CN114925864B - Emergency repair method under typhoon disaster - Google Patents
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Abstract
The application provides an emergency repair method under typhoon disasters, which comprises the steps of firstly determining a disaster area of a power grid according to typhoon monitoring information in a first stage before the disaster, collecting meteorological data, distribution network data, geographic data and other data of the disaster area, calculating line fault probability based on the collected data, establishing a temporary allocation center site selection model according to the fault probability and the position, and solving to obtain a temporary allocation center position. And then, a post-disaster broken line scene is generated by utilizing a Monte Carlo simulation and synchronous back-up scene reduction method in the post-disaster stage, a post-disaster distribution network emergency repair restoration optimization model is established based on the post-disaster broken line scene, and the model is solved to obtain a post-disaster area distribution network emergency repair restoration optimization strategy, so that rapid emergency repair restoration of the distribution network fault under typhoon disasters is realized.
Description
Technical Field
The invention relates to the technical field of typhoon disaster rush repair, in particular to an emergency rush repair method under typhoon disasters.
Background
Typhoon disasters can cause great damage to distribution network equipment, and great wind speed enables a power grid to fall down a pole (tower) and break a line, so that large-area power failure of users can be caused. In order to improve the disaster prevention and reduction capability of the power grid, the method has important practical significance in carrying out the research on the emergency repair recovery optimization strategy of the distribution network under typhoon disasters. The emergency repair recovery strategy of the distribution network under typhoon disasters is optimized, the disaster response capability of the power grid company under disasters can be effectively improved, the fault recovery task of the distribution network can be executed according to the emergency repair recovery strategy at the first time after the disasters, the loss of load loss of the distribution network caused by waiting for re-electricity in the disaster receiving process is reduced, and the influence of the distribution network power failure on society is reduced.
The present inventors have found that in the course of carrying out the present application, the method of the prior art has at least the following technical problems:
The emergency repair restoration optimization strategy under the extreme natural disaster condition considers the technical problems that the emergency repair restoration optimization strategy is on one side, is not fully aimed at the uncertainty of the distribution network fault under the specific disaster, such as typhoon disaster scene, and is less in consideration of the combined emergency repair electricity of a repair team by utilizing a mobile distributed power supply to supply power through an island under the typhoon scene.
Therefore, how to avoid the problems that the existing emergency repair recovery optimization strategy technology under the extreme natural disaster condition considers one side, does not fully aim at the uncertainty of the network distribution faults under the specific disaster, such as typhoon disaster scene, and less considers the inaccuracy and timeliness of the emergency repair caused by the combined emergency repair of the mobile distributed power supply and the emergency repair team in the typhoon scene through island power supply is still a urgent need of the technicians in the field.
Disclosure of Invention
The invention aims to overcome the technical defects, and provides an emergency repair method under typhoon disasters, which solves the technical problems that in the prior art, one-sided consideration is not fully carried out, the uncertainty of network distribution faults under specific disasters such as typhoon disasters is not fully solved, and the problem that in the typhoon scenes, the emergency repair is inaccurate and timely caused by the fact that a mobile distributed power supply is utilized to supply power through an island and the emergency repair team is combined to repair electricity is solved.
In order to achieve the technical purpose, the technical scheme of the invention provides an emergency repair method under typhoon disasters, which comprises the following steps:
Collecting monitoring information of a target typhoon, and determining a disaster area of a target power grid;
Collecting comprehensive data of the disaster affected area, and calculating a distribution network line of the target network by adopting a distribution network line fault probability model under typhoon disasters based on the comprehensive data to obtain the fault probability and the position of the distribution network line;
Constructing a first optimization model for temporary allocation center site selection based on the fault probability and the position, solving the first optimization model to obtain a target position of the temporary allocation center, and determining the target position as a departure point of a post-disaster emergency repair recovery optimization task;
Reducing a typhoon post-disaster fault scene generated by Monte Carlo simulation according to the fault probability to obtain a plurality of typical post-disaster fault scenes;
A typical post-disaster fault scene with the largest scene fault probability is selected as a scene for post-disaster rush repair restoration optimization;
and constructing a second optimization model of the post-disaster distribution network emergency repair recovery based on the departure point and the scene of the post-disaster emergency repair recovery optimization, and solving the second optimization model to obtain a target recovery optimization scheme of the disaster area distribution network emergency repair.
According to the emergency repair method under typhoon disasters provided by the invention, the monitoring information of target typhoons is collected, and the disaster affected area of the target power grid is determined, and the method specifically comprises the following steps:
Continuously monitoring target typhoons, and responding to the forecast of the target typhoons in the monitoring result, and collecting the monitoring information of the target typhoons;
and determining a disaster area of the target power grid based on the monitoring information.
According to the emergency repair method under typhoon disasters provided by the invention, the monitoring information comprises the following specific steps:
Typhoon development path, typhoon wind speed, typhoon wind circle, typhoon center position, and typhoon center air pressure.
According to the emergency repair method under typhoon disasters provided by the invention, the disaster affected area of the target power grid is determined, and the method specifically comprises the following steps:
And (3) based on geographic topology information data of the distribution network equipment, carrying out superposition analysis on the geographic topology information data and the P-level wind ring of typhoons, and determining a disaster area, wherein P is a positive integer.
According to the emergency repair method under typhoon disasters, the comprehensive data comprise meteorological data, distribution network data and geographic data; the meteorological data comprise the maximum gust wind speed of a disaster area; the distribution network data comprise the total number of towers, the positions of the towers, the lengths of the lines, the positions of the lines, the topology of the lines and the load information of the lines in the disaster area; the geographic data includes a longitude and a latitude.
