CN109390971B - Power distribution network multi-target active reconstruction method based on doorman pair genetic algorithm - Google Patents

Power distribution network multi-target active reconstruction method based on doorman pair genetic algorithm Download PDF

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CN109390971B
CN109390971B CN201811318328.2A CN201811318328A CN109390971B CN 109390971 B CN109390971 B CN 109390971B CN 201811318328 A CN201811318328 A CN 201811318328A CN 109390971 B CN109390971 B CN 109390971B
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李锰
王利利
刘巍
田春筝
李秋燕
李科
李鹏
全少理
付科源
郭勇
郭新志
杨卓
孙义豪
丁岩
马杰
张艺涵
罗潘
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Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
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    • H—ELECTRICITY
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Abstract

The invention discloses a multi-target active reconstruction method for a power distribution network based on a portal account pair genetic algorithm, which comprises the steps of firstly, fusing indexes such as power loss, voltage deviation, system stability margin and the like into a target function, and introducing a judgment matrix method and a linear weighting method to determine the weight of each index and realize the conversion of the multi-target function; secondly, aiming at the defects of the traditional genetic algorithm, a plurality of chromosome crossing strategies based on the principle of 'house-to-house' are provided, the diversity of population evolution modes is enriched, and the algorithm calculation efficiency and the optimization capability are greatly enhanced; and finally, by setting a critical value, self-perception and active reconstruction of the active power distribution network are realized, and the safety and stability of the power distribution network are improved. The simulation calculation shows that the model, the algorithm and the concept of active reconstruction provided by the invention conform to the idea of active operation and active control of the active power distribution network at present, and have wider application prospect.

Description

Power distribution network multi-target active reconstruction method based on doorman pair genetic algorithm
Technical Field
The invention belongs to the technical field of power distribution networks, and particularly relates to a multi-target active reconstruction method of a power distribution network based on a portal-to-portal genetic algorithm.
Background
The power distribution network is an important link for directly facing users and connecting a power transmission system and loads, and is the key for guaranteeing power supply quality and improving the operation efficiency of a power grid. The reconstruction of the power distribution network can effectively reduce the network loss of the system, improve the reliability and safe operation level of the system and has important significance for the construction of the current active power distribution network.
In recent years, a large amount of analysis and research are carried out on power distribution network reconstruction by scholars at home and abroad, and a series of research results are obtained: (1) in the aspect of reconstruction model construction, most researches take the minimum active network loss as an objective function, and do not consider factors such as voltage level, load balance and reliability of a system. On the other hand, the existing power distribution network reconstruction research is not only network structure optimization made by minimum network loss under normal conditions or reconstruction under fault conditions, but also passive, does not show forenotice and initiative, and does not accord with the development trend of the current active power distribution network; (2) in the aspect of solving algorithm analysis, the power distribution network reconstruction method mainly comprises a branch-and-bound method, a branch exchange method, an optimal flow pattern method, a simulated annealing method, a genetic algorithm, a particle swarm algorithm and the like. The branch-and-bound method is difficult to work when the calculated amount of a large-scale power distribution network is too large, the reconstructed result of the power distribution network by the branch exchange method is related to the initial structure of the power distribution network, the global optimum cannot be guaranteed, the optimal flow mode method can determine the opening and closing of a switch through two times of looped network load flow calculation, the calculated amount is large, and algorithm programming is complex. The heuristic algorithm has the problems of low search efficiency and difficulty in converging the global optimal solution; 3) in terms of processing modes of distributed power supplies, most of researches consider the distributed power supplies to be PQ type distributed power supplies, and influence analysis of uncertainty and volatility of the distributed power supplies on power distribution network reconstruction is not comprehensive and deep enough.
Disclosure of Invention
Aiming at the defects described in the prior art, the invention provides a power distribution network multi-target active reconstruction method based on a portal to genetic algorithm.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a multi-target active reconstruction method for a power distribution network based on a portal to genetic algorithm comprises the following steps:
and S1, establishing a multi-objective function.
The multi-objective function comprises an objective function f with minimum active loss1Objective function f with minimum voltage deviation2And an objective function f with minimum voltage stability margin3。
S1.1, constructing a target function f with minimum active loss1：
Figure BDA0001856889050000021
In the formula (f)1Active power loss is generated for the power distribution network; n is the total number of the branch circuits of the power distribution network; k is a radical ofzIndicating the branch switch state, kzA value of 0 indicates a branch disconnection, kzA 1 indicates a branch closure; rzResistance for branch z; pzActive power flowing through the z-terminal of the branch; qzThe reactive power flowing through the Z tail end of the branch circuit; u shapezIs the branch end node voltage.
S1.2, constructing an objective function f with minimum voltage offset2：
Figure BDA0001856889050000022
In the formula (f)2Is the sum of the voltage offsets; m is the number of nodes; u shapejIs the voltage value of node j, UNThe rated voltage value of the line.
