CN112036654B - Photovoltaic power station and electric vehicle charging network planning method based on co-evolution - Google Patents
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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
Technical Field
The invention relates to the technical field of electric vehicle charging networks, in particular to a photovoltaic power station and electric vehicle charging network planning method based on coevolution.
Background
The rapid development of renewable energy power generation represented by photovoltaic and clean energy vehicles represented by electric automobiles is one of the important ways to promote energy revolution and realize sustainable development. In the prior art, a power distribution system is an important place for distributed photovoltaic grid connection and electric vehicle charging, and obviously, an electric vehicle charging network and a photovoltaic power station affect the operation condition of the power distribution system together. For a power distribution system, unreasonable layout of photovoltaic power stations and electric vehicle charging stations deteriorates operation conditions, affects normal power supply to users, and is specifically represented by increased network loss electric quantity, overproof node voltage deviation, line tide out-of-limit and the like. Under the background, the photovoltaic power station and the electric vehicle charging network in the power distribution system need to be planned in a coordinated manner, and the operation cost of the power distribution system is minimized on the premise that the operation condition of the power distribution system meets the technical requirement. The charging load and the photovoltaic output of the electric vehicle charging station have random characteristics, and under the combined action of the two random factors, the operation condition of the power distribution system presents remarkable random characteristics, so that the photovoltaic power station and the electric vehicle charging network collaborative planning model inevitably becomes a random optimization model. In summary, it is necessary to provide a photovoltaic power station and electric vehicle charging network stochastic collaborative planning model considering the stochastic characteristics of the operation conditions of the power distribution system and a corresponding solution method.
In the literature, "Comprehensive optimization model for sizing and sizing of DG units, EV charging stations, and energy storage systems" (IEEE Transactions on Smart Grid,2018, volume 9, phase 4, pages 3871 to 3882), a second-order cone optimization model for simultaneously optimizing the construction addresses and capacities of a distributed photovoltaic power station, an electric vehicle charging station, and an energy storage power station in a power distribution system is established, and GAMS software is used for solving. In the research, the time-varying characteristics of the output of the distributed photovoltaic power station and the charging load of the electric automobile are considered, but the random characteristics of the output of the distributed photovoltaic power station and the charging load of the electric automobile are not considered, so that the method has certain limitations. Under the premise of considering that a distributed power supply including a photovoltaic power station supplies power to a power distribution network load and a charging station at the same time, a multi-objective optimization model for optimizing the construction address and capacity of the distributed power supply and the charging station of the electric vehicle at the same time is established in 'multi-objective planning research on the power distribution network including the distributed power supply and the charging station of the electric vehicle' (power grid technology, 2015, volume 39, phase 2, pages 450 to 456), and a Pareto solution set of the model is given by adopting a multi-objective free search algorithm. However, the random characteristics of the distributed power supply output and the charging load of the electric vehicle charging station are not considered in the document, and the given planning result has certain limitations. On the basis of considering the stochastic characteristics of photovoltaic power station output and charging load of a charging station, a stochastic programming model for optimizing the construction addresses of the distributed photovoltaic power station and the charging station of the electric vehicle is established in the third literature, namely, opportunity constraint planning of the charging station of the electric vehicle with the photovoltaic distributed power distribution network (electric power system and the automatic journal thereof, 2017, volume 29, period 6, pages 45 to 52), and is solved by adopting a bat algorithm. The photovoltaic power station charging system is insufficient in consideration of the random characteristics of the photovoltaic power station output and the charging load of the charging station, mainly focuses on optimizing the construction addresses of the electric vehicle charging station and the photovoltaic power station, and has certain limitation.
The electric vehicle charging network and the photovoltaic power station jointly influence the operation condition of the power distribution system, the unreasonable layout of the photovoltaic power station and the electric vehicle network deteriorates the operation condition of the power distribution system, and the normal power supply to users is influenced, specifically, the network loss electric quantity is increased, the node voltage deviation exceeds the standard, the line tide exceeds the limit, and the like. Therefore, it is necessary to plan the photovoltaic power station and the electric vehicle charging network in the power distribution system in a coordinated manner, and the operation cost of the power distribution system is minimized on the premise that the operation condition of the power distribution system meets the technical requirements. However, the method in the prior art does not fully consider the random characteristics of the output and charging load of the distributed photovoltaic power station, and has certain limitations.
