CN113629743B - Electric vehicle charging station power distribution network reconstruction method and system based on genetic algorithm - Google Patents
Electric vehicle charging station power distribution network reconstruction method and system based on genetic algorithm Download PDFInfo
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- H—ELECTRICITY
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Abstract
The application discloses a reconstruction method and a reconstruction system of an electric vehicle charging station distribution network based on a genetic algorithm, which can obtain a load curve of an electric vehicle in one day based on a Monte Carlo simulation method, further generate the load curve of the electric vehicle charging station, namely simulate the change trend of the load of the electric vehicle charging station in one day along with time, consider the uncertainty of the electric vehicle in time and space, and thus be close to the actual activity habit of human beings; meanwhile, a power distribution network reconstruction model is established by taking the minimum active network loss and the minimum node voltage offset as optimization targets, and the power distribution network reconstruction model is optimized by utilizing a multi-target genetic algorithm, so that an optimized network topology structure, the active network loss and the voltage offset are obtained, the network loss and the node voltage offset of the power distribution network can be reduced, the power supply reliability is improved, the dynamic reconstruction of the power grid is realized based on an electric vehicle charging station load curve and the power distribution network reconstruction model, and the time-varying load applicability is improved.
Description
Technical Field
The application relates to the technical field of power distribution network reconstruction, in particular to a power distribution network reconstruction method and system of an electric vehicle charging station based on a genetic algorithm.
Background
The electric automobile needs to be charged for a certain time before being used, but the charging place of the existing electric automobile is concentrated, and is usually carried out on a fixed charging pile.
With the gradual increase of electric vehicles, a large number of electric vehicles need higher charging power during charging, which can certainly have great influence on the operation and management of a power grid and also have influence on the economical efficiency and reliability of the power grid.
The charging voltage of the electric automobile is 220V generally, the access of the electric automobile can directly affect the power distribution network, and the time and place of charging the electric automobile are uncertain due to the uncertainty of activities of people using the electric automobile. Meanwhile, the electric automobile is connected to increase the network loss and the node voltage offset, at present, the power grid company is a method for reconstructing the network frame of the power distribution network, and the power distribution network has the characteristics of closed loop construction and open loop operation, namely, when the electric energy supply of each node is ensured, any node in the operation process is directly or indirectly connected with a power supply, and if the load of a certain node is increased due to the connection of the electric automobile, the network loss is increased, so that the topology diagram of the power distribution network can be changed, namely, the states of a sectionalizing switch and a connecting switch are changed, and the power flow in the network is changed under the condition of ensuring the power supply of a user, thereby reducing the loss of the network and saving the operation cost.
However, the current method for reconstructing the grid of the power distribution network cannot consider the uncertainty in time and space of the electric automobile when the electric automobile is accessed, and the selected objective function is single when the power distribution network is reconstructed, so that the reconstruction of a single objective can be realized only.
In the market, when the grid frame of the power distribution network is reconstructed, a plurality of selected objective functions are adopted, so that multi-objective reconstruction is realized, however, only static reconstruction of the power grid can be realized, and the load application capability for time variation is weak.
Disclosure of Invention
The application provides a reconstruction method of an electric vehicle charging station power distribution network based on a genetic algorithm, which is used for solving the technical problems that the existing power distribution network reconstruction can only realize static reconstruction of a power grid and has weak load applicability to time variation.
In view of this, the first aspect of the present application provides a method for reconstructing an electric vehicle charging station distribution network based on a genetic algorithm, comprising the following steps:
s1, collecting network parameters of a power distribution network, and initializing the network parameters of the power distribution network;
s2, according to the number scale of the preset electric vehicles, carrying out simulation sampling based on a Monte Carlo simulation method by considering the types of the electric vehicles, the corresponding duty ratio and the corresponding charging behaviors, and generating an electric vehicle charging station load curve according to the simulation sampling result;
S3, constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage offset as optimization targets;
and S4, optimizing the power distribution network reconstruction model based on a multi-objective genetic algorithm, and outputting a dynamic power distribution network reconstruction optimization structure, wherein the dynamic power distribution network reconstruction optimization structure comprises a network topology structure, a current active network loss and a voltage offset.
Preferably, the network parameters of the power distribution network include impedance data of each branch of the power distribution network and load data of each node, and the power distribution network adopts IEEE33 nodes to improve the power distribution system.
Preferably, step S2 specifically includes:
s201, determining the type of the electric automobile and the corresponding duty ratio of the electric automobile according to the number scale of the preset electric automobile;
s202, determining charging behaviors of different types of electric vehicles within 1 day, wherein the charging behaviors comprise daily charging times, charging initial charge state, initial charging time and charging speed;
s203, performing analog sampling by using a Monte Carlo simulation method according to the type and the charging behavior corresponding to the electric automobile, so as to obtain a load curve of the electric automobile of one type;
s204, repeating the step S203 to perform iterative simulation sampling until the load curve of each electric vehicle in the preset number scale of electric vehicles is obtained, and superposing the load curves of each electric vehicle in the preset number scale of electric vehicles to obtain an electric vehicle charging station load curve.
Preferably, step S3 specifically includes:
and constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage offset as optimization targets, and simultaneously determining constraint conditions, wherein the constraint conditions comprise power flow constraint, node voltage constraint, branch current constraint and power distribution network topology constraint.