According to the emergency repair method under typhoon disasters provided by the invention, the reduction utilizes Monte Carlo to simulate the generated post-typhoon disaster fault scene according to the fault probability, and the method concretely comprises the following steps:
And reducing the typhoon disaster post-fault scene generated by Monte Carlo simulation according to the fault probability by using a synchronous back-substitution scene reduction algorithm.
According to the emergency repair method under typhoon disasters provided by the invention, the fault scene after typhoon disasters is generated by using Monte Carlo simulation according to the fault probability, and the method specifically comprises the following steps:
assuming the fault probability of the line m is Pm, initializing the counting state of each distribution network line to be 0, the simulation times to be i=1 and the distribution network line traversing times to be m=1;
randomly generating a number random number S between 0 and 1, comparing the S with the fault probability Pm of each line, if S < Pm, the disconnection state of the line is 1, otherwise, the disconnection state is 0;
comparing whether m is smaller than N at the moment, if not, continuing traversing the rest branches of the system to obtain the broken line state of all the lines of the system, if so, judging whether a broken line scene SUM (L) at the moment is equal to 10, if so, proving that the simulation is effective, adding 1 to the simulation times i, continuing the next simulation, and if not, not counting the simulation at the time, and regenerating a random number S;
Repeating the steps until a post-disaster wire breakage scene of MM N-10 is generated, wherein MM is a positive integer.
According to the emergency repair method under typhoon disasters provided by the invention, MM post-typhoon disaster fault scenes generated by Monte Carlo simulation according to the fault probability are reduced by utilizing a synchronous back-substitution scene reduction algorithm, so as to obtain NN typical post-disaster fault scenes, and the method specifically comprises the following steps:
Calculating the distance d (ζ i,ζj) between any two scenes in the MM scenes;
Determining a scene ζ n with the smallest distance from the scene ζ i, multiplying the probability p (ζ i) of the scene ζ i by the distance d (ζ i,ζn) of the scene ζ i、ζn to obtain p (ζ i)·d(ζi,ζn), and repeating the above steps until all traversals are completed;
Finding two scenes that minimize the p (ζ i)·d(ζi,ζn) value, namely a scene pair (ζ i,ζn);
Update p (ζ n)＝p(ζn)+p(ζi) while reducing scene ζ i;
Updating the total scene number MM-1;
Returning to the first of the above steps, repeating the above steps until the number of remaining scenes is NN, NN being a positive integer and NN < MM.
According to the emergency repair method under typhoon disasters provided by the invention, a first optimization model for temporary allocation center site selection is constructed, and the method specifically comprises the following steps:
constructing a model to be optimized based on a first optimization formula, wherein the first optimization formula is as follows:
wherein P i is the predicted fault probability of the line i; m L is the total number of fault lines; n B (i) is the total number of load losing nodes caused by the predicted fault line i; w ij is the grade weight of the load j of the power failure caused by the fault of the fault line i; t ij is expressed in terms of the journey time from the temporary transfer center to the faulty line i in the temporary transfer center address selection phase.
According to the emergency repair method under typhoon disasters provided by the invention, the second optimization model for post-disaster distribution network emergency repair recovery is constructed based on the departure point and the scene of post-disaster repair recovery optimization, and specifically comprises the following steps:
constructing a model to be optimized based on a second optimization formula, wherein the second optimization formula is as follows:
Wherein M is the total number of fault points in the distribution network system, N is the total number of load loss at the fault point i, w ij represents the load grade weight of the load loss node j at the fault point i, p ij represents the active power of the load loss node j at the fault point i, and T i represents the power failure time at the fault point i;
The calculation formula of the power failure time is as follows:
Wherein t i-1>i represents the path time spent from the last failure point i-1 to failure point i; Representing the sum of the time taken to complete the duplicate from the beginning to the previous fault point i-1; t re,i represents the time spent on rush repair of the fault point i; t TC>i represents the journey time from temporary dispatch center TC to failure point i; x i is a binary variable of 0,1, when x i =1, the load cluster in the fault point is supplied with power by the mobile distributed power supply in an island mode, so that the power failure time of the fault point is only the time spent by the path of the mobile distributed power supply from the starting point to the fault point, namely T i＝xi·TTC>i; when x i =0, no mobile distributed power supply is used for supplying power to the fault, and the power can be recovered only by a subsequent repair team, so that x i·TTC>i =0,
The post-disaster emergency repair recovery optimization model also needs to meet preset constraint conditions.
Compared with the prior art, the invention has the beneficial effects that: and the rapid emergency repair and recovery of the distribution network faults under typhoon disasters are realized.
Drawings
FIG. 1 is a schematic flow chart of an emergency repair method under typhoon disasters;
fig. 2 is a schematic flow chart of a distribution network emergency repair restoration optimization strategy under typhoon disasters;
FIG. 3 is a geographic topology of an IEEE-33 node system provided by the present invention;
FIG. 4 is a schematic diagram of the relationship between the distance from the center of typhoon and the change of the fault probability;
FIG. 5 is a schematic diagram of the relationship between failure probability of a distribution network tower and wind speed;
Fig. 6 is a schematic diagram of fault probability of each line of the 33 node system under typhoon disasters;
FIG. 7 is a block diagram of a node system after temporary dial-up center site selection optimization provided by the present invention;
FIG. 8 is a flow chart of generating 1000 post typhoon disaster fault scenarios using Monte Carlo simulation provided by the present invention;
fig. 9 is a schematic diagram of a broken line scene obtained according to scene 1 provided by the present invention;
FIG. 10 is a simplified broken line scene schematic diagram provided by the present invention;
FIG. 11 is a convergence graph of the distribution network emergency repair restoration optimization model provided by the invention;
FIG. 12 is a schematic view of a fault repair optimization sequence provided by the present invention;
fig. 13 is a schematic diagram showing a system recovery degree according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, fig. 1 to 13, and examples. 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.
