CN110222889B - Power distribution network feeder automation terminal configuration method based on multiple intelligent algorithms - Google Patents
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Abstract
The invention discloses a power distribution network feeder automation terminal configuration method based on various intelligent algorithms, which comprises the following steps: (1) Inputting data required by the power distribution network feeder automation terminal configuration; (2) Randomly generating a plurality of feeder automation terminal configuration schemes to form an original population; (3) calculating the fitness of each individual in the population; (4) Selecting an individual with the minimum objective function value in the current population, and updating the historical optimal solution; (5) Judging whether the set iteration times are reached, if so, outputting the current historical optimal solution, and finishing the optimization; if not, executing the step (6); (6) And (4) performing operation according to optimizing rules of different intelligent algorithms, and generating a new population on the basis of the previous generation population. Then, the step (3) is skipped. The invention can provide a more scientific and reasonable distribution network feeder automation terminal configuration scheme, and can avoid the situations of feeder automation terminal configuration redundancy, investment waste and the like on the premise of ensuring the power supply reliability to reach the standard.
Description
Technical Field
The invention belongs to the field of secondary system configuration of a power distribution network, relates to a power distribution network feeder line automatic terminal configuration method based on multiple intelligent algorithms, and is suitable for automatic terminal configuration for optimizing a power distribution network feeder line switch.
Background
The power distribution network is a bridge connecting the power transmission network and users, and the power supply reliability of the power distribution network directly influences the development of national economy and the improvement of the living standard of people. The Distribution Automation (DA) technology is an important means for improving the power supply reliability and the power supply quality of a power Distribution network, and is also an important component of an intelligent power grid. The distribution network automation system generally comprises a distribution main station, a distribution substation and a distribution terminal, wherein the distribution terminal is a basic component unit of the distribution network automation system. Due to different functions of different types of power distribution terminals, the improvement degree of the power supply reliability of the power distribution network is different.
The feeder automation is used as an important content of distribution automation, and the first remote terminal, the second remote terminal and the third remote terminal are configured on the switch equipment or the ring network unit, so that the switch equipment and the ring network unit are monitored and controlled, the time for completing fault positioning, fault isolation and switching is shortened, and the power supply reliability of a power distribution network is improved. The remote terminal, namely a fault indicator, has the function of reducing the time for a worker to patrol the line to check a fault point of the line; the two remote terminals have remote signaling and remote measuring functions, can measure the system state quantities such as current, voltage and the like of the switch equipment or the ring network unit when a circuit fails, and upload the system state quantities to the power distribution substation or the power distribution main station to help a worker to remotely determine the range of a fault point; the three-remote terminal has remote signaling, remote measuring and remote controlling functions, and can enable a worker to remotely control the switch besides the function of the two-remote terminal. It is assumed that if all switches are configured with three remote terminals, the configuration scheme will be the one with the highest reliability of power supply of the distribution network in all feeder automation terminal configuration schemes, but the economical efficiency is not fully considered. Therefore, the method comprehensively considers the power supply reliability and the economic cost of the power distribution network, and provides the power distribution network feeder automation terminal configuration method.
Considering that the configuration optimization of the distribution network automatic terminal is a nonlinear combined optimization problem, an optimization model of the optimization problem is an NP problem, and along with the increase of the scale of a distribution network to be configured, the calculated amount increases exponentially, and the problem of dimension disaster exists, so that the global optimal solution is difficult to obtain by using a traditional method; and the solving result of the intelligent algorithm has randomness, and the existing various intelligent algorithms have no clear application relation with the optimization problem, so that various classical intelligent algorithms are adopted to compare and obtain the satisfactory solution.
Disclosure of Invention
The invention provides a power distribution network feeder automation terminal configuration method based on multiple intelligent algorithms. Firstly, randomly generating a plurality of feeder automation terminal configuration schemes to form an original population; then inputting the data into a genetic algorithm module, an ant colony algorithm module and a particle swarm algorithm module respectively for iterative optimization; in the iteration process, the full life cycle cost of an optimally configured target function configuration scheme is used as a fitness function, a power distribution network reliability analysis method considering user importance difference under the power distribution automation condition is adopted to calculate the reliability index of the power distribution network, the reliability index is converted into power failure loss and then is calculated into the target function together with other economic indexes, and the reliability constraint condition is considered to be converted into penalty and is included in the calculation of a target function value; updating individuals in the population and the optimal solution of the population according to new population generation rules of different algorithms; and outputting the optimal solution given by the three algorithms until the iteration times reach a set value, and taking the solution with the minimum objective function as a final configuration scheme.
The method for configuring the feeder automation terminal of the power distribution network based on various intelligent algorithms comprises the following steps:
(1) Inputting data required by the power distribution network feeder automation terminal configuration;
(2) Randomly generating a plurality of distribution network feeder automation terminal configuration schemes to form an original population;
(3) Calculating the fitness of each individual in the population;
(4) Selecting an individual with the minimum objective function value in the current population, and updating the historical optimal solution;
(5) Judging whether the set iteration times are reached, if so, outputting the current historical optimal solution, and finishing the optimization; if not, executing the step (6);
(6) And performing operation according to optimizing rules of different intelligent algorithms to generate a new population on the basis of the previous generation population. And (4) jumping to the step (3), wherein different intelligent algorithms comprise a genetic algorithm, an ant colony algorithm and a particle swarm algorithm.
In the step (1), the data required by the power distribution network feeder automation terminal configuration is as follows:
the data required by the reliability calculation of the power distribution network considering the user importance difference under the power distribution automation condition comprise: reliability parameters of all elements of the power distribution network to be analyzed, element connection relations, main feeder end nodes, length of each section of line, average load and importance parameters of each load point, and a plurality of times related to fault processing of a power supply company; the reliability parameters of all elements of the power distribution network to be analyzed comprise element annual fault rate and element mean fault repair time; the importance parameters comprise life importance parameters, economic importance parameters and special importance parameters; the several times related to the fault handling of the power supply company comprise the time of remotely controlling the action of a single three-remote switch, the average time of a maintainer arriving at a fault feeder, the time of the maintainer checking the listing condition of a single fault indicator, the line patrol time of the maintainer for determining the position of a fault point in a unit-length line, and the time of the maintainer operating the action of a single non-remote switch on site.
