AU2021335236A1 - Comprehensive resilience evaluation method and system for distribution network - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
Abstract
The present disclosure relates to a comprehensive resilience evaluation method and system for a
distribution network. The method includes: obtaining a parameter of a distribution network, and
performing evaluation based on a preset comprehensive resilience evaluation system of the
distribution network, where the comprehensive resilience evaluation system of the distribution
network includes first-level evaluation indexes and second-level evaluation indexes, each
first-level evaluation index is provided with a corresponding second-level evaluation index, and
the first-level evaluation indexes include a perception capability index, an disturbance response
capability index, a defense capability index, a restoration capability index, a collaboration
capability index, and a learning capability index; and performing comprehensive calculation
based on an evaluation result of each second-level evaluation index, a weight of each
second-level evaluation index, and a weight of each first-level evaluation index to obtain a
comprehensive resilience evaluation result of the distribution network. Compared with the prior
art, the present disclosure focuses on situation perception, disturbance coping, and
self-improvement capabilities of the distribution network based on six categories of key features
of a resilient power grid, such that a more all-round and refined comprehensive evaluation
system can be established based on a resilience demand, to improve accuracy and reliability of
an evaluation result.
Start
Construct a comprehensive resilience si
evaluation index system of the distribution
network
Calculate the value of each micro index of S2
the evaluation scheme
Calculate the Calculate the S3
subjective weight objective weight
Obtain the final weight through S4
comprehensive weight optimization
Obtain the final evaluation result based on S5
the index weight and the index value
End
Description
TECHNICAL FIELD 0001] The present disclosure relates to the technical field of resilience evaluation of a distribution network, and in particular, to a comprehensive resilience evaluation method and system for a distribution network.
BACKGROUND 0002] At present, the gradual depletion of traditional fossil energy and the increasingly severe global environmental situation make it urgent to vigorously develop renewable energy, including wind power and photovoltaic energy. There is an urgent need to build a stronger and smarter power system, especially to improve its capability of coping a low-probability and high-risk event. Therefore, the concept of "resilient power grid" comes into being. 0003] For the concept of "resilient power grid", some scholars have conducted preliminary research on relevant theories, methods, and key technologies, and Chinese scholars have also creatively put forward the connotation and concept of the resilient power grid based on the current development status and features of China's power system, including six key features of the resilient power grid: "perception capability", "disturbance response capability", "defense capability" "restoration capability", "collaboration capability", and "learning capability". The existing research on resilience evaluation of a power grid mainly focuses on features of resilience in a narrow sense, namely, disturbance response, defense, and restoration capabilities of the system before, during, and after an accident. The document "Evaluating Resilience of Island Integrated Energy Systems with Earthquake" (Li Xue, Sun Tingkai, Hou Kai, Jiang Tao, Chen Houhe, Li Guoqing, and Jia Hongjie, Proceedings of the CSEE, 2020, 40(17):5476-5493) puts forward robustness, rapidity and redundancy to evaluate an island integrated energy system. The document "Modeling on Shaping and Assessment of System Safety Resilience" (Huang Lang, Wu Chao, and Wang Bing, Journal of Safety Science and Technology, 2016, 12 (12): 15-21) constructs a quantitative safety resilience evaluation model from a system constituent element, a correlation relationship, and a resilience function. The document "Method for Assessing and Constructing for Power System Resilience under Emergency Events" (Xiao Zhiwen, Wang Guoqing, Zhu Jianming, et al., Systems Engineering-Theory & Practice, 2019, 39 (10): 26372645) quantitatively evaluates a resilience capability of the power grid in a whole accident cycle by using a performance curve. However, the evaluation research considering the three generalized resilience features, namely, the perception capability, the collaboration capability, and the learning capability, is relatively limited. At present, there are only some discussions on the perception capability, but most of them focus on evaluating specific operation performance of the power grid with reference to a situation perception technology, and evaluation on self-realization effects of the generalized features under a resilience demand is still in a preliminary development stage. 0004] As one of main next-stage development objectives of the power system, the resilient power grid is huge, complex, and highly coupled with other systems in a resilient city, which makes it more difficult to accurately evaluate the resilience capability of the power grid. In order to quickly and accurately grasp a current resilience capability of a distribution network and provide reliable data support for making a subsequent operation decision, it is of great research significance to analyze a distribution network resilience evaluation method and establish a comprehensive evaluation system.
SUMMARY 0005] The present disclosure is intended to provide a comprehensive resilience evaluation method and system for a distribution network to overcome the above defects in the prior art: the evaluation research considering three generalized resilience features, namely, a perception capability, a collaboration capability, and a learning capability, is relatively limited, there are only some discussions on the perception capability, but most of them focus on evaluating specific operation performance of a power grid with reference to a situation perception technology, and evaluation on self-realization effects of the generalized features under a resilience demand is still in a preliminary development stage. 0006] The objective of the present disclosure can be achieved by the following technical solutions: 00071 A comprehensive resilience evaluation method for a distribution network includes the following steps: 0008] obtaining parameters of a distribution network, and performing evaluation based on a preset comprehensive resilience evaluation system of the distribution network, where the comprehensive resilience evaluation system of the distribution network includes first-level evaluation indexes and second-level evaluation indexes, and each first-level evaluation index is provided with a corresponding second-level evaluation index; 0009] the first-level evaluation indexes of the comprehensive resilience evaluation system of the distribution network include a perception capability index, a disturbance response capability index, a defense capability index, a restoration capability index, a collaboration capability index, and a learning capability index;
0010] a second-level evaluation index corresponding to the perception capability index includes one or more of coverage of a smart ammeter, observability of a weak node, power grid measurement redundancy, average transmission delay time, situation visibility, and an operation index of a distribution automation system; 0011] a second-level evaluation index corresponding to the collaboration capability index includes one or more of a distribution line tie rate, a proportion of a flexible load that can be coordinated, a distribution network transfer rate, and an accommodation rate of local clean energy; and 0012] a second-level evaluation index corresponding to the learning capability index includes one or more of an error expectation between situation prediction data and actual data and a proportion of a vulnerability that can be fixed by a perception system after a disaster; and 0013] performing comprehensive calculation based on an evaluation result of each second-level evaluation index, a preset weight of each second-level evaluation index, and a preset weight of each first-level evaluation index to obtain a comprehensive resilience evaluation result of the distribution network. 0014] Further, in the second-level evaluation index corresponding to the perception capability
index, the coverage A, of the smart ammeter is calculated according to the following formula:
0015] n.
0016] where ns represents a quantity of smart ammeters in a region of a power grid, and
mn represents a total quantity of ammeters in the region of the power grid;
00171 the observability A 2 of the weak node is calculated according to the following formula:
A2 = n,~ 0018] n,
0019] where nwm represents a quantity of observable weak nodes, and nw represents a total
quantity of weak nodes; and
0020] the power grid measurement redundancy A3 is calculated according to the following
formula: n A3 = PM
00211 n,
0022] where n'"n represents a quantity of observable nodes, and n'P represents a total quantity of nodes of the power grid. 0023] Further, in the second-level evaluation index corresponding to the perception capability index, the average transmission delay time A is calculated according to the following formula:
A4 0024]
0025] wheretu represents time at which a collection quantity of an ammeter i is measured,
tai represents time at which measured data of the ammeter I is updated to a database, and n
represents a total quantity of ammeters in a region of a power grid;
0026] the situation visibility A is calculated according to the following formula:
A 5= - (N,-N) 2 00271 n
0028] where n represents a quantity of blocks obtained by dividing a situation map, N
represents a quantity of nodes in a block , and N represents an arithmetic average value of
N; and
0029] the operation index A of the distribution automation system is calculated according to
the following formula:
0030 A 6=aa x P ,+a, x P +a, x P +a, xJP
0031] where "or represents an average online rate of a distribution automation terminal,
represents a remote control success rate, re represents an accuracy rate of a remote signaling
action, Pc represents a feeder automation success rate, a"o, a_, a, and a* represent
a +a +a +a =1 weights of the corresponding indexes respectively, and "°r " r a .
0032] Further, in the second-level evaluation index corresponding to the collaboration
capability index, the distribution line tie rate E, is calculated according to the following
formula:
E,(a XI +al,L X l)X100% 0033 n nl,L
00341 where nl,H represents a total quantity of 35 kV to 110 kV high-voltage lines in a region,
tl,H represents a quantity of 35 kV to 110 kV high-voltage tie lines in the region, nl,
represents a total quantity of 10 (20) kV low-voltage lines in the region, ntL represents a
quantity of 10 (20) kV low-voltage tie lines in the region, al,H and alL represent weights of a
high-voltage line index and a low-voltage line index respectively, and aH+aI,L=1;and
0035] the distribution network transfer rate E2 is calculated according to the following
formula:
E2= n''-x 100% 0036] n,
00371 where n'," represents a quantity of lines that can be transferred in the distribution
network, and n" represents a total quantity of lines.
0038] Further, in the second-level evaluation index corresponding to the collaboration
capability index, the proportion E3 of the flexible load that can be coordinated is calculated
according to the following formula:
E,= SFL x1OO% 0039] SoLmax
0040] where SFL represents a peak value of the flexible load that can be coordinated, and
SLm represents maximum annual load provided by the network; and
0041] the accommodation rate E4 of the local clean energy is calculated according to the
following formula:
E4 = " +0 x100% 00421 P Po
P. P 0043] where oi represents a net injected electricity quantity outside the region,
represents an agreed electricity quantity outside the region, co represents an on-grid electricity
quantity of the local clean energy, and f represents a generating capacity of the local clean
energy.
00441 Further, in the second-level evaluation index corresponding to the learning capability
index, the error expectation F between the situation prediction data and the actual data is
calculated according to the following formula: 1T 1 No 1 Fl:- I[ ' Z(,(t)-h(x,.,j))2 2
0045] T =1 N
00461 where T represents total measurement duration, Nz, represents a total quantity of
measurements predicted by the perception system at time t, t) represents an estimated value
that is of an * measurement and obtained by the perception system at the time t, and h(x)
represents a true value of a system status corresponding to the j th measurement at the time t;
and F 0047] the proportion 2 of the vulnerability that can be fixed by the perception system after
the disaster is calculated according to the following formula:
F2=nv x100% 0048] n
0049] where nvf represents a total quantity of vulnerabilities discovered by the perception
system after the disaster, and nvr represents a quantity of vulnerabilities that can be fixed by the
perception system after the disaster. 00501 Further, a second-level evaluation index corresponding to the disturbance response capability index includes one or more of a voltage transient rate, a power flow limit exceeding rate, a voltage harmonic distortion rate, a frequency deviation rate, a distribution demand rate, an N-i verification pass rate, an active power reservation rate, and topology integrity.
