Method and device for evaluating disaster prevention capability of power distribution network

Abstract

The application relates to the technical field of power systems, and provides a power distribution network disaster prevention capability evaluation method and device. Based on disaster data and power grid data, a multi-disaster superposition probability model is constructed, and a comprehensive failure rate of a power distribution network element under a multi-disaster superposition scenario is solved; based on an improved analytic hierarchy process, a flow intermediate number, a population density and a unit load economic loss index are fused to calculate economic and social security influence weights of each load node; based on the comprehensive failure rate, a post-disaster failure scenario is generated, a network reconstruction model with a minimum weighted load shedding amount as an objective is constructed in combination with the economic and social security influence weights, and a power distribution network disaster prevention capability evaluation result is solved; wherein, constraint conditions of the network reconstruction model include operation constraints of a power system, power distribution network reconstruction constraints, user demand response constraints and operation constraints of flexible resources. The evaluation result can comprehensively reflect actual effects of disaster resistance capability of the power distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a power distribution network disaster prevention capability evaluation method and device. BACKGROUND

[0002] With the increasing frequency of extreme weather and disasters, power grids face risks such as large-area power outages, and higher requirements are placed on the improvement of power grid disaster resistance. Coastal areas are prone to extreme weather disasters such as thunderstorms and heavy rains caused by typhoons, and equipment pollution flashover accidents are also common in local industrial areas. Therefore, the influence of multiple disasters has become a major challenge to the safe and stable development of power grids. Under extreme compound disasters, the stability of the power supply system directly affects the stability of the load, and further affects social production and life. Therefore, it is necessary to evaluate the disaster prevention capability of the power distribution network under multiple disasters.

[0003] Traditional power system resilience evaluation focuses on the recovery level of the grid load, i.e., it ignores the impact of power distribution network disasters on users and society, and less evaluates the disaster resistance and prevention capability of the power distribution network from the economic and social security aspects. Existing research on the resilience of power distribution networks under multiple disaster scenarios is mostly limited to the improvement of technical aspects such as rapid response, resource optimization, and self-repairing capability of the power system, and fails to fully integrate power grid disaster prevention capability with social and economic security to comprehensively evaluate the social security risks that may be caused during the recovery process of the power distribution network after a disaster. In addition, in the development and implementation of recovery strategies, there is a lack of in-depth analysis of economic factors, social acceptance, public participation, and social stability.

[0004] Specifically, the technical problems faced by the power distribution network disaster prevention capability evaluation field and the limitations of existing methods mainly include the following three points:

[0005] First, single focus on grid load recovery. Traditional resilience evaluation methods mainly focus on the physical recovery capability of the grid, such as fault repair time, power supply recovery rate, etc. These indicators can reflect the technical performance of the grid, but they ignore the impact of power outages on users and society. For example, the grid may have restored power supply in a short time, but some critical users (such as hospitals, transportation hubs, communication facilities, etc.) have suffered serious economic losses or social impacts during the power outage.

[0006] Second, ignoring the active participation of users in disasters and the calling of flexible resources. The ultimate goal of the power system is to provide reliable power supply to users. Traditional evaluation methods mainly focus on the recovery capability and power supply reliability of the grid, while ignoring the active participation of users in disasters and the calling of flexible resources.

[0007] Third, the impact on economic and social security is ignored. Power interruption not only affects the daily life of users, but also has a profound impact on the economy and social security. For example, power outage can cause factories to shut down, traffic to be paralyzed, communication to be interrupted, and even social disorder to be triggered. The traditional evaluation method fails to take these economic and social impacts into account, resulting in evaluation results that cannot fully reflect the actual effect of the disaster resistance capability of the distribution network.

[0008] Therefore, the existing distribution network disaster prevention capability evaluation method only focuses on power load recovery, ignores the active participation of the user side in disasters and the calling of flexible resources, and ignores the impact on economic and social security, and the evaluation results cannot fully reflect the actual effect of the disaster resistance capability of the distribution network. SUMMARY

[0009] Therefore, the existing distribution network disaster prevention capability evaluation method only focuses on power load recovery, ignores the active participation of the user side in disasters and the calling of flexible resources, and ignores the impact on economic and social security, and the evaluation results cannot fully reflect the actual effect of the disaster resistance capability of the distribution network.

[0010] A first aspect of the embodiments of the present application provides a distribution network disaster prevention capability evaluation method, comprising:

[0011] Based on disaster data and power grid data, a multi-disaster superposition probability model is constructed, and the comprehensive failure rate of the distribution network element under the multi-disaster superposition scenario is solved;

[0012] Based on the improved analytic hierarchy process, the economic and social security impact weight of each load node is calculated by fusing the flow betweenness, population density and unit load economic loss index;

[0013] Based on the comprehensive failure rate, a post-disaster failure scenario is generated, a network reconfiguration model with the minimum weighted load shedding amount as the target is constructed in combination with the economic and social security impact weight, and the distribution network disaster prevention capability evaluation result is solved;

[0014] The constraint conditions of the network reconfiguration model include the operation constraint of the power system, the distribution network reconfiguration constraint, the user demand response constraint and the operation constraint of the flexible resource.

[0015] A second aspect of the embodiments of the present application provides a distribution network disaster prevention capability evaluation device, comprising:

[0016] The failure rate calculation module is configured to construct a multi-disaster superposition probability model based on disaster data and power grid data, and solve the comprehensive failure rate of the distribution network element under the multi-disaster superposition scenario;

[0017] The weight calculation module is configured to calculate the economic and social security impact weight of each load node by fusing the flow betweenness, population density and unit load economic loss index based on the improved analytic hierarchy process;

[0018] a network reconstruction module configured to generate a post-disaster failure scenario based on the comprehensive failure rate, construct a network reconstruction model with the minimum weighted load shedding as the target in combination with the economic and social safety influence weight, and obtain a power distribution network disaster prevention capability evaluation result by solving the network reconstruction model;

[0019] The constraint condition of the network reconstruction model includes an operation constraint of a power system, a power distribution network reconstruction constraint, a user demand response constraint, and an operation constraint of a flexible resource.

[0020] A third aspect of the embodiments of the present application provides an electronic device including a processor, a memory, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the electronic device implements the power distribution network disaster prevention capability evaluation method provided in the first aspect of the embodiments of the present application.

[0021] A fourth aspect of the embodiments of the present application provides a computer program product including a computer program, and when the computer program is executed, the method provided in the first aspect of the embodiments of the present application is executed.

[0022] The power distribution network disaster prevention capability evaluation method provided in the first aspect of the embodiments of the present application obtains a comprehensive failure rate of a power distribution network element in a multi-disaster superimposed scenario by constructing a multi-disaster superimposed probability model based on disaster data and power grid data; calculates an economic and social safety influence weight of each load node by fusing a power flow betweenness, a population density, and a unit load economic loss index based on an improved analytic hierarchy process; generates a post-disaster failure scenario based on the comprehensive failure rate, constructs a network reconstruction model with the minimum weighted load shedding as the target in combination with the economic and social safety influence weight, and obtains a power distribution network disaster prevention capability evaluation result by solving the network reconstruction model; and the constraint condition of the network reconstruction model includes an operation constraint of a power system, a power distribution network reconstruction constraint, a user demand response constraint, and an operation constraint of a flexible resource. The social and economic loss dimension is included in the evaluation system, and a collaborative mechanism of user-side flexible resources participating in power grid recovery is constructed. By establishing a quantitative correlation among disaster intensity, equipment failure probability, and social influence degree, a cross-dimension disaster evaluation from a physical power grid to a social and economic system is realized, and the evaluation result can comprehensively reflect the actual effect of the power distribution network disaster prevention capability, thereby providing a new technical path for improving the urban power grid resilience.

[0023] It can be understood that the beneficial effects of the above-mentioned second aspect to fourth aspect can be referred to the related description in the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative effort.

[0025] Figure 1 is a flowchart of a power distribution network disaster prevention capability evaluation method provided by an embodiment of the present application;

[0026] Figure 2 is a structural diagram of a power distribution network disaster prevention capability evaluation device provided by an embodiment of the present application;

[0027] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] In the following description, specific details are set forth in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art will understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary details.