S1.3, constructing an objective function f with minimum voltage stability margin3Voltage stability is an important index for measuring the safety and reliability of the power grid. The voltage stabilization criterion of the existence of the power distribution network flow solution is as follows:
Figure BDA0001856889050000023
f3＝min max(L1,L2,...,LN) (4)；
in the formula, LijIs the voltage stability index of branch ij; pjActive power flowing into branch node j; qjIs the reactive power flowing into branch node j; rijResistance for branch ij; xijReactance for branch ij; u shapeiIs the voltage value of node i; f. of3As an indication of voltage stability margin, f3The smaller the system, the more stable.
And S2, constructing constraint conditions of the multi-objective function.
The power distribution system network reconfiguration should simultaneously satisfy the following constraint conditions, including power flow constraint of the power distribution network, network topology constraint, voltage and current constraint of the power distribution network, and output constraint of the distributed power supply.
S2.1, obtaining the power flow constraint of the power distribution network.
S2.2, obtaining network topology constraint gk；
gk∈G；
gkFor the reconstructed network topology, G is the set of all possible radial network structures.
And S2.3, constructing voltage and current constraints of the power distribution network.
Figure BDA0001856889050000031
In the formula of Uj minAnd Uj maxLower and upper limits of the voltage at node j, Ij maxThe upper limit of the current at node j.
And S2.4, obtaining the output constraint of the distributed power supply.
Figure BDA0001856889050000032
PDGj maxRepresenting the maximum value of the distributed power output; pload,jRepresents the node j load; n isDGRepresenting the number of nodes containing the distributed power supply; pDGjRepresenting the distributed power output of node j; n represents the number of nodes; j denotes a node number.
The total output of the distributed power supply generally does not exceed 25% of the total load; the distributed power supply at each node is limited by the upper limit and the lower limit of the output of the distributed power supply.
And S3, constructing a model of the distributed power supply.
The distributed power sources include PQ type distributed power sources, PV type distributed power sources, and PQ (v) type distributed power sources.
And S3.1, constructing a model of the PQ type distributed power supply.
A distributed power supply controlled by a power factor is used as a PQ type distributed power supply, and loads considered to be negative in load flow calculation are treated as PQ nodes;
Figure BDA0001856889050000041
in the formula, PjActive power flowing into branch node j; qjIs the reactive power flowing into branch node j; psIndicating the active power of the PQ type distributed power supply; qsIndicating reactive power of the PQ type distributed power supply;
and S3.2, constructing a model of the PV type distributed power supply.
The method comprises the following steps that a micro gas turbine and a fuel cell are connected in a grid to output constant voltage and controllable active power, the constant voltage is output and is regarded as a PV type distributed power supply, the active power and the voltage are regarded as constant for the PV type distributed power supply, and reactive power is corrected through voltage deviation in each iteration process;
Figure BDA0001856889050000042
Figure BDA0001856889050000043
in the formula, PjActive power flowing into branch node j; psRepresents the active power of the PV type distributed power supply; u represents a node voltage; u shapesRepresents a PV-type distributed power supply voltage; qj t+1Representing the reactive power injected by the PV type distributed power supply during t +1 iterations; qj tRepresenting the reactive power injected by the PV distributed power supply during t iterations; t is the number of iterations, f (Δ U)t) The reactive correction quantity of the t iteration; f (Delta U)t-1) The reactive correction quantity of the t-1 th iteration; qj t-1Representing the reactive power injected by the PV type distributed power supply during t-1 iterations;
Figure BDA0001856889050000044
is the upper limit of reactive power;
Figure BDA0001856889050000045
is the lower reactive power limit.
And S3.3, constructing a model of the PQ (V) type distributed power supply.
Because the asynchronous wind driven generator with constant speed and constant frequency does not have an excitation device, the asynchronous wind driven generator provides excitation current for the asynchronous wind driven generator by a synchronous generator of a power grid, constant active power is considered to be output in load flow calculation, and the absorbed reactive power meets the following relation:
Figure BDA0001856889050000051
in the formula, xmAnd x is the sum of the reactance of the stator and the rotor of the generator.
And S4, converting the multi-objective function into a single objective function.
The multi-objective problem can only reserve one objective function, and other sub-objective functions are embodied in the form of constraint conditions, but a series of infeasible solutions are usually generated by the method, the weight is determined by adopting a judgment matrix method, and the multi-objective function is converted into a single objective function which is easy to solve through a linear weighting method, wherein the expression is as follows:
F＝min(c1f1'+c2f2'+c3f3') (11)；
in the formula, c1,c2,c3Is the weight, f1',f2',f3' is a normalized value in order to eliminate the effect of magnitude and dimension differences on the results.
