CN112668737B - SAA optimization method of power distribution network multi-fault rush-repair model under disaster condition - Google Patents
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Abstract
The application discloses an SAA optimization method of a multi-fault first-aid repair model of a power distribution network under a disaster condition, which comprises the steps of taking an optimization solution in the first-aid repair model as solid particles and taking an objective function as an energy function of the solid; heating the solid to melt, waiting for it to cool in combination with a Metropolis sampling strategy; randomly searching a global optimal solution in an optimal solution space, and repeating the sampling process along with temperature reduction until the global optimal solution is obtained. According to the application, the requirements on the performance of the optimization algorithm under the disaster scene are analyzed, and based on the requirements, the simulated annealing algorithm is selected to solve the power distribution network rush-repair strategy optimization problem, so that the advantages of the calculation speed, stability and optimizing performance of the rush-repair model are improved.
Description
Technical Field
The application relates to the technical field of power distribution network rush-repair optimization algorithms, in particular to an SAA optimization method of a power distribution network multi-fault rush-repair model under disaster conditions.
Background
The natural disasters can damage the power system, and more than 10-level hurricanes and other foreign matters blown up by typhoons can cause serious faults such as pole falling, broken wires, permanent short circuit, collision damage to the transformer and the like; flood can loosen the foundation of the power pole tower, induce pole reversing accidents, and cause serious accidents such as water inflow and shutdown of a transformer substation; the ice and snow disasters can cause the wires and the towers to be covered with ice, so that the power towers collapse due to dead weight and are connected with nearby power transformation equipment through wires, and the power towers are damaged; geological disasters such as earthquakes can destroy various electric power buildings including towers and power substations, and even completely destroy the power supply network in disaster areas.
When a first-aid repair strategy is formulated, the position of a fault, a preset first-aid repair scheme, time and the like are considered to help to further improve the first-aid repair efficiency of the power distribution network, wherein the position of the fault is helpful to analyze the influence range of the fault, and faults with larger influence range are usually arranged earlier for first-aid repair; the rush-repair time, the rush-repair scheme and the like are expected to be beneficial to the rush-repair management personnel to better comprehensively arrange the rush-repair time, reduce the time wasted on unnecessary things by the rush-repair team and improve the rush-repair efficiency.
When a power distribution network fault rush-repair strategy under disaster conditions is formulated, two points should be noted: 1. under disaster conditions, the number of faults is huge, so that the scale of the optimization problem is larger; 2. the emergency repair under the disaster condition is easy to occur, so that the initial parameters such as faults, external environment and the like are frequently updated in the optimization problem, and the optimization algorithm is required to perform frequent repeated operation, and the emergency repair strategy is updated in time.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems occurring in the prior art.
Therefore, the SAA optimization method for the power distribution network multi-fault first-aid repair model under the disaster condition can solve the problems of unstable solving and optimizing of the power distribution network first-aid repair model and low calculation speed.
In order to solve the technical problems, the application provides the following technical scheme: taking an optimized solution in a rush-repair model as solid particles and taking an objective function as an energy function of the solid; heating the solid to melt, waiting for it to cool in combination with a Metropolis sampling strategy; randomly searching a global optimal solution in an optimal solution space, and repeating the sampling process along with temperature reduction until the global optimal solution is obtained.
As a preferable scheme of the SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition, the application comprises the following steps: the Metropolis sampling strategy includes defining an initial state of the solid particles as i and an energy as E i At a temperature T i A random disturbance is carried out on the initial state to obtain a new state j, and the corresponding energy state is E j The method comprises the steps of carrying out a first treatment on the surface of the If E j <E i Judging whether to accept the new state according to the probability that the solid is in the new state or not;
the probability ratio formula of the solid in the states i and j is as follows
Wherein E is i 、E j The energy of the molecule in the i and j states respectively, T is Kelvin temperature, k b Is the boltzmann constant.
As a preferable scheme of the SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition, the application comprises the following steps: also included is a method of manufacturing a semiconductor device,
generating a 0-1 random number delta using a random number generator, accepting the new state if delta < P, otherwise discarding the new state, as follows,
as a preferable scheme of the SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition, the application comprises the following steps: comprising rationally controlling the execution progress of the SAA algorithm using a cooling schedule, which is a set of parameters including an initial value t of a control parameter t 0 Chain length L of Markov chain, decay function of control parameter k And stopping criteria.
