Risk event-oriented urban power grid comprehensive elasticity assessment method and system

Abstract

The invention discloses a risk event-oriented urban power grid comprehensive elasticity evaluation method and system. The method comprises the steps of constructing an urban power grid comprehensive elasticity index system which comprises a plurality of first-level indexes, and each first-level index comprises a plurality of second-level indexes; calculating an initial weight of each secondary index as a corresponding fuzzy measure value, constructing fuzzy measure values of all subsets in the secondary index set, and introducing a time adjustment parameter to perform dynamic weighting processing on the fuzzy measure values of all subsets; based on the fuzzy measure value after dynamic weighting processing, fusing the secondary index values by adopting an improved Choquet integration method to obtain primary index values; for urban power grid risk events, multiple elastic resource scheduling schemes are designed, a first-level index value of each elastic resource scheduling scheme is calculated according to the mode, and an improved VIKOR method is utilized to calculate and obtain an elastic comprehensive evaluation value of each scheme and perform optimization. According to the method, the comprehensive response and recovery level of the system under uncertain disturbance can be accurately reflected.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of power distribution system operation evaluation, and particularly relates to a risk event-oriented comprehensive resilience evaluation method and system for urban power grids. BACKGROUND

[0002] With the increasing proportion of new energy access and the increasing complexity of power distribution system structure, the resilience of urban power grids in the face of natural disasters, equipment failures and other extreme disturbance situations has become a key element to ensure the safe operation of power grids. As a comprehensive index for measuring the impact resistance, adaptability and recovery ability of the system under disturbance, resilience has gradually become an important evaluation dimension of modern urban power grids, especially for complex systems containing distributed power sources, energy storage devices and other flexible resources.

[0003] At present, the mainstream urban power grid resilience evaluation method is mostly based on a linear weighting mechanism to establish a performance index system. Although it has certain operability, it is difficult to accurately depict the coupling relationship, interaction and nonlinear characteristics between multiple indexes, resulting in distorted evaluation results and poor robustness.

[0004] The VIKOR method can take into account the overall satisfaction and maximum deviation during the ranking process, and has a certain balance, but its penalty mechanism is fixed, and it is difficult to sensitively reflect the subtle differences between different schemes in the case of large differences in index distribution or high-risk disturbance scenarios, resulting in limited discrimination and robustness of the ranking results.

[0005] To solve the above problems, the present application provides a risk event-oriented comprehensive resilience evaluation method and system for urban power grids based on an improved Choquet integral-VIKOR. The method introduces a non-equilibrium factor based on the traditional VIKOR framework, enhances the penalty effect of the inferior index, avoids the dominance of a single index masking the overall deficiency, and thus ensures the balance and rationality of the ranking results. At the same time, a dynamic control factor is introduced into the Choquet integral to realize the dynamic nonlinear aggregation of the index interaction, which is more in line with the resilience characteristics of the power grid under different risk situations. Based on the above two improvements, the method can more accurately reflect the comprehensive response and recovery level of the system under uncertain disturbance, and improve the discrimination and engineering practicability of the evaluation results. SUMMARY

[0006] To solve the problems in the prior art, the present application provides a risk event-oriented comprehensive resilience evaluation method and system for urban power grids.

[0007] The present application adopts the following technical solutions.

[0008] In a first aspect, the present application discloses a risk event-oriented comprehensive resilience evaluation method for urban power grids, which comprises the following steps: S1, constructing a city power grid comprehensive resilience index system including multiple primary indexes, and each primary index including multiple secondary indexes; S2, calculating initial weights of each secondary index as corresponding fuzzy measure values, constructing fuzzy measure values of all subsets in the secondary index set, and introducing a time adjustment parameter to dynamically weight process the fuzzy measure values of all subsets; S3, introducing power supply shortage rate as a control factor to improve the Choquet integral method, based on the dynamically weighted fuzzy measure values, using the improved Choquet integral method to fuse the secondary index values to obtain the primary index values; S4, for city power grid risk events, designing multiple resilience resource scheduling schemes, calculating corresponding primary index values of each resilience resource scheduling scheme according to S1-S3, and using the improved VIKOR method to calculate the resilience comprehensive evaluation value of each scheme and selecting the optimal scheme.

[0009] Further preferably, The primary index includes reliability, economy, response capability and recovery capability; the reliability includes four secondary indexes of important load average interruption time, user average power outage times, system load average loss rate and user average power outage time; the economy includes four secondary indexes of network loss rate, new energy consumption rate, economic load recovery proportion and real-time economic loss rate caused by system power outage; the response capability includes four secondary indexes of repair resource completeness rate, repair group capability rate, fault detection capability and load scheduling efficiency; the recovery capability includes four secondary indexes of load recovery time, load recovery speed, power supply shortage rate and important load recovery time.

[0010] Further preferably, The time adjustment parameter is introduced to dynamically weight process the fuzzy measure values of all subsets, and the calculation method is specifically:

[0011] Among them, is the stage number of the city power grid risk event, is the time adjustment parameter, is the initial fuzzy measure value of each subset in the secondary index set, is the fuzzy measure value of each subset in the secondary index set in t stage.

[0012] Further preferably, The power supply shortage rate is determined in the following manner:

[0013] Among them, is the power supply shortage rate, is the load importance weight of the th load node, is the power supply shortage power of the th load node at the th time, is the rated load power of the th load node, is the voltage quality coefficient of the th load node at the th time, is the total number of load nodes, is the recovery duration of the system, is the total time of scheduling corresponding to the recovery duration of the system, is the time step of scheduling.

[0014] Further preferably, the voltage quality coefficient of the th load node at the th time is determined in the following manner:

[0015] wherein, is the voltage amplitude of the th load node at the th time, is the rated voltage under the voltage level.

[0016] Further preferably, in S3, the improved Choquet integral method is used to fuse the secondary index values to obtain the primary index values, specifically: the control parameter is calculated based on the power supply shortage rate , the control parameter is multiplied by the primary index value calculated by the Choquet integral method to obtain a first weighted result, 1 is subtracted from the value of the control parameter and the primary index value obtained by linearly weighting each secondary index value to obtain a second weighted result, and the first weighted result and the second weighted result are summed to obtain the primary index value calculated based on the improved Choquet integral method.

