CN113537703B - Power distribution network pre-disaster emergency resource deployment method and device and computer equipment - Google Patents
Power distribution network pre-disaster emergency resource deployment method and device and computer equipment Download PDFInfo
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Abstract
The application relates to a method and a device for deploying emergency resources before a power distribution network disaster, computer equipment and a storage medium. The method comprises the following steps: acquiring emergency resources before a power distribution network disaster; decomposing the emergency resources before the power distribution network disaster according to preset resource deployment constraint conditions to generate at least two candidate emergency resource deployment combinations; determining a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function; the target optimization function comprises a distribution network disaster-suffered power failure risk prediction model and a post-disaster power failure loss prediction model; the function value is used for representing the sum of a distribution network disaster-suffering power failure risk predicted value and a post-disaster power failure loss predicted value corresponding to the candidate emergency resource deployment combination; and taking the candidate emergency resource deployment combination with the minimum function value as a target emergency resource deployment combination. By adopting the method, the disaster resistance of the power distribution network can be improved.
Description
Technical Field
The application relates to the technical field of electric power, in particular to a method and a device for deploying emergency resources before a power distribution network disaster, computer equipment and a storage medium.
Background
In recent years, typhoon disasters have high strength, great hidden dangers are brought to the safety of a power grid in the coastal zone, and electric power system accidents caused by the typhoon disasters cause great economic losses to the society.
Aiming at predictable typhoon disasters, the power failure risk can be reduced to a certain extent by deploying personnel and materials in advance before the disasters, the power restoration after the disasters is accelerated, and the power failure loss is reduced. However, the related art still stays in a relatively simple state depending on manual experience when emergency resources before a disaster are deployed, and the emergency resources before the disaster cannot be reasonably deployed for the power distribution network, which is not beneficial to effectively improving the disaster resistance of the power distribution network.
Disclosure of Invention
Therefore, in order to solve the technical problems, a method, a device, a computer device and a storage medium for deploying emergency resources before a power distribution network disaster are provided, wherein the method, the device, the computer device and the storage medium can effectively improve the disaster resistance of the power distribution network.
A pre-disaster emergency resource deployment method for a power distribution network comprises the following steps:
acquiring emergency resources before a power distribution network disaster; the power distribution network pre-disaster emergency resources comprise the total number of emergency substances and the total number of emergency personnel deployed at a target position point;
decomposing the emergency resources before the power distribution network disaster according to preset resource deployment constraint conditions to generate at least two candidate emergency resource deployment combinations;
determining a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function; the target optimization function comprises a distribution network disaster power failure risk prediction model and a post-disaster power failure loss prediction model; the function value is used for representing the sum of a distribution network disaster-suffering power failure risk predicted value and a post-disaster power failure loss predicted value corresponding to the candidate emergency resource deployment combination;
and taking the candidate emergency resource deployment combination with the minimum function value as a target emergency resource deployment combination.
In one embodiment, the target location point comprises at least one of a distribution network device location and a rescue center location in the power distribution network.
In one embodiment, the function value corresponding to the target emergency resource deployment combination is represented as:
wherein x represents a candidate emergency resource deployment combination; argmin represents the value of the variable when the following formula reaches the minimum value; r s Representing a distribution network disaster-suffering power failure risk prediction model; r e A power failure loss prediction model after the disaster is expressed; x = [ r = d ,h d ,r m ,h m ]，d∈D，m∈M，r d Representing the number of emergency resources deployed at the distribution network device d; h is d Representing the number of emergency personnel deployed at the distribution network device d; r is a radical of hydrogen m Representing the number of emergency resources deployed at the emergency rescue center m; h is m Representing the number of emergency personnel deployed at the emergency rescue center m.
In one embodiment, the resource deployment constraint is expressed as:
Σ d∈D r d +Σ m∈M r m ＝r total
Σ d∈D h d +Σ m∈M h m ＝h total
wherein r is total Representing a total number of emergency materials for deployment at the target location point; h is a total of total Indicating the total number of people and supplies available for dispatch.
In one embodiment, the distribution network disaster-tolerant power outage risk prediction model is represented as:
wherein C represents a load number, and C represents a set of all loads; t is s Indicating the moment when the typhoon weakens to be harmless; p c (x, t) represents the outage probability of the load c at time t; v c Is the value coefficient of the load; f c (t) represents the power lost by the load c at time t.
In one embodiment, the outage probability of the load c at time t is represented as:
wherein, P rt (x, t) represents the failure probability of the transmission line rt, expressed as:
P rt (x，t)＝1-Π d∈rt (1-P d (x，t))
wherein d e rt represents that the distribution network equipment d is on the distribution network route rt, P d (x, t) represents the failure probability of the distribution network equipment d.
