CN112036654B - Photovoltaic power station and electric vehicle charging network planning method based on co-evolution - Google Patents

Abstract

The invention provides a photovoltaic power station and electric vehicle charging network planning method based on co-evolution, and S10, a planning boundary condition is given; s20, establishing a photovoltaic power station and electric vehicle charging network random collaborative planning model by using the planning boundary condition; and S30, designing a chromosome coding strategy and corresponding intersection and mutation operators respectively used for representing a photovoltaic power station and electric vehicle charging network construction scheme, solving a photovoltaic power station and electric vehicle charging network random collaborative planning model by adopting a collaborative evolution algorithm, and providing an optimal planning scheme of the photovoltaic power station and electric vehicle charging network. According to the photovoltaic power station and electric vehicle charging network planning method based on co-evolution, the construction positions and the construction capacities of the electric vehicle charging stations and the photovoltaic power stations are cooperatively optimized, the operation cost of a power distribution system is minimized on the premise that the operation working condition of the power distribution system meets the technical requirements, and a reference is provided for engineering technicians.

Description

Planning method of photovoltaic power station and electric vehicle charging network based on co-evolution

technical field

The invention relates to the technical field of electric vehicle charging networks, in particular to a photovoltaic power station and an electric vehicle charging network planning method based on co-evolution.

Background technique

Vigorously developing renewable energy power generation represented by photovoltaics and clean energy transportation represented by electric vehicles is one of the important ways to promote energy reform and achieve sustainable development. Under the existing technology, the power distribution system is an important place for distributed photovoltaic grid connection and electric vehicle charging. Obviously, the electric vehicle charging network and the photovoltaic power station will jointly affect the operating conditions of the power distribution system. For the power distribution system, unreasonable layout of photovoltaic power stations and electric vehicle charging stations will deteriorate the operating conditions and affect the normal power supply to users. The specific manifestations are the increase of power loss in the network, the excessive node voltage deviation and the line power flow exceeding the limit. In this context, it is necessary to coordinate the planning of the photovoltaic power station and the electric vehicle charging network in the power distribution system, so as to minimize the operating cost of the power distribution system on the premise of ensuring that the operating conditions of the power distribution system meet the technical requirements. Both the charging load and photovoltaic output of electric vehicle charging stations have random characteristics. Under the combined action of these two random factors, the operating conditions of the power distribution system exhibit significant random characteristics. Therefore, the collaborative planning model of photovoltaic power station and electric vehicle charging network It must be a stochastic optimization model. To sum up, it is urgent to propose a stochastic collaborative programming model and a corresponding solution method of photovoltaic power station and electric vehicle charging network considering the stochastic characteristics of power distribution system operating conditions.

Document 1 "Comprehensive optimization model for sizing and sitting of DGunits, EV charging stations, and energy storage systems" (IEEE Transactions on Smart Grid, 2018, Vol. 9, No. 4, pp. 3871-3882) established At the same time, the second-order cone optimization model of the construction address and capacity of the distributed photovoltaic power station, electric vehicle charging station and energy storage power station in the power distribution system is optimized, and the GAMS software is used to solve it. In the study, the time-varying characteristics of the output of distributed photovoltaic power plants and the charging load of electric vehicles were considered, but the random characteristics of the two were not taken into account, which had certain limitations. Under the premise that distributed power sources including photovoltaic power plants can supply power to distribution network loads and charging stations at the same time, Literature 2 "Research on Multi-objective Planning of Distribution Networks with Distributed Power Sources and Electric Vehicle Charging Stations" (Grid Technology , 2015, Vol. 39, No. 2, pp. 450 to 456) established a multi-objective optimization model for simultaneously optimizing the construction address and capacity of distributed power and electric vehicle charging stations, and adopted a multi-objective free search algorithm The Pareto solution set of the model is given. However, this paper does not consider the random characteristics of distributed power output and charging load of electric vehicle charging stations, and the planning results given have certain limitations. On the basis of considering the random characteristics of the output of photovoltaic power plants and the charging load of charging stations, Literature 3 "Opportunity Constrained Planning of Electric Vehicle Charging Stations with Photovoltaic Distributed Power Distribution Networks" (Journal of Electric Power Systems and Automation, 2017, Vol. 29) , Issue 6, pp. 45 to 52) established a stochastic programming model for the optimization of the construction address of distributed photovoltaic power stations and electric vehicle charging stations, and used the bat algorithm to solve the model. This document does not sufficiently consider the random characteristics of photovoltaic power station output and charging station charging load, and mainly focuses on optimizing the construction addresses of electric vehicle charging stations and photovoltaic power stations, which has certain limitations.

The electric vehicle charging network and the photovoltaic power station will jointly affect the operating conditions of the power distribution system. The unreasonable layout of the photovoltaic power station and the electric vehicle network will worsen the operating conditions of the power distribution system and affect the normal power supply to users. increase, the node voltage deviation exceeds the standard and the line power flow exceeds the limit. Therefore, it is necessary to coordinate the planning of the photovoltaic power station and the electric vehicle charging network in the power distribution system, so as to minimize the operating cost of the power distribution system under the premise of ensuring that the operating conditions of the power distribution system meet the technical requirements. However, the prior art method does not fully consider the random characteristics of the output and charging load of the distributed photovoltaic power station, and has certain limitations.

SUMMARY OF THE INVENTION

In order to solve the above problems, the present invention provides a photovoltaic power station and electric vehicle charging network planning method based on co-evolution, which can coordinately optimize the construction location and construction capacity of the electric vehicle charging station and the photovoltaic power station, and ensure that the operating conditions of the power distribution system meet the technical requirements. Under the premise of requirements, minimize the operating cost of the power distribution system and provide reference for engineering and technical personnel.