In the above data, the rest of the input data except for the time related to the fault handling of the power supply company can be unified into a matrix branch. Each row of the matrix branch represents each element of the power distribution network; the first row and the second row are respectively a first node and a last node of the element and represent the connection relation of each element of the power distribution network; the third column indicates the type of the component, and codes 1,2, 3, 4, 5, 6, 7 indicate the component is line, transformer, fuse, breaker, sectionalizer, tie switch, load, respectively; the fourth column and the fifth column are the annual failure rate and the average failure repair time of the element, respectively; the sixth row stores the length of the line element, and if the element is not a line, the sixth row element is null; the seventh, eighth, ninth, and tenth rows store the average load, the life importance parameter, the economic importance parameter, and the special importance parameter of the load point, respectively, and if the component is not a load, the seventh, eighth, ninth, and tenth rows are null.
The data required by the economic index calculation of the power distribution network feeder automation terminal configuration scheme comprises the following data: the power distribution automation terminal comprises social and economic losses caused by power shortage of each degree, unit price of a 'one remote' terminal, unit price of a 'two remote' terminal, unit price of a 'three remote' terminal, proportion of maintenance cost for the power distribution automation terminal to initial investment every year, current rate, planning period and reliability constraint conditions.
The parameters required by the optimization solution of the various intelligent algorithms comprise: population size n, iteration number t. Different intelligent algorithms also need different parameters, for example, a genetic algorithm needs to set variation probability, genetic probability, inter-generation difference rate and the like; the ant colony algorithm needs to set pheromone volatilization coefficients, pheromone intensity and the like; the particle swarm optimization needs to set inertia weight, cognitive acceleration constant, cooperative acceleration constant and the like. All three algorithms also need to set variables to store historical optimal solutions of the whole population, and particularly, the particle swarm algorithm also needs to set variables to store historical optimal solutions of all individuals.
In the step (2), each individual in the population provides a possible configuration scheme, and the configuration schemes given by all the individuals form the population. Wherein the configuration scheme is represented as a decision vector X consisting of decision variables X i Namely:
X i ＝[x 1 x 2 ...x k ]
wherein, X i A configuration scheme vector, namely a decision vector, is given for the ith individual; k is the number of the section switches needing to be optimally configured with the terminal. The decision variable x is the type of termination on the switching element of each conditionally configured feeder automation terminal. In order to ensure that the power distribution network has high power supply reliability, the circuit breaker at the outlet end of the feeder line and the interconnection switch between the feeder line and the feeder line are provided with three remote terminals, namely, the types of the terminals on the circuit breaker at the outlet end and the interconnection switch do not belong to decision variables of the optimization model. The value of the decision variable x may be one of 0, 1,2, and 3, which respectively correspond to no feeder automation terminal installation, one remote terminal installation, two remote terminal installation, and three remote terminal installation.
Randomly generating an original population, i.e. randomly generating n decision vectors X i (i =1,2, \8230;, n), the k decision variables x in each decision vector are designated by random numbers to one of 0, 1,2, 3.
In the step (3), the fitness is an objective function value, and the calculation of the objective function value already includes a penalty rule converted from the reliability constraint condition.
Before calculating the objective function value of the decision vector, reliability analysis needs to be performed on the power distribution network represented by the decision vector, and the system average power failure time (SAIDI) of the power distribution network are calculated by using a power distribution network reliability analysis method considering user importance difference under the power distribution automation conditionFrequency (SAIFI) and expected power shortage (ens) at each load point i )。
The method for analyzing the reliability of the power distribution network considering the user importance difference under the power distribution automation condition comprises the following steps:
s1, inputting data required by reliability analysis of a power distribution network;
wherein the required data comprises the input data in the step (1) and the matrix branch.
S2, topology searching of a main feeder of the power distribution network is carried out;
and performing topology search on the first and second columns of elements of the matrix branch, wherein a path from a power supply point to a main feeder tail end node is the main feeder.
S3, carrying out downstream combination on the reliability parameters of the switching elements;
the switch elements refer to fuses, circuit breakers, section switches and interconnection switches. And S3, converting the reliability parameters of the switching elements to the adjacent downstream line elements, wherein the reliability parameters of the switching elements comprise the failure rate and the average repair time of the switching elements, and the failure rate and the average repair time of the converted switching elements are zero.
S4, calculating the fault isolation time of the non-switching element;
time t of fault isolation 2 Including the time t of the journey of the worker to the faulty feeder 21 And time t for locating fault 22 And field operation switch time t 23 The calculation formula is as follows:
t 2 ＝t 21 +t 22 +t 23
wherein, t 1 Time, t, for remotely controlling a 'three remote' switch by a distribution main station 3 The time for fault recovery.
Defining the remote non-visual segment to be the feeder segment sandwiched by the switching elements of the no less than two remote terminal configuration upstream and downstream of the nearest fault point; defining a minimum routing segment as a feeder segment sandwiched by switching elements configured by upstream and downstream terminals nearest to the fault point and not lower than one remote terminal; the fault feeder section is defined as the feeder section sandwiched by the upstream and downstream switching elements that are closest to the fault point.
Time t for locating fault 22 The calculation formula of (a) is as follows:
t 22 ＝n yiyao t yiyao +l patrol t patrol
wherein n is yiyao The number of all 'one remote' terminals in the remote non-visual segment, t yiyao Time spent to check a single "one remote" terminal,/ patrol Is the length of the minimum tour segment, t patrol Is the line patrol time of the line with unit length.
On-site operation switch time t 23 The calculation formula of (c) is as follows:
t 23 ＝n manual t manual
wherein n is mamual A switch without a remote control condition in the first and last switching elements of the faulty feeder section, t mamual Time to operate a single switch in the field.
