CN116862149A - Power distribution network mobile emergency resource pre-configuration method considering extreme weather influence - Google Patents
Power distribution network mobile emergency resource pre-configuration method considering extreme weather influence Download PDFInfo
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Abstract
The invention discloses a power distribution network mobile emergency resource pre-allocation method considering extreme weather influence, which comprises the following steps: based on the power grid topology, setting traffic network nodes and power grid nodes to be sequentially coupled and corresponding, and collecting power grid information and traffic network information to obtain a power and traffic coupling network; carrying out mobile emergency resource driving path analysis based on Dijkstra algorithm; constructing a power distribution network fault scene under extreme weather based on a blind number theory; constructing a comprehensive fault rate model of the line in a single weather; constructing a comprehensive fault probability model of the circuit affected by multiple weather factors; building a mobile emergency resource pre-configuration model which takes the power and traffic coupling network into account in extreme weather; and performing resource pre-configuration based on the mobile emergency resource pre-configuration model. According to the method, the power distribution network fault recovery requirement under extreme weather and the influence of the extreme weather on the traffic network traffic capacity are comprehensively considered, so that the path decision is more matched with the actual working condition.
Description
Technical Field
The invention relates to the technical field of power resource allocation, in particular to a power distribution network mobile emergency resource pre-allocation method considering extreme weather influence.
Background
Along with the development of global warming trend and frequent extreme weather disasters, large-scale power failure accidents of a power system are caused for many times, a serious challenge is provided for safe and stable operation of a power grid, and how to improve the resistance and recovery capability of the power grid to similar extreme events becomes a currently focused hot spot; meanwhile, under the construction background of a novel power system, mobile emergency resources are rapidly developed, and the solving means of fault recovery is further expanded, so that the novel flexible resources are fully utilized to develop related researches on power grid recovery in extreme weather, and the method has important practical significance in reducing power failure loss and improving power grid elasticity.
On one hand, extreme weather can generate extra burden on power distribution network elements, the failure rate of the power distribution network elements is increased, for example, the lines are fluctuated or even broken in the weather of thunder, storm, typhoon and the like, and a tower is collapsed possibly to cause large-area damage of a power grid; on the other hand, extreme weather can influence the traffic network, so that the visibility of the road is poor, the road is slippery, and the like, and the traffic capacity of the road is reduced, so that the running speed of the vehicle is seriously slowed down.
In extreme weather conditions, the risk of load blackout is great, and particularly important load power supply is affected. In order to reduce power outage loss and improve the elasticity of a power grid, the power grid is generally configured with mobile emergency resources, such as mobile energy storage vehicles and the like; considering that the fault risk of the power distribution network is influenced by multiple extreme weather and the traffic network flow information is influenced in the transportation process of the mobile resources, for effectively improving the engineering applicability and feasibility of a post-disaster recovery scheme, it is necessary to research a mobile emergency resource pre-configuration technology considering the extreme weather influence and give a certain engineering application reference.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides a power distribution network mobile emergency resource pre-allocation method considering extreme weather influence, which is beneficial to reducing power failure loss and improving the elastic recovery capacity of a power grid.
A second object of the present invention is to provide a power distribution network mobile emergency resource pre-configuration system that accounts for extreme weather effects.
A third object of the present invention is to provide a computer-readable storage medium.
A fourth object of the present invention is to provide a computer device.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a power distribution network mobile emergency resource pre-configuration method considering extreme weather influence comprises the following steps:
based on the power grid topology, setting traffic network nodes and power grid nodes to be sequentially coupled and corresponding, and collecting power grid information and traffic network information to obtain a power and traffic coupling network;
carrying out mobile emergency resource driving path analysis based on Dijkstra algorithm;
constructing a power distribution network fault scene under extreme weather based on a blind number theory, obtaining the fault rate of the circuit when the circuit is powered off under different grades of weather factors based on statistical data, constructing a judgment matrix among different grades of factors, solving a feature vector under the maximum feature root of the matrix, and carrying out normalization processing on the feature vector to obtain reliability values under different grades to obtain a comprehensive fault rate model of the circuit under single weather;
constructing judgment matrixes under different weather factors, and solving the proportion of the different weather factors in the line fault rate, namely the reliability under the different weather factors, so as to obtain a comprehensive fault probability model of the line affected by multiple weather factors;
constructing a mobile emergency resource pre-configuration model which takes the electric power and traffic coupling network into account under extreme weather, wherein the mobile emergency resource pre-configuration model comprises decision variables, objective functions and constraint conditions;
the decision variables comprise positions of movable emergency resource collection points and the quantity of different types of movable emergency resources, the objective function comprises comprehensive load power failure loss cost, movable emergency resource driving fuel cost and movable emergency resource investment acquisition cost, and the constraint conditions comprise time response constraint, load recovery demand constraint, movable emergency resource supply quantity constraint and movable emergency resource allocation quantity constraint;
and performing resource pre-configuration based on the mobile emergency resource pre-configuration model.