Preferably, step S4 is preceded by:
s401, generating a closed-loop branch matrix based on a topological radiation structure of closed-loop construction of a power distribution network, wherein the closed-loop branch matrix is used for storing closed-loop branch data, the closed-loop branch matrix is a two-column matrix, elements in a first column are branch head nodes, and elements in a second column are branch tail nodes;
s402, searching a path structure in the closed loop branch matrix based on a growth tree algorithm, wherein a plurality of distribution network reconstruction schemes are obtained, the path structure is judged according to whether a path exists from any load node to a power supply node, the distribution network reconstruction scheme comprises a scheme number, a reconstruction topological graph and corresponding branch data, and the branch data comprises a square head node, a tail node, a branch resistor and a branch reactance.
Preferably, step S4 specifically includes:
S411, generating an initial population in a random generation mode according to the scale of the power distribution network reconstruction scheme, coding the initial population by using decimal numbers, and converting the decimal numbers into 5-bit binary numbers;
s412, each power distribution network reconstruction scheme in the initial population forms an individual, and individual fitness is calculated according to an objective function of the power distribution network reconstruction model;
s413, performing copying, crossing and mutation operation on individuals in the initial population based on a multi-objective genetic algorithm to obtain a new power distribution network, calculating active network loss and node voltage offset of the new power distribution network, and calculating individual fitness according to a fitness function according to the active network loss and the node voltage offset;
and S414, judging whether the convergence condition is met according to the individual fitness, if so, judging that the corresponding individual is an optimal solution, and decoding the optimal solution to obtain a network topology structure corresponding to the optimal solution, and the current active network loss and the node voltage offset.
In a second aspect, the present invention further provides an electric vehicle charging station power distribution network reconstruction system based on a genetic algorithm, including:
The initialization module is used for collecting network parameters of the power distribution network and initializing the network parameters of the power distribution network;
the load curve generation module is used for carrying out simulation sampling based on a Monte Carlo simulation method according to the number scale of the preset electric vehicles, considering the types of the electric vehicles, the corresponding duty ratio and the corresponding charging behavior of the electric vehicles, and generating an electric vehicle charging station load curve according to the simulation sampling result;
the model construction module is used for constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage offset as optimization targets;
the optimization module is used for optimizing the power distribution network reconstruction model based on a multi-objective genetic algorithm and outputting a dynamic power distribution network reconstruction optimization structure, and the dynamic power distribution network reconstruction optimization structure comprises a network topology structure, current active network loss and voltage offset.
Preferably, the load curve generation module comprises a first determination sub-module, a second determination sub-module, a sampling sub-module and a superposition module;
the first determining submodule is used for determining the type of the electric automobile and the corresponding duty ratio of the electric automobile according to the number and the scale of the preset electric automobile;
The second determining submodule is used for determining charging behaviors of different types of electric automobiles within 1 day, wherein the charging behaviors comprise daily charging times, charging initial charge state, initial charging time and charging speed;
the sampling submodule is used for carrying out analog sampling by using a Monte Carlo simulation method according to the type and the charging behavior corresponding to the electric automobile, so as to obtain a load curve of the electric automobile of one type;
the superposition module is used for obtaining the electric vehicle charging station load curve by superposing the load curve of each electric vehicle in the preset number scale of the electric vehicles.
Preferably, the system further comprises:
the matrix module is used for generating a closed-loop branch matrix based on a topological radiation structure of closed-loop construction of the power distribution network, wherein the closed-loop branch matrix is used for storing closed-loop branch data, the closed-loop branch matrix is a two-column matrix, elements in a first column are branch head nodes, and elements in a second column are branch tail nodes;
the power distribution network reconstruction method comprises a growth tree module and a power supply node, wherein the growth tree module is used for searching a path structure in the closed loop branch matrix based on a growth tree algorithm, so as to obtain a plurality of power distribution network reconstruction schemes, the path structure is judged according to whether a path exists from any load node to the power supply node, the power distribution network reconstruction schemes comprise scheme numbers, reconstruction topological diagrams and corresponding branch data, and the branch data comprise a square head node, a tail node, branch resistances and branch reactances.
Preferably, the optimization module specifically comprises a coding module, an adaptability module, a genetic algorithm module and a convergence judgment module;
the coding module is used for generating an initial population in a random generation mode according to the scale of the power distribution network reconstruction scheme, coding the initial population by using decimal numbers and converting the decimal numbers into 5-bit binary numbers;
the fitness module is used for calculating individual fitness according to an objective function of the power distribution network reconstruction model, and each power distribution network reconstruction scheme in the initial population forms an individual;
the genetic algorithm module is used for carrying out copying, crossing and mutation operation on individuals in the initial population based on a multi-objective genetic algorithm to obtain a new power distribution network, calculating active network loss and node voltage offset of the new power distribution network, and calculating individual fitness according to an fitness function according to the active network loss and the node voltage offset;
the convergence judging module is used for judging whether convergence conditions are met according to the individual fitness, judging that the corresponding individual is an optimal solution if the convergence conditions are met, and obtaining a network topology structure corresponding to the optimal solution and the current active network loss and the node voltage offset by decoding the optimal solution.