S5, analyzing the consequence of the failure mode of the non-switching element;
assuming that the non-switching element j fails, there are three possibilities to analyze the outage time T of the load i:
1) If the load i can realize fault isolation and recover power supply by remotely controlling the three-remote switch action of the power distribution main station, T = T 1 ；
2) If the load i can not realize fault isolation and power supply recovery through the remote control of the three-remote switch action of the power distribution main station, but can realize fault isolation and power supply recovery through the field operation of the switch action, T = T 1 +t 2 ；
3) If the load i can not realize fault isolation and power restoration through any switching operation, T = T 1 +t 2 +t 3 ；
Traversing all non-switching elements in the power distribution network, selecting fault elements in all the non-switching elements, analyzing the influence of the faults on all load points, and finally obtaining the influence of all possible fault events of the non-switching elements of the power distribution network on all load points of the power distribution network;
s6, calculating the reliability index of each load point;
the reliability index of each load point comprises the annual fault rate lambda of the load point i i Average power outage duration γ for load point i i Annual average fault power failure time U of load point i i Expected power shortage amount ens of load point i i The specific calculation formula is as follows:
annual failure rate λ of load point i i ：
Wherein D is i Set of elements for powering down load point i after fault, lambda k Is the annual failure rate of element k.
Average outage duration γ for load point i i ：
Wherein, T ik The time when a failure of element k results in a power outage at load point i.
Annual average fault power failure time U of load point i i ：
U i ＝λ i γ i
Expected power shortage amount ens of load point i i ：
ens i ＝P i U i
Wherein, P i The annual average load at load point i.
And S7, calculating the reliability index of the power distribution network system.
The reliability indexes of the power distribution network system comprise system average power failure time (SAIDI), system average power failure frequency (SAIFI), average power failure time (CAIDI) of power failure users and power supply reliability index (ASAI). The calculation formula is the same as that defined in the evaluation regulation of the power supply reliability of the power supply system.
The objective function is the minimum value of the full life cycle cost LCC of the power distribution network feeder automation terminal configuration scheme. In order to reflect the influence of the power distribution network feeder automation terminal configuration scheme on the power supply reliability of the power distribution network, the power failure loss of the power distribution network is incorporated into the full life cycle cost LCC of the terminal configuration scheme by the model. The calculation formula of the objective function is as follows:
wherein, C inv For initial investment costs, C maintain For annual maintenance costs for feeder automation terminals, i.e. maintenance costs, C ens The loss caused by power failure every year, namely power failure loss, of the power distribution network. Maintenance cost C per year due to long planning period maintain And loss of power failure C ens It needs to be converted into the current value for consideration,
for the discount coefficient, r is the discount rate, n is the planning period, and t is the number of years the terminal configuration scheme has been put into use.
Initial investment cost C of power distribution network feeder automation terminal configuration scheme inv The sum of the purchase installation costs of the terminal selections determined for each decision variable is:
C inv ＝n 1 p 1 +n 2 p 2 +n 3 p 3
wherein n is 1 、n 2 、n 3 The number of switch elements, p, respectively for configuring 'one remote' terminal, 'two remote' terminal and 'three remote' terminal 1 、p 2 、p 3 The purchase cost and the installation cost of a single 'one remote' terminal, a 'two remote' terminal and a 'three remote' terminal are respectively.
Maintenance cost C of power distribution network feeder automation terminal configuration scheme maintain According to the method for managing the maintenance and overhaul costs of power supply equipment by most of the current power supply enterprises, the initial investment proportion is:
C maintain ＝ηC inv
wherein eta is the proportion of the maintenance cost to the initial investment cost.
Loss of power failure C ens The expected power shortage of the power distribution network is converted. Considering that different load points of the same power distribution network have different importance differences due to different user properties, which will cause different power outage losses caused by unit power shortage amount of different load points, the expected power shortage amount of each load point introduces a weight coefficient for measuring the importance difference of users. The expected power shortage ENS calculation formula of the power distribution network is as follows:
where D is the set of all load points, ens i The expected power shortage at the ith load point, (k α) i +β i +γ i ) Is a weight coefficient for measuring the load difference. The parameters alpha, beta and gamma respectively represent the difference of the load in three aspects of life safety, economy and specificity, and the larger the value of the parameter is, the larger the loss of the load in the aspect of power failure is. Alpha is alpha i 、β i 、γ i The life safety difference weight coefficient, the economical difference weight coefficient and the special difference weight coefficient of the load point i are respectively.
Loss of power failure C ens The calculation formula of (a) is as follows:
C ens ＝ENS×σ
wherein, σ is the power failure loss of unit power supply shortage amount, and the power failure loss considers the direct loss such as income loss brought to power supply enterprises and production loss brought to power utilization enterprises due to power failure, and also considers the indirect loss brought by the condition that power failure accidents influence the enterprise image of the power supply and power utilization enterprises.
The reliability constraints are as follows:
SAIDI≤SAIDI max
SAIFI≤SAIFI max
wherein, SAIDI max 、SAIFI max Respectively to allow the distribution of powerThe average outage time of the grid system and the upper limit of the average outage frequency of the system.
If the reliability index calculated by the reliability analysis can not meet the constraint, the objective function value is directly set to be infinite without calculating the initial investment cost C inv Terminal maintenance cost C maintain And power loss C ens 。
In the step (4), the configuration scheme with the minimum objective function value in the population of the current generation and the evaluation index thereof need to be selected and stored in the optimal solution matrix nbest of the current generation; if the optimal solution of the generation is better than the historical optimal solution, the historical optimal solution of the population needs to be updated along with the optimal solution of the generation, and the updated historical optimal solution is stored in a historical optimal solution matrix gbest of the population. Particularly, the particle swarm optimization also needs to update the historical optimal solution of each individual, and the historical optimal solution is stored in the individual historical optimal solution matrix pbest.