Fig. 1 is a schematic flow chart of an emergency repair method under typhoon disaster, as shown in fig. 1, the method includes:
Step 110, collecting monitoring information of a target typhoon, and determining a disaster area of a target power grid;
Step 120, collecting comprehensive data of the disaster affected area, and calculating a distribution network line of the target network by adopting a distribution network line fault probability model under typhoon disasters based on the comprehensive data to obtain fault probability and position of the distribution network line;
Step 130, constructing a first optimization model for temporary transfer center site selection based on the fault probability and the position, solving the first optimization model to obtain a target position of the temporary transfer center, and determining the target position as a departure point of a post-disaster emergency repair recovery optimization task;
step 140, reducing the typhoon post-disaster fault scene generated by Monte Carlo simulation according to the fault probability to obtain a plurality of typical post-disaster fault scenes;
step 150, selecting a typical post-disaster fault scene with the largest scene fault probability as a scene for post-disaster rush repair restoration optimization;
and 160, constructing a second optimization model of the post-disaster distribution network emergency repair restoration based on the departure point and the scene of the post-disaster emergency repair restoration optimization, and solving the second optimization model to obtain a target restoration optimization scheme of the disaster area distribution network emergency repair.
Specifically, S1: monitoring typhoons, and collecting monitoring information of typhoons after typhoons are received for forecasting; s2: determining a disaster area of the power grid according to the monitored typhoon information; s3: collecting meteorological data, distribution network data, geographic data and other data of a disaster-stricken area; s4: calculating the fault probability of the distribution network line by using a distribution network line fault probability model calculation line under typhoon disasters based on the collected data; s5: establishing a temporary allocation center site selection optimization model according to the fault probability and the position of the distribution network line, and solving the model to obtain the position of the temporary allocation center as a starting point of a post-disaster emergency repair recovery optimization task; s6: simulating and generating MM typhoon disaster fault scenes by utilizing Monte Carlo according to the line fault probability; s7: reducing the scene generated in the step S7 by using a synchronous back-up scene reduction method to obtain NN typical post-disaster fault scenes, and selecting the scene with the highest scene fault probability as the post-disaster rush repair restoration optimized scene; s8: and establishing a post-disaster distribution network emergency repair restoration optimization model, and solving the model to obtain a disaster-affected area distribution network emergency repair restoration optimization strategy.
According to the emergency repair method under typhoon disasters, through the combination of the two stages of pre-disaster and post-disaster, the positions of resources required by post-disaster emergency repair restoration and a temporary allocation center are determined in the pre-disaster stage, the uncertainty of a post-disaster distribution network fault scene is processed by using a scene generation and scene reduction method, meanwhile, an optimized scene of post-disaster emergency repair restoration is obtained, and the mobile distributed power island power supply and the cooperative repair of a repair team are used as a strategy for the distribution network emergency repair restoration optimization, so that the rapid emergency repair restoration of the distribution network faults under typhoon disasters is realized.
Based on the above-described embodiments, in this method,
The method for acquiring the monitoring information of the target typhoons and determining the disaster area of the target power grid specifically comprises the following steps:
Continuously monitoring target typhoons, and responding to the forecast of the target typhoons in the monitoring result, and collecting the monitoring information of the target typhoons;
And determining a disaster area of the target power grid based on the monitoring information. In the embodiment, the specific description is carried out on the acquisition process of the monitoring information, so that the further description on the acquisition of the target typhoon monitoring information is realized, and the accurate definition is carried out.
Based on the foregoing embodiment, in the method, the monitoring information specifically includes:
Typhoon development path, typhoon wind speed, typhoon wind circle, typhoon center position, and typhoon center air pressure.
In this embodiment, by specifically explaining the kind of the monitoring information, further explanation of the monitoring information is achieved, and accurate limitation is performed on the monitoring information.
Based on the foregoing embodiment, in the method, the determining the disaster-stricken area of the target power grid specifically includes:
And (3) based on geographic topology information data of the distribution network equipment, carrying out superposition analysis on the geographic topology information data and the P-level wind ring of typhoons, and determining a disaster area, wherein P is a positive integer.
Specifically, the method for determining the disaster area of the power grid is to perform superposition analysis on data such as geographical topology information of distribution network equipment obtained from a power grid company and 10-level wind rings and 12-level wind rings of typhoons so as to determine the disaster area.
In this embodiment, by further explaining the method for determining the disaster-stricken area, a convenient and accurate determination method for the disaster-stricken area is defined.
Based on the above embodiment, in the method, the comprehensive data includes meteorological data, distribution network data and geographic data; the meteorological data comprise the maximum gust wind speed of a disaster area; the distribution network data comprise the total number of towers, the positions of the towers, the lengths of the lines, the positions of the lines, the topology of the lines and the load information of the lines in the disaster area; the geographic data includes a longitude and a latitude.
Specifically, the meteorological data is provided by a meteorological department and mainly comprises the maximum gust wind speed of a disaster area; the distribution network data mainly comprises the total number of towers, the positions of the towers, the lengths of the lines, the positions of the lines, the topology of the lines, the load information of the lines and the like in the disaster area; geographic data mainly includes longitude, latitude, and the like.
In the embodiment, by specifically explaining the types of the meteorological data, the distribution network data and the geographic data, further explanation of the meteorological data, the distribution network data and the geographic data is realized, and accurate limitation is carried out.