[0029] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0030] In the present specification, the reference to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment", "in some embodiments", "in other embodiments", "in additional embodiments", and so on, in various places in the specification are not necessarily all referring to the same embodiment, unless otherwise specified. The terms "comprise", "comprising", "have", "having", and "include" and their variants mean "including but not limited to", unless otherwise specified.

[0031] As shown in Figure 1 The power distribution network disaster prevention capability evaluation method provided by the embodiments of the present application includes the following steps S101-S103:

[0032] Step S101, based on disaster data and power grid data, a multi-disaster superposition probability model is constructed, and a comprehensive failure rate of power distribution network elements under a multi-disaster superposition scenario is solved;

[0033] Step S102, based on the improved analytic hierarchy process, the economic and social security influence weight of each load node is calculated by fusing the power flow betweenness, population density and unit load economic loss index;

[0034] Step S103, based on the comprehensive failure rate, a post-disaster failure scenario is generated, a network reconfiguration model with the minimum weighted load shedding amount as the target is constructed by combining the economic and social security influence weight, and a power distribution network disaster prevention capability evaluation result is solved;

[0035] The constraint conditions of the network reconfiguration model include the operation constraints of the power system, the power distribution network reconfiguration constraints, the user demand response constraints and the operation constraints of the flexibility resources.

[0036] In application, in view of the technical short board of the power system safety evaluation under extreme climate conditions, in the background that the existing resilience evaluation system mainly focuses on the physical recovery index of the power grid and ignores the user-side resource regulation potential and the social influence of disaster loss, the embodiment of the application constructs a multi-dimensional evaluation model. The specific implementation process includes three core modules:

[0037] Firstly, a multi-disaster coupling probability model is established, through typhoon disaster decoupling analysis, the compound disaster is decomposed into two related disaster factors of storm and flood, a disaster chain reaction model is constructed by using a Bayesian network, and the failure probability of equipment under a multi-disaster superposition scenario is accurately quantified. Secondly, based on the social influence elements, a node recovery priority system is constructed, the network topology structure, the population distribution characteristics and the load economic loss coefficient are comprehensively considered, the entropy weight correction analytic hierarchy process is used to determine the recovery weight coefficient of each load node. Finally, a post-disaster reconstruction model coordinated with demand response is constructed, the user-side energy storage system, the dispatchable load and other flexibility resources are integrated, a mixed integer programming model with the minimum weighted load shedding amount as the target is established, and the quantitative evaluation of the disaster prevention efficiency is realized through disaster loss scenario simulation.

[0038] The embodiment of the application breaks through the traditional evaluation framework, for the first time, the social and economic loss dimension is included in the evaluation system, and a collaborative mechanism of user-side flexibility resources participating in power grid recovery is constructed. By establishing the quantitative correlation between disaster intensity, equipment failure probability and social influence degree, cross-dimensional disaster evaluation from physical power grid to social and economic system is realized, and the evaluation result can fully reflect the actual effect of the disaster resistance capability of the power distribution network, and provides a new technical path for the resilience improvement of urban power grid

[0039] In one embodiment, based on disaster data and power grid data, a multi-disaster superposition probability model is constructed, including:

[0040] The typhoon disaster is decomposed into a rainstorm disaster and a flood disaster, and a rainstorm disaster joint probability model and a flood disaster probability model are respectively constructed;

[0041] Based on the rainstorm disaster joint probability model and the flood disaster probability model, a multi-disaster superimposed probability model is constructed.

[0042] In one embodiment, the multi-disaster superimposed probability model is:

[0043]

[0044] Wherein, m = 1, 2; P d is the comprehensive failure rate of the power distribution network element under the multi-disaster superimposed scenario; P m is the failure probability of the power distribution line under each disaster independently; n d is the number of disasters considered for superposition.

[0045] In one embodiment, it further comprises:

[0046] When m = 1, P m is calculated by the following formula:

[0047]

[0048] When m = 2, P m is calculated by the following formula:

[0049]

[0050] Wherein, P1 is the failure probability of the power distribution line under the rainstorm disaster; P2 is the failure probability of the power distribution line under the flood disaster; y is the influence variable of the flood disaster on the power grid element; is the joint probability density function of the rainstorm disaster; is the rainfall intensity that the transmission line t5 can withstand in the s5 area at time t5; is the wind speed that the transmission line t5 can withstand in the s5 area at time t5, D ine is the wind speed and rainfall intensity interval under multi-disasters.

[0051] In application, based on disaster data and power grid data, the disaster is modeled, and the power grid element failure rate is obtained based on the multi-disaster superimposed probability model. The disaster scenario is modeled, the coupling effect between the single disaster and even the multi-disasters is fully considered, the typhoon is decomposed into two typical disaster factors of rainstorm and flood according to the disaster mechanism and influence range, a multi-disaster superimposed probability model is proposed, and a relatively accurate power distribution equipment failure probability can be quickly obtained.

[0052] Typhoon is the most frequent disaster weather event in the southeast coastal areas of China, and its disaster-causing factors mainly include three types: strong wind, heavy rain, and storm surge (big wave). These disaster-causing factors often interact with each other, forming a complex chain disaster effect, leading to the frequent occurrence of secondary disasters in the disaster evolution process, further exacerbating the disaster situation, causing huge economic losses, and significantly increasing the difficulty of emergency rescue. In order to deeply study the influence of typhoon disaster, the method of constructing typhoon disaster chain network is usually used in meteorology. As an important disaster-bearing body in the typhoon disaster chain, the vulnerability of urban power distribution network in typhoon disaster has attracted much attention, and some studies have analyzed the impact of typhoon and its secondary disasters on urban power distribution network.

[0053] Among the three types of disaster-causing factors, the impact of strong wind on urban power distribution network is the most direct and frequent. Unlike heavy rain and storm surge, strong wind usually directly acts on urban power distribution network without going through a complex secondary disaster evolution process. Strong wind directly or indirectly causes power system failures such as tower collapse, wire breakage, windage flashover, and foreign object hanging on the line, leading to frequent power outages. This kind of failure often cannot be restored by simple reclosing operation, further exacerbating the risk of power system paralysis. Therefore, strong wind is one of the most destructive disaster-causing factors in typhoon disasters.

[0054] Heavy rain indirectly affects the stability of the power system by inducing urban flood disasters. The disaster-causing mechanism lies in that when the rainfall intensity exceeds the design threshold of the urban drainage system, the surface runoff increases sharply, and the drainage system cannot effectively cope with it, especially in low-lying areas and areas with poor drainage, where waterlogging or even waterlogging is easily formed. The damage of flood to power system infrastructure mainly includes short circuit, insulation layer damage, and mechanical damage of substation, cable corridor, and distribution facilities, etc., which further leads to local or regional power supply interruption. In addition, flood may also wash away power supply lines, causing physical rupture of power transmission network, further exacerbating the problem of power shortage in urban areas during flood period, and posing a serious challenge to emergency response capability. Therefore, the impact of heavy rain and the flood disaster it causes on the power system is indirect but significant.

[0055] In summary, the disaster-causing factors such as strong wind, heavy rain, and storm surge in typhoon disasters have different characteristics, and their impact mechanisms on the power system are also different. Strong wind directly acts on power facilities, heavy rain indirectly damages power system through flood disaster, and storm surge may cause serious impact on power infrastructure in coastal areas. In order to comprehensively evaluate the impact of typhoon disaster on the power system, it is necessary to model the three types of disaster-causing factors respectively and deeply analyze their action mechanisms, so as to provide scientific basis for improving the disaster resistance of urban power distribution network.