As an preferable technical solution, the moving emergency resource driving path analysis based on Dijkstra algorithm specifically includes:
the introduced road resistance function is used as a standard for measuring the running time of a road section, and the function form is as follows:
t w0 ＝l w /v w
wherein t is w Time t required for the vehicle to pass the road section w w0 For the mean free path time, l, of the vehicle passing the road section w w Representing the length of the basic road section, v w Represents the road design speed, Q w For the traffic flow of road section w, C w The traffic capacity of the road section w is represented by alpha and beta, and the parameters to be calibrated are represented by alpha and beta;
dividing the traffic network roads into different road types, and assigning values to the parameters to be calibrated according to the different road types;
and calculating the running time of each road by using a road resistance function, constructing an adjacent matrix of the running time of the road according to a communication structure of the traffic network, and solving by using a Dijkstra algorithm to obtain the shortest running time and the corresponding running path between any road network nodes, thereby obtaining all the node site selection candidate schemes meeting the response time limit.
As a preferable technical scheme, the objective function comprises the minimum total cost of comprehensive load power outage loss cost, mobile emergency resource driving fuel cost and mobile emergency resource investment acquisition cost, and is specifically expressed as:
min f(x)＝α 1 f 1 +α 2 f 2 +α 3 f 3
wherein f 1 For loss of cost in load outage, f 2 F, driving fuel cost for moving emergency resources 3 To mobile emergency resource investment acquisition cost, alpha 1 、α 2 、α 3 Respectively f 1 、f 2 、f 3 The normalized weight coefficient of the load node can be flexibly determined according to the importance and the priority of the optimization target, N is the number of the load nodes, t i,x To move the emergency resource deposit location to the shortest time of node i,the power failure loss in unit time of the load of the node i is calculated; y is Y i Failure rate of power failure for node i load, < >>Loss of load unit for node i, P i load For the load capacity of the node i, H is the type/number of the mobile energy storage vehicle type number, +.>Driving cost for moving emergency resources to node i, < >>To move the unit fuel cost in the emergency resource driving to the target node, S h,i The number of configurations of the h kinds of mobile emergency resources coupled with the node i is +.>Unit investment cost for h mobile emergency resource, < > for h mobile emergency resource>Maximum output power for h-th mobile resource, beta h,y,i And (3) moving the connection state of the emergency resource and the node i for the y h type.
As a preferable technical solution, the time response constraint limits the longest response time from the aggregation point to any fault load node, which is expressed as:
t i,x ≤T res ,i∈N
wherein T is res For mobile emergency resource response time limitation, N is the number of load nodes;
the load recovery demand constraint is expressed as:
0≤P i re ≤P i load
wherein P is i re 、Respectively load nodesi active and reactive recovery amount, < ->Active and reactive recovery requirements of important loads are respectively met;
the mobile emergency resource supply constraints are expressed as:
wherein P is h,i 、Q h,i Active power and reactive power respectively output by ith node and h mobile emergency resource, lambda h Discharging efficiency for mobile emergency resources;
the mobile emergency resource allocation number constraint is expressed as:
wherein S is H The maximum configuration quantity of the h-th mobile emergency resource.
In order to achieve the second object, the present invention adopts the following technical scheme:
a power distribution network mobile emergency resource pre-configuration system that accounts for extreme weather effects, comprising: the system comprises an electric power and traffic coupling network construction module, a path analysis module, a fault scene construction module, a single weather fault rate model construction module, a multiple weather fault probability model construction module, a resource pre-configuration model construction module and a resource pre-configuration module;
the power and traffic coupling network construction module is used for setting traffic network nodes and power network nodes to be sequentially coupled and corresponding based on power grid topology, collecting power grid information and traffic network information, and obtaining a power and traffic coupling network;
the path analysis module is used for analyzing the running path of the mobile emergency resource based on Dijkstra algorithm;
the fault scene construction module is used for constructing a power distribution network fault scene under extreme weather based on a blind number theory;
the single weather fault rate model construction module is used for obtaining the fault rate of the circuit which is powered off under different levels of weather factors based on the statistical data, constructing a judgment matrix among the different levels of factors, solving a feature vector under the maximum feature root of the matrix, and carrying out normalization processing on the feature vector to obtain reliability values under the different levels to obtain a comprehensive fault rate model of the circuit under the single weather;
the multiple weather fault probability model construction module is used for constructing a judgment matrix under different weather factors, solving the proportion of the different weather factors in the line fault rate, namely the reliability under the different weather factors, and obtaining a multiple weather factor influence line comprehensive fault probability model;
the resource pre-allocation model construction module is used for constructing a mobile emergency resource pre-allocation model which takes the electric power and traffic coupling network into account in extreme weather, wherein the mobile emergency resource pre-allocation model comprises decision variables, objective functions and constraint conditions;
the decision variables comprise positions of movable emergency resource collection points and the quantity of different types of movable emergency resources, the objective function comprises comprehensive load power failure loss cost, movable emergency resource driving fuel cost and movable emergency resource investment acquisition cost, and the constraint conditions comprise time response constraint, load recovery demand constraint, movable emergency resource supply quantity constraint and movable emergency resource allocation quantity constraint;
the resource pre-configuration module is used for carrying out resource pre-configuration based on the mobile emergency resource pre-configuration model.
