CN113629743A - 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
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/10—Internal combustion engine [ICE] based vehicles
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The application discloses a method and a system for reconstructing a power distribution network of an electric vehicle charging station based on a genetic algorithm, which can obtain a one-day load curve of an electric vehicle 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 along with time in one day, and consider the uncertainty of the electric vehicle in time and space, thereby being 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 deviation as optimization targets, the power distribution network reconstruction model is optimized by utilizing a multi-objective genetic algorithm, an optimized network topology structure, active network loss and voltage deviation are obtained, the network loss and the node voltage deviation of the power distribution network can be reduced, the power supply reliability is improved, dynamic reconstruction of the power grid is realized on the basis of 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 method and a system for reconstructing a power distribution network 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, and the charging places of the existing electric automobiles are concentrated and are usually carried out in fixed charging piles.
With the gradual increase of electric vehicles, a large number of electric vehicles need high charging power during charging, which undoubtedly has a great influence on the operation and management of the power grid, and also has an influence on the economy and reliability of the power grid.
The charging voltage of the electric automobile is generally 220V, the access of the electric automobile can directly affect a power distribution network, and the charging time and the charging place of the electric automobile are uncertain due to the uncertainty of people using the electric automobile for activities. Meanwhile, the access of an electric automobile can increase the grid loss of a power grid and the voltage deviation of nodes, at present, most power grid companies are methods for reconstructing a grid structure of the power distribution grid, the characteristics that the power distribution grid has closed-loop construction and open-loop operation are utilized, namely when the power supply of each node is ensured, any node in the operation process is required to be directly or indirectly connected with a power supply, and in addition, only one path is provided, if the load of a certain node is increased due to the access of the electric automobile, so that the grid loss is increased, the topological diagram of the power distribution grid can be changed, namely the states of a section switch and a contact switch are changed, and the power flow in the network is changed under the condition that the power supply of a user is ensured, so that the loss of the network is reduced, and the operation cost is saved.
However, the current method for reconstructing the power distribution network frame cannot consider the uncertainty of the electric vehicle in time and space when the access of the electric vehicle is considered, and the selected objective function is single when the power distribution network is reconstructed, but only the reconstruction of a single objective can be realized.
In the market, when a power distribution network frame is reconstructed, a plurality of selected objective functions are provided, so that multi-objective reconstruction is realized, but only static reconstruction of a power grid can be realized, and the applicability to time-varying loads is weak.
Disclosure of Invention
The application provides an electric vehicle charging station power distribution network reconstruction method based on a genetic algorithm, which is used for solving the technical problems that the conventional power distribution network reconstruction can only realize static reconstruction of a power grid and has weaker time-varying load applicability.
In view of this, the first aspect of the present application provides an electric vehicle charging station power distribution network reconstruction method based on a genetic algorithm, including the following steps:
s1, collecting network parameters of the power distribution network, and initializing the network parameters of the power distribution network;
s2, according to the number scale of the preset electric automobiles, considering the types of the electric automobiles, the corresponding proportion of the electric automobiles and the corresponding charging behaviors, carrying out analog sampling based on a Monte Carlo simulation method, and generating a load curve of the electric automobile charging station according to the analog sampling result;
s3, constructing a power distribution network reconstruction model by taking minimum active network loss and minimum node voltage deviation as optimization targets;
and S4, optimizing the power distribution network reconstruction model based on a multi-objective genetic algorithm, and outputting a power distribution network dynamic reconstruction optimization structure, wherein the power distribution network dynamic reconstruction optimization structure comprises a network topology structure, the current active network loss and the current voltage deviation.
Preferably, the network parameters of the power distribution network comprise impedance data of each branch of the power distribution network and load data of each node, and the power distribution network adopts an IEEE33 node to improve a power distribution system.
Preferably, step S2 specifically includes:
s201, determining the type and the corresponding proportion of the electric automobiles according to the number scale of the preset electric automobiles;
s202, 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;
s203, performing analog sampling by using a Monte Carlo analog method according to the type and the charging behavior corresponding to the electric automobile, so as to obtain a load curve of one type of electric automobile;
and S204, repeating the step S203 to carry out iterative analog sampling until a load curve of each electric vehicle in the preset quantity scale of the electric vehicles is obtained, and overlapping the load curves of each electric vehicle in the preset quantity scale of the electric vehicles to obtain a load curve of the electric vehicle charging station.