[0017] Further preferably, the control parameter increases with the increase of the power supply shortage rate , and is specifically determined in the following manner:

[0018] wherein, This represents the basic nonlinear aggregation level in low-risk scenarios of the system. This represents the upper limit of nonlinear aggregation in high-risk scenarios of the system, and satisfies... ; This serves as the threshold for differentiating between low-risk and medium-risk power shortage rates in the system. The threshold value for dividing the power shortage rate between risky and high-risk levels in the system, and satisfying the following conditions. ; The power supply shortage rate; To adjust the parameters, and satisfy .

[0019] More preferably, In S4, the improved VIKOR method is specifically as follows: Based on the original VIKOR method's comprehensive ranking formula, an imbalance factor in the form of an exponential perturbation is introduced, specifically: , in, For the first i The overall elasticity evaluation value of the flexible resource scheduling scheme. For the first i The group utility value of a flexible resource scheduling scheme. The minimum group utility value among all options. The maximum group utility value among all options. For the first i The individual regret value of a flexible resource scheduling scheme. The minimum individual regret value among all options. The maximum individual regret value among all options; To adjust the decision coefficients, and satisfy the following conditions: , For the first i The imbalance factor of the flexible resource scheduling scheme.

[0020] More preferably, The first i The imbalance factor of a flexible resource scheduling scheme is determined as follows: The first i The average of all primary indicator values ​​in the elastic resource scheduling scheme divided by the standard deviation of all primary indicator values ​​is used as the first... i The imbalance factor of the flexible resource scheduling scheme.

[0021] In a second aspect, the present application discloses a risk event-oriented urban power grid comprehensive resilience evaluation system based on the foregoing method, comprising a comprehensive resilience index system construction module, a two-level index set subset fuzzy measure value calculation module, a first-level index value calculation module, and a resilience comprehensive evaluation value calculation module. The comprehensive resilience index system construction module constructs a comprehensive resilience index system of the urban power grid, which comprises multiple first-level indexes, and each first-level index comprises multiple two-level indexes. The two-level index set subset fuzzy measure value calculation module calculates the initial weight of each two-level index as the corresponding fuzzy measure value, constructs the fuzzy measure values of all subsets in the two-level index set, and introduces a time adjustment parameter to dynamically weight and process the fuzzy measure values of all subsets. The first-level index value calculation module introduces the power supply shortage rate as a control factor to improve the Choquet integral method, and based on the dynamically weighted fuzzy measure values, uses the improved Choquet integral method to fuse the two-level index values to obtain the first-level index values. The resilience comprehensive evaluation value calculation module designs multiple resilience resource scheduling schemes for the urban power grid risk event, calculates the corresponding first-level index values according to the resilience index system construction module, the two-level index set subset fuzzy measure value calculation module, and the first-level index value calculation module, and uses the improved VIKOR method to calculate the resilience comprehensive evaluation value of each scheme and performs optimization.

[0022] In a third aspect, the present application provides a terminal comprising a processor and a storage medium. The storage medium is used to store instructions. The processor is used to operate according to the instructions to perform the steps of the method of any one of the first aspect of the present application.

[0023] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of any one of the first aspect of the present application.

[0024] The present application has the following advantages compared with the prior art: 1) The present application can depict the comprehensive resilience level of the urban power grid system from the four dimensions of reliability, economy, response ability and recovery ability when facing multiple risks such as natural disasters, load disturbances, distributed power fluctuations, etc. The present application introduces a dynamic control factor and a nonlinear aggregation mechanism to dynamically represent the interaction relationship of multiple indexes, thereby improving the scientificity and rationality of the evaluation results under complex operation scenarios.

[0025] 2) This invention introduces an imbalance factor into the VIKOR framework, enabling the penalty effect of the weak index to be dynamically adjusted according to the degree of index dispersion, overcoming the shortcomings of the fixed penalty intensity in traditional methods. This improvement increases the sensitivity of the ranking results to the weak index, enhances the discriminative power and robustness under multi-scheme comparison, and thus more realistically reflects the resilience differences of the system under uncertain disturbances.

[0026] 3) The technical framework provided by this invention has good portability and scalability, and can adapt to the access needs of various flexible resources such as distributed power sources, energy storage devices, and electric vehicle charging and discharging. It can maintain consistency and comparability in different operating scenarios, thereby improving the universality and engineering application value of the evaluation method. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the process for the comprehensive resilience assessment method of urban power grids oriented towards risk events according to the present invention; Figure 2 This is the comprehensive elasticity index system in this invention; Figure 3 These are the scores of each secondary indicator for the four schemes in the embodiments of this invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0029] This invention discloses a comprehensive resilience assessment method for urban power grids oriented towards risk events, such as... Figure 1 As shown, the specific steps are as follows: S1. Construct a comprehensive resilience index system for urban power grids that includes multiple primary indicators, and each primary indicator includes multiple secondary indicators; The primary indicators include reliability, economy, responsiveness, and recovery capability; the reliability includes four secondary indicators: average interruption time of critical loads, average number of power outages per user, average system load loss rate, and average power outage time per user; the economy includes four secondary indicators: network loss rate, renewable energy absorption rate, proportion of economic load recovery, and real-time economic loss rate caused by system power outages; the responsiveness includes four secondary indicators: repair resource availability rate, repair team capacity rate, fault detection capability, and load dispatch efficiency; the recovery capability includes four secondary indicators: load recovery time, load recovery speed, power shortage rate, and recovery time of critical loads.

[0030] S2, calculate the initial weight of each secondary index as the corresponding fuzzy measure value, construct the fuzzy measure value of all subsets in the secondary index set, and introduce a time adjustment parameter to dynamically weight all subset fuzzy measure values; The time adjustment parameter is introduced to dynamically weight all subset fuzzy measure values, and the calculation method is specifically:

[0031] Among them, is the phase number of the urban power grid risk event, is the time adjustment parameter, is the initial fuzzy measure value of each subset in the secondary index set, is the fuzzy measure value of each subset in the secondary index set in t phase.