As an preferable technical solution, the path analysis module is configured to perform mobile emergency resource driving path analysis based on Dijkstra algorithm, and specifically includes:
the introduced road resistance function is used as a standard for measuring the running time of a road section, and the function form is as follows:
t w0 ＝l w /v w
wherein t is w Time t required for the vehicle to pass the road section w w0 For the mean free path time, l, of the vehicle passing the road section w w Representing the length of the basic road section, v w Represents the road design speed, Q w For the traffic flow of road section w, C w The traffic capacity of the road section w is represented by alpha and beta, and the parameters to be calibrated are represented by alpha and beta;
dividing the traffic network roads into different road types, and assigning values to the parameters to be calibrated according to the different road types;
and calculating the running time of each road by using a road resistance function, constructing an adjacent matrix of the running time of the road according to a communication structure of the traffic network, and solving by using a Dijkstra algorithm to obtain the shortest running time and the corresponding running path between any road network nodes, thereby obtaining all the node site selection candidate schemes meeting the response time limit.
As a preferable technical scheme, the objective function comprises the minimum total cost of comprehensive load power outage loss cost, mobile emergency resource driving fuel cost and mobile emergency resource investment acquisition cost, and is specifically expressed as:
min f(x)＝α 1 f 1 +α 2 f 2 +α 3 f 3
wherein f 1 For loss of cost in load outage, f 2 F, driving fuel cost for moving emergency resources 3 To mobile emergency resource investment acquisition cost, alpha 1 、α 2 、α 3 Respectively f 1 、f 2 、f 3 The normalized weight coefficient of the load node can be flexibly determined according to the importance and the priority of the optimization target, N is the number of the load nodes, t i,x To move the emergency resource deposit location to the shortest time of node i,the power failure loss in unit time of the load of the node i is calculated; y is Y i Failure rate of power failure for node i load, < >>Loss of load unit for node i, P i load For the load capacity of the node i, H is the type/number of the mobile energy storage vehicle type number, +.>Driving cost for moving emergency resources to node i, < >>To move the unit fuel cost in the emergency resource driving to the target node, S h,i The number of configurations of the h kinds of mobile emergency resources coupled with the node i is +.>Unit investment cost for h mobile emergency resource, < > for h mobile emergency resource>Maximum output power for h-th mobile resource, beta h,y,i Moving the connection of emergency resources to node i for the y h-th classStatus of the device.
As a preferable technical solution, the time response constraint limits the longest response time from the aggregation point to any fault load node, which is expressed as:
t i,x ≤T res ,i∈N
wherein T is res For mobile emergency resource response time limitation, N is the number of load nodes;
the load recovery demand constraint is expressed as:
0≤P i re ≤P i load
wherein P is i re 、Active and reactive recovery amounts of load node i respectively, < ->Active and reactive recovery requirements of important loads are respectively met;
the mobile emergency resource supply constraints are expressed as:
wherein P is h,i 、Q h,i Active power and reactive power respectively output by ith node and h mobile emergency resource, lambda h Discharging efficiency for mobile emergency resources;
the mobile emergency resource allocation number constraint is expressed as:
wherein S is H The maximum configuration quantity of the h-th mobile emergency resource.
In order to achieve the third object, the present invention adopts the following technical scheme:
a computer readable storage medium storing a program which when executed by a processor implements a power distribution network mobile emergency resource pre-configuration method as described above that accounts for extreme weather effects.
In order to achieve the fourth object, the present invention adopts the following technical scheme:
a computer device comprising a processor and a memory for storing a program executable by the processor, the processor implementing a power distribution network mobile emergency resource pre-configuration method as described above that accounts for extreme weather effects when executing the program stored by the memory.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) Aiming at the power distribution network fault recovery requirement under extreme weather, the influence of multiple extreme weather on the power distribution network fault risk and the traffic network traffic capacity is considered based on the coupling background of the power distribution network and the traffic network, and the Dijkstra algorithm is used for analyzing the mobile emergency resource driving path, so that the power distribution network mobile emergency resource pre-allocation method is more matched with the actual working condition.
(2) The invention comprehensively considers multiple extreme weather influences and mobile emergency resource cost, and the pre-configuration scheme can lay a foundation for resource scheduling after disaster, is beneficial to reducing power failure loss and improving power grid elasticity, and gives a certain engineering application reference.