Preferably, step S3 specifically includes:
the method comprises the steps of constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage deviation as optimization targets, and meanwhile 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, and 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;
s402, searching a path structure in the closed-loop branch matrix based on a growing tree algorithm to obtain a plurality of distribution network reconstruction schemes, wherein the path structure is judged according to whether a path exists from any load node to a power supply node, the distribution network reconstruction schemes comprise scheme numbers, a reconstruction topological graph and corresponding branch data, and the branch data comprise a square head node, a tail node, a branch resistance 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 the individual fitness is calculated according to the objective function of the power distribution network reconstruction model;
s413, carrying out copy, cross and variation operations on individuals in the initial population based on a multi-target genetic algorithm to obtain a new power distribution network, calculating active network loss and node voltage deviation of the new power distribution network, and calculating individual fitness according to the active network loss and the node voltage deviation and a fitness function;
and S414, judging whether a 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, the current active network loss and the node voltage offset.
In a second aspect, the present invention further provides a system for reconstructing a power distribution network of an electric vehicle charging station 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 analog sampling based on a Monte Carlo simulation method according to the number scale of the preset electric automobiles and in consideration of the types and the corresponding proportions of the electric automobiles and the corresponding charging behaviors, and generating a load curve of the electric automobile charging station according to an analog sampling result;
the model building module is used for building a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage deviation as optimization targets;
and the optimization module is used for optimizing the power distribution network reconstruction model based on a multi-objective genetic algorithm and outputting a power distribution network dynamic reconstruction optimization structure, and the power distribution network dynamic reconstruction optimization structure comprises a network topology structure, the current active network loss and the current voltage offset.
Preferably, the load curve generating module comprises a first determining submodule, a second determining submodule, a sampling submodule and a superposition module;
the first determining submodule is used for determining the type and the corresponding proportion of the electric automobiles according to the number scale of the preset electric automobiles;
the second determining submodule is used for determining charging behaviors of different types of electric automobiles within 1 day, and the charging behaviors comprise daily charging times, charging initial charge state, initial charging time and charging speed;
the sampling submodule is used for 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 one type of electric automobile;
and the superposition module is used for superposing the load curve of each electric automobile in the preset quantity scale of the electric automobiles to obtain the load curve of the electric automobile charging station.
Preferably, the system further comprises:
the system comprises a matrix module, a data processing module and a data processing module, wherein the matrix module is used for generating a closed-loop branch matrix based on a topological radiation structure constructed by a closed loop of a power distribution network, the closed-loop branch matrix is used for storing closed-loop branch data, and 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;
and the growth tree module is used for searching a path structure in the closed-loop branch matrix based on a growth tree algorithm to obtain a plurality of distribution network reconstruction schemes, wherein the path structure judges whether a path exists from any load node to a power supply node, the distribution network reconstruction schemes comprise scheme numbers, a reconstruction topological graph and corresponding branch data, and the branch data comprise a square head node, a tail node, a branch resistance and a branch reactance.
Preferably, the optimization module specifically comprises a coding module, a fitness module, a genetic algorithm module and a convergence judgment module;
the encoding module is used for generating an initial population in a random generation mode according to the scale of the power distribution network reconstruction scheme, encoding the initial population by using a decimal number and converting the decimal number into a 5-digit binary number;
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 copying, crossing and mutating the individuals in the initial population based on a multi-target genetic algorithm to obtain a new power distribution network, calculating the active network loss and node voltage offset of the new power distribution network, and calculating the individual fitness according to the active network loss and the node voltage offset and a fitness function;
and the convergence judging module is used for judging whether a 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 decoding the optimal solution to obtain a network topology structure corresponding to the optimal solution and the current active network loss and node voltage offset.