From the above technical solutions, the embodiments of the present application have the following advantages:
according to the method, a one-day load curve of the electric vehicle can be obtained based on a Monte Carlo simulation method, so that an electric vehicle charging station load curve is generated, namely, the change trend of the load of the electric vehicle charging station along with time in one day is simulated, the uncertainty in time and space of the electric vehicle is considered, and therefore the method is close to the actual activity habit of human beings, and charging behaviors are provided for electric vehicle charging to serve as charging references; meanwhile, a power distribution network reconstruction model is established by taking the minimum active network loss and the minimum node voltage offset as optimization targets, and the power distribution network reconstruction model is optimized by utilizing a multi-target genetic algorithm, so that an optimized network topology structure, the active network loss and the voltage offset are obtained, the network loss and the node voltage offset of the power distribution network can be reduced, the power supply reliability is improved, the dynamic reconstruction of the power grid is realized based on an electric vehicle charging station load curve and the power distribution network reconstruction model, and the time-varying load applicability is improved.
Drawings
Fig. 1 is a flowchart of a method for reconstructing an electric vehicle charging station power distribution network based on a genetic algorithm according to an embodiment of the present application;
FIG. 2 is a topology block diagram of an IEEE33 node improved power distribution system provided in an embodiment of the present application;
FIG. 3a is a 16-node incorporated electric vehicle charging station load graph provided by an embodiment of the present application;
FIG. 3b is a 23-node incorporated electric vehicle charging station load graph provided by an embodiment of the present application;
FIG. 3c is a 30-node incorporated electric vehicle charging station load graph provided by an embodiment of the present application;
FIG. 4 is a graph of population fitness as a function of iteration number according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electric vehicle charging station power distribution network reconstruction system based on a genetic algorithm according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
For easy understanding, please refer to fig. 1, the method for reconstructing the electric vehicle charging station power distribution network based on the genetic algorithm provided by the invention comprises the following steps:
s1, collecting network parameters of a power distribution network, and initializing the network parameters of the power distribution network;
s2, according to the number scale of the preset electric vehicles, carrying out simulation sampling based on a Monte Carlo simulation method by considering the types of the electric vehicles, the corresponding duty ratio and the corresponding charging behaviors, and generating an electric vehicle charging station load curve according to the simulation sampling result;
s3, constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage offset as optimization targets;
and S4, optimizing a power distribution network reconstruction model based on a multi-objective genetic algorithm, and outputting a dynamic power distribution network reconstruction optimization structure which comprises a network topology structure, current active network loss and voltage offset.
According to the electric vehicle charging station distribution network reconstruction method based on the genetic algorithm, a one-day load curve of an electric vehicle can be obtained based on a Yu Mengte Carlo simulation method, and then the electric vehicle charging station load curve is generated, namely, the change trend of the load of the electric vehicle charging station along with time in one day is simulated, the uncertainty of the electric vehicle in time and space is considered, and therefore the actual activity habit of human beings is closed, and charging behaviors are provided for electric vehicle charging to serve as charging references; meanwhile, a power distribution network reconstruction model is established by taking the minimum active network loss and the minimum node voltage offset as optimization targets, and the power distribution network reconstruction model is optimized by utilizing a multi-target genetic algorithm, so that an optimized network topology structure, the active network loss and the voltage offset are obtained, the network loss and the node voltage offset of the power distribution network can be reduced, the power supply reliability is improved, the dynamic reconstruction of the power grid is realized based on an electric vehicle charging station load curve and the power distribution network reconstruction model, and the time-varying load applicability is improved.
The following is a specific description of an embodiment of a reconstruction method of an electric vehicle charging station distribution network based on a genetic algorithm.
S100, collecting network parameters of a power distribution network, and initializing the network parameters of the power distribution network;
in this embodiment, the network parameters of the power distribution network include impedance data of each branch of the power distribution network and load data of each node, and the power distribution network adopts IEEE33 nodes to improve the power distribution system.
As shown in fig. 2, an IEEE33 node-modified power distribution system is used as the power distribution network of the present embodiment, in the IEEE33 node-modified power distribution system, 3 nodes, that is, 16, 23, and 30, are selected as access points of electric vehicles, node 1 is a power source node, the remaining nodes are load nodes, the branches connected by solid lines are segment switches, and the branches connected by broken lines are tie switches. The branch and load data in the IEEE33 node modified power distribution system is shown in table 1.
Table 1IEEE33 node improved Branch and load data Table in a Power distribution System
S200, according to the number scale of the preset electric vehicles, taking the type of the electric vehicles, the corresponding duty ratio and the corresponding charging behavior of the electric vehicles into consideration, performing simulation sampling based on a Monte Carlo simulation method, and generating an electric vehicle charging station load curve according to a simulation sampling result;
Specifically, step S200 specifically includes:
s201, determining the type of the electric automobile and the corresponding duty ratio of the electric automobile according to the number scale of the preset electric automobile;
in this embodiment, for a specific charging pile, the scale may be set manually, that is, the total number of electric vehicles may be variable, and for a determined total number, the proportion of various types of vehicles may be determined, for example, beijing, the electric vehicles in the whole city may be kept for about 16 ten thousand by 2017, wherein the private vehicles may be kept for about 11 ten thousand, the taxies may be about 1.5 ten thousand, the commercial vehicles may be about 3 ten thousand, and the buses may be about 5000, so that the proportion of various types of vehicles may be calculated to be about: 68.75% of private car, 9.38% of taxi, 18.75% of public service car and 3.12% of bus. The statistical knowledge shows that the distribution of the proportion of various vehicle types of the charging load of any charging pile is the same as the whole, so that the number of various vehicles which are possible at most can be determined by the proportion when the Monte Carlo simulation is carried out on the charging pile of any scale.