Drawings
FIG. 1 is a flow chart of a method for pre-configuring mobile emergency resources of a power distribution network, which takes extreme weather effects into account;
FIG. 2 is a diagram of a power and traffic coupling network framework in accordance with the present invention;
fig. 3 is a topology diagram of a node traffic network according to the present embodiment 33;
FIG. 4 is a graph showing the shortest travel time profile of the emergency resource pool node to each node.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, the embodiment provides a power distribution network mobile emergency resource pre-configuration method considering extreme weather effects, which includes the following steps:
s1: as shown in fig. 2, constructing a power and traffic coupling network framework;
in this embodiment, step S1 is specifically as follows:
s11: the coupling network construction is based on the power grid topology, and the resource connection requirements of recovery of all load nodes are considered, so that the traffic network nodes and the power grid nodes are arranged to be coupled and correspond in sequence, and one power grid node can be coupled with only one road network node;
s12: the power grid acquisition information mainly comprises load recovery requirements and resource output conditions; the traffic network acquired information mainly comprises traffic flow conditions, and the traffic flow influences the running speed of the vehicle, so that the paths of different traffic flows take different time, and the selection of the running paths of the mobile emergency resources can be directly influenced;
as shown in fig. 3, a power grid topological structure and a load grading condition are obtained, wherein the traffic network topological structure comprises 33 nodes and 51 roads, and each node of the traffic network is respectively coupled and corresponds to each load node of the power distribution network;
as shown in table 1 below, load node parameters are obtained:
TABLE 1 load node parameters
As shown in table 2 below, the basic parameters of the traffic network are obtained:
table 2 road parameters of traffic network
As shown in the following table 3, parameter information such as a mobile emergency resource model number, an upper limit of quantity and the like, which can be selected by the pre-configuration scheme in this embodiment, is obtained:
TABLE 3 Mobile Emergency resource parameters
S2: performing a mobile emergency resource driving path analysis based on Dijkstra algorithm;
in this embodiment, step S2 is specifically as follows:
s21: the introduced road resistance function is used as a standard for measuring the running time of a road section, and the function form is as follows:
wherein t is w The time (min) required for the vehicle to traverse the road segment w; t is t w0 For averaging the self-travel of vehicles over road sections wFrom the travel time (min), t w0 ＝l w /v w Basic road section Length l w Road design vehicle speed v w ；Q w Traffic flow (vehicle/h) for road segment w; c (C) w Traffic capacity (vehicle/h) for road segment w; alpha and beta are parameters to be calibrated.
S22: considering the influence of road types on parameters to be calibrated, dividing the road of the traffic network into four types of expressways, main roads, secondary main roads and branch roads, and assigning values to the parameters to be calibrated;
as shown in the following table 4, the values of the parameters to be calibrated under different road types are obtained:
TABLE 4 parameters to be calibrated for different road types
| α | β | |
| Expressway | 1.5 | 5 |
| Main road | 2.5 | 4 |
| Secondary trunk road | 3 | 4 |
| Branch circuit | 3.5 | 4 |
S23: calculating the running time of each road by using a road resistance function, constructing an adjacent matrix of the running time of the road according to a traffic network communication structure, wherein the adjacent matrix is a matrix for indicating whether road network nodes are directly connected or not, for example, the road network node i is directly connected with the road network node j, the element of the ith row j of the matrix is the running time tij between the two nodes, and the element of the ith row j of the matrix is 0 if the element of the ith row j is not connected with the road network node j;
as shown in fig. 4, the Dijkstra algorithm is used to obtain the shortest running time and the corresponding running path between any road network nodes, so as to obtain all the node site selection candidate schemes meeting the response time limit;
s3: determining the comprehensive fault risk of the line under the influence of multiple weather factors;
in this embodiment, step S3 is specifically as follows:
s31: the weather information has randomness, ambiguity, gray property and unknown property, and accords with the definition of blind information, so that the construction of the power distribution network fault scene under extreme weather can be processed by using blind mathematical theory.
Blind number is defined as the value of [0,1 ]]The actual value of the gray function above does not fall at a certain point, but more likely falls within a certain interval around that point, for an object with uncertainty. The blind number includes two operations of possible value and credibility, and g (lambda) is set as interval gray number set, and the interval number sequence lambda of multiple possible values u Forming, the credibility of each interval is alpha u ∈[0,1]The confidence sequence of the distribution of the intervals forms a blind number f (lambda), and the formula is as follows:
s32: firstly, considering the influence of a single weather factor; let be provided with W u (u=1, 2,., n) weather factors affect the distribution network, and each weather factor is classified into m classes w uv (v=1, 2,., m) based on statistical data, the weather factors of the line at different levels are availableFailure rate Y at which power failure occurs uv Constructing a judgment matrix among different levels of factors, further solving a feature vector under the maximum feature root of the matrix, and finally normalizing the feature vector to obtain a credibility value alpha under different levels uv Obtaining a comprehensive fault rate model of the circuit under single weatherThe concrete steps are as follows:
s33: further, considering the influence of multiple weather factors, the proportion of different weather factors in the line fault rate, namely the reliability of different weather factors, can be solved by constructing a judgment matrix under different weather factorsThe method comprises the steps of obtaining a comprehensive fault probability model Y of the multiple weather factors affecting the circuit, wherein the model is specifically expressed as follows:
in this embodiment, multiple weather factors mainly consider three types of extreme weather including lightning, storm and typhoon, and as shown in the following table 5, the line fault rate under different levels of each extreme weather is obtained:
TABLE 5 line failure rates at different levels for extreme weather
Respectively establishing line fault rate models under different meteorological factors to obtain lightning W 1 Storm W 2 Typhoon W 3 The mean value of the failure rate of the element under the influence is as follows:
further, constructing judgment matrixes under different weather factors, and obtaining weight values under each weather influence to obtain the comprehensive fault probability of the circuit under the influence of multiple weather factors, wherein the comprehensive fault probability is as follows: y= 0.1628.