In one embodiment, the post-disaster power outage loss prediction model is represented as:
wherein s represents the scene of the damage of the distribution network equipment after disaster, and omega x The method comprises the following steps that a scene set of disaster-suffering outage of equipment in a distribution network is shown after emergency personnel and emergency material allocation schemes are given; t is a unit of e Representing the time of the first-aid repair end;
the power loss function of the load c at the time t shows that the load c is still in a power loss state when the value is 1, and shows that the load c recovers power supply when the value is 0;
indicating a power supply path first-aid repair scheme.
A pre-disaster emergency resource deployment device for a power distribution network, the device comprising:
the acquisition module is used for acquiring emergency resources before the power distribution network disaster; the power distribution network pre-disaster emergency resources comprise the total number of emergency substances and the total number of emergency personnel deployed at a target position point;
the decomposition module is used for decomposing the emergency resources before the power distribution network disaster according to preset resource deployment constraint conditions to generate at least two candidate emergency resource deployment combinations;
the determining module is used for determining a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function; the target optimization function comprises a distribution network disaster power failure risk prediction model and a post-disaster power failure loss prediction model; the function value is used for representing the sum of a distribution network disaster-suffering power failure risk predicted value and a post-disaster power failure loss predicted value corresponding to the candidate emergency resource deployment combination;
and the deployment module is used for taking the candidate emergency resource deployment combination with the minimum function value as a target emergency resource deployment combination.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
According to the method, the device, the computer equipment and the storage medium for deploying the emergency resources before the power distribution network disaster, the emergency resources before the power distribution network disaster are obtained; the pre-disaster emergency resources of the power distribution network comprise the total number of emergency substances and the total number of emergency personnel for deployment at a target location point; decomposing emergency resources before the power distribution network disaster according to preset resource deployment constraint conditions to generate at least two candidate emergency resource deployment combinations; determining a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function; the target optimization function comprises a distribution network disaster-suffered power failure risk prediction model and a post-disaster power failure loss prediction model; the function value is used for representing the sum of a distribution network disaster-suffering power failure risk predicted value and a post-disaster power failure loss predicted value corresponding to the candidate emergency resource deployment combination; and finally, taking the candidate emergency resource deployment combination with the minimum function value as a target emergency resource deployment combination. Therefore, pre-disaster emergency resource deployment aiming at minimizing the disaster risk and post-disaster loss of the power distribution network can be realized, and the disaster resistance of the power distribution network is effectively improved.
Drawings
Fig. 1 is an application environment diagram of a method for deploying emergency resources before a power distribution network disaster in an embodiment;
fig. 2 is a schematic flow chart of a method for deploying emergency resources before a power distribution network disaster in an embodiment;
FIG. 3 is a schematic illustration of an apparatus brittleness curve according to one embodiment;
FIG. 4 is a flow chart illustrating a dynamic personnel allocation process according to an embodiment;
FIG. 5 is a schematic diagram of a topology of a standard distribution network in one embodiment;
FIG. 6 is a schematic diagram illustrating a comparison of outage probabilities for nodes before and after a transition in another embodiment;
fig. 7 is a block diagram illustrating a configuration of an emergency resource deployment device before a power distribution network disaster occurs in an embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for deploying the emergency resources before the power distribution network disaster can be applied to the application environment shown in fig. 1. The server 110 obtains emergency resources before the power distribution network disaster; the power distribution network pre-disaster emergency resources comprise the total number of emergency substances and the total number of emergency personnel deployed at a target position point; the server 110 decomposes the emergency resources before the power distribution network disaster according to preset resource deployment constraint conditions to generate at least two candidate emergency resource deployment combinations; the server 110 determines a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function; the target optimization function comprises a distribution network disaster-suffered power failure risk prediction model and a post-disaster power failure loss prediction model; the function value is used for representing the sum of a distribution network disaster-suffered power failure risk prediction value corresponding to the candidate emergency resource deployment combination and a post-disaster power failure loss prediction value; the server 110 takes the candidate emergency resource deployment combination with the minimum function value as a target emergency resource deployment combination. In practical applications, the server 110 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
In an embodiment, as shown in fig. 2, a method for deploying emergency resources before a power distribution network disaster is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
step S210, acquiring emergency resources before a power distribution network disaster; the emergency resources before the power distribution network disaster comprise the total number of emergency substances and the total number of emergency personnel for deployment at the target position point.
The target location point comprises at least one of a distribution network device position and an emergency rescue center position in the power distribution network.
In specific implementation, when the server deploys emergency resources before a disaster, the server may obtain the total number of emergency materials and the total number of emergency personnel used for deployment at a target location point. Specifically, the server may obtain the total number of all emergency materials and the total number of emergency personnel for deployment at the distribution network equipment and the emergency rescue center as emergency resources before the power distribution network disaster.