Based on the above embodiment, in the method, the calculation is performed on the distribution network line of the target network by using the distribution network line fault probability model under the typhoon disaster based on the comprehensive data, so as to obtain the fault probability and the position of the distribution network line, which specifically includes:
according to Batts wind field model, knowing that the distance from the point A to the typhoon center is r, the wind speed V of the point A is:
wherein R max is the maximum wind speed radius, and τ is a typhoon intensity decay related parameter;
Under the condition that the wind speed V of the point A is known, only the distribution network risk of the pole reverse pole fault is considered, and then the wind load W c born by the lead when the wind speed is V under typhoon disasters is as follows:
Wherein alpha is the wind pressure non-uniformity coefficient of the distribution network line; mu z is the wind pressure height change coefficient of the distribution network line; mu sc is the body form factor of the distribution network line; d is the outer diameter of the distribution line; l H is the horizontal span of the distribution network line; beta is the included angle between the direction of the direct blowing wind received by the distribution network line and the distribution network line;
wind load W p borne by the distribution network tower is calculated according to the formula:
Wherein lambda is the wind vibration coefficient of the distribution network tower; mu s is the wind load body form factor of the distribution network tower; a is the actual projection area of the structural member of the distribution network tower on the windward side;
the wind load W in applied to the insulator has a calculation formula:
Wherein c 1 is the number of insulator strings owned by the single-phase wire; c 2 is the number of pieces contained in each string of insulators; a in is the windward area of the single insulator under typhoon;
The bending moment at any section y-y of the main body of the pole body of the distribution network under typhoon disasters is as follows:
Wcom＝Wc+Win
Where W com represents the aggregate wind load of the insulator and wire, W SZ represents the shaft wind load, Representing the height from the section y-y to the action point of the wind pressure of the shaft; h 1 is the distance from the top of the electric pole to the section y-y; h 2 represents the distance from the pole cross arm to the section y-y; d 0 is the diameter of the electric pole; d y is the diameter of the pole at section y-y; m y is an additional bending moment coefficient generated due to the disturbance degree;
the design wind load of the distribution network tower obeys normal distribution, and the probability density function is as follows:
Wherein sigma d is the standard deviation of the design wind load of the distribution rod, and mu d is the average value of the design wind load of the distribution rod;
According to the stress intensity interference model, when the wind load intensity of the distribution pole under typhoon disasters is M x, the probability of the pole failure can be expressed as follows:
According to the tower line series theory, when the target distribution network line l has NP distribution network towers, any one distribution network tower fails, and the whole distribution network line fails, so that the failure probability P l of the target distribution network line l under typhoon disasters can be expressed as:
In the formula, p i is the fault probability of the ith distribution network tower on the distribution network line l, and the position corresponding to the fault probability is the ith distribution network tower on the distribution network line l.
Specifically, according to the stress intensity interference model, it is known that the power distribution pole is broken down under the typhoon disaster mainly because the actual wind load received by the power distribution pole under the typhoon disaster exceeds the design wind load of the power distribution network, therefore according to the stress intensity interference model, when the wind load intensity received by the power distribution pole under the typhoon disaster is x i, the probability of the power distribution pole failure can be expressed as:
Generally, one distribution network line includes a plurality of distribution network towers, so according to the tower-line series theory, when a certain distribution network line l has NP distribution network towers, any one distribution network tower fails, and the entire distribution network line fails, so that the failure probability P l of the distribution network line l under a typhoon disaster can be expressed as:
wherein p i is the fault probability of the ith distribution network tower on the distribution network line l.
The embodiment introduces a formula for calculating the distribution network line of the target network by adopting a distribution network line fault probability model under typhoon disasters, and provides an accurate method for calculating the distribution network line.
Based on the above embodiment, in the method, the reducing the typhoon post-disaster fault scenario generated by using monte carlo simulation according to the fault probability specifically includes:
And reducing the typhoon disaster post-fault scene generated by Monte Carlo simulation according to the fault probability by using a synchronous back-substitution scene reduction algorithm.
Specifically, a synchronous back-substitution scene reduction algorithm is preferably used for reducing a typhoon post-disaster fault scene generated by utilizing Monte Carlo simulation according to the fault probability.
The method provided by the embodiment further limits that the optimal scheme for obtaining the fault scene after the typhoon disaster is to use a synchronous generation scene reduction algorithm.
Based on the above embodiment, in the method, a post-typhoon fault scenario is generated by utilizing Monte Carlo simulation according to the fault probability, and the method specifically includes:
assuming the fault probability of the line m is Pm, initializing the counting state of each distribution network line to be 0, the simulation times to be i=1 and the distribution network line traversing times to be m=1;
randomly generating a number random number S between 0 and 1, comparing the S with the fault probability Pm of each line, if S < Pm, the disconnection state of the line is 1, otherwise, the disconnection state is 0;
comparing whether m is smaller than N at the moment, if not, continuing traversing the rest branches of the system to obtain the broken line state of all the lines of the system, if so, judging whether a broken line scene SUM (L) at the moment is equal to 10, if so, proving that the simulation is effective, adding 1 to the simulation times i, continuing the next simulation, and if not, not counting the simulation at the time, and regenerating a random number S;
Repeating the steps until a post-disaster wire breakage scene of MM N-10 is generated, wherein MM is a positive integer.
The embodiment introduces a scheme for generating the MM typhoon post-disaster fault scenes by utilizing Monte Carlo simulation according to the fault probability, and provides an accurate method for generating the MM typhoon post-disaster fault scenes.