[0056] Due to the significant differences in disaster mechanisms, evolution processes, and disaster range characteristics, it is necessary to consider the unique physical characteristics of each disaster when analyzing its impact on electrical components of the power system. To accurately assess the extent of damage caused by disasters to the power system, a mathematical model must be constructed for each type of disaster to simulate its mechanism and evolution process. Based on this, further analysis of the common and unique characteristics of various disasters is conducted to establish a probability model that reflects the probability of disaster occurrence and its impact. For example, typhoon disasters are mainly characterized by the combined effects of strong winds, heavy rain, and storm surges, while earthquakes can trigger a variety of secondary disasters such as ground shaking and soil liquefaction. By establishing a disaster probability model, the potential impact of disasters on the power system can be more accurately predicted, providing a scientific basis for developing targeted disaster prevention and mitigation strategies. This mathematical model-based analysis method not only improves the accuracy of disaster assessment but also provides important references for the planning and optimization of the power system.

[0057] In the application, for the storm disaster joint probability model, in typhoon disasters, heavy rain and strong wind are common disaster factors, and the events they cause, such as power distribution line breakage and distributed power interruption, usually belong to the "small probability-high loss" type. Traditional probability description methods are usually based on unconditional expectation and have the characteristic of risk neutrality, often ignoring the differences in system operating states under different probability-loss combinations, making it difficult to effectively reflect the special importance of low-probability-high-loss events under extreme weather conditions. Given the serious threat posed by heavy rain and strong winds to the power system, the Generalized Pareto Distribution (GPD) and Extreme Value Distribution (EVD) statistical models are used to jointly analyze the intensity probability density of heavy rain and strong winds. This method can better capture the tail characteristics of extreme events, thus more accurately describing the distribution of disaster intensity.

[0058] On this basis, a fault risk model for distribution network elements is further constructed, considering the probability distribution characteristics of disaster intensity and the tolerance capacity of distribution network elements. By calculating the probability of disaster intensity exceeding the maximum tolerance value of the element, the fault risk of distribution network elements under extreme weather conditions can be quantified. The Extreme Value Distribution is used to model the probability density function of rainfall due to its good fitting ability for tail extreme values, and its expression can effectively describe the probability distribution characteristics of extreme rainfall events. This analysis method based on extreme value theory not only improves the accuracy of risk assessment for extreme weather events but also provides scientific basis for disaster prevention planning and emergency response of distribution networks, thereby enhancing the disaster resistance of the power system under extreme weather conditions. Its expression is:

[0059]

[0060] Where, h′ s Let ξ be the rainfall intensity in region s at time t; ξ be the rain pattern shape parameter; σ be the scale parameter; and N be the rainfall intensity h′. s The number of samples in the sequence; h is the threshold; N h For each rainfall intensity sequence, the sample sequence contains rainfall intensities greater than or equal to h; f R (h′ s ) represents the rainfall probability density function; Nσ is the product of the sample size and the scale parameter.

[0061] Fitting the extreme value distribution to the strong wind speed yields the following:

[0062]

[0063] in, Let a be the strong wind speed in region s at time t; W b is the scale parameter; W For position parameters; γ W For shape parameters; Let be the probability density function of wind speed intensity.

[0064] Based on the Copula function, a joint probability distribution model of storms is constructed to describe the probabilistic correlation between different extreme weather events. The joint probability density function for storm disasters is... for:

[0065]

[0066] Where λ is the chain parameter of the joint probability density function of the storm disaster; Let be the rainfall intensity that the transmission line can withstand in region s at time t; Let be the wind speed that the transmission line can withstand in region s at time t; Let be the probability density function of wind speed intensity; Let be the probability density function of rainfall intensity.

[0067] Considering that the damage to the power distribution network caused by storms mainly manifests as line breaks and insulator flashovers, it is necessary to construct a fault probability model for power distribution lines under storm disasters:

[0068]

[0069] In the formula, P1 represents the failure probability of the power distribution line under a storm disaster; and the maximum rainfall intensity that the power distribution line in region s can withstand at time t is... Critical rainfall intensity due to insulator flashover Critical value of substation water accumulation shutdown intensity The smaller quantities together determine:

[0070]

[0071] wherein V is the critical flashover voltage of the insulator; in is the critical flashover voltage of the insulator; is the atmospheric pressure at the location of the insulator; is the standard atmospheric pressure; a in , b in , c in is a constant; is the design flow of rainwater for the substation; ψ is the runoff coefficient of the substation; ζ is the catchment area of the substation.

[0072] is the maximum wind speed that the distribution line in region s can withstand at time t, which can be calculated by the following formula:

[0073]

[0074] wherein: is the maximum wind load that the distribution line can withstand; is the wind pressure unevenness coefficient of the line; is the line body shape coefficient; is the number of phase conductors; is the length of the line under wind load; is the outer diameter of the line; is the angle between the wind direction and the line direction.

[0075] In application, for the flood disaster probability model, flood is another important disaster factor in typhoon disasters, and its impact on urban distribution networks mainly reflects the physical damage caused by flooding and soaking. When the rainstorm accompanying the typhoon exceeds the carrying capacity of the urban drainage system, waterlogging or even flood disasters are likely to occur in low-lying areas or along rivers. In this case, key equipment such as underground cables, substations, distribution boxes, and the like of the distribution network may suffer severe water immersion, leading to insulation damage, short circuit, equipment failure, and even the paralysis of the entire system. Water immersion not only directly damages the operational capacity of electrical equipment, but also increases the difficulty and cost of subsequent repair, because after the water recedes, thorough drying, cleaning, and detection work must be carried out to ensure the safety and reliability of the equipment before it is put back into operation.

[0076] In addition, flood disasters may also cause traffic disruptions, affecting the timely arrival of repair teams, further prolonging the time to restore power supply. When constructing the disaster factor probability model, the impact of flood disasters on distribution network nodes must be fully considered, including the flooding risk of different geographical locations, equipment types, and their water resistance, etc., to accurately assess the potential threat of flood disasters to the overall reliability of the distribution network.

[0077] Therefore, considering that the generation and development of flood disaster mainly depends on meteorological factors and geographical factors, when analyzing the influence of rainstorm flood disaster on power system, research should be carried out on meteorological factors and geographical factors first, and the failure probability model of power system components can be further derived according to the basic model of rainstorm flood.

[0078] Rainstorm is the disaster factor of urban waterlogging disaster. In the research of urban rainstorm waterlogging disaster, the measured rainstorm data or the rainstorm model is generally used to complete the disaster risk research. The rainstorm model needs to consider the rainstorm amount and the rainstorm process, and the rainstorm amount is generally determined by the urban rainstorm intensity formula. The rainstorm intensity of the city can be expressed as:

[0079]

[0080] In the formula, q r is the design rainstorm intensity, the unit is liter / (second·hectare); P rain is the return period of the design rainstorm, the unit is year; t rain is the design rainfall duration, the unit is minute; b r and n r are statistical parameters related to rainfall, which are given by the water bureau of each city after statistics.

[0081] Typical rainstorm models mainly include uniform rain type, triangular rain type and Chicago rain type. Among them, the uniform rain type assumes that the rainfall intensity remains unchanged throughout the rainfall process, although the calculation is simple, but the calculated runoff and flood peak flow are usually smaller than the actual value, and it is difficult to accurately reflect the real influence of rainstorm. The triangular rain type simulates the rainstorm process by assuming that the rainfall intensity changes linearly with time, but the calculation result of the flood peak flow is sensitive to the value of the rainfall duration, and a large amount of rainfall data is needed to ensure the accuracy of the model. In comparison, the Chicago rain type model can generate a non-uniform design rain type based on the frequency relationship between rainfall intensity and duration, which is closer to the actual rainfall process. The research results show that the Chicago rain type model has good simulation effect on the characteristics of urban rainstorm in China, and can usually meet the accuracy requirements, and the determination of the rain intensity process is relatively simple, so it has been widely used in the research of urban rainstorm waterlogging.

