect, a method for mitigating the risk of natural disasters in a power system, the method comprising:

[0013] Build an hourly composite cascading event set generation module to generate hourly datasets under various natural disaster scenarios, capture the cascading effects of different disasters, and provide data support for the quantification of the risk impact on the power system and the generation of strategies for the risk mitigation module;

[0014] Build a distributed resource assessment module to evaluate the availability and power generation potential of distributed resources within a third-level region, and analyze their power generation potential during various natural disasters in combination with weather and disaster factors;

[0015] Build a natural disaster impact module to simulate the impact of various natural disasters on the power system based on the hourly composite cascading event set, analyze the damage degree of disasters to loads, power generation facilities, transmission and distribution lines, etc., and quantify the risk impact on the power system;

[0016] Build a risk mitigation module. For areas with risk impacts, formulate disaster emergency plans based on a three-tier collaborative mechanism. Based on the power generation potential of the distributed resource assessment module, reduce the disaster impact by dispatching various distributed resources, repairing the power grid, and adjusting the load, and ensure the rapid restoration and stable operation of the power grid.

[0017] Among them, the distributed resource assessment module is:

[0018] Evaluate distributed energy based on GIS spatial distribution and resource characteristics, and calculate the weighted distance from each resource point to the evaluation center (x c ,y c ) by processing the spatial resolution grid coordinates (x,y):

[0019] z = ||x - x c | + 4 × |y - y c || + 1

[0020] Calculate the number of resources n(z) at each layer of distance through piecewise integration, and compare it with the total initial resources N p for comparison:

[0021] n'(z) = n(z) / N<> p

[0022] When the ratio n′(z) > α, where α is a preset threshold, the module determines that there are enough resources for optimization analysis.

[0023] Among them, the natural disaster impact module is:

[0024] The impact of the scenario S(t) based on the fuzzy function on the first-level power nodes is expressed as:

[0025] Among them, is the load of the third-level power node c, and ξ represents the event impact function;

[0026] The load characteristics are directly affected by natural disasters and indirectly affected by geographical and human factors:

[0027]

[0028] Among them, and are the power loads in the extreme cold scenario and the extreme high temperature scenario respectively, is the power load in the typical baseline scenario; and are the power correction loads in the extreme cold scenario and the extreme high temperature scenario respectively; and are the event-load impact functions in the extreme cold scenario and the extreme high temperature scenario respectively. For the TCS, EWS, TFS, and TWS scenarios, the power grid is affected by damaging the power connection lines between the first-level power nodes:

[0029]

[0030] Among them, d is the node index; P c,d (i) is the actual power flow of the branch; u c,d (i) is the binary variable of the branch state; is the rated power flow; is the event-branch impact function.

[0031] Among them, the risk mitigation module is a bottom-up risk mitigation model, including a three-stage risk mitigation model;

[0032] The first stage: Self-mitigation of the first-level power system risk:

[0033] It is composed of pre-configured distributed energy storage and flexible load scheduling together, aiming to reduce the impact of extreme natural disasters through load-side control. The goal of the power system self-mitigation is:

[0034]

[0035] Among them, p is the natural disaster index; c is the number of the first level, that is, the corresponding power node; M county is the number of low-voltage distribution networks; y, d, t are the time indexes respectively; is the configuration cost of distributed energy storage, is the flexible load scheduling cost; is the economic loss cost;

[0036] The second stage: Mutual mitigation of the second-level power system risk:

[0037] The first - level nodes participate in the second - level power system mutual mitigation. The purpose of the second - level power system mutual mitigation model is to achieve independent operation by forming a micro - grid, and the power supply is borne by the internal generators of the micro - grid and the distributed generation resources obtained from the distributed resource evaluation module; within the micro - grid, the grid framework is further strengthened through line reinforcement. The objectives of the second - level power system mutual mitigation model are as follows:

[0038]

[0039] where \(R_s=(1 + i)\) -1 ; \(i\) is the discount rate; \(pro\) is the third - level index; \(re\) is the renewable energy type index; \(M\) re is the number of distributed generation resources; \(P\) re,pro,c,p,y,d,t is the output of the distributed resources; is the micro - grid division cost; is the line reinforcement cost; is the levelized energy cost of the distributed resources;

[0040] The third stage: The third - level power system risk mitigation:

[0041] Among them, the scheduling cost of the generator is characterized by the maintenance cost operation cost Then, the third - level power system risk mitigation is expressed as:

[0042]

[0043] where \(p\) is the natural disaster index; \(M\) stage is the number of natural disasters; is the extreme cascading event mitigation cost.

