Distribution network disaster prevention capability assessment method and device

Abstract

The invention relates to the technical field of power systems, and provides a power distribution network disaster prevention capability assessment method and device. Based on the disaster data and the power grid data, constructing a multi-disaster superposition probability model, and solving to obtain the comprehensive failure rate of the power distribution network element in the multi-disaster superposition scene; on the basis of an improved analytic hierarchy process, the load flow betweenness, the population density and the unit load economic loss index are fused, and the economic and social safety influence weight of each load node is calculated; a post-disaster fault scene is generated based on the comprehensive fault rate, a network reconstruction model with the minimum weighted load shedding capacity as the target is constructed in combination with economic and social safety influence weights, and a power distribution network disaster prevention capability evaluation result is obtained through solving; wherein the constraint conditions of the network reconstruction model comprise the operation constraint of the power system, the reconstruction constraint of the power distribution network, the user demand response constraint and the operation constraint of the flexibility resource. The evaluation result can comprehensively reflect the actual effect of the anti-disaster capability of the power distribution network.

Description

Technical Field

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

[0002] The increasing frequency of extreme weather and disasters has exposed power grids to numerous risks, including widespread power outages and losses, placing higher demands on improving their disaster resilience. Coastal areas are prone to extreme weather disasters such as typhoon-induced thunderstorms and heavy rains, and some industrial areas are also prone to equipment flashover accidents. The combined impact of multiple disasters has become a major challenge to the safe and stable development of power grids. Under extreme, compound disasters, the power system's power supply stability directly impacts the stability of the load, which in turn impacts social production and life. The resilience of the distribution network reflects its ability to guarantee load protection, making it essential to assess the distribution network's disaster resilience under multiple disasters.

[0003] Traditional power system resilience assessments focus primarily on grid load recovery, ignoring the impact of distribution network disasters on users and society. Disaster resilience assessments of distribution networks are rarely conducted from the perspective of economic and social security. Existing research assessing the resilience of resilient distribution networks in multi-hazard scenarios is largely limited to rapid technical response, optimal resource allocation, and improvements in the self-repair capabilities of power systems. These studies fail to fully integrate grid resilience with social and economic security, and fail to comprehensively assess the potential social security risks associated with distribution network recovery after a disaster. Furthermore, the formulation and implementation of recovery strategies lack in-depth analysis of economic factors, social acceptance, public participation, and social stability.

[0004] Specifically, the technical problems and limitations of existing methods in the field of distribution network disaster prevention capacity assessment are mainly the following three points:

[0005] First, a singular focus on grid load recovery. Traditional resilience assessment methods primarily focus on the physical resilience of the grid, such as fault repair time and power restoration rate. While these metrics can reflect the technical performance of the grid, they overlook the impact of power outages on users and society. For example, while the grid may restore power within a short period of time, certain critical users (such as hospitals, transportation hubs, and communications facilities) may have already suffered severe economic losses or social impacts during the outage.

[0006] Second, it overlooks the active participation of users in disasters and the mobilization of flexible resources. The ultimate goal of a power system is to provide reliable power supply to users. Traditional assessment methods focus primarily on the grid's resilience and power supply reliability, while ignoring the active participation of users in disasters and the mobilization of flexible resources.

[0007] Third, they ignore the impact on economic and social security. Power outages not only disrupt users' daily lives but can also have profound consequences for the economy and social security. For example, power outages can cause factory shutdowns, paralyze transportation, disrupt communications, and even lead to social unrest. Traditional assessment methods fail to factor in these economic and social impacts, resulting in assessments that fail to fully reflect the actual effectiveness of the distribution network's disaster resilience.

[0008] Therefore, the existing distribution network disaster prevention capacity assessment method only focuses on grid load recovery, ignores the active participation of users in disasters and the use of flexibility resources, and ignores the impact on economic and social security. Its assessment results cannot fully reflect the actual effect of the distribution network's disaster resistance capacity. Summary of the Invention

[0009] In view of this, an embodiment of the present application provides a method and device for evaluating the disaster resistance capability of a distribution network, the evaluation results of which can fully reflect the actual effect of the disaster resistance capability of the distribution network.

[0010] A first aspect of an embodiment of the present application provides a method for evaluating the disaster prevention capability of a distribution network, comprising:

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

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

[0013] Based on the comprehensive failure rate, a post-disaster failure scenario is generated. In combination with the economic and social security impact weights, a network reconstruction model with the goal of minimizing weighted load shedding is constructed to obtain a distribution network disaster prevention capability assessment result;

[0014] Among them, the constraints of the network reconstruction model include the operation constraints of the power system, the distribution network reconstruction constraints, the user demand response constraints and the operation constraints of the flexibility resources.

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

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

[0017] The weight calculation module is used to calculate the economic and social security impact weight of each load node based on the improved hierarchical analysis method, integrating the flow betweenness, population density and unit load economic loss indicators;

[0018] A network reconstruction module is used to generate a post-disaster failure scenario based on the comprehensive failure rate, and to construct a network reconstruction model with the goal of minimizing weighted load shedding in combination with the economic and social security impact weights, so as to obtain a distribution network disaster prevention capability assessment result;

[0019] Among them, the constraints of the network reconstruction model include the operation constraints of the power system, the distribution network reconstruction constraints, the user demand response constraints and the operation constraints of the flexibility resources.

[0020] A third aspect of an embodiment of the present application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the electronic device implements the distribution network disaster prevention capability assessment method provided in the first aspect of the embodiment of the present application.

[0021] A fourth aspect of the embodiments of the present application provides a computer program product, including a computer program. When the computer program is executed, the method according to the first aspect of the embodiments of the present application is executed.

[0022] The first aspect of the embodiment of the present application provides a method for evaluating the disaster prevention capability of a distribution network. By constructing a multi-disaster superposition probability model based on disaster data and power grid data, the comprehensive failure rate of distribution network components under the multi-disaster superposition scenario is solved; based on the improved hierarchical analysis method, the flow betweenness, population density and unit load economic loss index are integrated to calculate the economic and social security impact weight of each load node; based on the comprehensive failure rate, a post-disaster failure scenario is generated, and a network reconstruction model with the goal of minimizing weighted load shedding is constructed in combination with the economic and social security impact weight to obtain the evaluation result of the disaster prevention capability of the distribution network; wherein the constraints of the network reconstruction model include the operation constraints of the power system, the distribution network reconstruction constraints, the user demand response constraints and the operation constraints of the flexibility resources. The social and economic loss dimension is incorporated into the evaluation system, and a collaborative mechanism for the user-side flexibility resources to participate in the power grid restoration is constructed. By establishing a quantitative correlation between disaster intensity-equipment failure probability-social impact, a cross-dimensional disaster assessment from the physical power grid to the social and economic system is realized. The evaluation results can fully reflect the actual effect of the disaster resistance capability of the distribution network, and provide a new technical path for improving the resilience of urban power grids.