S4: constructing a mobile emergency resource pre-configuration model which takes an electric power-traffic coupling network into account in extreme weather, wherein the constructed pre-configuration model comprises decision variables, objective functions and constraint conditions;
specifically, the decision variables of the pre-configured model include the mobile emergency resource set point position x and the number S of different types of mobile emergency resources h,i ；
The objective functions of the pre-configured model include: the total cost of comprehensive load power failure loss cost, mobile emergency resource driving fuel cost and mobile emergency resource investment acquisition cost is minimum, and the method is specifically expressed as:
minf(x)＝α 1 f 1 +α 2 f 2 +α 3 f 3
wherein f 1 The cost is lost for load power failure; f (f) 2 Driving fuel cost for mobile emergency resources; f (f) 3 Purchase cost for mobile emergency resource investment; alpha 1 、α 2 、α 3 Respectively f 1 、f 2 、f 3 The normalized weight coefficient of the optimization target can be flexibly determined according to the importance and the priority of the optimization target; n is the number of load nodes; t is t i,x For the shortest time to move the emergency resource deposit location to node i;the power failure loss in unit time of the load of the node i is calculated; y is Y i The failure rate of power failure for the load of the node i; />Loss of load units for node i; p (P) i load Load capacity for node i; h is the type number of the mobile energy storage vehicle; />The running cost for moving the emergency resource to the node i; />A unit fuel cost in driving to the target node for the mobile emergency resource; s is S h,i The configuration number of h kinds of mobile emergency resources coupled with the node i; />The unit investment cost for the h mobile emergency resource; />Maximum output power for the h mobile resource; beta h,y,i The connection state of the emergency resource and the node i is moved for the y h type;
the constraint conditions of the pre-configuration model comprise four types, namely, an immediate response constraint, a load recovery requirement constraint, a mobile emergency resource supply constraint and a mobile emergency resource configuration quantity constraint.
Wherein the time response constraint: when the power distribution network is affected by extreme weather and fails, in order to ensure that the mobile emergency resource can respond quickly, the longest response time from the centralized point to any fault load node needs to be limited, and a time response constraint formula is shown as follows:
t i,x ≤T res ,i∈N
wherein T is res Response time limits for mobile emergency resources.
Load recovery demand constraint: the recovery amount of each load node is not more than the total load amount, the total load recovery amount is configured by taking important load recovery capable of supporting the power distribution network area as a standard, and a constraint formula of the load recovery requirement is shown as follows:
0≤P i re ≤P i load
wherein P is i re 、Active and reactive recovery amounts of the load nodes i are respectively; />The active and reactive recovery requirements of the important load are respectively.
Mobile emergency resource supply constraints: the total electric energy provided by the mobile emergency resources connected to any load node is larger than the recovery quantity of the load node, so that sufficient mobile emergency resources are ensured to be configured, and the constraint formula of the mobile emergency resources is shown as follows:
wherein P is h,i 、Q h,i Active power and reactive power which are respectively output by the ith mobile emergency resource of the ith node; lambda (lambda) h Discharging efficiency for mobile emergency resources.
Mobile emergency resource allocation quantity constraint: for the distribution network load nodes, the total number of the mobile emergency resource configurations of various types should not exceed the maximum allowable number limit. The mobile emergency resource allocation quantity constraint formula is shown as follows:
wherein S is H The maximum configuration quantity of the h-th mobile emergency resource.
By applying the mobile emergency resource pre-configuration method of the invention, the response time of the mobile emergency resource is set to be limited to 20min, so as to obtain the shortest running time distribution from the mobile emergency resource collection point to each node, and the shortest time from the mobile emergency resource collection point to each node is obtained as shown in the following table 6:
table 6 move emergency resource set nodes to each node for the shortest time
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According to the shortest running time among the nodes under the road resistance, factors such as load outage risk, mobile emergency resource cost and the like are comprehensively considered, the optimal set point of the mobile emergency power supply in the power distribution network area of the embodiment can be obtained to be the node 17, the shortest running time from the node 17 to other nodes of the power distribution network is within the response time limit range, and the site selection requirement is met.
Example 2
This embodiment is the same as embodiment 1 except for the following technical matters;
the embodiment provides a power distribution network mobile emergency resource pre-configuration system considering extreme weather influence, which comprises the following components: the system comprises an electric power and traffic coupling network construction module, a path analysis module, a fault scene construction module, a single weather fault rate model construction module, a multiple weather fault probability model construction module, a resource pre-configuration model construction module and a resource pre-configuration module;
in this embodiment, the power and traffic coupling network construction module is configured to set a traffic network node and a power network node to be coupled and corresponding in sequence based on a power network topology, and collect power network information and traffic network information to obtain a power and traffic coupling network;
in this embodiment, the path analysis module is configured to perform mobile emergency resource driving path analysis based on Dijkstra algorithm;
in this embodiment, the fault scenario construction module is configured to construct a fault scenario of the power distribution network under extreme weather based on a blind mathematical theory;
in this embodiment, the single weather fault rate model building module is configured to obtain, based on statistical data, a fault rate of a line that is powered off under different levels of weather factors, build a judgment matrix between different levels of factors, calculate a feature vector under a maximum feature root of the matrix, and normalize the feature vector to obtain reliability values under different levels, so as to obtain a comprehensive fault rate model of the line under a single weather;
in this embodiment, the multiple weather fault probability model building module is configured to build a judgment matrix under different weather factors, and solve the proportion of the different weather factors in the line fault rate, that is, the reliability under the different weather factors, to obtain a multiple weather factor influence line comprehensive fault probability model;
in this embodiment, the resource pre-configuration model building module is configured to build a mobile emergency resource pre-configuration model for accounting for a power and traffic coupling network in extreme weather, where the mobile emergency resource pre-configuration model includes decision variables, objective functions, and constraint conditions;
in the embodiment, the decision variables comprise positions of the movable emergency resource collection points and the quantity of different movable emergency resources, the objective function comprises comprehensive load power failure loss cost, movable emergency resource driving fuel cost and movable emergency resource investment acquisition cost, and the constraint conditions comprise time response constraint, load recovery requirement constraint, movable emergency resource supply quantity constraint and movable emergency resource configuration quantity constraint;
in this embodiment, the resource pre-configuration module is configured to perform resource pre-configuration based on a mobile emergency resource pre-configuration model.