Disclosure of Invention
In order to solve the problems, the invention provides a photovoltaic power station and electric vehicle charging network planning method based on co-evolution, which is used for carrying out co-optimization on the construction positions and the construction capacities of an electric vehicle charging station and a photovoltaic power station, minimizing the operation cost of a power distribution system on the premise of ensuring that the operation working condition of the power distribution system meets the technical requirements, and providing reference for engineering technicians.
In order to realize the purpose, the invention adopts the technical scheme that:
a photovoltaic power station and electric vehicle charging network planning method based on coevolution comprises the following steps: s10, planning boundary conditions are given, wherein the planning boundary conditions comprise: the method comprises the following steps that topological parameters of a power distribution system and loads in a planned typical day, a scene set of charging loads and photovoltaic output probability in the planned typical day, the total number of candidate addresses of a charging station, the total number of construction and total construction capacity of the charging station, the construction type and corresponding construction capacity of the charging station, the total number of candidate addresses of the photovoltaic station, the total number and total construction capacity of the photovoltaic station, the construction type and corresponding construction capacity of the photovoltaic station, the maximum allowable deviation percentage of node voltage, and the confidence coefficient of node voltage threshold and tidal current threshold are set; 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 which are respectively used for representing the construction scheme of the photovoltaic power station and the electric automobile charging network, solving a random collaborative planning model of the photovoltaic power station and the electric automobile charging network by adopting a collaborative evolution algorithm, and giving an optimal planning scheme of the photovoltaic power station and the electric automobile 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 power loss of the power distribution system within a typical day of planning, as shown in formula (1),
wherein, F loss Planning the expected grid loss capacity in a typical day for the power distribution system; t is a tide analysis time interval index, T f The number of time periods for power flow analysis in a typical day; l is distribution line index; omega br Indexing a set for a distribution line; delta P loss,l,t The loss power of the distribution line l in the power flow analysis time period t is a random variable; e (-) is an operator to expect a random variable; the S20 further comprises determination constraints and opportunity constraints, the determination constraints comprise opportunity constraints representing total charging station construction number, opportunity constraints representing total photovoltaic station construction number, opportunity constraints representing total charging station construction capacity and opportunity constraints representing total photovoltaic station construction capacity, which are obtained through formulas (2) to (5) respectively, the opportunity constraints comprise opportunity constraints representing node voltage deviation and opportunity constraints representing line power flow out-of-limit obtained through formulas (6) to (7) respectively,
wherein, M ch Building a total number for the charging station; n is a radical of hydrogen ch A total number of candidate addresses for the charging station; i is a candidate address index; x is the number of i The variable is a 0-1 variable for representing whether charging stations are built at the candidate address i or not, the variable is a 0-1 optimization variable in a stochastic collaborative planning model of the photovoltaic power station and the electric vehicle charging network, a '1' is taken to represent that the charging stations are built at the candidate address i, a '0' is taken to represent that the charging stations are not built at the candidate address i, i =1,2,3, ·, N ch ；
Wherein, M pv The total number of the photovoltaic power station is built; n is a radical of pv The total number of the candidate addresses of the photovoltaic power station is; j is a candidate address index; y is j Is a 0-1 variable for representing whether a charging station is built at a candidate address j or not and is used for a photovoltaic power station and an electric vehicle charging networkAnd (3) optimizing variables 0-1 in the machine collaborative planning model, wherein the condition that 1 is taken to represent that the photovoltaic power station is built at the candidate address j, and 0 is taken to represent that the photovoltaic power station is not built at the candidate address j, j =1,2,3, ·, N pv ；
Wherein z is i Dividing the electric vehicle charging station to be built into Q for the charging station building capacity of the candidate address i ev A class; c ch Total construction capacity for the charging station;
wherein, W j For the photovoltaic power station construction capacity of the candidate address j, for the photovoltaic power station, the photovoltaic power station to be constructed is divided into Q pv A class; c pv The total construction capacity of the photovoltaic power station is obtained;
wherein, P r {. Denotes the probability of a random event occurring in parentheses; k is a power distribution node index; omega bus Indexing a collection for a power distribution node; u shape k The voltage of the node k is a random variable, and the probability distribution characteristic is given by a probability load flow analysis result; u shape N Rated voltage for the distribution system; alpha% is the maximum voltage allowed deviation percentage of the node; beta is a beta 1 Is the voltage out-of-limit confidence;
P r {I l ＞I l，max }≤β 2 l∈Ω br (7)
wherein, I l The load current in the distribution line l is a random variable, and the probability distribution characteristic is given by a probability load flow analysis result; i is l,max The maximum allowable current of the distribution line l; beta is a beta 2 Is the confidence of the power flow out-of-limit.
