A method for managing power distribution system faults within a smart grid using an improved measure of toughness

Abstract

The application relates to a method, device, computer equipment, computer readable storage medium and computer program product for managing power distribution system faults in a smart grid using an improved resilience metric. The method comprises: after a target power distribution system decays at the end of a fault scenario, quantifying the key load recovery of the target power distribution system under a network reconstruction technology by a second index to determine a target fault recovery strategy; wherein if the key load recovery represents that all key loads will be recovered by the network reconstruction technology, the target fault recovery strategy comprises the network reconstruction technology; if the key load recovery represents that there will be key loads that are not recovered by the network reconstruction technology, the target fault recovery strategy comprises the network reconstruction technology and a mobile emergency resource deployment technology; and the mobile emergency resource deployment technology is used to recover the key loads that are not recovered by the network reconstruction technology. The method can improve the intelligence of the fault recovery scheme for the power distribution system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, and in particular to a method, device, computer equipment, computer readable storage medium and computer program product for managing power distribution system failure in smart grid by using improved resilience metric. BACKGROUND

[0002] Catastrophic events, including cyber-attacks, natural disasters and wars, can have a serious impact on the operation of distribution systems (DSs), causing serious economic losses and large-scale power failure. Therefore, it is crucial to strengthen the resilience of DSs. Resilience mainly measures the support and recovery capability of distribution network to critical loads in natural disasters. The resilience of DSs can be defined as the ability of the distribution system to take proactive measures to ensure power supply to critical loads in disasters and quickly restore power failure loads. With the emergence of smart grid systems and the inclusion of various technologies and energy resources (such as microgrids (MGs), network reconfiguration (NR), mobile emergency resources (MEGs), resilience metrics, etc.) in the system, the resilience of DSs has been greatly improved.

[0003] The existing distribution network model framework for managing power distribution system failure in smart grid cannot independently analyze the maximum failure response potential of network reconfiguration technology and mobile emergency generator respectively, resulting in the inevitable need for both to participate in coordinated management in any case, and cannot provide a failure recovery scheme that can maximize the resilience of the system for power distribution system failure management.

[0004] Therefore, the failure recovery scheme for the power distribution system in the related art has the problem of low intelligence. SUMMARY

[0005] Therefore, it is necessary to provide a method, device, computer equipment, computer readable storage medium and computer program product for managing power distribution system failure in smart grid by using improved resilience metric, which can improve the intelligence of the failure recovery scheme for the power distribution system.

[0006] In a first aspect, the present application provides a method for managing power distribution system failure in smart grid by using improved resilience metric, comprising:

[0007] generating a failure scenario under disaster by time series Monte Carlo simulation according to the grid element vulnerability curve of the target distribution system;

[0008] quantify and record the attenuation state of the target power distribution system by a first index until the attenuation ends when the target power distribution system starts to attenuate under the fault scenario;

[0009] After the attenuation ends, quantify the critical load recovery situation of the target power distribution system under the network reconstruction technology by a second index to determine a target fault recovery strategy;

[0010] Wherein, in the case that the critical load recovery situation represents that all critical loads in the target power distribution system will be recovered by the network reconstruction technology, the target fault recovery strategy includes the network reconstruction technology; in the case that the critical load recovery situation represents that there will be critical loads that are not recovered by the network reconstruction technology, the target fault recovery strategy includes the network reconstruction technology and a mobile emergency resource deployment technology; the mobile emergency resource deployment technology is used to recover the critical loads that are not recovered by the network reconstruction technology.

[0011] After the repair process of the target fault recovery strategy ends, quantify the total resilience of the target power distribution system based on the recovery situation of the target power distribution system by a third index.

[0012] In one embodiment, the value range of the quantified value of the total resilience is 0-100%; the quantification of the total resilience of the target power distribution system based on the recovery situation of the target power distribution system by a third index includes:

[0013] In the case that the recovery situation represents that the target power distribution system is completely recovered, the quantified value of the total resilience of the target power distribution system is determined to be 100%;

[0014] In the case that the recovery situation represents that the target power distribution system has been degraded to the extent that it cannot alleviate power outages, the quantified value of the total resilience of the target power distribution system is determined to be 0.

[0015] In one embodiment, the quantification of the critical load recovery situation of the target power distribution system under the network reconstruction technology by a second index to determine a target fault recovery strategy after the attenuation ends includes:

[0016] Obtain the quantified value corresponding to the second index; the quantified value corresponding to the second index is used to represent the critical load outage rate of the target power distribution system;

[0017] In the case that the quantified value is equal to 0, it is determined that the critical load recovery situation is that all critical loads will be recovered by the network reconstruction technology;

[0018] In a case that the quantized value is greater than 0 and less than or equal to 1, it is determined that the critical load recovery condition is that there is a critical load that is not recovered by the network reconfiguration technology.

[0019] In one of the embodiments, the method further comprises:

[0020] quantifying, by an expected load quantization index, an expected load provided by the target fault recovery strategy;

[0021] quantifying, by a fourth index, a pre-recovery time of the target fault recovery strategy after the target fault recovery strategy is determined;

[0022] quantifying, by a fifth index, a load served by the target fault recovery strategy during recovery after the target fault recovery strategy is started;

[0023] quantifying, by a sixth index, a time required for the target fault recovery strategy to complete service recovery after a repair process of the target fault recovery strategy is completed.