In the step (6), the optimization rules of different intelligent algorithms are different, so that the operation is different. The operations that the genetic algorithm needs to perform are breeding, crossover and mutation, thereby creating new individuals, and composing the next generation of population with new individuals and some of the better performing old individuals. The operation to be executed by the ant colony algorithm is to update the pheromone concentration matrix and the ant critical number matrix according to the configuration scheme provided by the optimal solution of the current generation, and then regenerate the next generation of population according to the latest pheromone concentration matrix and ant critical number matrix. The particle swarm algorithm needs to execute the operation of updating the velocity vector of each particle according to the population history optimal solution and the individual history optimal solution, and then obtaining the new position of each particle according to the latest velocity vector of each particle and the current position of each particle, wherein the new position represents the new solution given by the particle, and the new solutions of all the particles form the population of the next generation.
Compared with the prior art, the invention has the following advantages and effects:
(1) The invention provides a method for optimizing configuration of a feeder automation terminal of a power distribution network, which fully considers the influence of different types of feeder automation terminals on the power supply reliability of the power distribution network and simultaneously considers the difference of loads with different importance in power failure loss calculation. Compared with the traditional power distribution network planning technical guide, the method can provide a more scientific and reasonable power distribution network feeder automation terminal configuration scheme, and can avoid the situations of redundant configuration, investment waste and the like of the feeder automation terminal on the premise of ensuring the power supply reliability to reach the standard.
(2) In the optimization configuration model, an objective function is a configuration scheme full-life cycle cost function consisting of a 'one remote' terminal, a 'two remote' terminal, a 'three remote' terminal total investment, a yearly terminal maintenance cost and a yearly power failure loss cost caused by total power shortage of a power distribution network, the power supply reliability of the power distribution network and the economical efficiency of feeder automation terminal configuration are comprehensively considered, various intelligent algorithms are adopted to carry out optimization solution on the objective function, the probability of falling into a local optimal solution caused by the randomness of an intelligent algorithm solution result and the ambiguous correspondence between the intelligent algorithm and an optimization problem is avoided to a great extent, the global optimal solution is easy to find efficiently, and reference is provided for power distribution automation planning design and transformation.
Drawings
Fig. 1 is a distribution network and its configuration range division in the embodiment of the present invention.
Fig. 2 is a flowchart illustrating a terminal configuration method provided in the present invention.
In the distribution network shown in FIG. 1, S 1 、S 9 Being an outlet breaker, S 2 ～S 7 、S 10 ～S 12 Being a section switch, S 8 For the interconnection switch, LD 1-LD 11 are load points.
Detailed Description
The embodiment of the invention provides a power distribution network feeder automation terminal configuration method based on various intelligent algorithms, and in order to make the purposes, characteristics and advantages of the invention more obvious and understandable, the following description is provided in detail with reference to the accompanying drawings.
In the distribution network shown in FIG. 1, S 1 、S 9 Being an outlet breaker, S 2 ～S 7 、S 10 ～S 12 Being a section switch, S 8 For the interconnection switch, LD 1-LD 11 are load points. In the power distribution network, circuit breakers are installed at the outgoing line end of a feeder line and the outgoing line end of a branch line, and three remote terminals are arrangedAnd the method does not belong to the consideration range of the optimal configuration problem of the power distribution terminal. And the fault on the branch line cannot affect the load points on the main feeder line and other branch lines due to the circuit breaker arranged at the outlet end of the branch line. Therefore, the power distribution network in this embodiment can be divided into two dotted frame areas shown in fig. 1, and the problem of terminal configuration is considered respectively.
As shown in fig. 2, the present invention provides an embodiment of a power distribution network feeder automation terminal configuration method, including the following steps:
step (1): inputting data required by the power distribution network feeder automation terminal configuration;
the data required for the distribution network feeder automation terminal configuration are as follows:
the data required by the power distribution network reliability calculation considering the user importance difference under the power distribution automation condition comprise: reliability parameters of all elements of the power distribution network to be analyzed, element connection relation, main feeder tail end nodes, length of each section of line, average load and importance parameters of each load point, and a plurality of times related to fault processing of a power supply company; the reliability parameters of all elements of the power distribution network to be analyzed comprise element annual fault rate and element mean fault repair time; the importance parameters comprise a life importance parameter, an economic importance parameter and a special importance parameter; the several times related to the fault handling of the power supply company comprise the time of remotely controlling the action of a single three-remote switch, the average time of a maintainer arriving at a fault feeder, the time of the maintainer checking the listing condition of a single fault indicator, the line patrol time of the maintainer for determining the position of a fault point in a unit-length line, and the time of the maintainer operating the action of a single non-remote switch on site.
In the above data, the rest of the input data except for the time related to the fault handling of the power supply company can be unified into a matrix branch. Each row of the matrix branch represents each element of the power distribution network; the first row and the second row are respectively a first node and a last node of the element and represent the connection relation of each element of the power distribution network; the third column indicates the type of the component, and codes 1,2, 3, 4, 5, 6, 7 indicate the component is line, transformer, fuse, breaker, sectionalizer, tie switch, load, respectively; the fourth column and the fifth column are the annual failure rate and the average failure repair time of the element, respectively; the sixth row stores the length of the line element, and if the element is not a line, the sixth row element is null; the seventh, eighth, ninth, and tenth rows store the average load, the life importance parameter, the economic importance parameter, and the special importance parameter of the load point, respectively, and if the component is not a load, the seventh, eighth, ninth, and tenth rows are null.
The data required by the economic index calculation of the power distribution network feeder automation terminal configuration scheme comprises the following data: the power distribution automation terminal comprises social and economic losses caused by power shortage of each degree, unit price of a 'one remote' terminal, unit price of a 'two remote' terminal, unit price of a 'three remote' terminal, proportion of maintenance cost for the power distribution automation terminal to initial investment every year, current rate, planning period and reliability constraint conditions.