[0044] In the second aspect, a power system natural disaster risk mitigation device is provided. The device includes: a processor and a memory. Program instructions are stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to execute the method described in any item of the first aspect.

[0045] In the third aspect, a computer - readable storage medium is provided. The computer - readable storage medium stores a computer program. The computer program includes program instructions, and when the program instructions are executed by a processor, the processor is enabled to execute the method described in any item of the first aspect.

[0046] The beneficial effects of the technical solution provided by the present invention are:

[0047] 1. Based on the historical data fitting method, the present invention establishes an hourly composite cascading event set, which can quantify seven natural disaster scenarios including typical baseline scenarios, extreme cold scenarios, extreme high temperature scenarios, tropical cyclone scenarios, extreme wind scenarios, typical flood scenarios, and typical wildfire scenarios with an hourly event resolution; it can accurately evaluate the cascading effects of disasters and provide basic data support for the resilience analysis and post-disaster recovery of power systems.

[0048] 2. The present invention establishes a risk mitigation model for a three-level power system to cope with extreme natural disasters of load, and reduces the risk cost through self-mitigation, mutual mitigation, and comprehensive risk mitigation strategies.

[0049] 3. The present invention evaluates the renewable energy resource endowment, optimizes the allocation and utilization of energy resources in each region, and provides a scientific basis for resource allocation and recovery under disasters by analyzing the distributed power generation of power grids at all levels.

[0050] 4. The present invention formulates a natural disaster risk mitigation strategy with multi-measure coordination: by formulating a multi-level coordinated natural disaster risk mitigation strategy, the present invention realizes flexible dispatching of grid resources, load adjustment, and line reinforcement in the event of disasters, minimizes power outages to the greatest extent, and ensures the stable operation and rapid recovery of the power grid.

[0051] 5. The risk mitigation model proposed by the present invention can reduce the excess cost caused by extreme natural disasters to 53%; the present invention also verifies the accuracy of the risk mitigation model for a three-level power system to cope with extreme natural disasters of load. Experimental analysis shows that the evaluation accuracy of the present invention for the average power outage time and power outage frequency indicators caused by natural disasters is as high as 97.7% and 93.0% respectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of a three-level power system natural disaster risk mitigation method;

[0053] Figure 2 It is a graph of the evaluation accuracy rate of the average power outage time;

[0054] Figure 3 It is a graph of the evaluation accuracy rate of the average power outage frequency. DETAILED DESCRIPTION OF THE INVENTION

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the embodiments of the present invention in detail.

[0056] Embodiment 1

[0057] An embodiment of the present invention provides a method for mitigating natural disaster risks in a three - tier power system. It includes: an hourly composite cascading event set generation module, a distributed photovoltaic resource assessment module, a risk mitigation module, and a natural disaster impact module. The purpose is to achieve the configuration of emergency resources and the pre - formulation of load adjustment plans in the pre - disaster stage of natural disasters. Among them:

[0058] Hourly composite cascading event set generation module: Generates an hourly data set under various natural disaster scenarios, captures the cascading effects of different disasters, and is used for disaster risk assessment and optimization of response strategies;

[0059] Distributed resource assessment module: Evaluates the availability and power generation potential of distributed resources within a three - level region, and analyzes their power supply and restoration capabilities during natural disasters in combination with weather and disaster factors.

[0060] Natural disaster impact module: Simulates the impact of natural disasters on the power system, analyzes the damage degree of disasters to loads, power generation facilities, transmission and distribution lines, etc., and quantifies the impact on the resilience of the power system.

[0061] Risk mitigation module: Formulates a disaster emergency plan based on a three - tier collaborative mechanism, and reduces the disaster impact through measures such as dispatching resources, repairing the power grid, and adjusting the load to ensure the rapid restoration and stable operation of the power grid.