[0023] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0025] Figure 1 This is a flow chart of a method for evaluating the disaster prevention capability of a distribution network provided in one embodiment of the present application;

[0026] Figure 2 This is a schematic diagram of the structure of the distribution network disaster prevention capability assessment device provided in an embodiment of the present application;

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

[0028] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

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

[0030] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0031] like Figure 1 As shown, the method for evaluating the disaster prevention capability of a distribution network provided in the embodiment of the present application includes the following steps S101 to S103:

[0032] Step S101: Based on disaster data and power grid data, a multi-disaster superposition probability model is constructed to obtain the comprehensive failure rate of distribution network components under the multi-disaster superposition scenario;

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

[0034] Step S103: generating a post-disaster failure scenario based on the comprehensive failure rate, constructing a network reconstruction model with the goal of minimizing weighted load shedding in combination with the economic, social and security impact weights, and solving to obtain a distribution network disaster prevention capability assessment result;

[0035] Among them, the constraints of the network reconstruction model include the operation constraints of the power system, the distribution network reconstruction constraints, the user demand response constraints and the operation constraints of the flexibility resources.

[0036] In application, to address the technical shortcomings of power system security assessment under extreme climate conditions, the present embodiment of the application constructs a multi-dimensional assessment model in the context of existing resilience assessment systems that mainly focus on physical grid recovery indicators while ignoring the resource regulation potential on the user side and the social impact of disaster losses. The specific implementation process includes three core modules:

[0037] First, a multi-disaster coupling probability model is established. Through typhoon disaster decoupling analysis, the complex disaster is decomposed into two related disaster-causing factors: storms and floods. A disaster chain reaction model is constructed using a Bayesian network to accurately quantify the failure probability of equipment in multi-disaster superposition scenarios. Secondly, a node recovery priority system is constructed based on social influencing factors. The three dimensions of network topology, population distribution characteristics, and load economic loss coefficient are comprehensively considered, and the entropy-weighted hierarchical analysis method is used to determine the recovery weight coefficient of each load node. Finally, a demand response-coordinated post-disaster reconstruction model is constructed, integrating flexible resources such as user-side energy storage systems and dispatchable loads. A mixed integer programming model with the goal of minimizing weighted load shedding is established, and a quantitative evaluation of disaster prevention effectiveness is achieved through disaster loss scenario simulation.

[0038] This embodiment of the application breaks through the traditional assessment framework, incorporates the dimension of social and economic losses into the assessment system for the first time, and builds a collaborative mechanism for user-side flexibility resources to participate in grid restoration. By establishing a quantitative correlation between disaster intensity, equipment failure probability, and social impact, a cross-dimensional disaster assessment from the physical grid to the socio-economic system is achieved. The assessment results can fully reflect the actual effect of the distribution network's disaster resistance, providing a new technical path for improving the resilience of urban power grids.

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

[0040] Decompose typhoon disasters into storm disasters and flood disasters, and construct a joint probability model for storm disasters and a probability model for flood disasters respectively;

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

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

[0043] ;

[0044] Where m=1,2; is the comprehensive failure rate of distribution network components under multi-disaster superposition scenarios; The failure probability of the distribution line under each disaster independently; The number of disasters to be considered superimposed.

[0045] In one embodiment, it further includes:

[0046] When m=1, Calculated using the following formula:

[0047] ;

[0048] When m=2, Calculated using the following formula:

[0049] ;

[0050] in, is the failure probability of distribution lines under storm disasters; is the failure probability of distribution lines under flood disasters; is the variable affecting the power grid components caused by flood disasters; is the joint probability density function of storm disaster; is the rainfall intensity that the transmission line can withstand in area s at time t; is the wind speed that the transmission line can withstand in area s at time t.

[0051] In this application, disaster modeling is performed based on disaster and power grid data, and the failure rate of power grid components is calculated using a multi-disaster superposition probability model. Disaster scenarios are modeled, fully considering the coupling effects of individual and multiple disasters. Typhoons are decomposed into two typical disaster-causing factors: storms and floods, based on their mechanisms and impact ranges. A multi-disaster superposition probability model is proposed, which can quickly and accurately determine the failure probability of distribution equipment.

[0052] Typhoons are the most frequent catastrophic meteorological events in my country's southeastern coastal areas. Their primary hazard factors include strong winds, heavy rains, and storm surges (high waves). These hazard factors often interact with each other, forming a complex chain of disaster effects. This leads to frequent secondary disasters during the disaster's evolution, further exacerbating the situation, causing enormous economic losses, and significantly increasing the difficulty of emergency rescue. To further study the impact of typhoon disasters, meteorology often uses the method of constructing typhoon disaster chain networks. As a key hazard-bearing component in the typhoon disaster chain, the vulnerability of urban distribution networks to typhoon disasters has attracted considerable attention. Several studies have analyzed the impact of typhoons and their secondary hazards on urban distribution networks.

[0053] Among the three types of disaster-causing factors, high winds have the most direct and frequent impact on urban distribution networks. Unlike heavy rain and storm surges, high winds typically affect urban distribution networks directly, without requiring a complex secondary disaster process. Through wind power, high winds directly or indirectly trigger power system failures, such as tower collapses, line breaks, wind-induced flashovers, and foreign objects hanging on wires, leading to frequent power outages. These failures often cannot be restored through simple reclosing operations, further exacerbating the risk of power system failure. Therefore, high winds are one of the most destructive disaster-causing factors affecting urban distribution networks during typhoon disasters.

[0054] Heavy rain indirectly affects the stability of the power system by inducing urban flooding. The mechanism of disaster occurrence lies in the fact that when rainfall intensity exceeds the design threshold of urban drainage systems, surface runoff increases dramatically, making it difficult for the drainage system to effectively cope. This is particularly true in low-lying areas and areas with poor drainage, where waterlogging and even urban waterlogging are likely to occur. Flood damage to power system infrastructure primarily manifests through short circuits, insulation damage, and mechanical damage to substations, cable corridors, and distribution facilities, leading to localized or regional power outages. Furthermore, floods can also destroy power lines, causing physical disruptions in the power transmission network, further exacerbating urban power shortages during floods and posing severe challenges to emergency response capabilities. Therefore, heavy rain and the resulting flooding have an indirect but significant impact on the power system.

[0055] In summary, the catastrophic factors of typhoon disasters—high winds, heavy rains, and storm surges—have distinct characteristics and impact mechanisms on power systems. High winds directly impact power facilities, heavy rains indirectly damage power systems through flooding, and storm surges can severely impact power infrastructure in coastal areas. To comprehensively assess the impact of typhoon disasters on power systems, it is necessary to model each of these three catastrophic factors and conduct in-depth analysis of their mechanisms, thereby providing a scientific basis for improving the disaster resilience of urban distribution networks.

[0056] Because different types of disasters exhibit significant differences in their causative mechanisms, evolutionary processes, and affected areas, their unique physical characteristics must be fully considered when analyzing their impact on electrical components in power systems. To accurately assess the extent of damage to power systems, it is first necessary to construct a mathematical model for each disaster to simulate its mechanism and evolutionary process. This model then analyzes the common and unique characteristics of each type of disaster to establish a probabilistic model that reflects the probability of disaster occurrence and its impact. For example, typhoon disasters primarily manifest as the combined effects of high winds, heavy rain, and storm surges, while earthquakes can trigger multiple secondary hazards such as ground shaking and soil liquefaction. By establishing a probabilistic disaster model, the potential impact of disasters on power systems can be more accurately predicted, providing a scientific basis for developing targeted disaster prevention and mitigation strategies. This mathematical model-based analysis approach not only improves the accuracy of disaster assessments but also provides an important reference for power system planning and optimization.