[0024] In one of the embodiments, the method further comprises:

[0025] establishing an optimization model of the target fault recovery strategy; the optimization model takes maximizing the resilience of the target power distribution system as an objective function;

[0026] The expression of the objective function is:

[0027] ;

[0028] wherein, the resilience of the target power distribution system; weighted by the critical load; the total amount of load stored in the bus S at time t; a set of bus nodes; used to define the load factor at the bus S.

[0029] In one of the embodiments, the constraint condition of the network reconfiguration technology is:

[0030] ;

[0031] wherein, a set of bus branches; is a set of virtual bus branches; A is artificial power flow; K is a virtual power station; M is a sampling number; P is active power; Q is reactive power; R is resistance; X is reactance; x represents a branch connection state, and x is 0 or 1; d is 0 or 1, and d is 0 indicates that a node is restored, and d is 1 indicates that a node is not restored; C is current carrying capacity; V is node potential; I is node current; f is frequency; subscript s is node S; and t is t time; , is a virtual bus branch; sr is an energy-consuming bus branch; ps is an energy-supplying bus branch; superscript g is a newly generated quantity; loss is a lost quantity; d is a decay quantity; and MEG is a quantity provided by a mobile emergency generator.

[0032] In a second aspect, the present application further provides a device for managing power distribution system failure in an intelligent power grid by using an improved resilience measurement index, comprising:

[0033] A generating module is configured to generate a failure scenario under a disaster by time-series Monte Carlo simulation according to a grid element vulnerability curve of a target power distribution system;

[0034] A decay quantifying module is configured to quantize and record a decay state of the target power distribution system by a first index when the target power distribution system starts to decay under the failure scenario until the decay ends;

[0035] A strategy determining module is configured to quantize a key load recovery situation of the target power distribution system under a network reconstruction technology by a second index after the decay ends, so as to determine a target failure recovery strategy;

[0036] In a case where the key load recovery situation represents that all key loads in the target power distribution system are recovered by the network reconstruction technology, the target failure recovery strategy comprises the network reconstruction technology; in a case where the key load recovery situation represents that there is a key load that is not recovered by the network reconstruction technology, the target failure recovery strategy comprises the network reconstruction technology and a mobile emergency resource deployment technology; and the mobile emergency resource deployment technology is used to recover the key load that is not recovered by the network reconstruction technology.

[0037] A resilience quantifying module is configured to quantize total resilience of the target power distribution system by a third index based on a recovery situation of the target power distribution system after a repair process of the target failure recovery strategy ends.

[0038] In a third aspect, the present application further provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to realize steps of the above method.

[0039] In a fourth aspect, the present application provides a computer readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method described above.

[0040] In a fifth aspect, the present application provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of the method described above.

[0041] The method, device, computer equipment, computer readable storage medium and computer program product for managing power distribution system fault in smart grid by using improved resilience metric described above, by generating fault scenarios under disasters through time series Monte Carlo simulation according to the vulnerability curve of the grid elements of the target power distribution system; when the target power distribution system starts to decay under the fault scenarios, quantifying and recording the decay state of the target power distribution system by the first index until the decay ends; after the decay ends, quantifying the key load recovery situation of the target power distribution system under the network reconstruction technology by the second index to determine the target fault recovery strategy; wherein, in the case that the key load recovery situation represents that all key loads in the target power distribution system will be recovered by the network reconstruction technology, the target fault recovery strategy includes the network reconstruction technology; in the case that the key load recovery situation represents that there will be key loads that are not recovered by the network reconstruction technology, the target fault recovery strategy includes the network reconstruction technology and the mobile emergency resource deployment technology; the mobile emergency resource deployment technology is used to recover the key loads that are not recovered by the network reconstruction technology; after the repair process of the target fault recovery strategy ends, the total resilience of the target power distribution system is quantified based on the recovery situation of the target power distribution system by the third index.

[0042] Thus, in the fault scenario, the network reconfiguration technology is preferred to be used to restore the power distribution system, and in the key load recovery case of the power distribution system under the network reconfiguration technology, there are key loads that are not recovered by the network reconfiguration technology, and the mobile emergency resource deployment technology is considered to be used to restore the key loads that are not recovered by the network reconfiguration technology; thus, the power outage management strategy of comprehensively utilizing the network reconfiguration and the mobile emergency generator is realized, which emphasizes the influence of the quantitative index in selecting the best power outage fault management method, the introduction of the index in the strategy will help the user to deploy the mobile emergency resource only when necessary, and after the repair process of the determined target fault recovery strategy ends, the total resilience of the target power distribution system is quantified based on the recovery of the target power distribution system through the third index, the target is to maximize the resilience of the system under the consideration of the priority load, which solves the problem that the maximum fault response potential of the network reconfiguration technology and the mobile emergency generator cannot be independently analyzed in the related technology, and the influence of the quantitative index in selecting the optimal power outage management strategy is not considered, which leads to the problem that both of them are inevitably required to participate in the coordinated management in any case, and the intelligence of the fault recovery scheme for the power distribution system is effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creating labor.