[0062] I. Hourly composite cascading event set generation module

[0063] This module is based on seven sets of generated scenario data: typical baseline scenario (TBS), extreme cold scenario (ECS), extreme high - temperature scenario (EHS), tropical cyclone scenario (TCS), extreme wind scenario (EWS), typical flood scenario (TFS), and typical wildfire scenario (TWS). Each set of data contains 8760 hourly scenarios. The typical baseline scenario is generated by weighting different atmospheric radiation forcing scenarios, and other disaster scenarios are generated at a resolution of 0.005° * 0.005°.

[0064] The scenario data S(t) can be expressed as a matrix:

[0065] S(t) = {s i (t)}, i ∈ {TBS, ECS, EHS, TCS, EWS, TFS, TWS} (1)

[0066] Where s i (t) represents the i - th scenario generated in the historical year t. Each scenario contains 8760 hours of data:

[0067] s i (t) = {h j}, j ∈ {1, 2,..., 8760} (2)

[0068] Among them, h j represents the data vector at the j-th hour.

[0069] TBS is calculated based on weighted calculations for different radiative forcing scenarios, and the TBS scenarios can be expressed as:

[0070] s i (t) = ∑ω k r k (t), i = TBS(3)

[0071] Among them, k is the index of the radiative forcing scenario; ω k is the weight for each radiative forcing scenario; the data for each radiative forcing scenario is r k (t).

[0072] II. Distributed Resource Evaluation Module

[0073] The core function of the distributed resource evaluation module is to accurately evaluate distributed energy based on GIS spatial distribution and resource characteristics. First, by processing the spatial resolution grid coordinates (x, y) to calculate the weighted distance from each resource point to the evaluation center (x c , y c ).

[0074] z = ||x - x c | + 4 × |y - y c || + 1 (4)

[0075] The determination of the center point is based on the spatial mean of all distributed resource points, and further adjusts the center position by selecting the minimum distance to make it more representative. For any resource distribution grid, this module first determines the mean position and determines the optimal center point according to the method of minimizing the weighted distance, and uses this as the starting point for evaluation.

[0076] Next, the distributed resource evaluation module conducts cumulative evaluation on resources at different distance levels through piecewise integration. By calculating the number of resources n(z) at each layer of distance and comparing it with the total initial resources N p as follows:

[0077] n'(z) = n(z) / N p (5)

[0078] In addition, grids with too small total resources have limited effect on mitigating system risks but will increase the computational burden. Therefore, the embodiments of the present invention stipulate that when the ratio n′(z) > α (α is a preset threshold), this module determines that there are enough resources for optimization analysis.

[0079] III. Natural Disaster Impact Module

[0080] The impacts of natural disasters include three parts: 1) the direct impact on power load; 2) the impact on power topology; 3) the stochastic impact on population characteristics. Different from the direct impact on power load, the stochastic impact on population characteristics may be related to factors such as geography and population activities, and then indirectly affect the power load. The impact of scenario S(t) based on the fuzzy function on the first-level power nodes can be expressed as:

[0081]

[0082] where is the load of the third-level power node c, and ξ represents the event impact function.

[0083] Extreme natural disaster scenarios will affect the load characteristics of the first-level power nodes (for example, extreme cold scenarios will increase the power output of electric air conditioners or heat pumps, and extreme heat scenarios will increase the power output of electric refrigerators). The load characteristics under extreme natural disasters can be characterized by the direct impact of natural disasters and the indirect impact of geographical and human factors:

[0084]

[0085] where and are the power loads of extreme cold scenarios and extreme heat scenarios respectively. is the power load of the typical baseline scenario; and are the power corrected loads of extreme cold scenarios and extreme heat scenarios respectively; and are the event-load impact functions of extreme cold scenarios and extreme heat scenarios respectively. For TCS, EWS, TFS, and TWS scenarios, they affect the power grid by damaging the power connection lines between the first-level power nodes.

[0086]

[0087] where d is the node index; P c,d (i) is the actual power flow of the branch; u c,d (i) is the binary variable of the branch state; is the rated power flow; is the event-branch impact function.