In this embodiment, the path analysis module is configured to perform mobile emergency resource driving path analysis based on Dijkstra algorithm, and specifically includes:
the introduced road resistance function is used as a standard for measuring the running time of a road section, and the function form is as follows:
t w0 ＝l w /v w
wherein t is w Time t required for the vehicle to pass the road section w w0 For the mean free path time, l, of the vehicle passing the road section w w Representing the length of the basic road section, v w Represents the road design speed, Q w For the traffic flow of road section w, C w The traffic capacity of the road section w is represented by alpha and beta, and the parameters to be calibrated are represented by alpha and beta;
dividing the traffic network roads into different road types, and assigning values to the parameters to be calibrated according to the different road types;
and calculating the running time of each road by using a road resistance function, constructing an adjacent matrix of the running time of the road according to a communication structure of the traffic network, and solving by using a Dijkstra algorithm to obtain the shortest running time and the corresponding running path between any road network nodes, thereby obtaining all the node site selection candidate schemes meeting the response time limit.
In this embodiment, the objective function includes the minimum total cost of the integrated load outage loss cost, the mobile emergency resource driving fuel cost and the mobile emergency resource investment acquisition cost, which is specifically expressed as:
minf(x)＝α 1 f 1 +α 2 f 2 +α 3 f 3
wherein f 1 For loss of cost in load outage, f 2 F, driving fuel cost for moving emergency resources 3 To mobile emergency resource investment acquisition cost, alpha 1 、α 2 、α 3 Respectively f 1 、f 2 、f 3 The normalized weight coefficient of the load node can be flexibly determined according to the importance and the priority of the optimization target, N is the number of the load nodes, t i,x To move the emergency resource deposit location to the shortest time of node i,the power failure loss in unit time of the load of the node i is calculated; y is Y i Failure rate of power failure for node i load, < >>Loss of load unit for node i, P i load For the load capacity of the node i, H is the type/number of the mobile energy storage vehicle type number, +.>Driving cost for moving emergency resources to node i, < >>To move the unit fuel cost in the emergency resource driving to the target node, S h,i The number of configurations of the h kinds of mobile emergency resources coupled with the node i is +.>Unit investment cost for h mobile emergency resource, < > for h mobile emergency resource>Maximum output power for h-th mobile resource, beta h,y,i And (3) moving the connection state of the emergency resource and the node i for the y h type.
In this embodiment, the time response constraint limits the longest response time of the aggregate node to any faulty load node, expressed as:
t i,x ≤T res ,i∈N
wherein T is res For mobile emergency resource response time limitation, N is the number of load nodes;
the load recovery demand constraint is expressed as:
0≤P i re ≤P i load
wherein P is i re 、Active and reactive recovery amounts of load node i respectively, < ->Active and reactive recovery requirements of important loads are respectively met;
the mobile emergency resource supply constraints are expressed as:
wherein P is h,i 、Q h,i Active power and reactive power respectively output by ith node and h mobile emergency resource, lambda h Discharging efficiency for mobile emergency resources;
the mobile emergency resource allocation number constraint is expressed as:
wherein S is H The maximum configuration quantity of the h-th mobile emergency resource.
Example 3
The present embodiment provides a computer readable storage medium, where the storage medium may be a storage medium such as ROM, RAM, a magnetic disk, or an optical disk, and the storage medium stores one or more programs, and when the programs are executed by a processor, implement the power distribution network mobile emergency resource pre-allocation method of embodiment 1 that accounts for extreme weather effects.
Example 4
The present embodiment provides a computing device, which may be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer, or other terminal devices with display functions, where the computing device includes a processor and a memory, where the memory stores one or more programs, and when the processor executes the programs stored in the memory, the method for pre-configuring mobile emergency resources of a power distribution network according to embodiment 1, which takes into account extreme weather effects.