The weight is obtained by a judgment matrix method; the core of the judgment matrix method is to construct a judgment matrix according to the experience of a decision maker or an expert and use aijAnd (3) representing the comparison result of the ith factor relative to the jth factor, wherein the judgment matrix is represented as follows:
Figure BDA0001856889050000052
wherein the content of the first and second substances,
Figure BDA0001856889050000053
a decision maker or an expert assigns values according to the importance degrees of the ith factor and the jth factor;
when the ith factor is as important as the jth factor, the weight is 1;
when the ith factor is between equally and slightly important relative to the jth factor, the weight is 2;
when the ith factor is slightly more important than the jth factor, the weight is 3;
when the ith factor is between slightly and significantly important relative to the jth factor, the weight is 4;
when the ith factor is significantly more important than the jth factor, the weight is 5;
when the ith factor is between significant importance and strong importance relative to the jth factor, the weight is 6;
when the ith factor is more important than the jth factor, the weight is 7;
when the ith factor is between strongly important and extremely important relative to the jth factor, the weight is 8;
when the ith factor is extremely important than the jth factor, the weight is 9;
the determination matrix for the indexes of active loss, voltage deviation and voltage stability margin is constructed as follows:
Figure BDA0001856889050000061
after matrix processing, each target weight vector is obtained as C ═ 0.49340.19580.3108.
And S5, performing active reconstruction of the power distribution network.
S5.1, initializing the population by using a Monte Carlo method, and calculating a fitness function of the initial population.
S5.2, setting a random number and crossing the random number and the single point with a probability pc1Comparing, if the random number is less than the single point cross probability pc1Performing single-point crossing, otherwise, performing multi-point crossing; and cross individuals are selected on the principle of door-house pairs during the cross operation.
The house-to-house principle is that the large target function value is matched with the large target function value, and the small target function value is matched with the small target function value.
The single-point crossing is characterized in that n looped networks are arranged after all switches of the closed distribution network are set, and chromosomes of two parents are randomly collected from a chromosome library:
father＝w1w2...wn，mother＝e1e2...en。
randomly choosing a cross position t, and exchanging partial genes after the chromosome positions t of the two parents:
Figure BDA0001856889050000071
the multipoint crossing sets n looped networks after all switches are closed, and two chromosomes with similar objective function values are collected from a chromosome library in a crossing process according to the principle of door-to-door house pairing:
father＝w1w2...wn，mother＝e1e2...en。
randomly choosing the crossing position t1And t2Dividing t in two parent chromosomes1And t2Partial chromosomal exchange between:
Figure BDA0001856889050000072
s5.3, setting another random number and combining the random number with the multiple diversity probability pc2Comparing, if the random number is less than the multiple diversity probability pc2If not, the next step is carried out.
The single point mutation randomly selects the position t of one chromosome mutation1Randomly extracting a branch from the corresponding ring network to replace the original t1The gene at the position:
Figure BDA0001856889050000073
and S5.4, checking whether the newly generated chromosome meets the topological structure constraint, and if not, carrying out individual correction.
And S5.5, calculating the fitness of the corrected individual.
And S5.6, judging whether the maximum evolution times is reached, if so, outputting a result, and otherwise, returning to the step S5.2.
Firstly, indexes such as power loss, voltage deviation and system stability margin are fused into a target function, a judgment matrix method and a linear weighting method are introduced to determine the weight of each index and realize the conversion of a multi-target function; secondly, aiming at the defects of the traditional genetic algorithm, a plurality of chromosome crossing strategies based on the principle of 'house-to-house' are provided, the diversity of population evolution modes is enriched, and the algorithm calculation efficiency and the optimization capability are greatly enhanced; and finally, by setting a critical value, self-perception and active reconstruction of the active power distribution network are realized, and the safety and stability of the power distribution network are improved. The simulation calculation shows that the model, the algorithm and the concept of active reconstruction provided by the invention conform to the idea of active operation and active control of the active power distribution network at present, and have wider application prospect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of reconstruction of a power distribution network according to the present invention.
Fig. 2 is a diagram of a 33-node power distribution system including a DG for simulation in accordance with the present invention.
FIG. 3 is a schematic diagram showing the comparison of node voltages before and after reconstruction according to the present invention.
FIG. 4 is a graph of the objective function value of the on-line calculation variation of the present invention.
Fig. 5 is a graph showing the variation trend of the objective function according to the present invention with the number of switching operations.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
A multi-target active reconstruction method for a power distribution network based on a portal to genetic algorithm comprises the following steps:
and S1, establishing a multi-objective function.
The multi-objective function comprises an objective function f with minimum active loss1Objective function f with minimum voltage deviation2And an objective function f with minimum voltage stability margin3。
S1.1, constructing a target function f with minimum active loss1：
Figure BDA0001856889050000091
In the formula (f)1Active power loss is generated for the power distribution network; n is the total number of the branch circuits of the power distribution network; k is a radical ofzIndicating the branch switch state, kzA value of 0 indicates a branch disconnection, kzA 1 indicates a branch closure; rzResistance for branch z; pzActive power flowing through the z-terminal of the branch; qzThe reactive power flowing through the Z tail end of the branch circuit; u shapezIs the branch end node voltage.