[0032] S3, introduce the power supply shortage rate as a control factor to improve the Choquet integral method, based on the dynamically weighted fuzzy measure value, and use the improved Choquet integral method to fuse each secondary index value to obtain each primary index value; The power supply shortage rate is determined as follows:

[0033] Among them, is the power supply shortage rate; is the load importance weight of the th load node, is the power supply shortage of the th load node at the th moment, is the rated load power of the th load node, is the voltage quality coefficient of the th load node at the th moment, is the total number of load nodes, is the recovery time of the system, is the total number of scheduling moments corresponding to the recovery time of the system, is the time step of scheduling.

[0034] The voltage quality coefficient of the th load node at the th moment is determined as follows:

[0035] Among them, is the voltage quality coefficient of the th load node at the th moment.The voltage amplitude of the load node, is the rated voltage under the voltage level.

[0036] The improved Choquet integral method is used to fuse the secondary index values to obtain the primary index values, and the specific process is as follows: The control parameter is calculated based on the power supply shortage rate The control parameter is multiplied by the primary index value calculated by the Choquet integral method to obtain a first weighted result, and 1 is subtracted from the value of the control parameter and the primary index value obtained by linearly weighting each secondary index value to obtain a second weighted result, and the first weighted result and the second weighted result are summed to obtain the primary index value calculated based on the improved Choquet integral method.

[0037] The control parameter increases with the increase of the power supply shortage rate , and is determined in the following manner:

[0038] wherein, is the basic nonlinear aggregation degree under the low-risk scenario of the system, is the upper limit value of the nonlinear aggregation under the high-risk scenario of the system, and satisfies ; is the power supply shortage rate threshold between the low-risk and the medium-risk of the system, is the power supply shortage rate threshold between the medium-risk and the high-risk of the system, and satisfies ; is the power supply shortage rate; is an adjustment parameter, and satisfies .

[0039] S4, for the risk event of the urban power grid, a plurality of flexible resource scheduling schemes are designed, and the primary index values corresponding to the flexible resource scheduling schemes are calculated in the manner of S1-S3. The improved VIKOR method is used to calculate the flexible comprehensive evaluation value of each scheme and to perform optimization.

[0040] The improved VIKOR method is as follows: On the basis of the original VIKOR method comprehensive sorting formula, an unbalanced factor in the form of exponential disturbance is introduced, and the specific process is as follows: , wherein, is the flexible comprehensive evaluation value of the i-th flexible resource scheduling scheme, i is the flexible comprehensive evaluation value of the j-th flexible resource scheduling scheme, is the difference between the flexible comprehensive evaluation value of the i-th flexible resource scheduling scheme and the flexible comprehensive evaluation value of the j-th flexible resource scheduling scheme, and ia group utility value of the i-th flexible resource scheduling scheme, a minimum value of the group utility value among all schemes, a maximum value of the group utility value among all schemes, a minimum value of the individual regret value among all schemes, i an individual regret value of the i-th flexible resource scheduling scheme, a minimum value of the individual regret value among all schemes, a maximum value of the individual regret value among all schemes; an adjustment decision coefficient, and satisfying , a minimum value of the individual regret value among all schemes, i an unbalance factor of the i-th flexible resource scheduling scheme.

[0041] The unbalance factor of the i-th flexible resource scheduling scheme is determined in the following manner: i The average value of all primary index values in the i-th flexible resource scheduling scheme is divided by the standard deviation of all primary index values to obtain the unbalance factor of the i-th flexible resource scheduling scheme. The average value of all primary index values in the i-th flexible resource scheduling scheme is divided by the standard deviation of all primary index values to obtain the unbalance factor of the i-th flexible resource scheduling scheme. i i The unbalance factor of the i-th flexible resource scheduling scheme is determined in the following manner:

[0042] The application discloses a city power grid comprehensive elasticity evaluation system for a risk event based on the foregoing method, comprising a comprehensive elasticity index system construction module, a secondary index set subset fuzzy measure value calculation module, a primary index value calculation module and an elasticity comprehensive evaluation value calculation module. The comprehensive elasticity index system construction module constructs a city power grid comprehensive elasticity index system comprising multiple primary indexes, and each primary index comprises multiple secondary indexes. The secondary index set subset fuzzy measure value calculation module calculates the initial weight of each secondary index as the corresponding fuzzy measure value, constructs the fuzzy measure values of all subsets in the secondary index set, and introduces a time adjustment parameter to dynamically weight process the fuzzy measure values of all subsets. The primary index value calculation module introduces the power supply shortage rate as a control factor to improve the Choquet integral method, and adopts the improved Choquet integral method to fuse the secondary index values to obtain the primary index values based on the dynamically weighted processed fuzzy measure values. The elasticity comprehensive evaluation value calculation module designs multiple flexible resource scheduling schemes for the city power grid risk event, calculates the corresponding primary index values of each flexible resource scheduling scheme in the manner of the elasticity index system construction module, the secondary index set subset fuzzy measure value calculation module and the primary index value calculation module, and calculates the elasticity comprehensive evaluation value of each scheme by using the improved VIKOR method and performs optimization.

[0043] Embodiment one:​ This invention discloses a comprehensive resilience assessment method for urban power grids oriented towards risk events, the specific steps of which are as follows: S1. Construct a comprehensive resilience index system for urban power grids that includes multiple primary indicators, and each primary indicator includes multiple secondary indicators; In S1, such as Figure 2 In the comprehensive resilience index system for urban power grids, the primary indicators include: reliability, economy, response capability, and recovery capability. Reliability indicators include four secondary indicators: average downtime of critical loads, average number of power outages per user, average system load loss rate, and average downtime per user.

[0044] The economic indicators include four secondary indicators: grid loss rate, renewable energy consumption rate, economic load recovery rate, and real-time economic loss rate caused by system power outages.

[0045] The response capability indicators include four secondary indicators: the availability rate of emergency repair resources, the capacity rate of emergency repair teams, the fault detection capability, and the efficiency of load dispatching.

[0046] The recovery capability indicators include four secondary indicators: load recovery time, load recovery speed, power shortage rate, and recovery time of critical loads.