[0082] Because the rainfall of single-peak rain type is concentrated in a short period of time, it is easy to cause large flood, and the impact on power system is particularly significant, therefore, when analyzing the influence of rainstorm on power system, the single-peak rain type is mainly considered. The single-peak Chicago rain type is selected to design the rainfall process, so as to more accurately simulate the space-time distribution characteristics of rainstorm. During the occurrence of rainstorm, the design rainfall intensity at each time can be expressed as:

[0083]

[0084] In the formula: t1 is the pre-peak duration, t2 is the post-peak duration, q(t1), q(t2) respectively represent the rainfall intensity at t1 and t2, the unit is mm / min; parameter A r , b r and n r are statistical parameters related to rainfall, which are given by the water bureau of each city after statistics; r ∈ (0, 1) represents the rain peak coefficient of the rainstorm, which describes the time when the rain peak occurs, and divides the entire rainfall time series into two parts, the pre-peak time series and the post-peak time series, for rain type calculation. The rain peak coefficient r should be based on the local rainfall process data statistics.

[0085] The waterlogging caused by rainstorm disaster is characterized by "no source submergence", that is, regional large-area uniform rainfall, and all low-lying places may be waterlogged. For a large spatial scale study area, the equal volume method is commonly used for rainstorm waterlogging disaster risk simulation. The waterlogged area is discretely divided into several grid areas, and spatial interpolation is used to assign values to the ground elevation grid:

[0086]

[0087] In the formula: Δσ is the grid area, N zone is the total number of waterlogged grid areas, E g (k) is the elevation of the kth grid. At this time, the unknown quantities are N zone and E w , and the N zone parameter can be obtained through the relationship between E w and E g . The waterlogged surface elevation E w is solved by using the dichotomy method, so that the total water volume P and the total water volume W in the waterlogged submerged range gradually approach each other until the difference is less than the allowable error. Finally, the waterlogged surface elevation E w is obtained. Through this method, all the grids in the study area are calculated once, and the waterlogged spatial distribution data of the study area can be obtained.

[0088] Based on the disaster model of rainstorm flood, considering the complexity and randomness of flood disaster, the characteristic quantities are extracted from meteorology, geography, power and other factors that cause power equipment failure, and a higher explanatory power of power equipment failure influence model is established, and the least square method is used to obtain the parameters.

[0089] On the basis of statistical analysis of various data under flood disaster, a statistical regression model of the influence of flood disaster on power equipment failure is established. Through the Lasso method for feature extraction, there are two processes, namely extracting significant variables and estimating corresponding coefficients, which is equivalent to solving the following problem:

[0090]

[0091] In the formula, X is the influence factor vector of flood disaster, including total rainfall W, temperature, humidity and other environmental variables; β is the model parameter; y is the influence variable of the power grid element, satisfying the linear relationship and where p is the equipment failure percentage; β s is the s-th linear parameter; is the model error with unbiasedness, equal variance and independence, and the inverse solution can obtain the fault probability of the transmission line:

[0092]

[0093] where P2 is the fault probability of the distribution line under flood disaster; y is the influence variable of the flood disaster on the power grid element.

[0094] In application, for the multi-disaster superposition probability model, given the significant differences in disaster-causing mechanisms, disaster evolution processes, and disaster range characteristics between gales, storms, and flood disasters, a hierarchical and phased strategy needs to be adopted when building a comprehensive fault probability model. First, the load effects of gales and storms on distribution network lines and towers should be quantitatively analyzed. By combining meteorological data, topographic information, and the structural characteristics of the distribution network, wind pressure distribution models and rainwater accumulation models can be established to predict the occurrence probability of line breakage, tower collapse, and other faults under different intensity of wind and rain conditions. This process needs to rely on historical disaster data and experimental research results to calibrate and optimize the model to ensure its accuracy and applicability.

[0095] Secondly, for flood disasters, geographic information systems (GIS) and hydrological models need to be used to simulate the water accumulation on urban surfaces under different rainfall intensities and identify high-risk areas of flood disasters. On this basis, the flooding risk of distribution network nodes (especially underground cables, substations, and other critical facilities) needs to be evaluated. Meanwhile, factors such as equipment type, installation height, waterproof level, and others need to be considered to establish a probability model of equipment damage caused by flood disasters. This step not only quantifies the direct impact of flood disasters on the power system, but also provides targeted suggestions for the disaster prevention design of the distribution network.

[0096] Finally, based on the above analysis, by integrating the failure probability of different disaster scenarios, a multi-disaster scenario comprehensive failure probability model based on the typhoon-urban distribution network disaster chain network is constructed. This model should be able to dynamically reflect the interaction and superposition effect between different disasters, such as the combined effect of strong wind and heavy rain on the damage of distribution network. Given the differences and independence of wind, rain and flood disasters in causing damage to the power system, it can be assumed that the influence of these disasters on conductors and towers is independent of each other. Therefore, based on the establishment of the probability model of each disaster-causing factor, a multi-disaster superposition probability model is proposed, which can be effectively applied to the calculation of the failure rate of power grid elements under different number and type of natural disaster superposition scenarios:

[0097]

[0098] where m = 1, 2; P d is the comprehensive failure rate of distribution network elements under multi-disaster superposition scenarios; P m is the failure probability of distribution lines under each disaster independently; n d is the number of disasters considered for superposition.

[0099] The line failure rate under multi-disaster superposition can be used to effectively analyze the vulnerability of power grid system under complex disaster environment, evaluate the impact of disasters on power grid operation, and provide the basic scenario of power grid failure under disasters for subsequent consideration of economic and social security impact and user-side demand response disaster prevention capability assessment.

[0100] In one embodiment, the improved analytic hierarchy process is used to calculate the economic and social security impact weight of each load node by integrating the power flow betweenness, population density and unit load economic loss index, including:

[0101] A three-level evaluation model including a target layer, a criterion layer and an object layer is constructed; wherein the evaluation indexes of the criterion layer include node power loss c i,1 , population density c i,2 and power flow betweenness c i,3 , i is the load node;

[0102] An exponential scale method is used to construct a judgment matrix to calculate the weight coefficient ω p of each evaluation index, and the economic and social security impact weight of each load node is obtained.

[0103] In the application, the improved analytic hierarchy process is used to comprehensively consider the economic and social safety influence factors to obtain the recovery weight of each node as an important influence factor of the target function in the next step. Based on the three economic and social safety influence factors of power flow betweenness, population density and unit load economic loss, the importance and difference of each node after disaster recovery are fully considered, and the improved analytic hierarchy process (IAHP) is used to comprehensively consider the three factors to obtain the recovery weight of each node, so that the method can combine the actual load loss of the disaster area to preferentially restore important nodes.

[0104] The power outage economic loss generally refers to the economic loss borne by the whole society when the power supply is not completely reliable or is expected to be not completely reliable (i.e. when power outage or power rationing occurs due to power supply interruption or shortage). The power outage economic loss is not only an important basis for power system planning and design, but also an important reference for power system disaster prevention capability assessment.

[0105] The main task of the power industry is to provide reliable and high-quality power to users at a reasonable price. In the disaster situation, load shedding of the power system will bring serious power outage economic loss to the power industry. The loss caused by the reduction of power sales accounts for a large proportion of the total loss of the power industry, and the power loss caused by load shedding is the main aspect of direct economic loss assessment of the power industry. The load shedding power loss c1 of node i is calculated as follows:

[0106] c i,1 (n)=p u P shed ;

[0107] In the formula: u = 1, 2, 3 represents the user type of the power outage node, which respectively represents industrial, commercial and residential users; p u (u = 1, 2, 3) respectively represents the power sales price of industrial, commercial and residential users; P shed represents the load shedding amount of the node; c i,1 (n) is the load shedding power loss of node i load n.

[0108] Disaster scenarios may cause large-scale power outages, causing production and life to stagnate, communication and information to be blocked, medical education to be suspended, and traffic order to be chaotic, and a series of public safety problems. Therefore, it is very important to consider the social impact in the process of distribution network disaster prevention assessment, and relevant indexes need to be set to describe the social impact caused by power outage due to disaster.

[0109] High population density areas have greater demand for electricity, and there are relatively more power facilities. Studies at home and abroad have shown that there is a positive correlation between power facility density and population density. Therefore, population density can be directly considered as an important indicator for the social safety impact assessment of nodes. The higher the population density of a node, the more the public will be affected by load shedding, and the greater the social impact caused by power outage. The population density of node i is defined as i,2 .