Step S220, decomposing the emergency resources before the power distribution network disaster according to preset resource deployment constraint conditions, and generating at least two candidate emergency resource deployment combinations.
In the specific implementation, after the server obtains the emergency resources before the power distribution network disaster, the server decomposes the emergency resources before the power distribution network disaster according to the preset resource deployment constraint conditions to generate at least two candidate emergency resource deployment combinations.
Specifically, the server can pre-distribute emergency materials and emergency personnel for each distribution network device and each emergency rescue center in the distribution network based on the resource deployment constraint conditions, and generate candidate emergency resource deployment combinations as many as possible. For example, if it is known that the total number of emergency materials for deployment is 1, and the total number of emergency personnel is 1, the power distribution network has a distribution network device a and an emergency assistance center B, the server may decompose emergency resources before the power distribution network is in a disaster, and generate the following candidate emergency resource deployment combinations, including:
candidate emergency resource deployment combinations 1: the emergency material quality number deployed by the distribution network equipment A is 1, the emergency material personnel deployed by the distribution network equipment A is 0, the emergency material quality number deployed by the emergency rescue center B is 0, and the deployed emergency material personnel are a resource deployment combination of 1;
candidate emergency resource deployment combinations 2: the emergency material quality number deployed by the distribution network equipment A is 0, the emergency material personnel deployed by the distribution network equipment A is 0, the emergency material quality number deployed by the emergency rescue center B is 1, and the resource deployment combination of the emergency material personnel deployed by the emergency rescue center B is 1;
candidate emergency resource deployment combinations 3: the emergency material quality number of the distribution network equipment A is 0, the emergency material personnel of the distribution network equipment A are 1, the emergency material quality number of the emergency rescue center B is 1, and the emergency material personnel of the distribution network equipment A are resource deployment combinations of 0.
And step S230, determining a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function.
The objective optimization function comprises a distribution network disaster-suffered power failure risk prediction model and a post-disaster power failure loss prediction model.
And the function value is used for representing the sum of the distribution network disaster-suffered power failure risk predicted value and the post-disaster power failure loss predicted value corresponding to the candidate emergency resource deployment combination.
In specific implementation, the server determines a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function. Specifically, the server may input the candidate emergency resource deployment combination to the distribution network disaster-tolerant power failure risk prediction model to obtain a distribution network disaster-tolerant power failure risk prediction value corresponding to the candidate emergency resource deployment combination. And the server inputs the candidate emergency resource deployment combination into the post-disaster power failure loss prediction model to obtain a post-disaster power failure loss prediction value corresponding to the candidate emergency resource deployment combination.
And then, adding the distribution network disaster-suffered power failure risk predicted value corresponding to the candidate emergency resource deployment combination and the post-disaster power failure loss predicted value corresponding to the candidate emergency resource deployment combination by the server to obtain a function value corresponding to the candidate emergency resource deployment combination.
And step S240, taking the candidate emergency resource deployment combination with the minimum function value as a target emergency resource deployment combination.
In specific implementation, the server takes the candidate emergency resource deployment combination with the smallest function value as the target emergency resource deployment combination in each candidate emergency resource deployment combination. Namely, when the target emergency resource deployment combination is adopted for pre-disaster deployment, the sum of the distribution network disaster-suffered power failure risk prediction value and the post-disaster power failure loss prediction value corresponding to the target emergency resource deployment combination is minimum.
In the distribution network pre-disaster emergency resource deployment method, the distribution network pre-disaster emergency resources are acquired; the pre-disaster emergency resources of the power distribution network comprise the total number of emergency substances and the total number of emergency personnel for deployment at a target location point; decomposing emergency resources before the power distribution network disaster according to preset resource deployment constraint conditions to generate at least two candidate emergency resource deployment combinations; determining a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function; the target optimization function comprises a distribution network disaster power failure risk prediction model and a post-disaster power failure loss prediction model; the function value is used for representing the sum of a distribution network disaster-suffered power failure risk predicted value and a post-disaster power failure loss predicted value corresponding to the candidate emergency resource deployment combination; and finally, taking the candidate emergency resource deployment combination with the minimum function value as a target emergency resource deployment combination. Therefore, pre-disaster emergency resource deployment aiming at minimizing the disaster risk and post-disaster loss of the power distribution network can be realized, and the disaster resistance of the power distribution network is effectively improved.
In one embodiment, the function value corresponding to the target emergency resource deployment combination is expressed as:
wherein x represents a candidate emergency resource deployment combination; argmin represents the value of the variable when the following formula reaches the minimum value; r is s Representing a distribution network disaster-suffering power failure risk prediction model; r e To representA post-disaster power failure loss prediction model; x = [ r = d ,h d ,r m ,h m ]，d∈D，m∈M，r d Representing the number of emergency resources deployed at distribution network device d; h is d Represents the number of emergency personnel deployed at distribution network device d; r is m Representing the number of emergency resources deployed at emergency rescue center m; h is m Representing the number of emergency personnel deployed at the emergency rescue center m.