As a preferable scheme of the SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition, the application comprises the following steps: also included is a method of manufacturing a semiconductor device,
t k ＝αt k-1 ＝α k t 0
where k is the current iteration number of the SAA algorithm, and α is a constant, preferably close to 1, and generally takes 0.5-0.99, and the smaller α is, the faster the control parameter decays.
As a preferable scheme of the SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition, the application comprises the following steps: the method comprises the steps of arranging solutions generated by operation at the same temperature in sequence to obtain a Markov chain; length L of the Markov chain k The number of times of generating new solution operation in the k-th round of iterative computation is the number of times of generating new solution operation in the k-th round of iterative computation; set fixed L k ，L k And t k In inverse proportion, i.e. t k At 0, L k →∞。
As a preferable scheme of the SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition, the application comprises the following steps: and further comprises adding a small perturbation to generate a new solution, unconditionally accepting the new solution when the new solution is better than the old solution, and otherwise calculating the probability of accepting the new solution according to a probability strategy, as follows,
P accept and accept ＝f(m)＝e -m/T
Wherein m is the difference value of the objective function value corresponding to the new solution and the old solution, T is the current temperature, and e is a natural constant.
As a preferable scheme of the SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition, the application comprises the following steps: the objective function may comprise a function of the object,
wherein t is i Is the time from the start of the execution of the rush-repair plan to the power supply recovered from the ith fault, the mathematical optimization is aimed at minimizing the function value, ω i The importance coefficient of the ith load node is directly determined according to the load level, P i The average value of active power consumed by the ith load node is represented, and the change of the system function F (t) of the power distribution network along with time represents toughness.
As a preferable scheme of the SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition, the application comprises the following steps: the first-aid repair model comprises a first-aid repair model,
R real (t)＝R*inc(t)
where R is a journey time matrix, inc (t) is journey increase coefficient, R is a fixed value, inc (t) is a dynamic parameter, which varies in hours, which is a positive real number.
The application has the beneficial effects that: according to the application, the requirements on the performance of the optimization algorithm under the disaster scene are analyzed, and based on the requirements, the simulated annealing algorithm is selected to solve the power distribution network rush-repair strategy optimization problem, so that the advantages of the calculation speed, stability and optimizing performance of the rush-repair model are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a flow chart of a SAA optimization method of a multi-fault emergency repair model of a power distribution network under a disaster condition according to a first embodiment of the present application;
fig. 2 is a schematic diagram of a power grid structure and fault distribution of an SAA optimization method of a multi-fault emergency repair model of a power distribution network under a disaster condition according to a second embodiment of the present application;
fig. 3 is a schematic diagram of a relationship trend between an objective function value and a dead time ratio of a part of feasible solutions of the SAA optimization method of the multi-fault emergency repair model of the power distribution network under a disaster condition according to the second embodiment of the present application.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present application have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, for a first embodiment of the present application, there is provided a SAA (Simulated Annealing Algorithm, SAA, simulated annealing algorithm) optimization method of a multi-fault emergency repair model of a power distribution network under disaster conditions, including:
s1: and taking the optimal solution in the rush-repair model as solid particles and taking the objective function as an energy function of the solid. It should be noted that, the objective function includes:
wherein t is i Is the time from the start of the execution of the rush-repair plan to the power supply recovered from the ith fault, the mathematical optimization is aimed at minimizing the function value, ω i The importance coefficient of the ith load node is directly determined according to the load level, P i The average value of active power consumed by the ith load node is represented, and the change of a system function F (t) of the power distribution network along with time represents toughness;
the first-aid repair model comprises a first-aid repair model,
R real (t)＝R*inc(t)
where R is a journey time matrix, inc (t) is journey increase coefficient, R is a fixed value, inc (t) is a dynamic parameter, which varies in hours, which is a positive real number.
S2: the solid is heated to melt and awaits its cooling in conjunction with the Metropolis sampling strategy. The step is to be noted, the Metropolis sampling strategy includes:
defining the initial state of the solid particles as i and the energy as E i At a temperature T i A random disturbance is carried out on the initial state to obtain a new state j, and the corresponding energy state is E j ；
If E j <E i If the solid is in the new state, judging whether to accept the new state or not according to the probability that the solid is in the new state;
the probability ratio formula of the solid in the states i and j is as follows
Wherein E is i 、E j The energy of the molecule in the i and j states respectively, T is Kelvin temperature, k b Is the boltzmann constant;
a random number delta of 0-1 is generated by means of a random number generator, and if delta < P, a new state is accepted, otherwise the new state is discarded, as follows,
rational control of the execution progress of the SAA algorithm by means of a cooling schedule, which is a set of parameters including an initial value t of the control parameter t 0 Chain length L of Markov chain, decay function of control parameter k And a stopping criterion;
t k ＝αt k-1 ＝α k t 0
where k is the current iteration number of the SAA algorithm, and α is a constant, preferably close to 1, and generally takes 0.5-0.99, and the smaller α is, the faster the control parameter decays.