[0047] The critical loads are classified as Level 1 loads based on distribution network planning data, user importance classification, and relevant regulations of the local power supply department. Specifically, they include the following three types: loads related to public safety and emergency services, such as hospitals, fire-fighting facilities, and emergency command centers; loads that maintain the basic functions of infrastructure, such as water supply pumping stations and communication hubs; and other industrial and civil loads that require continuous power supply due to special processes or safety requirements.

[0048] The secondary indicators are calculated as follows: 1) Reliability Indicators The average interruption time of critical loads is used to quantify the continuous power supply capacity of critical loads in the distribution network involved in public safety, emergency services, and critical infrastructure during faults; the specific calculation formula is as follows:

[0049] in, The average downtime of critical loads. For the first i The cumulative power outage time of a critical load node during a single fault event; It is the set of all important loads in the system; This represents the total number of critical loads in the set.

[0050] The formula for the average number of power outages per user is as follows:

[0051] where, is the average number of power outages for users, is the number of users affected in the j th power outage event; is the total number of users powered by the system.

[0052] System load average loss rate, used to depict the resistance ability of the system to deal with disasters. Its calculation formula is specifically:

[0053] where, is the system load average loss rate, is the load loss power in the time period k is the total power of the load in the time period is the total power of the load in the time period k is the total number of statistical time periods. User average power outage time, used to measure the severity of power interruption events in the power system, and is calculated in the following way:

[0054]

[0055] where, is the user average power outage time, is the duration of the j th power outage event, is the number of power outages of the j th power outage event.

[0056] 2) Economic indicators Network loss rate, reflecting the proportion of power loss in the transmission process relative to the power supply power of the power grid, is a key indicator to measure the efficiency and economy of the power grid. Its calculation method is specifically:

[0057] where, is the network loss rate, is the total power input to the grid; is the recorded actual load power of the grid.

[0058] New energy consumption rate, which refers to the proportion of actual output of wind turbines and photovoltaic devices to their maximum power output, and its calculation method is specifically:

[0059] where, is the new energy consumption rate, is the set of wind power equipment, ​A collection of photovoltaic equipment; For the first The active power output of each wind turbine. For the first j The active power output of each photovoltaic device; To maximize wind power output, To maximize the output of photovoltaic power.

[0060] The economic load recovery ratio refers to the proportion of economic losses reduced by restoring failed loads in the event of load loss, relative to the total economic losses. The calculation method is as follows:

[0061] in, The percentage of economic load recovery, To restore economic value, This represents the total economic loss caused by load loss.

[0062] The real-time economic loss rate caused by a system power outage refers to the ratio of real-time economic losses caused by the power outage to the total losses. The calculation method is as follows:

[0063] in, The economic cost per unit power loss, For a moment t node i Power loss, For the set of load nodes, For the power grid in time t Total economic losses within the country.

[0064] 3) Response capability indicators Emergency repair resource readiness rate is an indicator used to measure the completeness of emergency repair equipment configuration in urban power grids. This indicator reflects the reserve level of equipment resources configured by the power grid for emergency repair tasks under different risk events; the calculation formula is:

[0065] in, In order to improve the availability of emergency repair resources, This indicates the total number of resource categories used for emergency repairs; For the first The number of device types included in a resource category; For the first Devices under similar resources Minimum number of configurations required; This indicates the number of [units / items] currently actually owned by the city's power grid. Number of devices of each type.

[0066] The repair team capacity rate, which is used to comprehensively reflect the personnel scale, professional skill level and operation efficiency of the repair team, is an important indicator for measuring the emergency disposal capacity of the team in the event of an emergency. Its calculation expression is as follows:

[0067] wherein, is the repair team capacity rate, is the number of team members; is the total number of repair resources; is the professional knowledge reserve coefficient of the q resource; is the repair efficiency of the q resource; is the total number of devices of the q resource; represents the average value of the operation capacity coefficient of the device q of the d resource in the repair team.

[0068] The fault detection capability is used to measure the real-time mastery of the operation state of the power distribution network monitoring system and the accuracy of identifying and reporting faults. Its calculation formula is as follows:

[0069] wherein, is the fault detection capability, represents the number of faults successfully identified by the monitoring system, represents the total number of faults actually occurring within the statistical period.

[0070] The load scheduling efficiency is used to evaluate the ability of the power grid to meet the electricity demand through resource reconfiguration and load management under the condition of disaster or disturbance, and reflects the level of maintaining power supply stability and reliability of the system under external impact. Its calculation formula is as follows:

[0071] wherein, is the load scheduling efficiency, represents the amount of load demand successfully met by the power grid, represents the total load demand of the region.

[0072] 4) Recovery capability indicators The load recovery time is used to represent the total time required for the power system to make recovery strategies and gradually repair the infrastructure to make the affected load regain normal power supply. Its calculation formula is as follows:

[0073] wherein, Indicates the first i The fault diagnosis time of each load node, that is, the time required from the occurrence of a fault to determining the cause of the fault and the repair plan; Indicates the first i The repair time for each load node is the time required from the start of the recovery operation to the point where the load node regains normal power supply. N Indicates the total number of load nodes; This is the load recovery time.

[0074] Load recovery rate measures how quickly a system returns to normal operating conditions during load recovery; it reflects the power grid's recovery capability and efficiency after a fault. The calculation formula is as follows:

[0075] in, Indicates the load recovery speed; Indicates the first t Time period, the i Recovery power of each load node; This represents the total number of time periods in the recovery process. A unified time reference for use during the recovery process; This represents the total number of load nodes participating in the recovery.

[0076] The power shortage rate is an important indicator used to assess the degree of power restoration to the load after the power grid encounters various risk events. After a fault occurs in the urban power grid, a phased restoration plan must be implemented to ensure the restoration of power supply to the load. This indicator, through a weighted method, measures the ratio between the actual amount of unsupplied load and the total amount of power that should be supplied. It can better highlight the severity of power outages at critical load nodes, and thus reflect the emergency response and restoration efficiency of the power grid under various sudden risk scenarios.

