Method and device for relieving natural disaster risk of power system

Abstract

The invention discloses a natural disaster risk relieving method and device for a power system, and the method comprises the steps: constructing an hour-level composite cascade event set generation module which is used for generating hour-level data sets in various natural disaster scenes, capturing the cascade effects of different disasters, and generating a data set; data support is provided for power system risk influence quantification and risk mitigation module strategy generation; a distributed resource evaluation module is constructed and used for evaluating the availability and power generation potential of distributed resources in the third-level region, and the power generation potential of the distributed resources in various natural disaster periods is analyzed in combination with weather and disaster factors; a natural disaster influence module is constructed, the influence of various natural disasters on the power system is simulated based on the hour-level composite cascade event set, the damage degree of the disasters on loads, power generation facilities, power transmission and distribution lines and the like is analyzed, and the risk influence on the power system is quantified; and constructing a risk relief module, for an area with risk influence, formulating a disaster emergency scheme based on a three-level cooperation mechanism, and based on the power generation potential of the distributed resource assessment module, by scheduling various distributed resources, repairing the power grid, adjusting the load and reducing the disaster influence, ensuring 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 in particular to a method and device for alleviating natural disaster risks in an electric power system. Background Art

[0002] The power system is an important part of social infrastructure, and its stability is directly related to the normal operation of economic development and social operations. However, with the intensification of global climate change, the frequency and intensity of natural disasters are gradually increasing, such as earthquakes, floods, typhoons, etc., which pose a serious threat to the safe operation of the power system. In the multi-level power system of transmission network, high-voltage distribution network and low-voltage distribution network, natural disasters may not only cause power outages in local areas, but also trigger cross-regional chain reactions, causing large-scale power outages. Therefore, studying and proposing effective natural disaster risk mitigation methods, especially comprehensive mitigation technologies for multi-level power systems, is one of the major challenges facing the current power industry. Therefore, natural disaster risk mitigation technology based on multi-level power systems needs to take into account global and local coordinated optimization.

[0003] At present, most of the power system protection measures against natural disasters are concentrated at a single level or in a local area, lacking a systematic and coordinated overall risk mitigation plan. At the same time, traditional risk mitigation technologies often have problems such as slow response and low recovery efficiency when dealing with sudden disasters such as extreme weather events.

[0004] Therefore, a natural disaster risk mitigation method and device based on a global perspective and combined with the actual needs of power systems at all levels is proposed, which can not only improve the resilience of the power grid, but also more effectively coordinate the recovery process between multi-level power grids and achieve an organic combination of the global and the local.

[0005] References

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

[0007] [2] Staffell I, Pfenninger S, Johnson NA 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 electricity generation, grid exchange and storage for the transition to a 100% renewableenergy system in 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 from multiple levels, as described below:

[0012] A first aspect is a method for mitigating natural disaster risks in a power system, the method comprising:

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

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

[0015] Construct a natural disaster impact module to simulate the impact of various natural disasters on the power system based on hourly composite cascade event sets, analyze the degree of damage 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 affected by risks, formulate disaster emergency plans based on a three-level coordination 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 to ensure rapid recovery and stable operation of the power grid.

[0017] Wherein, the distributed resource evaluation module is:

[0018] Distributed energy is evaluated based on GIS spatial distribution and resource characteristics. The spatial resolution grid coordinates (x, y) are processed to calculate the distance from each resource point to the evaluation center (x c ,y c )’s weighted distance:

[0019] z=||xx c |+4×|yy c ||+1

[0020] By means of piecewise integration, the number of resources n(z) at each layer distance is calculated and compared with the initial total 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 enough resources have been included for optimization analysis.

[0023] Wherein, 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] in, is the load of the third-level power node c, ξ represents the event impact function;

[0026] Load characteristics are characterized by direct impacts of natural disasters and indirect impacts of geographical and human factors:

[0027]

[0028] in, and They are the power loads in the extreme cold scenario and the extreme high temperature scenario, is the power load for a typical baseline scenario; and They are the power correction loads for the extreme cold scenario and the extreme high temperature scenario, respectively; and They are the event-load impact functions for the extreme cold scenario and the extreme high temperature scenario. 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:

[0029]

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

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

[0032] Phase 1: First-level power system risk self-mitigation:

[0033] The goal of self-mitigation of the power system is to reduce the impact of extreme natural disasters through load-side control through distributed energy storage pre-configuration and flexible load scheduling.