[0057] In applications, the joint probability model for storm disasters is used. Heavy rain and strong winds are common catastrophic factors in typhoon disasters, and the resulting events, such as distribution line breakage and distributed power outages, are typically classified as "low probability, high loss." Traditional probabilistic description methods, typically based on unconditional expectations and characterized by risk neutrality, often overlook the variability in system operating conditions 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 severe threat posed by heavy rain and strong winds to power systems, a joint analysis of the probability density of heavy rain and strong wind disasters is conducted using the Generalized Pareto Distribution (GPD) and Extreme Value Distribution (EVD) statistical models. This approach better captures the tail characteristics of extreme events, thereby more accurately describing the distribution of disaster intensity.

[0058] On this basis, a failure risk model of distribution network components is further constructed, comprehensively considering the probability distribution characteristics of disaster intensity and the tolerance of distribution network components. By calculating the probability that the disaster intensity exceeds the maximum tolerance value of the component, the failure risk of distribution network components under extreme weather conditions can be quantified. The extreme value distribution is used to model the rainfall probability density function due to its good fitting ability for the extreme value characteristics of the tail. Its expression can effectively describe the probability distribution characteristics of extreme rainfall events. This analysis method based on extreme value theory can not only improve the accuracy of risk assessment of extreme weather events, but also provide a scientific basis for disaster prevention planning and emergency response of distribution networks, thereby enhancing the disaster resistance of power systems under extreme weather conditions. Its expression is:

[0059] ;

[0060] in, is the rainfall intensity in region s at time t; is the rain pattern shape parameter; is the scale parameter; N is the rainfall intensity The number of samples in the sequence; is the threshold; The rainfall intensity in the rainfall intensity sequence is greater than or equal to The sample sequence of is the rainfall probability density function; is the product of the sample size and the scale parameter.

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

[0062] ;

[0063] in, is the strong wind speed in area s at time t; is the scale parameter; is a positional parameter; is the shape parameter; is the probability density function of wind speed intensity.

[0064] Based on the Copula function, a storm joint probability distribution model is constructed to describe the probability correlation between different extreme weather conditions, and a storm disaster joint probability density function is constructed. for:

[0065] ;

[0066] in, is the joint probability density function parameter of storm disaster; is the rainfall intensity that the transmission line can withstand in area s at time t; is the wind speed that the transmission line can withstand in area s at time t; is the probability density function of wind speed intensity; is the probability density function of rainfall intensity.

[0067] Considering that the damage caused by storm disasters to the distribution network is mainly reflected in line disconnection, insulator flashover and other faults, it is necessary to build a failure probability model of distribution lines under storm disasters:

[0068] ;

[0069] ;

[0070] Where, is the failure probability of the distribution line under storm disaster; the maximum rainfall intensity that the distribution line in area s can withstand at time t , the critical rainfall intensity of insulator flashover and critical value of substation waterlogging shutdown intensity The smaller amount jointly determines:

[0071] ;

[0072] ;

[0073] ;

[0074] Where: is the critical voltage value at which the insulator flashes over; is the atmospheric pressure at the location of the insulator; is standard atmospheric pressure; 、 、 is a constant; Design flow for substation stormwater; is the substation runoff coefficient; is the substation catchment area.

[0075] is the maximum wind speed that the distribution line in area s can withstand at time t, which can be calculated by the following formula:

[0076] ;

[0077] Where: is the maximum wind load that the distribution line can withstand; is the line wind pressure unevenness coefficient; is the line shape coefficient; is the number of phase conductor splits; is the length of the line under wind load; is the outer diameter of the line; It is the angle between wind direction and line direction.

[0078] In practical applications, flooding is another significant hazard factor in typhoon disasters, and its impact on urban distribution networks is primarily manifested in physical damage caused by submersion and immersion. When typhoon-related heavy rain exceeds the carrying capacity of urban drainage systems, low-lying areas or riverside areas are prone to waterlogging, even leading to flooding. In such cases, critical equipment in the distribution network, such as underground cables, substations, and distribution boxes, can suffer severe flooding, leading to insulation damage, short circuits, equipment failure, and even complete system failure. Flooding not only directly impairs the operational capacity of electrical equipment but also increases the difficulty and cost of subsequent repairs, as thorough drying, cleaning, and inspection are required after the water recedes to ensure the safety and reliability of the equipment before it can be put back into operation.

[0079] Furthermore, floods can disrupt traffic, hindering the timely arrival of repair teams and further delaying power restoration. When constructing a probabilistic model of hazard factors, it is crucial to fully consider the impact of floods on distribution network nodes, including the inundation risk at different locations, equipment type, and its water resistance, to accurately assess the potential threat of floods to the overall reliability of the distribution network.

[0080] Therefore, considering that the occurrence and development of flood disasters are mainly determined by meteorological and geographical factors, when analyzing the impact of rainstorm and flood disasters on power systems, it is necessary to first study meteorological and geographical factors and then, based on the established basic rainstorm and flood model, further derive the failure probability model of power system components.

[0081] Heavy rain is a contributing factor to urban waterlogging. In urban waterlogging research, measured rainstorm data or rainstorm models are often used to assess disaster risk. Rainstorm models must consider both the amount of rain and the process of the rainstorm. Rainstorm amount is typically determined using the urban rainstorm intensity formula. Urban rainstorm intensity can be expressed as:

[0082] ;

[0083] Where: is the design rainstorm intensity, in liters / (seconds·hectare); is the return period of the design rainstorm, in years; is the design rainfall duration, in minutes; and It is a statistical parameter related to rainfall conditions, and its value is determined by the water bureau of each city after statistics.

[0084] Typical rainstorm models include the uniform, triangular, and Chicago rain patterns. The uniform rain pattern assumes that rainfall intensity remains constant throughout the entire rainfall process. While computationally simple, the resulting runoff and peak flow are often smaller than the actual values, making it difficult to accurately reflect the true impact of rainstorms. The triangular rain pattern simulates rainstorms by assuming that rainfall intensity varies linearly over time. However, its peak flow calculations are sensitive to rainfall duration, requiring extensive rainfall data to ensure model accuracy. In contrast, the Chicago rain pattern, based on the frequency relationship between rainfall intensity and duration, can generate an uneven design rainfall pattern that more closely resembles the actual rainfall process. Research results indicate that the Chicago rain pattern effectively simulates urban rainstorm characteristics in my country, generally meeting accuracy requirements. Furthermore, its rainfall intensity process is relatively simple to determine, and therefore has been widely used in urban rainstorm waterlogging research.

[0085] Because unimodal rainfall concentrates its amount over a relatively short period of time, it can easily cause large floods, with particularly significant impacts on power systems. Therefore, when analyzing the impact of rainstorms on power systems, unimodal rainfall is the primary consideration. The unimodal Chicago rainfall pattern is selected for designing the rainfall process to more accurately simulate the spatial and temporal distribution characteristics of rainstorms. During a rainstorm, the design rainfall intensity at each moment can be expressed as:

[0086] ;

[0087] ;

[0088] Where: The duration before the peak, The duration after the peak, 、 Respectively represent the peak Time and Peak The rainfall intensity at the moment, in mm / min; parameter 、 and It is a statistical parameter related to rainfall conditions, and its value is determined by the water bureau of each city after statistics; The rain peak coefficient represents the rainstorm, which is used to describe the time when the rainfall peak occurs. The entire rainfall time series is divided into two parts: the pre-peak time series and the post-peak time series for rain type calculation. The rain peak coefficient Statistics should be based on local rainfall process data.