Go toStep S30 includes the following steps: s301, setting genetic algorithm parameters, wherein the genetic algorithm parameters comprise a population size N for optimizing a photovoltaic power station construction scheme pop1 Population scale N for optimizing electric vehicle charging station construction scheme pop2 Cross over ratio P c The rate of mutation P m And maximum evolutionary algebra G of co-evolution max (ii) a S302 initializing population Ψ ev Of (3) chromosomes, using an integer coding scheme for Ψ pairs of populations ev Wherein Ψ encodes ev A population optimized for a charging network construction scheme; s303 initializing the population Ψ pv Chromosome of (2), using integer coding scheme to pair populations psi pv Wherein Ψ encodes pv The population is used for optimizing the photovoltaic power station construction scheme; s304 from the population Ψ ev To Ψ pv Randomly selecting a chromosome in the system, and constructing an initial ecosystem; s305, initializing an evolutionary algebra index g to 0, i.e., letting g =0; s306, starting to carry out the g generation evolution when g = g +1, and carrying out the group psi ev Chromosome index m and population Ψ pv The chromosome index n in (a) is initialized to 1, i.e. m =1, n =1; s307 Pair population Ψ ev M chromosome of (1) is decoded to determine M ch Construction position, construction capacity and total construction capacity C of electric vehicle charging station t-ev Decoding the chromosome representing the construction scheme of the photovoltaic power station in the ecosystem to determine M ev The construction position, construction capacity and total construction capacity C of each photovoltaic power station t-pv (ii) a Calculating the probability load flow of the power distribution system by adopting a scene probability method, and determining the expected F of the network loss electric quantity in a planning typical day loss Calculating the population psi according to the formulas (8) - (12) based on the probability distribution characteristics of the voltage amplitude of each node and the power flow of each line ev Fitness of the mth chromosome in (a);
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)
wherein, F max Operator for presetting positive number to ensure chromosome fitness is not negative
Express get
The larger number in the middle is treated by a penalty function method according to the constraints eta given by the formulas (4) to (7) 1 、η 2 、η 3 And η 4 A penalty factor; v p1 、V p2 、V p3 And V p4 Respectively expressing the violation degrees of the constraints given by formulas (4) to (7); s308, judging whether the calculation of the group psi is finished ev Fitness of all chromosomes in the population, i.e. determining whether the chromosome index m is equal to the population size N pop2 If m is<N pop2 If m = m +1, and go to step S307 to continue calculating the population Ψ ev Fitness of the next chromosome; otherwise, continue to step S309; s309 from the population Ψ ev Selecting the most excellent chromosome, replacing the chromosome representing the construction scheme of the charging network in the ecosystem, and updating the ecosystem; s310 pairs of populations Ψ pv The nth chromosome in (1) is decoded to determine M ev Construction position, construction capacity and total construction capacity C of individual photovoltaic power station t-pv Decoding chromosomes representing charging network construction schemes in the ecosystem to determine M ch Construction position, construction capacity and total construction capacity C of individual electric vehicle charging station t-ev (ii) a On the basis of the aboveCalculating the probability load flow of the power distribution system by adopting a scene probability method, and determining the expected F of the network loss electric quantity in a planning typical day loss The voltage amplitude of each node and the probability distribution characteristic of each line load flow, and calculating the population psi according to the formula (13) pv Fitness V of the nth chromosome in (1) fit-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 calculation of the group psi is finished pv Fitness of all chromosomes in the population, i.e. determining whether the chromosome index N is equal to the population size N pop1 If n is<N pop1 If n = n +1, and go to step S310, continue to calculate the population Ψ pv Fitness of the next chromosome; otherwise, continue to step S312; s312 based on fitness, selecting from the population psi pv Selecting the most elegant chromosome, replacing the chromosome representing the construction scheme of the photovoltaic power station in the ecological system, and updating the ecological system; s313, judging whether the maximum evolutionary algebra is reached, and if G = G max Then, go on to step S314; otherwise, based on fitness, the population psi is respectively matched ev To Ψ pv Performing copying, crossing and mutation operations, updating the two populations, and skipping to the step S306; s314, decoding two chromosomes respectively representing the electric vehicle charging network construction scheme and the photovoltaic power station construction scheme in the ecological system, outputting the two chromosomes as the optimal solution of the photovoltaic power station and electric vehicle charging network random collaborative planning model, and ending the algorithm process.