S202, determining charging behaviors of different types of electric vehicles within 1 day, wherein the charging behaviors comprise daily charging times, charging initial charge state, initial charging time and charging speed;
In the present embodiment, the average daily driving range of the private car is short, and the charging start charge state thereof is assumed to be charged once a day, and the start charging time thereof is in compliance with the normal distribution N (0.6,0.12) to satisfy the normal distribution N (19,1.52). The taxi has long driving distance per day, and one-time charging is difficult to meet the requirement, so that the initial charge state of charging is assumed to be charged twice per day and is subjected to normal distribution N (0.3,0.12), and the initial charge time is 2:00-5:00 and 11:30-14:30 to meet uniform distribution. The service vehicle has a shorter daily driving distance, and the charging initial charge state is subjected to normal distribution N (0.4,0.12) and the initial charge time is subjected to uniform distribution at 18:00-24:00 on the assumption that the charging is carried out once daily. The bus has longer daily driving distance, one-time charging cannot meet the requirement, the bus is supposed to be charged for 2 times per day, the initial charge state of charging is subjected to normal distribution N (0.5,0.12), and the initial charge time is subjected to uniform distribution in the ranges of 11:00-14:00 and 23:30-5:30. The charging modes of the taxi and the bus can be two, the charging modes are set to be a rapid charging mode in the daytime section, the charging modes of the private car and the public bus are set to be a conventional charging mode at night.
S203, performing analog sampling by using a Monte Carlo simulation method according to the type and the charging behavior corresponding to the electric automobile, so as to obtain a load curve of the electric automobile of one type;
S204, repeating the step S203 to perform iterative simulation sampling until the load curve of each electric vehicle in the preset number scale of electric vehicles is obtained, and superposing the load curves of each electric vehicle in the preset number scale of electric vehicles to obtain the electric vehicle charging station load curve.
In this embodiment, for an electric vehicle, the type of the electric vehicle is simulated first, then the charging mode is determined, if the electric vehicle is in a conventional charging mode, a charging initial charge state is generated according to the distribution satisfied by the electric vehicle, then the initial charge time is simulated according to the distribution satisfied by the initial charge time, and when the electric vehicle is charged, that is, the charging is stopped when the charging initial charge state reaches 1, because the charging power is determined, the time required for charging the electric vehicle can also be determined, and the load curve of the electric vehicle can be obtained from the above matters. For a given number of electric vehicles, the system is simulated for N times to obtain the load curve of each vehicle, and the load curve of a certain charging pile or charging station can be obtained after superposition.
In this embodiment, 3 nodes are selected to be incorporated into the electric vehicle, namely 16 nodes, 23 nodes and 30 nodes, and the scales of the incorporated electric vehicles are 500, 800 and 1000, respectively, and the load curves of the three nodes can be obtained by a Monte Carlo simulation method, as shown in fig. 3 a-3 c.
S300, constructing a power distribution network reconstruction model by taking minimum active network loss and minimum node voltage offset as optimization targets
Specifically, step S300 specifically includes:
and constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage offset as optimization targets, and simultaneously determining constraint conditions, wherein the constraint conditions comprise power flow constraint, node voltage constraint, branch current constraint and power distribution network topology constraint.
In this embodiment, the objective function of the reconstruction model of the power distribution network is,
F＝minP loss +minU max
wherein F is the output solution of the power distribution network reconstruction, minP loss Objective function representing minimum active net loss, minU max Representing an objective function with minimum node voltage offset of the power distribution network;
wherein the active loss of the distribution network is the algebraic sum of the active loss of each line, which is expressed as,
wherein P is i 、Q i Active power and reactive power of node i load respectively, P ij 、Q ij Active power and reactive power of node j load connected with node i respectively, R ij The impedance of a line between the nodes i and j is represented by n, the node number of the power distribution network system is represented by U i The voltage of each node of the power distribution network system.
The maximum value of the node voltage offset of the whole power distribution network is set as U max Then, the first and second data are obtained,
U max ＝max(|U i -U N |)
in U N Representing the rated voltage of the distribution network system.
Determining constraint conditions, wherein the constraint conditions comprise power flow constraint, node voltage constraint, branch current constraint and power distribution network topology constraint;
specifically, the power flow constraint is that,
where j ε i represents all nodes connected to an inode and includes an inode. P (P) Gi 、P EVi 、P Li Respectively is power supply power, electric automobile power, load power, Q Gi 、Q Evi 、Q Li Respectively, reactive power of power supply, reactive power of electric automobile and reactive power of load, theta ij G is the phase angle difference between the i node and the j node ij B is the conductance between the i node and the j node ij Susceptance between node i and node j;
the node voltage is constrained to be,
U min ≤U i ≤U max
in U min And U max Respectively lower bound and upper bound allowed by node voltage, and U is generally taken in power distribution network min 0.9, U max 1.1 (per unit value).