In order to realize the above purpose, the technical scheme that the present invention takes is:

The method for planning a photovoltaic power station and an electric vehicle charging network based on co-evolution includes the following steps: S10, a planning boundary condition is given, and the planning boundary conditions include: power distribution system topology parameters and planning load on a typical day, and planning a charging load on a typical day and PV output probability scenario set, total number of candidate addresses of charging stations, total number of charging station construction and total construction capacity, type of charging station construction and corresponding construction capacity, total number of candidate addresses of photovoltaic power stations, total number of photovoltaic power station constructions and total construction capacity, photovoltaic power station construction Type and corresponding construction capacity, the maximum allowable deviation percentage of node voltage, and the confidence level of node voltage over-limit and power flow over-limit; S20 uses the planning boundary conditions to establish a random collaborative planning model for photovoltaic power plants and electric vehicle charging networks; and S30 design respectively Chromosome coding strategy and corresponding crossover and mutation operators are used to represent the construction scheme of photovoltaic power station and electric vehicle charging network. Co-evolutionary algorithm is used to solve the stochastic collaborative planning model of photovoltaic power station and electric vehicle charging network, and the photovoltaic power station and electric vehicle are given. Optimal planning scheme of charging network.

Further, the step S20 includes: the optimization goal of the stochastic collaborative planning model of the photovoltaic power station and the electric vehicle charging network is to reduce the grid loss power in a typical day of the power distribution system planning, as shown in formula (1),

Among them, F _loss is the expected power loss of the power distribution system in a typical day; t is the power flow analysis period index, T _f is the number of power flow analysis periods in a typical day; l is the distribution line index; Ω _br is the distribution line index set ; ΔP _{loss, l, t} is the power loss of the distribution line l in the power flow analysis period t, which is a random variable; E( ) is an operator that seeks the expectation of the random variable; the S20 also includes a determination constraint and a chance constraint, The determination constraints include the opportunity constraints representing the total number of construction of charging stations, the opportunity constraints representing the total number of photovoltaic power station constructions, the opportunity constraints representing the total construction capacity of charging stations, and the opportunity constraints representing the total construction capacity of photovoltaic power stations, which are obtained through formulas (2) to (5). Opportunity constraints, the opportunity constraints include the node voltage offset opportunity constraints and the line power flow over-limit opportunity constraints obtained through formulas (6) to (7), respectively,

Among them, M _ch is the total number of charging stations built; N _ch is the total number of candidate addresses of charging stations; i is the index of candidate addresses; _xi is a 0-1 variable indicating whether to build a charging station at candidate address i to charge photovoltaic power stations and electric vehicles The 0-1 optimization variable in the network random collaborative planning model, taking "1" means building a charging station at candidate address i, taking "0" means not building a charging station at candidate address i, i=1,2,3,... , _Nch ;

Among them, M _pv is the total number of photovoltaic power stations constructed; N _pv is the total number of candidate addresses of photovoltaic power stations; j is the index of candidate addresses; y _j is a 0-1 variable that indicates whether to build a charging station at candidate address j, for charging photovoltaic power stations and electric vehicles The 0-1 optimization variable in the network stochastic collaborative planning model, taking "1" means building a photovoltaic power station at the candidate address j, taking "0" means not building a photovoltaic power station at the candidate address j, j=1,2,3,... ,N _pv ;

Among them, _zi is the charging station construction capacity of candidate address i, and the electric vehicle charging stations to be built are divided into Q _ev categories; C _ch is the total construction capacity of charging stations;

Among them, W _j is the construction capacity of the photovoltaic power station at the candidate address j. For the photovoltaic power station, the photovoltaic power station to be built is divided into Q _pv categories; C _pv is the total construction capacity of the photovoltaic power station;

Among them, P _r {·} represents the probability of random events in parentheses; k is the distribution node index; Ω _bus is the distribution node index set; U _k is the voltage of node k, which is a random variable, and the probability distribution characteristics are determined by the probability power flow The analysis results are given; U _N is the rated voltage of the power distribution system; α% is the maximum allowable deviation percentage of the node voltage; β ₁ is the confidence level of the voltage exceeding the limit;

P _r {I _l >I _{l, max} }≤β ₂ l∈Ω _br (7)

Among them, I _l is the load current in the distribution line l, which is a random variable, and the probability distribution characteristics are given by the probabilistic power flow analysis results; I _l,max is the maximum allowable current of the distribution line l; β ₂ is the power flow over-limit confidence Spend.

Further, the step S30 includes the following steps: S301 sets genetic algorithm parameters, the genetic algorithm parameters include a population size N _pop1 for optimizing the photovoltaic power station construction scheme, and a population size N for optimizing the electric vehicle charging station construction scheme. _pop2 , crossover rate P _c , mutation rate P _m and the maximum evolutionary generation G _max of co-evolution; S302 initializes the chromosomes in the population Ψ _ev , and uses an integer coding scheme to encode the chromosomes in the population Ψ _ev , where Ψ _ev is the It is the population optimized for the charging network construction plan; S303 initializes the chromosomes in the population Ψ _pv , and uses the integer coding scheme to encode the chromosomes in the population Ψ _pv , where Ψ _pv is used for the optimized population of the photovoltaic power station construction plan; S304 From the population Ψ One chromosome is randomly selected from each of _ev and Ψ _pv to construct the initial ecosystem; _S305 , the evolutionary algebra index g is initialized to 0, that is, g=0; The chromosome index m and the chromosome index n in the population Ψ _pv are both initialized to 1, that is, m=1, n=1; S307 decodes the mth chromosome in the population Ψ _ev , and determines the construction of M _ch electric vehicle charging stations Location, construction capacity and total construction capacity C _t-ev , decode the chromosome representing the photovoltaic power plant construction plan in the ecosystem, and determine the construction positions, construction capacity and total construction capacity C _t-pv of M _ev photovoltaic power plants; use scenarios The probability method is used to calculate the probability power flow of the power distribution system, to determine the expected power loss F _loss in a typical planned day, the probability distribution characteristics of the voltage amplitude of each node and the power flow of each line, and calculate the population Ψ _ev according to formulas (8) to (12). The fitness of the mth chromosome;

V _fit-ev,m =F _max -F _loss -η ₁ ×V _p1 -η ₂ ×V _p2 -η ₃ ×V _p3 -η ₄ ×V _p4 (8)

V _p1 =|C _ch -C _t-ev | (9)

V _p2 =|C _pv -C _t-pv | (10)