S1.2, constructing a target function with minimum voltage deviationNumber f2：
Figure BDA0001856889050000092
In the formula (f)2Is the sum of the voltage offsets; m is the number of nodes; u shapejIs the voltage value of node j, UNThe rated voltage value of the line.
S1.3, constructing an objective function f with minimum voltage stability margin3Voltage stability is an important index for measuring the safety and reliability of the power grid. The voltage stabilization criterion of the existence of the power distribution network flow solution is as follows:
Figure BDA0001856889050000093
f3＝min max(L1,L2,...,LN) (4)；
in the formula, LijIs the voltage stability index of branch ij; pjActive power flowing into branch node j; qjIs the reactive power flowing into branch node j; rijResistance for branch ij; xijReactance for branch ij; u shapeiIs the voltage at node i; f. of3As an indication of voltage stability margin, f3The smaller the system, the more stable.
And S2, constructing constraint conditions of the multi-objective function.
The power distribution system network reconfiguration should simultaneously satisfy the following constraint conditions, including power flow constraint of the power distribution network, network topology constraint, voltage and current constraint of the power distribution network, and output constraint of the distributed power supply.
S2.1, obtaining the power flow constraint of the power distribution network.
S2.2, obtaining network topology constraint gk；
gk∈G；
gkFor the reconstructed network topology, G is the set of all possible radial network structures.
And S2.3, constructing voltage and current constraints of the power distribution network.
Figure BDA0001856889050000101
In the formula of Uj minAnd Uj maxLower and upper limits of the voltage at node j, Ij maxThe upper limit of the current at node j.
And S2.4, obtaining the output constraint of the distributed power supply.
Figure BDA0001856889050000102
PDGj maxRepresenting the maximum value of the distributed power output; pload,jRepresents the node j load; n isDGRepresenting the number of nodes containing the distributed power supply; pDGjRepresenting the distributed power output of node j; n represents the number of nodes; j denotes a node number.
The total output of the distributed power supply generally does not exceed 25% of the total load; the distributed power supply at each node is limited by the upper limit and the lower limit of the output of the distributed power supply.
And S3, constructing a model of the distributed power supply.
The distributed power sources include PQ type distributed power sources, PV type distributed power sources, and PQ (v) type distributed power sources.
And S3.1, constructing a model of the PQ type distributed power supply.
A distributed power supply controlled by a power factor is used as a PQ type distributed power supply, and loads considered to be negative in load flow calculation are treated as PQ nodes;
Figure BDA0001856889050000111
in the formula, PjActive power flowing into branch node j; qjIs the reactive power flowing into branch node j; psIndicating the active power of the PQ type distributed power supply; qsIndicating the reactive power of the PQ-type distributed power supply.
And S3.2, constructing a model of the PV type distributed power supply.
The method comprises the following steps that a micro gas turbine and a fuel cell are connected in a grid to output constant voltage and controllable active power, the constant voltage is output and is regarded as a PV type distributed power supply, the active power and the voltage are regarded as constant for the PV type distributed power supply, and reactive power is corrected through voltage deviation in each iteration process;
Figure BDA0001856889050000112
Figure BDA0001856889050000113
in the formula, PjActive power flowing into branch node j; psRepresents the active power of the PV type distributed power supply; u represents a node voltage; u shapesRepresents a PV-type distributed power supply voltage; qj t+1Representing the reactive power injected by the PV type distributed power supply during t +1 iterations; qj tRepresenting the reactive power injected by the PV distributed power supply during t iterations; t is the number of iterations, f (Δ U)t) The reactive correction quantity of the t iteration; f (Delta U)t-1) The reactive correction quantity of the t-1 th iteration; qj t-1Representing the reactive power injected by the PV type distributed power supply during t-1 iterations;
Figure BDA0001856889050000114
is the upper limit of reactive power;
Figure BDA0001856889050000115
is the lower reactive power limit.
And S3.3, constructing a model of the PQ (V) type distributed power supply.
Because the asynchronous wind driven generator with constant speed and constant frequency does not have an excitation device, the asynchronous wind driven generator provides excitation current for the asynchronous wind driven generator by a synchronous generator of a power grid, constant active power is considered to be output in load flow calculation, and the absorbed reactive power meets the following relation:
Figure BDA0001856889050000121
in the formula, xmAnd x is the sum of the reactance of the stator and the rotor of the generator.
And S4, converting the multi-objective function into a single objective function.
The multi-objective problem can only reserve one objective function, and other sub-objective functions are embodied in the form of constraint conditions, but a series of infeasible solutions are usually generated by the method, the weight is determined by adopting a judgment matrix method, and the multi-objective function is converted into a single objective function which is easy to solve through a linear weighting method, wherein the expression is as follows:
F＝min(c1f1'+c2f2'+c3f3') (11)；
in the formula, c1,c2,c3Is the weight, f1',f2',f3' is a normalized value in order to eliminate the effect of magnitude and dimension differences on the results.
