CN111864728A - Identification method and system for important equipment of reconfigurable power distribution network - Google Patents

Identification method and system for important equipment of reconfigurable power distribution network Download PDF

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CN111864728A
CN111864728A CN201910333554.6A CN201910333554A CN111864728A CN 111864728 A CN111864728 A CN 111864728A CN 201910333554 A CN201910333554 A CN 201910333554A CN 111864728 A CN111864728 A CN 111864728A
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equipment
importance
distribution network
node
scene
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CN111864728B (en
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赵晓龙
杨红磊
方恒福
周勐
杨尚晴
宋祺鹏
王利
刘永梅
王越
王罡
孙辰军
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Hebei Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Hebei Electric Power Co Ltd
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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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention provides a method and a system for identifying important equipment of a reconfigurable power distribution network, which comprise the following steps: step 1: generating a random scene based on the basic aging process, the weather condition and the overhaul rate of the equipment; step 2: based on each random scene, respectively calculating an optimal reconstruction operation scheme of the power distribution network under the two conditions that the equipment is always reliable and the equipment is always unreliable, and calculating the importance of the equipment in the two reconstruction operation schemes under the random scene; and step 3: judging whether the importance of the equipment is converged under all random scenes: if yes, executing the step 4, otherwise, turning to the step 1 to generate a new scene; and 4, step 4: and taking the average value of the importance of the equipment in all random scenes as the importance of the equipment and finishing. The method and the system can quantitatively analyze the influence of different influence factors on the importance indexes of the power distribution network equipment, such as equipment aging state, weather state, network reconstruction and overhaul rate, so that the importance indexes of the equipment are more comprehensive, and the evaluation result is closer to the actual situation.

Description

Identification method and system for important equipment of reconfigurable power distribution network
Technical Field
The invention belongs to the technical field of equipment importance evaluation, and particularly relates to a method and a system for identifying important equipment of a reconfigurable power distribution network.
Background
In the power system, the higher the importance of the system equipment, the greater the influence on the economy and reliability of the system power supply, and therefore, the greater the attention. When system equipment with high importance in the system is forced to be shut down, negative effects such as system load shedding, user interruption cost rising, even major power failure and the like are easily caused. The importance of the system equipment is effectively identified, so that a decision maker can reasonably distribute human resource resources, prevent potential risks, adjust a maintenance and outage plan and the like.
In the evaluation of importance of system equipment, the existing research is mainly divided into three directions. The first direction is equipment importance evaluation based on graph theory, the second direction is equipment importance evaluation based on reliability mathematics, and the third direction is equipment importance evaluation considering the running state of the power distribution network. However, the following disadvantages exist in the conventional research for evaluating the importance of power distribution network equipment. First, no consideration is given to the reconstruction scheme of the distribution network. The power distribution network can perform operations such as topology reconstruction and load transfer according to actual conditions, reliability of the system is improved, and importance comparison of elements can be influenced. Second, previous research overlooks key factors that affect forced plant outages, such as inclement weather, equipment aging status, maintenance rates, and the like.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a system for identifying important equipment of a reconfigurable power distribution network. The method and the system aim at the reliability maintenance and operation work of the intelligent power distribution network, dynamically identify key equipment in the reconfigurable power distribution network on the basis of considering the reconfiguration scheme of the power distribution network, and aim at establishing an importance evaluation framework from the aspect of influence of the equipment on the operation of the whole system and forming corresponding importance evaluation indexes. The invention also considers the influence of various factors, such as equipment aging, external weather, maintenance rate and the like, and comprehensively evaluates the importance of the equipment. The evaluation framework is different from the existing importance evaluation, can obtain more realistic and accurate importance indexes, and improves the scientificity and economy of distribution network overhaul operation and management decision.
The adopted solution for realizing the purpose is as follows:
the improvement of a method for identifying important equipment of a reconfigurable power distribution network is that the method comprises the following steps:
step 1: generating a random scene based on the basic aging process, the weather condition and the overhaul rate of the equipment;
step 2: on the basis of each random scene, respectively calculating an optimal reconstruction operation scheme of the power distribution network under the conditions that the equipment is always reliable and the equipment is always unreliable, and calculating the importance of the equipment in the random scene on the basis of the two optimal reconstruction operation schemes;
And step 3: judging whether the change range of the importance of the equipment is in a set range under all random scenes: if yes, executing step 4; otherwise, turning to the step 1;
and 4, step 4: and taking the average value of the importance degrees of the equipment in all random scenes as the importance degree of the equipment and finishing.
In a first preferred technical solution provided by the present invention, the improvement is that the calculating the importance of the device in the random scene based on the two optimal reconstruction operation schemes includes:
calculating the reliability of the power distribution network system under the two optimal reconstruction operation schemes based on the random scene;
and calculating the difference of the reliability of the power distribution network system under the two optimal reconstruction operation schemes to obtain the importance of the equipment in the random scene.
In a second preferred technical solution provided by the present invention, the improvement is that the calculating the reliability of the power distribution network system under two optimal reconstruction operation schemes based on the random scene includes:
respectively calculating the system electricity shortage index and per-unit system voltage of each node of the equipment at each moment under the conditions of constant reliability and constant unreliability of the equipment under the optimal reconstruction operation scheme of the power distribution network based on the random scenes;
And taking the system electric quantity insufficiency index and the per-unit system voltage of each node as the reliability of the power distribution network system.