[0044] Figure 1 A flowchart of a method for managing power distribution system faults in smart grid using an improved resilience metric in an embodiment;

[0045] Figure 2 A flowchart of a system-level method for power outage management of a target power distribution system in an embodiment;

[0046] Figure 3 A comparison chart of mobile emergency generator deployment before and after using the proposed target fault recovery strategy in an embodiment;

[0047] Figure 4 A flowchart of a method for managing power distribution system faults in smart grid using an improved resilience metric in another embodiment;

[0048] Figure 5 A structural block diagram of a device for managing power distribution system faults in smart grid using an improved resilience metric in an embodiment;

[0049] Figure 6 Fig. 1 is a schematic diagram of an internal structure of a computer device according to an embodiment. DETAILED DESCRIPTION

[0050] In order to make the purposes, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0051] It should be noted that the terms "first", "second", and the like in the description and claims of the present disclosure and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0052] In one embodiment, as shown in Figure 1 a method for managing power distribution system failures within a smart grid using an improved resilience metric is provided, and the present embodiment is exemplified by the method being applied to a computer device. The computer device can be a terminal, a server, or a system including a terminal and a server. In the present embodiment, the method includes the following steps:

[0053] Step S110, generating a failure scenario under a disaster according to a grid element vulnerability curve of a target power distribution system through time-series Monte Carlo simulation.

[0054] The target power distribution system can be a power distribution system that needs to be analyzed for failure management strategies.

[0055] The failure scenario can include a failure scenario that causes the target power distribution system to be out of service.

[0056] In order to facilitate the understanding of those skilled in the art, Figure 2 a flowchart of a system layer method for outage management of a target power distribution system is provided. In conjunction with Figure 2 the embodiments of the present application are explained and described.

[0057] In a specific implementation, the computer device can generate a failure scenario under a disaster according to a grid element vulnerability curve of a target power distribution system through time-series Monte Carlo simulation. Specifically, the number of failure scenarios and the duration of failure events can be generated using time-series Monte Carlo simulation.

[0058] In practical applications, a fault scenario that has a greater impact on the target power distribution network can be selected, and it is assumed that only a few fault events will occur in the target power distribution system.

[0059] At step S120, when the target power distribution system starts to decay under the fault scenario, the decay state of the target power distribution system is quantified and recorded by the first index until the decay ends.

[0060] In specific implementations, the computer device can quantify and record the decay state of the target power distribution system by the data operation rule corresponding to the first index when the target power distribution system starts to decay under the fault scenario, until the decay ends.

[0061] Specifically, in combination with Figure 2 During phase 1 after the fault, the RST d metric index at t∈[t db ,t de ] is used as the first index to quantify the actual degradation of the target power distribution system under the fault scenario, where t db represents the start time of the decay of the target power distribution system, and t de represents the end time of the decay of the target power distribution system. The first index measures the severity of the decay and the interruption rate. In this embodiment, quantitative analysis is performed every 30 minutes, and the maximum decay time Td is assumed to be 8 hours. The proposed first index will continue to quantify and record the decay state until t= t de .

[0062] At step S130, after the decay ends, the key load recovery situation of the target power distribution system under the network reconfiguration technology is quantified by the second index to determine the target fault recovery strategy.

[0063] Wherein, when the key load recovery situation represents that all key loads in the target power distribution system will be recovered by the network reconfiguration technology, the target fault recovery strategy includes the network reconfiguration technology.

[0064] Wherein, when the key load recovery situation represents that there will be key loads that are not recovered by the network reconfiguration technology, the target fault recovery strategy includes the network reconfiguration technology and the mobile emergency resource deployment technology.

[0065] Wherein, the mobile emergency resource deployment technology is used to recover key loads that are not recovered by the network reconfiguration (NR) technology.

[0066] Wherein, the key load can include load devices that have high requirements for power supply reliability and will cause serious economic losses or safety risks once power failure occurs.

[0067] The key load can include a full key load and a half key load. The power supply reliability requirement of the full key load is higher than that of the half key load.

[0068] The key load can be named as a critical load in actual application, the full key load can be named as a full critical load in actual application, and the half key load can be named as a half critical load in actual application.

[0069] The mobile emergency resource deployment technology is a technology for recovering power failure by deploying a mobile emergency generator (MEG).

[0070] In the specific implementation, after the attenuation ends, the computer device can quantify the key load recovery situation of the target power distribution system under the network reconstruction technology by using a data operation rule corresponding to the second index, to determine the target fault recovery strategy. When the key load recovery situation represents that all the key loads in the target power distribution system are recovered by the network reconstruction technology, the target fault recovery strategy includes the network reconstruction technology. When the key load recovery situation represents that there is a key load that is not recovered by the network reconstruction technology, the target fault recovery strategy includes the network reconstruction technology and the mobile emergency resource deployment technology, and the key load that is not recovered by the network reconstruction technology is recovered by the mobile emergency resource deployment technology. In this way, a network reconstruction scheme considering the deployment of mobile emergency generators (MEGs) is provided for fault recovery after the target power distribution system fails.

[0071] Specifically, in combination with Figure 2 , the present application provides two candidate fault recovery strategies. The first strategy (NR technology) is considered to be preferentially used for supplying loads, and the second strategy for serving load interruption considers NR and MEG technology. It should be noted that the maintenance staff (RC) will only perform the optimized measures, i.e., the target fault recovery strategy finally selected.