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
Claims (10)
1. The power distribution network mobile emergency resource pre-allocation method considering extreme weather influence is characterized by comprising the following steps of:
based on the power grid topology, setting traffic network nodes and power grid nodes to be sequentially coupled and corresponding, and collecting power grid information and traffic network information to obtain a power and traffic coupling network;
carrying out mobile emergency resource driving path analysis based on Dijkstra algorithm;
constructing a power distribution network fault scene under extreme weather based on a blind number theory;
obtaining failure rate of line power failure under different grades of weather factors based on statistical data, constructing a judgment matrix among different grades of factors, solving a feature vector under the maximum feature root of the matrix, carrying out normalization processing on the feature vector to obtain reliability values under different grades, and obtaining a comprehensive failure rate model of the line under single weather;
constructing judgment matrixes under different weather factors, and solving the proportion of the different weather factors in the line fault rate, namely the reliability under the different weather factors, so as to obtain a comprehensive fault probability model of the line affected by multiple weather factors;
constructing a mobile emergency resource pre-configuration model which takes the electric power and traffic coupling network into account under extreme weather, wherein the mobile emergency resource pre-configuration model comprises decision variables, objective functions and constraint conditions;
the decision variables comprise positions of movable emergency resource collection points and the quantity of different types of movable emergency resources, the objective function comprises comprehensive load power failure loss cost, movable emergency resource driving fuel cost and movable emergency resource investment acquisition cost, and the constraint conditions comprise time response constraint, load recovery demand constraint, movable emergency resource supply quantity constraint and movable emergency resource allocation quantity constraint;
and performing resource pre-configuration based on the mobile emergency resource pre-configuration model.
2. The method for pre-configuring the mobile emergency resources of the power distribution network, which takes extreme weather effects into account, according to claim 1, wherein the moving emergency resource driving path analysis is performed based on Dijkstra algorithm, and the specific steps include:
the introduced road resistance function is used as a standard for measuring the running time of a road section, and the function form is as follows:
t w0 ＝l w /v w
wherein t is w Time t required for the vehicle to pass the road section w w0 For the mean free path time, l, of the vehicle passing the road section w w Representing the length of the basic road section, v w Represents the road design speed, Q w For the traffic flow of road section w, C w The traffic capacity of the road section w is represented by alpha and beta, and the parameters to be calibrated are represented by alpha and beta;
dividing the traffic network roads into different road types, and assigning values to the parameters to be calibrated according to the different road types;
and calculating the running time of each road by using a road resistance function, constructing an adjacent matrix of the running time of the road according to a communication structure of the traffic network, and solving by using a Dijkstra algorithm to obtain the shortest running time and the corresponding running path between any road network nodes, thereby obtaining all the node site selection candidate schemes meeting the response time limit.
3. The method for pre-configuring mobile emergency resources of a power distribution network, according to claim 1, wherein said objective function comprises a total cost minimum of integrated load outage loss cost, mobile emergency resource driving fuel cost, mobile emergency resource investment acquisition cost, specifically expressed as:
minf(x)＝α 1 f 1 +α 2 f 2 +α 3 f 3
wherein f 1 For loss of cost in load outage, f 2 F, driving fuel cost for moving emergency resources 3 To mobile emergency resource investment acquisition cost, alpha 1 、α 2 、α 3 Respectively f 1 、f 2 、f 3 The normalized weight coefficient of the load node can be flexibly determined according to the importance and the priority of the optimization target, N is the number of the load nodes, t i,x To move the emergency resource deposit location to the shortest time of node i,for node iPower failure loss per unit time of load; y is Y i Failure rate of power failure for node i load, < >>Loss of load unit for node i, P i load For the load capacity of the node i, H is the type/number of the mobile energy storage vehicle type number, +.>Driving cost for moving emergency resources to node i, < >>To move the unit fuel cost in the emergency resource driving to the target node, S h,i The number of configurations of the h kinds of mobile emergency resources coupled with the node i is +.>Unit investment cost for h mobile emergency resource, < > for h mobile emergency resource>Maximum output power for h-th mobile resource, beta h,y,i And (3) moving the connection state of the emergency resource and the node i for the y h type.
4. The method of pre-configuring mobile emergency resources of a power distribution network, taking into account extreme weather effects, according to claim 1, characterized in that said time response constraint limits the longest response time of the aggregation point to any faulty load node, expressed as:
t i,x ≤T res ,i∈N
wherein T is res For mobile emergency resource response time limitation, N is the number of load nodes;
the load recovery demand constraint is expressed as:
0≤P i re ≤P i load
wherein P is i re 、Active and reactive recovery amounts of load node i respectively, < ->Active and reactive recovery requirements of important loads are respectively met;
the mobile emergency resource supply constraints are expressed as:
wherein P is h,i 、Q h,i Active power and reactive power respectively output by ith node and h mobile emergency resource, lambda h Discharging efficiency for mobile emergency resources;
the mobile emergency resource allocation number constraint is expressed as:
wherein S is H The maximum configuration quantity of the h-th mobile emergency resource.