[0034]

[0035] Among them, p is the natural disaster index; c is the number of the first level, i.e., the corresponding power nodes; M county is the number of low voltage distribution networks; y, d, t are time indexes respectively; Allocate costs for distributed energy storage, Scheduling costs for flexible loads; For economic loss costs;

[0036] Phase II: Second-level power system risk mutual mitigation:

[0037] The first-level nodes participate in the mutual mitigation of the second-level power system. The purpose of the second-level power system mutual mitigation model is to achieve independent operation by forming a microgrid. The power supply is provided by the generator inside the microgrid and the distributed generation resources obtained by the distributed resource evaluation module. The grid is further strengthened by line reinforcement inside the microgrid. The goals of the second-level power system mutual mitigation model are:

[0038]

[0039] 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,t Contribution to distributed resources; allocating costs for microgrids; Cost of reinforcing the line; the levelized cost of energy for distributed resources;

[0040] Phase 3: Tertiary Power System Risk Mitigation:

[0041] Among them, the dispatch cost of the generator is composed of the maintenance cost Operating costs Characterization, the third-level power system risk mitigation is expressed as:

[0042]

[0043] Among them, p is the natural disaster index; M stage is the number of natural disasters; Mitigation costs for extreme cascading events.

[0044] In a second aspect, a device for mitigating natural disaster risks in a power system comprises: a processor and a memory, wherein program instructions are stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to execute any one of the methods described in the first aspect.

[0045] In a third aspect, a computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes any one of the methods described in 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 cascade 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 hourly event resolution; it can accurately assess the cascading effects of disasters and provide basic data support for 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 in load, and reduces risk costs through self-mitigation, mutual mitigation and comprehensive risk mitigation strategies;

[0049] 3. The present invention evaluates the endowment of renewable energy resources, 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 multi-measure coordinated natural disaster risk mitigation strategy: The present invention formulates a multi-level coordinated natural disaster risk mitigation strategy to achieve flexible dispatching of power grid resources, load adjustment and line reinforcement in disaster situations, minimize power outages, and ensure stable operation and rapid recovery of the power grid;

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

[0052] Figure 1 A flowchart of a three-tiered approach to natural disaster risk mitigation for power systems;

[0053] Figure 2 Evaluate accuracy graph for average outage time;

[0054] Figure 3 Evaluate the accuracy graph for the average outage frequency. DETAILED DESCRIPTION

[0055] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below.

[0056] Example 1

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

[0058] Hourly composite cascade event set generation module: Generates hourly data sets under various natural disaster scenarios to capture the cascading effects of different disasters for disaster risk assessment and response strategy optimization;

[0059] Distributed resource assessment module: Assess the availability and power generation potential of distributed resources in the third-level region, and analyze their power supply and recovery 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 degree of damage 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: Develop disaster emergency response plans based on a three-level coordination mechanism to mitigate the impact of disasters and ensure rapid recovery and stable operation of the power grid through measures such as dispatching resources, repairing the power grid, and adjusting loads.

[0062] 1. Hourly composite cascade event set generation module

[0063] This module is based on seven sets of scenario data generated: 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 scene 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] Among them, s i (t) represents the i-th scenario generated in 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 for the jth hour.

[0069] TBS is a weighted calculation based on different radiative forcing scenarios. The TBS scenario can be expressed as:

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

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

[0072] 2. Distributed Resource Evaluation Module

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

[0074] z=||xx c |+4×|yy c ||+1 (4)

[0075] The determination of the center point is based on the spatial mean of all distributed resource points, and the center position is further adjusted through the minimum distance selection to make it more representative. For any resource distribution grid, the module first determines the mean position and determines the optimal center point based on the weighted distance minimization method, which is used as the starting point for evaluation.

[0076] Next, the distributed resource evaluation module performs cumulative evaluation of resources at different distance levels by means of segmented integration. By calculating the number of resources n(z) at each distance level and comparing it with the initial total resource amount N p For comparison:

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

[0078] In addition, a grid with too small a total amount of resources has limited effect on system risk mitigation, but increases the computational burden. To this end, the embodiment of the present invention stipulates that when the ratio n′(z)>α (α is a preset threshold), the module determines that enough resources have been included for optimization analysis.

[0079] 3. Natural Disaster Impact Module

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

[0081]

[0082] in, 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 (e.g., extreme cold scenarios will increase the power output of electric air conditioners or heat pumps, and extreme high temperature 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 factors:

[0084]

[0085] in, and They are the power loads in the extreme cold scenario and the extreme high temperature scenario respectively. is the power load for a typical baseline scenario; and They are the power correction loads for the extreme cold scenario and the extreme high temperature scenario, respectively; and They are the event-load impact functions for the extreme cold scenario and the extreme high temperature scenario, respectively. For the TCS, EWS, TFS, and TWS scenarios, they affect the power grid by destroying 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 a binary variable of branch state; is the rated power flow; is the event-branch influence function.