The branch current is constrained to be,
0≤I i ≤I imax
wherein I is i For the current passing through the ith branch, I imax The maximum value of the current allowed to pass through for the ith branch.
The topology constraints of the distribution network are that,
the distribution network is generally in a radiation operation state (open loop), namely, each node of N must be connected by N-1 branches, a ring structure cannot appear in a topological graph of the power grid, and isolated nodes cannot appear.
S400, generating a closed-loop branch matrix based on a topological radiation structure of closed-loop construction of a power distribution network, wherein the closed-loop branch matrix is used for storing closed-loop branch data, the closed-loop branch matrix is a two-column matrix, elements in a first column are branch head nodes, and elements in a second column are branch tail nodes;
In the topology diagram of closed loop construction of the power distribution network, N-1 branches are found to connect the circuits, which is a radial structure for the topology diagram, and each node needs to be guaranteed to be connected with a power supply, so that electric energy can be obtained from the power supply.
Assuming a distribution network with N nodes, b branches in closed loop, all cases with b-1 branches are common
Seed, however, thisIn some cases, the requirements cannot be met, and there may be cases where internal looping cannot be guaranteed that each node can be connected.
In order to find satisfactory conditions, these must be judged. A closed-loop branch matrix is given, the specification of the closed-loop branch matrix is b rows and 2 columns, the closed-loop branch matrix is used for storing closed-loop branch data, the first column is a branch head node, and the second column is a branch tail node; after the closed-loop branch matrix is generated, whether the structure containing b branches is established or not can be judged by using the paths from other nodes to power supply nodes or not for each structure containing b branches obtained by arrangement and combination, and the situation is established if the paths with the power supply can be directly or indirectly found in the closed-loop branch matrix for the rest N-1 load nodes in a sequential cycle, or else, the situation is not established.
S500, searching a path structure in a closed loop branch matrix based on a growth tree algorithm, wherein a plurality of distribution network reconstruction schemes are obtained, the path structure is judged according to whether a path from any load node to a power supply node exists or not, the distribution network reconstruction scheme comprises a scheme number, a reconstruction topological graph and corresponding branch data thereof, and the branch data comprises a square head node, a tail node, a branch resistor and a branch reactance.
In the embodiment of the IEEE33 node improved power distribution system, 37 total branches are calculated by using a spanning tree algorithm, and 53056 kinds of reconstruction schemes to be selected are obtained.
And S600, optimizing a power distribution network reconstruction model based on a multi-objective genetic algorithm, and outputting a dynamic power distribution network reconstruction optimization structure, wherein the dynamic power distribution network reconstruction optimization structure comprises a network topology structure, current active network loss and voltage offset.
Specifically, step S600 specifically includes:
s611, generating an initial population in a random generation mode according to the scale of a power distribution network reconstruction scheme, coding the initial population by using decimal numbers, and converting the decimal numbers into 5-bit binary numbers;
it should be noted that, when the initial structure of the distribution network is given, all the alternatives are obtained by searching all the spanning trees, each alternative stores a topological graph, and corresponding branch data and scheme numbers, and the branch data includes a first node, a last node, a branch resistor and a branch reactance.
When binary numbers are used for coding, the problem of unequal range is encountered, namely 30 schemes are adopted, however, a total of 5-bit binary numbers can represent 32 numbers, the coding of two binary numbers is meaningless and cannot correspond to a specific scheme, in order to solve the problem, a random number from 1 to 30 can be randomly generated from 10 binary numbers and then converted into binary numbers, and in this way, all initial populations can be ensured to be within a feasible domain range.
For each alternative scheme, there are 30 alternative schemes, because 30 binary codes are 11110, which is a 5-bit binary number, all numbers are coded by a 5-bit binary number, and if the number is insufficient, the front is complemented by 0, for example, the scheme corresponding to the number 6 is correspondingly coded as 00110.
S612, each power distribution network reconstruction scheme in the initial population forms an individual, and individual fitness is calculated according to an objective function of a power distribution network reconstruction model;
it should be noted that, since the individual is corresponding to the scheme, and the fitness of each individual should correspond to the calculation result of each scheme, specifically, the power flow calculation may be performed according to the topology structure data and the branch impedance data stored in each scheme, so as to obtain the active network loss and the node voltage offset of the scheme, however, since the minimum active network loss and the minimum node voltage offset are inversely related, that is, the greater the active network loss is, the smaller the node voltage offset is, and vice versa, in the calculation process, it is necessary to find an optimal scheme to perform the trade-off. In this embodiment, the two weights are equal, and the fitness function is the reciprocal of the product of the two weights, i
Wherein fit (i) is an fitness function solution, f 1 (i) F is the minimum function of active network loss 2 (i) Is the node voltageThe minimum function is shifted.
The individual fitness is the proportion of the individual fitness to the sum of the population fitness, the relative fitness determines the probability of the individual inheriting to the next generation, the calculation formula is as follows,
wherein Bfit (i) represents individual fitness.