[0077] Introducing voltage quality factor Improvements have been made to the voltage shortage rate. The assessment has expanded from a static description of whether power is available to a perceptual metric of the quality of effective power supply. In post-disaster scenarios, there is a common situation where power is available but voltage quality is insufficient. Traditional equal-weighted cumulative shortage rates systematically underestimate the difficulty of recovery. [The text then abruptly shifts to a different topic:] ...adding... Subsequently, the power supply during the undervoltage period is reduced according to the voltage compliance rate. This indicator can simultaneously reflect the power supply scale and power supply quality, accurately revealing the weak links in the recovery of key areas. The specific calculation method is as follows:

[0078]

[0079] in, The power supply shortage rate; For the first The load importance weight of each load node For the first Time of the first Power shortage at each load node For the first The rated load power of each load node, For the first Time of the first Voltage quality factor of each load node, This represents the total number of load nodes. The system recovery time. This represents the total number of scheduling moments corresponding to the system's recovery time. The time step for scheduling; For the first Time of the first Voltage amplitude at each load node This is the rated voltage for this voltage level.

[0080] Critical load restoration time refers to the time required for the power system to restore normal power supply to critical loads after a fault or disaster occurs, in order to ensure the power supply security of critical users. The specific calculation method is as follows:

[0081] in, The critical load recovery time is the time when the fault occurred. The set of important loads is , of which j One important load is , No. j The time when the power supply to the critical load is restored to normal is , No. j The weight of each important load is .

[0082] The urban power grid comprehensive resilience index system includes positive and negative indicators. To unify the direction, these indicators are normalized, and the normalized interval falls within [0,1]. The specific normalization method is as follows: The positive indicators are normalized as follows:

[0083] Negative indicators are normalized as follows:

[0084] In the formula, Representing the i The first scheme is in the j Normalized results for each indicator Representing the iThe original numerical value of the first index under the first scheme, j The original numerical value of the first index under the first scheme, The maximum value of the first index taken in all schemes, j The minimum value of the first index taken in all schemes. The minimum value of the first index taken in all schemes. j The minimum value of the first index taken in all schemes.

[0085] Preferably, in order to avoid absolute extreme values of the index score, improve the aesthetics and robustness of the chart and evaluation, the following mapping method is used:

[0086] In the formula, is the conventional normalized numerical value, is the numerical value after compression mapping, is the compression coefficient.

[0087] S2, calculate the initial weight of each secondary index as its corresponding fuzzy measure value, construct the fuzzy measure value of all subsets in the secondary index set, and introduce a time adjustment parameter to dynamically weight the fuzzy measure value of all subsets; In this embodiment, preferably, in the example study of the IEEE33 node power distribution system, four types of representative schemes are designed. Scheme 1 (baseline scheme) only relies on a single path of the main power grid for power supply, and the system does not configure any local distributed power supply or energy storage. Scheme 2 (conventional emergency enhancement scheme) adds 2 emergency diesel generator sets and configures a fast switching switch based on the main power grid, which preferentially supplies power locally and prohibits reverse sending. Scheme 3 (new energy localization scheme) configures distributed rooftop photovoltaic, simultaneously constructs 2 sets of battery energy storage systems, and introduces adjustable load (DR), which can reduce about 10% of the power demand. Scheme 4 (comprehensive joint energy supply scheme) configures a gas turbine unit, photovoltaic and energy storage system, and introduces a demand response mechanism (adjustable load ratio 15%). Based on the Monte Carlo method, risk scenarios are constructed, and the failure conditions of lines, distributed power supply, energy storage and demand response are considered in the scenarios; thus, the failure performance of various resources under risk events can be obtained, providing input for subsequent resilience evaluation. Combined with the designed dispatching and operation scheme, simulation is performed, and thus performance data of the system in terms of reliability, economy, responsiveness and recovery, etc. are obtained.

[0088] Each secondary index of the four different schemes is normalized according to the actual scene data to obtain an index score vector, as shown in Figure 3 The specific formula is as follows:

[0089] In the formula indicates the secondary index score vector, indicates the firsti an index score, is expressed as a n-dimensional vector whose value range is [0, 1].

[0090] Preferably, the importance of each secondary index is determined by expert scoring method, and the initial weight of each secondary index is calculated as a fuzzy measure value, which can be expressed as:

[0091] In the formula, represents the weight of the single index set in the fuzzy measure, represents the weight obtained by the corresponding expert scoring method.

[0092] In the traditional fuzzy measure, each subset is assigned a value separately, and the calculation complexity is When there are many indexes, it cannot be realized. Therefore, preferably, in the present embodiment, the -measure method is used, which constructs the fuzzy measure values of all subsets through a parameter and the initial single index weight .

[0093] After introducing , for the universal set , the following λ -equation needs to be satisfied:

[0094] When λ > 0, it indicates that there is a positive interaction between the indexes and a synergistic enhancement effect, making the overall performance better than the linear additive value; when λ < 0, it indicates that there is a negative interaction between the indexes and leads to index redundancy, and the overall performance is worse than the linear additive value; when λ = 0, the indexes are independent of each other, and the system degenerates into a traditional linear weighted sum model.

[0095] The union measure value of any two disjoint subsets and can be calculated by the following recursive formula:

[0096] This formula satisfies the monotonicity and non-additivity requirements of the fuzzy measure, and can be used to gradually construct the measure values of all multi-element subsets. The recursive process can start from the single-element set, and then merge and calculate to form binary groups, ternary groups, and finally the whole set, thereby establishing a complete fuzzy measure system .

[0097] Furthermore, to enhance the adaptability of fuzzy measures to temporal and dynamic correlation characteristics of indicators during the evaluation phase, a time-adjustment parameter is introduced to dynamically weight the fuzzy measure values, constructing a time-varying fuzzy measure function. Its calculation formula is as follows:

[0098] in, This embodiment breaks down the urban power grid risk event process into four stages, numbered as follows: ① Pre-failure stage, where the system is in a normal or early warning state, corresponding to... t =0; ②During the disturbance phase, an external event triggers and causes a sudden change in the operating state, at which point the corresponding... t =1; ③ During the interruption and emergency response phase, when fault expansion and load damage occur, and an emergency response is initiated, the corresponding... t =2; ④ Recovery phase: Power supply is gradually restored through reconstruction and emergency repairs, at which time the corresponding t =3. Let be the initial fuzzy measure value for each subset. For each subset in t The fuzzy measure value of the stage; This is a time-adjusting parameter used to balance historical information with current-stage measurements. It is used when it is desirable to maintain the stability of the assessment results and when the risk is relatively stable. Choose a smaller value, such as 0.1-0.3, where historical information carries higher weight; this is important when the risk situation changes rapidly and greater sensitivity to the latest situation is required. A larger value, such as 0.7–0.9, is selected, and the new metric value dominates the update result. In this embodiment, a larger value is selected. A typical value is 0.8. This function uses the initial fuzzy measure... With the new stage of fuzzy measurement By introducing exponential weights, a dynamic balance between old and new information is achieved. This not only maintains the non-additive nature of fuzzy measures but also endows them with adaptive capabilities to temporal sequence and indicator dynamics. This mechanism enables a more flexible characterization of the interactions and weight evolution among indicators in multi-stage, multi-risk scenarios, thereby significantly enhancing the model's performance and applicability in dynamic evaluation and resilient response analysis.