[0088] IV. Risk Mitigation Module

[0089] Among them, the risk mitigation module is a bottom-up risk suppression model, which consists of a three-stage risk suppression model.

[0090] The first stage: self-mitigation of the first-level power system risk:

[0091] The power system self - mitigation model is composed of pre - configured distributed energy storage and flexible load scheduling, aiming to reduce the impact of extreme natural disasters through load - side control. The objectives of power system self - mitigation are:

[0092]

[0093] Among them, p is the natural disaster index; c is the number of the first - level (e.g., medium - and low - voltage distribution networks within a county - level administrative region), that is, the corresponding power nodes; M county is the number of low - voltage distribution networks; y, d, t are time indices respectively; is the configuration cost of distributed energy storage, and is the flexible load scheduling cost; is the economic loss cost.

[0094] The second stage: Risk mutual - mitigation of the second - level power system:

[0095] When the first - level power nodes cannot fully self - mitigate the impact of extreme natural disasters, these first - level nodes participate in the risk mutual - mitigation of the second - level (e.g., municipal high - voltage distribution network) power system. The purpose of the second - level power system risk mutual - mitigation model is to achieve independent operation by forming micro - grids, and the power supply is borne by the internal generators of the micro - grids and the distributed generation resources obtained from the distributed resource evaluation module. In addition, the grid framework is further strengthened through line reinforcement within the micro - grids. Therefore, the objectives of the second - level power system risk mutual - mitigation model are:

[0096]

[0097] Among them, Rs=(1 + i) -1 ; i is the discount rate; pro is the index of the third - level (e.g., the power transmission network between provinces and cities); re is the index of renewable energy types; M re is the number of distributed generation resources. P re,pro,c,p,y,d,t is the output of distributed resources; is the micro - grid division cost; is the line reinforcement cost; is the levelized energy cost of distributed resources.

[0098] The third stage: Risk mitigation of the third - level power system:

[0099] Among them, the risk mitigation of the third - level power system is achieved by dispatching large - scale generators, aiming to solve the nodes where the load loss cannot be reduced through micro - grid strategies and self - mitigation strategies. The scheduling cost of the generators is characterized by the maintenance cost operation cost and can be expressed as:

[0100]

[0101] Among them, p is the natural disaster index; M stage is the number of natural disasters; Mitigation costs for extreme cascading events.

[0102] The operational strategy of the three-stage risk mitigation module is as follows:

[0103] 1) Distributed energy storage scheduling based on the first-level model: The distributed energy storage system is pre-deployed based on the node demand and historical disaster data based on Equation (10) before the disaster, ensuring rapid scheduling response when the disaster occurs.

[0104] 2) Flexible load scheduling based on the first-level model: Flexible load scheduling prioritizes the normal power supply of critical loads by reducing or shifting non-critical loads.

[0105] 3) Secondary model triggering: When the first-level power network cannot withstand the operational risk caused by natural disasters (i.e. economic loss cost), When it is not 0), the second phase mutual mitigation strategy is triggered to further achieve risk mitigation.

[0106] 4) Microgrid networking based on the second-level model: A mutual assistance mechanism is formed between the first-level power nodes through the establishment of microgrids, which mainly uses distributed resources and combines emergency power sources such as generators in the microgrid and distributed power generation resources obtained by the evaluation module to maintain regional power supply.

[0107] 5) Distributed resource scheduling potential assessment based on the distributed resource assessment module: The distributed resource assessment module calculates the available renewable energy output within each microgrid, such as wind power and photovoltaic power, to provide support for the low-voltage distribution network when power is scarce.

[0108] 6) Line reinforcement based on the second-level model: Based on Equation (11), the internal lines of the power grid are reinforced to ensure that under disaster conditions, the second-level power system can effectively support the operation of the microgrid through interconnected lines and ensure that renewable energy can safely and reliably transmit electricity to the microgrid.

[0109] 7) Triggering of the third-level model: When the second-level model cannot fully cover all internal power nodes, or there are still load loss nodes (i.e. economic loss cost The third-level model is triggered when the power supply is not 0. This stage relies on the dispatch of large generators to solve the power gap that cannot be solved by the first-level self-mitigation or the second-level mutual mitigation.