The parameters required by the optimization solution of the various intelligent algorithms comprise: population size n, iteration number t. Different intelligent algorithms also need different parameters, for example, the genetic algorithm needs to set mutation probability, genetic probability, inter-generation difference rate and the like; the ant colony algorithm needs to set pheromone volatilization coefficients, pheromone intensity and the like; the particle swarm optimization needs to set inertia weight, cognitive acceleration constant, cooperative acceleration constant and the like. All three algorithms also need to set variables to store historical optimal solutions of the whole population, and particularly, the particle swarm algorithm also needs to set variables to store historical optimal solutions of all individuals.
Step (2): randomly generating a plurality of feeder automation terminal configuration schemes to form an original population;
each individual represents one possible profile, and the profiles represented by all individuals make up the population. Wherein the configuration scheme is represented as a decision vector X consisting of decision variables X i Namely:
X i ＝[x 1 x 2 ...x k ]
wherein, X i A configuration scheme vector, namely a decision vector, is given for the ith individual; k is the number of the section switches needing to be optimally configured with the terminal. Decision makingThe variable x is the type of termination on the switching element of each conditionally configured feeder automation terminal. In order to ensure that the power distribution network has high power supply reliability, the circuit breaker at the outlet end of the feeder line and the interconnection switch between the feeder line and the feeder line are provided with three-remote terminals, namely, the types of the terminals on the circuit breaker at the outlet end and the interconnection switch do not belong to decision variables of the optimization model. The value of the decision variable x may be one of 0, 1,2, and 3, which respectively correspond to no feeder automation terminal installation, one remote terminal installation, two remote terminal installation, and three remote terminal installation.
And randomly generating an original population, namely randomly generating n decision vectors X, wherein k decision variables X in each decision vector are designated as one value of 0, 1,2 and 3 by random numbers.
And (3): calculating the fitness of each individual in the population;
the fitness is an objective function value, and the calculation of the objective function value already comprises a penalty rule converted from a reliability constraint condition.
Before calculating the objective function value of the decision vector, reliability analysis needs to be performed on the power distribution network represented by the decision vector, and the system average power failure time (SAIDI), the system average power failure frequency (SAIFI) and the expected power shortage amount (ens) of each load point of the power distribution network are calculated by using a power distribution network reliability analysis method taking account of user importance difference under the power distribution automation condition i )。
The method for analyzing the reliability of the power distribution network considering the user importance difference under the power distribution automation condition comprises the following steps:
s1, inputting data required by reliability analysis of a power distribution network;
wherein the required data comprises the input data in the step (1) and the matrix branch.
S2, topology searching of a main feeder of the power distribution network is carried out;
and performing topology search on the first and second columns of elements of the matrix branch, wherein a path from a power supply point to a main feeder terminal node is the main feeder.
S3, carrying out downstream combination on the reliability parameters of the switching elements;
the switch elements refer to fuses, circuit breakers, section switches and interconnection switches. The method comprises the following steps of converting reliability parameters of the switching elements to adjacent downstream line elements, wherein the reliability parameters of the switching elements comprise the failure rate and the average repair time of the switching elements, and the failure rate and the average repair time of the converted switching elements are zero.
S4, calculating the fault isolation time of the non-switching element;
time t of fault isolation 2 Including the time t of the journey of the worker to the faulty feeder 21 And time t for locating fault 22 And field operation switch time t 23 The calculation formula is as follows:
t 2 ＝t 21 +t 22 +t 23
wherein, t 1 Time, t, for remote control of 'three remote' switches by a distribution main station 3 Is the time for fault recovery.
Defining the remote non-visual segment to be the feeder segment sandwiched by the switching elements of the no less than two remote terminal configuration upstream and downstream of the nearest fault point; defining a minimum routing segment as a feeder segment sandwiched by switching elements configured by no less than one remote terminals located upstream and downstream of a nearest fault point; defining a faulty feeder segment is a feeder segment sandwiched by switching elements that are most adjacent to the upstream and downstream of the fault point.
Time t for locating fault 22 The calculation formula of (a) is as follows:
t 22 ＝n yiyao t yiyao +l patrol t patrol
wherein n is yiyao The number of all 'one remote' terminals in the remote non-visual segment, t yiyao Time spent to check a single "one remote" terminal,/ patrol Is the length of the minimum tour segment, t patrol Is the line patrol time of a unit length line.
Switching time t of field operation 23 The calculation formula of (c) is as follows:
t 23 ＝n manual t manual
wherein n is mamual A switch without a remote control condition in the first and last switching elements of the faulty feeder section, t mamual Time to operate a single switch in the field.
S5, analyzing the consequence of the failure mode of the non-switching element;
assuming that the non-switching element j fails, there are three possibilities to analyze the outage time T of the load i:
(1) if the load i can realize fault isolation and power supply recovery by remotely controlling the three-remote switch action of the power distribution main station, T = T 1 ；
(2) If the load i can not realize fault isolation and power supply recovery through the remote control of the three-remote switch action of the power distribution main station, but can realize fault isolation and power supply recovery through the field operation of the switch action, T = T 1 +t 2 ；
(3) If the load i cannot realize fault isolation and power supply restoration through any switching operation, T = T 1 +t 2 +t 3 ；
Traversing all non-switching elements in the power distribution network, selecting fault elements in all non-switching elements, analyzing the influence of the faults on all load points, and finally obtaining the influence of all possible fault events of the non-switching elements of the power distribution network on all load points of the power distribution network;
s6, calculating the reliability index of each load point;
the reliability index of each load point comprises the annual fault rate lambda of the load point i i Average power outage duration γ for load point i i Annual average fault power failure time U of load point i i Expected power shortage amount ens of load point i i The specific calculation formula is as follows:
annual failure rate λ of load point i i ：
Wherein D is i Set of elements for powering down load point i after fault, lambda k Is the k year failure rate of the element.
Average outage duration γ for load point i i ：
Wherein, T ik The time when a failure of element k results in a power outage at load point i.
Annual average fault power failure time U of load point i i ：
U i ＝λ i γ i
Expected power shortage amount ens of load point i i ：
ens i ＝P i U i
Wherein, P i The annual average load at load point i.