[0110] Betweenness was first used in social networks and information networks to measure the importance of nodes (or edges) in the network. The greater the betweenness of a node (or branch), the more intermediary functions it undertakes in the network. In power grids, the node power flow betweenness is used to represent the importance of nodes in network structure, and truly reflects the occupation of power transmission between "power generation-load" nodes on each line and node, quantifies the importance of elements in the whole network power transmission process, and can reflect the influence of elements on electricity and social safety. The power flow betweenness index of node i is defined as i,3 As follows:

[0111]

[0112] Where: L i represents the set of lines connected to node i in the power network; represents the power flow betweenness of branch l k ; P i represents the outflow power of node i; c i,3 (n) is the power flow betweenness of node i.

[0113] The power flow betweenness of a branch is used to measure the importance of a line in the power network, which can be represented as:

[0114]

[0115] Where: F ij is the power flow betweenness index of branch; G is the set of generator nodes; B is the set of system nodes; represents the minimum value of the actual output of generator m g and the active power demand of load n; P ij (m g , n) represents the active power flowing through the branch when generator m g supplies load n; represents the active power actually supplied by generator m g to load n.

[0116] The improved analytical hierarchy process (IAHP) is used to evaluate the anti-disaster ability of urban distribution network in typhoon. According to the relationship between the node, the social and economic security index and the state of urban distribution network in disaster scenario, the hierarchy model of disaster prevention ability evaluation considering the influence of social and economic security is divided into three layers: target layer, criterion layer and object layer.

[0117] 1) The target layer is the disaster degree of distribution network node, which is the evaluation result of urban distribution network disaster prevention ability considering the influence of social and economic security; 2) The criterion layer is the index describing the influence of social and economic security caused by the power outage of urban distribution network in disaster; 3) The object layer is the node of urban distribution network.

[0118] Compared with the traditional analytical hierarchy process, the improved analytical hierarchy process is different in weight scale method. The improved analytical hierarchy process uses exponential scale, which has the characteristics of optimal consistency of judgment matrix, greatly improves the accuracy of weight calculation and reduces the subjective influence.

[0119] Step 1: Construct the index judgment matrix A. According to the improved exponential scale method, the importance of the same level evaluation index z and index w about the importance of the last level index is compared, and the judgment matrix A is as follows:

[0120]

[0121] The element a zw in A is the scale value of the importance of the same level evaluation index z and index w. According to the Weber-Fechner law, the form of importance scale is 1.316 e (e = 0, 1, 2, … 8). The specific importance scale method is shown in Table 1:

[0122] Table 1 IAHP exponential scale

[0123]

[0124] Step 2: The consistency of the judgment matrix A is checked, and the consistency index C1 and the consistency ratio C R are calculated.

[0125]

[0126] In the formula: λ max is the maximum eigenvalue of the judgment matrix A; n A is the order of the judgment matrix A; R1 is the average random consistency check index corresponding to the exponential scale, which can be obtained from Table 2.

[0127] Table 2 Random consistency check index of exponential scale

[0128]

[0129] When the consistency ratio meets C R <0.1, it is judged that the matrix A passes the consistency test, and the calculated weight meets the requirements, otherwise the matrix A needs to be restructured.

[0130] In one embodiment, the economic and social security influence weight of each load node is calculated by the following formula:

[0131] Ω i1 = ω i1,1 c i1,1 + ω i1,2 c i1,2 + ω i1,3 c i1,3 ;

[0132]

[0133] wherein, Ω i1 is the economic and social security influence weight of the load node i1; ω i1,1 is the electricity sales loss weight; ω i1,2 is the population density weight; ω i1,3 is the power flow intermediate weight; ω p is the weight coefficient of the evaluation index; x p is the pth element of the characteristic vector; n A is the order of the judgment matrix A.

[0134] In application, it includes Step 3: calculating the weight coefficient ω p of each evaluation index. The judgment matrix A meets the consistency test standard, and the corresponding characteristic vector X is obtained by the largest eigenvalue λ max of the judgment matrix A. The characteristic vector X is normalized:

[0135]

[0136] Therefore, the economic and social security influence weight of each load node, i.e. the economic and social security influence weight of the load node i, is:

[0137] Ω i1 = ω i1,1 c i1,1 + ω i1,2 c i1,2 + ω i1,3 c i1,3 ;

[0138] wherein, Ω i1 is the economic and social security influence weight of the load node i1; ω i1,1 is the electricity sales loss weight; ω i1,2 is the population density weight; ωi1,3 is the weight of the flow between node i and node j; ω p is the weight coefficient of the evaluation index; x p is the pth element of the feature vector; n A is the order of the judgment matrix A.

[0139] Through the above process, the influence of each power distribution network node on economic and social safety can be quantified, and the corresponding weight can be assigned. The size of the weight directly reflects the importance and priority of the node in post-disaster recovery. The greater the weight, the more significant the influence of the node on economic and social safety, and the higher the urgency of recovery. For example, the weight of key load nodes such as hospitals, transportation hubs, and communication facilities is usually high, because their power outage can cause serious social disorder or economic loss. Based on these weights, a multi-disaster scenario power distribution network disaster prevention capability evaluation model considering economic and social safety influence can be further constructed, so as to not only evaluate the physical recovery capability of the power distribution network in disaster, but also optimize resource allocation and recovery strategy from the perspective of economic and social safety, and ensure the priority recovery of key loads.

[0140] In one embodiment, the objective function of the network reconstruction model is:

[0141]

[0142] wherein Ω i1 is the economic and social safety influence weight of load node i1; P shed,i1 is the load reduction of load node i1, and B is the system node set.

[0143] In application, based on step S101, the most probable post-disaster power distribution network failure scenario is generated and screened based on the multi-disaster superimposed power grid element failure rate sampling. Based on step S102, the weight coefficient of each load point considering economic and social safety influence is obtained. The minimum network reconstruction load reduction of the post-disaster urban power distribution network considering economic and social safety influence is taken as the target, and a multi-disaster scenario power distribution network disaster prevention capability evaluation model considering economic and social safety influence is constructed. It is proposed to consider the participation of user-side flexible resources such as storage batteries and user-side transferable load, and to construct a corresponding mathematical model. Finally, based on the weight results of each node recovery, the load recovery under disaster simulation is carried out, and the disaster prevention capability of the power distribution network is evaluated.

[0144] The post-disaster distribution network disaster prevention capability evaluation is a complex and multi-dimensional process. Traditional evaluation methods mainly focus on the physical recovery capability of the power grid itself, such as fault repair time, power supply recovery rate and other technical indicators. However, with the continuous development of the power system and the increasing diversification of user demand, evaluation from the perspective of the power grid alone cannot fully reflect the actual response capability of the distribution network in disasters. Therefore, in the post-disaster distribution network disaster prevention capability evaluation, the demand response of the user side and the role of flexible resources (such as batteries) must be fully considered. This comprehensive evaluation method not only improves the scientificity and comprehensiveness of the evaluation, but also provides more targeted decision support for the disaster prevention planning and emergency response of the distribution network.

[0145] User-side demand response plays an important role in the post-disaster distribution network recovery process. Demand response refers to the use of price signals or incentive mechanisms to guide users to adjust their electricity consumption behavior during a specific time period, thereby optimizing power supply and demand balance. After a disaster occurs, the power supply capability of the distribution network is usually severely limited. At this time, user-side demand response can effectively alleviate the power supply pressure of the grid by reducing unnecessary loads, shifting electricity consumption time, or enabling backup power sources, etc. For example, in extreme weather conditions such as typhoons or floods, the distribution network may face widespread power outages. At this time, through the demand response mechanism, the power supply of critical loads such as hospitals, transportation hubs, and communication facilities can be prioritized, reducing the impact of power outages on the economy and society. In addition, demand response can also improve the flexibility of the distribution network, allowing it to recover and operate stably more quickly in disasters. Therefore, incorporating user-side demand response into the disaster prevention capability evaluation system can more comprehensively reflect the actual performance of the distribution network in disasters.