[0110] 8) Generator scheduling based on the third-level model: The scheduling of large generators needs to be optimized according to power demand. The scheduling plan comprehensively considers the maintenance cost and operating cost of the generators and is optimized based on Equation (12).

[0111] Embodiment 2

[0112] The best implementation manner of the embodiment of the present invention relates to a method for mitigating the risk of natural disasters in a power system, aiming to achieve the efficient, intelligent and safe operation of the energy system through a highly integrated modular design. This implementation manner includes the following key components:

[0113] Hourly composite cascading event set generation module: Seven sets of scenario data are generated, namely typical baseline scenario (TBS), extreme cold scenario (ECS), extreme high temperature scenario (EHS), tropical cyclone scenario (TCS), extreme wind scenario (EWS), typical flood scenario (TFS) and typical wildfire scenario (TWS). Each set of data contains 8760 hours of scenario information.

[0114] The typical baseline scenario (TBS) is generated by weighting different atmospheric radiative forcing scenarios, specifically by performing weighted calculations based on the weights and data of multiple radiative forcing scenarios. The scenario matrix is generated based on historical years, and the hourly data vectors are stored in the matrix.

[0115] Other extreme disaster scenarios (ECS, EHS, TCS, EWS, TFS, TWS) are generated based on a resolution of 0.005° * 0.005°. These scenarios determine the impact of disasters on the power system through statistical and predictive analysis of historical data, so as to provide reference data for subsequent optimization and mitigation.

[0116] Distributed resource evaluation module: First, based on the GIS system, by processing the spatial coordinates (x, y) of each distributed resource point, the weighted distance from each resource point to the evaluation center (xc, yc) is calculated. The center point is determined by the spatial mean of the distributed resource points and the method of minimizing the weighted distance, so as to make the evaluation more representative.

[0117] The next step of the evaluation is to perform cumulative evaluation on the distance resources at different levels through piecewise integration, calculate the resource quantity layer by layer, and compare it with the total resource quantity.

[0118] When the total amount of the resource grid is lower than the preset threshold α, the system will automatically ignore this area to reduce the calculation burden and focus on important resource areas, ensuring calculation efficiency and accuracy.

[0119] Natural disaster impact module: It is realized by simulating three aspects of impacts: the direct impact on power load, the impact on power topology, and the indirect impact of random changes in population characteristics.

[0120] Direct impact on power load: This part considers the impact of extreme cold scenarios (ECS) and extreme heat scenarios (EHS) on power demand, increasing the loads of electric heating and electric refrigeration equipment respectively.

[0121] Impact on power topology: Scenarios such as tropical cyclones, extreme winds, floods, and wildfires can damage the connections between grid nodes, affecting the power flow distribution of power transmission.

[0122] Random impact of population characteristics: Based on geographical and activity data, fuzzy logic is used to estimate the load impact of disaster scenarios on the first-level nodes. This part will be more dynamic and randomized to ensure coverage of the indirect impact of disasters on the power system.

[0123] Risk mitigation module: Adopt a hierarchical risk mitigation strategy, consisting of risk mitigation models of the first-level, second-level, and third-level power systems from bottom to top.

[0124] First stage: Self-mitigation of the first-level power system: The first-level power system reduces the direct impact of disasters through pre-configured distributed energy storage and flexible load scheduling. The distributed energy storage system is optimized and configured to be dispatched when the power load is affected by disasters, reducing load losses.

[0125] Second stage: Mutual mitigation of the second-level power system: When the first-level nodes cannot self-mitigate, the second-level power system conducts mutual mitigation by forming microgrids. Distributed generation resources and line reinforcement measures ensure the independent operation of the microgrids.

[0126] Third stage: Risk mitigation of the third-level power system: When the second-level cannot further mitigate the disaster impact, the third-level power system solves the remaining load loss problems by dispatching generators to ensure the stable operation of the overall system.