Based on the above embodiment, in the method, the method uses a synchronous back-substitution scene reduction algorithm to reduce the post-disaster fault scenes of the MM stations to obtain NN typical post-disaster fault scenes, which specifically includes:
Calculating the distance d (ζ i,ζj) between any two scenes in the MM scenes;
Determining a scene ζ n with the smallest distance from the scene ζ i, multiplying the probability p (ζ i) of the scene ζ i by the distance d (ζ i,ζn) of the scene ζ i、ζn to obtain p (ζ i)·d(ζi,ζn), and repeating the above steps until all traversals are completed;
Finding two scenes that minimize the p (ζ i)·d(ζi,ζn) value, namely a scene pair (ζ i,ζn);
Update p (ζ n)＝p(ζn)+p(ζi) while reducing scene ζ i;
Updating the total scene number MM-1;
Returning to the first of the above steps, repeating the above steps until the number of remaining scenes is NN, NN being a positive integer and NN < MM.
Preferably, the MM takes a value of 1000 and NN takes a value of 10.
The scheme of the synchronous back-substitution scene reduction algorithm is specifically introduced, and an accurate method for reducing the MM typhoon post-disaster fault scenes to obtain NN typical post-disaster fault scenes is provided.
Based on the above embodiment, in the method, the constructing a first optimization model of temporary transfer center site selection specifically includes:
constructing a model to be optimized through a first optimization formula, wherein the first optimization formula is as follows:
wherein P i is the predicted fault probability of the line i; m L is the total number of fault lines; n B (i) is the total number of load losing nodes caused by the predicted fault line i; w ij is the grade weight of the load j of the power failure caused by the fault of the fault line i; t ij is expressed in terms of the journey time from the temporary transfer center to the faulty line i in the temporary transfer center address selection phase.
In the embodiment, a formula for constructing a first optimization model for temporary allocation center address selection is introduced, and an accurate method for optimizing temporary allocation center address selection is provided.
Based on the above embodiment, in the method, the constructing a second optimization model of post-disaster distribution network emergency repair restoration based on the departure point and the scene of post-disaster repair restoration optimization specifically includes:
Constructing a model to be optimized through a second optimization formula, wherein the second optimization formula is as follows:
Wherein M is the total number of fault points in the distribution network system, N is the total number of load loss at the fault point i, w ij represents the load grade weight of the load loss node j at the fault point i, p ij represents the active power of the load loss node j at the fault point i, and T i represents the power failure time at the fault point i;
The calculation formula of the power failure time is as follows:
Wherein t i-1>i represents the path time spent from the last failure point i-1 to failure point i; Representing the sum of the time taken to complete the duplicate from the beginning to the previous fault point i-1; t re,i represents the time spent on rush repair of the fault point i; t TC>i represents the journey time from temporary dispatch center TC to failure point i; x i is a binary variable of 0,1, when x i =1, the load cluster in the fault point is supplied with power by the mobile distributed power supply in an island mode, so that the power failure time of the fault point is only the time spent by the path of the mobile distributed power supply from the starting point to the fault point, namely T i＝xi·TTC>i; when x i =0, no mobile distributed power supply is used for supplying power to the fault, and the power can be recovered only by a subsequent repair team, so that x i·TTC>i =0,
The post-disaster emergency repair recovery optimization model needs to meet preset constraint conditions. Specifically, the preset constraint conditions in this embodiment are the following constraint conditions:
Distribution network radial radiation topology constraint:
gre∈GM
Wherein G re represents a distribution network topological structure after the repair is completed and normal power supply is recovered, and G M represents a structure set capable of guaranteeing radial topology of the distribution network;
Distribution network node voltage constraint
UMIN≤U≤UMAX
Wherein, U MIN is the lower limit of the node voltage, and U MAX is the upper limit of the node voltage;
Distribution network tide constraint
|SL|≤SL,max
Wherein S L is the flow of line L, and S L,max is the maximum value of the flow of line L.
The method provided by the embodiment further specifically introduces a second optimization model for constructing the post-disaster distribution network emergency repair restoration, and provides an accurate method for constructing the second optimization model for the post-disaster distribution network emergency repair restoration so as to solve the second optimization model later and obtain a target restoration optimization scheme for the disaster-affected area distribution network emergency repair.
Based on the above embodiment, the following describes an emergency repair recovery optimization strategy for a distribution network under typhoon disaster with reference to fig. 2. Fig. 2 is a schematic flow chart of a network distribution emergency repair restoration optimization strategy under typhoon disasters, and as shown in fig. 2, the strategy includes the following steps:
The invention provides a distribution network emergency repair restoration optimization strategy under typhoon disasters, which specifically comprises the following steps:
s1: monitoring typhoons, and collecting monitoring information of typhoons after typhoons are received for forecasting;
s2: determining a disaster area of the power grid according to the monitored typhoon information;
s3: collecting meteorological data, distribution network data and geographic data of a disaster-stricken area;
S4: calculating the fault probability of the distribution network line by using a distribution network line fault probability model calculation line under typhoon disasters based on the collected data;
s5: establishing a temporary allocation center site selection optimization model according to the fault probability and the position of the distribution network line, and solving the model to obtain the position of the temporary allocation center as a starting point of a post-disaster emergency repair recovery optimization task;
S6: generating 1000 typhoon disaster post-disaster fault scenes by utilizing Monte Carlo simulation according to the line fault probability;
S7: reducing the scene generated in the step S7 by using a synchronous back-up scene reduction method to obtain 10 typical post-disaster fault scenes, and selecting the scene with the highest scene fault probability as a post-disaster rush repair restoration optimization scene;
S8: and establishing a post-disaster distribution network emergency repair restoration optimization model, and solving the model to obtain a disaster-affected area distribution network emergency repair restoration optimization strategy.