Further, to the population Ψ ev In other words, to ensure that the crossed chromosomes satisfy the equation constraint given in equation (2), the crossing operation is performed as follows: s41 deriving Ψ from the population ev Randomly selecting two chromosomes as chromosomes to be crossed; s42, repeatedly and randomly generating cross bit N to be selected can1 Wherein 1 is<N can1 <N ch Until a feasible cross position N is found cr1 (ii) a And S43 with a crossover probability P c Exchange two chromosomes to be crossedN th cr1 And (5) completing the cross operation of the code strings after the code bits.
Further, Ψ a population pv In other words, to ensure that the crossed chromosomes satisfy the equation constraint given in equation (3), the crossing operation is performed as follows: s51 deriving from the population Ψ pv Randomly selecting two chromosomes as chromosomes to be crossed; s52, repeatedly and randomly generating cross bit N to be selected can2 Wherein 1 is<N can2 <N pv Until a feasible cross position N is found cr2 (ii) a And S53 with crossover probability P c Crossover of the Nth of two chromosomes to be crossed cr2 And (5) finishing the cross operation of the code string after the code bit.
Further, the population Ψ ev The mutation operator comprises the following steps: s61 from the population Ψ ev Randomly selecting a chromosome as a chromosome to be mutated; s62, randomly generating two code bits N to be varied mu1 And N mu2 Wherein 1 is<N mu1 <N ch ，1<N mu2 <N ch Ensuring that one of the two code bits to be varied is '0' and the other is a non-0 integer; and S63 mutation probability P m Treating simultaneously the ectopic N mu1 And N mu2 Performing mutation operation, namely changing the position to be varied with the value of 0 to be not more than Q ev The random integer of not 0, the bit to be mutated which takes the value of not 0 is mutated into 0, and the mutation operation is completed.
Further, the population Ψ pv The mutation operator comprises the following steps: s71 from the population Ψ pv Randomly selecting a chromosome as a chromosome to be mutated; s72 randomly generating two code bits N to be varied mu3 And N mu4 In which 1 is<N mu3 <N pv ，1<N mu4 <N pv Ensuring that one of the two code bits to be varied is '0' and the other is a non-0 integer; and S73 mutation probability P m Treating simultaneously the ectopic N mu3 And N mu4 Performing mutation operation, namely changing the position to be mutated with the value of 0 to be not more than Q pv The non-0 random integer is taken as the value of the to-be-mutated bit with the non-0 value to be mutated into the 0 value, and the mutation operation is finished。
Compared with the prior art, the technical scheme of the invention has the following advantages:
(1) According to the photovoltaic power station and electric vehicle charging network planning method based on the coevolution, under the condition that the construction number and the total construction total capacity of the photovoltaic power station/charging station are given, the construction positions and the construction capacities of the electric vehicle charging station and the photovoltaic power station 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 requirement, and reference is provided for engineering technicians.