According to the technical scheme, the embodiment of the application has the following advantages:
the invention can obtain the one-day load curve of the electric automobile based on the Monte Carlo simulation method, and further generate the load curve of the electric automobile charging station, namely simulate the change trend of the load of the electric automobile charging station along with the time in one day, and consider the uncertainty of the electric automobile in time and space, thereby being close to the actual activity habit of human beings and providing the charging behavior of the electric automobile as the charging reference; meanwhile, a power distribution network reconstruction model is established by taking the minimum active network loss and the minimum node voltage deviation as optimization targets, the power distribution network reconstruction model is optimized by utilizing a multi-objective genetic algorithm, an optimized network topology structure, active network loss and voltage deviation are obtained, the network loss and the node voltage deviation of the power distribution network can be reduced, the power supply reliability is improved, dynamic reconstruction of the power grid is realized on the basis of 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 a power distribution network of an electric vehicle charging station based on a genetic algorithm according to an embodiment of the present application;
fig. 2 is a topology structure diagram of an IEEE33 node improved power distribution system according to an embodiment of the present application;
FIG. 3a is a load graph of a 16-node incorporated electric vehicle charging station provided by an embodiment of the present application;
FIG. 3b is a load graph of an electric vehicle charging station incorporated into the 23-node provided in the embodiments of the present application;
FIG. 3c is a load graph of an electric vehicle charging station incorporating 30 nodes provided by an embodiment of the present application;
fig. 4 is a graph of population fitness varying with iteration number according to the embodiment of the present application;
fig. 5 is a schematic structural diagram of an electric vehicle charging station power distribution network reconfiguration system based on a genetic algorithm according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For easy understanding, please refer to fig. 1, the method for reconstructing a power distribution network of an electric vehicle charging station based on a genetic algorithm according to the present invention includes the following steps:
s1, collecting network parameters of the power distribution network, and initializing the network parameters of the power distribution network;
s2, according to the number scale of the preset electric automobiles, considering the types of the electric automobiles, the corresponding proportion of the electric automobiles and the corresponding charging behaviors, carrying out analog sampling based on a Monte Carlo simulation method, and generating a load curve of the electric automobile charging station according to the analog sampling result;
s3, constructing a power distribution network reconstruction model by taking minimum active network loss and minimum node voltage deviation as optimization targets;
and S4, optimizing the power distribution network reconstruction model based on the multi-objective genetic algorithm, and outputting a power distribution network dynamic reconstruction optimization structure, wherein the power distribution network dynamic reconstruction optimization structure comprises a network topology structure, the current active network loss and the current voltage deviation.
According to the electric vehicle charging station power distribution network reconstruction method based on the genetic algorithm, a one-day load curve of an electric vehicle can be obtained based on a Monte 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 the time in one day is simulated, the uncertainty of the electric vehicle in time and space is considered, so that the actual activity habits of human beings are met, and the charging behavior is provided for the electric vehicle charging as the charging reference; meanwhile, a power distribution network reconstruction model is established by taking the minimum active network loss and the minimum node voltage deviation as optimization targets, the power distribution network reconstruction model is optimized by utilizing a multi-objective genetic algorithm, an optimized network topology structure, active network loss and voltage deviation are obtained, the network loss and the node voltage deviation of the power distribution network can be reduced, the power supply reliability is improved, dynamic reconstruction of the power grid is realized on the basis of 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 detailed description of an embodiment of a method for reconstructing a power distribution network of an electric vehicle charging station based on a genetic algorithm according to the present invention.
S100, collecting network parameters of the power distribution network, and initializing the network parameters of the power distribution network;
in the embodiment, the network parameters of the power distribution network comprise impedance data of each branch of the power distribution network and load data of each node, and the power distribution network adopts an IEEE33 node to improve a power distribution system.
As shown in fig. 2, an IEEE33 node improved power distribution system is used as the power distribution network of the present embodiment, in the IEEE33 node improved power distribution system, 3 nodes, namely 16, 23, and 30, are selected as access points of an electric vehicle, node 1 is a power supply node, the remaining nodes are load nodes, a branch connected by a solid line is a section switch, and a branch connected by a dotted line is a tie switch. The branch and load data in the IEEE33 node improved power distribution system is shown in table 1.
Table 1IEEE33 node improved branch and load data table in power distribution system
S200, according to the number scale of the preset electric automobiles, considering the types of the electric automobiles, the corresponding proportion of the electric automobiles and the corresponding charging behaviors, carrying out simulation sampling based on a Monte Carlo simulation method, and generating a load curve of the electric automobile charging station according to a simulation sampling result;
specifically, step S200 specifically includes:
s201, determining the type and the corresponding proportion of the electric automobiles according to the number scale of the preset electric automobiles;
in this embodiment, for a specific charging pile, the scale of the charging pile can be set manually, that is, the total number of electric vehicles is variable, for a certain total number, the proportion of various vehicle types is approximately determined, taking beijing as an example, by 2017, the holding amount of electric vehicles in the whole market is about 16 thousands, wherein the holding amount of private vehicles is about 11 thousands, taxies is about 1.5 thousands, business vehicles are about 3 thousands, and buses are about 5000, so that the proportion of various types of vehicles can be calculated as: 68.75% of private cars, 9.38% of taxis, 18.75% of official cars and 3.12% of buses. As can be known from statistical knowledge, the distribution satisfied by the proportion of various vehicle types of any charging pile is the same as the whole distribution, so that the maximum possible quantity of various automobiles of the charging pile of any scale can be determined by the proportion when Monte Carlo simulation is carried out.