0051] Further, the voltage transient rate B1 is calculated according to the following formulas:
B1 = (t)dt /(T xn,) 0052]
Iv,.- Vi(t1)]/ i V (t) < V,
V''t)= 0 Vim>!Vi(t) > V,i
00531 1[V(t)- Vm]mV, V(t)> Vi,
0054] where n represents a quantity of voltage transients, J'(t) represents a voltage
V transient value of a node i at current transient time, ''ax represents an upper limit of a
transient voltage of the node , represents a lower limit of the transient voltage of the
node i , (t) represents a voltage of the node at the current transient time, and T
represents a statistical cycle;
0055] the power flow limit exceeding rate B 2 is calculated according to the following
formulas: B2 =YJ S,"-(t)dt / (T x n,) 00561 1=0
im (00 Si(t) Smax
00571 (t)- S max]/Smax S,(t)>Smax
0058] where S?"(t) represents a quantity of power flows that are of a branch i and exceed a
limit at time t , Si,max represents an upper rated power flow limit of the branch i, st)
represents a power flow size of the branch i at the time t , and nI represents a total quantity
of branches of a power grid;
0059] the voltage harmonic distortion rate B3 is calculated according to the following
formula:
B3 =max( V 2 I/VlI) 0060] i" k-2
V V 0061] where l' represents an effective value of a fundamental voltage of the node ,
represents an effective value of a kth harmonic voltage of the node i, and n " represents a total
quantity of nodes; and
0062] the frequency deviation rate B4 is calculated according to the following formula:
- fN B4 B=if 0063] A,
0064] where f represents a current system frequency, N represents a rated frequency, and
4fh represents afrequency deviation limit.
0065] Further, the distribution demand rate B 5 is calculated according to the following formula: B5 =-"x100% 00661 P 00671 where a represents total actual power consumption of users in a resilient power grid, and N represents a total rated frequency of the users in the resilient power grid;
0068] the N-1 verification pass rate B6 is calculated according to the following formula:
B 6 =a, x nt(N-1) X % , X n(N-1) X 100% 0069] nt n,
00701 where nt represents a total quantity of substations in the power grid, nt(N-1) represents
a quantity of substations passing N-1 verification, n, represents a total quantity of lines in the
power grid, nl(N-1) represents a quantity of lines passing N-1 verification, at and a,
represent weights of a substation index and aline index respectively, and a,+a,=1
00711 the active power reservation rate B 7 is calculated according to the following formula: P
0072] F.i
0073] where r represents a reserved capacity of active power of the power grid, and a.im
represents a limit of the reserved capacity of the active power of the power grid; and
0074] the topology integrity B8 is calculated according to the following formula:
B, = f s,(t)dt/(Txn,) 00751 1_1
0076] where s(t) represents an operation status of a line i at time, s(t)=1when the
line operates normally, s(t)=Owhen the line stops operating, T represents a statistical
cycle, and n, represents the total quantity of lines.
00771 Further, a second-level evaluation index corresponding to the defense capability index includes one or more of a performance index, a distribution network capacity-load ratio, and a power grid fault rate.
0078] Further, a calculation expression of a performance index C of the fuel cell system is as
follows:
Cti P Po 00791 to ,
, 00801 where o represents an original capacity of a power grid before the disaster, d
represents a capacity of the power grid after an active defense measure is taken, lO' represents
a capacity of the power grid when performance of the power grid decreases to a lowest level,
tio' represents time at which the performance of the power grid decreases to the lowest level,
and td represents time at which the active defense measure is taken;
0081] the distribution network capacity-load ratio C 2 is calculated according to the following
formula:
C2 ST 0082] SLmax
0083] where ST represents a total capacity of transformation devices of the distribution
network, and SLmax represents maximum annual load provided by the network; and
0084] the power grid fault rate C3 is calculated according to the following formula:
C 3=1- (1-p 0085] i=1
0086] where n'e represents a total quantity of power devices in the power grid, and J"
represents a fault probability of a device i .
00871 Further, a second-level evaluation index corresponding to the restoration capability index includes one or more of average outage time of users, a fault self-healing rate, a black-start success rate, and average scheduled system outage time.
0088] Further, the average outage time D, of the users is calculated according to the following
formula:
009]DI= tc,~x n,,,,j)/n, 00891
0090] where t represents outage duration in an jth fault, nucut,i represents a quantity of
users experiencing an outage in the j th fault, and nu represents a total quantity of users of a
power grid;
0091] the fault self-healing rate D 2 is calculated according to the following formula:
n,hi D2= X100%
0092]
0093] where nush,i represents a quantity of self-healed users in the jth fault, and
represents a total quantity of users affected by the j th fault; and
0094] the black-start success rate D3 is calculated according to the following formula:
D3 =x 100% 0095]
0096] where n represents a quantity of users for which power supply is resumed through
black start in the jth fault, and ntjt,,,' represents the total quantity of users affected by the jth
fault.
00971 Further, the average scheduled system outage time D4 is calculated according to the
following formula: t ,' x n,,,i
0098] n,,
0099] where trc,' represents time of an jth scheduled outage, n''c i represents a quantity of
users experiencing the jth scheduled outage, and n, represents a total quantity of users of a
power grid. 0100] Further, weight setting processes of the second-level evaluation index and the first-level evaluation index each include the following steps: 0101] subjective weighting: weighting each index subjectively; 0102] objective weighting: weighting each index objectively; and 0103] comprehensive weight optimization: obtaining a weight vector obtained by using each weighting method in the subjective weighting and the objective weighting, and calculating an overall weight of a set of each weighting method according to the following formula:
d,=1 H(u,,,)M 0104] n- l _I(UM )M
0105] where M represents a total quantity of weighting methods, u, =(um1 I,urn2 ,..., UmN)
represents a weight vector of an mthweighting method, u- represents a weight that is of an n
th index and obtained by using the m th weighting method, N represents a total quantity of
indexes, and d, represents the weight of the set;
0106] calculating relative entropy between a result of each weighting method and the weight of the set according to the following formula: h(u,d)= u, In
0108] where h(ud) represents relative entropy between the weight vector of the M th
weighting method and the weight of the set; 0109] calculating a preference coefficient of each weighting method based on the relative entropy and the following formula:
h(um,d)
0111] where a- represents a preference coefficient of the m weighting method; and
0112] calculating a comprehensive index weight coefficient of each index according to the preference coefficient and the following formula:
0113] W" amu,
0114] where represents a comprehensive index weight coefficient of the n th index.
0115] Further, the subjective weighting includes: weighting each index subjectively by using a binomial coefficient method, where the binomial coefficient method includes the following steps: 0116] performing, by M experts, pairwise comparison on a total of N evaluation indexes .0 to independently obtain a importance ranking rn of an index set, and taking an average
ranking value of each expert to obtain an average importance ranking of the nth index, where the average importance ranking of the nth index is calculated according to the following formula: M
YO, (n)
O(xM)='" n=1,2,...,N 01171
0118] where O(xn) represents the average importance ranking of the n th index, and 0, (n) represents an importance ranking obtained by anmt expert for the thindX;
0119] re-ranking the N evaluation indexes in ascending order based on the average importance ranking to obtain a new index sequence:
FX 1 ,X 2 ,.. I XN
0120] | s.t. O(X,) <O(Xi) i < j
0121] where l'X2'- X - xN represents ranked evaluation indexes;
0122] performing symmetrical ranking on the index set
0123] N ""I 2JIJ39"."'XN-1 ;ad 0124] re-numbering each index based on a symmetrically-ranked index set, denoting a number as i,and calculating a subjective weight of each index according to the following formula:
u.= i=1,2,...,N 0125] 2],.N C-1 0126] where u" represents a subjective weight of an index whose number is i, andCN
represents a calculation result of an index permutation and combination. 01271 Before the objective weighting, the method further includes: performing normalization processing on each index, where the normalization processing specifically includes: 0128] normalizing a benefit index according to the following formula:
f - Xnmi) /(Xn,max - Xn,min) Xn,max #Xn,min
0129 "nmax
0130] where X'H represents an actually calculated value of an n th index of an M th
to-be-selected scheme, Y'n represents a normalized value of a benefit index of the n th index of
the mth to-be-selected scheme, n,-in represents a minimum value of the n th index, nmax
represents a maximum value of the nth index, and the to-be-selected scheme is each actual index value obtained by using the index; and 0131] normalizing a cost index according to the following formula:
S (Xnmax - Xmn (Xn,max - Xn,min Xn,max Xn,min
0132 = Xnmax Xnmin
0133] where "'n represents a normalized value of a cost index of an nth index of an mth
objective weighting scheme. 0134] Further, the objective weighting includes: weighting each index objectively by using an anti-entropy weight method, where a calculation process of the anti-entropy weight method includes:
01351 calculating an anti-entropy value h of each index according to the following formulas:
0136] m"" M1 '""
0137] hM=- n1-,_
01381 where Y"2 represents a normalized value of the n th index of the m h objective
weighting scheme, and M represents the total quantity of to-be-selected schemes; and 0139] determining a weight of each index according to the anti-entropy value and the following formula:
0140] un =hn-1Zhn
0141] where u" represents a weight of the n th index, and N represents the total quantity of
indexes. 0142] Further, the objective weighting includes: weighting each index objectively by using a Criteria Importance Through Intercriteria Correlation (CRITIC) method, where a calculation process of the CRITIC method includes:
0143] calculating redundant information entropy PH of each index according to the following
formulas:
p,=l+ 0144] InM
0145] rm"" m M1 '""
0146] where " represents redundant information entropy of the nth index, Ynn represents a
normalized value of the n th index of the mth objective weighting scheme, and M represents the total quantity of to-be-selected schemes;
01471 calculating an inter-column covariance and an index variation coefficient so by using a
normalized matrix, so as to calculate an inter-index correlation coefficient according to the following formulas:
r. =n n, n = 1,2,..., N "O(ny' 0148] ss
=77 0149] n
0150] M
0151] where 5n represents a normalized value of the nth index, sn represents a variation
coefficientofthe ntindex, N represents the total quantity of indexes, nn represents
correlation coefficient of the n th index and an n*th index, "n represents a value of the n'th
index, cov(yny.) represents a covariance between an index value Yn and the index value Yn,
and Sn. represents a variation coefficient of the n*th index;
0152] evaluating an information amount contained in each index, where the information amount is calculated according to the following formula:
0153] "n=(sn+p A (1-rn)
0154] where 'n represents an information amount of the n th index;and
0155] determining a weight of the index according to the information amount and the following formula:
0156] Un =in/l inn
01571 where un represents a weight of the n th index.