The third preferred technical solution provided by the present invention is improved in that the calculating of the difference between the reliabilities of the power distribution network systems under the two optimal reconfiguration operation schemes to obtain the importance of the device in the random scene includes:
calculating the difference of system electric quantity insufficiency indexes of the equipment at each moment under the conditions of constant reliability and constant unreliability of the equipment in the optimal reconstruction operation scheme of the power distribution network to obtain the load loss support importance of the equipment in the random scene;
calculating the difference of per-unit system voltages of each node at each moment under the optimal reconstruction operation scheme of the power distribution network under the conditions that the equipment is always reliable and is always unreliable to obtain the voltage support importance of the equipment under the random scene;
the importance includes a load loss support importance and a voltage support importance.
In a fourth preferred embodiment of the present invention, the improvement is that the importance of the load loss support is calculated as follows:
Figure BDA0002038413880000021
in the formula (I), the compound is shown in the specification,
Figure BDA0002038413880000022
representing the load loss support importance of the equipment l in the scene s; l represents a set of all devices;
Figure BDA0002038413880000023
Representing the index of insufficient system electric quantity at the time t when the equipment l is reliable in the scene s;
Figure BDA0002038413880000024
representing the index of insufficient system electric quantity at the time t when the equipment l is unreliable in the scene s, and nt representing the total simulation duration;
the voltage support importance is calculated as follows:
Figure BDA0002038413880000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002038413880000032
representing the voltage support importance of device i in scene s,
Figure BDA0002038413880000033
representing devices l in a scene sThe per unit system voltage of the node n at the time t,
Figure BDA0002038413880000034
representing the per-unit system voltage of a node n at the time t when the device l is unreliable in the scene s; nb represents the total number of nodes in the power distribution network system.
The fifth preferred technical solution provided by the present invention is improved in that the determining whether the change range of the importance of the device in all random scenes is within a set range includes:
calculating variance coefficients of the importance of the equipment under all random scenes, and judging whether the variance coefficients are less than or equal to a set threshold value:
if so: the change range of the importance of the equipment is within the set range, otherwise, the change range of the importance of the equipment is not within the set range.
The improvement of the sixth preferred technical solution provided by the present invention is that, the generating a random scene based on the basic aging process, the weather condition and the overhaul rate of the equipment includes:
Step 1-1: based on a proportional risk model, considering the basic aging process and the weather condition of the equipment, and establishing an equipment fault rate model;
step 1-2: generating a fault moment and running time based on the fault moment by adopting an inverse transformation sampling method based on the equipment fault rate model;
step 1-3: based on the fault moment, randomly generating maintenance time according to the historical maintenance rate of the equipment;
step 1-4: judging whether the current moment reaches a preset simulation time length after the maintenance time is passed: if so, sampling the running time and the maintenance time of the equipment, and then turning to the step 1-5, otherwise, turning to the step 1-2;
step 1-5: judging whether the running time and the overhaul time sampling of all other equipment except the equipment with the importance to be identified are finished: if so, generating a random scene according to the running time and the maintenance time of all other equipment; otherwise, turning to the step 1-1;
in the proportional risk model, the basic aging process of the equipment is represented by a Weibull distributed benchmark function, and the weather condition is represented by an exponentially distributed connection function.
The improvement of the seventh preferred technical scheme provided by the invention is that the calculation of the optimal reconfiguration operation scheme of the power distribution network comprises the following steps:
And (3) adopting a second-order cone planning distribution network reconstruction model, taking the minimum system power shortage as a target function, taking distribution network structure constraint, power balance constraint, node voltage constraint, second-order cone constraint and line capacity constraint as constraint conditions, and calculating an optimal reconstruction operation scheme of the distribution network under the conditions that the equipment is reliable all the time and unreliable all the time based on a random scene.
In an eighth preferred embodiment, the improvement is that the objective function is as follows:
Figure BDA0002038413880000041
in the formula, subscript m represents a node; n represents a set of all nodes; ENSmRepresenting the load loss of the node m;
the distribution network structural constraints are as follows:
Figure BDA0002038413880000042
Figure BDA0002038413880000043
Figure BDA0002038413880000044
Figure BDA0002038413880000045
in the formula, alphalIs a binary variable, which is 1 if the device l is connected, or 0 otherwise; beta is amnIs a binary variable, is 1 if node n is the parent node of node m,otherwise, the value is 0; l represents a set of all devices; n denotes the set of all nodes, NsRepresenting a set of all substation nodes, N \ NsRepresenting the set of all nodes except the substation node, N(m)Representing a set of all nodes connected to the m-node;
the power balance constraint is as follows:
Figure BDA0002038413880000046
Figure BDA0002038413880000047
in the formula, PDmRepresenting the active load of node m, QD mRepresenting reactive load of node m, QRmRepresenting the reactive load shedding proportion of the node m; p is a radical ofmnRepresenting the active power between node m and node n, qmnRepresenting the reactive power between the node m and the node n;
the node voltage constraint is as follows:
Figure BDA0002038413880000048
Figure BDA0002038413880000049
Figure BDA00020384138800000410
-Vm，maxVn，max≤tl≤Vm，maxVn，max
Figure BDA0002038413880000051
Figure BDA0002038413880000052
in the formula (I), the compound is shown in the specification,
Figure BDA0002038413880000053
um、rland tlAs an auxiliary variable, the number of variables,
Figure BDA0002038413880000054
when the device l is on-line
Figure BDA0002038413880000055
Otherwise
Figure BDA0002038413880000056
Figure BDA0002038413880000057
rl＝VmVncosθmn，tl＝VmVnsinθmnIn which V ismRepresenting the voltage magnitude, V, of node mnRepresenting the magnitude of the voltage at node n, θmnRepresents the voltage angle difference between nodes m and n; vm，maxRepresents the maximum voltage amplitude, V, of node mn，maxRepresenting the maximum voltage amplitude of the node n;
the second order cone constraint is shown as follows:
Figure BDA0002038413880000058
in the formula (I), the compound is shown in the specification,
Figure BDA0002038413880000059
as an auxiliary variable, when the plant l is on-line
Figure BDA00020384138800000510
Otherwise
Figure BDA00020384138800000511
The line capacity constraint is given by:
Figure BDA00020384138800000512
in the formula Il，maxIndicates that the device l satisfies the maximum allowable current, A, for thermal stabilityl、Bl、ClAnd DlRespectively coefficients of the variance of the thermostabilization constraints.