S3: randomly searching a global optimal solution in the optimized solution space, and repeating the sampling process along with the temperature reduction until the global optimal solution is obtained. The following are also to be described:
the solutions generated by operation at the same temperature are arranged in sequence to obtain a Markov chain;
length L of Markov chain k The number of times of generating new solution operation in the k-th round of iterative computation is the number of times of generating new solution operation in the k-th round of iterative computation;
set fixed L k ，L k And t k In inverse proportion, i.e. t k At 0, L k →∞；
Adding a small perturbation to generate a new solution, unconditionally accepting the new solution when the new solution is better than the old solution, otherwise calculating the probability of accepting the new solution according to a probability strategy, as follows,
P accept and accept ＝f(m)＝e -m/T
Wherein m is the difference value of the objective function value corresponding to the new solution and the old solution, T is the current temperature, and e is a natural constant.
Colloquially, the stopping criterion may be a numerical value or criterion, the stopping criterion in numerical form being typically the maximum number of iteration rounds k max Or the lowest control parameter (lowest temperature) t of the continued execution algorithm min Same as t 0 Is similarly arranged as t min The algorithm operation efficiency is considered, and meanwhile, the algorithm operation is small; the criterion for stopping the algorithm to continue to be executed can be unchanged or in other forms of continuous iterative calculation results; the reasonable cooling schedule not only ensures that the SAA algorithm can converge, but also shortens the running time of the algorithm as far as possible on the premise of not affecting the solving quality of the algorithm.
Preferably, this embodiment also needs to illustrate that, compared to the traditional intelligent optimization algorithm based on the population, the SAA has the following three advantages: 1. only one solution exists in the solution group, and the calculation process of generating and receiving the new solution is simple, so that the programming is convenient to realize; 2. although the SAA algorithm has low convergence speed and needs more iterative computation times to achieve a better optimization effect, the single iterative computation time is shorter because the number of times of computing the objective function value is smaller in single iterative computation, so that the total time spent in computation is possibly shorter; 3. the SAA algorithm has theoretical demonstration on convergence, so long as the algorithm meets a certain condition, the condition can converge to an optimal solution, and the condition is irrelevant to an optimization problem, while the intelligent optimization algorithm based on a group generally lacks theoretical support on the convergence, which means that the SAA algorithm can obtain the optimal solution of the problem with higher confidence than other algorithms, namely, the SAA algorithm can have better optimizing capability and better calculation stability.
Example 2
Referring to fig. 2 and 3, in a second embodiment of the present application, unlike the first embodiment, there is provided verification of a SAA optimization method of a multi-fault rush-repair model of a power distribution network under disaster conditions, including:
referring to fig. 2, the faults occurring between load nodes are line faults, the faults occurring on the load nodes are faults of the load nodes, do not affect the line operation, and comprise 1 source node, 32 load nodes, 10 faults, 5 repair teams and 5 repair teamsThe team comprises 2 line rush-repair classes, 1 cable rush-repair class and 2 large engineering vehicles; defining only one starting point of a team, setting efficiency loss coefficient k=0.64 of faults at each position, and enabling the lowest efficiency coefficient a of rush repair by the rush repair team min ＝0.3。
The fault problem is solved by three algorithms of SAA, GA and COA, and the respective solving results are compared, and the parameters of the three algorithms involved in the comparison are as follows:
(1) The method (simulated annealing algorithm) of the application: initial temperature t 0 ＝10 30 Final temperature t min ＝10 -30 ，L k =1, α=0.9 (iterative calculation 1312 times);
(2) Traditional method one (genetic algorithm): population number 20, gene crossover probability 70%, gene mutation probability 10%, and iterative calculation 300 times;
(3) Traditional method two (ant colony algorithm): the number of the ant colony individuals is 20, the pheromone descending coefficient is 0.9975, the ascending coefficient is 1.0025, and the iterative calculation is 500 times.