[0127] Example 3

[0128] In this study, the embodiments of the present invention are verified and optimized by using actual energy capacity and historical meteorological data to optimize 124 data sets at the second level between 2017 and 2020. Seven are compared, including conventional costs, emergency costs under extreme natural disasters, only considering energy storage configuration, line hardening, flexible load, microgrid strategy, and collaborative optimal strategy. Then, the average outage duration (AOD) and frequency (AOF) of the power system caused by natural disasters are fitted to evaluate the system performance.

[0129] Figure 2 and Figure 3Respectively show the AOD and AOF indicators caused by natural disasters in the natural disaster impact module and the risk mitigation module during the five years from 2019 to 2023. It can be seen that the accuracy rates of natural disaster risk assessment for AOD and AOF are as high as 97.7% and 93% respectively. This proves that the present invention has high accuracy in evaluating the impact of natural disasters on large-scale systems.

[0130] According to the table data, the total cost of the conventional plan is 9,564.278 billion yuan, and the total costs under various disaster scenarios far exceed the conventional plan, especially the total cost under extreme scenarios. For example, the total cost of the energy storage configuration plan is 61,561.59 billion yuan, and the total cost of the line hardening plan is 64,278.79 billion yuan. The total cost of the microgrid plan is 38,733.14 billion yuan, which is relatively low, and its demand response cost (827.567 billion yuan) and carbon emission cost (1,702.13 billion yuan) are significantly reduced. This shows that the microgrid plan has strong economic efficiency and environmental friendliness. The total cost of the optimal plan is the lowest, only 37,805.87 billion yuan, indicating that comprehensively using strategies such as energy storage configuration, demand response, and line hardening can significantly reduce costs under extreme disasters while maintaining high reliability.

[0131] Table 1 Cost comparison

[0132]

[0133]

[0134] Example 4

[0135] A device for mitigating the risk of natural disasters in a power system, the device includes: a processor and a memory, and program instructions are stored in the memory. The processor calls the program instructions stored in the memory to enable the device to execute the following method steps in Example 1:

[0136] Build an hourly composite cascading event set generation module for generating an hourly data set under various natural disaster scenarios, capturing the cascading effects of different disasters, and providing data support for power system risk impact quantification and risk mitigation module strategy generation;

[0137] Build a distributed resource assessment module for evaluating the availability and power generation potential of distributed resources in a three-level area, and analyzing their power generation potential during various natural disasters in combination with weather and disaster factors;

[0138] Build a natural disaster impact module for simulating the impact of various natural disasters on the power system based on the hourly composite cascading event set, analyzing the damage degree of disasters to loads, power generation facilities, transmission and distribution lines, etc., and quantifying the risk impact on the power system;

[0139] Construct a risk mitigation module. For areas affected by risks, develop a disaster emergency plan based on a three - level collaborative mechanism. Based on the power generation potential of the distributed resource assessment module, reduce the impact of disasters by dispatching various distributed resources, repairing the power grid, and adjusting the load, ensuring the rapid restoration and stable operation of the power grid.

[0140] Among them, the distributed resource assessment module is as follows:

[0141] Evaluate distributed energy based on GIS spatial distribution and resource characteristics. By processing the spatial resolution grid coordinates (x, y), calculate the weighted distance from each resource point to the assessment center (x c , y c ):

[0142] z = ||x - x c | + 4×|y - y c || + 1

[0143] Calculate the number of resources n(z) at each layer of distance through piece - wise integration, and compare it with the total initial resources N p for comparison:

[0144] n'(z) = n(z) / N p

[0145] When the ratio n′(z)>α, where α is a preset threshold, the module determines that there are enough resources for optimization analysis.

[0146] Among them, the natural disaster impact module is as follows:

[0147] The impact of the scenario S(t) based on the fuzzy function on the first - level power nodes is expressed as:

[0148]

[0149] Among them, is the load of the third - level power node c, and ξ represents the event impact function;

[0150] The load characteristics are directly affected by natural disasters and indirectly characterized by geographical and human factors:

[0151]

[0152] Among them, and are the power loads of the extremely cold scenario and the extremely hot scenario respectively, is the power load of the typical baseline scenario; and are the power correction loads of the extremely cold scenario and the extremely hot scenario respectively; and Event-load impact functions for the extremely cold scenario and the extremely hot scenario respectively. For the TCS, EWS, TFS, and TWS scenarios, the power grid is affected by disrupting the power connection lines between the first-level power nodes:

[0153]

[0154] where d is the node index; P c,d (i) is the actual power flow of the branch; u c,d (i) is the binary variable of the branch state; is the rated power flow; is the event-branch impact function.