[0099] S3. The Choquet integral method is improved by introducing the power shortage rate as a control factor. Based on the fuzzy measure value after dynamic weighting, the improved Choquet integral method is used to fuse the values ​​of each secondary index to obtain the values ​​of each primary index. In the comprehensive evaluation system of flexibility index, the first-level indicators such as reliability, economy, response capability and recovery capability are composed of multiple second-level indicators. The traditional linear weighting method assumes that these sub-indicators are independent of each other, and cannot reflect their potential redundancy, complementarity and synergy. In order to improve the aggregation quality, this paper uses the improved Choquet integral to weight and fuse the scores of multiple second-level indicators under the first-level indicators, reflecting the interdependence between indicators.

[0100] Further, the improved Choquet integral is calculated to realize the nonlinear weighted aggregation of multiple evaluation index scores under the consideration of their interaction.

[0101] Choquet integral is a kind of aggregation operator based on fuzzy measure, which is used to replace the traditional linear weighted sum and can fully model the redundancy, synergy or complementarity between multiple indicators. The calculation steps are as follows: First, the score vector is reordered from small to large, and the sorted index sequence is obtained:

[0102] In the formula, represents the score of the th indicator in the second-level indicator set, represents the original index of the sorted th indicator.

[0103] For each position of the sorted index sequence, a set containing all indicators after the current one is constructed:

[0104] where, represents the i th second-level evaluation indicator after sorting, n is the total number of second-level indicators.

[0105] The standard definition of Choquet integral is as follows. This formula regards the marginal increment of each evaluation indicator, i.e. the difference between the adjacent scores after sorting, as the new contribution, and weights the sum with the criticality of the subset to which the indicator belongs at the moment . The physical meaning is that when a certain indicator just falls into a more critical subset, i.e. is larger, its same marginal increment will be given a greater value; otherwise, the value will be compressed. Therefore, even if the difference between the scores of two indicators is very small, as long as the importance of the subsets they belong to is significantly different, the integral result can accurately reflect this difference, thus more finely describing the nonlinear interaction between indicators.

[0106] The first-level indicator value calculated by the Choquet integral method​ It can be represented as:

[0107] Therefore, the final calculation result is obtained. This represents the comprehensive weighted score of the current primary indicator after considering the interaction relationships of its subordinate secondary indicators. Preferably, it is defined as follows: Used for difference calculation.

[0108] Furthermore, control parameters are introduced. The Choquet integral result is improved, and the calculation method is as follows:

[0109]

[0110] In the formula, The values ​​for the primary indexes are calculated based on the improved Choquet integral method. For control parameters and , For the first The linear weighting coefficients of each secondary indicator, Indicates the first The standardized score of each secondary indicator. This represents the basic nonlinear aggregation level in low-risk scenarios of the system. This represents the upper limit of nonlinear aggregation in high-risk scenarios of the system, and satisfies... ; This serves as the threshold for differentiating between low-risk and medium-risk power shortage rates in the system. The threshold value for dividing the power shortage rate between risky and high-risk levels in the system, and satisfying the following conditions. ; The power supply shortage rate; To adjust the parameters, and satisfy .

[0111] In this embodiment, The preferred value is 0.3, which means that if the power shortage rate is less than 0.3, the system is considered to have a low risk. The preferred value is 0.6, which means that if the power shortage rate is greater than 0.6, the system is considered to have a high risk; when the power shortage rate is between the two, the system is considered to have a medium risk level.

[0112] Based on the actual application scenario The value range is [0, 0.4]. The value range is [0.7, 1]. In this embodiment, The value is 0.2. The value is 0.8.

[0113] The Choquet integral is improved by introducing a regulating coefficient to the comprehensive score expression . The physical meaning of this formula is that by introducing the control factor , a balanced adjustment is achieved between the Choquet nonlinear weighted aggregation result and the traditional linear weighted result. When , the model completely adopts the Choquet integral aggregation method, emphasizing the mutual interaction effect between indicators; when , it degenerates into the traditional linear weighted aggregation form, which is suitable for the case where each indicator is independent; when , the degree of linear and nonlinear fusion can be dynamically adjusted according to the actual situation, enhancing the flexibility of the model's expression ability. This improved form further enhances the flexibility and interpretability of the model in different evaluation scenarios.

[0114] It is controlled by a nonlinear function, whose value monotonically increases with the change of the risk indicator in a given interval, to achieve the goal of the more fragile the system operation state, the more dependent the aggregation process on nonlinear synergy. Through this mechanism, the model can maintain strong linear interpretability in low-risk scenarios and fully exert the synergy between indicators in high-risk conditions, thereby effectively improving the accuracy and robustness of flexible evaluation.

[0115] S4, for urban power grid risk events, design multiple flexible resource scheduling schemes, calculate the corresponding primary indicator values for each flexible resource scheduling scheme in the manner of S1-S3, and calculate the flexible comprehensive evaluation value of each scheme using the improved VIKOR method and perform optimization.

[0116] Further, there are flexible resource scheduling schemes, each corresponding to primary indicator values , where is the i primary indicator value in the j scheme, and the weight vector is defined as , .

[0117] To clarify the relative performance of each indicator, the optimal and worst scores of each primary indicator need to be identified among all schemes as the upper and lower bounds for subsequent normalization processing:

[0118] where represents the optimal score of the j indicator among all schemes; represents the worst score of the jThe worst score for each indicator among all options.