S202, 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;
in the present embodiment, the average daily mileage of a private car is short, and assuming that the car is charged once per day, the charging start state of charge satisfies the normal distribution N (19,1.52) following the normal distribution N (0.6,0.12) start charging time. The taxi is long in driving distance every day, and the requirement cannot be met by one-time charging, so that the charging is assumed to be performed twice every day, the charging initial charge state follows normal distribution N (0.3 and 0.12), and the initial charging time meets the uniform distribution at 2:00-5:00 and 11:30-14: 30. The distance of the bus to be driven every day is short, the charging initial charge state is assumed to be charged once a day, the normal distribution N (0.4, 0.12) is obeyed, and the uniform distribution is obeyed in the initial charging time of 18:00-24: 00. The bus is long in driving distance every day, one-time charging cannot meet the requirement, the bus is assumed to be charged 2 times every day, the charging initial charge state obeys normal distribution N (0.5, 0.12), and the initial charging time obeys uniform distribution at 11:00-14:00 and 23:30-5: 30. The charging modes of the taxi and the bus can be two, the charging mode is set to be a quick charging mode in the daytime, the charging mode is set to be a conventional charging mode at night, and the charging modes of the private car and the public bus are both the conventional charging modes.
S203, performing analog sampling by using a Monte Carlo analog method according to the type and the charging behavior corresponding to the electric automobile, so as to obtain a load curve of one type of electric automobile;
and S204, repeating the step S203 to carry out iterative analog sampling until a load curve of each electric vehicle in the preset quantity scale of the electric vehicles is obtained, and superposing the load curves of each electric vehicle in the preset quantity scale of the electric vehicles to obtain a load curve of the electric vehicle charging station.
In this embodiment, for an electric vehicle, the type of the electric vehicle is simulated, the charging mode is determined, if the electric vehicle is in the normal charging mode, a charging start state of charge is generated according to the distribution that the electric vehicle satisfies, the charging start time is simulated according to the distribution that the charging start time satisfies, after the charging of the electric vehicle is completed, that is, the charging start state of charge reaches 1, the charging is stopped, and the charging power is determined, so that the time required by a full charge of the electric vehicle can be determined, and the load curve of the electric vehicle can be obtained through the above. For a given number of electric automobiles, the load curve of each automobile can be obtained by simulating the system for N times, 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 merged into the electric vehicle load, which are respectively 16 nodes, 23 nodes and 30 nodes, and the sizes of the merged electric vehicles are respectively 500 vehicles, 800 vehicles and 1000 vehicles, and the load curves of the three nodes can be obtained by the monte carlo simulation method, as shown in fig. 3a to 3 c.
S300, constructing a power distribution network reconstruction model by taking minimum active network loss and minimum node voltage deviation as optimization targets
Specifically, step S300 specifically includes:
the method comprises the steps of constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage deviation as optimization targets, and meanwhile 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 power distribution network reconstruction model is,
F＝minPloss+minUmax
wherein F is the output solution of the power distribution network reconstruction, minPlossAn objective function, minU, representing minimum active network lossmaxRepresenting an objective function with minimum voltage deviation of the distribution network node;
wherein the active network loss of the distribution network is the algebraic sum of the active losses of the individual lines, which is expressed as,
in the formula, Pi、QiActive and reactive power, P, respectively, of the node i loadij、QijActive and reactive power, R, respectively, of the load at node j connected to node iijIs the impedance of the line between nodes i and j, n is the number of nodes in the distribution network system, UiThe voltages of the nodes of the power distribution grid system.
Wherein, the maximum value of the node voltage deviation of the whole power distribution network is set as UmaxThen, the first step is executed,
Umax＝max(|Ui-UN|)
in the formula of UNRepresenting the rated voltage of the power distribution grid system.