[0089] Flooding caused by heavy rainstorms manifests as "sourceless flooding," meaning that even rainfall over a large area can cause flooding in all low-lying areas. For study areas with larger spatial scales, the equal volume method is a common approach for simulating the risk of flooding caused by heavy rainstorms. The flooded area is discretely divided into several grid regions, and spatial interpolation is used to assign values ​​to the ground elevation grid:

[0090] ;

[0091] Where: is the grid area, is the total number of grids in the waterlogged area, For the The unknown quantity at this time is and ,and Parameters can be passed and The relationship between the water surface elevation and the water level is obtained by using the dichotomy method. Solve so that the total amount of accumulated water Total water volume within the flooded area The value of gradually approaches until the difference is less than the allowable error, and finally the water surface elevation can be obtained. By performing a single calculation on all grids in the study area using this method, the spatial distribution data of waterlogging in the study area can be obtained.

[0092] Based on the disaster model of rainstorm and flood, considering the complexity and randomness of flood disasters, characteristic quantities are extracted from various factors that cause power equipment failure, such as meteorological, geographical, and electrical factors, to establish a more explanatory power equipment failure impact model, and the least squares method is used to obtain the parameters.

[0093] Based on the statistical analysis of various data on flood disasters, a statistical regression model for the impact of flood disasters on power equipment failures was established. The feature extraction process using the Lasso method involves two steps: extracting significant variables and estimating the corresponding coefficients, which is equivalent to solving the following problem:

[0094] ;

[0095] Where: X is the influencing factor vector of flood disasters, including total rainfall W, temperature, humidity and other environmental variables; parameter are model parameters; is the influencing variable on the grid components, satisfying the linear relationship ,and , where p is the percentage of equipment failure; is the sth linear parameter; The model error is unbiased, equal-scedastic, and uncorrelated, and the failure probability of the transmission line can be obtained by inverse solution:

[0096] ;

[0097] in, is the failure probability of distribution lines under flood disasters; is the variable affecting the power grid components due to flood disasters.

[0098] In application, for multi-hazard superposition probability models, given the significant differences in disaster mechanisms, evolution processes, and affected area characteristics between high winds, heavy rains, and floods, a hierarchical and phased approach is required when constructing comprehensive failure probability models. First, a quantitative analysis of the loads exerted by high winds and heavy rains on distribution network lines and towers should be conducted. By combining meteorological data, topographic information, and the structural characteristics of the distribution network, a wind pressure distribution model and a rainwater accumulation model are established to predict the probability of failures such as line breaks and tower collapses under wind and rain conditions of varying intensities. This process requires verification and optimization of the model based on historical disaster data and experimental research results to ensure its accuracy and applicability.

[0099] Secondly, for flooding, Geographic Information Systems (GIS) and hydrological models are needed to simulate urban surface water accumulation under varying rainfall intensities and identify high-risk areas. Based on this, the inundation risk of distribution network nodes (especially critical facilities such as underground cables and substations) is assessed. Furthermore, a probabilistic model of equipment damage from flooding must be developed, taking into account factors such as equipment type, installation height, and waterproofing rating. This step not only quantifies the direct impact of flooding on the power system but also provides targeted recommendations for the disaster-resistant design of the distribution network.

[0100] Finally, based on the above analysis, by integrating the failure probabilities of different disaster scenarios, a comprehensive failure probability model for multiple disaster scenarios based on the typhoon-urban distribution network disaster chain network is constructed. This model should be able to dynamically reflect the interaction and superposition effects between different disasters, such as the degree of damage to the distribution network caused by the combined effects of strong winds and heavy rains. Given that the disaster-causing mechanisms of strong winds, heavy rains, and floods on the power system are different and independent, it can be assumed that the effects of these disasters on conductors and towers are independent of each other. Therefore, based on the establishment of probability models for various types of disaster-causing factors, a multi-disaster superposition probability model is proposed, which can be effectively applied to the calculation of the failure rate of power grid components under scenarios with superposition of different numbers and types of natural disasters:

[0101] ;

[0102] Where m=1,2; is the comprehensive failure rate of distribution network components under multi-disaster superposition scenarios; The failure probability of the distribution line under each disaster independently; The number of disasters to be considered superimposed.

[0103] The line failure rate under the superposition of multiple disasters can be used to effectively analyze the vulnerability of the power grid system in a complex disaster environment, assess the impact of disasters on the operation of the power grid, and provide a basic scenario for power grid failure under disasters for subsequent disaster prevention capacity assessment that takes into account the impact on economic and social security and user-side demand response.

[0104] In one embodiment, the economic and social security impact weight of each load node is calculated based on the improved analytic hierarchy process, integrating the power flow betweenness, population density, and unit load economic loss index, including:

[0105] Construct a three-level evaluation model including target layer, criterion layer and object layer; wherein the evaluation index of the criterion layer includes node power sales loss , population density and trend betweenness , i is the load node;

[0106] The exponential scaling method is used to construct the judgment matrix and calculate the weight coefficient of each evaluation index. , and obtain the economic and social security impact weight of each load node.

[0107] In this application, an improved analytic hierarchy process (AHP) was used to comprehensively consider economic and social security factors, deriving restoration weights for each node. These weights serve as key factors influencing the order of load restoration in the next objective function. Based on three economic and social security factors—flow betweenness, population density, and economic loss per unit load—the importance and diversity of post-disaster recovery at each node in the distribution network were fully considered. An improved analytic hierarchy process (IAHP) was used to comprehensively consider these three factors and derive restoration weights for each node. This approach, combined with the actual load loss in the affected area, prioritizes the restoration of important nodes.

[0108] Power outage losses generally refer to the economic losses borne by society when power supply is unreliable or expected to be unreliable (i.e., when power outages or brownouts occur due to power outages or shortages). This information is not only a crucial factor in power system planning and design, but also serves as a crucial reference for assessing power system disaster preparedness.

[0109] The primary mission of the power industry is to provide users with reliable, high-quality electricity at reasonable prices. In the event of a disaster, load shedding in the power system will inevitably lead to severe economic losses from power outages. Losses caused by reduced electricity sales account for a significant proportion of the power industry's total losses, and electricity sales losses caused by load shedding are a key aspect of direct economic loss assessments for the power industry. Losses from load shedding and electricity sales The calculation formula is as follows:

[0110] ;

[0111] Where: Indicates the user type of the power outage node, which includes industrial, commercial and residential users; Represents the electricity sales prices for industrial, commercial and residential users respectively: Indicates the load shedding amount of the node; is the power sales loss caused by load shedding at node i and load n.

[0112] Disaster scenarios can cause widespread power outages, leading to a range of public safety concerns, including stagnation of production and life, disruption of communications and information, interruption of medical care and education, and disruption of traffic order. Therefore, it is crucial to consider social impacts during distribution network disaster preparedness assessments, and relevant indicators should be established to capture the social impacts of disaster-induced power outages.

[0113] Areas with high population density have a greater demand for electricity and a relatively large number of power facilities. Domestic and foreign studies have shown that there is a positive correlation between the density of power facilities and population density. Therefore, population density can be directly considered as an important indicator for evaluating the social security impact of a node. The higher the population density of a node, the more people are affected by power outages and load shedding, and the greater the social impact caused by the power outage. The population density is .