0158] The present disclosure further provides a comprehensive resilience evaluation system for a distribution network, including a memory and a processor, where the memory stores a computer program, and the processor invokes the computer program to perform steps of the above method. 0159] The present disclosure has the following advantages over the prior art. 0160] (1) Based on characteristics of the resilient power grid, from six macro resilience measurement dimensions, namely, the perception capability index, the disturbance response capability index, the defense capability index, the restoration capability index, the collaboration capability index, and the learning capability index, the present disclosure constructs the comprehensive resilience evaluation index system for the distribution network, including 27 micro indexes, and especially sets second-level indexes of the perception capability index, the collaboration capability index, and the learning capability index creatively. This can realize more comprehensive evaluation on situation perception, disturbance coping, and self-improvement capabilities of the distribution network based on a resilience demand. The present disclosure focuses on situation perception, disturbance coping, and self-improvement functions of the distribution network based on six categories of key features of the resilient power grid, such that a more all-round and refined comprehensive evaluation system can be established based on the resilience demand, to improve accuracy and reliability of an evaluation result. 01611 (2) The present disclosure comprehensively weights the index system through comprehensive weight optimization based on the relative entropy in combination with the binomial coefficient method, the anti-entropy weight method, and the correlation weight method, which can combine subjective weight calculation and objective weight calculation to improve the accuracy and reliability of the evaluation result. 0162] (3) The present disclosure evaluates the perception capability index mainly by considering a hardware configuration and an operation level of the perception capability index, and a system status mastering degree. The specific perception capability indexes have the following technical effects: 0163] Coverage of the smart ammeter: As an important part of an advanced measurement system, the smart ammeter is a basic terminal device for enabling the situation perception system to measure, store, calculate the electricity quantity, and performing two-way communication with a data center. The coverage of the smart ammeter reflects a basic perception capability index of the resilient power grid. 0164] Observability of the weak node: When a high-risk event occurs, the weak node has a greater probability of degrading power performance. A degree of mastering a real-time status of the weak node can reflect a capability of the perception system of the resilient power grid in coping a potential disaster, removing a hidden danger in time, and preventing further expansion of a fault. 0165] Power grid measurement redundancy: Unlike the observability of the weak node, which mainly reflects a coping capability of the perception system of the power grid when the disaster occurs, the power grid measurement redundancy reflects whether the perception system of the resilient power grid can completely master, in a normal state, an operation status of the power grid and assist another system in making a decision, and can be used as a supplement to the observability of the weak node. 0166] Average transmission delay time: The average transmission delay time is defined as average time required to transmit operation data of the resilient power grid in the perception system and update the operation data to the database, and reflects a real-time requirement of the perception system of the resilient power grid. 01671 Situation visibility: The situation visibility reflects a visualization degree of the perception system. Convenient and clear situation information helps operation and management personnel to quickly discriminate a power event and make a corresponding decision when coping disasters and accidents. A visualization degree of a situation map is evaluated by using node distribution uniformity. Smaller node distribution uniformity leads to a more even sample data distribution and a higher visualization degree. 01681 Operation index of the distribution automation system: As a collection of a power grid data collection and monitoring system, a geographic information system, and a demand-side management system, the distribution automation system integrates power data monitoring and analysis, remote control, and other functions. Different from some system configuration indexes, the operation index of the distribution automation system mainly reflects perception results of decision execution accuracy and execution efficiency of the resilient power grid, and is a real-time dynamic index. 01691 (4) The collaboration capability index specified in the present disclosure represents a capability of the resilient power grid in reasonably and efficiently using internal and external resources and jointly concentrating on resisting a disturbance event. The related indexes of the collaboration capability index can be used to improve and reflect a capability of the distribution network in controlling internal and external defense resources, multi-dimensional system information, and the like. The specific collaboration capability indexes have the following technical effects: 01701 Distribution line tie rate: As a most intuitive and basic tie mode, a direct tie of a distribution line provides flexibility for dispatching and cooperation of physical systems inside and outside the region. In addition, more coupling elements are equipped to provide higher measurement redundancy for the distribution network. 01711 Proportion of the flexible load that can be coordinated: As an important part of an active distribution network, a flexible load is responsible for maintaining electric power and energy balance and collaborative disaster restoration. The proportion of the flexible load that can be coordinated determines a peak capability of the resilient power grid in cooperatively resisting a disturbance, and reflects a collaborative power grid management function of the flexible load. 0172] Distribution network transfer rate: Line transfer can effectively reduce load losses of the resilient power grid in the case of a local fault, give play to a greater collaborative disaster coping capability of the system, and provide greater flexibility for risk situation evaluation and decision effect simulation of the distribution network. 01731 Accommodation rate of the local clean energy: An amount of accommodated clean energy can comprehensively reflect a collaborative effect of the resilient power grid. Different from the proportion of the flexible load that can be coordinated, the accommodation rate of the local clean energy dynamically represents a collaborative electric power and energy balance capability and a flexible anti-disturbance capability inside and outside the region.
01741 (5) As common support for other features, the learning capability index specified in the present disclosure represents a capability of the resilient power grid in performing self-correction and self-improvement based on historical experience and improving innovation in combination with a new technology, and can be used to reflect a capability of the resilient distribution network in making independent error correction and continuous improvement. The specific learning capability indexes have the following technical effects: 01751 Error expectation between the situation prediction data and the actual data: An error between each piece of predicted data of the resilient power grid and corresponding actual data can be obtained based on historical operation data and recurrence and analysis of a risk situation after an accident, and the error expectation and a change trend can effectively reflect a self-correction and independent improvement capability of the distribution network. 0176] Proportion of the vulnerability that can be fixed by the perception system after the disaster: The proportion of the vulnerability that can be fixed by the perception system after the disaster directly reflects vulnerability identification, error correction, and self-learning capabilities of a resilient power grid system.
BRIEF DESCRIPTION OF THE DRAWINGS 01771 FIG. 1 is a schematic flowchart of a binomial coefficient method according to an embodiment of the present disclosure; 0178] FIG. 2 is a schematic flowchart of an anti-entropy weight method according to an embodiment of the present disclosure; 01791 FIG. 3 is a schematic diagram of a CRITIC method according to an embodiment of the present disclosure; 0180] FIG. 4. is a schematic flowchart of comprehensive weight optimization based on relative entropy according to an embodiment of the present disclosure; and 0181] FIG. 5 is a schematic flowchart of comprehensive resilience evaluation of a distribution network according to an embodiment of the present disclosure.
DETAILED DESCRIPTION 0182] In order to make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, rather than all of the embodiments of the present disclosure. Generally, components of the embodiments of the present disclosure described and shown in the accompanying drawings may be arranged and designed in various manners.
01831 Therefore, the following detailed description of the examples of the present disclosure in the accompanying drawings is not intended to limit the protection scope of the present disclosure, but merely represent selected examples of the present disclosure. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the disclosure without creative efforts shall fall within the protection scope of the present disclosure. 0184] Embodiment 1 0185] This embodiment provides a comprehensive resilience evaluation method for a distribution network, including the following steps: 0186] obtaining parameters of a distribution network, and performing evaluation based on a preset comprehensive resilience evaluation system of the distribution network, where the comprehensive resilience evaluation system of the distribution network includes first-level evaluation indexes and second-level evaluation indexes, and each-level evaluation index is provided with a corresponding second-level evaluation index; 01871 the first-level evaluation indexes of the comprehensive resilience evaluation system of the distribution network include a perception capability index, an disturbance response capability index, a defense capability index, a restoration capability index, a collaboration capability index, and a learning capability index; 0188] a second-level evaluation index corresponding to the perception capability index includes one or more of coverage of a smart ammeter, observability of a weak node, power grid measurement redundancy, average transmission delay time, situation visibility, and an operation index of a distribution automation system, where the coverage of the smart ammeter, the observability of the weak node, and the power grid measurement redundancy are used to reflect a perception capability index of an edge of the distribution network, and the average transmission delay time, the situation visibility, and the operation index of the distribution automation system are used to reflect a perception capability index of a regulation and control center of the distribution network; 01891 a second-level evaluation index corresponding to the disturbance response capability index includes one or more of a voltage transient rate, a power flow limit exceeding rate, a voltage harmonic distortion rate, a frequency deviation rate, a distribution demand rate, an N-1 verification pass rate, an active power reservation rate, and topology integrity, where the voltage transient rate, the power flow limit exceeding rate, the voltage harmonic distortion rate, the frequency deviation rate are used to reflect electrical quantity parameters and indexes during operation of the distribution network, and the distribution demand rate, the N-1 verification pass rate, the active power reservation rate, and the topology integrity are used to reflect macro planning indexes of the distribution network;
0190] a second-level evaluation index corresponding to the defense capability index includes one or more of a performance index, a distribution network capacity-load ratio, and a power grid fault rate; 0191] a second-level evaluation index corresponding to the restoration capability index includes one or more of average outage time of users, a fault self-healing rate, a black-start success rate, and average scheduled system outage time, where the average scheduled system outage time is used to reflect an early overhaul warning capability of the distribution network before an accident; and the average outage time of the users, the fault self-healing rate, and the black-start success rate are used to reflect a restoration capability index of the distribution after the accident; 0192] a second-level evaluation index corresponding to the collaboration capability index includes one or more of a distribution line tie rate, a proportion of a flexible load that can be coordinated, a distribution network transfer rate, and an accommodation rate of local clean energy, where the distribution line tie rate and the distribution network transfer rate are used to reflect a coordination capability of physical lines of the distribution network, and the proportion of the flexible load that can be coordinated and the accommodation rate of the local clean energy are used to reflect a coordination capability of flexible resources of the distribution network; and 01931 a second-level evaluation index corresponding to the learning capability index includes one or more of an error expectation between situation prediction data and actual data and a proportion of a vulnerability that can be fixed by a perception system after a disaster; and 0194] performing comprehensive calculation based on an evaluation result of each second-level evaluation index, a preset weight of each second-level evaluation index, and a preset weight of each first-level evaluation index to obtain a comprehensive resilience evaluation result of the distribution network. 0195] A quantity of second-level evaluation indexes corresponding to the perception capability index, the disturbance response capability index, the defense capability index, the restoration capability index, the collaboration capability index, and the learning capability index is not limited. The corresponding second-level evaluation indexes may be selected arbitrarily from the second-level evaluation indexes provided above, and all the corresponding second-level evaluation indexes provided in this embodiment are preferably used. An optimal implementation is described in detail below. 01961 1. Construction of the comprehensive resilience evaluation system of the distribution network 01971 Based on a relationship between key features of a resilient power grid and a selection principle of a micro evaluation index, an actual resilience capability of the distribution network is comprehensively evaluated from six dimensions, namely, the perception capability index, the disturbance response capability index, the defense capability index, the restoration capability index, the collaboration capability index, and the learning capability index. A comprehensive resilience evaluation index system of the distribution network is established, as shown in Table 1. Table 1 Comprehensive resilience evaluation index system of the distribution network First-level evaluation index Second-level evaluation index Coverage of the smart ammeter Observability of the weak node Power grid measurement redundancy Perception capability index Average transmission delay time Situation visibility Operation index of the distribution automation system Voltage transient rate Power flow limit exceeding rate Voltage harmonic distortion rate Disturbance response capability Frequency deviation rate index Distribution demand rate N-1 verification pass rate Active power reservation rate Topology integrity Performance index Defense capability index Distribution network capacity-load ratio Power grid fault rate Average outage time of the users Faultself-healingrate Restoration capability index Black-start success rate Average scheduled system outage time Distribution line tie rate Distribution network transfer rate Proportion of the flexible load that can be coordinated Accommodation rate of the local clean energy Error expectation between the situation prediction data and the actual data Learning capability indexanthaculda Proportion of the vulnerability that can be fixed by the perception system after the disaster 01981 Each second-level evaluation index is described in detail below: 0199] (1) Perception capability index 0200] According to a principle of convenience and practicability of data obtaining, the perception capability index is evaluated mainly by considering a hardware configuration and an operation level of the perception capability index, and a system status mastering degree. Algorithm efficiency, risk situation prediction, and other indexes that are related to the perception capability index of the resilient power grid and have some difficulties in direct quantitative evaluation will be indirectly reflected by subsequent indexes of other dimensions. 0201] a. Coverage of the smart ammeter 0202] As an important part of an advanced measurement system, the smart ammeter is a basic terminal device for enabling the situation perception system to measure, store, calculate an electricity quantity, and performing two-way communication with a data center. The coverage of the smart ammeter reflects a basic perception capability index of the resilient power grid, and is calculated according to the following formula:
,n
0204] In the above formula, ns represents a quantity of smart ammeters in a region of the n power grid, and n represents a total quantity of ammeters in the region of the power grid.