表示取

中较大的数，采用罚函数法分别处理公式(4)～(7)给出的约束，η₁、η₂、η₃以及η₄为罚系数；V_p1、V_p2、V_p3以及V_p4分别表示公式(4)～(7)给出约束的违背程度；S308判断是否计算完种群Ψ_ev中所有染色体的适应度，即判断染色体索引m是否等于种群规模N_pop2，若m<N_pop2，则令m＝m+1，并跳转至步骤S307，继续计算种群Ψ_ev中下一条染色体的适应度；否则，继续执行步骤S309；S309从种群Ψ_ev中挑选最优秀的染色体，替换生态系统中表示充电网络建设方案的染色体，更新生态系统；S310对种群Ψ_pv中的第n条染色体进行解码，确定M_ev个光伏电站的建设位置，建设容量与总建设容量C_t-pv，对生态系统中表示充电网络建设方案的染色体进行解码，确定M_ch个电动汽车充电站的建设位置，建设容量与总建设容量C_t-ev；在此基础上，采用场景概率法进行配电系统概率潮流计算，确定规划典型日内的网损电量期望F_loss，各节点电压幅值与各线路潮流的概率分布特性，并按公式(13)计算种群Ψ_pv中的第n条染色体的适应度V_fit-pv,n，Among them, _Fmax is a preset positive number to ensure that the chromosome fitness is non-negative, and the operator

means to take

For the larger number in , the penalty function method is used to deal with the constraints given by equations (4) to (7), respectively, η ₁ , η ₂ , η ₃ and η ₄ are penalty coefficients; V _p1 , V _p2 , V _p3 and V _p4 represents the degree of violation of the constraints given by formulas (4) to (7) respectively; S308 judges whether the fitness of all chromosomes in the population Ψ _ev has been calculated, that is, judges whether the chromosome index m is equal to the population size N _pop2 , if m<N _pop2 , then let m=m+1, and jump to step S307, and continue to calculate the fitness of the next chromosome in the population Ψ _ev ; otherwise, continue to perform step S309; S309 selects the best chromosome from the population Ψ _ev and replaces the ecological The chromosome representing the charging network construction plan in the system, and the ecosystem is updated; S310 decodes the nth chromosome in the population Ψ _pv to determine the construction positions of M _ev photovoltaic power stations, the construction capacity and the total construction capacity C _t-pv The chromosomes representing the charging network construction plan in the ecosystem are decoded to determine the construction locations of M _ch electric vehicle charging stations, the construction capacity and the total construction capacity C _t-ev ; Calculate the power flow, determine the expected power loss F _loss in a typical planned day, the probability distribution characteristics of the voltage amplitude of each node and the power flow of each line, and calculate the fitness V _fit of the nth chromosome in the population Ψ _pv according to formula (13). _-pv,n ,

V _fit-pv,n =F _max -F _loss -η ₁ ×V _p1 -η ₂ ×V _p2 -η ₃ ×V _p3 -η ₄ ×V _p4 (13)

S311 judges whether the fitness of all chromosomes in the population Ψ _pv has been calculated, that is, judges whether the chromosome index n is equal to the population size N _pop1 , if n<N _pop1 , then set n=n+1, and jump to step S310 to continue the calculation The fitness of the next chromosome in the population Ψ _pv ; otherwise, continue to step S312; S312 selects the best chromosome from the population Ψ _pv based on the fitness, replaces the chromosome representing the photovoltaic power station construction plan in the ecosystem, and updates the ecosystem system; S313 judges whether the maximum evolutionary algebra is reached, if g=G _max , then continue to perform step S314; otherwise, based on the fitness, the populations Ψ _ev and Ψ _pv are copied, crossed and mutated respectively, and the two are updated. population, and jump to step S306; S314 decodes the two chromosomes in the ecosystem representing the electric vehicle charging network construction scheme and the photovoltaic power station construction scheme respectively, as the optimal solution of the photovoltaic power station and the electric vehicle charging network stochastic collaborative planning model Output, end the algorithm flow.

Further, for the population Ψ _ev , in order to ensure that the crossed chromosomes satisfy the equality constraints given by formula (2), the crossover operation is performed as follows: S41 randomly selects two chromosomes from the population Ψ _ev as the chromosomes to be crossed. ; S42 repeatedly randomly generates the candidate crossover position N _can1 , wherein 1<N _can1 <N _ch , until a feasible crossover position N _cr1 is found; and S43 exchanges the N _cr1 th code position in the two chromosomes to be crossed with the crossover probability P _c After the code string, the crossover operation is completed.

Further, for the population Ψ _pv , in order to ensure that the crossed chromosomes satisfy the equality constraints given by formula (3), the crossover operation is performed as follows: S51 randomly selects two chromosomes from the population Ψ _pv as the chromosomes to be crossed. ; S52 repeatedly randomly generates the candidate crossover position N _can2 , wherein 1<N _can2 <N _pv , until a feasible crossover position N _cr2 is found; and S53 exchanges the N _cr2 th code position in the two chromosomes to be crossed with the crossover probability P _c After the code string, the crossover operation is completed.

Further, the population Ψ _ev mutation operation operator includes the following steps: S61 randomly selects a chromosome from the population Ψ _ev as the chromosome to be mutated; S62 randomly generates two code positions to be mutated N _mu1 and N _mu2 , where 1<N _mu1 <N _ch , 1<N _mu2 <N _ch , to ensure that one of the values of the two code bits to be mutated is “0” and the other is an integer other than “0”; and S63 treats the mutated bits N _mu1 and N mu1 simultaneously with the mutation probability P _m N _mu2 performs the mutation operation, that is, the bit to be mutated whose value is "0" is mutated into a non-"0" random integer not greater than Q _ev , and the bit to be mutated whose value is not "0" is mutated to "0", and the mutation operation is completed. .

Further, the population Ψ _pv mutation operator includes the following steps: S71 randomly selects a chromosome from the population Ψ _pv as the chromosome to be mutated; S72 randomly generates two code positions to be mutated N _mu3 and N _mu4 , where 1<N _mu3 < N _pv , 1<N _mu4 <N _pv , to ensure that the values of the two code bits to be mutated are one “0” and the other is an integer other than “0”; and S73 treats the mutated bits N _mu3 and N simultaneously with the mutation probability P _{m Mu4} performs the mutation operation, that is, the mutated bit with a value of "0" is mutated into a non-"0" random integer not greater than Q _pv , and the to-be-mutated bit with a value other than "0" is mutated to "0" to complete the mutation operation.