[0119] Next, the original scores of all solutions are normalized to obtain the score of the first solution. i In the first scheme j The disadvantage distance of each indicator relative to the optimal indicator value It is used to measure the degree of deviation from the ideal value, specifically:

[0120] Calculate the first i The group utility value of each option With individual regret value Specifically:

[0121] Among them, the group utility value represents the first i The overall satisfaction with the first option reflects the first i The weighted average deviation of all indicators for the first option; the individual regret value represents the first option. i The maximum regret value of each option reflects the impact of the worst-case scenario on the options.

[0122] To reflect the balance of the scheme's performance across multiple indicators, an imbalance factor for the elastic resource scheduling scheme, constructed based on the mean and standard deviation, is introduced as a disturbance term:

[0123]

[0124]

[0125] In the formula, The number of primary indicators. For the first i The average of all primary indicator values ​​for each option. For the first i The standard deviation of each scheme across all primary indicator values ​​reflects the degree of dispersion among the indicators. For the first i The imbalance factor of the flexible resource scheduling scheme.

[0126] Based on the original VIKOR comprehensive ranking formula, an imbalance factor in the form of an exponential perturbation is introduced to enhance the penalty effect of the most disadvantaged term, resulting in an improved comprehensive ranking index:

[0127] in, For the first i The overall elasticity evaluation value of the flexible resource scheduling scheme. the group utility value of the i-th flexible resource scheduling scheme, i the minimum value of the group utility value among all schemes, the maximum value of the group utility value among all schemes, the individual regret value of the i-th flexible resource scheduling scheme, the minimum value of the individual regret value among all schemes, i the maximum value of the individual regret value among all schemes, the non-equilibrium factor of the i-th flexible resource scheduling scheme. the adjustment decision coefficient, and satisfies , the non-equilibrium factor of the i-th flexible resource scheduling scheme. i By adjusting the decision coefficient

[0128] to balance the relationship between the group effect and the individual preference, when v is closer to 0, the decision result is more inclined to minimize the individual regret, avoid the worst result, and select a more conservative scheme; when v is closer to 1, the decision result is more inclined to maximize the group utility, that is, the best performance of the whole, and selects a higher-risk scheme; when v is closer to 0.5, that is, a balance is sought between the group utility and the individual regret. In the embodiment, in order to balance the group utility and the individual regret, v 0.5 is preferably selected. v

[0129] The application also claims a city power grid comprehensive flexibility evaluation system for a risk event based on the foregoing method, comprising a comprehensive flexibility index system construction module, a two-level index set subset fuzzy measure value calculation module, a one-level index value calculation module and a flexibility comprehensive evaluation value calculation module. The comprehensive flexibility index system construction module constructs a city power grid comprehensive flexibility index system comprising multiple one-level indexes, and each one-level index comprises multiple two-level indexes. The two-level index set subset fuzzy measure value calculation module calculates the initial weight of each two-level index as the corresponding fuzzy measure value, constructs the fuzzy measure values of all subsets in the two-level index set, and introduces a time adjustment parameter to dynamically weight process the fuzzy measure values of all subsets. The one-level index value calculation module introduces the power supply shortage rate as a control factor to improve the Choquet integral method, and based on the dynamically weighted processed fuzzy measure values, adopts the improved Choquet integral method to fuse the two-level index values to obtain the one-level index values. ​​The elastic comprehensive evaluation value calculation module designs multiple elastic resource scheduling schemes for the urban power grid risk event, calculates the corresponding first-level index value in the manner of the elastic index system construction module, the second-level index set subset fuzzy measure value calculation module and the first-level index value calculation module, and calculates the elastic comprehensive evaluation value of each scheme by using the improved VIKOR method and performs optimization.

[0130] The application comprehensively considers the index interaction characteristics and nonlinear aggregation problems of the urban power grid under various disturbance scenarios, guarantees the scientificity of the evaluation results, improves the decision explanation ability of the complex system elasticity evaluation and the rationality of the index fusion, constructs a fuzzy measure by combining the Choquet integral method, realizes nonlinear fusion aggregation when there is mutual promotion, redundancy or inhibition relationship between different indexes, and enhances the expression ability of the evaluation model to the multi-dimensional characteristics of system elasticity. On the basis of the traditional VIKOR framework, the method introduces a non-equilibrium factor, enhances the punishment effect of the inferior index, avoids the single index advantage covering the overall deficiency, and thus guarantees the balance and rationality of the ranking result. The method not only improves the accuracy of the comprehensive elasticity evaluation result of the urban power grid, but also provides weight support and quantitative reference for subsequent differentiated regulation strategy design, ensures that the urban power grid has strong adaptability, responsiveness and recovery under the impact of multi-source disturbance, and effectively supports the safe operation and resilience improvement of the power grid under extreme conditions.

[0131] Embodiment two: The terminal provided in the embodiment of the application comprises a processor and a storage medium. The storage medium is used for storing instructions. The processor is used for operating according to the instructions to perform the steps of the method according to any one of the embodiments one.

[0132] Embodiment three: The computer readable storage medium provided in the embodiment of the application has a computer program stored thereon, and the program is executed by a processor to realize the steps of the method according to any one of the embodiments one.

[0133] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions loaded thereon, the computer readable program instructions being configured to cause a processor to implement various aspects of the present disclosure.

[0134] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0135] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0136] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0137] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced, and any modification or replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A risk event-oriented integrated resilience assessment method for urban power grid, characterized in that, The method comprises the following steps: S1, constructing a city power grid comprehensive resilience index system comprising multiple primary indexes, each primary index comprising multiple secondary indexes; S2, calculating the initial weight of each secondary index as its corresponding fuzzy measure value, constructing the fuzzy measure values of all subsets in the secondary index set, and introducing a time adjustment parameter to dynamically weight the fuzzy measure values of all subsets; S3, introducing the power supply shortage rate as a control factor to improve the Choquet integral method, based on the dynamically weighted fuzzy measure values, using the improved Choquet integral method to fuse the secondary index values to obtain the primary index values; S4, for the city power grid risk event, designing multiple resilience resource scheduling schemes, calculating the corresponding primary index values of each resilience resource scheduling scheme according to S1-S3, and using the improved VIKOR method to calculate the comprehensive evaluation value of each scheme and selecting the optimal scheme.