In a reconfigurable power distribution network critical equipment identification system, the improvement comprising: the device comprises a scene generation module, an equipment importance evaluation module, a convergence judgment module and an importance calculation module;
the scene generation module is used for generating a random scene based on the basic aging process, the weather condition and the overhaul rate of the equipment;
the equipment importance evaluation module is used for respectively calculating an optimal reconstruction operation scheme of the power distribution network under the conditions that the equipment is always reliable and is always unreliable based on each random scene, and calculating the importance of the equipment under the random scene based on the two optimal reconstruction operation schemes;
The convergence judging module is used for judging whether the change range of the importance of the equipment is within a set range under all random scenes: if yes, calling an importance calculation module; otherwise, continuing to call the scene generation module;
and the importance calculating module is used for taking the average value of the importance of the equipment in all random scenes as the importance of the equipment and ending.
In a ninth preferred aspect of the present invention, the improvement wherein the device importance evaluation module comprises: the reliability calculation submodule and the importance calculation submodule are connected with the importance calculation submodule;
the reliability calculation submodule is used for calculating the reliability of the power distribution network system under the two optimal reconstruction operation schemes based on the random scene;
and the importance degree calculation operator module is used for calculating the difference of the reliability of the power distribution network system under the two optimal reconstruction operation schemes to obtain the importance degree of the equipment under the random scene.
Compared with the closest prior art, the invention has the following beneficial effects:
(1) the invention provides a method and a system for identifying important equipment of a reconfigurable power distribution network, which comprise the following steps: step 1: generating a random scene based on the basic aging process, the weather condition and the overhaul rate of the equipment; step 2: based on each random scene, respectively calculating an optimal reconstruction operation scheme of the power distribution network under the two conditions that the equipment is always reliable and the equipment is always unreliable, and calculating the importance of the equipment in the two reconstruction operation schemes under the random scene; and step 3: judging whether the change range of the importance of the equipment is in a set range under all random scenes: if yes, executing the step 4, otherwise, turning to the step 1 to generate a new scene; and 4, step 4: taking the average value of the importance of the equipment in all random scenes as the importance of the equipment and ending; the influence of different influence factors on the importance indexes of the power distribution network equipment, such as equipment aging state, weather state, network reconstruction and overhaul rate, can be quantitatively analyzed, so that the importance indexes of the evaluation equipment are more comprehensive, and the evaluation result is closer to the actual condition.
(2) The method can quantitatively analyze the influence of the reconfiguration operation of the distribution network on the equipment importance degree, can adapt to the distribution network which operates flexibly, and can better identify the key equipment in the network in the area network with variable operation modes.
(3) The method and the device can quantify the influence of the weather state on the importance index, and can evaluate the equipment importance index under different weather conditions. When unfavorable weather occurs, corresponding resource allocation can be carried out according to the importance index of the equipment, and the emergency repair work after the fault is arranged in time.
(4) The method can quantitatively analyze the influence of the maintenance efficiency on the equipment importance, and can evaluate the equipment importance index under the condition of the maintenance rate. The distribution network has different maintenance rates in different areas, and the invention is beneficial for the working personnel to more comprehensively master the running state of the equipment and reasonably arrange the operation and maintenance plan.
(5) The method utilizes a mixed integer quadratic cone model in a mathematical programming method to model the reconstruction of the power distribution network, and finds out a global optimal solution under the given precision requirement;
(6) and introducing a proportional risk model to model the forced outage rate of the equipment. The model quantifies the influence of key factors such as health conditions and weather conditions on the reliability of the equipment. The service life and the health condition of different equipment in the power grid are different, and equipment with deteriorated health condition has potential fault risks. Also, in severe weather, the failure rate of some equipment is significantly higher than normal. The proportional risk model can well describe the change of the equipment fault characteristics caused by the covariates.