[0146] Flexible resources such as batteries play a crucial role in the post-disaster distribution network recovery. As an efficient energy storage device, batteries can provide emergency power support during disasters, reducing the impact of power outages on users. In the early stages of a disaster, batteries can provide backup power for critical loads, ensuring their normal operation. In the later stages of a disaster, batteries can also be part of distributed energy sources, participating in the recovery and reconstruction process of the distribution network. For example, in disasters such as typhoons or earthquakes, the main lines of the distribution network may be severely damaged, leading to widespread power outages. At this time, batteries distributed at various nodes of the distribution network can provide temporary power supply to local areas through island operation mode, reducing the duration and scope of power outages. In addition, batteries can also smooth the fluctuating output of renewable energy sources, improving the power supply reliability of the distribution network in disasters. Therefore, incorporating flexible resources such as batteries into the disaster prevention capability evaluation system can significantly improve the accuracy and practicality of the evaluation.

[0147] In addition, the synergy of user-side demand response and flexible resources can further enhance the disaster resistance of the distribution network. After a disaster occurs, the power supply capacity of the distribution network is usually severely limited. At this time, through the demand response mechanism, the power supply of critical loads can be prioritized, reducing the impact of power outages on the economy and society. At the same time, flexible resources such as batteries can provide backup power for critical loads to ensure their normal operation. For example, under extreme weather conditions such as typhoons or floods, the distribution network may face widespread power outages. At this time, through the demand response mechanism, the power supply of critical loads such as hospitals, transportation hubs, and communication facilities can be prioritized, reducing the impact of power outages on the economy and society. In addition, demand response can also improve the flexibility of the distribution network, allowing it to recover and operate stably more quickly in disasters. Therefore, incorporating user-side demand response into the disaster prevention capability evaluation system can more comprehensively reflect the actual performance of the distribution network in disasters.

[0148] The disaster prevention capability evaluation method considering user-side demand response and flexible resources can provide more targeted decision support for disaster prevention planning and emergency response of the distribution network. Traditional evaluation methods mainly focus on the physical recovery capability of the power grid itself, ignoring the role of user-side demand and flexible resources. This single perspective evaluation method often cannot fully reflect the actual performance of the distribution network in disasters, leading to a lack of scientific basis for the development of disaster prevention planning and emergency response strategies. By incorporating user-side demand response and flexible resources into the evaluation system, the vulnerable links of the distribution network in disasters can be more accurately identified, resource allocation and recovery strategies can be optimized, and the priority recovery of critical loads can be ensured.

[0149] The specific content and solution method of the model are as follows:

[0150] The line fault probability model based on multiple disaster scenarios can effectively quantify the main impact of typhoon disaster factors on urban distribution networks, obtaining the probability value of distribution network element failure under typhoon scenarios. The urban distribution network disaster prevention capability evaluation model needs to be established under the corresponding post-disaster failure scenario, based on the distribution network element failure probability formula, using the random sampling method to generate the distribution network post-disaster scenario, and selecting 10 typical scenarios.

[0151] Urban distribution networks often use network reconfiguration to restore power supply after a disaster. The objective is to minimize the loss of load, and the optimal solution is obtained through mathematical programming. The objective of the network reconfiguration of the post-disaster urban distribution network is to minimize the cut load, considering the economic and social safety impact. A multi-disaster scenario distribution network disaster prevention capability evaluation model is constructed considering the economic and social safety impact and user demand response, and the objective function is as follows:

[0152]

[0153] In one embodiment, the operation constraints of the network power system include:

[0154] Power balance constraint:

[0155] Node voltage relationship constraint:

[0156] Line operation safety constraint:

[0157] Node voltage amplitude upper and lower limit constraint:

[0158] Load shedding constraint:

[0159] Wherein, B is the system node set; π(j) and δ(j) represent the child node set and the parent node set of node j, respectively; P ij and Q ij are the active power and the reactive power flowing through line (i, j), respectively; P G,j and Q G,j are the active power output and the reactive power output of the generator; P L,j and P shed,j represent the active load and the load shedding of node j; Q L,j and Q shed,j are the reactive load and the load shedding of node j; E is the line set; U i and U j represent the voltages of node i and node j, respectively; r ij and x ij are the conductor resistance and reactance, respectively; U0 is the node rated voltage; M is a preset value; c ij is a state variable used to represent the open or closed state of line (i, j); 0 and 1 can be taken, and 0 represents that line (i, j) is open, and otherwise, it represents that the line is in a closed state; represents the maximum allowed power of the line; P js and Q js are the active power and the reactive power flowing through line (j, s), respectively; U min is the lowest voltage lower limit; U max is the highest voltage upper limit; i is the first end node of line (i, j); and j is the end node of line (i, j).

[0160] In one embodiment, according to graph theory, the topological radial condition is satisfied when each island topology of the network is connected and the number of lines and the number of nodes is equal to the difference between the number of islands. Therefore, the network reconfiguration constraints include:

[0161] Island topology constraints:

[0162] Virtual power balance constraints:

[0163] Wherein, γ j is a binary variable to determine whether node j is a source node; the number of closed lines is equal to the number of nodes N node minus 1 (i.e. the subgraph where the substation {Sub} is located) and the number of subgraphs composed of load islands; the method of virtual power flow is used to ensure the connectivity of the reconfigured distribution network, a virtual network with the same topological structure as the distribution network is set, and the connectivity between nodes is determined by virtual power. Each subgraph selects a node as a source node γ j , and other nodes as load nodes, W j is the output of the source node; F ij is the virtual power flow, which is zero when the branch is disconnected; c ij is a state variable used to represent the open or closed state of line (i, j); c js is a state variable used to represent the open or closed state of line (j, s); B is the set of system nodes; N node is the number of nodes; π(j), δ(j) respectively represent the child node set and the parent node set of node j; F js is the virtual power; M is a preset value, i is the first end node of line (i, j); j is the end node of line (i, j). The virtual power balance constraint restricts the virtual power to flow only on closed lines.

[0164] In one embodiment, the load types of users participating in demand response are mainly divided into transferable load and reducible load. The load types of users participating in demand response are mainly divided into transferable load and reducible load, and the two types of loads have different characteristics and functions in the operation of the power system. The transferable load refers to the electricity demand that can be flexibly adjusted in time, such as the use time of industrial production lines, electric vehicle charging, washing machines and other equipment. The characteristic of this type of load is that the electricity use time has a certain flexibility, which can relieve the pressure of the power grid through time adjustment when the supply and demand of the power system is tight. The reducible load refers to the electricity demand that can be reduced or interrupted in a short time, such as lighting, air conditioning, non-critical production equipment, etc. The characteristic of this type of load is that it can support the stable operation of the power grid by reducing the power consumption without affecting the core demand of the user. The synergistic effect of transferable load and reducible load can effectively improve the flexibility and reliability of the power system, especially in the case of disasters or extreme weather conditions, through reasonable scheduling of the two types of loads, the power-off time and range can be significantly reduced, the power supply of critical loads can be guaranteed, and the overall disaster resistance of the distribution network can be improved. The user demand response constraints include:

[0165] Load transfer state constraint:

[0166]

[0167] Transfer rate constraint:

[0168]

[0169] Transfer capacity constraint:

[0170]

[0171] Electricity balance constraint:

[0172]

[0173] Electricity price constraint:

[0174]

[0175] Electricity price elasticity matrix constraint:

[0176]

[0177] Reducible load constraint:

[0178]

[0179] Wherein, u shiftl,i,t and u shiftq,i,trespectively represent the state identification variables of the outgoing and incoming load in period t, taking 0 or 1, and only one kind of load transfer state can exist in the same period; is a set of transferable electric load points; S shiftl,i,t and S shiftq,i,t respectively represent the percentage of the outgoing and incoming electric load in the total load in period t, which is used to measure the capacity of transferable load per unit time; and respectively represent the maximum proportion of the outgoing and incoming power that the load node i can accept; σ 0,t is the original time-of-use electricity price; Δσ t is the time-of-use electricity price change; σ t is the adjusted time-of-use electricity price; is the elasticity coefficient; t1 and t2 are time nodes used to index the corresponding period, wherein t1=t2 is the self-elasticity coefficient, otherwise it is the mutual elasticity coefficient; D i represents the proportion of the active load reduction amount of electric load i in the total load, ξ is a set of reducible type load points in the subsystem; i is the first end node of the line (i, j); j is the end node of the line (i, j); P shiftl,i,t is the outgoing power; P shiftq,i,t is the incoming power; P load,i,t is the load amount; T is the simulation cycle time; is the price change at t2; is the original price at t2; P cut,i,t is the load reduction amount; is the outgoing power at t1; is the incoming power at t1; is the load amount at t1. The formula corresponding to the power balance constraint represents that the total load power balance must be satisfied within the given IES repair and recovery period, and the total amount of outgoing and incoming load is equal.