S613, performing copying, crossing and mutation operations on individuals in the initial population based on a multi-objective genetic algorithm to obtain a new power distribution network, calculating active network loss and node voltage offset of the new power distribution network, and calculating individual fitness according to an fitness function according to the active network loss and the node voltage offset;
it should be noted that, in the multi-objective genetic algorithm, genetic operations include duplication, crossover and mutation, and the combination of the three together form a genetic rule between generations, and for each generation of population, the individual with the highest relative fitness is extracted, and the optimal individual can be inherited to the next generation, and the process is duplication; for the rest individuals, two individuals are randomly selected for crossing, namely, a random number from 0 to 1 is randomly generated, if the random number is smaller than the crossing probability, the chromosomes of the two randomly selected individuals are partially exchanged, so that two new individuals are obtained, and the crossing probability set in the embodiment is 0.8; after the crossover operation, a random number of 0 to 1 is randomly generated, and if the value is smaller than the set mutation probability, all chromosomes are inverted after the current individual is at the random locus, and the mutation probability set in this embodiment is 0.05.
Since the total number of binary numbers of the corresponding bit number is larger than the number of schemes, after a series of genetic operations, some individuals may jump out of the feasible region, and in order to solve this problem, a comparator needs to be set, that is, after the genetic operations, each binary number is decoded and changed into a decimal number, and if not all the binary numbers are within the scheme numbers, it is randomly jumped back into the feasible region, which is specifically: and for the number of the jump-out feasible region, randomly generating a random number in the feasible region, and thus realizing the randomness of the jump-back.
S614, judging whether the convergence condition is met according to the individual fitness, if so, judging that the corresponding individual is an optimal solution, and decoding the optimal solution to obtain a network topology structure corresponding to the optimal solution, current active network loss and node voltage offset.
In the genetic algorithm, each generation of optimal individual can be used for judgment, and if the individual with the highest fitness in consecutive generations keeps unchanged for several generations, the optimal solution is considered, and the genetic operation is stopped.
After the optimal solution is obtained, the network topology structure corresponding to the optimal solution, the current active network loss and the current voltage offset can be obtained through decoding.
All schemes are coded and then solved by a genetic algorithm, and 53056 kinds of reconstruction schemes to be selected are obtained in total in the embodiment of the IEEE33 node improved distribution system, and 2 15 <53056<2 16 Thus, each scheme is represented by a 16-bit binary number. And (3) finishing calculation of the selected time in 24 hours a day, and thus realizing dynamic reconstruction of the network. Taking the first time period as an example, the variation trend of population fitness along with the iteration number is shown in fig. 4, and the variation trend is available according to the variation trend, and after a plurality of iterations, the optimal individual is not changed any more. And thus output the reconstruction optimization results of the distribution network, as shown in table 2.
Table 224 hour distribution network reconstruction optimization results
The embodiment can accurately simulate the change trend of the load of the electric vehicle charging station along with time in one day, and accords with the activity habit of human beings; the genetic algorithm is used for searching the optimal topological graph, a brand new scheme coding mode is adopted, the solving time of the model is greatly reduced, and the multi-objective reconstruction considering network loss and voltage offset is realized, so that a guiding scheme can be provided for the optimal operation problem of the power distribution network connected with a large number of electric vehicle charging stations.
The following is a specific description of an embodiment of a system for implementing the genetic algorithm-based reconstruction method of the electric vehicle charging station distribution network provided in this embodiment.
For easy understanding, please refer to fig. 5, the embodiment provides an electric vehicle charging station power distribution network reconstruction system based on genetic algorithm, which includes:
the initialization module 100 is configured to collect network parameters of the power distribution network, and initialize the network parameters of the power distribution network;
the load curve generation module 200 is configured to perform simulation sampling based on a monte carlo simulation method according to a preset number scale of electric vehicles, considering the type of the electric vehicles and the corresponding duty ratio thereof, and the corresponding charging behavior, and generate an electric vehicle charging station load curve according to a simulation sampling result;
the model construction module 300 is configured to construct a power distribution network reconstruction model with minimum active network loss and minimum node voltage offset as optimization targets;
the optimization module 400 is configured to optimize the power distribution network reconstruction model based on a multi-objective genetic algorithm, and output a dynamic reconstruction optimization structure of the power distribution network, where the dynamic reconstruction optimization structure of the power distribution network includes a network topology structure, a current active network loss and a voltage offset.
Further, the load curve generation module comprises a first determination sub-module, a second determination sub-module, a sampling sub-module and a superposition module;
the first determining submodule is used for determining the type of the electric automobile and the corresponding duty ratio of the electric automobile according to the number and the scale of the preset electric automobile;
The second determining submodule is used for determining charging behaviors of different types of electric automobiles within 1 day, wherein the charging behaviors comprise daily charging times, charging initial charge state, initial charging time and charging speed;
the sampling sub-module is used for carrying out simulation sampling by using a Monte Carlo simulation method according to the type and the charging behavior corresponding to the electric automobile, so as to obtain a load curve of the electric automobile of one type;
and the superposition module is used for superposing the load curves of each electric automobile in the preset quantity scale of the electric automobiles to obtain the load curve of the electric automobile charging station.