[0155] Among them, the risk mitigation module is a bottom-up risk mitigation model, including a three-stage risk mitigation model;

[0156] The first stage: Self-mitigation of the first-level power system risk:

[0157] It is composed of pre-configured distributed energy storage and flexible load scheduling together, aiming to reduce the impact of extreme natural disasters through load-side control. The goal of the power system self-mitigation is:

[0158]

[0159] where p is the natural disaster index; c is the quantity of the first level, that is, the corresponding power node; M county is the number of low-voltage distribution networks; y, d, t are time indexes respectively; is the configuration cost of distributed energy storage, is the flexible load scheduling cost; is the economic loss cost;

[0160] The second stage: Mutual mitigation of the second-level power system risk:

[0161] The first-level nodes participate in the mutual mitigation of the second-level power system. The purpose of the mutual mitigation model of the second-level power system is to achieve independent operation by forming a microgrid, and the power supply is borne by the internal generators of the microgrid and the distributed generation resources obtained from the distributed resource evaluation module; within the microgrid, the grid framework is further strengthened through line reinforcement. The goal of the mutual mitigation model of the second-level power system is:

[0162]

[0163] where Rs = (1 + i) -1 ; i is the discount rate; pro is the third-level index; re is the renewable energy type index; M re is the number of distributed generation resources; P re,pro,c,p,y,d,tis the output of distributed resources; is the microgrid division cost; is the line reinforcement cost; is the levelized energy cost of distributed resources;

[0164] The third stage: the third-level power system risk mitigation:

[0165] Among them, the scheduling cost of the generator is characterized by the maintenance cost operation cost and the third-level power system risk mitigation is expressed as:

[0166]

[0167] where p is the natural disaster index; M stage is the number of natural disasters; is the extreme cascading event mitigation cost.

[0168] It should be noted here that the device descriptions in the above embodiments correspond to the method descriptions in the embodiments, and the embodiments of the present invention will not be elaborated here.

[0169] The execution subjects of the above-mentioned processor and memory can be devices with computing functions such as computers, single-chip microcomputers, and microcontrollers. Specifically, in implementation, the embodiments of the present invention do not limit the execution subjects and are selected according to the needs in actual applications.

[0170] Data signals are transmitted between the memory and the processor through a bus, and the embodiments of the present invention will not elaborate on this.

[0171] Based on the same inventive concept, the embodiments of the present invention also provide a computer-readable storage medium. The storage medium includes a stored program that controls the device where the storage medium is located to execute the method steps in the above embodiments when the program runs.

[0172] The computer-readable storage medium includes, but is not limited to, flash memory, hard disks, solid-state drives, etc.

[0173] It should be noted here that the description of the readable storage medium in the above embodiments corresponds to the method description in the embodiments, and the embodiments of the present invention will not be elaborated here.

[0174] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part.

[0175] The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a computer-readable storage medium or transmitted through a computer-readable storage medium. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or a data center that incorporates one or more available media. The available medium can be a magnetic medium or a semiconductor medium, etc.

[0176] In the embodiments of the present invention, except for those with special specifications for the models of each device, the models of other devices are not limited, as long as the devices can perform the above functions.