The basic principle of the three algorithms is random search, so that when the iteration times are not infinite, the final optimization result can fluctuate, in order to show respective actual performances, after multiple independent repeated tests are carried out, the research thought of data is analyzed by using a statistical method, 500 independent repeated optimization calculations are respectively carried out by using the three algorithms, and compared indexes comprise program average running time, final optimization objective function mean value, optimizing rate, 1/10000 optimizing solution rate and 5/10000 optimizing solution rate, wherein the optimizing rate refers to the probability that the algorithm searches for the actual optimal solution of the optimizing problem, and the capability of the algorithm to search the optimal solution is represented; the optimal solution rate of 1/10000 (5/10000) shows that the higher the value, the stronger the stability of the algorithm is, the probability of the algorithm to solve the optimal solution of 1/10000 (5/10000) before the optimization problem is solved.
Table 1: results data comparison table of 500 independent replicates.
| Algorithm class | Simulated annealing algorithm | Genetic algorithm | Ant colony algorithm |
| Program average run time/s | 28.62 | 58.76 | 85.75 |
| Average value of final results/10 6 | 8.979 | 9.120 | 9.087 |
| Optimizing rate | 37.4％ | 2.4％ | 0.0％ |
| 1/10000 optimal solution rate | 78.4％ | 49.2％ | 45.6％ |
| 5/10000 optimal solution rate | 99.0％ | 80.8％ | 95.4％ |
Referring to table 1, compared with two traditional algorithms, the SAA algorithm has advantages in terms of running speed, stability and optimizing capability, so that analysis and speculation of the advantages of the SAA algorithm are verified, and compared with other common intelligent algorithms based on groups, the SAA algorithm is more suitable for solving the problem of power distribution network fault rush-repair strategy optimization under disaster conditions.
After the total 1500 times of solutions are carried out by using three algorithms, the obtained optimal solution is 5- & gt 9- & gt 6- & gt 3- & gt 4- & gt 1- & gt 8- & gt 2- & gt 10- & gt 7, wherein the sequences of No. 5 and No. 9, no. 4 and No. 1 can be interchanged, and the objective function value corresponding to the solution is 8.710 x 10 6 。
In the emergency repair practice, when the power company encounters multiple power distribution network faults and needs to arrange the emergency repair sequence, the power company generally takes the influence range of the faults as the basis of the sequence, namely, the larger the influence range of the faults is, the more preferentially arrange the emergency repair, sometimes, the emergency repair time of the faults is also considered, namely, the larger the influence range is, the shorter the emergency repair time is, the more preferentially arrange the emergency repair is, if the sequencing method is expressed by using a mathematical formula, the simplest expression mode is as follows
Wherein ρ (k) is the importance coefficient of the fault, Ω k In the expression of (2), D k For the collection of all load nodes positioned at the downstream of the kth fault, obviously, the larger the value of rho (k), the more the fault should be repaired first-aid, and the sorting method is simple and practical on the premise of not considering the blocking of the external environment to the repair, but when the blocking effect of the external environment to the repair is not neglected, the optimal repair strategy may be difficult to find by the simple method.
Preferably, the importance coefficient of each fault is obtained by combining the load and related fault parameters and the formula.
Table 2: and a fault importance degree coefficient table in the calculation example.
| Fault number k | Predicting rush repair time | Ω(k) | ρ(k) |
| 1 | 6 | 24750 | 4125 |
| 2 | 3.5 | 2400 | 685.714286 |
| 3 | 12 | 120000 | 10000 |
| 4 | 1 | 60000 | 60000 |
| 5 | 1 | 20500 | 20500 |
| 6 | 2 | 16100 | 8050 |
| 7 | 3 | 3000 | 1000 |
| 8 | 4 | 282000 | 70500 |
| 9 | 2 | 10000 | 5000 |
| 10 | 5 | 9600 | 1920 |
If the rho value is used for sorting, the rush repair sequence is 8-4-5-3-6-9-1-10-7-2, and the sequence is substituted into the model for calculation, so that the corresponding objective function value is 1.635 x 10 7 Not only far above the known optimal solution of the problem, but also the solution is known to be only at the middle and downstream level in all feasible solutions of the calculation example after random sampling, and under the disaster condition, the simple rush repair strategy formulation method is not applicable.
The evaluation of the effect of the rush-repair strategy finally falls in time, so that in the implementation process of the strategy, all rush-repair teams spend longer time on idle work such as on the way and waiting for the rush-repair to start, and the like, accounting for 65.33 percent of the total working time of all the rush-repair teams, and compared with the conventional optimal rush-repair strategy, the ratio corresponding to the optimal rush-repair strategy is only 38.05 percent; this phenomenon suggests that the merits of the power distribution network fault repair strategy may also be related to the proportion of the total working time that the sum of the "idle work" time of all the repair teams is occupied in the process of executing the strategy.