Further, the system further comprises:
the matrix module is used for generating a closed-loop branch matrix based on a topological radiation structure of closed-loop construction of the power distribution network, the closed-loop branch matrix is used for storing closed-loop branch data, the closed-loop branch matrix is a two-column matrix, wherein elements in a first column are branch head nodes, and elements in a second column are branch tail nodes;
the power distribution network reconstruction system comprises a growth tree module and a power distribution network reconstruction module, wherein the growth tree module is used for searching a path structure in a closed loop branch matrix based on a growth tree algorithm, so as to obtain a plurality of power distribution network reconstruction schemes, the path structure judges whether a path exists from any load node to a power supply node, the power distribution network reconstruction schemes comprise scheme numbers, reconstruction topological diagrams and corresponding branch data, and the branch data comprise a square head node, a tail node, a branch resistor and a branch reactance.
Further, the optimization module specifically comprises a coding module, an adaptability module, a genetic algorithm module and a convergence judgment module;
the coding module is used for generating an initial population in a random generation mode according to the scale of the power distribution network reconstruction scheme, coding the initial population by using decimal numbers and converting the decimal numbers into 5-bit binary numbers;
the fitness module is used for calculating individual fitness according to an objective function of the power distribution network reconstruction model, and each power distribution network reconstruction scheme in the initial population forms an individual;
the genetic algorithm module is used for carrying out copying, crossing and mutation operation on individuals in the initial population based on a multi-objective genetic algorithm to obtain a new power distribution network, calculating active network loss and node voltage offset of the new power distribution network, and calculating individual fitness according to the fitness function according to the active network loss and the node voltage offset;
the convergence judging module is used for judging whether the convergence condition is met according to the individual fitness, judging that the corresponding individual is an optimal solution if the convergence condition is met, and obtaining a network topology structure corresponding to the optimal solution, current active network loss and node voltage offset through decoding the optimal solution.
It should be noted that, the working process of the electric vehicle charging station power distribution network reconstruction system based on the genetic algorithm provided in this embodiment is consistent with the electric vehicle charging station power distribution network reconstruction method based on the genetic algorithm provided in the foregoing embodiment, and will not be described herein.
According to the electric vehicle charging station distribution network reconstruction system based on the genetic algorithm, a one-day load curve of an electric vehicle can be obtained based on a Yu Mengte Carlo simulation method, and then the electric vehicle charging station load curve is generated, namely, the change trend of the load of the electric vehicle charging station along with time in one day is simulated, the uncertainty of the electric vehicle in time and space is considered, and therefore the actual activity habit of human beings is closed, and charging behaviors are provided for electric vehicle charging to serve as charging references; meanwhile, a power distribution network reconstruction model is established by taking the minimum active network loss and the minimum node voltage offset as optimization targets, and the power distribution network reconstruction model is optimized by utilizing a multi-target genetic algorithm, so that an optimized network topology structure, the active network loss and the voltage offset are obtained, the network loss and the node voltage offset of the power distribution network can be reduced, the power supply reliability is improved, the dynamic reconstruction of the power grid is realized based on an electric vehicle charging station load curve and the power distribution network reconstruction model, and the time-varying load applicability is improved.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (6)
1. The electric vehicle charging station power distribution network reconstruction method based on the genetic algorithm is characterized by comprising the following steps of:
s1, collecting network parameters of a power distribution network, and initializing the network parameters of the power distribution network;
s2, according to the number scale of the preset electric vehicles, carrying out simulation sampling based on a Monte Carlo simulation method by considering the types of the electric vehicles, the corresponding duty ratio and the corresponding charging behaviors, and generating an electric vehicle charging station load curve according to the simulation sampling result;
the step S2 specifically comprises the following steps:
s201, determining the type of the electric automobile and the corresponding duty ratio of the electric automobile according to the number scale of the preset electric automobile;
S202, determining charging behaviors of different types of electric vehicles within 1 day, wherein the charging behaviors comprise daily charging times, charging initial charge state, initial charging time and charging speed;
s203, performing analog sampling by using a Monte Carlo simulation method according to the type and the charging behavior corresponding to the electric automobile, so as to obtain a load curve of the electric automobile of one type;
s204, repeating the step S203 to perform iterative simulation sampling until the load curve of each electric vehicle in the preset number scale of electric vehicles is obtained, and superposing the load curves of each electric vehicle in the preset number scale of electric vehicles to obtain an electric vehicle charging station load curve;
s3, constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage offset as optimization targets;
s4, optimizing the power distribution network reconstruction model based on a multi-objective genetic algorithm, and outputting a dynamic power distribution network reconstruction optimization structure, wherein the dynamic power distribution network reconstruction optimization structure comprises a network topology structure, a current active network loss and a voltage offset;
the step S4 specifically comprises the following steps:
s411, generating an initial population in a random generation mode according to the scale of the power distribution network reconstruction scheme, coding the initial population by using decimal numbers, and converting the decimal numbers into 5-bit binary numbers;
S412, each power distribution network reconstruction scheme in the initial population forms an individual, and individual fitness is calculated according to an objective function of the power distribution network reconstruction model;
s413, performing copying, crossing and mutation operation on individuals in the initial population based on a multi-objective genetic algorithm to obtain a new power distribution network, calculating active network loss and node voltage offset of the new power distribution network, and calculating individual fitness according to a fitness function according to the active network loss and the node voltage offset;
and S414, judging whether the convergence condition is met according to the individual fitness, if so, judging that the corresponding individual is an optimal solution, and decoding the optimal solution to obtain a network topology structure corresponding to the optimal solution, and the current active network loss and the node voltage offset.