Based on the above embodiment, the scientificity and effectiveness of the proposed strategy are verified by taking the IEEE-33 node system as an example for simulation. Table 1 is data of an IEEE-33 node system, and Table 2 is a load class list of the IEEE-33 node system. Fig. 3 is a geographical topological diagram of an IEEE-33 node system provided by the present invention, fig. 4 is a schematic diagram of a relationship between a distance from a typhoon center and a failure probability provided by the present invention, fig. 5 is a schematic diagram of a relationship between a failure probability of a distribution network tower provided by the present invention and a wind speed, fig. 6 is a schematic diagram of failure probability of each line of the 33 node system under a typhoon disaster provided by the present invention, fig. 7 is a block diagram of the node system after temporary allocation center site selection optimization provided by the present invention, fig. 8 is a flowchart of 1000 post typhoon disaster fault scenes generated by monte carlo simulation provided by the present invention, fig. 9 is a schematic diagram of a broken line scene obtained according to scene 1 provided by the present invention, fig. 10 is a simplified schematic diagram of a broken line scene provided by the present invention, fig. 11 is a converging graph of a distribution network emergency repair recovery optimization model provided by the present invention, fig. 12 is a schematic diagram of a repair optimization sequence of a failure provided by the present invention, and fig. 13 is a schematic diagram of a system recovery degree of the present invention varies with time.
TABLE 1 IEEE-33 node System data
Table 2 load class list
The line fault probability model under the typhoon disaster established by the S4 is utilized to simulate in the node system in the FIG. 3 to obtain the relationship between the distance from the center of typhoon to the change of the fault probability, the relationship between the failure probability of the distribution network towers and the wind speed in the FIG. 5 and the fault probability of each line of the 33 node system under the typhoon disaster in the FIG. 6, the fault rate of the distribution network towers is increased along with the increase of the wind speed as the wind load caused by the increase of the wind speed to the distribution network towers is also increased, and the failure probability of the distribution network towers is also increased as the maximum is not more than 0.5 as the whole distribution network area is positioned outside the wind circle radius in the pre-disaster prediction stage as shown in the FIG. 6. On the other hand, it can be seen that the farther the line is from the wind circle radius, the lower the fault probability of the line, for example, the line L22 is closer to the wind circle radius and the typhoon center than the line L14, so the fault probability of the line L22 is larger, and the fault probability of the line L14 is close to 0.
S5: establishing a temporary allocation center site selection optimization model according to the fault probability and the position of the distribution network line, and solving the model to obtain the position of the temporary allocation center as a starting point of a post-disaster emergency repair recovery optimization task;
the temporary allocation center site selection optimization model in S5 is as follows:
wherein P i is the predicted fault probability of the line i; m L is the total number of fault lines; n B (i) is the total number of load losing nodes caused by the predicted fault line i; w ij is the grade weight of the load j of the power failure caused by the fault of the fault line i; t ij is expressed in terms of the journey time from the temporary transfer center to the faulty line i in the temporary transfer center address selection phase.
According to the temporary transfer center site selection model, the fminearch function in MATLAB is utilized to solve the temporary transfer center site selection optimization model, and the objective function value is obtained by solving: 8.8401 X104 kW.h, coordinates (119.44, 95.22), and particularly as shown in FIG. 7, it can be seen that the optimized temporary dialing center site selection position is closer to the line group with high fault probability than the original departure point Depot.
S6: generating 1000 typhoon disaster post-disaster fault scenes by utilizing Monte Carlo simulation according to the line fault probability;
The flow of generating 1000 post-typhoon fault scenes by using Monte Carlo simulation in S6 is shown in FIG. 8. The specific flow is as follows: firstly, on the basis of the fault probability of each line of a known system, assuming the fault probability of a line m as Pm, initializing the counting state of each distribution network line as 0, the simulation times as i=1 and the distribution network line traversing times as m=1; randomly generating a number random number S between 0 and 1, comparing the S with the fault probability Pm of each line, if S < Pm, the disconnection state of the line is 1, otherwise, the disconnection state is 0; and comparing whether m is smaller than N at the moment, if not, continuing traversing the rest branches of the system to obtain the broken line state of all the lines of the system, if so, judging whether the SUM (L) of the broken line scene at the moment is equal to 10, if so, proving that the simulation is effective, adding 1 to the simulation times, continuing the next simulation, if not, not counting the simulation at the time, regenerating a random number S, and repeating the steps until 1000 post-disaster broken line scenes of N-10 are generated.
S7: reducing the scene generated in the step S7 by using a synchronous back-up scene reduction method to obtain 10 typical post-disaster fault scenes, and selecting the scene with the highest scene fault probability as a post-disaster rush repair restoration optimization scene;
S7, reducing the scene generated in the S7 by using a synchronous back-substitution scene reduction method, and obtaining 10 typical post-disaster fault scenes by using the following steps:
S7.1: calculating the distance d (ζ i,ζj) between any two scenes in the 1000 scenes;
S7.2: determining a scene ζ n with the smallest distance from the scene ζ i, multiplying the probability p (ζ i) of the scene ζ i by the distance d (ζ i,ζn) of the scene ζ i、ζn to obtain p (ζ i)·d(ζi,ζn), and repeating the above steps until all traversals are completed;
S7.3: finding two scenes that minimize the p (ζ i)·d(ζi,ζn) value, namely a scene pair (ζ i,ζn);
S7.4: update p (ζ n)＝p(ζn)+p(ζi) while reducing scene ζ i;
S7.5: updating the total scene number 1000-1;
S7.6: returning to S7.1, the steps are repeated until the number of remaining scenes is 10.