0205] b. Observability of the weak node 0206] When a high-risk event occurs, the weak node has a greater probability of degrading power performance. A degree of mastering a real-time status of the weak node can reflect a capability of the perception system of the resilient power grid in coping a potential disaster, removing a hidden danger in time, and preventing further expansion of a fault. A weakness degree of a node is determined based on a load-system short-circuit capacity ratio. The load-system short-circuit capacity ratio is calculated according to the following formula: U2 / ZLi
02071 E/ Z, (2)
0208] In the above formula, Epi and ZPi represent power grid-specific Thevenin equivalent
circuit parameters of the node, and ULi and ZLi represent load-specific Thevenin equivalent
circuit parameters of the node. A larger load-system short-circuit capacity ratio leads to a smaller allowable voltage margin of the node. When this index is greater than a preset threshold, the node is considered as the weak node. When a voltage phasor of the node can be measured or calculated directly, the node is observable. The observability of the weak node is calculated according to the following formula: n A2 = n 0209] nw (3)
0210] In the above formula, nm represents a quantity of observable weak nodes, and n represents a total quantity of weak nodes. 0211] c. Power grid measurement redundancy 0212] Unlike the observability of the weak node, which mainly reflects a coping capability of the perception system of the power grid when the disaster occurs, the power grid measurement redundancy reflects whether the perception system of the resilient power grid can completely master, in a normal state, an operation status of the power grid and assist another system in making a decision, and can be used as a supplement to the observability of the weak node. The power grid measurement redundancy is calculated according to the following formula: n
0213 n (4)
0214] In the above formula, n'p represents a quantity of observable nodes, and n,
represents a total quantity of nodes of the power grid. 0215] d. Average transmission delay time 0216] The average transmission delay time is defined as average time required to transmit operation data of the resilient power grid in the perception system and update the operation data to a database, and reflects a real-time requirement of the perception system of the resilient power grid. A calculation formula is as follows:
A4 02171 n (5)
0218] In the above formula, t"i represents time at which a collection quantity of an ammeter
is measured, and ti represents time at which measured data of the ammeter i is updated to
the database. 0219] e. Situation visibility 0220] The situation visibility reflects a visualization degree of the perception system. Convenient and clear situation information helps operation and management personnel to quickly discriminate a power event and make a corresponding decision when coping disasters and accidents. A visualization degree of a situation map is evaluated by using node distribution uniformity. Smaller node distribution uniformity leads to a more even sample data distribution and a higher visualization degree. A specific calculation formula is as follows:
A5 = (N, - N)2 0221] n j=1 (6)
0222] In the above formula, n represents a quantity of blocks obtained by dividing the situation map, N' represents aquantity of nodes ina block i, and Nrepresents an NN arithmetic average value of
0223] f. Operation index of the distribution automation system 0224] As a collection of a power grid data collection and monitoring system, a geographic information system, and a demand-side management system, the distribution automation system integrates power data monitoring and analysis, remote control, and other functions. Different from some system configuration indexes, the operation index of the distribution automation system mainly reflects perception results of decision execution accuracy and execution efficiency of the resilient power grid, and is a real-time dynamic index. The operation index of the distribution automation system includes four sub-items: an average online rate of a distribution automation terminal, a remote control success rate, an accuracy rate of a remote signaling action, and a feeder automation success rate.
0225] The operation index A of the distribution automation system is calculated to the
following formula:
0226] A6 =aaorxX P,+a, x P+a, x P[,+a xJP
02271 In the above formula, Paor represents the average online rate of the distribution
P P automation terminal, N represents the remote control success rate, re represents the
P accuracy rate of the remote signaling action, Jat represents the feeder automation success rate,
a,, a,, a, , and aaJc represent weights of the corresponding indexes respectively, and aI,
aaor+ar +arc + aja=1
0228] Preferably, an optimal weight configuration of the operation index A6 of the
distribution automation system in this embodiment is specifically calculated according to the following formula:
0229 A 6=0.25xP+0.25xP +0.2xP +0.3xP, (7)
0230] In the above formula, for represents the average online rate of the distribution
automation terminal, a represents the remote control success rate, re represents the
P accuracy rate of the remote signaling action, Jac represents the feeder automation success rate.
02311 Coefficients in the operation index A of the distribution automation system may be
adjusted based on an actual situation without any specific limitation. Optimal coefficients of the
operation index A of the distribution automation system are provided in this embodiment.
0232] (2) Disturbance response capability index 02331 The disturbance response capability index reflects a capability of the resilient power grid in resisting a disturbance, maintaining performance of the resilient power grid, and formulating a risk plan before an extreme event occurs. The related indexes of the disturbance response capability index can be used to reflect reliability, high efficiency, risk prediction, and pre-accident deployment efficiency of the power grid in a daily operation decision. 0234] a. Voltage transient rate 0235] The voltage transient rate is mainly used to reflect a frequency and an amplitude of a voltage sag (or rise) during normal operation of the power grid, and a capability of the power grid in monitoring a transient disturbance and providing an early warning. A low voltage
transient rate reflects strong transient stability and a strong anti-interference capability of the system. The voltage transient rate is calculated according to the following formulas:
B = F§(t)dt /(T xn,) 0236] =1(8)
- (t)] (t) < J(t)= 0 t!1 >',i
02371 Lt)-T]/T, T(t)> (9)
0238] In the above formulas, nP represents a quantity of voltage transients, W(t) represents
a voltage transient value of a node at current transient time, '''"x represents an upper limit
of a transient voltage of the node represents a lower limit of the transient voltage of
the node i, T(t) represents a voltage of the node i at the current transient time, and T
represents a statistical cycle. 0239] b. Power flow limit exceeding rate 0240] Frequent power flow limit exceeding reduces an anti-interference capability of the power grid, which is not conducive to risk control before the disaster. A low power flow limit exceeding frequency reflects a real-time power flow monitoring and risk situation prediction capability of the resilient power grid. The power flow limit exceeding rate is calculated according to the following formulas: n,
B 2= 0 S,"'(t)dt / (T x n,) 02411 iO (10)
S| (t)=0s )! S' 0242] ('(t)-S]/S S,(t1>
02431 In the above formulas, S'i i(t) represents a quantity of power flows that are of a branch
and exceed a limit at time t, Sirmnx represents an upper rated power flow limit of the branch
,i(t) represents a power flow size of the branch i at the time t , and n' represents a total
quantity of branches of the power grid. 0244] c. Voltage harmonic distortion rate 0245] Harmonic distortion of a voltage lowers power supply quality within the power grid, and excessive distortion even causes a heating fault to a transformer and a power line. A low voltage harmonic distortion rate meets a requirement of users in a resilient city for high power quality, is conducive to suppressing a small disturbance before the accident, and reflects a power quality monitoring capability of the distribution network. The voltage harmonic distortion rate is calculated according to the following formula:
B3=max( V /V 1 )
0246] i" k-2 (12)
02471 In the above formula, j represents an effective value of a fundamental voltage of the
node l,V represents an effective value of a kthharmonic voltage of the node i, and n,
represents a total quantity of nodes. 0248] d. Frequency deviation rate 0249] The frequency deviation rate is a steady-state electric energy index of the power grid, and reflects active power safety performance of the system. Power flow monitoring, load prediction and allocation, and other technologies are conducive to reducing a frequency deviation of the resilient power grid. The frequency deviation rate is calculated according to the following formula:
B4 = N 0250] 4fh (13)
0251] In the above formula, represents a current system frequency, fN presents a rated
frequency, and f represents a frequency deviation limit, namely, 0.2 Hz.