Determining constraint conditions, wherein the constraint conditions comprise power flow constraint, node voltage constraint, branch current constraint and distribution network topology constraint;
in particular, the power flow constraint is,
in the formula, j ∈ i represents all nodes connected to inode, and includes inode. PGi、PEVi、PLiRespectively power supply power, electric automobile power, load power, QGi、QEvi、QLiRespectively, the reactive power of the power supply, the reactive power of the electric automobile and the reactive power of the load thetaijIs the phase angle difference between the i and j nodes, GijAs conductance between node i and node j, BijIs the susceptance between the i node and the j node;
the node voltage is constrained to be,
Umin≤Ui≤Umax
in the formula of UminAnd UmaxThe lower bound and the upper bound allowed by the node voltage are respectively taken as U in the power distribution networkminIs 0.9, UmaxIs 1.1 (per unit value).
The branch current is constrained to be,
0≤Ii≤Iimax
in the formula IiFor the current passing through the ith branch, IimaxThe maximum value of the current allowed to pass through the ith branch.
The topological constraint of the power distribution network is that,
the distribution network is generally in a radiation operation state (open loop), namely N nodes are connected by N-1 branches, and a topological graph of the power grid cannot have a ring structure and cannot have isolated nodes.
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 and is a two-column matrix, elements in a first column are branch head nodes, and elements in a second column are branch tail nodes;
it should be noted that, in the topology diagram constructed by the closed loop of the power distribution network, N-1 branches are to be found to connect the circuits, which is a radial structure for the topology diagram, and it is required to ensure that each node is connected to the power supply and can ask for electric energy from the power supply.
Assuming that a distribution network has N nodes and b branches when closed, all cases with b-1 branches are common
However, these situations do not satisfy all the requirements, and there may be a case where there is an inner ring and it is not guaranteed that each node can be connected.
In order to find out the cases that meet the requirements, a judgment must be made about these cases. Firstly, a closed loop branch matrix is given, the specification of the closed loop branch matrix is b rows and 2 columns and 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 a closed loop branch matrix is generated, for each structure containing b branches obtained by permutation and combination, whether the structure is established or not can be judged by using whether paths from other nodes to power supply nodes exist or not, the rest N-1 load nodes are circulated in sequence, if the paths with the power supply can be directly or indirectly found in the closed loop branch matrix, the situation is established, and if not, the situation is not established.
S500, searching a path structure in the closed-loop branch matrix based on a growing tree algorithm to obtain a plurality of distribution network reconstruction schemes, wherein the path structure is judged according to whether a path exists from any load node to a power supply node, the distribution network reconstruction schemes comprise scheme numbers, a reconstruction topological graph and corresponding branch data, and the branch data comprise a square head node, a tail node, a branch resistor and a branch reactance.
In the IEEE33 node improved power distribution system example, the total number of branches is 37, and after calculation is performed by using a spanning tree algorithm, 53056 reconstruction schemes to be selected are obtained.
S600, optimizing the power distribution network reconstruction model based on the multi-objective genetic algorithm, and outputting a power distribution network dynamic reconstruction optimization structure, wherein the power distribution network dynamic reconstruction optimization structure comprises a network topology structure, current active network loss and voltage deviation.
Specifically, step S600 specifically includes:
s611, 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 a decimal number, and converting the decimal number into a 5-bit binary number;
it should be noted that, when the initial structure of the distribution network is given, all candidate schemes are obtained by searching all spanning trees, each candidate scheme stores a topological graph and branch data and a scheme number corresponding to the topological graph, and the branch data includes a first node, a last node, a branch resistance and a branch reactance.
When binary numbers are used for coding, the problem of unequal range is encountered, namely 30 schemes are used, however, a total of 5-bit binary numbers can represent 32 numbers, the coding of two binary numbers is meaningless, and the two binary numbers cannot correspond to the specific scheme.
For each alternative scheme, there are 30 alternative schemes, because 30 is 11110 after binary coding and is a 5-bit binary number, all numbers are coded by a 5-bit binary number, and when the number of bits is not enough, 0 is used for complementing, for example, the scheme corresponding to the number 6 is correspondingly coded as 00110.
S612, forming an individual by each power distribution network reconstruction scheme in the initial population, and calculating individual fitness according to an objective function of a power distribution network reconstruction model;
it should be noted that, because the individuals correspond to the solutions and the fitness of each individual corresponds to the calculation result of each solution, specifically, load flow calculation may be performed according to the topology data and the branch impedance data stored in each solution, so as to obtain the active network loss and the node voltage offset of the solution, however, since the minimum active network loss and the minimum node voltage offset are negatively correlated, that is, the node voltage offset is smaller when the active network loss is larger, and vice versa, in the calculation process, an optimal solution needs to be found to perform balancing. In this embodiment, the weight of the two is set to be equal, and the fitness function is the reciprocal of the product of the two, i.e. the fitness function
Where fit (i) is the fitness function solution, f1(i) As a function of the minimum loss of the active network, f2(i) Is a minimum function of node voltage offset.