The 10 fault scenarios obtained with scene cut-down are shown in table 3.
Table 3 scene cuts down 10 scenes
Fig. 9 is a schematic diagram of a broken line scene obtained according to the scene 1, and fig. 10 is simplified from fig. 9 for convenience in showing a subsequent rush repair recovery strategy.
S8: and establishing a post-disaster distribution network emergency repair restoration optimization model, and solving the model to obtain a disaster-affected area distribution network emergency repair restoration optimization strategy.
The post-disaster distribution network emergency repair recovery optimization model established in the S8 specifically comprises the following steps:
Wherein M is the total number of fault points in the distribution network system, N is the total number of load loss at the fault point i, w ij represents the load grade weight of the load loss node j at the fault point i, p ij represents the active power of the load loss node j at the fault point i, and T i represents the power failure time at the fault point i;
The calculation formula of the power failure time is as follows:
Wherein t i-1>i represents the path time spent from the last failure point i-1 to failure point i; Representing the sum of the time taken to complete the duplicate from the beginning to the previous fault point i-1; t re,i represents the time spent on rush repair of the fault point i; t TC>i represents the journey time from temporary dispatch center TC to failure point i; x i is a binary variable of 0,1, when x i =1, the load cluster in the fault point is supplied with power by the mobile distributed power supply in an island mode, so that the power failure time of the fault point is only the time spent by the path of the mobile distributed power supply from the starting point to the fault point, namely T i＝xi·TTC>i; when x i =0, no mobile distributed power supply is used for supplying power to the fault, and the power can be recovered only by a subsequent repair team, so that x i·TTC>i =0,
The post-disaster emergency repair recovery optimization model needs to meet certain constraint conditions:
Distribution network radial radiation topology constraint
The distribution network after fault repair should ensure that radial topology operation is continuously maintained, so that radial radiation topology constraint conditions need to be satisfied, namely:
gre∈GM
In the formula, G re represents a distribution network topological structure after the repair is completed and normal power supply is recovered, and G M represents a structure set capable of guaranteeing radial topology of the distribution network.
Distribution network node voltage constraint
UMIN≤U≤UMAX
Where U MIN is the lower limit of the node voltage and U MAX is the upper limit of the node voltage.
Distribution network tide constraint
|SL|≤SL,max
Where S L is the flow of line L and S L,max is the maximum value of the flow of line L.
The distances from the temporary allocation center to the fault points and the distances between the fault points are shown in tables 4 and 5, the time required for emergency repair of each fault generated randomly by Matlab is shown in table 6, and the load loss power values of the fault points obtained by taking the load class weights into consideration are shown in table 7.
TABLE 4 temporary dialing of the center to each faulty line distance
TABLE 5 distance between faulty wires
TABLE 6 rush repair time required for each failure point
TABLE 7 load loss power values for each failure point
And solving the post-disaster emergency repair recovery optimization model by using a genetic algorithm. The convergence curve of the distribution network emergency repair recovery optimization model obtained through solving is shown in fig. 11, and as can be seen from fig. 11, the objective function converges to the minimum value of 9.60×105 kw.h after 143 iterations, and the obtained post-disaster distribution network emergency repair recovery optimization strategy is: the mobile distributed power supply is connected into fault lines L17 and L29, and the emergency repair optimization sequence of faults is as follows: l2> L3> L8> L22> L23> L19> L27> L16> L17> L29, as shown in particular in fig. 12.
To illustrate the effectiveness of the present invention, 3 comparison schemes were set as follows:
scheme 1: under the condition that the temporary allocation center exists, island power supply is not carried out by adopting a mobile distributed power supply;
Scheme 2: under the condition that the temporary allocation center exists, a mobile distributed power supply is adopted to supply power to the load losing node cluster in an island mode (the invention);
scheme 3: and under the condition of no temporary transfer center, a mobile distributed power supply is adopted to supply power to the load losing node cluster in an island mode.
The comparison of the schemes is shown in Table 8.