2. The method for reconstructing an electric vehicle charging station distribution network based on a genetic algorithm according to claim 1, wherein the network parameters of the distribution network comprise impedance data of each branch of the distribution network and load data of each node, and the distribution network adopts an IEEE33 node to improve a distribution system.
3. The method for reconstructing an electric vehicle charging station distribution network based on a genetic algorithm according to claim 1, wherein step S3 specifically comprises:
And constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage offset as optimization targets, and simultaneously determining constraint conditions, wherein the constraint conditions comprise power flow constraint, node voltage constraint, branch current constraint and power distribution network topology constraint.
4. The method of claim 1, further comprising, prior to step S4:
s401, generating a closed-loop branch matrix based on a topological radiation structure of closed-loop construction of a power distribution network, wherein the closed-loop branch matrix is used for storing closed-loop branch data, the closed-loop branch matrix is a two-column matrix, elements in a first column are branch head nodes, and elements in a second column are branch tail nodes;
s402, searching a path structure in the closed loop branch matrix based on a growth tree algorithm, wherein a plurality of distribution network reconstruction schemes are obtained, the path structure is judged according to whether a path exists from any load node to a power supply node, the distribution network reconstruction scheme comprises a scheme number, a reconstruction topological graph and corresponding branch data, and the branch data comprises a square head node, a tail node, a branch resistor and a branch reactance.
5. Electric automobile charging station distribution network reconfiguration system based on genetic algorithm, characterized by comprising:
the initialization module is used for collecting network parameters of the power distribution network and initializing the network parameters of the power distribution network;
the load curve generation module is used for carrying out simulation sampling based on a Monte Carlo simulation method according to the number scale of the preset electric vehicles, considering the types of the electric vehicles, the corresponding duty ratio and the corresponding charging behavior of the electric vehicles, and generating an electric vehicle charging station load curve according to the simulation sampling result;
the load curve generation module comprises a first determination submodule, a second determination submodule, a sampling submodule and a superposition module;
the first determining submodule is used for determining the type of the electric automobile and the corresponding duty ratio of the electric automobile according to the number and the scale of the preset electric automobile;
the second determining submodule is used for determining charging behaviors of different types of electric automobiles within 1 day, wherein the charging behaviors comprise daily charging times, charging initial charge state, initial charging time and charging speed;
the sampling submodule is used for carrying out analog sampling by using a Monte Carlo simulation method according to the type and the charging behavior corresponding to the electric automobile, so as to obtain a load curve of the electric automobile of one type;
The superposition module is used for superposing the load curves of each electric vehicle in the preset quantity scale of the electric vehicles to obtain an electric vehicle charging station load curve;
the model construction module is used for constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage offset as optimization targets;
the optimization module is used for optimizing the power distribution network reconstruction model based on a multi-objective genetic algorithm and outputting a dynamic power distribution network reconstruction optimization structure, wherein the dynamic power distribution network reconstruction optimization structure comprises a network topology structure, current active network loss and voltage offset;
the optimization module specifically comprises a coding module, a fitness module, a genetic algorithm module and a convergence judgment module;
the coding module is used for generating an initial population in a random generation mode according to the scale of the power distribution network reconstruction scheme, coding the initial population by using decimal numbers and converting the decimal numbers into 5-bit binary numbers;
the fitness module is used for calculating individual fitness according to an objective function of the power distribution network reconstruction model, and each power distribution network reconstruction scheme in the initial population forms an individual;
The genetic algorithm module is used for carrying out copying, crossing and mutation operation on individuals in the initial population based on a multi-objective genetic algorithm to obtain a new power distribution network, calculating active network loss and node voltage offset of the new power distribution network, and calculating individual fitness according to an fitness function according to the active network loss and the node voltage offset;
the convergence judging module is used for judging whether convergence conditions are met according to the individual fitness, judging that the corresponding individual is an optimal solution if the convergence conditions are met, and obtaining a network topology structure corresponding to the optimal solution and the current active network loss and the node voltage offset by decoding the optimal solution.
6. The genetic algorithm-based electric vehicle charging station distribution network reconstruction system of claim 5, further comprising:
the matrix module is used for generating a closed-loop branch matrix based on a topological radiation structure of closed-loop construction of the power distribution network, wherein the closed-loop branch matrix is used for storing closed-loop branch data, the closed-loop branch matrix is a two-column matrix, elements in a first column are branch head nodes, and elements in a second column are branch tail nodes;
The power distribution network reconstruction method comprises a growth tree module and a power supply node, wherein the growth tree module is used for searching a path structure in the closed loop branch matrix based on a growth tree algorithm, so as to obtain a plurality of power distribution network reconstruction schemes, the path structure is judged according to whether a path exists from any load node to the power supply node, the power distribution network reconstruction schemes comprise scheme numbers, reconstruction topological diagrams and corresponding branch data, and the branch data comprise a square head node, a tail node, branch resistances and branch reactances.
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