Drawings
Fig. 1 is a schematic flow chart of a method for identifying important equipment of a reconfigurable power distribution network, provided by the invention;
fig. 2 is a technical framework schematic diagram of a method for identifying important devices of a reconfigurable power distribution network, provided by the invention;
fig. 3 is a schematic flow chart of a random scene generation technology in identification of important devices of a reconfigurable power distribution network, provided by the invention;
FIG. 4 is a schematic diagram of an IEEE 16 node in accordance with the present invention;
fig. 5 is a schematic diagram of a basic structure of a reconfigurable power distribution network important equipment identification system provided by the invention;
fig. 6 is a detailed structural schematic diagram of a reconfigurable power distribution network important equipment identification system provided by the invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Example 1:
the flow schematic diagram of the identification method of the reconfigurable power distribution network important equipment provided by the invention is shown in fig. 1, and the technical framework of the overall scheme is shown in fig. 2. The method and the device measure the importance of the equipment from the perspective of the whole system, and can simultaneously consider the influence of various factors on the importance. The invention considers the factors of equipment health condition, external weather condition, power distribution network reconstruction, maintenance rate and the like.
A proportional risk model is introduced to model the forced outage rate of the equipment, the influence of covariates such as the health condition of the equipment, the weather condition, the maintenance rate and the like on the forced outage rate of the equipment is quantified, and a recursive sampling method is provided for carrying out state sampling on the equipment with the time-varying discontinuous forced outage rate.
An iterative method for dynamically identifying the importance of equipment is provided. Based on the angle of the overall influence of the equipment on the system, two equipment importance degree defining and calculating methods are provided, namely the load loss supporting importance degree and the voltage supporting importance degree.
The method comprises three main parts of scene generation, equipment importance evaluation and convergence judgment, wherein equipment l always runs normally, namely the equipment l is always reliable, and the equipment l always breaks down, namely the equipment l is always unreliable. In the present invention, the step of evaluating the importance of the device l includes:
step 1: generating a random scene based on the basic aging process, the weather condition and the overhaul rate of the equipment;
step 2: on the basis of each random scene, respectively calculating an optimal reconstruction operation scheme of the power distribution network under the two conditions that the equipment is always reliable and is always unreliable, and calculating the importance of the equipment in the random scene on the basis of the two optimal reconstruction operation schemes;
Wherein, in the step 2, two substeps are specifically included:
step 2-1: calculating the reliability of the power distribution network system under two optimal reconstruction operation schemes based on a random scene;
step 2-2: calculating the difference of the reliability of the power distribution network system under the two optimal reconstruction operation schemes to obtain the importance of the equipment in a random scene;
and step 3: judging whether the importance of the equipment is converged under all random scenes, namely whether the change range of the importance is within a set range: if yes, executing step 4; otherwise, the step 1 is carried out to generate a new scene.
And 4, step 4: and taking the average value of the importance of the equipment in all random scenes as the importance of the equipment and finishing.
In the actual production process of the power industry, after the step 4, the following step 5 can be further included:
and 5: the decision maker is helped to reasonably distribute the human material resources according to the importance of the equipment, the potential risk is prevented, and the maintenance and outage plan and the like are adjusted.
The technical process for generating the random scene is shown in fig. 3, and specifically includes fault time sampling, overhaul time sampling, and random scene generation. The fault time sampling submodule considers the influence of factors such as weather state, equipment aging state and the like.
In the invention, when the equipment is reliable, the equipment can be represented online, and when the equipment is unreliable, the equipment is represented offline.
Specifically, the method comprises the following steps:
firstly, based on a proportional risk model, the failure rate of the equipment is modeled by formula (1):
Figure BDA0002038413880000081
equation (1) is based on a proportional risk model, and comprises two parts, namely a reference function h0(t) and the linkage function Ψ (Z)1(t)). The invention adopts a reference function of Weibull distribution and a connection function of exponential distribution. The reference function reflects the basic aging process of the device, while the connection function reflects the influencing factor Z1(t) change to failure rate. t represents time and is used to describe the age or actual state of aging of the device. Z1(t) {0, 1, 2} represents weather conditions, 0 represents normal weather, 1 represents adverse weather, and 2 represents severe disaster such as typhoon, thunderstorm, and the like. In the formula (1), the parameters φ, η and γ1The settings are made in advance according to the conditions of the respective apparatuses.
Then, generating a fault moment by adopting an inverse transformation sampling method, and mainly comprising the following steps of:
(1) sampling a uniform random variable zeta in the interval [0, 1] as a probability value;
(2) and (5) giving a fault time T, wherein T is a non-negative integer, and returning T which enables the fault probability P (T < T) to be less than or equal to zeta maximum.
(3) The probability of reliable operation of the device at time t is calculated, as shown in equation (2),
Figure BDA0002038413880000082
and taking the time from the current moment to the fault moment as the running time. And after the fault moment is generated, sampling the overhaul time. And randomly generating maintenance time based on the historical data of the equipment maintenance rate. And repeatedly sampling the running time and the overhaul time, and terminating after the simulation time length of the importance evaluation is reached. And generating a random scene after the sampling of all the devices except the device to be evaluated in the operation-maintenance period is completed.
And 2, the power distribution network reconstruction in the step 2 can be realized by adopting various methods such as an artificial intelligence algorithm, a second-order cone planning method and the like. Taking a second-order cone planning algorithm as an example, a power distribution network reconstruction model is given.
The traditional ac power flow model is as follows:
Figure BDA0002038413880000091
Figure BDA0002038413880000092
in the formula, pmnRepresenting the active power between node m and node n, qmnRepresenting the reactive power between the node m and the node n; vm，VnRepresenting the voltage amplitudes, G, of nodes m and n, respectivelyl、BlAnd Bsh lRespectively representing the series conductance, the series susceptance and the parallel susceptance of the device l in a pi model, thetamnRepresenting the voltage angle difference between nodes m and n.