Table 8 protocol comparison results
As can be seen from the comparison of the table 8, the scheme 2 is the optimal scheme, and the method proves that the method can more effectively reduce the load loss in the post-disaster rush repair process and quickly recover the system load loss in the post-disaster rush repair process in the distribution network emergency rush repair recovery task under the typhoon disaster, and simultaneously proves that the setting of the temporary transfer center can reduce the load loss in the distribution network rush repair process after the typhoon disaster, so that the consideration of the temporary transfer center is necessary under the typhoon disaster.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The emergency repair method under typhoon disasters is characterized by comprising the following steps:
Collecting monitoring information of a target typhoon, and determining a disaster area of a target power grid;
Collecting comprehensive data of the disaster affected area, and calculating a distribution network line of the target power grid by adopting a distribution network line fault probability model under typhoon disasters based on the comprehensive data to obtain fault probability and position of the distribution network line;
Constructing a first optimization model for temporary allocation center site selection based on the fault probability and the position, solving the first optimization model to obtain a target position of the temporary allocation center, and determining the target position as a departure point of a post-disaster emergency repair recovery optimization task;
Reducing a typhoon post-disaster fault scene generated by Monte Carlo simulation according to the fault probability to obtain a plurality of typical post-disaster fault scenes;
A typical post-disaster fault scene with the largest scene fault probability is selected as a scene for post-disaster rush repair restoration optimization;
constructing a second optimization model of the post-disaster distribution network emergency repair restoration based on the departure point and the scene of the post-disaster repair restoration optimization, and solving the second optimization model to obtain a target restoration optimization scheme of the post-disaster distribution network emergency repair;
the construction of the first optimization model of temporary transfer center site selection specifically comprises the following steps:
constructing a model to be optimized based on a first optimization formula, wherein the first optimization formula is as follows:
wherein P i is the predicted fault probability of the line i; m L is the total number of fault lines; n B (i) is the total number of load losing nodes caused by the predicted fault line i; w ij is the grade weight of the load j of the power failure caused by the fault of the fault line i; t ij is expressed by the path time from the temporary transfer center to the fault line i in the temporary transfer center address selection stage;
The constructing a second optimization model of post-disaster distribution network emergency repair restoration based on the departure point and the scene of post-disaster repair restoration optimization specifically comprises the following steps:
constructing a model to be optimized based on a second optimization formula, wherein the second optimization formula is as follows:
Wherein M is the total number of fault points in the distribution network system, N is the total number of load loss at the fault point i, w ij represents the load grade weight of the load loss node j at the fault point i, p ij represents the active power of the load loss node j at the fault point i, and T i represents the power failure time at the fault point i;
The calculation formula of the power failure time is as follows:
Wherein t i-1>i represents the path time spent from the last failure point i-1 to failure point i; Representing the sum of the time taken to complete the duplicate from the beginning to the previous fault point i-1; t re,i represents the time spent on rush repair of the fault point i; t TC>i represents the journey time from temporary dispatch center TC to failure point i; x i is a binary variable of 0,1, when x i =1, the load cluster in the fault point is supplied with power by the mobile distributed power supply in an island mode, so that the power failure time of the fault point is only the time spent by the path of the mobile distributed power supply from the starting point to the fault point, namely T i＝xi·TTC>i; when x i =0, no mobile distributed power supply is used for supplying power to the fault, and the power can be recovered only by a subsequent repair team, so that x i·TTC>i =0,
The post-disaster emergency repair recovery optimization model also needs to meet preset constraint conditions.
2. The emergency repair method according to claim 1, wherein the collecting the monitoring information of the target typhoons, determining the disaster-affected area of the target power grid, specifically comprises:
Continuously monitoring target typhoons, and responding to the forecast of the target typhoons in the monitoring result, and collecting the monitoring information of the target typhoons;
and determining a disaster area of the target power grid based on the monitoring information.
3. The emergency repair method under typhoon disasters according to claim 2, wherein the monitoring information specifically comprises:
Typhoon development path, typhoon wind speed, typhoon wind circle, typhoon center position, and typhoon center air pressure.
4. The emergency repair method for typhoon disasters according to claim 1, wherein the determining the disaster-affected area of the target power grid specifically comprises:
And (3) based on geographic topology information data of the distribution network equipment, carrying out superposition analysis on the geographic topology information data and the P-level wind ring of typhoons, and determining a disaster area, wherein P is a positive integer.
5. The method of claim 1, wherein the integrated data comprises meteorological data, distribution network data and geographic data; the meteorological data comprise the maximum gust wind speed of a disaster area; the distribution network data comprise the total number of towers, the positions of the towers, the lengths of the lines, the positions of the lines, the topology of the lines and the load information of the lines in the disaster area; the geographic data includes a longitude and a latitude.
6. The emergency repair method according to claim 1, wherein the reducing uses a monte carlo simulation to generate a post-typhoon disaster fault scenario according to the fault probability, and specifically comprises:
And reducing the typhoon disaster post-fault scene generated by Monte Carlo simulation according to the fault probability by using a synchronous back-substitution scene reduction algorithm.
7. The emergency repair method under typhoon disasters according to claim 6, wherein the generating of the post-typhoon disaster fault scene by utilizing Monte Carlo simulation according to the fault probability comprises the following steps:
assuming the fault probability of the line m is Pm, initializing the counting state of each distribution network line to be 0, the simulation times to be i=1 and the distribution network line traversing times to be m=1;
randomly generating a number random number S between 0 and 1, comparing the S with the fault probability Pm of each line, if S < Pm, the disconnection state of the line is 1, otherwise, the disconnection state is 0;
comparing whether m is smaller than N at the moment, if not, continuing traversing the rest branches of the system to obtain the broken line state of all the lines of the system, if so, judging whether a broken line scene SUM (L) at the moment is equal to 10, if so, proving that the simulation is effective, adding 1 to the simulation times i, continuing the next simulation, and if not, not counting the simulation at the time, and regenerating a random number S;
Repeating the steps until a post-disaster wire breakage scene of MM N-10 is generated, wherein MM is a positive integer.
8. The emergency repair method according to claim 7, wherein the synchronous back-substitution scene reduction algorithm is used to reduce MM post-typhoon fault scenes generated by monte carlo simulation according to the fault probability to obtain NN typical post-disaster fault scenes, and the method specifically comprises:
Calculating the distance d (ζ i,ζj) between any two scenes in the MM scenes;
Determining a scene ζ n with the smallest distance from the scene ζ i, multiplying the probability p (ζ i) of the scene ζ i by the distance d (ζ i,ζn) of the scene ζ i、ζn to obtain p (ζ i)·d(ζi,ζn), and repeating the above steps until all traversals are completed;
Finding two scenes that minimize the p (ζ i)·d(ζi,ζn) value, namely a scene pair (ζ i,ζn);
Update p (ζ n)＝p(ζn)+p(ζi) while reducing scene ζ i;
Updating the total scene number MM-1;
Returning to the first of the above steps, repeating the above steps until the number of remaining scenes is NN, NN being a positive integer and NN < MM.
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