In one embodiment, the resource deployment constraint is expressed as:
Σ d∈D r d +Σ m∈M r m ＝r total
Σ d∈D h d +Σ m∈M h m ＝h total
wherein r is total Representing a total number of emergency materials for deployment at the target location point; h is total Indicating the total number of personnel and supplies available for dispatch.
In one embodiment, the distribution network disaster-tolerant power outage risk prediction model is represented as follows:
wherein C represents a load number, and C represents a set of all loads; t is a unit of s Indicating the moment when the typhoon weakens to be harmless; p c (x, t) represents the outage probability of the load c at time t; v c Is the value coefficient of the load; f c (t) represents the power lost by the load c at time t.
To simplify the problem, all emergency resources and personnel are considered homogeneous here, i.e. no differences in resource type and personnel capacity are considered. And further, providing an analysis model of distribution network power failure risk and power failure loss influenced by emergency personnel and material allocation schemes.
In one embodiment, the outage probability for load c at time t is expressed as:
wherein, P rt (x, t) represents the failure probability of the transmission line rt, expressed as:
P rt (x,t)＝1-Π d∈rt (1-P d (x,t))
wherein d ∈ rt represents that the distribution network equipment d is on the distribution network route rt, and P d (x, t) represents the probability of failure of distribution network device d.
According to engineering structure analysis, at given disaster intensity S d (e.g., wind speed), the probability of the power plant reaching the damage state ds is described by a lognormal distribution function:
wherein
And beta ds Respectively, the expectation and standard deviation of the intensity of the hazard when the facility reaches a critical failure state.
After personnel and materials are allocated in advance and the disaster resistance intensity of the equipment is enhanced, the method is considered as follows:
wherein
The expectation of the intensity of the disaster that the equipment can reach the critical damage state when the equipment is brand new,
indicating the design disaster resistance, omega, of the device d d (a d ,h d ,r d ) Indicating the disaster resistance of the equipment d after reinforcement. Comprises the following steps:
wherein the content of the first and second substances,
indicating electrical equipment e i Designed service life of a d The actual commissioning time of the equipment; xi shape d The value of the disaster-resistant grade loss degree is [0,1 ] when the equipment reaches the service life]；h d Is the number of emergency repair personnel deployed at electrical equipment d; lambda [ alpha ] d The effect coefficient for throwing in rush repair personnel represents the maximum effect of disaster resistance improvement which can be played by maintenance personnel throwing in electrical equipment, and the value is not very large under the general condition and is [0,0.5 ]]In between.
Is to reinforce electrical equipment e i The number of basic resources required, e.g. a set of stiffeners, r d Is the amount of hardened resource deployment for the design deployment.
Therefore, the brittleness curve of the electric power facility under the typhoon disaster is obtained:
wherein v is critical Critical value of wind force, v, for possible damage of the plant collapse Is a critical value of wind power for almost certain damage of equipment. P is hw (v) And calculating the probability of the corresponding damage of the tower when the wind power is between the two values according to the formula (1). The brittleness curve of the device is shown in fig. 3:
in typhoon disasters, the typhoon propelling range and the maximum wind speed are known through weather forecast, and then the disaster and power failure risk of the distribution network can be calculated.
In one embodiment, the post-disaster power outage loss prediction model is represented as:
wherein s represents the scene of the damage of the distribution network equipment after disaster, and omega x The method comprises the following steps of representing a scene set of disaster-suffered and outage of equipment in a distribution network after emergency personnel and emergency material allocation schemes are given; t is a unit of e Representing the time of the first-aid repair end;
the power loss function of the load c at the time t shows that the load c is still in a power loss state when the value is 1, and shows that the load c recovers power supply when the value is 0;
and the specific scheduling and use conditions of emergency repair personnel and materials at the time t are shown.
In the optimization problem, in order to reduce the calculation amount and ensure that the algorithm has certain robustness, the disaster-tolerant shutdown probability of the equipment is considered to be positively correlated with the damage degree of the equipment and negatively correlated with the repair efficiency of the equipment. According to past experience, under the condition of abundant personnel and materials, the efficiency of restoring the electric power facility d from the damaged state is
(dimension%/h). Then the repair efficiency at time t after disaster is expressed as:
wherein the content of the first and second substances,
indicating the manpower and materials currently required by the electrical facility d. Expressed as:
wherein the content of the first and second substances,
indicating the manpower and materials required for the repair of the device d from the damaged state,
indicating the time at which the power device d was repaired. Satisfies the following conditions:
wherein the content of the first and second substances,
indicating the manpower and material present at d.