02521 e. Distribution demand rate 02531 The distribution demand rate is defined as a ratio of actual operation power of the users to rated power in the resilient power grid. This value reflects rationality and economical efficiency of a power resource configuration in the resilient power grid, and reflects a capability of the power grid in flexible load scheduling and resource allocation. The distribution demand rate is calculated according to the following formula:
B5 = X 100% 0254] (14)
P 0255] In the above formula, a represents total actual power consumption of the users in the
P resilient power grid, and N represents a total rated frequency of the users in the resilient power
grid. 0256] f. N-1 verification pass rate 02571 The N-1 verification pass rate includes N-1 pass rates of a substation and a transmission line within the power grid. As a most intuitive index reflecting reliability and an early risk warning capability of the power grid, the N-1 verification pass rate also reflects a capability of the resilient power grid in perceiving a risk situation and simulating a predicted accident.
0258] The N-1 verification pass rate B6 is calculated to the following formula:
B 6 =a t(N-1) X 100%+a, X (N-1) X 100% 0259] nt n,
0260] In the above formula, nt represents a total quantity of substations in the power grid,
1 n (N-1) represents a quantity of substations passing N-1 verification, n, represents a total
quantity of lines in the power grid, nl(N-1) represents a quantity of lines passing N-1
verification, a, and a' represent weights of a substation index and a line index respectively,
anda,+a,=1
0261] Preferably, an optimal weight configuration of the N-1 verification pass rate in this embodiment is calculated according to the following formula:
B6 =.5Xnt(N-1) X0%+0.5X nl(N-1) 0262] nt nI (15)
0263] In the above formula, nt represents the total quantity of substations in the power grid, n (N-1) represents the quantity of substations passing N-i verification, n, represents the total quantity of lines in the power grid, and nl(N-1) represents the quantity of lines passing N-i verification. 0264] g. Active power reservation rate 0265] Different from the distribution demand rate, the active power reservation rate pays more attention to pre-arrangement of an emergency resource. An appropriate amount of reserved active power is conducive to maintaining system performance in a risk accident, reducing a loss caused by the disaster, and meeting the economical efficiency of normal operation of the system. A specific calculation formula is as follows: P B= 0266] P11m(16)
P 02671 In the above formula, r represents a reserved capacity of active power of the power
grid, andf.1im represents a limit of the reserved capacity of the active power of the power grid,
and is generally 0.1 times maximum load of the system. 0268] h. Topology integrity 0269] The topology integrity of the power grid greatly affects a vulnerability of the power grid in coping the disaster. Reducing a quantity of outage lines and outage time is conducive to building a stronger and more reliable resilient power grid. The topology integrity is calculated according to the following formula: n, B 8= f si(t)dt/(Txn,) 02701 i=1 (17)
02711 In the above formula, si(t) represents an operation status of a line i at time t ,
s(t)=1 when the line operates normally, s(t)=O when the line stops operating, T
represents a statistical cycle, and n represents the total quantity of lines.
0272] (3) Defense capability index 0273] The defense capability index focuses on a capability of the system of the resilient power grid in using internal and external defense resources to actively resist a disaster damage and reducing overall impact of an extreme event during the extreme event. The related indexes of the defense capability index can be used to reflect operation efficiency of the power grid under an extreme condition and a response rate under a changeable condition. 0274] a. Performance index
02751 In resilience evaluation, a dynamic change of the resilient power grid during the extreme event may be depicted by using a performance curve, and a capacity of the power grid is selected as the performance index. 0276] The performance index is defined as a product of factors such as an active defense speed and an active defense effect. The index reflects a real-time response capability of the perception capability index, a prediction capability of disaster situation development, and a defense resource evaluation capability during the disaster. A specific calculation formula is as follows:
C,_tl- Px Po 02771 ta I, I' (18)
0278] In the above formula, o represents an original capacity of the power grid before the
disaster, d represents a capacity of the power grid after an active defense measure is taken,
Plow represents a capacity of the power grid when performance of the power grid decreases to a
lowest level, tI- represents time at which the performance of the power grid decreases to the
lowest level, and td represents time at which the active defense measure is taken.
02791 b. Distribution network capacity-load ratio 0280] The distribution network capacity-load ratio is defined as a ratio of a total capacity of transformation devices in a region of the distribution network to maximum annual load of the distribution network. A certain reserved transformer capacity is conducive to improving adaptability of the power grid during the disaster, realizing more flexible defense resource scheduling. In addition, a too large capacity-load ratio not only increases an investment cost and a construction cycle of the resilient power grid, but also reduces the economical efficiency of the operation of the power grid. How to reduce the distribution network capacity-load ratio while meeting the reliability depends on a more accurate load prediction and operation control technology of the distribution network. The distribution network capacity-load ratio is calculated according to the following formula:
C 2 = ST 0281] SLax (19)
0282] In the above formula, ST represents the total capacity of the transformation devices of
the distribution network, and SLax represents the maximum annual load provided by the
network. 0283] c. Power grid fault rate
02841 In order to minimize system performance degradation and a disturbance range caused by a disaster event, it is necessary to prevent occurrence of large-area cascading faults. Monitoring, early warning, and other functions are used to reduce a fault probability of each element of the power grid to improve an anti-setback capability of the resilient power grid in the high-risk event. The power grid fault rate considering a function of each element is calculated according to the following formula:
C3 =1- (1- p )
0285] i=1 (20)
0286] In the above formula, n P represents a total quantity of power devices in the power grid,
and "Pjhji represents a fault probability of a device , and can be obtained by using a situation
prediction technology based on historical data. 02871 (4) Restoration capability index 0288] The restoration capability index considers emergency guarantee and gradual restoration of performance of the power grid after the accident, and timeliness is a most important factor of the restoration capability index. The related indexes of the restoration capability index can be used to reflect a capability of the power grid in fault location, maintenance and overhaul, remote control, decision simulation evaluation, and the like. 0289] a. Average outage time of the users 0290] Outage time is a most intuitive index reflecting the restoration capability index of the resilient power grid after the disturbance. Fault location, fault data recording, and disturbance factor analysis, and other technologies can effectively reduce the average outage time. The average outage time of the users is calculated according to the following formula:
0291] (21)
0292] In the above formula, te11 represents outage duration in an j th fault, nucutl represents a
quantity of users experiencing an outage in the j th fault, and nu represents a total quantity of
users of the power grid. 0293] b. Fault self-healing rate 0294] Fault self-healing is mainly reflected as a self-healing capability of the power grid in an early stage of a small disturbance or extreme event. A fault self-healing cycle includes monitoring and early warning, diagnosis and analysis, and automatic restoration. A high-level resilient power grid can give an alarm and diagnose an inducing factor when a fault is still in a "potential" stage, and attempt to perform fault self-healing through remote control. The fault self-healing rate is calculated according to the following formula:
D2 =x 100%
0295] (22)
0296] In the above formula, nshi represents a quantity of self-healed users in the jth fault,
and n"*"l 1 t represents a total quantity of users affected by the j th fault.
02971 c. Black-start success rate 0298] Black start is a key measure for emergency guarantee after the extreme event. An optimal scheme of black start is formulated mainly depending on system status estimation performed by the distribution network under a fault set and an operation effect of the scheme in a simulation environment such as a twin system. The black-start success rate can effectively reflect a preliminary restoration capability index of the resilient power grid after the disaster, and is calculated according to the following formula:
D3 x100%
0299] (23)
0300] In the above formula, nus, represents a quantity of users for which power supply is
resumed through black start in the j th fault.
0301] d. Average scheduled system outage time 0302] A scheduled outage is a planned outage made by the power department in a related region to perform maintenance or capacity expansion, which is applicable to fault removal that can be delayed and does not cause a damage in a short time. The scheduled outage is mainly determined based on a failure rate, a fault rate, and other indexes. The average scheduled system outage time is calculated according to the following formula: Itwe x n,o .n D4 = '
0303] n (24)
0304] In the above formula, trei represents time of an j th scheduled outage, and n,
represents a quantity of users experiencing the j th scheduled outage.
0305] (5) Collaboration capability index 0306] The collaboration capability index represents a capability of the resilient power grid in using internal and external resources rationally and efficiently and jointly concentrating on resisting a disturbance event. Like the perception capability index, the collaboration capability index serves as a basic feature for serving three core features. The related collaboration capability indexes of the collaboration capability index can be used to improve and reflect a capability of the distribution network in controlling the internal and external defense resources, multi-dimensional system information, and the like. 03071 a. Distribution line tie rate 03081 As a most intuitive and basic tie mode, a direct tie of a distribution line provides flexibility for dispatching and cooperation of physical systems inside and outside the region. In addition, more coupling elements are equipped to provide higher measurement redundancy for the distribution network.
03091 The distribution line tie rate EL is calculated according to the following formula:
E, = (a , X naI -+ a, X na" -) X100% 0310 n n
03111 In the above formula nl,H represents a total quantity of 35 kV to 110 kV high-voltage
lines in a region, ntl,H represents a quantity of 35 kV to 110 kV high-voltage tie lines in the
region, nL represents a total quantity of 10 (20) kV low-voltage lines in the region, ntL
represents a quantity of 10 (20) kV low-voltage tie lines in the region, al,H andalL represent
weights of a high-voltage line index and a low-voltage line index respectively, andalH lL
0312] Preferably, an optimal weight configuration of the distribution network tie rate in this embodiment is calculated according to the following formula:
E, = (0.5 x na -+ 0.5 x n"L) x 100% 03131 nlH nlL (25)
0314] In the above formula, nl,H represents the total quantity of 35 kV to 110 kV
high-voltage lines in the region, ntl,H represents the quantity of 35 kV to 110 kV high-voltage
tie lines in the region, nl represents the total quantity of 10 (20) kV low-voltage lines in the
region, and nl re esents the quantity of 10 (20) kV low-voltage tie lines in the region.
0315] Coefficients in the calculation formula of the distribution network tie rate may be adjusted based on an actual situation without any specific limitation. Optimal coefficients in the calculation formula of the distribution network tie rate are provided in this embodiment.