[0072] In actual application, the first strategy (NR technology) is preferentially considered for recovering the target power distribution system. CL The index as the second index aims to quantify the critical load outage rate. CL The index is composed of three parameters, namely, the critical load outage CL o，s , the half critical load outage SCL o，s , and the initial load demand SCL T + CL T . The value can vary between 0 and 1. CL

[0073] In step S140, after the repair process of the target fault recovery strategy ends, the total resilience of the target power distribution system is quantified based on the recovery situation of the target power distribution system by using a third index.​

[0074] In specific implementation, the computer device can execute the target fault recovery strategy for the target power distribution system, and after the repair process of the target fault recovery strategy ends, the computer device can quantify the total resilience of the target power distribution system based on the recovery situation of the target power distribution system by the third index. The target is to maximize the resilience of the system while considering the priority load.

[0075] In actual application, the SR index can be used as the third index to quantify the total resilience of the target power distribution system, and the value range is 0%-100%. That is, the value range of the quantified value of the total resilience is 0-100

[0076] In this way, in the process of quantifying the total resilience of the target power distribution system based on the recovery situation of the target power distribution system by the third index, when the recovery situation represents that the target power distribution system is completely recovered, the computer device can determine that the quantified value of the total resilience of the target power distribution system is 100% (SR is 100%); when the recovery situation represents that the target power distribution system has been degraded to the extent that it cannot alleviate power outages, the computer device can determine that the quantified value of the total resilience of the target power distribution system is 0 (SR is 0%).

[0077] In the above method for managing power distribution system failures in smart grids using improved resilience measurement indexes, the failure scenario under the disaster is generated by time series Monte Carlo simulation according to the grid element vulnerability curve of the target power distribution system; when the target power distribution system starts to decay under the failure scenario, the decay state of the target power distribution system is quantified and recorded by the first index until the decay ends; after the decay ends, the key load recovery situation of the target power distribution system under the network reconstruction technology is quantified by the second index to determine the target fault recovery strategy; wherein, when the key load recovery situation represents that all key loads in the target power distribution system will be recovered by the network reconstruction technology, the target fault recovery strategy includes the network reconstruction technology; when the key load recovery situation represents that there will be key loads that are not recovered by the network reconstruction technology, the target fault recovery strategy includes the network reconstruction technology and the mobile emergency resource deployment technology; the mobile emergency resource deployment technology is used to recover the key loads that are not recovered by the network reconstruction technology; after the repair process of the target fault recovery strategy ends, the total resilience of the target power distribution system is quantified based on the recovery situation of the target power distribution system by the third index.

[0078] Thus, in the fault scenario, the network reconfiguration technology is prioritized to restore the power distribution system, and in the case of key load recovery of the power distribution system under the network reconfiguration technology, there may be key loads that are not recovered by the network reconfiguration technology, and the mobile emergency resource deployment technology is considered to be used to restore the key loads that are not recovered by the network reconfiguration technology; thus, the power outage management strategy that comprehensively utilizes the network reconfiguration and the mobile emergency generator is realized, which emphasizes the influence of the quantitative index in selecting the best power outage fault management method, and the introduction of the index in the strategy will help users to deploy mobile emergency resources only when necessary, and after the repair process of the determined target fault recovery strategy ends, the total resilience of the target power distribution system is quantified based on the recovery of the target power distribution system through the third index, which aims to maximize the resilience of the system under the consideration of priority loads, solves the problem that the maximum fault response potential of the network reconfiguration technology and the mobile emergency generator cannot be independently analyzed in the related technology, and fails to consider the influence of the quantitative index in selecting the optimal power outage management strategy, resulting in the inevitable need for both to participate in coordinated management in any case, and effectively improves the intelligence of the fault recovery scheme for the power distribution system.

[0079] In one embodiment, after the attenuation ends, the key load recovery situation of the target power distribution system under the network reconfiguration technology is quantified by the second index to determine the target fault recovery strategy, including: obtaining a quantitative value corresponding to the second index; the quantitative value corresponding to the second index is used to represent the critical load outage rate of the target power distribution system; in the case that the quantitative value is equal to 0, it is determined that all key loads will be recovered by the network reconfiguration technology; in the case that the quantitative value is greater than 0 and less than or equal to 1, it is determined that there will be key loads that are not recovered by the network reconfiguration technology.

[0080] Wherein, the second index is RS CL Index, RS CL The value can vary between 0-1.

[0081] In a specific implementation, in the process of quantifying the key load recovery situation of the target power distribution system under the network reconfiguration technology by the second index corresponding data operation rule after the attenuation ends to determine the target fault recovery strategy, the computer device can obtain a quantitative value corresponding to the second index; wherein, the quantitative value corresponding to the second index is used to represent the critical load outage rate of the target power distribution system; in the case that the quantitative value is equal to 0, it is determined that all key loads will be recovered by the network reconfiguration technology; in the case that the quantitative value is greater than 0 and less than or equal to 1, it is determined that there will be key loads that are not recovered by the network reconfiguration technology.

[0082] Further, in combination with Figure 2The first strategy (NR technology) should be given priority in restoring the target power distribution system. The metric is used to quantify the expected load served by NR technology, while RS CL The metric aims to quantify the critical load outage rate. RS CL The index consists of three parameters, namely, critical load shutdown CL. o，s Semi-critical load shutdown SCL o，s ; and initial load demand SCL T +CL T RS CL The value can vary between 0 and 1.