The embodiment adopts a second-order cone relaxation transformation method and introduces auxiliary variables
Figure BDA0002038413880000093
rl＝VmVncosθmnAnd tl＝VmVnsinθmn(ii) a At the same time, auxiliary variables are established
Figure BDA0002038413880000094
And
Figure BDA0002038413880000095
when the device l is on-line
Figure BDA0002038413880000096
Figure BDA0002038413880000097
When the device/is taken off-line,
Figure BDA0002038413880000098
equations (3) - (4) can be expressed as:
Figure BDA0002038413880000099
Figure BDA00020384138800000910
wherein:
Figure BDA00020384138800000911
Figure BDA00020384138800000912
in the formula, alphalIs a binary variable, which is 1 if the device l is connected, or 0 otherwise;
a second order cone constraint can be established as:
Figure BDA00020384138800000913
in this embodiment, a power distribution network reconstruction model based on second-order cone programming is as follows:
Figure BDA00020384138800000914
Figure BDA00020384138800000915
Figure BDA00020384138800000916
Figure BDA00020384138800000917
Figure BDA00020384138800000918
Figure BDA0002038413880000101
Figure BDA0002038413880000102
Figure BDA0002038413880000103
Figure BDA0002038413880000104
-Vm，maxVn，max≤tl≤Vm，maxVn，max(19)
Figure BDA0002038413880000105
Figure BDA0002038413880000106
in equation (10), power distribution network reconfiguration is targeted to minimize system power shortage (ENS). Subscripts m and n denote nodes; n represents a set of all nodes; ENSmIndicating that the off-load quantity of the node m is insufficient, i.e. the electric quantity of the node m is insufficient.
Equations (11) - (14) limit the distribution networkThere is a tree structure, which is the power distribution network structure constraint. In the formula, alphalIs a binary variable, which is 1 if the device l is connected, or 0 otherwise; beta is amnIs a binary variable, if the node n is the parent node of the node m, it is 1, otherwise it is 0; l represents a set of all devices; n denotes the set of all nodes, NsRepresenting a set of all substation nodes, N \ NsRepresenting the set of all nodes except the substation node, N(m)Representing the set of all nodes connected to the m node.
Equation (15) represents the node active power balance, and equation (16) represents the node reactive power balance, which constitutes the power balance constraint. In the formula, PDmRepresenting the active load of node m, QDmRepresenting reactive load of node m, QRmRepresenting the reactive load shedding proportion of node m.
Equation (17) limits the line capacity, i.e., the line capacity constraint. In the formula, Il，maxIndicates that the device l satisfies the maximum allowable current, A, for thermal stabilityl、Bl、ClAnd DlRespectively, coefficients of the variance of the thermal stability constraint, and the coefficient calculation method is well known.
The auxiliary variables r are given by the equations (18) to (21)l，tl，um，
Figure BDA0002038413880000107
The upper and lower limits of (c) together with equations (7) and (8) form the node voltage constraint. In the formula, V m，maxRepresents the maximum voltage amplitude, V, of node mn，maxRepresenting the maximum voltage amplitude at node n.
When the equipment for importance identification is a line, a fault condition constraint is also included, as shown in the following formula:
Figure BDA0002038413880000108
wherein, ylThe fault state representing the line l is generated by performing state sampling by a recursive sampling method, and belongs to an input quantity in a reconstructed model. If forced shutdown occurs, it is 1Otherwise, it is 0.
When the importance degree is calculated, the importance degree characteristics of the equipment in different aspects can be represented by calculating the variable quantity of various reliability indexes. In the embodiment, two reliability indexes of the load loss amount and the voltage out-of-limit are taken as examples, and a calculation method of the load loss support importance and the voltage out-of-limit importance is provided.
Defining the importance of the unloaded support of the device llComprises the following steps:
Figure BDA0002038413880000111
wherein the content of the first and second substances,
Figure BDA0002038413880000112
in the formula (I), the compound is shown in the specification,
Figure BDA0002038413880000113
representing the load loss support importance of the equipment l in the scene s; l represents a set of all devices;
Figure BDA0002038413880000114
representing a system electric quantity insufficiency index of the device l at the online time t in the scene s; nt denotes the total simulation duration, and ns denotes the total number of random scenes.
Defining the voltage support importance ω of the device llComprises the following steps:
Figure BDA0002038413880000115
Figure BDA0002038413880000116
in the formula, nb represents the total number of nodes of the power distribution network system;
Figure BDA0002038413880000117
representing the voltage support importance of device i in scene s,
Figure BDA0002038413880000118
Represents the per-unit system voltage of the node n at the moment t when the device l is online in the scene s,
Figure BDA0002038413880000119
and representing the per-unit system voltage of the node n at the time t when the device l is offline in the scene s.
A variance Coefficient (CV) of the apparatus importance index is calculated at the time of convergence determination, and the evaluation process is terminated when the variance coefficient satisfies a convergence condition. If the convergence condition is not met, the current sample cannot represent the overall characteristics, and the scene sampling is returned to enter the next calculation to generate a new random scene. Loss-of-load support importance variance coefficient cv1The calculation formula of (a) is as follows:
Figure BDA0002038413880000121
variance coefficient cv of voltage support importance2The calculation formula of (a) is as follows:
Figure BDA0002038413880000122
in the formula, a preset threshold value is represented.