[0114] Betweenness was first used in social networks and information networks to measure the importance of nodes (or edges) in the network. The larger the betweenness of a node (or branch), the more intermediary functions the node (or branch) assumes in the network. In the power grid, the node flow betweenness is used to characterize the importance of the node in the network structure, truly reflecting the occupancy of each line and node by power transmission between "generation-load" node pairs, quantifying the importance of components in the power transmission process of the entire network, and reflecting the impact of components on electricity consumption and social security. Define node The trend betweenness index as follows:

[0115] ;

[0116] Where: represents the set of lines connected to node i in the power network; express The flow betweenness of the branch; represents the outflow power of node i; is the power flow betweenness of node i.

[0117] The power flow betweenness of a branch is used to measure the importance of a line in the power network and can be represented as:

[0118] ;

[0119] Where: is the power flow betweenness index of the branch; G is the generator node set; is the system node set; Indicates a generator The actual output and the minimum value of the active power demand of load n; Indicates a generator The active power flowing through the branch when supplying power to load n; Indicates a generator Active power actually supplied to load n.

[0120] An improved analytic hierarchy process (IAHP) was used to comprehensively evaluate the resilience of urban distribution networks during typhoons. Based on the relationships among nodes, socioeconomic security impact indicators, and the state of the urban distribution network under disaster scenarios, the hierarchical model for disaster resilience assessment, considering socioeconomic security impacts, was divided into three layers: the target layer, the criterion layer, and the object layer.

[0121] 1) Target layer: The degree of damage to distribution network nodes, that is, the evaluation result of urban distribution network disaster prevention capacity considering the impact on economic and social security; 2) Criterion layer: The indicators that characterize the social and economic security impact of urban distribution network power outages caused by disasters; 3) Object layer: Urban distribution network nodes.

[0122] Compared with the traditional AHP, the improved AHP differs in its weight scaling method. The improved AHP adopts an exponential scaling method, which has the characteristics of optimal consistency of the judgment matrix, greatly improves the accuracy of weight calculation, and reduces subjective influence.

[0123] Step 1: Construct indicator judgment matrix , based on the improved index scaling method, the importance of the evaluation indicators at the same level to the indicators at the previous level is compared pairwise, and the judgment matrix as follows:

[0124] ;

[0125] Chinese elements Evaluation indicators for the same layer and indicators The scale value of importance. According to the Weber-Fechner law, the form of importance scale is obtained , the specific importance scaling method is shown in Table 1:

[0126] Table 1 IAHP index scale

[0127]

[0128] Step 2: Judgment matrix Perform consistency test and calculate consistency index and consistency ratio :

[0129] ;

[0130] ;

[0131] Where: is the judgment matrix The maximum eigenvalue of is the judgment matrix The order of is the average random consistency test index corresponding to the exponential scale, which can be obtained from Table 2.

[0132] Table 2 Index of random consistency test of exponential scaling

[0133]

[0134] When the consistency ratio satisfies When If the calculated weights meet the requirements after consistency test, otherwise the judgment matrix needs to be reconstructed. .

[0135] In one embodiment, the economic and social security impact weight of each load node is calculated using the following formula:

[0136] ;

[0137] ;

[0138] in, is the economic and social security impact weight of load node i; is the weight of electricity sales loss; is the population density weight; is the trend betweenness weight; is the weight coefficient of the evaluation index; is the first eigenvector elements; is the judgment matrix The order of .

[0139] In the application, including Step 3: Calculate the weight coefficient of each evaluation index Judgment Matrix Satisfy the consistency test standard, by the judgment matrix Maximum eigenvalue The corresponding eigenvector Get. For the eigenvector Perform normalization:

[0140] ;

[0141] Therefore, the economic and social security impact weight of each load node, that is, the economic and social security impact weight of load node i, is:

[0142] ;

[0143] in, is the economic and social security impact weight of load node i; is the weight of electricity sales loss; is the population density weight; is the trend betweenness weight; is the weight coefficient of the evaluation index; is the first eigenvector elements; is the judgment matrix The order of .

[0144] Through the above process, the impact of each distribution network node on economic and social security can be quantified and assigned corresponding weights. The size of the weight directly reflects the node's importance and priority in post-disaster recovery. The larger the weight, the more significant the node's impact on economic and social security, and the greater the urgency of recovery. For example, critical load nodes such as hospitals, transportation hubs, and communication facilities are typically given higher weights because power outages at these nodes could cause serious social disorder or economic losses. Based on these weights, a multi-disaster scenario distribution network disaster resilience assessment model can be further constructed that comprehensively considers the impact on economic and social security. This model not only assesses the distribution network's physical resilience to disasters but also optimizes resource allocation and recovery strategies from the perspective of economic and social security, ensuring the priority recovery of critical loads.

[0145] In one embodiment, the objective function of the network reconstruction model is:

[0146] ;

[0147] in, is the economic and social security impact weight of load node i; is the load reduction at load node i.

[0148] In the application, based on step S101, the failure rate of grid components after the superposition of multiple disasters is sampled and generated, and the most likely post-disaster distribution network failure scenario is screened. Based on step S102, the weight coefficient of each load point is obtained considering the impact of economic and social security. With the goal of minimizing the load shedding amount during network reconstruction of the urban distribution network after the disaster considering the impact of economic and social security, a multi-disaster distribution network disaster prevention capacity assessment model considering the impact of economic and social security is constructed. It is proposed to consider user-side flexible resources such as batteries and user-side transferable and curtailable loads in demand response and construct a corresponding mathematical model. Finally, based on the recovery weight results of each node, load recovery under disaster simulation is carried out and the disaster prevention capacity of the distribution network is evaluated.

[0149] Evaluating the resilience of distribution networks after disasters is a complex and multidimensional process. Traditional assessment methods primarily focus on the physical resilience of the grid itself, such as technical indicators like fault repair time and power restoration rate. However, with the continuous development of power systems and the increasing diversification of user needs, assessments solely from the grid's perspective are no longer able to fully reflect the distribution network's actual resilience during disasters. Therefore, in assessing the resilience of distribution networks after disasters, it is essential to fully consider user-side demand response and the role of flexible resources (such as batteries). This comprehensive assessment approach not only improves the scientific and comprehensive nature of the assessment but also provides more targeted decision-making support for the distribution network's disaster prevention planning and emergency response.

[0150] Demand response (DR) plays a crucial role in regulating the post-disaster recovery of distribution networks. Demand response involves using price signals or incentives to guide users to adjust their electricity usage during specific time periods, thereby optimizing the balance between electricity supply and demand. Following a disaster, the distribution network's supply capacity is often severely constrained. DR can effectively alleviate this pressure by reducing non-essential loads, shifting electricity usage during certain hours, or activating backup power sources. For example, during extreme weather conditions like typhoons or floods, the distribution network may face widespread power outages. DR can prioritize power supply to critical loads such as hospitals, transportation hubs, and communications facilities, mitigating the impact of power outages on the economy and society. Furthermore, DR can enhance the resilience of the distribution network, enabling it to more quickly resume stable operations after a disaster. Therefore, incorporating DR into disaster preparedness assessment systems can more comprehensively reflect the actual performance of the distribution network during disasters.