[0083] index and RS CL The distinction between critical and semi-critical loads in the target distribution system is crucial for early warning of utilities, ensuring sufficient resources are available to guarantee the full recovery of all critical and semi-critical loads. Based on the binary decision variable λ, an RS (Recovery System) is proposed. pr Metrics are used to select the optimal solution for performing recovery services. If all critical loads are satisfied, meaning the expected load served by the NR technology can satisfy all critical loads, then λ will equal 1; when using NR technology, RS cl =0, thus making RS pr = That is, the target fault recovery strategy at this time is the first strategy. Conversely, when there are critical loads not served by the NR method under the expected load served by the NR technology, λ will be equal to 0; therefore, 0 <RS cl ≤1, consider the second strategy with RS pr = That is, the target fault recovery strategy at this time is the second strategy. Indicators were proposed to quantify the expected load provided by the second strategy.

[0084] In some embodiments, the method further includes: quantifying the expected load provided by the target fault recovery strategy using data calculation rules corresponding to the expected load quantification index; quantifying the pre-recovery time of the target fault recovery strategy after determining the target fault recovery strategy using data calculation rules corresponding to the fourth index; quantifying the load of the target fault recovery strategy during the recovery period after starting the target fault recovery strategy using data calculation rules corresponding to the fifth index; and quantifying the time required for the target fault recovery strategy to complete service recovery after the repair process of the target fault recovery strategy is completed using data calculation rules corresponding to the sixth index.

[0085] Among them, the expected load quantification indicators include those used to quantify the expected load served by NR technology. Indicators, and the expected loads provided by the second strategy (which takes into account both NR and MEG technologies) used to quantify them. index.

[0086] Among these, computer equipment can quantify the pre-recovery time of the target power distribution system under the target fault recovery strategy after determining the target fault recovery strategy, based on the data calculation rules corresponding to the fourth indicator. Specifically, such as... Figure 2 As shown, in stage 2, T pr The fourth indicator aims to quantify the total pre-recovery time (i.e., pre-recovery time) of the target power distribution system under the target fault recovery strategy. For example... Figure 2 As shown, assuming the target power distribution system is ready to be restored, T pr Evaluation of the metrics begins after the first RC or MEG team arrives at the selected bus. Specifically, for the first strategy considering NR technology, the pre-recovery time is quantified after recording all selected RC groups; for the second strategy considering both NR and MEG technologies, the outage is eliminated using the MEG method, specifically by deploying all selected MEGs to execute the MEG method, thereby quantifying the pre-recovery time.

[0087] Among them, the computer equipment uses the data operation rules corresponding to the fifth indicator to quantify the service load of the target fault recovery strategy during the recovery period after the target fault recovery strategy is started; and uses the data operation rules corresponding to the sixth indicator to quantify the time required for the target fault recovery strategy to complete service recovery after the repair process of the target fault recovery strategy is completed.

[0088] Specifically, in combination Figure 2 In Phase 3, after the target fault recovery strategy is initiated, The indicator, as the fifth indicator, will begin to quantify t∈[t]. rb ,t re The load on services during the recovery period can be understood as being quantified by quantifying the current recovery level. Once the affected area resumes service, the target distribution system will immediately begin quantifying the recovery status. It should be noted that the quantification process is affected by the travel distance and switching time of the MEGs. Considering that only two RC groups are responsible for making the switches, the number of connected tie lines also affects the quantification process. After the repair process is completed, the metric t is used. re -t rb The sixth indicator is used to quantify the time T required for the target fault recovery strategy to complete service restoration. rIt is noted that the time for manual switching of tie lines is assumed to be 30 minutes. The SR index corresponds to a data operation rule that quantifies the total resilience of the system, with a value range of 0%-100%. If the system has been completely restored, the SR is 100%; if the system has been degraded to the extent that it cannot alleviate power outages, the SR is 0%.

[0089] Thus, the first index, the second index, the third index, the expected load quantification index, the fourth index, the fifth index, the sixth index, and various other measurement indexes mentioned in the above embodiments can cover all operating stages after a fault of the target power distribution system to comprehensively depict the resilience of the target power distribution system in the operating stage. The various indexes mentioned above can be used as a set of improved resilience measurement indexes for considering network reconstruction schemes of mobile emergency generators (MEGs) deployment, and are suitable for constructing a real-time monitoring system covering all operating stages during fault recovery of a power distribution network, so that users will benefit from the all-around monitoring system constructed based on these indexes. In addition, these indexes can be combined with the proposed network reconstruction strategy to determine a suitable automated solution as a target fault recovery strategy, and select an optimal solution for power outage management in a smart grid system.

[0090] In some embodiments, the method further comprises: establishing an optimization model of the target fault recovery strategy; the optimization model takes maximizing the resilience of the target power distribution system as an objective function; and an expression of the objective function is:

[0091] ;

[0092] wherein, is the resilience of the target power distribution system; with the critical load as a weight; a total amount of loads stored in the bus S at time t; is a set of bus nodes; is used to define a load factor at the bus S.

[0093] Thus, the resilience of the target power distribution system can be enhanced based on the optimization model of the target fault recovery strategy, and the resilience of the system is maximized in consideration of priority loads.