The weight is obtained by a judgment matrix method; the core of the judgment matrix method is to construct a judgment matrix according to the experience of a decision maker or an expert and use aijAnd (3) representing the comparison result of the ith factor relative to the jth factor, wherein the judgment matrix is represented as follows:
Figure BDA0001856889050000122
wherein the content of the first and second substances,
Figure BDA0001856889050000123
and (3) a decision maker or an expert assigns values according to the importance degrees of the ith factor and the jth factor:
when the ith factor is as important as the jth factor, the weight is 1;
when the ith factor is between equally and slightly important relative to the jth factor, the weight is 2;
when the ith factor is slightly more important than the jth factor, the weight is 3;
when the ith factor is between slightly and significantly important relative to the jth factor, the weight is 4;
when the ith factor is significantly more important than the jth factor, the weight is 5;
when the ith factor is between significant importance and strong importance relative to the jth factor, the weight is 6;
when the ith factor is more important than the jth factor, the weight is 7;
when the ith factor is between strongly important and extremely important relative to the jth factor, the weight is 8;
when the ith factor is extremely important than the jth factor, the weight is 9;
specifically, as shown in table 1:
TABLE 1 criteria for decision matrix formation
Dimension Means of
1 The ith factor is as important as the jth factor
3 The i-th and j-th factors are slightly important
5 Significant importance of the ith and jth factors
7 The strong importance of the ith and jth factors
9 Extreme importance of the ith and jth factors
Note: 2,4,6,8 indicate that the importance of the ith factor relative to the jth factor lies between two adjacent levels.
The determination matrix for the indexes of active loss, voltage deviation and voltage stability margin is constructed as follows:
Figure BDA0001856889050000131
after matrix processing, each target weight vector is obtained as C ═ 0.49340.19580.3108.
And S5, performing active reconstruction of the power distribution network.
S5.1, initializing the population by using a Monte Carlo method, and calculating a fitness function of the initial population.
S5.2, setting a random number and crossing the random number and the single point with a probability pc1Comparing, if the random number is less than the single point cross probability pc1Performing single-point crossing, otherwise, performing multi-point crossing; and cross individuals are selected on the principle of door-house pairs during the cross operation. The invention provides a plurality of crossover operators, ensures that more genes are crossed, and solves the premature problem based on single-point crossover.
The house-to-house principle is that the large target function value is matched with the large target function value, and the small target function value is matched with the small target function value.
The single-point crossing is characterized in that n looped networks are arranged after all switches of the closed distribution network are set, and chromosomes of two parents are randomly collected from a chromosome library:
father＝w1w2...wn，mother＝e1e2...en。
randomly choosing a cross position t, and exchanging partial genes after the chromosome positions t of the two parents:
Figure BDA0001856889050000141
the multipoint crossing sets n looped networks after all switches are closed, and two chromosomes with similar objective function values are collected from a chromosome library in a crossing process according to the principle of door-to-door house pairing:
father＝w1w2...wn，mother＝e1e2...en。
randomly choosing the crossing position t1And t2Dividing t in two parent chromosomes1And t2Partial chromosomal exchange between:
Figure BDA0001856889050000142
s5.3, setting another random number and combining the random number with the multiple diversity probability pc2Comparing, if the random number is less than the multiple diversity probability pc2If not, the next step is carried out.
The single point mutation randomly selects the position t of one chromosome mutation1Randomly extracting a branch from the corresponding ring network to replace the original t1The gene at the position:
Figure BDA0001856889050000151
in terms of variation, the convergence precision of the algorithm can be guaranteed by adopting single-point variation, and the problem of optimization buffeting caused by overlarge cross strength is weakened.
And S5.4, checking whether the newly generated chromosome meets the topological structure constraint, and if not, carrying out individual correction.
And S5.5, calculating the fitness of the corrected individual.
And S5.6, judging whether the maximum evolution times is reached, if so, outputting a result, and otherwise, returning to the step S5.2.
The invention connects 3 types of distributed power supplies into an IEEE33 node power distribution system respectively, and the topological structure is shown in figure 2.1 is a balance node, the reference power is 10MVA, the voltage of the balance node is 10.5kV, and the reference voltage is 10 kV. The DG parameters are shown in table 2.
Table 2 DG parameter table for access
Figure BDA0001856889050000152
Firstly, for the load level at a certain moment, the influence of DG access on the power distribution network is evaluated, and active reconstruction is performed on the DG power distribution network with different types by considering factors such as active loss, voltage deviation and voltage stability margin. The network states before and after DG access before reconfiguration are shown in table 3.