And when the two variance coefficients meet the requirements, the convergence condition is met, the calculation can be terminated, otherwise, the step 1 is returned to enter the next calculation, and a new random scene is generated.
Example 2:
the specific calculation example of the identification method of the important equipment of the reconfigurable power distribution network is given below. In this embodiment, a line is taken as an example of a device in a power distribution network.
The present embodiment employs an IEEE 16 node test system, as shown in fig. 4. And carrying out simulation verification on MATLAB simulation software on a 2.3-GHz computer. Parameters related to the forced outage rate of the plant: alpha 10950, beta 1.8, gamma 1＝1.2，γ2＝1.8，P(Z1＝0)＝0.7，P(Z1＝1)＝0.4，P(Z 12, the maintenance rate mu is 2.5, and the parameters related to the reconfiguration of the distribution network are as follows: heavy loadThe evaluation simulation duration was 7 days (168 hours), and the maximum number of switching actions was 3, regardless of the switching on/off cost. The convergence condition in equations (27) and (28) is 0.05.
Step 201: and (5) initializing. Inputting data such as power distribution network information, load information, weather conditions, equipment parameters and the like;
step 202: a random scene is sampled. And (3) sampling the normal running time of the equipment except the line l by using formulas (1) and (2) according to the weather and aging information to obtain the fault moment. The overhaul time is sampled according to exponential distribution. And after the overhaul is finished, obtaining the next random fault moment by using the formulas (1) and (2) again. This sampling process is repeated until the states of all elements within the simulation interval are determined, i.e. a random scene s is generated as a sample.
Step 203: and calculating the reconfiguration of the distribution network. Under the obtained scene s, supposing that the line l is always normal and the line l is always in fault, carrying out power distribution network reconstruction calculation based on formulas (10) - (22) of second-order cone programming to obtain reliability indexes such as load loss, voltage out-of-limit and the like.
Step 204: and calculating an equipment importance index. The line importance calculation is performed according to equations (23) to (26).
Step 205: and (6) evaluating the overall convergence. The variance coefficient of the equipment importance index is calculated by equations (27) and (28), and the entire process is terminated when the variance coefficient satisfies the convergence condition. Otherwise, returning to step 202, generating a new random scene to continue the calculation.
Example 3:
based on the same invention concept, the invention also provides a system for identifying the important equipment of the reconfigurable power distribution network, and the principle of solving the technical problems of the equipment is similar to the identification method of the important equipment of the reconfigurable power distribution network, so repeated parts are not repeated.
The basic structure of the system is shown in fig. 5, and comprises:
the device comprises a scene generation module, an equipment importance evaluation module, a convergence judgment module and an importance calculation module;
the scene generation module is used for generating a random scene based on the basic aging process, the weather condition and the overhaul rate of the equipment;
the equipment importance evaluation module is used for respectively calculating an optimal reconstruction operation scheme of the power distribution network under the conditions that the equipment is reliable all the time and unreliable all the time based on each random scene, and calculating the importance of the equipment under the random scene based on the two optimal reconstruction operation schemes;
the convergence judging module is used for judging whether the change range of the importance of the equipment in all random scenes is within a set range: if yes, calling an importance calculation module; otherwise, continuing to call the scene generation module;
And the importance calculating module is used for taking the average value of the importance of the equipment in all random scenes as the importance of the equipment and ending.
The detailed structure of the identification system of the important equipment of the reconfigurable power distribution network is shown in fig. 6.
Wherein, equipment importance degree evaluation module includes: the reliability calculation submodule and the importance calculation submodule are connected with the importance calculation submodule;
the reliability calculation submodule is used for calculating the reliability of the power distribution network system under two optimal reconstruction operation schemes based on a random scene;
and the importance degree calculation operator module is used for calculating the difference of the reliability of the power distribution network system under the two optimal reconstruction operation schemes to obtain the importance degree of the equipment under the random scene.
Specifically, the reliability calculation sub-module is respectively based on random scenes, and the calculation equipment is used for reconstructing the system electricity shortage index and the per-unit system voltage of each node at each moment under the operation scheme by the optimal reconstruction of the power distribution network under the conditions of constant reliability and constant unreliability; and the reliability of the power distribution network system is determined by the system power shortage index and the per-unit system voltage of each node.
The importance degree calculation operator module comprises a load loss supporting importance degree unit and a voltage supporting importance degree unit;
the load loss support importance degree unit is used for calculating the difference of system electric quantity insufficiency indexes of the equipment at each moment under the conditions of constant reliability and constant unreliability of the equipment under the optimal reconstruction operation scheme of the power distribution network to obtain the load loss support importance degree of the equipment under a random scene;
The voltage support importance degree unit is used for calculating the per-unit system voltage difference of each node at each moment under the optimal reconstruction operation scheme of the power distribution network under the conditions that the equipment is always reliable and is always unreliable to obtain the voltage support importance degree of the equipment under a random scene;
the importance includes a load loss support importance and a voltage support importance.
The equipment importance evaluation module further comprises a distribution network reconstruction submodule, and the distribution network reconstruction submodule is used for respectively calculating an optimal reconstruction operation scheme of the power distribution network on the basis of each random scene under the two conditions that the equipment is reliable all the time and unreliable all the time. Specifically, the distribution network reconstruction submodule adopts a second-order cone planning distribution network reconstruction model, the minimum system electricity shortage is taken as a target function, the distribution network structure constraint, the power balance constraint, the node voltage constraint, the second-order cone constraint and the line capacity constraint are taken as constraint conditions, and based on a random scene, the optimal reconstruction operation scheme of the distribution network is calculated under the two conditions that equipment is always reliable and equipment is always unreliable.