The above-mentioned technical scheme of the present invention has the following advantages compared with the prior art:

(1) In the co-evolution-based photovoltaic power station and electric vehicle charging network planning method of the present invention, given the number of photovoltaic power stations/charging stations and the total construction capacity, the construction positions of the electric vehicle charging station and the photovoltaic power station are determined. Coordinate optimization with the construction capacity, minimize the operating cost of the power distribution system on the premise of ensuring that the operating conditions of the power distribution system meet the technical requirements, and provide a reference for engineers and technicians.

(2) In the co-evolution-based photovoltaic power station and electric vehicle charging network planning method of the present invention, both the charging load and photovoltaic power generation output have random characteristics, and the photovoltaic power station and electric vehicle charging network collaborative planning planning problem is modeled as a chance constraint-based Stochastic optimization model, the model optimization variables are: photovoltaic power station access location and access capacity, electric vehicle charging station access location and access capacity; the optimization goal is to minimize the operating cost of the power distribution system, which is embodied in the typical day of the power distribution system planning. The expected minimum power loss of the network; the optimization constraints are: the number and capacity constraints of charging stations, the constraints on the number and capacity of photovoltaic power stations, node voltage offset opportunity constraints and line power flow over-limit opportunities constraints.

(3) The co-evolution-based photovoltaic power station and electric vehicle charging network planning method of the present invention adopts two populations to represent the construction scheme of photovoltaic power station and electric vehicle charging network respectively. The operation operator iteratively updates the two populations and the ecosystem composed of the optimal chromosomes of the population until the final optimal construction scheme of photovoltaic power station and electric vehicle charging network is given. The coding scheme of the optimal construction scheme of electric vehicle charging network and the corresponding crossover and mutation operators.

Description of drawings

The technical solutions of the present invention and its beneficial effects will be apparent through the detailed description of the specific embodiments of the present invention below in conjunction with the accompanying drawings.

1 is a flowchart of a method for planning a photovoltaic power station and an electric vehicle charging network based on co-evolution according to an embodiment of the present invention;

FIG. 2 is a flowchart of the step S30 according to an embodiment of the present invention;

FIG. 3 shows a flow chart of the population Ψ _ev crossover operation for optimizing the charging network construction scheme according to an embodiment of the present invention;

FIG. 4 shows a flow chart of population Ψ _pv crossover operation for optimization of photovoltaic power station construction scheme according to an embodiment of the present invention;

FIG. 5 is a flow chart of population Ψ _ev mutation operation for optimization of charging network construction scheme according to an embodiment of the present invention;

FIG. 6 is a flow chart of population Ψ _pv mutation operation for optimization of a photovoltaic power plant construction scheme according to an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

This embodiment provides a method for planning a photovoltaic power station and an electric vehicle charging network based on co-evolution, as shown in FIG. 1 , including the following steps: S10, a planning boundary condition is given. S20 uses the planning boundary conditions to establish a random collaborative planning model of the photovoltaic power station and the electric vehicle charging network. And S30 is designed to represent the chromosome coding strategy and the corresponding crossover and mutation operators of the photovoltaic power station and electric vehicle charging network construction scheme respectively. The co-evolutionary algorithm is used to solve the random collaborative planning model of the photovoltaic power station and the electric vehicle charging network, and the photovoltaic power station and the electric vehicle charging network are given. Optimal planning scheme for power station and electric vehicle charging network.

The planning boundary conditions include: distribution system topology parameters and planned loads in a typical day, a set of charging loads and photovoltaic output probability scenarios in a typical planned day, the total number of candidate addresses for charging stations, the total number of charging station constructions and the total construction capacity, and the construction of charging stations. Type and corresponding construction capacity, total number of candidate locations for photovoltaic power plants, total number and total construction capacity of photovoltaic power plants, construction type and corresponding construction capacity of photovoltaic power plants, maximum allowable deviation percentage of node voltage, and confidence level of node voltage over-limit and power flow over-limit .

The step S20 is to deal with the stochastic characteristics of the operating conditions of the power distribution system, and the node voltage offset constraints and the line power flow out-of-limit constraints in the photovoltaic power station and the electric vehicle charging network stochastic collaborative programming model are modeled as chance constraints,

The step S20 includes: the optimization goal of the stochastic collaborative planning model of the photovoltaic power station and the electric vehicle charging network is to reduce the grid loss power in a typical day of the power distribution system planning, as shown in formula (1),

Among them, F _loss is the expected power loss of the power distribution system in a typical day; t is the power flow analysis period index, T _f is the number of power flow analysis periods in a typical day; l is the distribution line index; Ω _br is the distribution line index set ;ΔP _loss,l,t is the power loss of distribution line l in the power flow analysis period t, which is a random variable, which can be obtained by probabilistic power flow calculation; E(·) is the operator for the expectation of random variables. The cost of network loss is one of the important components of the operating cost of the power distribution system. The charging load and photovoltaic output have random characteristics. Therefore, the power loss of the power distribution system in a typical day is also a random variable. The optimization objective of the stochastic collaborative planning model for the vehicle charging network is to minimize the expected grid loss in a typical day of the distribution system planning.

The S20 further includes a determination constraint and an opportunity constraint, and the determination constraint includes the opportunity constraint representing the total number of construction of the charging station, the opportunity constraint representing the total number of photovoltaic power station construction, and the total construction capacity of the charging station obtained through formulas (2) to (5) respectively. and the opportunity constraint representing the total construction capacity of the photovoltaic power station, and the opportunity constraint includes the opportunity constraint representing the node voltage offset and the opportunity constraint representing the line power flow exceeding the limit obtained by formulas (6) to (7), respectively,

Among them, M _ch is the total number of charging stations built; N _ch is the total number of candidate addresses of charging stations; i is the index of candidate addresses; _xi is a 0-1 variable indicating whether to build a charging station at candidate address i to charge photovoltaic power stations and electric vehicles The 0-1 optimization variable in the network random collaborative planning model, taking "1" means building a charging station at candidate address i, taking "0" means not building a charging station at candidate address i, i=1,2,3,... , _Nch ;