Is shown as
Indicating the quantity of materials and personnel transported from the emergency repair center to the electrical facilities, and between the electrical facilities to each other at time t. The constraint conditions should be satisfied:
expressions (9) to (10) indicate that the manpower and equipment transported from the emergency repair center are not greater than the existing amount of personnel supplies, expressions (11) to (12) indicate that the manpower and equipment transported from the supply demand point are not greater than the current redundancy amount, and expressions (13) to (14) indicate that the personnel and equipment transported to a certain point should not be greater than the gap amount at the point (in view of the limitation of personnel supplies).
Wherein the content of the first and second substances,
can be expressed as:
wherein
Indicating the time when the load c resumes power. Because the first-aid repair is a parallel process, the method comprises the following steps:
wherein
Indicating the time of transmission line rt repair. Comprises the following steps:
in this optimization problem, it is desirable that the loss of power outage during a first-aid repair is a variable related only to the pre-disaster deployment x. For simplicity, the dynamic personnel material distribution process can be simulated as shown in FIG. 4
Wherein the evaluation index of the priority of the power facility point is the sum of the power loss weighted by the value factors of the downstream loads. The specific calculation formula is as follows:
wherein C is d Is the set of all load nodes downstream of device d.
Supplementary explanation:
1) And (4) at the time t, after the goods and materials are transported out from a certain point, the goods and materials need to arrive at the destination after a period of traffic time. In general, the transit time between two places can be estimated by the existing map tool platform.
2) Constraint conditions for repeated allocation of personnel and materials:
for emergency repair personnel: on the premise of meeting the working time limit, the blending can be repeated; after the personnel continuously work for the maximum time limit, the personnel can also allocate again after the rest time with a specific length.
For first-aid repair materials: are considered to be disposable, i.e., not to be repeatedly dispensable.
According to the technical scheme, the post-disaster power failure loss prediction value corresponding to each candidate emergency resource deployment combination can be output through the post-disaster power failure loss prediction model.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
In order to facilitate understanding of the technical personnel in the field, the disclosure also provides an example of a pre-disaster emergency resource deployment method for the power distribution network; an example is constructed on the basis of the topological structure and the load information of the distribution network based on the IEEE33 node standard. All the computational tests were performed on a personal computer with a processor Ryzen 5 2500U, and the development environment was MATLAB R2019a. The topology of the distribution network based on the IEEE33 node standard can be seen in fig. 5.
The power distribution network model is described as follows:
wherein the node is a generator/power access node. For simplicity, the role of the tie switch (i.e. the dashed line in the figure) is not taken into account, i.e. the distribution network is considered radial, with a unique path from node 1 to each node. In the problem, a pole tower, a cable, a transformer and the like connected between two nodes are considered as a set of electrical equipment, and the disaster resistance parameters of the set of electrical equipment are based on the parameters of the weakest link; and numbering each set of equipment.
For simplicity, consider that each set of electrical equipment parameters are identical:
wherein, the wind speed reaching the critical damage state when the equipment leaves the factory is 100m/s, and the standard deviation thereof is 0.5m/s; two critical values v of wind speed critical ＝20m/s、v collapse ＝160m/s；
Wherein the design service life of the equipment is 20 years, and the actual operation is 10 years;
wherein, 2 sets of material bags are needed for reinforcing one unit of electrical equipment;
wherein, the action coefficient lambda of the personnel for first-aid repair is =0.3, and the loss degree xi of the disaster-resistant grade when the equipment reaches the service life is =0.5.
And considering the power loss amount of each node after the shutdown as the apparent power of each node in normal operation.
The load figure is defined as the economic loss per 1 unit of electricity missing from the load, and is set to a number between 0 and 100 in this example. The power loss and the value factor after the node is shut down are correspondingly as follows:
TABLE 1 loss of power after shutdown of node and its value factor
Wherein, the maximum wind speed of typhoon disaster is set to be 65m/s (the maximum wind speed of 'mangosteen' super typhoon in 2018). Since only the overall outage risk until the disaster is weakened to harmless needs to be calculated, the equipment disaster probability is calculated only by the maximum wind speed in the example without considering the process of typhoon development. At present, 50 sets of reinforcing equipment are available for allocation by 20 people. Obviously, when the power loss in the post-disaster repair process is not considered, the global power failure risk can be reduced to the maximum extent by configuring all resources to power equipment points.
10000 solutions (namely candidate emergency resource deployment combinations) are randomly generated, the distribution network disaster-suffered risk in the current distribution mode is calculated according to the distribution network disaster-suffered power failure risk prediction model, and the optimal solution is selected. The results of 5 operations are as follows:
table 2 distribution network outage risk assessment results in distribution modes without considering transshipment
Wherein (1) is the situation when personnel and materials are not prepared in advance, and (2) to (6) are the better preparation situations obtained after 5 times of calculation and screening. According to the table, after optimized allocation, the distribution network outage risk measurement index R s Reduce to about 1/2 of the original value.