[0094] In some embodiments, the constraint condition of the network reconstruction technology is:

[0095] ;

[0096] Each row represents the following: 1: active power constraint; 2: reactive power constraint (note that when NR technology is used, the active and reactive power parameters in the equations related to MEGs are negligible); 3, 4: bus voltage magnitude constraints; 5: line apparent power magnitude; 6: emphasizes that only virtual buses will generate artificial power flow; 7: ensures artificial power flow in the circuit by monitoring the circuit's operating state; 8, 9: limits the active and reactive power generated by virtual buses; 10: specifies that at least one circuit must be connected to a load bus; 11, 12: limits the active and reactive power flow in the branch to the maximum apparent power when the branch is connected, otherwise ensures that the value is zero; 13: indicates the time of fault occurrence

[0097] wherein, is the set of bus branches; is the set of virtual bus branches; A is the artificial power flow; K is the virtual power plant; M is the number of samples; P is the active power; Q is the reactive power; R is the resistance; X is the reactance; x represents the branch connection state, which can be 0 or 1; d can be 0 or 1, where d = 0 indicates that the node has been restored, and d = 1 indicates that the node has not been restored; C is the current carrying capacity; V is the node potential; I is the node current; f is the frequency; subscript s is the node S; t is the time t; , is the virtual bus branch; sr is the energy-consuming bus branch; ps is the energy-supplying bus branch; the superscript g is a newly generated quantity; loss is a quantity lost; d is a decaying quantity; MEG is a quantity provided by a mobile emergency generator.

[0098] In some embodiments, the constraints of the mobile emergency generator in the mobile emergency resource deployment technology include:

[0099]

[0100] wherein each row represents the following: 1: ensures that all selected MEGs on the selected bus are counted; 2: ensures that the selected MEGs for each microgrid will not be the same; 3: determines the optimal active power fed into the selected bus; 4: prohibits the MEG from selecting a capacity that has already been selected by another MEG; 5: specifies that the total amount of selected MEGs must fall within the allowed range; 6: limits the size of the reactive power fed into the bus from the selected MEGs; 7: determines the active power injected into the selected bus based on the power capacity of the available MEGs.

[0101] wherein G represents a partial MEG quantity, Y represents a total MEG quantity, Perc represents a percentage coefficient, represents the rated capacity, represents the set of selectable MEGs, and dg1 represents the MEG selected by a single microgrid.

[0102] Thus, the power classification scheme related to the MEG scale in the above constraint helps to limit the number of deployed MEGs by utilizing the full potential of each MEG, thereby determining the optimal MEG number and capacity configuration, and restoring the disconnected load through the development of several microgrids.

[0103] In order to facilitate the understanding of those skilled in the art, Figure 3 A comparison chart of the deployment of mobile emergency generators before and after the adoption of the target fault recovery strategy is provided. It can be seen that after the adoption of the target fault recovery strategy proposed in the present application, the mobile emergency generators can serve more nodes, thereby saving the number of mobile emergency generators deployed.

[0104] In another embodiment, as Figure 4 shown, a method for managing power distribution system failures within a smart grid using an improved resilience metric is provided. The method is described by way of example with reference to a computer device, and includes the following steps:

[0105] Step S402, according to the vulnerability curve of the grid elements of the target power distribution system, generate the failure scenario under the disaster through time series Monte Carlo simulation.

[0106] Step S404, when the target power distribution system starts to decay under the failure scenario, quantify and record the decay state of the target power distribution system through the first index until the decay ends.

[0107] Step S406, after the decay ends, quantify the key load recovery of the target power distribution system under the network reconstruction technology through the second index to determine the target fault recovery strategy.

[0108] Step S408, quantify the expected load provided by the target fault recovery strategy through the expected load quantification index.

[0109] Step S410, after the target fault recovery strategy is determined, quantify the pre-recovery time of the target fault recovery strategy through the fourth index.

[0110] Step S412, after the target fault recovery strategy is started, quantify the amount of load served by the target fault recovery strategy during the recovery period through the fifth index.

[0111] Step S414, after the repair process of the target fault recovery strategy ends, quantify the time required for the target fault recovery strategy to complete the service recovery through the sixth index.

[0112] Step S416, after the repair process of the target fault recovery strategy ends, quantify the total resilience of the target power distribution system based on the recovery of the target power distribution system through the third index.

[0113] It is to be noted that the specific definitions of the above steps can refer to the specific definitions of the method for managing power distribution system faults in smart grid by using improved resilience metric index as described above.

[0114] It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts related to the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least some of the other steps or steps or stages in other steps.

[0115] Based on the same inventive concept, the embodiments of the present application also provide a device for implementing the above-mentioned method for managing power distribution system faults in smart grid by using improved resilience metric index. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific definitions of one or more device embodiments for managing power distribution system faults in smart grid by using improved resilience metric index provided below can refer to the definitions of the method for managing power distribution system faults in smart grid by using improved resilience metric index described above, which will not be repeated here.

[0116] In one exemplary embodiment, as shown in Figure 5 a device for managing power distribution system faults in smart grid by using improved resilience metric index is provided, comprising: a generation module 510, a decay quantification module 520, a strategy determination module 530, and a resilience quantification module 540, wherein:

[0117] The generation module 510 is configured to generate a failure scenario under a disaster according to a grid element vulnerability curve of a target power distribution system through a time series Monte Carlo simulation.