Table 3 network states before reconstruction
Figure BDA0001856889050000153
Table 3 shows that when PQ-type and PQ (v) -type distributed power sources with certain capacities are independently connected, the network loss is reduced, the voltage deviation is reduced, and the voltage stability margin index is reduced. Even though the PV distributed power supply is independently connected, certain network loss can be reduced, the voltage deviation and voltage stability margin indexes of the network are increased, and the safety of the power distribution network is not facilitated. The 3 distributed power supplies with certain capacity are comprehensively accessed, so that the indexes of network loss reduction, voltage offset and voltage stability margin are reduced.
Setting the initial population number as 200, the gene length as 5 bits of the ring network number and the probability p of single-point crossingc10.5, probability of multipoint intersection pc20.5, probability of variation pb0.1, the maximum evolution passage number is 200. The network states before and after reconstruction are shown in table 4 and fig. 3.
Table 4 network states after reconstruction
Figure BDA0001856889050000161
From table 4, it can be seen that the network loss after reconstruction is greatly reduced, and meanwhile, the voltage offset and voltage stability margin indexes of the simulation model are significantly reduced, which indicates that the power distribution network reconstruction model provided herein can significantly improve the economy and reliability of the system. In addition, it can be seen from fig. 3 that the lowest voltage of the nodes of the system after reconstruction is increased, the voltage distribution is relatively uniform, and the voltage distribution of the whole network is improved.
To verify the performance of the improved genetic algorithm presented herein, the genetic algorithm was tested 50 times before and after improvement, and the results obtained are shown in table 5.
TABLE 5 comparison of conventional genetic algorithms and improved genetic algorithms
Figure BDA0001856889050000162
It can be seen from table 5 that the proposed gatekeeper can obtain accurate results for both genetic algorithms and traditional genetic algorithms, but the accuracy and convergence rate of the algorithm proposed herein are superior to those of traditional genetic algorithms, and the stability of the algorithm is enhanced compared with that of traditional genetic algorithms, because various crossover strategies are introduced, the diversity of biological evolution is enriched, and the search capability and stability of genetic algorithms are improved.
The IEEE33 node power distribution system is still taken as an example. Dividing one day into 24 moments, monitoring the change of a network topological structure and a load level in the whole course, and calculating an objective function value. Based on the reconstructed objective function constructed by the method and the genetic algorithm of the doormen, the threshold value of the actively reconstructed objective function is set to be 2 (the threshold value is set according to different topological structures and the running characteristics of the power distribution network). Obviously, when the calculated target function value is larger than the set critical value, the power distribution network actively carries out reconstruction optimization to improve the current power distribution network operation state. Generally speaking, in the strategy of active reconfiguration, if the turn-off times of the switch are not set, the distribution network is actively reconfigured to the operation state with the optimal objective function, and in order to better fit the actual situation of the distribution network operation, 6 different reconfiguration schemes are set in the scheme based on the turn-off times of the switch. Under these conditions, a comparison of 6 reconstitution protocols over a 24 hour period is shown in FIG. 4.
As can be seen from fig. 4, if the distribution network has no active reconfiguration capability (i.e. the case of the scheme 1), the objective function values of the system at 3 times of 9h, 12h and 20h are all greater than the critical value 2, which is not favorable for safe and economic reliable operation of the distribution network. If the power distribution network has the active reconstruction capability, the monitoring system feeds back the network operation state at the moment of 9h, and calculates that the objective function value exceeds the critical value, and the power distribution network can actively reconstruct and optimize at the moment. After once reconstruction, the target function values in subsequent operation time are all lower than the set threshold, which indicates that the system operates in a safe and economic interval, and the system can continuously perform reconstruction optimization along with the increase of the set switching times until the optimal operation state is met. The objective function values for the 6 different reconstruction schemes are shown in table 6.
Table 6 comparison of different active reconstruction schemes
Figure BDA0001856889050000171
Figure BDA0001856889050000181
From table 6 the following conclusions can be drawn: (1) with the increase of the times of switching actions, the power distribution network can be actively reconstructed to an optimal operation state, which is reflected in that the sum of the objective functions is smaller and smaller within 24 hours; (2) the optimal results of the three sub-objective functions are mutually contradictory, so that when the multi-objective is reconstructed, the current operation state and the reconstruction purpose of the power distribution network are combined to perform switching action; (3) when the operating state of the power distribution network exceeds the set threshold, there are many feasible reconstruction schemes, and in order to determine the optimal reconstruction scheme, the reconstruction schemes taking the number of switching actions into account need to be compared, and a comparison schematic diagram of the multiple reconstruction schemes is shown in fig. 5.
As can be seen from fig. 5, as the number of switching operations increases, the reconstructed objective function decreases, the sensitivity of the corresponding objective function changes, and a point that has the largest slope change and can satisfy the system safety should be found from an economic viewpoint. In the simulation model used herein, the scheme 2 is the best, and the optimal reconstruction scheme of the power distribution network can be achieved only by once switching off and switching on different switches.