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 inheritance to the next generation, and the calculation formula is,
in the formula, bfit (i) represents the individual fitness.
S613, copying, crossing and mutating the individuals in the initial population based on the multi-target genetic algorithm to obtain a new power distribution network, calculating the active network loss and node voltage deviation of the new power distribution network, and calculating the individual fitness according to the active network loss and the node voltage deviation and a fitness function;
it should be noted that, in the multi-objective genetic algorithm, genetic operations include replication, crossover and variation, the three are combined to form a genetic rule among generations, for each generation of population, the individual with the highest relative fitness is extracted, the optimal individual can be inherited to the next generation, and the process is replication; for the remaining individuals, randomly selecting two individuals to be crossed, namely randomly generating a random number from 0 to 1, and if the random number is smaller than the cross probability, partially exchanging chromosomes of any selected two individuals to obtain two new individuals, wherein the cross probability set in the embodiment is 0.8; after the crossover operation is performed, a random number from 0 to 1 is randomly generated, and if the value of the random number is smaller than the set mutation probability, all chromosomes of the current individual are negated after the random site, where the set mutation probability is 0.05.
In order to solve the problem that after a series of genetic operations, a comparator is needed, namely after the genetic operations, each binary number is decoded and changed back to a decimal number, and if the binary number is not in all scheme numbers, the binary number is randomly jumped back to the feasible domain, and the specific method is as follows: for the serial number of the jumping-out feasible domain, a random number in the feasible domain is randomly generated, and the jumping-back randomness can be realized.
And S614, judging whether a 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 node voltage offset.
It should be noted that, in the genetic algorithm, the optimal individual of each generation can be used for judgment, and if the individual with the highest fitness in the consecutive generations keeps unchanged for a plurality of 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 are solved by a genetic algorithm, and 2 reconstruction schemes to be selected are obtained 53056 in the IEEE33 node improved power distribution system calculation example in the embodiment15<53056<216Thus, each scheme is represented by a 16-bit binary number. And the dynamic reconfiguration of the network can be realized by calculating the selected time within 24 hours in one day. Taking the first time period as an example, the variation trend of the population fitness along with the number of iterations is as shown in fig. 4, and the population fitness is obtained according to the variation trend, and after a plurality of iterations, the optimal individual does not change any more. Thereby outputting the power distribution network reconstruction optimization result, as shown in table 2.
Table 224-hour power distribution network reconstruction optimization result
The embodiment can correctly simulate the change trend of the load of the electric vehicle charging station along with the time in one day, and is consistent 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, multi-objective reconstruction considering network loss and voltage deviation is adopted, and a guidance scheme can be provided for the optimal operation problem of the power distribution network accessed to a large number of electric vehicle charging stations.
The following is a detailed description of an embodiment of a system for implementing the electric vehicle charging station power distribution network reconstruction method based on the genetic algorithm provided in this embodiment.
For convenience of understanding, please refer to fig. 5, the system for reconstructing a power distribution network of an electric vehicle charging station based on a genetic algorithm according to the present embodiment includes:
the initialization module 100 is configured to collect network parameters of a power distribution network, and initialize the network parameters of the power distribution network;
the load curve generation module 200 is configured to perform analog sampling based on a monte carlo simulation method according to the number scale of the preset electric vehicles, in consideration of the types and corresponding ratios of the electric vehicles and corresponding charging behaviors, and generate a load curve of the electric vehicle charging station according to an analog sampling result;
the model building module 300 is used for building a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage deviation as optimization targets;
and the optimization module 400 is configured to optimize the power distribution network reconstruction model based on a multi-objective genetic algorithm and output a power distribution network dynamic reconstruction optimization structure, where the power distribution network dynamic reconstruction optimization structure includes a network topology structure, current active network loss and voltage offset.