[0118] The decay quantification module 520 is configured to, when the target power distribution system starts to decay under the failure scenario, quantize and record the decay state of the target power distribution system by a first index until the decay ends.

[0119] The strategy determination module 530 is configured to, after the decay ends, quantize the key load recovery situation of the target power distribution system under a network reconstruction technology by a second index to determine a target failure recovery strategy.

[0120] wherein, in a case that the critical load recovery condition represents a case that all critical loads in the target power distribution system are recovered by the network reconfiguration technology, the target fault recovery strategy comprises the network reconfiguration technology; in a case that the critical load recovery condition represents a case that there is a critical load that is not recovered by the network reconfiguration technology, the target fault recovery strategy comprises the network reconfiguration technology and a mobile emergency resource deployment technology; the mobile emergency resource deployment technology is used to recover the critical load that is not recovered by the network reconfiguration technology;

[0121] a resilience quantification module 540, configured to quantify total resilience of the target power distribution system based on a recovery condition of the target power distribution system by a third index after a repair process of the target fault recovery strategy ends.

[0122] In one of the embodiments, the quantification value of the total resilience ranges from 0 to 100%; the resilience quantification module 540 is specifically configured to determine that the quantification value of the total resilience of the target power distribution system is 100% in a case that the recovery condition represents a case that the target power distribution system is completely recovered; and determine that the quantification value of the total resilience of the target power distribution system is 0 in a case that the recovery condition represents a case that the target power distribution system has been degraded to a degree that cannot alleviate power outage.

[0123] In one of the embodiments, the strategy determination module 530 is specifically configured to obtain a quantification value corresponding to the second index; the quantification value corresponding to the second index is used to represent a critical load outage rate of the target power distribution system; determine that the critical load recovery condition is that all critical loads are recovered by the network reconfiguration technology in a case that the quantification value is equal to 0; and determine that the critical load recovery condition is that there is a critical load that is not recovered by the network reconfiguration technology in a case that the quantification value is greater than 0 and less than or equal to 1.

[0124] In one of the embodiments, the apparatus further comprises a quantification module configured to quantify an expected load provided by the target fault recovery strategy by an expected load quantification index; quantify a pre-recovery time of the target fault recovery strategy after the target fault recovery strategy is determined by a fourth index; quantify a load served by the target fault recovery strategy during recovery after the target fault recovery strategy is started by a fifth index; and quantify a time required for the target fault recovery strategy to complete service recovery after a repair process of the target fault recovery strategy ends by a sixth index.

[0125] In one of the embodiments, the apparatus further comprises a model establishment module configured to establish an optimization model of the target fault recovery strategy; the optimization model takes maximizing resilience of the target power distribution system as an objective function; and an expression of the objective function is: ; wherein, is the resilience of the target power distribution system; S R weighted by the critical load; total load stored in bus S at time t; is the set of bus nodes; is used to define the load factor at bus S.

[0126] In one embodiment, the constraints of the network reconfiguration technique are:

[0127] ;

[0128] wherein, is the set of bus branches; is the set of virtual bus branches; A is the artificial power flow; K is the virtual power station; M is the number of samples; P is the active power; Q is the reactive power; R is the resistance; X is the reactance; x represents the branch connection state, and x takes the value of 0 or 1; d takes the value of 0 or 1, and d takes the value of 0 indicates that the node has been restored, and d takes the value of 1 indicates that the node has not been restored; C is the current carrying capacity; V is the node potential; I is the node current; f is the frequency; subscript s is the node S; t is the time t; , is the virtual bus branch; sr is the energy-consuming bus branch; ps is the energy-supplying bus branch; superscript g is a newly generated quantity; loss is a lost quantity; d is a decay quantity; MEG is a quantity provided by a mobile emergency generator.

[0129] The above-mentioned various modules in the device for managing power distribution system faults in an intelligent power grid by using an improved resilience measurement index can be all or partially implemented by software, hardware, or a combination thereof. The above-mentioned various modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned various modules.

[0130] In one exemplary embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 6As shown in the figure. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the data operation rule data corresponding to the index. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor to realize a method for managing the internal power distribution system fault of the smart grid by using the improved resilience measurement index.

[0131] Those skilled in the art can understand that, Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0132] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the steps in each of the method embodiments described above.

[0133] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by the processor to realize the steps in each of the method embodiments described above.

[0134] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by the processor to realize the steps in each of the method embodiments described above.

[0135] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0136] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0137] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0138] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for managing faults in the power distribution system within a smart grid using an improved resilience metric, characterized in that, The method includes: Based on the vulnerability curves of the power grid components of the target power distribution system, fault scenarios under disasters are generated through time-series Monte Carlo simulation. When the target power distribution system begins to degrade under the fault scenario, the degradation state of the target power distribution system is quantified and recorded by a first indicator until the degradation ends; the first indicator is a degradation measurement indicator. After the attenuation ends, the critical load recovery status of the target power distribution system under network reconfiguration technology is quantified by a second indicator to determine the target fault recovery strategy; the second indicator is the critical load outage rate indicator. Wherein, if the critical load recovery status indicates that all critical loads in the target power distribution system will be recovered by the network reconfiguration technology, then the target fault recovery strategy includes the network reconfiguration technology; if the critical load recovery status indicates that there are critical loads that have not been recovered by the network reconfiguration technology, then the target fault recovery strategy includes the network reconfiguration technology and the mobile emergency resource deployment technology; the mobile emergency resource deployment technology is used to recover critical loads that have not been recovered by the network reconfiguration technology. After the repair process of the target fault recovery strategy is completed, the overall resilience of the target power distribution system is quantified based on the recovery status of the target power distribution system using a third indicator.