The scene generation module comprises a fault modeling submodule, an operation time submodule, a maintenance time submodule, a time judgment submodule and a scene generation submodule;
The fault modeling submodule is used for establishing an equipment fault rate model by considering the basic aging process and the weather condition of the equipment based on the proportional risk model;
the operation time submodule is used for generating a fault moment and operation time based on the fault moment by adopting an inverse transformation sampling method based on the equipment fault rate model;
the maintenance time submodule is used for randomly generating maintenance time according to the historical maintenance rate of the equipment based on the fault moment;
the time judgment submodule is used for judging whether the current moment reaches the preset simulation time length after the maintenance time is passed: if so, sampling the running time and the overhaul time of the equipment and then calling a scene generation sub-module, otherwise calling the running time sub-module;
and the scene generation submodule is used for judging whether the running time and the overhaul time sampling of all other equipment except the equipment with the importance to be identified are finished: if so, generating a random scene according to the running time and the overhaul time of all other equipment, and otherwise, calling a fault modeling submodule.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present application and not for limiting the scope of protection thereof, and although the present application is described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that after reading the present application, they can make various changes, modifications or equivalents to the specific embodiments of the application, but these changes, modifications or equivalents are all within the scope of protection of the claims to be filed.

Claims (11)

1. A method for identifying important equipment of a reconfigurable power distribution network is characterized by comprising the following steps:
step 1: generating a random scene based on the basic aging process, the weather condition and the overhaul rate of the equipment;
step 2: on the basis of each random scene, respectively calculating an optimal reconstruction operation scheme of the power distribution network under the conditions that the equipment is always reliable and the equipment is always unreliable, and calculating the importance of the equipment in the random scene on the basis of the two optimal reconstruction operation schemes;
And step 3: judging whether the change range of the importance of the equipment is in a set range under all random scenes: if yes, executing step 4; otherwise, turning to the step 1;
and 4, step 4: and taking the average value of the importance degrees of the equipment in all random scenes as the importance degree of the equipment and finishing.
2. The method of claim 1, wherein said calculating the importance of the device in the stochastic scenario based on the two optimal reconstruction run solutions comprises:
calculating the reliability of the power distribution network system under the two optimal reconstruction operation schemes based on the random scene;
and calculating the difference of the reliability of the power distribution network system under the two optimal reconstruction operation schemes to obtain the importance of the equipment in the random scene.
3. The method of claim 2, wherein said calculating reliability of the power distribution network system under two of said optimal reconfiguration operating schemes based on said stochastic scenarios comprises:
respectively calculating the system electricity shortage index and per-unit system voltage of each node of the equipment at each moment under the conditions of constant reliability and constant unreliability of the equipment under the optimal reconstruction operation scheme of the power distribution network based on the random scenes;
And taking the system electric quantity insufficiency index and the per-unit system voltage of each node as the reliability of the power distribution network system.
4. The method of claim 3, wherein calculating the difference in reliability of the power distribution network system under the two optimal reconfiguration operating schemes to obtain the importance of the equipment in the stochastic scenario comprises:
calculating the difference of system electric quantity insufficiency indexes of the equipment at each moment under the conditions of constant reliability and constant unreliability of the equipment in the optimal reconstruction operation scheme of the power distribution network to obtain the load loss support importance of the equipment in the random scene;
calculating the difference of per-unit system voltages of each node at each moment under the optimal reconstruction operation scheme of the power distribution network under the conditions that the equipment is always reliable and is always unreliable to obtain the voltage support importance of the equipment under the random scene;
the importance includes a load loss support importance and a voltage support importance.
5. The method of claim 4, wherein the off-load support importance is calculated as:
Figure FDA0002038413870000011
in the formula (I), the compound is shown in the specification,
Figure FDA0002038413870000021
representing the load loss support importance of the equipment l in the scene s; l represents a set of all devices;
Figure FDA0002038413870000022
Representing the index of insufficient system electric quantity at the time t when the equipment l is reliable in the scene s;
Figure FDA0002038413870000023
representing the index of insufficient system electric quantity at the time t when the equipment l is unreliable in the scene s, and nt representing the total simulation duration;
the voltage support importance is calculated as follows:
Figure FDA0002038413870000024
in the formula (I), the compound is shown in the specification,
Figure FDA0002038413870000025
representing the voltage support importance of device i in scene s,
Figure FDA0002038413870000026
representing the per-unit system voltage at node n at time t when device l is reliable in scene s,
Figure FDA0002038413870000027
representing the per-unit system voltage of a node n at the time t when the device l is unreliable in the scene s; nb represents the total number of nodes in the power distribution network system.
6. The method of claim 1, wherein the determining whether the change range of the importance of the device in all the random scenes is within a set range comprises:
calculating variance coefficients of the importance of the equipment under all random scenes, and judging whether the variance coefficients are less than or equal to a set threshold value:
if so: the change range of the importance of the equipment is within the set range, otherwise, the change range of the importance of the equipment is not within the set range.