Further, 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 proportion of the electric automobile according to the number scale of the preset electric automobiles;
the second determining submodule is used for determining the charging behaviors of different types of electric automobiles within 1 day, and 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 one type of electric automobile;
and the superposition module is used for superposing the load curve 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 also includes:
the system comprises a matrix module, a data processing module and a data processing module, wherein the matrix module is used for generating a closed-loop branch matrix based on a topological radiation structure constructed by a closed loop of a power distribution network, the closed-loop branch matrix is used for storing closed-loop branch data, and the closed-loop branch matrix is a two-column matrix, wherein an element in a first column is a branch head node, and an element in a second column is a branch tail node;
and the growth tree module is used for searching a path structure in the closed-loop branch matrix based on a growth tree algorithm to obtain a plurality of distribution network reconstruction schemes, wherein the path structure is judged according to whether a path exists from any load node to a power supply node, the distribution network reconstruction schemes comprise scheme numbers, a reconstruction topological graph and corresponding branch data, and the branch data comprise a square head node, a tail node, a branch resistance and a branch reactance.
Further, the optimization module specifically comprises a coding module, a fitness module, a genetic algorithm module and a convergence judgment module;
the encoding module is used for generating an initial population in a random generation mode according to the scale of the power distribution network reconstruction scheme, encoding the initial population by using a decimal number and converting the decimal number into a 5-bit binary number;
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 copying, crossing and mutating individuals in the initial population based on a multi-target genetic algorithm to obtain a new power distribution network, calculating the active network loss and node voltage deviation of the new power distribution network, and calculating individual fitness according to the active network loss and the node voltage deviation and a fitness function;
and the convergence judging module is used for judging whether a convergence condition is met according to the individual fitness, judging the corresponding individual to be an optimal solution if the convergence condition is met, and decoding the optimal solution to obtain a network topology structure corresponding to the optimal solution and the current active network loss and node voltage offset.
It should be noted that the working process of the electric vehicle charging station power distribution network reconfiguration system based on the genetic algorithm provided in this embodiment is consistent with the electric vehicle charging station power distribution network reconfiguration method based on the genetic algorithm provided in the foregoing embodiment, and is not described herein again.
The electric vehicle charging station power distribution network reconstruction system based on the genetic algorithm can obtain a one-day load curve of an electric vehicle 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 along with time in one day, and consider the uncertainty of the electric vehicle in time and space, so that the system is close to the actual activity habit of human beings and provides charging behaviors for charging the electric vehicle as charging references; meanwhile, a power distribution network reconstruction model is established by taking the minimum active network loss and the minimum node voltage deviation as optimization targets, the power distribution network reconstruction model is optimized by utilizing a multi-objective genetic algorithm, an optimized network topology structure, active network loss and voltage deviation are obtained, the network loss and the node voltage deviation of the power distribution network can be reduced, the power supply reliability is improved, dynamic reconstruction of the power grid is realized on the basis of 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 the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above embodiments are only used for illustrating the technical solutions 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
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 the power distribution network, and initializing the network parameters of the power distribution network;
s2, according to the number scale of the preset electric automobiles, considering the types of the electric automobiles, the corresponding proportion of the electric automobiles and the corresponding charging behaviors, carrying out analog sampling based on a Monte Carlo simulation method, and generating a load curve of the electric automobile charging station according to the analog sampling result;
s3, constructing a power distribution network reconstruction model by taking minimum active network loss and minimum node voltage deviation as optimization targets;
and S4, optimizing the power distribution network reconstruction model based on a multi-objective genetic algorithm, and outputting a power distribution network dynamic reconstruction optimization structure, wherein the power distribution network dynamic reconstruction optimization structure comprises a network topology structure, the current active network loss and the current voltage deviation.
2. The genetic algorithm-based electric vehicle charging station power distribution network reconstruction method according to claim 1, wherein the network parameters of the power distribution network comprise impedance data of each branch of the power distribution network and load data of each node, and the power distribution network adopts IEEE33 node to improve a power distribution system.
3. The genetic algorithm-based electric vehicle charging station power distribution network reconstruction method according to claim 1, wherein the step S2 specifically comprises:
s201, determining the type and the corresponding proportion of the electric automobiles according to the number scale of the preset electric automobiles;
s202, 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;
s203, performing analog sampling by using a Monte Carlo analog method according to the type and the charging behavior corresponding to the electric automobile, so as to obtain a load curve of one type of electric automobile;
and S204, repeating the step S203 to carry out iterative analog sampling until a load curve of each electric vehicle in the preset quantity scale of the electric vehicles is obtained, and overlapping the load curves of each electric vehicle in the preset quantity scale of the electric vehicles to obtain a load curve of the electric vehicle charging station.