2. The method according to claim 1, characterized in that, The quantified value of the total toughness ranges from 0 to 100%; The third indicator, based on the recovery status of the target power distribution system, quantifies the overall resilience of the target power distribution system, including: When the recovery status characterizes the target power distribution system as fully recovered, the total resilience of the target power distribution system is determined to be quantified as 100%. When the recovery status indicates that the target power distribution system has degraded to the point where it can no longer alleviate the power outage, the total resilience of the target power distribution system is determined to be 0.

3. The method according to claim 1, characterized in that, After the attenuation ends, the recovery status of key loads in the target power distribution system under network reconfiguration technology is quantified using a second indicator to determine the target fault recovery strategy, including: Obtain the quantized value corresponding to the second indicator; the quantized value corresponding to the second indicator is used to characterize the critical load outage rate of the target power distribution system. When the quantization value is equal to 0, the critical load recovery status is determined to be that all critical loads will be recovered by the network reconstruction technology; If the quantization value is greater than 0 and less than or equal to 1, the critical load recovery status is determined to be that there are critical loads that have not been recovered by the network reconstruction technology.

4. The method according to claim 1, characterized in that, The method further includes: The expected load provided by the target fault recovery strategy is quantified by the expected load quantification index. The fourth indicator quantifies the pre-recovery time of the target fault recovery strategy after it has been determined. The fifth indicator quantifies the load on the target fault recovery strategy during the recovery period after the target fault recovery strategy is initiated. The sixth metric quantifies the time required for the target fault recovery strategy to complete service restoration after the repair process of the target fault recovery strategy is completed.

5. The method according to claim 1, characterized in that, The method further includes: An optimization model for the target fault recovery strategy is established; the optimization model takes maximizing the resilience of the target power distribution system as its objective function. The expression for the objective function is: ； in, This refers to the resilience of the target power distribution system; Time-lapse storage on bus node Total load in the system; The set of bus nodes; Used to define bus nodes Load factor at the location.

6. The method according to claim 5, characterized in that, The constraints of the network reconstruction technology are: ； Wherein, formula (1) represents active power constraint; formula (2) represents reactive power constraint; formulas (3) and (4) represent voltage amplitude constraints of each bus; formula (5) represents the apparent power amplitude of the line; formula (6) represents that only the virtual bus generates artificial power flow; formula (7) represents that the artificial power flow in the circuit is ensured by monitoring the operating status of the circuit; formulas (8) and (9) represent limiting the active and reactive power generated by the virtual bus; formula (10) represents that at least one circuit must be connected to a load bus; formulas (11) and (12) represent limiting the active and reactive power flow in the branch to not exceed the maximum apparent power when the branch is connected; formula (13) represents the time of fault occurrence; in, It is the set of busbar branches; A is the set of virtual bus branches; A is the artificial power flow; K is the virtual power station; M is the number of samples; P is the active power; Q is the reactive power; R is the resistance; X is the reactance; x represents the branch connection state, and the value of x is 0 or 1. express Time bus node The recovery status is represented by D, which takes the value 0 or 1. A value of D of 0 indicates that the node has recovered, and a value of D of 1 indicates that the node has not recovered; C is the current carrying capacity; V is the node potential; I is the node current; and f is the frequency. , sr is the virtual bus branch; sr is the energy-consuming bus branch; ps is the energy-supplying bus branch; g in the superscript is the newly generated quantity; loss is the quantity lost; d is the quantity attenuated; MEG is the quantity provided by the mobile emergency generator.

7. An apparatus for managing faults in the power distribution system within a smart grid using an improved resilience metric, characterized in that, The device includes: The generation module is used to generate fault scenarios under disasters through time-series Monte Carlo simulation based on the vulnerability curves of the power grid components of the target power distribution system. The attenuation quantization module is used to quantify and record the attenuation state of the target power distribution system by a first indicator when the target power distribution system begins to attenuate under the fault scenario, until the attenuation ends; the first indicator is an attenuation measurement indicator. The strategy determination module is used to quantify the critical load recovery status of the target power distribution system under network reconfiguration technology after the attenuation ends, so as to determine the target fault recovery strategy; the second indicator is the critical load outage rate indicator. Wherein, if the critical load recovery status indicates that all critical loads in the target power distribution system will be recovered by the network reconfiguration technology, then the target fault recovery strategy includes the network reconfiguration technology; if the critical load recovery status indicates that there are critical loads that have not been recovered by the network reconfiguration technology, then the target fault recovery strategy includes the network reconfiguration technology and the mobile emergency resource deployment technology; the mobile emergency resource deployment technology is used to recover critical loads that have not been recovered by the network reconfiguration technology. The resilience quantification module is used to quantify the overall resilience of the target power distribution system based on the recovery status of the target power distribution system by using a third indicator after the repair process of the target fault recovery strategy is completed.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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