In summary, the present invention has the following features:
(1) the invention provides an active power distribution network reconstruction model containing various distributed power supplies on the basis of considering a plurality of factors such as active loss, voltage deviation, voltage stability margin and the like, and introduces an improved genetic algorithm into the active power distribution network reconstruction problem, thereby meeting the reconstruction requirement of the optimization problem of the power distribution network containing different types of distributed power supplies.
(2) Aiming at typical problems of the genetic algorithm in the aspect of power distribution network reconstruction solving, the principle of the door-to-door house pair is introduced to improve the cross strategy and the pairing scheme, and the advantages of the improved genetic algorithm in the aspects of convergence speed and convergence accuracy are verified through a simulation example.
(3) Based on an improved genetic algorithm and the intelligent level of the current power distribution network, a multi-target power distribution network active reconstruction strategy containing distributed power sources is provided, and the power distribution network is actively reconstructed by setting a threshold value of a target function so as to meet the development requirements of flexibility and adaptability of the active power distribution network.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (6)

1. A multi-target active reconstruction method for a power distribution network based on a portal to genetic algorithm is characterized by comprising the following steps:
s1, establishing a multi-objective function;
said pluralityObjective function including objective function f with minimum active loss1Objective function f with minimum voltage deviation2And an objective function f with minimum voltage stability margin3；
S2, constructing constraint conditions of the multi-objective function;
the constraint conditions comprise power flow constraint of the power distribution network, network topology constraint, voltage and current constraint of the power distribution network and output constraint of the distributed power supply;
s3, constructing a model of the distributed power supply;
the distributed power sources include PQ type distributed power sources, PV type distributed power sources, and PQ (v) type distributed power sources;
s4, converting the multi-objective function into a single objective function;
determining the weight by adopting a judgment matrix method and converting a multi-objective function into a single objective function which is easy to solve by adopting a linear weighting method, wherein the expression is as follows:
F＝min(c1f1'+c2f′2+c3f′3) (11)；
in the formula, c1,c2,c3Is the weight, f1',f′2,f′3Is a normalized value;
s5, performing active reconstruction of the power distribution network;
in step S5, the specific steps are as follows:
s5.1, initializing a population by using a Monte Carlo method, and calculating a fitness function of the initial population;
s5.2, setting a random number and crossing the random number and the single point with a probability pc1Comparing, if the random number is less than the single point cross probability pc1Performing single-point crossing, otherwise, performing multi-point crossing; and selecting crossed individuals according to the principle of door-house pair in the process of crossing operation;
in step S5.2, the house pair rule is a pair with a large objective function value and a pair with a small objective function value;
the single-point crossing is characterized in that n looped networks are arranged after all switches of the closed distribution network are set, and chromosomes of two parents are randomly collected from a chromosome library:
father＝w1w2...wn，mother＝e1e2...en；
randomly choosing a cross position t, and exchanging partial genes after the chromosome positions t of the two parents:
Figure FDA0002826680150000021
the multipoint crossing sets n looped networks after all switches are closed, and two chromosomes with similar objective function values are collected from a chromosome library in a crossing process according to the principle of door-to-door house pairing:
father＝w1w2...wn，mother＝e1e2...en；
randomly choosing the crossing position t1And t2Dividing t in two parent chromosomes1And t2Partial chromosomal exchange between:
Figure FDA0002826680150000022
s5.3, setting another random number and combining the random number with the multiple diversity probability pc2Comparing, if the random number is less than the multiple diversity probability pc2If not, carrying out the next step;
s5.4, checking whether the newly generated chromosome meets the topological structure constraint, and if not, carrying out individual correction;
s5.5, calculating the fitness of the corrected individual;
and S5.6, judging whether the maximum evolution times is reached, if so, outputting a result, and otherwise, returning to the step S5.2.
2. The multi-target active reconstruction method for the power distribution network based on the doorman-to-genetic algorithm as claimed in claim 1, wherein in step S1, the specific steps are as follows:
s1.1, constructing a target function f with minimum active loss1：
Figure FDA0002826680150000023
In the formula (f)1Active power loss is generated for the power distribution network; n is the total number of the branch circuits of the power distribution network; k is a radical ofzIndicating the branch switch state, kzA value of 0 indicates a branch disconnection, kzA 1 indicates a branch closure; rzResistance for branch z; pzActive power flowing through the z-terminal of the branch; qzThe reactive power flowing through the Z tail end of the branch circuit; u shapezIs the branch end node voltage;
s1.2, constructing an objective function f with minimum voltage offset2：
Figure FDA0002826680150000031
In the formula (f)2Is the sum of the voltage offsets; m is the number of nodes; u shapejIs the voltage value of node j, UNThe rated voltage value of the line is obtained;
s1.3, constructing an objective function f with minimum voltage stability margin3：
Figure FDA0002826680150000032
f3＝min max(L1,L2,...,LN) (4)；
In the formula, LijIs the voltage stability index of branch ij; pjActive power flowing into branch node j; qjIs the reactive power flowing into branch node j; rijResistance for branch ij; xijReactance for branch ij; u shapeiIs the voltage value of node i; f. of3Is an index of voltage stability margin.