[0151] Flexible resources, such as batteries, play a crucial role in post-disaster distribution network recovery. As efficient energy storage devices, batteries can provide emergency power support during disasters, mitigating 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 participate in the recovery and reconstruction of the distribution network as part of distributed energy resources. For example, during disasters such as typhoons and earthquakes, the backbone lines of the distribution network may suffer severe damage, causing widespread power outages. In these situations, batteries distributed across the distribution network nodes can operate in an islanded mode to provide temporary power to localized areas, reducing the duration and scope of power outages. Furthermore, batteries can smooth the fluctuating output of renewable energy, improving the reliability of the distribution network during disasters. Therefore, incorporating flexible resources such as batteries into disaster resilience assessment systems can significantly improve the accuracy and practicality of assessments.

[0152] Furthermore, the synergistic effect of user-side demand response and flexible resources can further enhance the distribution network's resilience to disasters. Following a disaster, the distribution network's power supply capacity is often severely limited. In these situations, demand response mechanisms can prioritize power supply to critical loads, mitigating the impact of power outages on the economy and society. Simultaneously, flexible resources such as batteries can provide backup power for critical loads, ensuring their normal operation. For example, during extreme weather conditions such as typhoons or floods, the distribution network may face widespread power outages. In these situations, demand response mechanisms can prioritize power supply to critical loads such as hospitals, transportation hubs, and communications facilities, mitigating the impact of power outages on the economy and society. Furthermore, demand response can enhance the resilience of the distribution network, enabling it to more quickly resume stable operations after a disaster. Therefore, incorporating user-side demand response into disaster resilience assessment systems can more comprehensively reflect the distribution network's actual performance during disasters.

[0153] Disaster prevention capacity assessment methods that consider user-side demand response and flexibility resources can provide more targeted decision-making support for the disaster prevention planning and emergency response of distribution networks. Traditional assessment methods mainly focus on the physical recovery capacity of the power grid itself, while ignoring the role of user-side demand and flexibility resources. This single-perspective assessment method often fails to fully reflect the actual performance of the distribution network during disasters, resulting in a lack of scientific basis for the formulation of disaster prevention planning and emergency response strategies. By incorporating user-side demand response and flexibility resources into the assessment system, the vulnerable links of the distribution network during disasters can be more accurately identified, resource allocation and recovery strategies can be optimized, and the priority restoration of critical loads can be ensured. By incorporating user-side demand response and flexibility resources into the assessment system, the vulnerable links of the distribution network during disasters can be more accurately identified, resource allocation and recovery strategies can be optimized, and the priority restoration of critical loads can be ensured.

[0154] The following is a detailed description of the model's content and solution method:

[0155] A line failure probability model based on multiple disaster scenarios can effectively quantify the primary impact of typhoon-induced disaster factors on urban distribution networks and derive the probability of failure of distribution network components in typhoon scenarios. An urban distribution network disaster resilience assessment model must be established based on appropriate post-disaster failure scenarios. Based on the distribution network component failure probability formula, a random sampling method was used to generate post-disaster distribution network scenarios, and 10 representative scenarios were selected.

[0156] Urban distribution networks often use network reconstruction to restore power after disasters. With the goal of minimizing the amount of lost load, a power supply path for restoring the lost load is optimized, and mathematical programming can be used to ensure the optimality of the solution. With the goal of minimizing the amount of load shedding during post-disaster network reconstruction, considering the impact of economic and social security on urban distribution networks, a multi-disaster scenario distribution network disaster resilience assessment model is constructed, which takes into account economic and social security impacts and user demand response. The objective function is as follows:

[0157] .

[0158] In one embodiment, the operation constraints of the network power system include:

[0159] Power balance constraints: ;

[0160] Node voltage relationship constraints: ;

[0161] Line operation safety constraints: ;

[0162] Node voltage amplitude upper and lower limit constraints: ;

[0163] Load shedding constraints: ;

[0164] in, is the system node set; 、 Represents nodes respectively The set of child nodes and parent nodes; 、 Flow through the lines Active power and reactive power; 、 are the active output and reactive output of the generator respectively; 、 Represents nodes respectively Active load and load shedding capacity; 、 Node Reactive load and load shedding capacity; is a collection of lines; and Represents node i and node Voltage; 、 are the wire resistance and reactance respectively; is the node rated voltage; is the default value; To indicate a line State variable for open or closed state; can be 0 or 1, 0 represents the line Open, otherwise it means the circuit is in closed state; Indicates the maximum allowable power of the line; and Flow through the lines Active power and reactive power; is the lowest voltage limit; is the upper limit of voltage; i is the line The first end node; j is the line End node.

[0165] In one embodiment, according to graph theory, the radial topology condition is satisfied when each island in the distribution network is connected and the difference between the number of lines and the number of nodes is equal to the number of islands. Therefore, the network distribution network reconstruction constraints include:

[0166] Island topology constraints: ;

[0167] Virtual power balance constraints: ;

[0168] in, To determine the node A binary variable indicating whether it is a source node; the number of closed circuits is equal to the number of nodes Subtract 1 (that is, the substation The number of subgraphs (subgraphs where the subgraphs are located) and load islands; the virtual power flow method is used to ensure the connectivity of the reconstructed distribution network, and a virtual network with the same topology as the distribution network is set up. The connectivity between nodes is determined by virtual power. Each subgraph selects a node as the source node , other nodes are used as load nodes, Output of the source node; is the virtual power flow, which takes zero when the branch is disconnected; To indicate a line State variables for open or closed states; To indicate a line State variables for open or closed states; is the system node set; is the number of nodes; 、 Represents nodes respectively The set of child nodes and parent nodes; is the virtual power; is the preset value, i is the line The first end node; j is the line The virtual power balance constraint also constrains the virtual power to flow only on closed circuits.

[0169] In one embodiment, the load types for which users participate in demand response are mainly divided into transferable loads and curtailable loads. The load types for which users participate in demand response are mainly divided into transferable loads and curtailable loads. These two types of loads have different characteristics and roles in the operation of the power system. Transferable loads refer to those electricity demands that can be flexibly adjusted in time, such as the usage time of industrial production lines, electric vehicle charging, washing machines and other equipment. The characteristic of this type of load is that its electricity usage time has a certain degree of flexibility, and it can alleviate the pressure on the power grid by adjusting the time when the supply and demand of the power system is tight. Curtailable loads refer to those electricity demands that can be reduced or interrupted in a short period of 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 electricity consumption without affecting the core needs of users. The synergistic effect of transferable loads and curtailable loads can effectively improve the flexibility and reliability of the power system, especially under disaster or extreme weather conditions. By rationally dispatching these two types of loads, the time and scope of power outages can be significantly reduced, and the power supply of key loads can be guaranteed, thereby improving the overall disaster resistance of the distribution network. The user demand response constraints include:

[0170] Load transfer state constraints:

[0171] ;

[0172] Transfer rate constraint:

[0173] ;

[0174] ;

[0175] Transfer capacity constraints:

[0176] ;

[0177] ;

[0178] Power balance constraints:

[0179] ;

[0180] Electricity price constraints:

[0181] ;

[0182] Electricity quantity and price elasticity matrix constraints:

[0183] ;

[0184] Reducible load constraints:

[0185] ;