[0088] 4. Risk Mitigation Module

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

[0090] Phase 1: First-level power system risk self-mitigation:

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

[0092]

[0093] Where p is the natural disaster index; c is the number of the first level (for example, the medium and low voltage distribution network within the county-level administrative area), that is, the corresponding power node; M county is the number of low-voltage distribution networks; y, d, t are time indexes respectively; The configuration cost of distributed energy storage is Flexible load dispatching costs; The cost of economic loss.

[0094] Phase II: Second-level power system risk mutual mitigation:

[0095] When the first-level power node cannot completely self-mitigate the impact of extreme natural disasters, the first-level node participates in the second-level (e.g., municipal high-voltage distribution network) power system mutual mitigation. The purpose of the second-level power system mutual mitigation model is to achieve independent operation by forming a microgrid, and the power supply is provided by the generator inside the microgrid and the distributed generation resources obtained by the distributed resource evaluation module. In addition, the grid is further strengthened by line reinforcement inside the microgrid. Therefore, the objectives of the second-level power system mutual mitigation model are:

[0096]

[0097] Where Rs = (1 + i) -1 ; i is the discount rate; pro is the third-level (e.g., inter-provincial transmission network) index; re is the renewable energy type index; M re is the number of distributed generation resources. re,pro,c,p,y,d,t Contribution to distributed resources; allocating costs for microgrids; Cost of reinforcing the line; is the levelized cost of energy for distributed resources.

[0098] Phase 3: Tertiary Power System Risk Mitigation:

[0099] Among them, the third-level power system risk mitigation is achieved by dispatching large generators, aiming to solve the nodes where load losses cannot be reduced through microgrid strategies and self-mitigation strategies. The dispatch cost of the generator is composed of maintenance costs. Operating costs Characterization, the third-level power system risk mitigation 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 operation 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 formula (10) before the disaster to ensure 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-important loads.

[0105] 3) Secondary model triggering: When the first-level power network cannot withstand the operational risks caused by natural disasters (i.e. economic loss costs) 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 use distributed resources and combine 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) Evaluation of distributed resource scheduling potential based on distributed resource assessment module: The distributed resource assessment module calculates the available renewable energy output within each microgrid, such as wind power, photovoltaic power, etc., 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 formula (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) The third-level model is triggered 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 grid is not equal to 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 dispatch based on the third-level model: The dispatch of large generators needs to be optimized according to the power demand. Its dispatch plan comprehensively considers the maintenance cost and operation cost of the generator and is optimized based on formula (12).

[0111] Example 2

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

[0113] Hourly composite cascade event set generation module: by generating seven sets of scenario data, 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 based on the weights and data of multiple radiative forcing scenarios. The scenario matrix is ​​generated for 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, thereby providing reference data for subsequent optimization and mitigation.

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

[0117] The next step of the evaluation is to cumulatively evaluate 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 resource grids is lower than the preset threshold α, the system will automatically ignore the area to reduce the computing burden and focus on important resource areas to ensure computing efficiency and accuracy.

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

[0120] Direct impact of electricity load: This section considers the impact of extreme cold scenario (ECS) and extreme heat scenario (EHS) on electricity demand, increasing the load of electric heating and electric cooling equipment respectively.

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

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

[0123] Risk Mitigation Module: A hierarchical risk mitigation strategy is adopted, which consists of bottom-up risk mitigation models for the first-level, second-level and third-level power systems.

[0124] Phase 1: 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 optimizes its configuration and dispatches when the power load is affected by the disaster to reduce load losses.

[0125] Phase II: Second-level power system mutual relief: When the first-level nodes cannot self-mitigate, the second-level power system performs mutual relief by forming a microgrid. Distributed generation resources and line reinforcement measures ensure the independent operation of the microgrid.

[0126] Phase 3: Risk mitigation of the tertiary power system: When the second level cannot further mitigate the impact of the disaster, the tertiary power system solves the remaining load loss problem by dispatching generators to ensure the stable operation of the overall system.

[0127] Example 3

[0128] In this study, the embodiment of the present invention uses actual energy capacity and historical meteorological data to optimize 124 data sets of the second level from 2017 to 2020 for validation optimization. Compare the conventional cost, emergency cost under extreme natural disasters, only consider energy storage configuration, line hardening, flexible load, microgrid strategy, and collaborative optimal strategy. Then fit the average power outage time (AOD) and frequency (AOF) of the power system caused by natural disasters to evaluate system performance.