03161 b. Distribution network transfer rate 03171 Line transfer can effectively reduce load losses of the resilient power grid in the case of a local fault, give play to a greater collaborative disaster response capability of the system, and provide greater flexibility for risk situation evaluation and decision effect simulation of the distribution network. The distribution network transfer rate is calculated according to the following formula:
E2 = x 1OO% 03181 n, (26)
03191 c. Proportion of the flexible load that can be coordinated 0320] As an important part of an active distribution network, a flexible load is responsible for maintaining electric power and energy balance and collaborative disaster restoration. The proportion of the flexible load that can be coordinated determines a peak capability of the resilient power grid in cooperatively resisting the disturbance, and reflects a collaborative power grid management function of the flexible load. The proportion of the flexible load that can be coordinated is calculated according to the following formula:
E, = SFL X 100% 0321] SLM. (27)
0322] In the above formula, SFL represents a peak value of the flexible load that can be
coordinated, and nlt represents a quantity of lines that can be transferred in the distribution network.
03231 d. Accommodation rate of the local clean energy 0324] An amount of accommodated clean energy can comprehensively reflect a collaborative effect of the resilient power grid. Different from the proportion of the flexible load that can be coordinated, the accommodation rate of the local clean energy dynamically represents a collaborative electric power and energy balance level and a flexible anti-disturbance capability inside and outside the region. The accommodation rate of the local clean energy is calculated according to the following formula:
E4 = X 100% 0325] a (28)
03261 In the above formula, of represents a net injected electricity quantity outside the
P P region, oa represents an agreed electricity quantity outside the region, CO represents an
on-grid electricity quantity of the local clean energy, and ] represents a generating capacity of the local clean energy. 03271 (6) Learning capability index 03281 As common support for other features, the learning capability index represents a capability of the resilient power grid in performing self-correction and self-improvement based on historical experience and improving innovation in combination with a new technology. The related indexes of the learning capability index can be used to reflect a capability of the resilient distribution network in making independent error correction and continuous improvement. 03291 a. Error expectation between the situation prediction data and the actual data 03301 An error between each piece of predicted data of the resilient power grid and corresponding actual data can be obtained based on historical operation data and recurrence and analysis of a risk situation after the accident, and the error expectation and a change trend can effectively reflect a self-correction and independent improvement capability of the distribution network. The error expectation between the situation prediction data and the actual data is calculated according to the following formula: 1T 1 N, F, - [ (2, (t) -h(x,.,,i))2]2 0331] T1 N 1 (29)
0332] In the above formula, T represents total measurement duration, Nz,'t represents a total
quantity of measurements predicted by the perception system at time t, #i(t) represents an
estimated value that is of an j th measurement and obtained by the perception system at the time
t, and h(x ) represents a true value of a system status corresponding to the j th measurement
at the time t .
0333] b. Proportion of the vulnerability that can be fixed by the perception system after the disaster 0334] The proportion of the vulnerability that can be fixed by the perception system after the disaster directly reflects vulnerability identification, error correction, and self-learning capabilities of a resilient power grid system.
F2 = n- x 100% 0335] nvf (30)
0336] In the above formula, n f represents a total quantity of vulnerabilities discovered by the
perception system after the disaster, and n, represents a quantity of vulnerabilities that can be
fixed by the perception system after the disaster.
03371 2. Weighting methods for comprehensive resilience evaluation of the power grid 0338] The weighting methods can be classified into a subjective weighting method and an objective weighting method based on their bases. There is a possibility of one-sided evaluation if any kind of method is used alone. Therefore, the present disclosure uses a comprehensive weighting optimization method based on relative entropy to optimize subjective and objective weights obtained by using a binomial coefficient method, an anti-entropy weight method, and a CRITIC method, to realize weight calculation of comprehensive resilience evaluation of the power grid. 0339] (1) Subjective weighting method 0340] As shown in FIG. 1, the subjective weighting method in this embodiment adopts the binomial coefficient method. Compared with the Delphi method and AHP, the binomial coefficient method is characterized by simpler calculation, and comprehensively considers opinions of all parties to avoid ignoring minority viewpoints. FIG. 1 is a flowchart of the binomial coefficient method. Weighting steps are as follows: 0341] S1O: M experts perform pairwise comparison on a total of N evaluation indexes to
independently obtain an importance ranking 0 of an index set, where a smaller ranking leads
to higher index importance. An average ranking value of each expert is taken to obtain an average importance ranking of the nth index according to the following formula: M
OQn(n)
0342] O(xM)='" n=1,2,...,N (31)
0343] S102: Re-rank the N evaluation indexes in ascending order based on the average importance ranking to obtain a new index sequence: FX 1 ,X 2 ,'. I XN
03441 js.t. O(x)< O(xy) i < j (32)
0345] S103: Perform symmetrical ranking on the index set, and calculate a binomial coefficient based on a ranking result:
0346] XN I...1 21 X1, 331... I N-1 ( 3
03471 S104: Re-number each index from left to right based on a symmetrically-ranked index set, and denote a number as . The binomial coefficient can be determined based on a numbering result to calculate a subjective weight of each index according to the following formula:
u0 = N1 i=1,2,...,N 0348] 2N (34)
03491 In the above formula, u represents a subjective weight of an index whose number is',
andCN represents a calculation result of an index permutation and combination.
03501 (2) Objective weighting method 03511 The subjective weighting method depends on subjective cognition of the evaluation experts on the evaluation index set, which may cause a random deviation and one-sidedness, and needs to be used together with the objective weighting method depending on objective data. In the present disclosure, the anti-entropy weight method and the CRITIC method are selected as objective weighting methods. Based on the concept of information entropy, the anti-entropy weight method can effectively evaluate quality of information provided by an index for decision-making, and avoid an extreme case that a too small weight is caused in an entropy weight method. In addition, considering high coupling of the key features in the resilient power grid, the CRITIC method suitable for weighting highly-correlated indexes is supplemented to improve effectiveness of objective weighting. 0352] It is assumed that there are a total of M to-be-selected schemes, each scheme includes
N evaluation indexes, and " is used to represent an actually calculated value of an nth
index of an mth scheme. Since a range of an actually calculated value of each micro index varies greatly and there are different dimensions, which is not conducive to determining an objective weight of the index, each index is normalized first. Specific formulas are as follows: 0353] a. A benefit index whose value is positively correlated with an evaluated capability is normalized according to the following formula:
[(x.-x,nn)/(x -.x,,) x e.x,,n
0354] "" (35) 0355] b. A cost index whose value is negatively correlated with the evaluated capability is normalized according to the following formula:
F(xw x._X)/(x ._,nn) Xxx,
0356] "1" (36)
03571 A normalized value Y"H of each index is a dimensionless constant in a range of 01,
and a larger index value reflects a stronger capability of the index of the scheme. Specific weight calculation processes of the anti-entropy weight method and the CRITIC are separately described below. 0358] (3) Anti-entropy method 0359] FIG. 2 shows a weighting process of the anti-entropy method. Specific steps are as follows: 03601 S201: Obtain a normalized index.
03611 S202: Calculate an anti-entropy value h of each index according to the following
formulas:
0362] r" 1'" (37)
03631 h M_=-Z r n(1-r..) (38)
0364] S203: Determine a weight of each index according to the anti-entropy value and the following formula:
0365] u =h / nh" (39) 03661 (4) CRITIC method 03671 FIG. 3. shows a weighting process of the CRITIC method. Specific steps are as follows: 0368] S301: Obtain a normalized index.
0369] S302: Calculate redundant information entropy PH of each index according to the
following formulas:
03701 "= / 1 1 '"" (40)
p, =1+ 03711 InM (41)
0372] S303: Calculate an inter-column covariance and an index variation coefficient s' by
using a normalized matrix, so as to calculate an inter-index correlation coefficient according to the following formulas: y
0373] M (42)
1 &_
0374] 32 (43)
r. =~ (nY- n,n =1, 2,..., N 03751 s 44)
0376] In the above formulas, Yn represents a normalized value of the n th index, sn
represents a variation coefficient of the n th index, N represents the total quantity of indexes,
nr" represents a correlation coefficient of the nth index and an n th index, Y represents a value of the n*th index, cov(yny9,* represents a covariance between an index value n and the indexvalue Y", and s-. represents a variation coefficient of the n*th index.
0377] S304: Evaluate an information amount contained in each index, where the information amount is calculated according to the following formula:
03781 "n=(s +p )E.,(-r.) (45)
0379] S305: Determine a weight of the index according to the information amount and the following formula:
03801 Uf = if /Zn-1i" (46) 03811 (5) Comprehensive weight optimization 0382] After subjective and objective weighting results are obtained, considering a method for determining subjective and objective preference coefficients based on the relative entropy, a comprehensive weight optimization process based on the relative entropy is shown in FIG. 4, and optimization steps are as follows: 03831 S401: Obtain a weight of a set by using an optimization model based on a principle of
the relative entropy and each weight vector U, = (U,1,Ur2,---, umN obtained by usingM
weighting methods, where the weight of the set is an intermediate variable in a process of obtaining a comprehensive weight. 0384] For a specific optimization model, reference may be made to the prior art. For example, the following model formula may be used:
minH(d)= j= dlog
S. di= 1,d,'>0(i C-M) 0385]
0386] In this embodiment, the following calculation formula of a weight dn of a set of an nth
second-level evaluation index is directly provided:
nd
0387] =n-1 =1(,)U (47)
0388] S402: Calculate relative entropy between a result of each weighting method and the weight of the set according to the following formula, where the relative entropy represents a proximity between the result of the weighting method and the weight of the set.
h(u, ,d)= NIU, In 03891 d, (48)
03901 S403: Obtain a preference coefficient of the weighting method according to the proximity and the following formula, where a larger proximity leads to a more important role of the method in determining the comprehensive weight, and a higher preference coefficient.
a. = h(u,,d) 0391] = 1 h(u, ,d) (49)
0392] S404: Obtain, based on the preference coefficient, a comprehensive index weight coefficient combined with subjective and objective weighting. M
0393] W n la, u'" (50)
0394] Therefore, a comprehensive resilience evaluation process of the distribution network is obtained based on the above content, as shown in FIG. 5. The process specifically includes the following steps: 0395] Si: Construct the comprehensive resilience evaluation index system of the distribution network. 0396] S2: Calculate a value of each micro index in the comprehensive resilience evaluation index system of the distribution network based on parameter data of the distribution network. 03971 S3: Calculate subjective and objective weights of each first-level evaluation index and each second-level evaluation index. 0398] S4: Perform comprehensive weight optimization on the subjective and objective weights of each index to obtain a final weight. 0399] S5: Obtain a final evaluation result based on the final weight and the calculated value of the index. 0400] This embodiment further provides a comprehensive resilience evaluation system for a distribution network, including a memory and a processor, where the memory stores a computer program, and the processor invokes the computer program to perform steps of the above comprehensive resilience evaluation method for a distribution network. 0401] The foregoing is detailed description of the preferred specific embodiments of the present disclosure. It should be understood that a person of ordinary skill in the art can make various modifications and variations according to the concept of the present disclosure without creative efforts. Therefore, all technical solutions that a person skilled in the art can obtain based on the prior art through logical analysis, reasoning, or finite experiments according to the concept of the present disclosure shall fall within the protection scope defined by the appended claims.