5. A power distribution network mobile emergency resource pre-configuration system that accounts for extreme weather effects, comprising: the system comprises an electric power and traffic coupling network construction module, a path analysis module, a fault scene construction module, a single weather fault rate model construction module, a multiple weather fault probability model construction module, a resource pre-configuration model construction module and a resource pre-configuration module;
the power and traffic coupling network construction module is used for setting traffic network nodes and power network nodes to be sequentially coupled and corresponding based on power grid topology, collecting power grid information and traffic network information, and obtaining a power and traffic coupling network;
the path analysis module is used for analyzing the running path of the mobile emergency resource based on Dijkstra algorithm;
the fault scene construction module is used for constructing a power distribution network fault scene under extreme weather based on a blind number theory;
the single weather fault rate model construction module is used for obtaining the fault rate of the circuit which is powered off under different levels of weather factors based on the statistical data, constructing a judgment matrix among the different levels of factors, solving a feature vector under the maximum feature root of the matrix, and carrying out normalization processing on the feature vector to obtain reliability values under the different levels to obtain a comprehensive fault rate model of the circuit under the single weather;
the multiple weather fault probability model construction module is used for constructing a judgment matrix under different weather factors, solving the proportion of the different weather factors in the line fault rate, namely the reliability under the different weather factors, and obtaining a multiple weather factor influence line comprehensive fault probability model;
the resource pre-allocation model construction module is used for constructing a mobile emergency resource pre-allocation model which takes the electric power and traffic coupling network into account in extreme weather, wherein the mobile emergency resource pre-allocation model comprises decision variables, objective functions and constraint conditions;
the decision variables comprise positions of movable emergency resource collection points and the quantity of different types of movable emergency resources, the objective function comprises comprehensive load power failure loss cost, movable emergency resource driving fuel cost and movable emergency resource investment acquisition cost, and the constraint conditions comprise time response constraint, load recovery demand constraint, movable emergency resource supply quantity constraint and movable emergency resource allocation quantity constraint;
the resource pre-configuration module is used for carrying out resource pre-configuration based on the mobile emergency resource pre-configuration model.
6. The mobile emergency resource pre-configuration system for a power distribution network, which takes extreme weather effects into account according to claim 5, wherein the path analysis module is configured to perform mobile emergency resource driving path analysis based on Dijkstra algorithm, and specifically comprises:
the introduced road resistance function is used as a standard for measuring the running time of a road section, and the function form is as follows:
t w0 ＝l w /v w
wherein t is w Time t required for the vehicle to pass the road section w w0 For the mean free path time, l, of the vehicle passing the road section w w Representing the length of the basic road section, v w Represents the road design speed, Q w For the traffic flow of road section w, C w The traffic capacity of the road section w is represented by alpha and beta, and the parameters to be calibrated are represented by alpha and beta;
dividing the traffic network roads into different road types, and assigning values to the parameters to be calibrated according to the different road types;
and calculating the running time of each road by using a road resistance function, constructing an adjacent matrix of the running time of the road according to a communication structure of the traffic network, and solving by using a Dijkstra algorithm to obtain the shortest running time and the corresponding running path between any road network nodes, thereby obtaining all the node site selection candidate schemes meeting the response time limit.
7. The mobile emergency resource pre-configuration system for a power distribution network, accounting for extreme weather effects, according to claim 5, wherein said objective function comprises a total cost minimum of integrated load outage loss cost, mobile emergency resource driving fuel cost, mobile emergency resource investment acquisition cost, expressed in detail as:
minf(x)＝α 1 f 1 +α 2 f 2 +α 3 f 3
wherein f 1 For loss of cost in load outage, f 2 F, driving fuel cost for moving emergency resources 3 To mobile emergency resource investment acquisition cost, alpha 1 、α 2 、α 3 Respectively f 1 、f 2 、f 3 The normalized weight coefficient of the load node can be flexibly determined according to the importance and the priority of the optimization target, N is the number of the load nodes, t i,x To move the emergency resource deposit location to the shortest time of node i,the power failure loss in unit time of the load of the node i is calculated; y is Y i Is node i minusFailure rate of the occurrence of a power failure of the charge, +.>Loss of load unit for node i, P i load For the load capacity of the node i, H is the type/number of the mobile energy storage vehicle type number, +.>Driving cost for moving emergency resources to node i, < >>To move the unit fuel cost in the emergency resource driving to the target node, S h,i The number of configurations of the h kinds of mobile emergency resources coupled with the node i is +.>Unit investment cost for h mobile emergency resource, < > for h mobile emergency resource>Maximum output power for h-th mobile resource, beta h,y,i And (3) moving the connection state of the emergency resource and the node i for the y h type.
8. The extreme weather effect-accounting power distribution network mobile emergency resource pre-configuration system of claim 5, wherein said time response constraint limits the longest response time of a collection node to any faulty load node, expressed as:
t i,x ≤T res ,i∈N
wherein T is res For mobile emergency resource response time limitation, N is the number of load nodes;
the load recovery demand constraint is expressed as:
0≤P i re ≤P i load
wherein P is i re 、Active and reactive recovery amounts of load node i respectively, < ->Active and reactive recovery requirements of important loads are respectively met;
the mobile emergency resource supply constraints are expressed as:
wherein P is h,i 、Q h,i Active power and reactive power respectively output by ith node and h mobile emergency resource, lambda h Discharging efficiency for mobile emergency resources;
the mobile emergency resource allocation number constraint is expressed as:
wherein the method comprises the steps of，S H The maximum configuration quantity of the h-th mobile emergency resource.
9. A computer readable storage medium storing a program which when executed by a processor implements a power distribution network mobile emergency resource pre-configuration method according to any of claims 1-4, taking into account extreme weather effects.
10. Computer device comprising a processor and a memory for storing a program executable by the processor, characterized in that the processor, when executing the program stored in the memory, implements a method for pre-configuring mobile emergency resources of a power distribution network taking into account extreme weather effects according to any of claims 1-4.
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