[0180] In an embodiment, the battery is an important flexible resource, and its operation constraints mainly include capacity limit, charge and discharge rate, cycle life and efficiency, etc. The capacity limit determines the maximum energy support that the battery can provide, and the charge and discharge rate affects the response speed. Although high charge and discharge rate can quickly balance power supply and demand, it may shorten the battery life. The cycle life limits the number of charge and discharge times of the battery, and frequent use will accelerate aging and reduce long-term availability. In addition, the charge and discharge efficiency (usually 80%-95%) directly affects the energy utilization rate, and low efficiency will cause energy loss and weaken the overall performance of the system. The above constraints jointly determine the actual application effect of the battery in the post-disaster distribution network recovery, and must be fully considered in the disaster prevention capability evaluation. The operation constraints of the flexible resource include:

[0181]

[0182] α c +α d ≤1;

[0183]

[0184] wherein, and are the charge and discharge power of the battery; and are the instantaneous maximum charge and discharge capacity; α c and α d are binary variables representing the charge and discharge state; is the state of charge; and are the upper and lower limits of the battery power; η c and η d are the charge and discharge efficiency of the battery; is the battery power at time t; is the battery power at time t-1; Δt is the unit time length.

[0185] By solving the above disaster prevention capability evaluation model under multiple sampling scenarios, the objective function value is obtained for comparative analysis, which can reflect the actual level of power distribution network disaster prevention considering economic and social safety influence and user demand side response under disaster.

[0186] The method has significant innovation and practicality in disaster modeling, fully considers the current reality of frequent multiple disasters and compound disasters, and deeply analyzes the coupling effect between multiple disasters and its superimposed influence on the power distribution network. By dividing typhoon disasters into two typical disaster factors of storm and flood according to the disaster mechanism and influence range, the method innovatively proposes a multiple disaster superimposed probability model. The model not only can quickly and accurately calculate the failure probability of power distribution equipment under multiple disaster scenarios, but also can more realistically reflect the actual situation of disaster occurrence, overcoming the limitations of traditional single disaster model. Compared with existing methods, the multiple disaster superimposed probability model of the method has higher precision and applicability, and can provide more scientific basis for disaster risk assessment.

[0187] The method fully integrates the power grid disaster prevention capability and social and economic security in the disaster prevention capability evaluation process, comprehensively evaluates the social security risks that may be caused in the power distribution network recovery process after a disaster, and can prioritize the recovery of important nodes in combination with the actual load loss of the disaster area, thereby improving the accuracy and practicality of the evaluation. In the disaster prevention capability evaluation process, the method innovatively integrates the power grid disaster prevention capability and social and economic security, and realizes comprehensive evaluation of potential social security risks in the power distribution network recovery process after a disaster. By comprehensively considering factors such as flow betweenness, population density, and unit load economic loss, the method can accurately quantify the importance of each node, and in combination with the actual load loss of the disaster area, the key nodes that have a greater impact on economic and social security are prioritized for recovery. This evaluation method not only significantly improves the pertinence and efficiency of power distribution network recovery, but also maximizes the negative impact of disasters on social order and economic activities. In addition, the method further optimizes the calculation process of node recovery weights by introducing an improved analytic hierarchy process (IAHP), ensuring the scientificity and reliability of the evaluation results. Compared with traditional methods, the method not only improves the accuracy of the evaluation, but also enhances the practicality, providing more operational decision support for disaster prevention planning and emergency response of the power distribution network, thereby achieving dual optimization of social and economic benefits in disaster response.

[0188] In the disaster prevention capability evaluation process, the method innovatively considers the role of demand response and flexible resources on the user side, significantly improving the comprehensiveness and practicality of the evaluation. Traditional evaluation methods mainly focus on the physical recovery capability of the power grid, while the method introduces demand response mechanisms (such as transferable load and reducible load) and flexible resources (such as batteries) on the user side, which can more accurately reflect the actual response capability of the power distribution network in disasters, and is more consistent with the real situation.

[0189] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0190] The embodiments of the present application also provide a power distribution network disaster prevention capability evaluation device for executing the steps in the power distribution network disaster prevention capability evaluation method embodiments described above. The power distribution network disaster prevention capability evaluation device can be a virtual appliance in an electronic device, run by a processor of the electronic device, or can be the electronic device itself.

[0191] As shown in Figure 2 The power distribution network disaster prevention capability evaluation device 100 provided by the embodiments of the present application includes:

[0192] The failure rate calculation module 101 is used to construct a multi-hazard superposition probability model based on disaster data and power grid data, and solve for the comprehensive failure rate of distribution network components under multi-hazard superposition scenarios.

[0193] The weight calculation module 102 is used to calculate the economic and social security impact weight of each load node based on the improved analytic hierarchy process, which integrates power flow betweenness, population density and unit load economic loss indicators.

[0194] Network reconfiguration module 103 is used to generate post-disaster fault scenarios based on the comprehensive fault rate, construct a network reconfiguration model with the goal of minimizing the weighted load shedding amount by combining the economic and social security impact weights, and solve the result of the distribution network disaster prevention capability assessment.

[0195] The constraints of the network reconfiguration model include power system operation constraints, distribution network reconfiguration constraints, user demand response constraints, and flexibility resource operation constraints.

[0196] In applications, the modules in the power distribution network disaster prevention capability assessment device can be software program modules, or they can be implemented through different logic circuits integrated in the processor, or they can be implemented through multiple distributed processors.

[0197] like Figure 3 As shown, this application embodiment also provides an electronic device 200, including: at least one processor 201 ( Figure 3 The diagram shows only one processor, memory 202, and computer program 203 stored in memory 202 and executable on at least one processor 201. When processor 201 executes computer program 203, it implements the steps in the various method embodiments described above.

[0198] In applications, electronic devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that... Figure 3 This is merely an example of an electronic device and does not constitute a limitation on the electronic device. It may include more or fewer components than shown, or a combination of certain components, or different components.

[0199] In applications, the processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0200] In applications, the memory can be an internal storage unit of the electronic device in some embodiments, for example, a hard disk or a memory of the electronic device. The memory can also be an external storage device of the electronic device in other embodiments, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory can include both the internal storage unit and the external storage device of the electronic device. The memory is used to store an operating system, an application program, a boot loader, data, and other programs, for example, program codes of a computer program, etc. The memory can also be used to temporarily store data that has been output or will be output.

[0201] It should be noted that the information interaction, execution process, etc. between the above apparatuses / units, since based on the same concept as the method embodiments of the present application, the specific functions and the brought technical effects can be referred to the method embodiments part, and will not be repeated here.

[0202] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the above-described functions. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software functional unit. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the above system can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

[0203] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to realize the steps in each of the above method embodiments.

[0204] The embodiment of the present application provides a computer program product, which includes a computer program. When the computer program product is run on an electronic device, the electronic device is caused to execute the steps in each of the above method embodiments.

[0205] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application realizes all or part of the processes in the above embodiment methods, which can be completed by a computer program instructing related hardware. The computer program can be stored in a computer readable storage medium. The computer program, when executed by a processor, can realize the steps in each of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the device / electronic device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and method practice, the computer readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0206] In the above embodiments, the description of each embodiment is focused on, and the part not described or recorded in a certain embodiment can be referred to the relevant description of other embodiments.