4. The genetic algorithm-based electric vehicle charging station power distribution network reconstruction method according to claim 1, wherein the step S3 specifically comprises:
the method comprises the steps of constructing a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage deviation as optimization targets, and meanwhile determining constraint conditions, wherein the constraint conditions comprise power flow constraint, node voltage constraint, branch current constraint and power distribution network topology constraint.
5. The genetic algorithm-based electric vehicle charging station power distribution network reconstruction method according to claim 1, wherein 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, and 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;
s402, searching a path structure in the closed-loop branch matrix based on a growing tree algorithm to obtain a plurality of distribution network reconstruction schemes, wherein the path structure is judged according to whether a path exists from any load node to a power supply node, the distribution network reconstruction schemes comprise scheme numbers, a reconstruction topological graph and corresponding branch data, and the branch data comprise a square head node, a tail node, a branch resistance and a branch reactance.
6. The genetic algorithm-based reconstruction method for the power distribution network of the electric vehicle charging station according to claim 5, wherein the step S4 specifically comprises:
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 the individual fitness is calculated according to the objective function of the power distribution network reconstruction model;
s413, carrying out copy, cross and variation operations on individuals in the initial population based on a multi-target genetic algorithm to obtain a new power distribution network, calculating active network loss and node voltage deviation of the new power distribution network, and calculating individual fitness according to the active network loss and the node voltage deviation and a fitness function;
and S414, judging whether a 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, the current active network loss and the node voltage offset.
7. Electric automobile charging station distribution network reconfiguration system based on genetic algorithm, its characterized in that includes:
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 analog sampling based on a Monte Carlo simulation method according to the number scale of the preset electric automobiles and in consideration of the types and the corresponding proportions of the electric automobiles and the corresponding charging behaviors, and generating a load curve of the electric automobile charging station according to an analog sampling result;
the model building module is used for building a power distribution network reconstruction model by taking the minimum active network loss and the minimum node voltage deviation as optimization targets;
and the optimization module is used for optimizing the power distribution network reconstruction model based on a multi-objective genetic algorithm and outputting a power distribution network dynamic reconstruction optimization structure, and the power distribution network dynamic reconstruction optimization structure comprises a network topology structure, the current active network loss and the current voltage offset.
8. The genetic algorithm-based electric vehicle charging station power distribution network reconfiguration system according to claim 7, wherein said load curve generation module comprises a first determination submodule, a second determination submodule, a sampling submodule and an overlap module;
the first determining submodule is used for determining the type and the corresponding proportion of the electric automobiles according to the number scale of the preset electric automobiles;
the second determining submodule is used for determining charging behaviors of different types of electric automobiles within 1 day, and the charging behaviors comprise daily charging times, charging initial charge state, initial charging time and charging speed;
the sampling submodule is used for 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 one type of electric automobile;
and the superposition module is used for superposing the load curve of each electric automobile in the preset quantity scale of the electric automobiles to obtain the load curve of the electric automobile charging station.
9. The genetic algorithm-based electric vehicle charging station power distribution network reconfiguration system according to claim 7, further comprising:
the system comprises a matrix module, a data processing module and a data processing module, wherein the matrix module is used for generating a closed-loop branch matrix based on a topological radiation structure constructed by a closed loop of a power distribution network, the closed-loop branch matrix is used for storing closed-loop branch data, and 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;
and the growth tree module is used for searching a path structure in the closed-loop branch matrix based on a growth tree algorithm to obtain a plurality of distribution network reconstruction schemes, wherein the path structure judges whether a path exists from any load node to a power supply node, the distribution network reconstruction schemes comprise scheme numbers, a reconstruction topological graph and corresponding branch data, and the branch data comprise a square head node, a tail node, a branch resistance and a branch reactance.
10. The genetic algorithm-based electric vehicle charging station power distribution network reconstruction method according to claim 9, wherein the optimization module specifically comprises a coding module, a fitness module, a genetic algorithm module and a convergence determination module;
the encoding module is used for generating an initial population in a random generation mode according to the scale of the power distribution network reconstruction scheme, encoding the initial population by using a decimal number and converting the decimal number into a 5-digit binary number;
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 copying, crossing and mutating the individuals in the initial population based on a multi-target genetic algorithm to obtain a new power distribution network, calculating the active network loss and node voltage offset of the new power distribution network, and calculating the individual fitness according to the active network loss and the node voltage offset and a fitness function;
and the convergence judging module is used for judging whether a 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 decoding the optimal solution to obtain a network topology structure corresponding to the optimal solution and the current active network loss and node voltage offset.
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