Among them, M _pv is the total number of photovoltaic power stations constructed; N _pv is the total number of candidate addresses of photovoltaic power stations; j is the index of candidate addresses; y _j is a 0-1 variable that indicates whether to build a charging station at candidate address j, for charging photovoltaic power stations and electric vehicles The 0-1 optimization variable in the network stochastic collaborative planning model, taking "1" means building a photovoltaic power station at the candidate address j, taking "0" means not building a photovoltaic power station at the candidate address j, j=1,2,3,... ,N _pv ;

Among them, _zi is the construction capacity of the charging station at the candidate address i, and the electric vehicle charging stations to be built are divided into Q _ev categories. In the random collaborative planning model of photovoltaic power station and electric vehicle charging network, the charging stations to be built are divided into For the Q _ev class, that is to say, _zi has different values of Q _ev , _zi is the discrete optimization variable in the joint stochastic programming model of the photovoltaic power station and the electric vehicle charging network; C _ch is the total construction capacity of the charging station, which is determined by the The number of electric vehicles in the planning area, the cost of charging station construction and the total investment to be invested in charging station construction are jointly determined;

Among them, W _j is the construction capacity of the photovoltaic power station at the candidate address j. In the stochastic collaborative planning model between the photovoltaic power station and the electric vehicle charging network, the photovoltaic power stations to be built are divided into Q _pv categories according to different capacities, that is to say, w _j has Q _pv At this time, W _j is the discrete optimization variable in the joint stochastic programming model of the photovoltaic power station and the electric vehicle charging network; C _pv is the total construction capacity of the photovoltaic power station, which is determined by the construction cost of the photovoltaic power station and the total investment in the construction of the photovoltaic power station, etc. factors are determined together.

Among them, P _r {·} represents the probability of random events in parentheses; k is the distribution node index; Ω _bus is the distribution node index set; U _k is the voltage of node k, which is a random variable, and the probability distribution characteristics are determined by the probability power flow The analysis results are given; U _N is the rated voltage of the power distribution system; α% is the maximum allowable deviation percentage of the node voltage; β ₁ is the confidence level of the voltage exceeding the limit;

P _r {I _l >I _{l, max} }≤β ₂ l∈Ω _br (7)

Among them, I _l is the load current in the distribution line l, which is a random variable, and the probability distribution characteristics are given by the probabilistic power flow analysis results; I _l,max is the maximum allowable current of the distribution line l; β ₂ is the power flow over-limit confidence Spend.

The constraints in the stochastic collaborative programming model of the photovoltaic power station and the electric vehicle charging network are shown in formulas (2) to (7), where formulas (2) to (5) are deterministic constraints. Considering the random characteristics of the operating conditions of the power distribution system, the node voltage offset constraints and line power flow out-of-limit constraints given by equations (6) to (7) are chance constraints.

As shown in FIG. 2 , the step S30 includes the following steps: S301 sets genetic algorithm parameters, and the genetic algorithm parameters include the population size N pop1 for optimizing the photovoltaic power station construction plan, and the population size N _pop1 for optimizing the electric vehicle charging station construction plan. Population size N _pop2 , crossover rate P _c , mutation rate P _m and maximum evolutionary generation G _max of co-evolution.

S302 initializes the chromosomes in the population Ψ _ev , and uses an integer encoding scheme to encode the chromosomes in the population Ψ _ev , where Ψ _ev is the population for optimization of the charging network construction scheme. According to the characteristics of the stochastic cooperative planning model of photovoltaic power station and electric vehicle charging network, an integer coding scheme is used to encode the chromosomes in the population Ψ _ev . Each chromosome is composed of N _ch code points, the ith code point represents the construction of the charging station of the ith candidate address (i=1, 2, 3, ···, N _ch ), and the value is "0" , indicating that no charging station will be built at candidate address i, and the value is "q", indicating that the q-type charging station (q=1,2,3,...,Q _ev ) will be built at candidate address i, and the corresponding construction capacity is _Cev,q . In order to satisfy the equality constraint given by formula (2), in the chromosome, there are and only M _ch code points whose value is an integer other than "0". Therefore, the chromosomes in the population Ψ _ev are initialized as follows: first, assign all code points of the chromosome to "0"; then, randomly select M _ch code points, and change the assignment from "0" to a random value not greater than Q _ev Integer.

S303 initializes the chromosomes in the population Ψ _pv , and uses an integer encoding scheme to encode the chromosomes in the population Ψ _pv , where Ψ _pv is used for the population optimized for the photovoltaic power station construction plan. According to the characteristics of the stochastic collaborative planning model of photovoltaic power station and electric vehicle charging network, an integer coding scheme is used to encode the chromosomes in the population Ψ _pv . Each chromosome is composed of N _pv code points, and the jth code point represents the photovoltaic power station construction status of the jth candidate address (j=1, 2, 3, ···, N _pv ), and takes the value "0" , indicating that the photovoltaic power station is not to be built at the candidate address j, the value is “m”, indicating that the m-th type photovoltaic power station (m=1, 2, 3, . . . , Q _pv ) is to be built at the candidate address j, and the corresponding construction capacity is C _pv,m . In order to satisfy the equality constraint given by the formula (3), in the chromosome, there are and only M _pv code points whose value is an integer other than "0". Therefore, the chromosomes in the population Ψ _pv are initialized as follows: first, all code points of the chromosome are assigned as "0"; then, M _pv code points are randomly selected, and the assignment is changed from "0" to a random value not greater than Q _pv Integer.

S304 randomly selects one chromosome from each of the populations Ψ _ev and Ψ _pv to construct an initial ecosystem.

S305, the evolution algebra index g is initialized to 0, that is, g=0.

S306 sets g=g+1, and starts the g-th generation evolution. Both the chromosome index m in the population Ψ _ev and the chromosome index n in the population Ψ _pv are initialized to 1, that is, m=1, n=1.