Considering only the global outage risk of the optimization distribution network (considering transshipment)
Only one route from the generator/power node to each load is considered before the transfer; after the action of the interconnection switch is considered, the routes for supplying power from the generator/power nodes to all the loads are increased, so that the global outage risk of the distribution network needs to be reevaluated.
Considering the convenience of the C + + language in the use of stack structures, programs are written in C + + to traverse all paths from the generator/power nodes to the various load nodes. Abstracting the current distribution network topology structure chart into an undirected graph G, abstracting all nodes into undirected graph nodes, traversing the graph, and finding out all paths between two specified nodes. The traversal process is as follows:
1. establishing a stack structure of a storage node, and converting the starting point x 0 Stacking the node x 0 Marking as a stack entering state;
2. slave node x 0 Starting from, find node x 0 First non-stacking state of neighbor node x 1 Node x will be 1 Marking as a stack entering state;
3. slave node x 1 Starting from, find node x 1 First non-stacking state of neighbor node x 2 Node x of 2 Marking as a stacking state;
4、……
5. slave node x n-1 Starting from, find node x n-1 First non-stacking state neighbor point x of (1) n Node x of n Marking as a stack entering state;
6. trestle top node x n Is the end point, so now the whole stack is a path from the start point to the end point, and outputs it;
7. popping node x from the top of the stack n X is to be n Marking as a non-push state;
8. the node at the top of the stack is x n-1 . If x n-1 Node x without just popping n Neighbor nodes in other non-stacking state, x n-1 Pop up from the top of the stack, so the top node is x n-2 Repeating the steps; otherwise, x is n-1 Pushing the adjacent node in the non-pushing state, and repeating the steps iii to viii;
9. repeat steps 1 through 8 until the stack is empty, i.e., x 0 To x n All paths have been found and the algorithm ends. And saving the path information as an input parameter for evaluating the outage probability of each load node. The probability of failure of the tie switch is considered to be 0. Randomly generating 5000 solutions (namely candidate emergency resource deployment combinations), and predicting the disaster-suffering power failure risk of the distribution networkAnd calculating the distribution network disaster risk in the current distribution mode by the model, and selecting the optimal solution. The results of 5 operations are as follows:
table 3 distribution network outage risk assessment results in consideration of supply transfer in each distribution mode
Wherein, the solution (1) is the situation that personnel and material deployment is not carried out, and the solution (2) to (6) are the better deployment situations obtained after 5 times of calculation and screening. Compared with the results in table 2, the risk of distribution network outage is hardly reduced much when personnel material deployment is not performed; this is because the outage probability of each power supply path is very close to 1 when personnel and materials are not deployed in a disaster setting scene; even if there are a plurality of power supply paths, the degree of reduction in the outage probability of each node is statistically limited. After proper personnel and material deployment is carried out, the outage probability of each power facility is reduced to different degrees, and the outage probability of each node can be obviously reduced due to the increase of the power supply paths. For example, in the distribution mode corresponding to the solution (6) in fig. 6, the shutdown probability comparison of each node before and after the supply is considered (before the supply is considered, the objective function value is 8.8 × 10 4 )。
In one embodiment, as shown in fig. 7, there is provided a pre-disaster emergency resource deployment device for a power distribution network, including:
the acquisition module 710 is used for acquiring pre-disaster emergency resources of the power distribution network; the emergency resources before the power distribution network disaster comprise the total number of emergency substances and the total number of emergency personnel for deployment at a target position point;
the decomposition module 720 is configured to decompose the pre-disaster emergency resources of the power distribution network according to preset resource deployment constraint conditions, and generate at least two candidate emergency resource deployment combinations;
a determining module 730, configured to determine, according to a preset target optimization function, a function value corresponding to each candidate emergency resource deployment combination; the target optimization function comprises a distribution network disaster power failure risk prediction model and a post-disaster power failure loss prediction model; the function value is used for representing the sum of a distribution network disaster-suffering power failure risk predicted value and a post-disaster power failure loss predicted value corresponding to the candidate emergency resource deployment combination;
and the deployment module 740 is configured to use the candidate emergency resource deployment combination with the smallest function value as a target emergency resource deployment combination.
In one embodiment, the target location point comprises at least one of a distribution network device location and a rescue center location in the power distribution network.