Claims (20)
- CLAIMS: 1. A comprehensive resilience evaluation method for a distribution network, comprising the following steps: obtaining parameters of a distribution network, and performing evaluation based on a preset comprehensive resilience evaluation system of the distribution network, wherein the comprehensive resilience evaluation system of the distribution network comprises first-level evaluation indexes and second-level evaluation indexes, and each first-level evaluation index is provided with a corresponding second-level evaluation index; the first-level evaluation indexes of the comprehensive resilience evaluation system of the distribution network comprise a perception capability index, an disturbance response capability index, a defense capability index, a restoration capability index, a collaboration capability index, and a learning capability index; a second-level evaluation index corresponding to the perception capability index comprises one or more of coverage of a smart ammeter, observability of a weak node, power grid measurement redundancy, average transmission delay time, situation visibility, and an operation index of a distribution automation system; a second-level evaluation index corresponding to the collaboration capability index comprises one or more of a distribution line tie rate, a proportion of a flexible load that can be coordinated, a distribution network transfer rate, and an accommodation rate of local clean energy; and a second-level evaluation index corresponding to the learning capability index comprises one or more of an error expectation between situation prediction data and actual data and a proportion of a vulnerability that can be fixed by a perception system after a disaster; and performing comprehensive calculation based on an evaluation result of each second-level evaluation index, a preset weight of each second-level evaluation index, and a preset weight of each first-level evaluation index to obtain a comprehensive resilience evaluation result of the distribution network.
- 2. The comprehensive resilience evaluation method for a distribution network according to claim 1, wherein in the second-level evaluation index corresponding to the perception capabilityindex, the coverage A' of the smart ammeter is calculated according to the following formula:n nnwherein n represents a quantity of smart ammeters in a region of a power grid, and n represents a total quantity of ammeters in the region of the power grid; the observability A 2 of the weak node is calculated according to the following formula:A 2 =n n iwherein nwm represents a quantity of observable weak nodes, and nw represents a totalquantity of weak nodes; andthe power grid measurement redundancy A3 is calculated according to the following formula:n A3 =-PM nwherein n'"n represents a quantity of observable nodes, and n'P represents a total quantity ofnodes of the power grid.
- 3. The comprehensive resilience evaluation method for a distribution network according to claim 1, wherein in the second-level evaluation index corresponding to the perception capabilityindex, the average transmission delay time A is calculated according to the following formula:A4 = 1 nwherein tmi represents time at which a collection quantity of an ammeter i is measured, turepresents time at which measured data of the ammeter is updated to a database, and nrepresents a total quantity of ammeters in a region of a power grid;the situation visibility A is calculated according to the following formula:2 A5 = (N -N)wherein n represents a quantity of blocks obtained by dividing a situation map, Ni representsa quantity of nodes in a block , and N represents an arithmetic average value of Ni; andthe operation index A of a distribution automation system is calculated according to thefollowing formula:A 6 =aaoxP,+axP aoP +arxP+axP, rP rcwherein "°r represents an average online rate of a distribution automation terminal,represents a remote control success rate, re represents an accuracy rate of a remote signaling actio Ja,a a..a. a action, represents a feeder automation success rate, a, a are, and af"c representa +a + a +a =1 weights of the corresponding indexes respectively, and aor " r la.
- 4. The comprehensive resilience evaluation method for a distribution network according to claim 1, wherein in the second-level evaluation index corresponding to the collaborationcapability index, the distribution line tie rate E, is calculated according to the followingformula: n,, nI, E, = (a, x + al,L X -) X100% l,H nl,Lwherein nl,H represents a total quantity of 35 kV to 110 kV high-voltage lines in a region, ntl,Hrepresents a quantity of 35 kV to 110 kV high-voltage tie lines in the region, nl,Ltotal quantity of 10 (20) kV low-voltage lines in the region, nt,L representsaquantityof10(20)kV low-voltage tie lines in the region, al,H and alL represent weights of a high-voltage lineindex and a low-voltage line index respectively, and a,H+aL andthe distribution network transfer rate E2 is calculated according to the following formula:E2 = n' X 100% nlwherein n'," represents a quantity of lines that can be transferred in the distribution network,and n, represents a total quantity of lines.
- 5. The comprehensive resilience evaluation method for a distribution network according to claim 1, wherein in the second-level evaluation index corresponding to the collaborationcapability index, the proportion E3 of the flexible load that can be coordinated is calculatedaccording to the following formula:E3 = SFL X100% SLwmwherein SFL represents a peak value of the flexible load that can be coordinated, and SL"" represents maximum annual load provided by the network; andthe accommodation rate E4 of the local clean energy is calculated according to the followingformula:E4= x100% P +P".P P wherein of represents a net injected electricity quantity outside the region, oa represents anP agreed electricity quantity outside the region, co represents an on-grid electricity quantity of P the local clean energy, and eg represents a generating capacity of the local clean energy.
- 6. The comprehensive resilience evaluation method for a distribution network according to claim 1, wherein in the second-level evaluation index corresponding to the learning capabilityindex, the error expectation F between the situation prediction data and the actual data iscalculated according to the following formula: 1 T 1 Nz,r F =- I[ (2i (t) - h(x,,))232 Tt=1 Nwherein T represents total measurement duration, Nz represents a total quantity ofmeasurements predicted by the perception system at time t, t) represents an estimated valuethat is of an i measurement and obtained by the perception system at the time t, and h ,.represents a true value of a system status corresponding to the j th measurement at the time t;and F the proportion 2 of the vulnerability that can be fixed by the perception system after thedisaster is calculated according to the following formula:F2 = x100% nvf nnwherein nvf represents a total quantity of vulnerabilities discovered by the perception system after the disaster, and nr represents a quantity of vulnerabilities that can be fixed by the perception system after the disaster.
- 7. The comprehensive resilience evaluation method for a distribution network according to claim 1, wherein a second-level evaluation index corresponding to the disturbance response capability index comprises one or more of a voltage transient rate, a power flow limit exceeding rate, a voltage harmonic distortion rate, a frequency deviation rate, a distribution demand rate, an N-1 verification pass rate, an active power reservation rate, and topology integrity.
- 8. The comprehensive resilience evaluation method for a distribution network according toclaim 7, wherein the voltage transient rate B1 is calculated according to the following formulas:B,= V(t)dt/(Txn,)[,min- K(t)]/ ,min VK(t)< VK'mi Vi'"(t)= 0 V" >!V,(t) > V,'m[U(t)- ]KMrn1x/ K"na J(t)>KV.wherein n] represents a quantity of voltage transients,j''(t) representsavoltagetransientvalue of a node i at current transient time, '''ax represents an upper limit of a transientvoltage of the node , represents a lower limit of the transient voltage of the nodeV(t) represents a voltage of the node at the current transient time, and T represents astatistical cycle;the power flow limit exceeding rate B2 is calculated according to the following formulas: n, B 2= [S,"'(t)dt / (T x n,) 1=0Si(t)Simax SlimW=0 i[Si(t)- max]/ S max S,(t)> _,maxwherein Si""'(t) represents a quantity of power flows that are of a branch i and exceed a limitat time t , Sirax represents an upper rated power flow limit of the branch, S(representsa power flow size of the branch i at the time t , and n' represents a total quantity of branchesof a power grid; the voltage harmonic distortion rate B 3 is calculated according to the following formula:B3=max( V/Vl) S- Pk-2Vj V wherein represents an effective value of a fundamental voltage of the node, represents an effective value of a kth harmonic voltage of the node i, and n" represents a totalquantity of nodes; andthe frequency deviation rate B4 is calculated according to the following formula:B4- = - l Af;hwherein represents a current system frequency, fN represents a rated frequency,handrepresents a frequency deviation limit.
- 9. The comprehensive resilience evaluation method for a distribution network according to claim7, wherein the distribution demand rate B5 is calculated according to the following formula: P B5 = " X100% Pwherein a represents total actual power consumption of users in a resilient power grid, andN represents a total rated frequency of the users in the resilient power grid;the N-1 verification pass rate B6 is calculated according to the following formula:B 6 =a, x nt(N-1) x 100%+a, X n,(N-1) X100% nt n,wherein nt represents a total quantity of substations in the power grid, nt(N-1) representsquantity of substations passing N-1 verification, n, represents a total quantity of lines in thepower grid, nI(N-1) represents a quantity of lines passing N-1 verification, a, and a,represent weights of a substation index and aline index respectively, and a,+a,=1the active power reservation rate B7 is calculated according to the following formula:P r.lim P PP wherein r represents a reserved capacity of active power of the power grid, and .imrepresents a limit of the reserved capacity of the active power of the power grid; andthe topology integrity B8 is calculated according to the following formula:B =Y si(t)dt / (T x n,) ;1wherein si(t) represents an operation status of a line i at time t , s(t)=1 when the lineoperates normally, s(t)=O when the line stops operating, T represents a statistical cycle,and n represents the total quantity of lines.
- 10. The comprehensive resilience evaluation method for a distribution network according to claim 1, wherein a second-level evaluation index corresponding to the defense capability index comprises one or more of a performance index, a distribution network capacity-load ratio, and a power grid fault rate.
- 11. The comprehensive resilience evaluation method for a distribution network according toclaim 10, wherein the performance index C, is calculated according to the following formula:t P, P,P PP wherein o represents an original capacity of a power grid before the disaster, d represents acapacity of the power grid after an active defense measure is taken, lo1' represents a capacity ofthe power grid when performance of the power grid decreases to a lowest level, tio- representstime at which the performance of the power grid decreases to the lowest level, and td representstime at which the active defense measure is taken;the distribution network capacity-load ratio C 2 is calculated according to the following formula:C2= ST SLmaxwherein ST represents a total capacity of transformation devices of the distribution network,and SLmax represents maximum annual load provided by the network; and the power grid fault rate C3 is calculated according to the following formula:C - --"',,wherein n' represents a total quantity of power devices in the power grid, andrepresents a fault probability of a device i.