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

[0130] According to the data in the table, the total cost of the conventional solution is 9564.278 billion yuan, while the total costs under various disaster scenarios are far higher than the conventional solution, especially the total costs under extreme scenarios. For example, the total cost of the energy storage configuration solution is 615615.9 billion yuan, and the total cost of the line hardening solution is 642787.9 billion yuan. The total cost of the microgrid solution is 387331.4 billion yuan, which is relatively low, and its demand response cost (8275.67 billion yuan) and carbon emission cost (17021.31 billion yuan) are significantly reduced. This shows that the microgrid solution has strong economic and environmental protection. The total cost of the optimal solution is the lowest, only 378058.7 billion yuan, indicating that the comprehensive use of energy storage configuration, demand response and line hardening strategies 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 natural disaster risks in a power system, the device comprising: a processor and a memory, wherein 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 following method steps in embodiment 1:

[0136] Construct an hourly composite cascade event set generation module to generate hourly data sets under various natural disaster scenarios, capture the cascading effects of different disasters, and provide data support for the quantification of power system risk impacts and the generation of risk mitigation module strategies;

[0137] Construct a distributed resource assessment module to evaluate the availability and power generation potential of distributed resources in the third-level region, and analyze their power generation potential during various natural disasters in combination with weather and disaster factors;

[0138] Construct a natural disaster impact module to simulate the impact of various natural disasters on the power system based on hourly composite cascade event sets, analyze the degree of damage to loads, power generation facilities, transmission and distribution lines, etc., and quantify the risk impact on the power system;

[0139] Build a risk mitigation module. For areas affected by risks, formulate disaster emergency plans based on a three-level coordination 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 to ensure rapid recovery and stable operation of the power grid.

[0140] Among them, the distributed resource evaluation module is:

[0141] Distributed energy is evaluated based on GIS spatial distribution and resource characteristics. The spatial resolution grid coordinates (x, y) are processed to calculate the distance from each resource point to the evaluation center (x c ,y c )’s weighted distance:

[0142] z=||xx c |+4×|yy c ||+1

[0143] By means of piecewise integration, the number of resources n(z) at each layer distance is calculated and compared with the initial total 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 enough resources have been included for optimization analysis.

[0146] Wherein, the natural disaster impact module is:

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

[0148]

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

[0150] Load characteristics are characterized by direct impacts of natural disasters and indirect impacts of geographical and human factors:

[0151]

[0152] in, and They are the power loads in the extreme cold scenario and the extreme high temperature scenario, is the power load for a typical baseline scenario; and They are the power correction loads for the extreme cold scenario and the extreme high temperature scenario, respectively; and They are the event-load impact functions for the extreme cold scenario and the extreme high temperature scenario. 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:

[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 a binary variable of branch state; is the rated power flow; is the event-branch influence function.

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

[0156] Phase 1: First-level power system risk self-mitigation:

[0157] The goal of self-mitigation of the power system is to reduce the impact of extreme natural disasters through load-side control through distributed energy storage pre-configuration and flexible load scheduling.

[0158]

[0159] Among them, p is the natural disaster index; c is the number of the first level, i.e., the corresponding power nodes; M county is the number of low voltage distribution networks; y, d, t are time indexes respectively; Allocate costs for distributed energy storage, Scheduling costs for flexible loads; For economic loss costs;

[0160] Phase II: Second-level power system risk mutual mitigation:

[0161] The first-level nodes participate in the mutual mitigation of the second-level power system. The purpose of the second-level power system mutual mitigation model is to achieve independent operation by forming a microgrid. The power supply is provided by the generator inside the microgrid and the distributed generation resources obtained by the distributed resource evaluation module. The grid is further strengthened by line reinforcement inside the microgrid. The goals of the second-level power system mutual mitigation model are:

[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,tContribution to distributed resources; allocating costs for microgrids; Cost of reinforcing the line; the levelized cost of energy for distributed resources;

[0164] Phase 3: Tertiary Power System Risk Mitigation:

[0165] Among them, the dispatch cost of the generator is composed of the maintenance cost Operating costs Characterization, the third-level power system risk mitigation is expressed as:

[0166]

[0167] Among them, p is the natural disaster index; M stage is the number of natural disasters; Mitigation costs for extreme cascading events.

[0168] It should be pointed out here that the device description in the above embodiment corresponds to the method description in the embodiment, and the embodiment of the present invention will not be described in detail 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. In specific implementation, the embodiments of the present invention do not limit the execution subjects and are selected according to the needs of actual applications.