In one embodiment, the function value corresponding to the target emergency resource deployment combination is represented as:
wherein x represents a candidate emergency resource deployment combination; argmin represents the value of the variable when the following expression reaches the minimum value; r is s Representing a distribution network disaster-suffering power failure risk prediction model; r e A power failure loss prediction model after the disaster is expressed; x = [ r ] d ,h d ,r m ,h m ]，d∈D，m∈M，r d Representing the number of emergency resources deployed at the distribution network device d; h is a total of d Representing the number of emergency personnel deployed at the distribution network device d; r is m Representing the number of emergency resources deployed at the emergency rescue center m; h is a total of m Representing the number of emergency personnel deployed at the emergency rescue center m.
In one embodiment, the resource deployment constraint is expressed as:
Σ d∈D r d +Σ m∈M r m ＝r total
Σ d∈D h d +Σ m∈M h m ＝h total
wherein r is total Representing a total number of emergency materials for deployment at the target location point; h is total Indicating the total number of personnel and supplies available for dispatch.
In one embodiment, the distribution network disaster-caused power outage risk prediction model is represented as:
wherein C represents a load number, and C represents a set of all loads; t is s Indicating the moment when the typhoon weakens to be harmless; p c (x, t) represents the outage probability of the load c at time t; v c Is the value coefficient of the load; f c (t) represents the power lost by the load c at time t.
In one embodiment, the outage probability of the load c at time t is expressed as:
wherein, P rt (x, t) represents the failure probability of the transmission line rt, expressed as:
P rt (x,t)＝1-Π d∈rt (1-P d (x,t))
wherein d e rt represents that the distribution network equipment d is on the distribution network route rt, P d (x, t) represents the failure probability of the distribution network equipment d.
In one embodiment, the post-disaster power failure loss prediction model is represented as:
wherein s represents a scene of damage to the distribution network equipment after a disaster, omega x The method comprises the following steps of representing a scene set of disaster-suffered and outage of equipment in a distribution network after emergency personnel and emergency material allocation schemes are given; t is e Representing the time of emergency repair ending;
is the power loss function of the load c at the time t, and when the value is 1, the load c is still positionedIn the power-off state, when the value is 0, the load c is represented to recover power supply;
indicating a power supply path first-aid repair scheme.
For specific limitations of the emergency resource deployment device before the power distribution network disaster, reference may be made to the above limitations on the emergency resource deployment method before the power distribution network disaster, which are not described herein again. All modules in the pre-disaster emergency resource deployment device for the power distribution network can be completely or partially realized through software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing emergency resource deployment data before the power distribution network disaster. The network interface of the computer device is used for communicating with an external terminal through a network connection. When the computer program is executed by the processor, the method for deploying the emergency resources before the power distribution network disaster is realized.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method for deploying emergency resources before a power distribution network disaster. The steps of the method for deploying emergency resources before the power distribution network disaster may be steps in the method for deploying emergency resources before the power distribution network disaster according to the above embodiments.
In one embodiment, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the method for deploying emergency resources before a power distribution network disaster. The steps of the method for deploying emergency resources before the power distribution network disaster may be steps in the method for deploying emergency resources before the power distribution network disaster according to the above embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. A pre-disaster emergency resource deployment method for a power distribution network is characterized by comprising the following steps:
acquiring emergency resources before a power distribution network disaster; the power distribution network pre-disaster emergency resources comprise the total number of emergency substances and the total number of emergency personnel deployed at a target position point;
decomposing the emergency resources before the power distribution network disaster according to preset resource deployment constraint conditions to generate at least two candidate emergency resource deployment combinations;
determining a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function; the target optimization function comprises a distribution network disaster power failure risk prediction model and a post-disaster power failure loss prediction model; the function value is used for representing the sum of a distribution network disaster-suffering power failure risk predicted value and a post-disaster power failure loss predicted value corresponding to the candidate emergency resource deployment combination; the function value corresponding to the target emergency resource deployment combination is expressed as follows:
wherein x represents a candidate emergency resource deployment combination; argmin represents the value of the variable when the following expression reaches the minimum value; r is s Representing a distribution network disaster-suffering power failure risk prediction model; r is e A power failure loss prediction model after the disaster is expressed; x = [ r = d ,h d ,r m ,h m ]，d∈D，m∈M，r d Representing the number of emergency resources deployed at the distribution network device d; h is a total of d Representing the number of emergency personnel deployed at the distribution network device d; r is m Representing the number of emergency resources deployed at the emergency rescue center m; h is a total of m Representing the number of emergency personnel deployed at the emergency rescue center m;
the distribution network disaster-suffering power failure risk prediction model is represented as follows:
wherein C represents a load number, and C represents a set of all loads; t is a unit of s Indicating the moment when the typhoon weakens to be harmless; p c (x, t) represents the outage probability of the load c at time t; v c A value coefficient for the load; f c (t) represents the power lost by the load c at time t;
wherein the outage probability of the load c at the time t is represented as:
wherein, P rt (x, t) represents the failure probability of the transmission line rt, expressed as:
P rt (x,t)＝1-Π d∈rt (1-P d (x,t))
wherein d e rt represents that the distribution network equipment d is on the distribution network route rt, P d (x, t) represents the probability of failure of the distribution network device d;
the post-disaster power failure loss prediction model is expressed as follows:
wherein s represents the scene of the damage of the distribution network equipment after disaster, and omega x Showing when given emergency personnel and emergency suppliesAfter the allocation scheme, the scene set of the equipment in the distribution network which is in disaster and shut down is collected; t is a unit of e Representing the time of the first-aid repair end;
the power loss function of the load c at the moment t is used for indicating that the load c is still in a power loss state when the value is 1 and indicating that the load c recovers power supply when the value is 0;
representing a power supply path emergency repair scheme;
and taking the candidate emergency resource deployment combination with the minimum function value as a target emergency resource deployment combination.