[0177] Those skilled in the art can understand that the drawings are only schematic diagrams of a preferred embodiment, and the serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0178] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for mitigating the risk of natural disasters in a power system, characterized in that, The method includes: Construct a hourly composite cascading event set generation module for generating hourly datasets under seven natural disaster scenarios, capturing the cascading effects of different disasters, and providing data support for power system risk impact quantification and risk mitigation module strategy generation; the seven natural disaster scenarios include: typical baseline scenario, extreme cold scenario, extreme heat scenario, tropical cyclone scenario, extreme wind scenario, typical flood scenario, and typical wildfire scenario; Construct a distributed resource assessment module for evaluating the availability and power generation potential of distributed resources within a tertiary region, and analyzing their power generation potential during various natural disasters in combination with weather and disaster factors; Construct a natural disaster impact module to simulate the impact of seven natural disasters on the power system based on the hourly composite cascading event set, analyze the damage degree of disasters to loads, power generation facilities, and transmission and distribution lines, and quantify the risk impact on the power system; the natural disaster impact includes the direct impact on power loads; the impact on the power topology; the random impact on population characteristics; Construct a risk mitigation module. For areas with risk impacts, formulate a disaster emergency plan based on a three-tier collaborative mechanism. Based on the power generation potential of the distributed resource assessment module, reduce the disaster impact by dispatching various distributed resources, repairing the power grid, and adjusting the load, and ensure the rapid recovery and stable operation of the power grid; The risk mitigation module is a bottom-up risk suppression model, including a three-stage risk suppression model; The first stage: the first-level power system risk self-mitigation: It is composed of pre-configured distributed energy storage and flexible load scheduling, aiming to reduce the impact of extreme natural disasters through load-side control; the first-level power system is the medium- and low-voltage distribution network within a county-level administrative region; The second stage: the second-level power system risk mutual mitigation: The first-level nodes participate in the second-level power system risk mutual mitigation. The purpose of the second-level power system risk mutual mitigation model is to achieve independent operation by forming a microgrid, and the power supply is borne by the internal generators of the microgrid and the distributed power generation resources obtained from the distributed resource assessment module; further strengthen the grid framework through line reinforcement within the microgrid; the second-level power system is the municipal high-voltage distribution network; The third stage: the third-level power system risk mitigation: Among them, the third-level power system risk mitigation is achieved by dispatching large generators, reducing the nodes with load loss through microgrid strategies and self-mitigation strategies, and the third-level power system is the transmission network between provinces and cities.

2. The method for mitigating natural disaster risks in a power system according to claim 1, characterized in that, The distributed resource assessment module is: Evaluate distributed energy based on GIS spatial distribution and resource characteristics. Calculate the weighted distance from each resource point to the evaluation center (x c , y c ) by processing the spatial resolution grid coordinates (x, y): z = ||x - x c | + 4×|y - y c || + 1 Calculate the resource quantity \(n(z)\) at each layer distance through piecewise integration and compare it with the total initial resource quantity \(N\). p Make a comparison: n'(z) = n(z) / N p When the ratio n′(z)>α, where α is a preset threshold, the module determines that there are enough resources for optimization analysis.

3. A method for mitigating natural disaster risks in a power system according to claim 1, characterized in that, The natural disaster impact module is: The impact of the scenario S(t) based on the fuzzy function on the first-level power nodes is expressed as: Among them, is the load of the first-level power node c, and ξ represents the event impact function; s i (t) represents the i-th scenario generated in the historical year t, TBS is the typical baseline scenario, ECS is the extreme cold scenario, EHS is the extreme high temperature scenario, TCS is the tropical cyclone scenario, EWS is the extreme wind scenario, TFS is the typical flood scenario, and TWS is the typical wildfire scenario; The load characteristics are directly affected by natural disasters and indirectly affected by geographical and human factors: Among them, and are the power loads in the extreme cold scenario and the extreme high temperature scenario respectively, is the power load in the typical baseline scenario; and are the power correction loads in the extreme cold scenario and the extreme high temperature scenario respectively; and are the event-load impact functions in the extreme cold scenario and the extreme high temperature scenario respectively. For the TCS, EWS, TFS, and TWS scenarios, the power grid is affected by destroying the power connection lines between the first-level power nodes: Among them, P c,d (i) is the actual power flow of the branch; u c,d (i) is the binary variable of the branch status; is the rated power flow; is the event-branch impact function.

4. A device for mitigating natural disaster risks in a power system, characterized in that, The device includes: a processor and a memory. The memory stores program instructions, and the processor calls the program instructions stored in the memory to enable the device to execute the method described in any one of claims 1-3.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to execute the method according to any one of claims 1-3.

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