[0186] in, and Respectively indicate time periods The state identification variable of the internal transfer-out and transfer-in loads takes the value 0 or 1. There can only be one load transfer state in the same period; is the set of transferable electric load points; and Represents time periods The percentage of the internally transferred outgoing and incoming electric loads in the total load is used to measure the capacity of load that can be transferred per unit time; and Represents load nodes The maximum ratio of acceptable outgoing and incoming power; The original time-of-use electricity price; is the change in time-of-use electricity price; The adjusted time-of-use electricity price; is the elastic coefficient; and is the time node for indexing the corresponding period, where hour is the self-elastic coefficient, otherwise it is the mutual elastic coefficient; Indicates electrical load Can actively reduce the proportion of load in the total load, is the set of curtailable load points in the subsystem; i is the line The first end node; j is the line terminal node; is the output power; is the input power; is the load; is the simulation cycle time; is the price change at time t2; is the original price at time t2; is the load reduction amount; is the power output at time t1; is the power transferred in at time t1; is the load at time t1. The formula corresponding to the power balance constraint indicates that the total load power balance must be satisfied within a given IES repair and restoration cycle, and the total amount of load transferred in and out must be equal.

[0187] In one embodiment, as an important flexibility resource, the operation constraints of batteries mainly include capacity limitations, charge and discharge rates, cycle life and efficiency. The capacity limitation determines the maximum energy support that the battery can provide, and the charge and discharge rate affects its response speed. Although a high charge and discharge rate can quickly balance the supply and demand of electricity, it may shorten the battery life. The cycle life limits the number of charge and discharge times of the battery. 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. Low efficiency will lead to energy loss and weaken the overall performance of the system. The above constraints jointly determine the actual application effect of batteries in the restoration of distribution networks after disasters, and must be fully considered in the disaster prevention capacity assessment. The operation constraints of the flexibility resources include:

[0188] ;

[0189] ;

[0190] ;

[0191] ;

[0192] ;

[0193] in, and is the charge and discharge power of the battery; and is the instantaneous maximum charge and discharge capacity; and It is a binary variable that represents the charge and discharge state; Is the charging status; and are the upper and lower limits of the battery charge; and is the battery's charge and discharge efficiency; is the battery capacity at time t; is the battery capacity at time t-1; The unit duration.

[0194] By solving the above-mentioned disaster prevention capacity assessment model under multiple sampling scenarios, the objective function values ​​are obtained for comparative analysis, which can reflect the actual level of disaster prevention of the distribution network under disasters, taking into account the impact of economic and social security and user demand-side response.

[0195] This method is significantly innovative and practical in disaster modeling. It fully considers the current reality of frequent multiple and compound disasters, and deeply analyzes the coupling between multiple disasters and their superimposed impact on the distribution network. By decomposing typhoon disasters into two typical disaster factors, storms and floods, based on the disaster mechanism and impact range, this method innovatively proposes a multi-disaster superposition probability model. This model can not only quickly and accurately calculate the failure probability of distribution equipment under various disaster scenarios, but also more realistically reflect the actual situation of disaster occurrence, overcoming the limitations of traditional single disaster models. Compared with existing methods, the multi-disaster superposition probability model of this method has higher accuracy and applicability, and can provide a more scientific basis for disaster risk assessment.

[0196] This method fully integrates power grid disaster resilience with social and economic security in the disaster resilience assessment process, comprehensively assessing potential social security risks associated with distribution network restoration after a disaster. It prioritizes the restoration of critical nodes based on the actual load losses in the affected area, improving the accuracy and practicality of the assessment. This innovative approach deeply integrates power grid disaster resilience with social and economic security in the disaster resilience assessment process, enabling a comprehensive assessment of potential social security risks associated with distribution network restoration after a disaster. By comprehensively considering factors influencing social and economic security, such as power flow betweenness, population density, and economic losses per unit load, this method accurately quantifies the importance of each node and prioritizes the restoration of key nodes with the greatest impact on social and economic security, taking into account the actual load losses in the affected area. This assessment method not only significantly improves the targetedness and efficiency of distribution network restoration, but also minimizes the negative impact of disasters on social order and economic activities. Furthermore, by introducing the improved analytic hierarchy process (IAHP), this method further optimizes the calculation of node restoration weights, ensuring the scientific and reliable assessment results. Compared with traditional methods, this method not only improves the accuracy of the assessment, but also enhances its practicality, providing more operational decision-making support for disaster prevention planning and emergency response of distribution networks, thereby achieving dual optimization of social and economic benefits in disaster response.

[0197] This method innovatively considers the role of user-side demand response and flexibility resources in disaster resilience assessment, significantly enhancing the comprehensiveness and practicality of the assessment. Traditional assessment methods primarily focus on the physical resilience of the power grid itself. By incorporating user-side demand response mechanisms (such as shiftable and curtailable loads) and flexibility resources (such as batteries), this method more accurately reflects the distribution network's actual disaster resilience, more closely resonating with real-world conditions.

[0198] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0199] The present application also provides a device for evaluating the disaster resilience of a distribution network, configured to perform the steps of the aforementioned method for evaluating the disaster resilience of a distribution network. The device can be a virtual appliance within an electronic device, executed by a processor within the electronic device, or it can be the electronic device itself.

[0200] like Figure 2 As shown, the distribution network disaster prevention capability assessment device 100 provided in the embodiment of the present application includes:

[0201] The failure rate calculation module 101 is used to construct a multi-disaster superposition probability model based on disaster data and power grid data, and solve the comprehensive failure rate of distribution network components under the multi-disaster superposition scenario;

[0202] The weight calculation module 102 is used to calculate the economic and social security impact weight of each load node based on the improved hierarchical analysis method, integrating the flow betweenness, population density and unit load economic loss index;

[0203] A network reconstruction module 103 is configured to generate a post-disaster failure scenario based on the comprehensive failure rate, construct a network reconstruction model with the goal of minimizing weighted load shedding in combination with the economic, social and security impact weights, and obtain a distribution network disaster prevention capability assessment result;

[0204] Among them, the constraints of the network reconstruction model include the operation constraints of the power system, the distribution network reconstruction constraints, the user demand response constraints and the operation constraints of the flexibility resources.

[0205] In application, each module in the distribution network disaster prevention capability assessment device may be a software program module, or may be implemented through different logic circuits integrated in a processor, or may be implemented through multiple distributed processors.

[0206] like Figure 3 As shown, the embodiment of the present application further provides an electronic device 200, including: at least one processor 201 ( Figure 3 Only one processor is shown in the figure), a memory 202, and a computer program 203 stored in the memory 202 and executable on at least one processor 201. When the processor 201 executes the computer program 203, the steps in the above-mentioned various method embodiments are implemented.

[0207] In applications, electronic devices may include, but are not limited to, processors and memories. Those skilled in the art will appreciate that Figure 3 The electronic device is merely an example and does not limit the electronic device. The electronic device may include more or fewer components than shown in the figure, or may include a combination of certain components or different components.

[0208] In applications, a processor may be a central processing unit (CPU), other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0209] In applications, in some embodiments, memory can be an internal storage unit of an electronic device, such as a hard drive or memory. In other embodiments, memory can also be an external storage device of the electronic device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a flash memory card. Furthermore, memory can include both internal storage units and external storage devices. Memory is used to store operating systems, application programs, boot loaders, data, and other programs, such as computer program code. Memory can also be used to temporarily store data that has been output or is about to be output.