- 12. The comprehensive resilience evaluation method for a distribution network according to claim 1, wherein a second-level evaluation index corresponding to the restoration capability index comprises one or more of average outage time of users, a fault self-healing rate, a black-start success rate, and average scheduled system outage time.
- 13. The comprehensive resilience evaluation method for a distribution network according toclaim 12, wherein the average outage time D, of the users is calculated according to thefollowing formula: D,= te, x n,,,,, ,)/n,,wherein represents outage duration in an j th fault, nucut,i represents a quantity of usersexperiencing an outage in the j th fault, and nu represents a total quantity of users of a powergrid;the fault self-healing rate D 2 is calculated according to the following formula:Z ,.h,i D2= x100% 2Zu/aul,iwherein nushj represents a quantity of self-healed users in the j th fault, andn t"Et'i represents atotal quantity of users affected by the j th fault; andthe black-start success rate D 3 is calculated according to the following formula:D3= xl100%wherein nues,' represents a quantity of users for which power supply is resumed through black start in the j th fault, and n"*"ha represents the total quantity of users affected by the th fault.
- 14. The comprehensive resilience evaluation method for a distribution network according toclaim 12, wherein the average scheduled system outage time D4 is calculated according to thefollowing formula: t,, x nu, D4= n' wherein tr< represents time of an jth scheduled outage, n'"i represents a quantity of usersexperiencing the jth scheduled outage, and n, represents a total quantity of users of a powergrid.
- 15. The comprehensive resilience evaluation method for a distribution network according to claim 1, wherein weight setting processes of the second-level evaluation index and the first-level evaluation index each comprise the following steps: subjective weighting: weighting each index subjectively; objective weighting: weighting each index objectively; and comprehensive weight optimization: obtaining a weight vector obtained by using each weighting method in the subjective weighting and the objective weighting, and calculating an overall weight of a set of each weighting method according to the following formula:d, Hn1 (u)M wherein M represents a total quantity of weighting methods, U,. = (U1, ur2 umN)represents a weight vector of an m th weighting method, u- represents a weight that is of an nth index and obtained by using the m th weighting method, N represents a total quantity ofindexes, and d, represents the weight of the set;calculating relative entropy between a result of each weighting method and the weight of the set according to the following formula: h (u, ,d))=( In u u,, in dwherein h(u, d) represents relative entropy between the weight vector of the m th weighting method and the weight of the set; calculating a preference coefficient of each weighting method based on the relative entropy and the following formula: a h(u, ,d) h(u, ,d) wherein am represents a preference coefficient of the m weighting method; and calculating a comprehensive index weight coefficient of each index according to the preference coefficient and the following formula: M wherein "'n represents a comprehensive index weight coefficient of the nth index.
- 16. The comprehensive resilience evaluation method for a distribution network according to claim 15, wherein the subjective weighting comprises: weighting each index subjectively by using a binomial coefficient method, wherein the binomial coefficient method comprises the following steps: performing, by M experts, pairwise comparison on a total of N evaluation indexes toindependently obtain a importance ranking 0. of an index set, and taking an average rankingvalue of each expert to obtain an average importance ranking of the nth index, wherein the average importance ranking of the nthindex is calculated according to the following formula:MO,,,(n) O(xn) = niIM n =1, 2,..., Nwherein O(xn) represents the average importance ranking of the n th index, and 0,,(n)represents an importance ranking obtained by an m th expert for the nth index; re-ranking the N evaluation indexes in ascending order based on the average importance ranking to obtain a new index sequence:x 1 ,,x2 ,-.. I XN|s.t. O(xi) <O(xj) i< jwherein l'E29- -- xN represents ranked evaluation indexes;performing symmetrical ranking on the index setXN ' I -"Z2 l 3 ... I ",4N-18 a d re-numbering each index based on a symmetrically-ranked index set, denoting a number as and calculating a subjective weight of each index according to the following formula: Ci- 1 u _=-N 1 ,2,...,N 2 wherein ui represents a subjective weight of an index whose number is i, and '- 1 represents a calculation result of an index permutation and combination.
- 17. The comprehensive resilience evaluation method for a distribution network according to claim 15, before the objective weighting, further comprising: performing normalization processing on each index, wherein the normalization processing specifically comprises: normalizing a benefit index according to the following formula: , (x,.- Xn,min) (Xn,max - Xn,min) Xn,max * In,min=1 x = xn,minwherein XMH represents an actually calculated value of an n th index of an th to-be-selectedscheme, Y-' represents a normalized value of a benefit index of the nth index of the mthto-be-selected scheme, xnmin represents a minimum value of the nth index, X""" represents amaximum value of the nth index, and the to-be-selected scheme is each actual index value obtained by using the index; and normalizing a cost index according to the following formula: S (Xn,max - Xnn) (Xn,max - Xn,min) Xn,max * Xn,minSmax = xn,minwherein Y-n represents a normalized value of a cost index of an n th index of an thobjectiveweighting scheme.
- 18. The comprehensive resilience evaluation method for a distribution network according to claim 17, wherein the objective weighting comprises: weighting each index objectively by using an anti-entropy weight method, and a calculation process of the anti-entropy weight method comprises:calculating an anti-entropy value h of each index according to the following formulas:49 wherein Y.n represents a normalized value of the n th index of the mth objective weighting scheme, and M represents the total quantity of to-be-selected schemes; and determining a weight of each index according to the anti-entropy value and the following formula: un=hnlIZNihn wherein un represents a weight of the nth index, and N represents the total quantity of indexes.
- 19. The comprehensive resilience evaluation method for a distribution network according to claim 17, wherein the objective weighting comprises: weighting each index objectively by using a Criteria Importance Through Intercriteria Correlation (CRITIC) method, and a calculation process of the CRITIC method comprises:calculating redundant information entropy PH of each index according to the followingformulas:r, r., In , =1+Er1n, InMr;,n = yn / ymwherein n represents redundant information entropy of the n th index, Ymn represents anormalized value of the n th index of the mth objective weighting scheme, and M represents the total quantity of to-be-selected schemes;calculating an inter-column covariance and an index variation coefficient so by using anormalized matrix, so as to calculate an inter-index correlation coefficient according to the following formulas: cov(y,,y .) ,r. = " n,n'=1,2,..., NYnfsn = (yn - 1) MM 71 1 ,. Mwherein Yn represents a normalized value of the n th index, Sn represents a variation coefficientofthenth index, N represents the total quantity of indexes, n, represents a correlation coefficient of the nth index and an n*th index, " represents a value of the n'th index, cov(yny,.) represents a covariance between an index value Yn and the index value Y' and sn* represents a variation coefficient of the n*th index; evaluating an information amount contained in each index, wherein the information amount is calculated according to the following formula: )N wherein n represents an information amount of the n th index;and determining a weight of the index according to the information amount and the following formula: wherein un represents a weight of the n th index.
- 20. A comprehensive resilience evaluation system for a distribution network, comprising a memory and a processor, wherein the memory stores a computer program, and the processor invokes the computer program to perform steps of the method according to any one of claims 1 to 19.
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| CN113283698A (en) * | 2021-04-21 | 2021-08-20 | 华北电力大学 | Relay protection anti-electromagnetic interference evaluation index system and evaluation method |
| CN116433073A (en) * | 2023-02-23 | 2023-07-14 | 华北电力大学 | Wind power plant operation efficiency evaluation method, device, equipment and medium |
| CN116136988B (en) * | 2023-04-19 | 2023-07-07 | 国网浙江省电力有限公司宁波供电公司 | Space-time compass distribution-based power distribution network power supply grid construction optimization sequencing method |
| CN116739417A (en) * | 2023-05-24 | 2023-09-12 | 国家电网有限公司华东分部 | Gateway electric energy meter state evaluation method and device, storage medium and computer equipment |
| CN116341992A (en) * | 2023-05-29 | 2023-06-27 | 广东电网有限责任公司佛山供电局 | Comprehensive evaluation method and device for power distribution network, electronic equipment and storage medium |
| CN116361925B (en) * | 2023-05-31 | 2023-11-03 | 西北工业大学 | Multi-scheme assessment method and system for ship transmission configuration |
| CN116485286B (en) * | 2023-06-21 | 2023-10-03 | 中铁第四勘察设计院集团有限公司 | Regional passenger dedicated line planning and evaluation method and system based on analytic hierarchy process |
| CN116629455B (en) * | 2023-07-19 | 2023-10-31 | 山西阳风新能售电有限公司 | Intelligent orientation prediction method based on power supply |
| CN116703368B (en) * | 2023-08-08 | 2023-10-13 | 国网信通亿力科技有限责任公司 | Synchronous line loss intelligent closed-loop monitoring method based on data mining |
| CN116744321B (en) * | 2023-08-11 | 2023-11-14 | 中维建技术有限公司 | Data regulation and control method for intelligent operation and maintenance integrated platform for 5G communication |
| CN116742645B (en) * | 2023-08-15 | 2024-02-27 | 北京中电普华信息技术有限公司 | Power load regulation and control task allocation method and device |
| CN117114491A (en) * | 2023-09-06 | 2023-11-24 | 青岛民航凯亚系统集成有限公司 | Airport information system operation and maintenance capability evaluation method and system based on entropy method |
| CN116901774B (en) * | 2023-09-11 | 2023-11-14 | 南京安充智能科技有限公司 | Flexible power distribution method, system and storage medium based on full-network charging pile |
| CN116933981B (en) * | 2023-09-15 | 2023-12-08 | 安徽方能电气技术有限公司 | Regional power stability analysis and evaluation method based on power outage and restoration event data monitoring |
| CN117200460B (en) * | 2023-11-06 | 2024-01-23 | 国网湖北省电力有限公司经济技术研究院 | Active power distribution network measurement optimization configuration method and system |
| CN117235373B (en) * | 2023-11-14 | 2024-03-15 | 四川省计算机研究院 | Scientific research hot spot recommendation method based on information entropy |
| CN117726079B (en) * | 2024-02-05 | 2024-04-16 | 肯拓（天津）工业自动化技术有限公司 | Automatic annular production line optimization method based on electromechanical integration |
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| CN111179117A (en) * | 2019-12-27 | 2020-05-19 | 天津大学 | Calculation method and device for situation awareness effect evaluation of intelligent power distribution network |
| CN112001626A (en) * | 2020-08-21 | 2020-11-27 | 广东电网有限责任公司广州供电局 | Method for evaluating toughness of power distribution network in typhoon weather, storage medium and equipment |
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