Referring to fig. 3, to verify the relationship, 1000 solutions are randomly selected from all possible solutions in this embodiment, the corresponding objective function and "dead time ratio" are calculated, a data scatter diagram is drawn and a linear fit is performed, as shown in fig. 3, it can be seen from the figure that the objective function value corresponding to the solution and the "dead time ratio" are approximately positively correlated, and as the objective function value increases, the "dead time ratio" corresponding to the solution approximately increases, which suggests that the rush-repair efficiency of the distribution network after the disaster is to be improved, and it is also necessary to reasonably plan the rush-repair period, and reduce the time wasted by the rush-repair personnel on non-rush-repair works such as the journey, waiting for the rush-repair start, etc.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Claims (6)
1. A SAA optimization method of a power distribution network multi-fault rush-repair model under disaster conditions is characterized by comprising the following steps of: comprising the steps of (a) a step of,
taking an optimized solution in the rush-repair model as solid particles and taking an objective function as an energy function of the solid;
the objective function may comprise a function of the object,
wherein t is i Is the time from the start of the execution of the rush-repair plan to the power supply recovered from the ith fault, the mathematical optimization is aimed at minimizing the function value, ω i The importance coefficient of the ith load node is directly determined according to the load level, P i Representing the average value of active power consumed by the ith load node, and representing toughness and P by the change of a system function F (t) of the power distribution network along with time i (t) is the average value of active power consumed by the ith load node at the moment t;
the first-aid repair model comprises a first-aid repair model,
R real (t)＝R*inc(t)
wherein R is a journey time matrix, inc (t) is a journey increase coefficient, R is a fixed value, inc (t) is a dynamic parameter, and changes in units of hours, which is a positive real number;
heating the solid to melt, waiting for it to cool in combination with a Metropolis sampling strategy;
generating a 0-1 random number delta using a random number generator, accepting the new state if delta < P, otherwise discarding the new state, as follows,
wherein E is i 、E j The energy of the molecule in the i and j states respectively, T is Kelvin temperature, k b Is Boltzmann constant, P is the acceptance of the energy value from E i Becomes E j Probability values of (2);
randomly searching a global optimal solution in an optimal solution space, and repeating the sampling process along with temperature reduction until the global optimal solution is obtained.
2. The SAA optimization method for a multi-fault emergency repair model of a power distribution network under disaster conditions according to claim 1, wherein the method comprises the following steps: the metapolis sampling strategy includes,
defining the initial state of the solid particles as i and the energy as E i At a temperature T i A random disturbance is carried out on the initial state to obtain a new state j, and the corresponding energy state is E j ；
If E j <E i Judging whether to accept the new state according to the probability that the solid is in the new state or not;
the probability ratio formula of the solid in the states i and j is as follows
Wherein E is i 、E j The energy of the molecule in the i and j states respectively, T is Kelvin temperature, k b Is the boltzmann constant.
3. The SAA optimization method for a multi-fault emergency repair model of a power distribution network under disaster conditions according to claim 2, wherein the method comprises the following steps: comprising rationally controlling the execution progress of the SAA algorithm using a cooling schedule, which is a set of parameters including an initial value t of a control parameter t 0 Chain length L of Markov chain, decay function of control parameter k And stopping criteria.
4. A SAA optimization method for a multi-fault rush-repair model of a power distribution network under disaster conditions according to claim 3, wherein: also included is a method of manufacturing a semiconductor device,
t k ＝αt k-1 ＝α k t 0
wherein k is the current iteration number of the SAA algorithm, alpha is a constant, alpha is 0.5-0.99, and the smaller the alpha is, the faster the control parameter decays.
5. The SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition according to claim 4 is characterized in that: comprising the steps of (a) a step of,
arranging solutions generated by operation at the same temperature in sequence to obtain a Markov chain;
length L of the Markov chain k The number of times of generating new solution operation in the k-th round of iterative computation is the number of times of generating new solution operation in the k-th round of iterative computation;
set fixed L k ，L k And t k In inverse proportion, i.e. t k At 0, L k →∞。
6. The SAA optimization method of the power distribution network multi-fault rush-repair model under the disaster condition according to claim 5 is characterized in that: also included is a method of manufacturing a semiconductor device,
adding a small perturbation to generate a new solution, unconditionally accepting the new solution when the new solution is better than the old solution, and otherwise calculating the probability of accepting the new solution according to a probability strategy, as follows,
P accept and accept ＝f(m)＝e -m/T
Wherein m is the difference value of the objective function value corresponding to the new solution and the old solution, T is the current temperature, and e is a natural constant.
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