2. The method of claim 1, wherein the target location point comprises at least one of a distribution network device and a rescue center in the distribution network.
3. The method of claim 1, wherein the resource deployment constraint is expressed as:
Σ d∈D r d +Σ m∈M r m ＝r total
Σ d∈D h d +Σ m∈M h m ＝h total
wherein r is total Representing a total number of emergency materials for deployment at the target location point; h is total Indicating the total number of people and supplies available for dispatch.
4. A pre-disaster emergency resource deployment device for a power distribution network, the device comprising:
the acquisition module is used for acquiring emergency resources before the power distribution network disaster; the power distribution network pre-disaster emergency resources comprise the total number of emergency substances and the total number of emergency personnel deployed at a target position point;
the decomposition module is used for decomposing the emergency resources before the power distribution network disaster according to preset resource deployment constraint conditions to generate at least two candidate emergency resource deployment combinations;
the determining module is used for determining a function value corresponding to each candidate emergency resource deployment combination through a preset target optimization function; the target optimization function comprises a distribution network disaster-suffered power failure risk prediction model and a post-disaster power failure loss prediction model; the function value is used for representing the sum of a distribution network disaster-suffered power failure risk prediction value corresponding to the candidate emergency resource deployment combination and a post-disaster power failure loss prediction value; the function value corresponding to the target emergency resource deployment combination is expressed as:
wherein x represents a candidate emergency resource deployment combination; argmin represents the value of the variable when the following formula reaches the minimum value; r is s Representing a distribution network disaster-suffering power failure risk prediction model; r e A power failure loss prediction model after the disaster is shown; x = [ r = d ,h d ,r m ,h m ]，d∈D，m∈M，r d Representing the number of emergency resources deployed at the distribution network device d; h is d Representing the number of emergency personnel deployed at the distribution network device d; r is m Representing the number of emergency resources deployed at the emergency rescue center m; h is m Representing the number of emergency personnel deployed at the emergency rescue center m;
the distribution network disaster-suffering power failure risk prediction model is expressed as follows:
wherein C represents a load number, and C represents a set of all loads; t is a unit of s Indicating the moment when the typhoon weakens to be harmless; p c (x, t) represents the outage probability of the load c at time t; v c Is the value coefficient of the load; f c (t) represents the power lost by the load c at time t;
wherein the outage probability of the load c at the time t is represented as:
wherein, P rt (x, t) represents the failure probability of the transmission line rt, expressed as:
P rt (x,t)＝1-Π d∈rt (1-P d (x,t))
wherein d ∈ rt represents that the distribution network device d is on the distribution network route rt, and P d (x, t) represents the probability of failure of the distribution network device d;
the post-disaster power failure loss prediction model is expressed as follows:
wherein s represents a scene of damage to the distribution network equipment after a disaster, omega x The method comprises the following steps that a scene set of disaster-suffering outage of equipment in a distribution network is shown after emergency personnel and emergency material allocation schemes are given; t is e Representing the time of the first-aid repair end;
the power loss function of the load c at the time t shows that the load c is still in a power loss state when the value is 1, and shows that the load c recovers power supply when the value is 0;
representing a power supply path emergency repair scheme;
and the deployment module is used for taking the candidate emergency resource deployment combination with the minimum function value as a target emergency resource deployment combination.
5. The apparatus of claim 4, wherein the determining module is further configured to represent the resource deployment constraint as:
Σ d∈D r d +Σ m∈M r m ＝r total
Σ d∈D h d +Σ m∈M h m ＝h total
wherein r is total Representing a total number of emergency materials for deployment at the target location point; h is total Indicating the total number of personnel and supplies available for dispatch.
6. The apparatus of claim 4, wherein the determining module is further configured to use the candidate emergency resource deployment combination with the smallest function value as a target emergency resource deployment combination.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 3 when executing the computer program.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 3.
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