S307 decodes the mth chromosome in the population Ψ _ev , determines the construction positions of M _ch electric vehicle charging stations, the construction capacity and the total construction capacity C _t-ev , and decodes the chromosome representing the photovoltaic power station construction plan in the ecosystem , determine the construction locations of M _ev photovoltaic power stations, the construction capacity and the total construction capacity C _t-pv ; use the scenario probability method to calculate the probability power flow of the power distribution system, determine the expected power loss F _loss in a typical planned day, and the voltage amplitude of each node value and the probability distribution characteristics of the power flow of each line, calculate the fitness V _fit-ev,m of the mth chromosome in the population Ψ _ev according to formulas (8) to (12);

V _fit-ev,m =F _max -F _loss -η ₁ ×V _p1 -η ₂ ×V _p2 -η ₃ ×V _p3 -η ₄ ×V _p4 (8)

V _p1 =|C _ch -C _t-ev | (9)

V _p2 =|C _pv -C _t-pv | (10)

表示取

中较大的数，采用罚函数法分别处理公式(4)～(7)给出的约束，η₁、η₂、η₃以及η₄为罚系数；V_p1、V_p2、V_p3以及V_p4分别表示公式(4)～(7)给出约束的违背程度，可分别通过公式(9)～(12)计算获得。Among them, _Fmax is a relatively large positive number given in advance to ensure that the chromosome fitness is non-negative, and the operator

means to take

For the larger number in , the penalty function method is used to deal with the constraints given by equations (4) to (7), respectively, η ₁ , η ₂ , η ₃ and η ₄ are penalty coefficients; V _p1 , V _p2 , V _p3 and V _p4 respectively represents the degree of violation of the constraints given by formulas (4) to (7), which can be calculated and obtained by formulas (9) to (12).

S308 judges whether the fitness of all chromosomes in the population Ψ _ev has been calculated, that is, judges whether the chromosome index m is equal to the population size N _pop2 , if m<N _pop2 , then set m=m+1, and jump to step S307 to continue the calculation The fitness of the next chromosome in the population Ψ _ev ; otherwise, continue to step S309;

S309 selects the best chromosome from the population Ψ _ev , replaces the chromosome representing the charging network construction plan in the ecosystem, and updates the ecosystem;

S310 decodes the nth chromosome in the population Ψ _pv , determines the construction positions of M _ev photovoltaic power plants, the construction capacity and the total construction capacity C _t-pv , decodes the chromosome representing the charging network construction plan in the ecosystem, and determines The construction locations of M _ch electric vehicle charging stations, the construction capacity and the total construction capacity C _t-ev ; on this basis, the probability flow calculation of the power distribution system is carried out by using the scenario probability method, and the expected power loss F _loss in a typical planned day is determined , the probability distribution characteristics of the voltage amplitude of each node and the power flow of each line, and the fitness V _fit-pv,n of the nth chromosome in the population Ψ _pv is calculated according to formula (13).

V _fit-pv,n =F _max -F _loss -η ₁ ×V _p1 -η ₂ ×V _p2 -η ₃ ×V _p3 -η ₄ ×V _p4 (13)

S311 judges whether the fitness of all chromosomes in the population Ψ _pv has been calculated, that is, judges whether the chromosome index n is equal to the population size N _pop1 , if n<N _pop1 , then set n=n+1, and jump to step S310 to continue the calculation The fitness of the next chromosome in the population Ψ _pv ; otherwise, continue to step S312.

Based on fitness, S312 selects the best chromosome from the population Ψ _pv , replaces the chromosome representing the photovoltaic power station construction plan in the ecosystem, and updates the ecosystem;

S313 judges whether the maximum evolutionary algebra is reached, that is, judges whether the evolutionary algebra index g is equal to the maximum evolutionary algebra G _max . If g=G _max , if g=G _max , continue to execute step S314; otherwise, based on the fitness, perform replication, crossover and mutation operations on the populations Ψ _ev and Ψ _pv respectively, update the two populations, and jump Go to step S306;

In order to improve the solution efficiency, according to the characteristics of the stochastic collaborative programming model of photovoltaic power station and electric vehicle charging network, the crossover operator and mutation operator for population Ψ _ev and population Ψ _pv are designed respectively,

For the population Ψ _ev , in order to ensure that the crossed chromosomes satisfy the equality constraints given by formula (2), as shown in Figure 3, the crossover operation is performed as follows:

S41 randomly selects two chromosomes from the population Ψ _ev as the chromosomes to be crossed. S42 repeatedly randomly generates candidate cross bits N _can1 , where 1<N _can1 <N _ch , until a feasible cross bit N _cr1 is found. And S43 exchanges the code string after the N _cr1 th code point in the two chromosomes to be crossed with the crossover probability P _c to complete the crossover operation.

For the population Ψ _pv , in order to ensure that the crossed chromosomes satisfy the equality constraints given by formula (3), as shown in Figure 4, the crossover operation is performed as follows:

S51 randomly selects two chromosomes from the population Ψ _pv as the chromosomes to be crossed. S52 repeatedly randomly generates candidate cross bits N _can2 , where 1<N _can2 <N _pv , until a feasible cross bit N _cr2 is found. And S53 exchanges the code string after the N _cr2 code point in the two chromosomes to be crossed with the crossover probability P _c to complete the crossover operation.

As shown in Figure 5, the population Ψ _ev mutation operator includes the following steps:

S61 randomly selects a chromosome from the population Ψ _ev as the chromosome to be mutated. S62 randomly generates two to-be-mutated code bits N _mu1 and N _mu2 , where 1<N _mu1 <N _ch , 1<N _mu2 <N _ch , to ensure that one of the two to-be-mutated code bits is "0" and the other is an integer other than "0". And S63 performs mutation operation on the mutation bits N _mu1 and N _mu2 at the same time with the mutation probability P _m , that is, the mutation bit whose value is "0" is mutated into a non-"0" random integer not greater than Q _ev , and the value is not "0" ” is mutated to “0” to complete the mutation operation.

As shown in Figure 6, the population Ψ _pv mutation operator includes the following steps:

S71 randomly selects a chromosome from the population Ψ _pv as the chromosome to be mutated. S72 randomly generates two to-be-mutated code bits N _mu3 and N _mu4 , where 1<N _mu3 <N _pv , 1<N _mu4 <N _pv , to ensure that one of the two to-be-mutated code bits is "0" and the other is Integer other than "0". And S73 performs mutation operation on the mutation bits N _mu3 and N _mu4 at the same time with the mutation probability P _m , that is, the mutation bit whose value is "0" is mutated into a non-"0" random integer not greater than Q _pv , and the value is not "0" ” is mutated to “0” to complete the mutation operation. S314 decodes the two chromosomes in the ecosystem representing the electric vehicle charging network construction scheme and the photovoltaic power station construction scheme respectively, and outputs the optimal solution of the photovoltaic power station and the electric vehicle charging network stochastic collaborative planning model, and ends the algorithm process.