3. The multi-target active reconstruction method for the power distribution network based on the doorman-to-genetic algorithm as claimed in claim 1, wherein in step S2, the specific steps are as follows:
s2.1, acquiring the power flow constraint of the power distribution network;
s2.2, obtaining network topology constraint gk；
gk∈G；
gkFor the reconstructed network topology, G is a set of all feasible radial network structures;
s2.3, constructing voltage and current constraints of the power distribution network;
Figure FDA0002826680150000033
in the formula of UjminAnd UjmaxLower and upper limits of the voltage at node j, IjmaxThe upper limit of the current at node j;
s2.4, obtaining output constraint of the distributed power supply;
Figure FDA0002826680150000034
PDGjmaxrepresenting the maximum value of the distributed power output; pload,jRepresents the node j load; n isDGRepresenting the number of nodes containing the distributed power supply; pDGjRepresenting the distributed power output of node j; n represents the number of nodes; j denotes a node number.
4. The multi-target active reconstruction method for the power distribution network based on the doorman-to-genetic algorithm as claimed in claim 1, wherein in step S3, the specific steps are as follows:
s3.1, constructing a model of the PQ type distributed power supply;
a distributed power supply controlled by a power factor is used as a PQ type distributed power supply, and loads considered to be negative in load flow calculation are treated as PQ nodes;
Figure FDA0002826680150000041
in the formula, PjRepresenting the active power of the outgoing branch node j; qjRepresenting the reactive power flowing out of branch node j; psIndicating the active power of the PQ type distributed power supply; qsIndicating reactive power of the PQ type distributed power supply;
s3.2, constructing a model of the PV type distributed power supply;
the method comprises the following steps that a micro gas turbine and a fuel cell are connected in a grid to output constant voltage and controllable active power, the constant voltage is output and is regarded as a PV type distributed power supply, the active power and the voltage are regarded as constant for the PV type distributed power supply, and reactive power is corrected through voltage deviation in each iteration process;
Figure FDA0002826680150000042
Figure FDA0002826680150000043
in the formula, PjThe active power of the outgoing branch node j; psRepresents the active power of the PV type distributed power supply; u represents a node voltage; u shapesRepresents a PV-type distributed power supply voltage; qj t+1Representing the reactive power injected by the PV type distributed power supply during t +1 iterations; qj tRepresenting the reactive power injected by the PV distributed power supply during t iterations; t is the number of iterations, f (. DELTA.U)t) The reactive correction quantity of the t iteration; f ([ delta ] U)t-1) The reactive correction quantity of the t-1 th iteration; qj t-1Representing the reactive power injected by the PV type distributed power supply during t-1 iterations;
Figure FDA0002826680150000044
is the upper limit of reactive power;
Figure FDA0002826680150000045
is the lower reactive power limit;
s3.3, constructing a model of the PQ (V) type distributed power supply;
because the asynchronous wind driven generator with constant speed and constant frequency does not have an excitation device, the asynchronous wind driven generator provides excitation current for the asynchronous wind driven generator by a synchronous generator of a power grid, constant active power is considered to be output in load flow calculation, and the absorbed reactive power meets the following relation:
Figure FDA0002826680150000051
in the formula, xmAnd x is the sum of the reactance of the stator and the rotor of the generator.
5. The multi-target active reconstruction method for power distribution network based on genetic algorithm of door holder as claimed in claim 1, wherein in step S4, the core of the decision matrix method is to construct decision matrix according to the experience of decision maker or expert, and use a asijAnd (3) representing the comparison result of the ith factor relative to the jth factor, wherein the judgment matrix is represented as follows:
Figure FDA0002826680150000052
wherein the content of the first and second substances,
Figure FDA0002826680150000053
the decision maker or expert according to the importance degree of the ith factor and the jth factor to the aijCarrying out assignment;
when the ith factor is as important as the jth factor, the weight is 1;
when the ith factor is between equally and slightly important relative to the jth factor, the weight is 2;
when the ith factor is slightly more important than the jth factor, the weight is 3;
when the ith factor is between slightly and significantly important relative to the jth factor, the weight is 4;
when the ith factor is significantly more important than the jth factor, the weight is 5;
when the ith factor is between significant importance and strong importance relative to the jth factor, the weight is 6;
when the ith factor is more important than the jth factor, the weight is 7;
when the ith factor is between strongly important and extremely important relative to the jth factor, the weight is 8;
when the ith factor is extremely important than the jth factor, the weight is 9.
6. The multi-objective active reconstruction method for power distribution network based on the doorman-to-genetic algorithm as claimed in claim 1, wherein in step S5.3, the single point mutation randomly selects a position t of the chromosome mutation1Randomly extracting a branch from the corresponding ring network to replace the original t1The gene at the position:
Figure FDA0002826680150000061
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