And S7, calculating the reliability index of the power distribution network system.
The reliability indexes of the power distribution network system comprise system average power failure time (SAIDI), system average power failure frequency (SAIFI), average power failure time (CAIDI) of power failure users and power supply reliability rate (ASAI). The calculation formula is the same as that defined in the evaluation regulation of power supply reliability of the power supply system.
The objective function is the minimum value of the full life cycle cost LCC of the power distribution network feeder automation terminal configuration scheme. In order to reflect the influence of the power distribution network feeder automation terminal configuration scheme on the power distribution network power supply reliability, the power failure loss of the power distribution network is incorporated into the life cycle cost LCC of the terminal configuration scheme by the model. The calculation formula of the objective function is as follows:
wherein, C inv For initial investment costs, C maintain For the annual costs for feeder automation terminal maintenance, i.e. maintenance costs, C ens The loss caused by power failure every year, namely power failure loss, of the power distribution network. Maintenance cost C per year due to long planning period maintain And power failureLoss C ens It needs to be converted into the current value for consideration,
in order to discount the coefficient, r is the discount rate, n is the planning period, and t is the number of years that the terminal configuration scheme has been put into use.
Initial investment cost C of power distribution network feeder automation terminal configuration scheme inv The sum of the purchase installation costs of the terminal selections determined for each decision variable is:
C inv ＝n 1 p 1 +n 2 p 2 +n 3 p 3
wherein n is 1 、n 2 、n 3 The number of switch elements, p, respectively for configuring 'one remote' terminal, 'two remote' terminal and 'three remote' terminal 1 、p 2 、p 3 The purchase cost and the installation cost of a single 'one remote' terminal, a 'two remote' terminal and a 'three remote' terminal are respectively.
Maintenance cost C of power distribution network feeder automation terminal configuration scheme maintain According to the method for managing the maintenance and overhaul costs of power supply equipment by most of the current power supply enterprises, the proportion of initial investment is taken as follows:
C maintain ＝ηC inv
wherein eta is the proportion of the maintenance cost to the initial investment cost.
The loss of the power distribution network due to power outage is translated from the expected amount of power shortage in the power distribution network. Considering that different load points of the same distribution network have different importance differences due to different user properties, which causes different power outage losses caused by unit power shortage amount of different load points, the expected power shortage amount of each load point introduces a weight coefficient for measuring the user importance difference. The expected power shortage ENS calculation formula of the power distribution network is as follows:
wherein D is the set of all load pointsHenens (Chinese character of' he) i For the expected power shortage of the ith load point, (k α i +β i +γ i ) Is a weight coefficient for measuring the load difference. The parameters alpha, beta and gamma respectively represent the difference of the load in three aspects of life safety, economy and specificity, and the larger the value of the parameter is, the larger the loss of the load in the aspect of power failure is. Alpha is alpha i 、β i 、γ i The life safety difference weight coefficient, the economical difference weight coefficient and the special difference weight coefficient of the load point i are respectively.
Loss of power failure C ens The calculation formula of (a) is as follows:
C ens ＝ENS×σ
the σ is the power failure loss of the unit power supply shortage amount, and the power failure loss not only considers the direct loss such as income loss brought to a power supply enterprise and production loss brought to a power utilization enterprise due to power failure, but also considers the indirect loss brought by the condition that a power failure accident affects the enterprise image of the power supply and the power utilization enterprise.
The reliability constraints are as follows:
SAIDI≤SAIDI max
SAIFI≤SAIFI max
wherein, SAIDI max 、SAIFI max The upper limit of the average system outage time and the upper limit of the average system outage frequency of the distribution network are allowed.
If the reliability index calculated by the reliability analysis can not meet the constraint, the objective function value is directly set to be infinite without calculating the initial investment cost C inv Terminal maintenance cost C maintain And power loss C ens 。
And (4): selecting an individual with the minimum objective function value in the current population, and updating a historical optimal solution;
selecting a configuration scheme with the minimum objective function value in the population of the current generation and evaluation indexes thereof, and storing the configuration scheme and the evaluation indexes in an optimal solution matrix nbest of the current generation; if the optimal solution of the generation is better than the historical optimal solution, the historical optimal solution of the population needs to be updated along with the optimal solution of the generation and is stored in a population historical optimal solution matrix gbest. Particularly, the particle swarm optimization also needs to update the historical optimal solution of each individual, and the historical optimal solution is stored in the individual historical optimal solution matrix pbest.
And (5): judging whether the set iteration times are reached, if so, outputting a current historical optimal solution, and finishing optimization; if not, executing the step (6);
the more iterations, the closer the result is to the optimal solution. The iteration times can be set to 100 times, and if the solution result is not satisfactory, the iteration times can be increased according to the actual situation.
And (6): and (4) performing operation according to optimizing rules of different intelligent algorithms, and generating a new population on the basis of the previous generation population. Then, the step (3) is skipped.
The optimization rules of different intelligent algorithms are different, so the calculation is different. As shown in fig. 2, if the set number of iterations is not reached, the configuration method sends the existing population, the fitness of the existing population, the current generation optimal individual and the population history optimal solution to the operators of the three intelligent algorithms for parallel operation (particularly, the particle swarm algorithm needs to send the individual history optimal solution), so as to generate respective new clusters. And (4) performing the step (3) again on the generated new cluster, and entering the next iteration.
The operations that the genetic algorithm needs to perform are breeding, crossover and mutation, thereby creating new individuals, and composing the next generation of population with new individuals and some of the better performing old individuals. The operation to be executed by the ant colony algorithm is to update the pheromone concentration matrix and the ant critical number matrix according to the configuration scheme provided by the optimal solution of the current generation, and then regenerate the next generation of population according to the latest pheromone concentration matrix and ant critical number matrix. The particle swarm algorithm needs to execute the operation of updating the velocity vector of each particle according to the historical optimal solution of the population and the historical optimal solution of each individual particle, and then obtaining the new position of each particle according to the latest velocity vector of each particle and the current position of each particle, wherein the new position represents the new solution given by the particle, and the new solutions of all the particles form the population of the next generation.