2. The city power grid comprehensive resilience evaluation method for risk events according to claim 1, wherein the primary indexes include reliability, economy, response capability and recovery capability; the reliability includes four secondary indexes of important load average interruption time, user average power outage times, system load average loss rate and user average power outage time; the economy includes four secondary indexes of network loss rate, new energy consumption rate, economic load recovery proportion and real-time economic loss rate caused by system power outage; the response capability includes four secondary indexes of repair resource completeness rate, repair group capability rate, fault detection capability and load scheduling efficiency; and the recovery capability includes four secondary indexes of load recovery time, load recovery speed, power supply shortage rate and important load recovery time.

3. The city power grid comprehensive resilience evaluation method for risk events according to claim 1, wherein the dynamic weighting processing of the fuzzy measure values of all subsets by introducing the time adjustment parameter is calculated in the following manner:

4. The city power grid comprehensive resilience evaluation method for risk events according to claim 1, wherein the power supply shortage rate is determined in the following manner:

5. The city power grid comprehensive resilience evaluation method for risk events according to claim 4, wherein wherein, is the phase number of the occurrence of the risk event of the urban power grid, is the time adjustment parameter, is the initial fuzzy measure value of each subset in the secondary index set, is the fuzzy measure value of each subset in the secondary index set in the phase t .

6. The city power grid comprehensive resilience evaluation method for risk events according to claim 4, wherein In S3, the improved Choquet integral method is used to fuse the secondary index values to obtain the primary index values, which is specifically: wherein, is the power supply deficiency rate; is the load importance weight of the th load node, is the power supply deficiency of the th load node at the th time instant, is the rated load power of the th load node, is the voltage quality coefficient of the th load node at the th time instant, is the total number of load nodes, is the recovery duration of the system, is the total number of dispatch time instants corresponding to the recovery duration of the system, is the time step of the dispatch.

7. The city power grid comprehensive resilience evaluation method for risk events according to claim 6, wherein The first The voltage quality coefficient of the first The voltage quality coefficient of the first The voltage quality coefficient of the first wherein, is the first is the second is the voltage amplitude of the kth load node at the nth time instant, is the rated voltage at the voltage class.

8. The city power grid comprehensive resilience evaluation method for risk events according to claim 1, wherein In S4, the improved VIKOR method is specifically: The control parameter is calculated based on the power supply shortage rate The control parameter is multiplied by the first-level index value calculated by the Choquet integral method to obtain a first weighted result, and 1 is subtracted from the value of the control parameter and each second-level index value is linearly weighted to obtain a first-level index value, the first weighted result and the second weighted result are summed to obtain a first-level index value calculated based on the improved Choquet integral method. On the basis of the original VIKOR method comprehensive sorting formula, a non-uniformity factor in the form of exponential disturbance is introduced, which is specifically: the control parameter , the power supply rate increases, and is determined in the following manner: wherein, is the basic nonlinear aggregation degree under the low-risk scenario of the system, is the upper limit value of nonlinear aggregation under the high-risk scenario of the system, and satisfies ; is the power supply shortage rate threshold for distinguishing between the low-risk and medium-risk scenarios of the system, is the power supply shortage rate threshold for distinguishing between the medium-risk and high-risk scenarios of the system, and satisfies ; is the power supply shortage rate; is the adjustment parameter, and satisfies .

9. The city power grid comprehensive resilience evaluation method for risk events according to claim 8, wherein ​ ​ ， wherein, is the first i elastic comprehensive evaluation value of the elastic resource scheduling scheme, is the first i group utility value of the elastic resource scheduling scheme, is the minimum value of the group utility value in all schemes, is the maximum value of the group utility value in all schemes, is the first i individual regret value of the elastic resource scheduling scheme, is the minimum value of the individual regret value in all schemes, is the maximum value of the individual regret value in all schemes; is the adjustment decision coefficient, and satisfies , is the first i imbalance factor of the elastic resource scheduling scheme. ​ The first i The non-uniformity factor of the item elastic resource scheduling scheme is determined in the following manner: The average value of all the first-level index values in the elastic resource scheduling scheme is divided by the standard deviation of all the first-level index values as the non-uniformity factor of the elastic resource scheduling scheme. i The average value of all the first-level index values in the elastic resource scheduling scheme is divided by the standard deviation of all the first-level index values as the non-uniformity factor of the elastic resource scheduling scheme. i The average value of all the first-level index values in the elastic resource scheduling scheme is divided by the 10. A risk event-oriented urban power grid comprehensive resilience evaluation system based on the method of any one of claims 1-9, comprising a comprehensive resilience index system construction module, a two-level index set subset fuzzy measure value calculation module, a one-level index value calculation module, and a resilience comprehensive evaluation value calculation module, characterized in that: the comprehensive resilience index system construction module constructs a comprehensive resilience index system for an urban power grid, which includes multiple one-level indexes, and each one-level index includes multiple two-level indexes; the two-level index set subset fuzzy measure value calculation module calculates the initial weight of each two-level index as its corresponding fuzzy measure value, constructs the fuzzy measure values of all subsets in the two-level index set, and introduces a time adjustment parameter to dynamically weight the fuzzy measure values of all subsets; the one-level index value calculation module introduces power supply shortage rate as a control factor to improve the Choquet integral method, and based on the dynamically weighted fuzzy measure values, uses the improved Choquet integral method to fuse the two-level index values to obtain one-level index values; the resilience comprehensive evaluation value calculation module designs multiple resilience resource scheduling schemes for urban power grid risk events, calculates the corresponding one-level index values for each resilience resource scheduling scheme in the manner of the resilience index system construction module, the two-level index set subset fuzzy measure value calculation module, and the one-level index value calculation module, and uses the improved VIKOR method to calculate the resilience comprehensive evaluation value of each scheme and performs optimization.

11. A terminal comprising a processor and a storage medium; characterized in that: the storage medium is used to store instructions; the processor is used to operate according to the instructions to perform the steps of the method according to any one of claims 1-9.

12. A computer readable storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to realize the steps of the method of any one of claims 1-9.

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