[0207] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0208] In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the above-described apparatus embodiments are only schematic, and the division of the modules or units is only a logical function division, and there can be another division in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0209] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0210] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for evaluating disaster prevention capability of a power distribution network, characterized by, The method comprises the following steps: Based on disaster data and power grid data, a multi-disaster superimposed probability model is constructed, and the comprehensive failure rate of the power distribution network elements under the multi-disaster superimposed scenario is solved; Based on the improved analytic hierarchy process, the economic and social security influence weight of each load node is calculated by combining the flow intermediate number, population density and unit load economic loss index; Based on the comprehensive failure rate, a post-disaster failure scenario is generated, and a network reconstruction model with the minimum weighted load shedding amount as the target is constructed by combining the economic and social security influence weight, and the disaster prevention capability evaluation result of the power distribution network is solved; The constraint conditions of the network reconstruction model include the operation constraint of the power system, the reconstruction constraint of the power distribution network, the demand response constraint of the user and the operation constraint of the flexible resource.

2. The power distribution network disaster prevention capability assessment method of claim 1, wherein, The method comprises the following steps: The typhoon disaster is divided into a rainstorm disaster and a flood disaster, and a rainstorm disaster joint probability model and a flood disaster probability model are constructed respectively; Based on the rainstorm disaster joint probability model and the flood disaster probability model, a multi-disaster superimposed probability model is constructed.

3. The power distribution grid disaster prevention capability assessment method of claim 1, wherein, The multi-disaster superimposed probability model is: where m = 1, 2; P d is the comprehensive failure rate of distribution network elements under the multi-disaster superimposed scenario; P m is the failure probability of the distribution line under each disaster independently; n d is the number of disasters considered superimposed.

4. The power distribution network disaster prevention capability assessment method of claim 3, wherein, The method further comprises the following steps: When m = 1, P m The calculation is made by the following equation: When m = 2, P m The calculation is made by the following equation: wherein P1 is the failure probability of the distribution line under the storm disaster; P2 is the failure probability of the distribution line under the flood disaster; y is the influence variable of the flood disaster on the power grid element; is the joint probability density function of the storm disaster; is the rainfall intensity that the transmission line t5 can withstand at s5 area at time t5; is the wind speed that the transmission line t5 can withstand at s5 area at time t5.

5. The power distribution grid disaster prevention capability assessment method of claim 1, wherein, The economic and social security influence weight of each load node is calculated by combining the flow intermediate number, population density and unit load economic loss index based on the improved analytic hierarchy process. A three-level evaluation model including a target layer, a criterion layer and an object layer is constructed; wherein evaluation indexes of the criterion layer include node power loss c i1,1 , population density c i1,2 and tide flow betweenness c i1,3 , and i1 is a load node; The weight coefficient ω of each evaluation index is calculated by constructing the judgment matrix with exponential scale method p , and the economic and social security influence weight of each load node is obtained.

6. The power distribution network disaster prevention capability assessment method of claim 5, wherein, The economic and social security influence weight of each load node is calculated by the following formula: Ω i1 = ω i1,1 c i1,1 + ω i1,2 c i1,2 + ω i1,3 c i1,3 ; wherein, ω i1 is the economic and social security influence weight of the load node i1; ω i1,1 is the weight of the loss of electricity sales; ω i1,2 is the population density weight; ω i1,3 is the weight of the power flow betweenness; ω p is the weight coefficient of the evaluation index; x p is the pth element of the feature vector; n A is the order of the judgment matrix A.

7. The power distribution grid disaster prevention capability assessment method of claim 1, wherein, The objective function of the network reconstruction model is: Wherein, Ω i1 is the economic and social security influence weight of load node i1; P shed,i1 is the load reduction of load node i1, and B is the system node set.

8. The power distribution grid disaster prevention capability assessment method of claim 1, wherein, The operation constraint of the power system includes: Where B is the set of system nodes; π(j), δ(j) are the set of child nodes and parent nodes of node j, respectively; P ij , Q ij are the active power and reactive power flowing through line (i, j), respectively; P G,j , Q G,j are the active power and reactive power output of generator; P L,j , P shed,j are the active load and load shedding of node j, respectively; Q L,j , Q shed,j are the reactive load and load shedding of node j, respectively; E is the set of lines; U i and U j are the voltages of node i and node j, respectively; r ij , x ij are the resistance and reactance of the conductor; U0 is the rated voltage of the node; M is a preset value; c ij is the state variable used to represent the open or closed state of line (i, j); is the maximum allowed power of the line; P js and Q js are the active power and reactive power flowing through line (j, s), respectively; U min is the lower limit of the voltage; U max is the upper limit of the voltage; i is the starting node of line (i, j); j is the ending node of line (i, j).

9. The power distribution grid disaster prevention capability assessment method of claim 1, wherein, The reconstruction constraint of the power distribution network includes: where γ j is a binary variable to determine whether node j is a source node; W j is the output of the source node; F ij is the virtual power flow; c ij is a state variable used to represent the open or closed state of line (i, j); c js is a state variable used to represent the open or closed state of line (j, s); B node is the set of system nodes; N js is the number of nodes; π(j), δ(j) represent the child node set and parent node set of node j, respectively; F js is the virtual power; M is a preset value, i is the head node of line (i, j), and j is the end node of line (i, j).

10. The power distribution grid disaster prevention capability assessment method of claim 1, wherein, The demand response constraint of the user includes: The load transfer state constraint includes: The transfer rate constraint includes: The transfer capacity constraint includes: The power balance constraint includes: The electricity price constraint includes: The electricity price elasticity matrix constraint includes: The reducible load constraint includes: wherein, u shiftl,i,t and u shiftq,i,t are the state identification variables of the export and import load in period t, respectively; is the set of transferable electric load points; S shiftl,i,t and S shiftq,i,t are the percentages of the export and import electric load in the total load in period t, respectively; and are the maximum percentages of the export and import power that load node i can accept, respectively; σ 0,t is the original time-of-use electricity price; Δσ t is the time-of-use electricity price change amount; σ t is the adjusted time-of-use electricity price; is the elasticity coefficient; t1 and t2 are the time nodes for indexing the corresponding period; D i represents the percentage of the active load reduction amount of electric load i in the total load, and ξ is the set of reducible type load points in the subsystem; i is the head node of line (i, j); j is the end node of line (i, j); P shiftl,i,t is the export power; P shiftq,i,t is the import power; P load,i,t is the load amount; T1 is the simulation cycle time; is the price change amount at t2; is the original price at t2; P cut,i,t is the load reduction amount; is the export power at t1; is the import power at t1; is the load amount at t1.

11. The power distribution grid disaster prevention capability assessment method of claim 1, wherein, The operation constraint of the flexible resource includes: α c +α d ≤1； wherein, and is the charge-discharge power of the battery; and is the instantaneous maximum charge-discharge capacity; a c and a d is a binary variable representing the charge-discharge state; is the state of charge; and are the upper and lower limits of the battery power; η c and η d is the charge-discharge efficiency of the battery; is the battery power at time t; is the battery power at time t-1; Δt is the unit time length.

12. A device for assessing the disaster prevention capabilities of a power distribution network, characterized in that, The method comprises the following steps: A failure rate calculation module is configured to construct a multi-disaster superimposed probability model based on disaster data and power grid data, and to solve the comprehensive failure rate of the power distribution network elements under the multi-disaster superimposed scenario; A weight calculation module is configured to calculate the economic and social security influence weight of each load node by combining the flow intermediate number, population density and unit load economic loss index based on the improved analytic hierarchy process; A network reconstruction module is configured to generate a post-disaster failure scenario based on the comprehensive failure rate, and to construct a network reconstruction model with the minimum weighted load shedding amount as the target by combining the economic and social security influence weight, and to solve the disaster prevention capability evaluation result of the power distribution network; The constraint conditions of the network reconstruction model include the operation constraint of the power system, the reconstruction constraint of the power distribution network, the demand response constraint of the user and the operation constraint of the flexible resource.

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