[0170] The data signal is transmitted between the memory and the processor via a bus, which is not described in detail in the embodiment of the present invention.

[0171] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, the storage medium includes a stored program, and when the program is running, the device where the storage medium is located is controlled to execute the method steps in the above embodiment.

[0172] The computer-readable storage medium includes but is not limited to a flash memory, a hard disk, a solid-state drive, and the like.

[0173] It should be pointed out here that the description of the readable storage medium in the above embodiment corresponds to the description of the method in the embodiment, and the embodiment of the present invention will not be described in detail here.

[0174] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented 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 a computer, all or part of the processes or functions according to the embodiments of the present invention are generated.

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

[0176] Unless otherwise specified, the models of the components in the embodiments of the present invention are not limited, and any device that can perform the above functions may be used.

[0177] Those skilled in the art will appreciate that the accompanying drawing is only a schematic diagram of a preferred embodiment, and the serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages 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 substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for mitigating natural disaster risks in a power system, characterized in that: The method comprises: Construct an hourly composite cascade event set generation module to generate hourly data sets under various natural disaster scenarios, capture the cascading effects of different disasters, and provide data support for the quantification of power system risk impacts and the generation of risk mitigation module strategies; Construct a distributed resource assessment module to evaluate the availability and power generation potential of distributed resources in the third-level region, and analyze 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 various natural disasters on the power system based on hourly composite cascade event sets, analyze the degree of damage to loads, power generation facilities, transmission and distribution lines, etc., and quantify the risk impact on the power system; Build a risk mitigation module. For areas affected by risks, formulate disaster emergency plans based on a three-level coordination 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 to ensure rapid recovery and stable operation of the power grid.

2. A method for mitigating natural disaster risks in a power system according to claim 1, characterized in that: The distributed resource evaluation module is: Distributed energy is evaluated based on GIS spatial distribution and resource characteristics. The spatial resolution grid coordinates (x, y) are processed to calculate the distance from each resource point to the evaluation center (x c ,y c )’s weighted distance: z=||xx c |+4×|yy c ||+1 By means of piecewise integration, the number of resources n(z) at each layer distance is calculated and compared with the initial total resources N p For comparison: n'(z)=n(z) / N p When the ratio n′(z)>α, where α is a preset threshold, the module determines that enough resources have been included 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: in, is the load of the third-level power node c, ξ represents the event impact function; Load characteristics are characterized by direct impacts of natural disasters and indirect impacts of geographical and human factors: in, and They are the power loads in the extreme cold scenario and the extreme high temperature scenario, is the power load for a typical baseline scenario; and They are the power correction loads for the extreme cold scenario and the extreme high temperature scenario, respectively; and They are the event-load impact functions for the extreme cold scenario and the extreme high temperature scenario. 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: Where d is the node index; P c,d (i) is the actual power flow of the branch; u c,d (i) is a binary variable of branch state; is the rated power flow; is the event-branch influence function.

4. A method for mitigating natural disaster risks in a power system according to claim 1, characterized in that: The risk mitigation module is a bottom-up risk mitigation model, including a three-stage risk mitigation model; Phase 1: First-level power system risk self-mitigation: The goal of self-mitigation of the power system is to reduce the impact of extreme natural disasters through load-side control through distributed energy storage pre-configuration and flexible load scheduling. Among them, p is the natural disaster index; c is the number of the first level, i.e., the corresponding power nodes; M county is the number of low voltage distribution networks; y, d, t are time indexes respectively; Allocate costs for distributed energy storage, Scheduling costs for flexible loads; Cost of economic loss; Phase II: Second-level power system risk mutual mitigation: The first-level nodes participate in the mutual mitigation of the second-level power system. The purpose of the second-level power system mutual mitigation model is to achieve independent operation by forming a microgrid. The power supply is provided by the generator inside the microgrid and the distributed generation resources obtained by the distributed resource evaluation module. The grid is further strengthened by line reinforcement inside the microgrid. The goals of the second-level power system mutual mitigation model are: 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,t Contribution to distributed resources; allocating costs for microgrids; Cost of reinforcing the line; the levelized cost of energy for distributed resources; Phase 3: Tertiary Power System Risk Mitigation: Among them, the dispatch cost of the generator is composed of the maintenance cost Operating costs Characterization, the third-level power system risk mitigation is expressed as: Among them, p is the natural disaster index; M stage is the number of natural disasters; Mitigation costs for extreme cascading events.

5. A device for mitigating natural disaster risks in a power system, characterized in that: The device comprises: a processor and a memory, wherein 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 according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is enabled to perform the method according to any one of claims 1 to 4.

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