7. The method of claim 1, wherein generating a random scenario based on the basic aging process, weather conditions, and overhaul rate of the device comprises:
step 1-1: based on a proportional risk model, considering the basic aging process and the weather condition of the equipment, and establishing an equipment fault rate model;
Step 1-2: generating a fault moment and running time based on the fault moment by adopting an inverse transformation sampling method based on the equipment fault rate model;
step 1-3: based on the fault moment, randomly generating maintenance time according to the historical maintenance rate of the equipment;
step 1-4: judging whether the current moment reaches a preset simulation time length after the maintenance time is passed: if so, sampling the running time and the maintenance time of the equipment, and then turning to the step 1-5, otherwise, turning to the step 1-2;
step 1-5: judging whether the running time and the overhaul time sampling of all other equipment except the equipment with the importance to be identified are finished: if so, generating a random scene according to the running time and the maintenance time of all other equipment; otherwise, turning to the step 1-1;
in the proportional risk model, the basic aging process of the equipment is represented by a Weibull distributed benchmark function, and the weather condition is represented by an exponentially distributed connection function.
8. The method of claim 1, wherein the calculating the optimal reconfiguration operating scheme for the distribution network comprises:
and (3) adopting a second-order cone planning distribution network reconstruction model, taking the minimum system power shortage as a target function, taking distribution network structure constraint, power balance constraint, node voltage constraint, second-order cone constraint and line capacity constraint as constraint conditions, and calculating an optimal reconstruction operation scheme of the distribution network under the conditions that the equipment is reliable all the time and unreliable all the time based on a random scene.
9. The method of claim 8, wherein the objective function is expressed as:
Figure FDA0002038413870000031
in the formula, subscript m represents a node; n represents a set of all nodes; ENSmRepresenting the load loss of the node m;
the distribution network structural constraints are as follows:
Figure FDA0002038413870000032
Figure FDA0002038413870000033
Figure FDA0002038413870000034
Figure FDA0002038413870000035
in the formula, alphalIs a binary variable, which is 1 if the device l is connected, or 0 otherwise; beta is amnIs a binary variable, if the node n is the parent node of the node m, it is 1, otherwise it is 0; l represents a set of all devices; n denotes the set of all nodes, NsRepresenting a set of all substation nodes, N \ NsRepresenting the set of all nodes except the substation node, N(m)Representing a set of all nodes connected to the m-node;
the power balance constraint is as follows:
Figure FDA0002038413870000036
Figure FDA0002038413870000037
in the formula, PDmRepresenting the active load of node m, QDmRepresenting reactive load of node m, QRmRepresenting the reactive load shedding proportion of the node m; p is a radical ofmnRepresenting the active power between node m and node n, qmnRepresenting the reactive power between the node m and the node n;
the node voltage constraint is as follows:
Figure FDA0002038413870000038
Figure FDA0002038413870000039
Figure FDA0002038413870000041
-Vm，maxVn，max≤tl≤Vm，maxVn，max
Figure FDA0002038413870000042
Figure FDA0002038413870000043
in the formula (I), the compound is shown in the specification,
Figure FDA0002038413870000044
μm、rland tlAs an auxiliary variable, the number of variables,
Figure FDA0002038413870000045
when the device l is on-line
Figure FDA0002038413870000046
Otherwise
Figure FDA0002038413870000047
＝0，rl＝VmVncosθmn，tl＝VmVnsinθmnIn which V ismRepresenting the voltage magnitude, V, of node mnRepresenting the magnitude of the voltage at node n, θ mnRepresents the voltage angle difference between nodes m and n; vm,maxRepresents the maximum voltage amplitude, V, of node mn,maxRepresenting the maximum voltage amplitude of the node n;
the second order cone constraint is shown as follows:
Figure FDA0002038413870000048
in the formula (I), the compound is shown in the specification,
Figure FDA0002038413870000049
as an auxiliary variable, when the plant l is on-line
Figure FDA00020384138700000410
Otherwise
Figure FDA00020384138700000411
The line capacity constraint is given by:
Figure FDA00020384138700000412
in the formula Il,maxIndicates that the device l satisfies the maximum allowable current, A, for thermal stabilityl、Bl、ClAnd DlRespectively coefficients of the variance of the thermostabilization constraints.
10. A system for identifying important equipment of a reconfigurable power distribution network is characterized by comprising: the device comprises a scene generation module, an equipment importance evaluation module, a convergence judgment module and an importance calculation module;
the scene generation module is used for generating a random scene based on the basic aging process, the weather condition and the overhaul rate of the equipment;
the equipment importance evaluation module is used for respectively calculating an optimal reconstruction operation scheme of the power distribution network under the conditions that the equipment is always reliable and is always unreliable based on each random scene, and calculating the importance of the equipment under the random scene based on the two optimal reconstruction operation schemes;
the convergence judging module is used for judging whether the change range of the importance of the equipment is within a set range under all random scenes: if yes, calling an importance calculation module; otherwise, continuing to call the scene generation module;
And the importance calculating module is used for taking the average value of the importance of the equipment in all random scenes as the importance of the equipment and ending.
11. The system of claim 10, wherein the device importance assessment module comprises: the reliability calculation submodule and the importance calculation submodule are connected with the importance calculation submodule;
the reliability calculation submodule is used for calculating the reliability of the power distribution network system under the two optimal reconstruction operation schemes based on the random scene;
and the importance degree calculation operator module is used for calculating the difference of the reliability of the power distribution network system under the two optimal reconstruction operation schemes to obtain the importance degree of the equipment under the random scene.
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