[0210] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0211] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0212] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0213] An embodiment of the present application provides a computer program product, including a computer program. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0214] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable media can include at least: any entity or device capable of carrying computer program code to a device / electronic device, recording media, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunications signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, due to legislation and methodological practices, computer-readable media cannot be electric carrier signals or telecommunications signals.

[0215] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0216] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0217] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0218] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0219] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for evaluating the disaster prevention capability of a distribution network, characterized in that: include: Based on disaster data and power grid data, a multi-disaster superposition probability model is constructed to obtain the comprehensive failure rate of distribution network components under multi-disaster superposition scenarios; Based on the improved analytic hierarchy process, the economic and social security impact weight of each load node is calculated by integrating the flow betweenness, population density and unit load economic loss indicators. Based on the comprehensive failure rate, a post-disaster failure scenario is generated. In combination with the economic and social security impact weights, a network reconstruction model with the goal of minimizing weighted load shedding is constructed to obtain a distribution network disaster prevention capability assessment result; Among them, the constraints of the network reconstruction model include the operation constraints of the power system, the distribution network reconstruction constraints, the user demand response constraints and the operation constraints of the flexibility resources.

2. The method for evaluating the disaster prevention capability of a distribution network according to claim 1, wherein: The multi-disaster superposition probability model is constructed based on disaster data and power grid data, including: Decompose typhoon disasters into storm disasters and flood disasters, and construct a joint probability model for storm disasters and a probability model for flood disasters respectively; Based on the storm disaster joint probability model and the flood disaster probability model, a multi-disaster superposition probability model is constructed.

3. The method for evaluating the disaster prevention capability of a distribution network according to claim 1, wherein: The multi-disaster superposition probability model is: ； Where m=1,2; is the comprehensive failure rate of distribution network components under multi-disaster superposition scenarios; The failure probability of the distribution line under each disaster independently; The number of disasters to be considered superimposed.

4. The method for evaluating the disaster prevention capability of a distribution network according to claim 3, wherein: Also includes: When m=1, Calculated using the following formula: ； When m=2, Calculated using the following formula: ； in, is the failure probability of distribution lines under storm disasters; is the failure probability of distribution lines under flood disasters; is the variable affecting the power grid components caused by flood disasters; is the joint probability density function of storm disaster; is the rainfall intensity that the transmission line can withstand in area s at time t; is the wind speed that the transmission line can withstand in area s at time t.

5. The method for evaluating the disaster prevention capability of a distribution network according to claim 1, wherein: The improved analytic hierarchy process is based on the integration of flow betweenness, population density and unit load economic loss indicators to calculate the economic and social security impact weight of each load node, including: Construct a three-level evaluation model including target layer, criterion layer and object layer; wherein the evaluation index of the criterion layer includes node power sales loss , population density and trend betweenness , i is the load node; The exponential scaling method is used to construct the judgment matrix and calculate the weight coefficient of each evaluation index. , and obtain the economic and social security impact weight of each load node.

6. The method for evaluating the disaster prevention capability of a distribution network according to claim 5, wherein: The economic and social security impact weight of each load node is calculated using the following formula: ； ； in, is the economic and social security impact weight of load node i; is the weight of electricity sales loss; is the population density weight; is the trend betweenness weight; is the weight coefficient of the evaluation index; is the first eigenvector elements; is the judgment matrix The order of .

7. The method for evaluating the disaster prevention capability of a distribution network according to claim 1, wherein: The objective function of the network reconstruction model is: ； in, is the economic and social security impact weight of load node i; is the load reduction at load node i.

8. The method for evaluating the disaster prevention capability of a distribution network according to claim 1, wherein: The operating constraints of the power system include: ； ； ； ； ； in, is the system node set; 、 Represents nodes respectively The set of child nodes and parent nodes; 、 Flow through the lines Active power and reactive power; 、 are the active output and reactive output of the generator respectively; 、 Represents nodes respectively Active load and load shedding capacity; 、 Node Reactive load and load shedding capacity; is a collection of lines; and denote the voltages at nodes i and j respectively; 、 are the wire resistance and reactance respectively; is the node rated voltage; is the default value; To indicate a line State variables for open or closed states; Indicates the maximum allowable power of the line; and Flow through the lines Active power and reactive power; is the lowest voltage limit; is the upper limit of voltage; i is the line The first end node; j is the line End node.

9. The method for evaluating the disaster prevention capability of a distribution network according to claim 1, wherein: The distribution network reconstruction constraints include: ； ； in, To determine the node A binary variable indicating whether it is a source node; Output of the source node; For virtual trends; To indicate a line State variables for open or closed states; To indicate a line State variables for open or closed states; is the system node set; is the number of nodes; 、 Represents nodes respectively The set of child nodes and parent nodes; is the virtual power; is the preset value, i is the line The first end node; j is the line End node.

10. The method for evaluating the disaster prevention capability of a distribution network according to claim 1, wherein: The user demand response constraints include: Load transfer state constraints: ； Transfer rate constraint: ； ； Transfer capacity constraints: ； ； Power balance constraints: ； Electricity price constraints: ； Electricity quantity and price elasticity matrix constraints: ； Reducible load constraints: ； in, and Respectively indicate time periods Status identification variables for internal outgoing and incoming loads; is the set of transferable electric load points; and Represents time periods The percentage of the internal outgoing and incoming electric load in the total load; and Represents load nodes The maximum ratio of acceptable outgoing and incoming power; The original time-of-use electricity price; is the change in time-of-use electricity price; The adjusted time-of-use electricity price; is the elastic coefficient; and is the time node for indexing the corresponding period; Indicates electrical load Can actively reduce the proportion of load in the total load, is the set of curtailable load points in the subsystem; i is the line The first end node; j is the line terminal node; is the output power; is the input power; is the load; is the simulation cycle time; is the price change at time t2; is the original price at time t2; is the load reduction amount; is the power output at time t1; is the power transferred in at time t1; is the load at time t1.

11. The method for evaluating the disaster prevention capability of a distribution network according to claim 1, wherein: The operational constraints of the flexibility resources include: ； ； ； ； ； in, and is the charge and discharge power of the battery; and is the instantaneous maximum charge and discharge capacity; and It is a binary variable that represents the charge and discharge state; Is the charging status; and are the upper and lower limits of the battery charge; and is the battery's charge and discharge efficiency; is the battery capacity at time t; is the battery capacity at time t-1; The unit duration.

12. A device for evaluating the disaster prevention capability of a distribution network, characterized in that: include: The failure rate calculation module is used to build a multi-disaster superposition probability model based on disaster data and power grid data, and solve the comprehensive failure rate of distribution network components under the multi-disaster superposition scenario; The weight calculation module is used to calculate the economic and social security impact weight of each load node based on the improved hierarchical analysis method, integrating the flow betweenness, population density and unit load economic loss indicators; A network reconstruction module is used to generate a post-disaster failure scenario based on the comprehensive failure rate, construct a network reconstruction model with the goal of minimizing weighted load shedding in combination with the economic and social security impact weights, and solve to obtain a distribution network disaster prevention capability assessment result; Among them, the constraints of the network reconstruction model include the operation constraints of the power system, the distribution network reconstruction constraints, the user demand response constraints and the operation constraints of the flexibility resources.

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