The above embodiments are only used to illustrate the technical solution of the present invention, and are not limited thereto, and the method of the present invention is also applicable to the optimization configuration problem of feeder automation terminals of power distribution networks with other structures.
Claims (8)
1. The method for configuring the feeder automation terminal of the power distribution network based on multiple intelligent algorithms is characterized in that optimal configuration of the feeder automation terminal of the power distribution network is achieved through the multiple intelligent algorithms, the economy and reliability of the whole life cycle of a configuration scheme are considered, and influences of a first remote feeder automation terminal, a second remote feeder automation terminal and a third remote feeder automation terminal on the power supply reliability of the power distribution network are considered in the power supply reliability calculation of the power distribution network; the configuration method comprises the following steps:
(1) Inputting data required by the power distribution network feeder automation terminal configuration; the data required by the power distribution network feeder automation terminal configuration comprises data required by power distribution network reliability calculation considering user importance difference under the power distribution automation condition, data required by economic index calculation of a power distribution network feeder automation terminal configuration scheme and parameters required by optimization solution of various intelligent algorithms;
the data required by the reliability calculation of the power distribution network considering the user importance difference under the power distribution automation condition comprises the following data: reliability parameters of all elements of the power distribution network to be analyzed, element connection relations, main feeder end nodes, length of each section of line, average load and importance parameters of each load point, and a plurality of times related to fault processing of a power supply company; the reliability parameters of all elements of the power distribution network to be analyzed comprise element annual fault rate and element mean fault repair time; the importance parameters comprise life importance parameters, economic importance parameters and special importance parameters; the plurality of times related to the fault handling of the power supply company comprise the time of remotely controlling the action of a single three-remote switch, the average time of a maintainer arriving at a fault feeder, the time of the maintainer checking the listing condition of a single fault indicator, the line patrol time of the maintainer for determining the line of unit length at the position of a fault point, and the time of the maintainer operating the action of a single non-remote switch on site;
the data required by the economic index calculation of the power distribution network feeder automation terminal configuration scheme comprises the following data: social economic loss caused by lack of power supply per degree, unit price of a remote terminal, unit price of a two remote terminal, unit price of a three remote terminal, proportion of maintenance cost for the distribution automation terminal to initial investment every year, current rate, planning period and reliability constraint conditions;
the parameters required by the optimization solution of the multiple intelligent algorithms comprise: population scale n and iteration times t; different intelligent algorithms also need different parameters respectively, and the variation probability, the genetic probability and the inter-generation difference rate which are needed to be set by the genetic algorithm; pheromone volatilization coefficients and pheromone intensities which are required to be set by the ant colony algorithm; inertia weight, cognitive acceleration constant and cooperation acceleration constant which are required to be set by the particle swarm algorithm; the three algorithms also need to store the historical optimal solutions of the whole population by the set variables, and particularly, the particle swarm algorithm also needs to store the historical optimal solutions of all individuals by the set variables;
the method for analyzing the reliability of the power distribution network considering the user importance difference under the power distribution automation condition comprises the following steps:
s1, inputting data required by reliability analysis of a power distribution network;
s2, topology searching of a main feeder of the power distribution network is carried out;
s3, carrying out downstream combination on the reliability parameters of the switching elements;
s4, calculating the fault isolation time of the non-switching element;
s5, analyzing the consequence of the failure mode of the non-switching element;
s6, calculating the reliability index of each load point;
s7, calculating a reliability index of the power distribution network system;
(2) Randomly generating a plurality of distribution network feeder automation terminal configuration schemes to form an original population;
(3) Calculating the fitness of each individual in the population;
(4) Selecting an individual with the minimum objective function value in the current population, and updating the historical optimal solution;
(5) Judging whether the set iteration times are reached, if so, outputting a current historical optimal solution, and finishing optimization; if not, executing the step (6);
(6) And (4) performing operation according to optimization rules of different intelligent algorithms, generating a new population on the basis of the previous generation of population, and then skipping to the step (3), wherein the different intelligent algorithms comprise a genetic algorithm, an ant colony algorithm and a particle swarm algorithm.
2. The distribution network feeder automation terminal configuration method based on multiple intelligent algorithms according to claim 1, characterized in that, in the data required for calculating the reliability of the distribution network considering the difference of the importance of the users under the distribution automation condition, the rest input data except for several times related to the fault handling of the power supply company can be unified into a matrix branch; each row of the matrix branch represents each element of the power distribution network; the first row and the second row are respectively a first node and a last node of the elements and represent the connection relation of each element of the power distribution network; the third column indicates the type of the component, and codes 1,2, 3, 4, 5, 6, 7 indicate the component is line, transformer, fuse, breaker, sectionalizer, tie switch, load, respectively; the fourth column and the fifth column respectively represent the annual failure rate and the average failure repair time of the element; the sixth row stores the length of the line element, and if the element is not a line, the element in the sixth row is null; the seventh, eighth, ninth and tenth rows store the average load, the life importance parameter, the economic importance parameter and the special importance parameter of the load point, respectively, and if the element is not a load, the seventh, eighth, ninth and tenth rows are null.
3. The method for configuring feeder automation terminals of power distribution networks based on multiple intelligent algorithms according to claim 1, wherein in step (2), each individual represents one possible configuration scheme, and the configuration schemes represented by all the individuals form a population; wherein the configuration scheme is represented as a decision vector X consisting of decision variables X i Namely:
X i ＝[x 1 x 2 ...x k ]
wherein, X i A configuration scheme vector, namely a decision vector, is given for the ith individual; k is the number of section switches of the terminal needing to be optimally configured; decision variables x are each stripedA terminal type on a switching element of the piece configuration feeder automation terminal; the value of the decision variable x may be one of 0, 1,2 and 3, and respectively corresponds to no feeder automation terminal installation, one-remote terminal installation, two-remote terminal installation and three-remote terminal installation;
randomly generating an original population, i.e. randomly generating n decision vectors X i (i =1,2, \ 8230;, n), the k decision variables x in each decision vector are designated by random numbers to one of 0, 1,2, 3.