The above descriptions are only exemplary embodiments of the present invention, and are not intended to limit the scope of the patent protection of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied to other related The technical field of the present invention is similarly included in the scope of patent protection of the present invention.

Claims (5)

1. A method for planning a photovoltaic power station and an electric vehicle charging network based on co-evolution, characterized in that it comprises the following steps: S10 is given planning boundary conditions, the planning boundary conditions include: power distribution system topology parameters and loads in a typical planned day, a set of charging loads and photovoltaic output probability scenarios in a typical planned day, the total number of candidate addresses for charging stations, the total number of charging station construction and Total construction capacity, construction type of charging station and corresponding construction capacity, total number of candidate addresses for photovoltaic power stations, total number of photovoltaic power station construction and total construction capacity, construction type of photovoltaic power station and corresponding construction capacity, maximum allowable deviation percentage of node voltage, and the higher the node voltage Confidence of limit and flow crossing limit; S20 uses the planning boundary conditions to establish a stochastic collaborative planning model for the photovoltaic power station and the electric vehicle charging network; and The step S20 includes: The optimization goal of the stochastic collaborative planning model of photovoltaic power station and electric vehicle charging network is to reduce the grid loss in a typical day of the distribution system planning, as shown in formula (1),

Among them, F _loss is the expected power loss of the power distribution system in a typical day; t is the power flow analysis period index, T _f is the number of power flow analysis periods in a typical day; l is the distribution line index; Ω _br is the distribution line index set ;ΔP _loss,l,t is the power loss of distribution line l in the power flow analysis period t, which is a random variable; E( ) is the operator to find the expectation of the random variable; The S20 further includes a determination constraint and an opportunity constraint, and the determination constraint includes the opportunity constraint representing the total number of construction of the charging station, the opportunity constraint representing the total number of photovoltaic power station construction, and the total construction capacity of the charging station obtained through formulas (2) to (5) respectively. and the opportunity constraint representing the total construction capacity of the photovoltaic power station, and the opportunity constraint includes the opportunity constraint representing the node voltage offset and the opportunity constraint representing the line power flow exceeding the limit obtained by formulas (6) to (7), respectively,

Among them, M _ch is the total number of charging stations built; N _ch is the total number of candidate addresses of charging stations; i is the index of candidate addresses; _xi is a 0-1 variable indicating whether to build a charging station at candidate address i to charge photovoltaic power stations and electric vehicles The 0-1 optimization variable in the network random collaborative planning model, taking "1" means building a charging station at candidate address i, taking "0" means not building a charging station at candidate address i, i=1,2,3,... , _Nch ;

Among them, M _pv is the total number of photovoltaic power stations constructed; N _pv is the total number of candidate addresses of photovoltaic power stations; j is the index of candidate addresses; y _j is a 0-1 variable that indicates whether to build a charging station at candidate address j, for charging photovoltaic power stations and electric vehicles The 0-1 optimization variable in the network stochastic collaborative planning model, taking "1" means building a photovoltaic power station at the candidate address j, taking "0" means not building a photovoltaic power station at the candidate address j, j=1,2,3,... ,N _pv ;

Among them, _zi is the charging station construction capacity of candidate address i, and the electric vehicle charging stations to be built are divided into Q _ev categories; C _ch is the total construction capacity of charging stations;

Among them, W _j is the construction capacity of the photovoltaic power station at the candidate address j. For the photovoltaic power station, the photovoltaic power station to be built is divided into Q _pv categories; C _pv is the total construction capacity of the photovoltaic power station;

Among them, P _r {·} represents the probability of random events in parentheses; k is the distribution node index; Ω _bus is the distribution node index set; U _k is the voltage of node k, which is a random variable, and the probability distribution characteristics are determined by the probability power flow The analysis results are given; U _N is the rated voltage of the power distribution system; α% is the maximum allowable deviation percentage of the node voltage; β ₁ is the confidence level of the voltage exceeding the limit; P _r {I _l >I _l,max }≤β ₂ l∈Ω _br (7) Among them, I _l is the load current in the distribution line l, which is a random variable, and the probability distribution characteristics are given by the probabilistic power flow analysis results; I _l,max is the maximum allowable current of the distribution line l; β ₂ is the power flow over-limit confidence Spend; S30 is designed to represent the chromosome coding strategy and the corresponding crossover and mutation operators for the construction scheme of photovoltaic power station and electric vehicle charging network respectively. Co-evolutionary algorithm is used to solve the random collaborative planning model of photovoltaic power station and electric vehicle charging network, and the photovoltaic power station is given. Optimal planning scheme with electric vehicle charging network; The step S30 includes the following steps: S301 sets genetic algorithm parameters, the genetic algorithm parameters include population size N _pop1 for optimizing the construction scheme of photovoltaic power plants, population size N _pop2 for optimizing the construction scheme for electric vehicle charging stations, crossover rate P _c , and mutation rate P _m and the maximum evolutionary generation G _max of co-evolution; S302 initializes the chromosomes in the population Ψ _ev , and uses an integer encoding scheme to encode the chromosomes in the population Ψ _ev , where Ψ _ev is the population used for optimization of the charging network construction scheme; S303 initializes the chromosomes in the population Ψ _pv , and uses an integer coding scheme to encode the chromosomes in the population Ψ _pv , where Ψ _pv is used for the population optimized for the photovoltaic power station construction plan; S304 randomly selects one chromosome from each of the populations Ψ _ev and Ψ _pv to construct an initial ecosystem; S305 The evolutionary algebra index g is initialized to 0, that is, g=0; S306 sets g=g+1, starts the g-th generation evolution, and the chromosome index m in the population Ψ _ev and the chromosome index n in the population Ψ _pv are both initialized to 1, that is, let m=1, n=1; S307 decodes the mth chromosome in the population Ψ _ev , determines the construction positions of M _ch electric vehicle charging stations, the construction capacity and the total construction capacity C _t-ev , and decodes the chromosome representing the photovoltaic power station construction plan in the ecosystem , determine the construction locations of M _ev photovoltaic power stations, the construction capacity and the total construction capacity C _t-pv ; use the scenario probability method to calculate the probability power flow of the power distribution system, determine the expected power loss F _loss in a typical planned day, and the voltage amplitude of each node value and the probability distribution characteristics of the power flow of each line, calculate the fitness of the mth chromosome in the population _Ψev according to formulas (8) to (12); V _fit-ev,m =F _max -F _loss -η ₁ ×V _p1 -η ₂ ×V _p2 -η ₃ ×V _p3 -η ₄ ×V _p4 (8) V _p1 =|C _ch -C _t-ev | (9) V _p2 =|C _pv -C _t-pv | (10)

Among them, _Fmax is a preset positive number to ensure that the chromosome fitness is non-negative, and the operator

means to take

For the larger number in , the penalty function method is used to deal with the constraints given by equations (4) to (7), respectively, η ₁ , η ₂ , η ₃ and η ₄ are penalty coefficients; V _p1 , V _p2 , V _p3 and V _p4 represents the degree of violation of the constraints given by formulas (4) to (7), respectively; S308 judges whether the fitness of all chromosomes in the population Ψ _ev has been calculated, that is, judges whether the chromosome index m is equal to the population size N _pop2 , if m<N _pop2 , then set m=m+1, and jump to step S307 to continue the calculation The fitness of the next chromosome in the population Ψ _ev ; otherwise, continue to step S309; S309 selects the best chromosome from the population Ψ _ev , replaces the chromosome representing the charging network construction plan in the ecosystem, and updates the ecosystem; S310 decodes the nth chromosome in the population Ψ _pv , determines the construction positions of M _ev photovoltaic power plants, the construction capacity and the total construction capacity C _t-pv , decodes the chromosome representing the charging network construction plan in the ecosystem, and determines The construction locations of M _ch electric vehicle charging stations, the construction capacity and the total construction capacity C _t-ev ; on this basis, the probability flow calculation of the power distribution system is carried out by using the scenario probability method, and the expected power loss F _loss in a typical planned day is determined , the probability distribution characteristics of the voltage amplitude of each node and the power flow of each line, and the fitness V _fit-pv,n of the nth chromosome in the population Ψ _pv is calculated according to formula (13), V _fit-pv,n =F _max -F _loss -η ₁ ×V _p1 -η ₂ ×V _p2 -η ₃ ×V _p3 -η ₄ ×V _p4 (13) S311 judges whether the fitness of all chromosomes in the population Ψ _pv has been calculated, that is, judges whether the chromosome index n is equal to the population size N _pop1 , if n<N _pop1 , then set n=n+1, and jump to step S310 to continue the calculation The fitness of the next chromosome in the population Ψ _pv ; otherwise, continue to step S312; Based on fitness, S312 selects the best chromosome from the population Ψ _pv , replaces the chromosome representing the photovoltaic power station construction plan in the ecosystem, and updates the ecosystem; S313 judges whether the maximum evolutionary algebra is reached, if g=G _max , then continue to perform step S314; otherwise, based on the fitness, the populations Ψ _ev and Ψ _pv are copied, crossed and mutated respectively, and the two populations are updated, and jump to step S306; S314 decodes the two chromosomes in the ecosystem representing the electric vehicle charging network construction scheme and the photovoltaic power station construction scheme respectively, and outputs the optimal solution of the photovoltaic power station and the electric vehicle charging network stochastic collaborative planning model, and ends the algorithm process. 2. The co-evolution-based photovoltaic power station and electric vehicle charging network planning method according to claim 1, characterized in that, for population Ψ _ev , in order to ensure that the crossed chromosomes satisfy the equation given by formula (2) Constraints, perform the intersection operation as follows: S41 randomly selects two chromosomes from the population Ψ _ev as the chromosomes to be crossed; S42 repeatedly randomly generates candidate cross bits N _can1 , where 1<N _can1 <N _ch , until a feasible cross bit N _cr1 is found; and S43 exchanges the code string after the N _cr1 code point in the two chromosomes to be crossed with the crossover probability P _c to complete the crossover operation. 3. The co-evolution-based photovoltaic power station and electric vehicle charging network planning method according to claim 1, characterized in that, for population Ψ _pv , in order to ensure that the crossed chromosomes satisfy the equation given by formula (3) Constraints, perform the intersection operation as follows: S51 randomly selects two chromosomes from the population Ψ _pv as the chromosomes to be crossed; S52 repeatedly randomly generates candidate cross bits N _can2 , where 1<N _can2 <N _pv , until a feasible cross bit N _cr2 is found; and S53 exchanges the code string after the N _cr2 code point in the two chromosomes to be crossed with the crossover probability P _c to complete the crossover operation. 4. The co-evolution-based photovoltaic power station and electric vehicle charging network planning method according to claim 1, wherein the population Ψ _ev mutation operator comprises the following steps: S61 randomly selects a chromosome from the population Ψ _ev as the chromosome to be mutated; S62 randomly generates two to-be-mutated code bits N _mu1 and N _mu2 , where 1<N _mu1 <N _ch , 1<N _mu2 <N _ch , to ensure that one of the two to-be-mutated code bits is "0" and the other is an integer other than "0"; and S63 performs mutation operation on the mutation bits N _mu1 and N _mu2 at the same time with the mutation probability P _m , that is, the mutation bit whose value is "0" is mutated into a non-"0" random integer not greater than Q _ev , and the value is not "0" The to-be-mutated bit is mutated to "0" to complete the mutation operation. 5. The co-evolution-based photovoltaic power station and electric vehicle charging network planning method according to claim 1, wherein the population Ψ _pv mutation operator comprises the following steps: S71 randomly selects a chromosome from the population Ψ _pv as the chromosome to be mutated; S72 randomly generates two to-be-mutated code bits N _mu3 and N _mu4 , where 1<N _mu3 <N _pv , 1<N _mu4 <N _pv , to ensure that one of the two to-be-mutated code bits is "0" and the other is an integer other than "0"; and S73 performs mutation operation on the mutation bits N _mu3 and N _mu4 at the same time with the mutation probability P _m , that is, the mutation bit whose value is "0" is mutated into a non-"0" random integer not greater than Q _pv , and the value is not "0" The to-be-mutated bit is mutated to "0" to complete the mutation operation.

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