4. The method for configuring feeder automation terminals for power distribution networks based on various intelligent algorithms according to claim 1, wherein in the step (3), the fitness is an objective function value, and the calculation of the objective function value already includes penalties converted from reliability constraints;
before the decision vector is calculated to obtain the target function value, reliability analysis needs to be performed on the power distribution network represented by the decision vector, and the system average outage time (SAIDI), the system average outage frequency (SAIFI) and the expected power shortage amount (ens) of each load point of the power distribution network are calculated by using a power distribution network reliability analysis method which considers user importance difference under the power distribution automation condition i )；
The objective function is the minimum value of the full life cycle cost LCC of the power distribution network feeder automation terminal configuration scheme; in order to reflect the influence of the power distribution network feeder automation terminal configuration scheme on the power supply reliability of the power distribution network, the power failure loss of the power distribution network is incorporated into the full life cycle cost LCC of the terminal configuration scheme by the model; the calculation formula of the objective function is as follows:
wherein, C inv For initial investment costs, C maintain For annual maintenance costs for feeder automation terminals, i.e. maintenance costs, C ens The loss caused by power failure of the power distribution network every year, namely power failure loss; due to the long duration of the planning period,annual maintenance costs C maintain And loss of power failure C ens It needs to be converted into the current value for consideration,
in order to discount the coefficient, r is the discount rate, n is the planning period, and t is the number of years that the terminal configuration scheme has been put into use.
5. The method for configuring feeder automation terminals of power distribution networks based on multiple intelligent algorithms according to claim 4,
initial investment cost C of power distribution network feeder automation terminal configuration scheme inv The sum of the purchase installation costs of the terminal selection decided by each decision variable is as follows:
C inv ＝n 1 p 1 +n 2 p 2 +n 3 p 3
wherein n is 1 、n 2 、n 3 The number of switch elements, p, respectively for configuring 'one remote' terminal, 'two remote' terminal and 'three remote' terminal 1 、p 2 、p 3 The purchase cost and the installation cost of a single 'one remote' terminal, a 'two remote' terminal and a 'three remote' terminal are respectively;
maintenance cost C of power distribution network feeder automation terminal configuration scheme maintain According to the method for managing the maintenance and overhaul costs of power supply equipment by most of the current power supply enterprises, the initial investment proportion is:
C maintain ＝ηC inv
wherein eta is the proportion of maintenance cost to initial investment cost;
the power failure loss C ens The expected power shortage of the power distribution network is converted; considering that different load points of the same power distribution network have different importance differences due to different user properties, which causes different power failure losses of different load points caused by unit power shortage amount, expected power shortage amount of each load point introduces a weight coefficient for measuring the importance difference of users; expected power shortage ENS calculation for a power distribution networkThe formula is as follows:
where D is the set of all load points, ens i The expected power shortage at the ith load point, (k α) i +β i +γ i ) A weight coefficient for measuring the difference of the load i; the parameters alpha, beta and gamma respectively represent the differences of the load in the aspects of life safety, economy and specificity, and the larger the value of the parameter alpha, beta and gamma is, the larger the loss of the load in the aspect of power failure is; alpha (alpha) ("alpha") i 、β i 、γ i Respectively representing life safety difference weight coefficients, economical difference weight coefficients and special difference weight coefficients of the load points i; k is the number of section switches needing to be optimized and configured with the terminal;
the power failure loss C ens The calculation formula of (a) is as follows:
C ens ＝ENS×σ
the σ is the power failure loss of the unit power supply shortage amount, and the power failure loss not only considers the direct loss such as income loss brought to a power supply enterprise and production loss brought to a power utilization enterprise due to power failure, but also considers the indirect loss brought by the condition that a power failure accident affects the enterprise image of the power supply and the power utilization enterprise.
6. The method according to claim 4, wherein the reliability constraint is as follows:
SAIDI≤SAIDI max
SAIFI≤SAIFI max
wherein, SAIDI max 、SAIFI max Respectively allowing the upper limit of the average system power failure time and the upper limit of the average system power failure frequency of the power distribution network;
if the reliability index calculated by the reliability analysis can not meet the constraint, the objective function value is directly set to be infinite without calculating the initial investment cost C inv Terminal maintenance charge C maintain And power loss C ens 。
7. The method for configuring the feeder automation terminal of the power distribution network based on various intelligent algorithms according to claim 1, wherein in the step (4), the configuration scheme with the minimum objective function value in the population of the current generation and the evaluation index thereof are selected and stored in the optimal solution matrix nbest of the current generation; if the current generation optimal solution is better than the previous historical optimal solution, the historical optimal solution of the population needs to be updated along with the current generation optimal solution, and the updated historical optimal solution is stored in a historical optimal solution matrix gbest of the population; particularly, the particle swarm optimization also needs to update the historical optimal solution of each individual, and the historical optimal solution is stored in the individual historical optimal solution matrix pbest.
8. The power distribution network feeder automation terminal configuration method based on multiple intelligent algorithms according to claim 1, characterized in that in step (6), the optimization rules of different intelligent algorithms are different, so the calculation is different;
the operations to be executed by the genetic algorithm are reproduction, crossover and variation, so that new individuals are generated, and the new individuals and part of old individuals with better performance form a population of the next generation;
the operation to be executed by the ant colony algorithm is to update the pheromone concentration matrix and the ant critical number matrix according to a configuration scheme provided by the current generation of optimal solution, and then regenerate the next generation of population according to the latest pheromone concentration matrix and ant critical number matrix;
the particle swarm algorithm needs to execute the operation of updating the velocity vector of each particle according to the historical optimal solution of the swarm and the historical optimal solution of each individual particle, and then obtaining the new position of each particle according to the latest velocity vector of each particle and the current position of each particle, wherein the new position represents the new solution given by the particle, and the new solutions of all the particles form the swarm of the next generation.
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