(2) According to the photovoltaic power station and electric vehicle charging network planning method based on co-evolution, both the charging load and the photovoltaic power generation output have random characteristics, the photovoltaic power station and electric vehicle charging network co-planning problem is modeled into a random optimization model based on opportunity constraint, and model optimization variables are as follows: the photovoltaic power station access position and the access capacity, and the electric vehicle charging station access position and the access capacity; the optimization target is that the operation cost of the power distribution system is minimum, and the network loss electric quantity expectation in a typical day of the power distribution system planning is minimum; the optimization constraints are: the method comprises the following steps of charging station construction number and capacity constraint, photovoltaic power station construction number and capacity constraint, node voltage deviation opportunity constraint and line tide out-of-limit opportunity constraint.
(3) The photovoltaic power station and electric vehicle charging network planning method based on co-evolution adopts two populations to respectively represent the construction schemes of the photovoltaic power station and an electric vehicle charging network, iteratively updates the two populations and an ecosystem consisting of population optimal chromosomes by means of replication, intersection and variation operators based on chromosome fitness evaluation results until a final optimal construction scheme of the photovoltaic power station and the electric vehicle charging network is given, and respectively designs a coding scheme and corresponding intersection and variation operators for representing the optimal construction scheme of the photovoltaic power station and the electric vehicle charging network for improving solution efficiency.
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The technical solution and the advantages of the present invention will be apparent from the following detailed description of the embodiments of the present invention with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a method for planning a charging network of a photovoltaic power station and an electric vehicle 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 population Ψ for optimization of a charging network construction scheme according to an embodiment of the present invention ev A cross-operation flow diagram;
FIG. 4 shows a population Ψ for optimizing a photovoltaic plant construction scheme according to an embodiment of the present invention pv A cross-operation flow diagram;
FIG. 5 shows a population Ψ for optimization of a charging network construction scheme according to an embodiment of the present invention ev A variant operation flow chart;
FIG. 6 shows the population Ψ for optimizing the photovoltaic plant construction scheme according to an embodiment of the invention pv And (4) a variant operation flow chart.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be 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 invention.
The embodiment provides a photovoltaic power station and electric vehicle charging network planning method based on co-evolution, and as shown in fig. 1, the method comprises the following steps: s10, planning boundary conditions are given. And S20, establishing a photovoltaic power station and electric vehicle charging network random collaborative planning model by using the planning boundary conditions. And S30, designing a chromosome coding strategy and corresponding intersection and mutation operators which are respectively used for representing the construction scheme of the photovoltaic power station and the electric automobile charging network, solving a random collaborative planning model of the photovoltaic power station and the electric automobile charging network by adopting a collaborative evolution algorithm, and giving an optimal planning scheme of the photovoltaic power station and the electric automobile charging network.
The planning boundary conditions include: the method comprises the following steps of power distribution system topology parameters, loads in a planned typical day, a scene set of charging loads and photovoltaic output probability in the planned typical day, the total number of candidate addresses of a charging station, the total number of construction of the charging station and total construction capacity of the charging station, the construction type of the charging station and corresponding construction capacity, the total number of candidate addresses of the photovoltaic station, the total number of construction of the photovoltaic station and total construction capacity of the photovoltaic station, the construction type of the photovoltaic station and corresponding construction capacity of the photovoltaic station, the maximum allowable deviation percentage of node voltage, and the confidence coefficient of node voltage overrun and tidal current overrun.
The step S20 is to deal with the random characteristics of the operation conditions of the power distribution system, node voltage deviation constraint and line power flow out-of-limit constraint in a photovoltaic power station and electric vehicle charging network random collaborative planning model are modeled as opportunity constraint,
the step S20 includes: the optimization goal of the photovoltaic power station and electric vehicle charging network stochastic collaborative planning model is to reduce the network loss electric quantity within a typical day of power distribution system planning, as shown in formula (1),
wherein, F loss Planning the expected grid loss capacity in a typical day for a power distribution system; t is a tide analysis time interval index, T f The number of time periods for power flow analysis in a typical day; l is distribution line index; omega br A distribution line index set is obtained; delta P loss,l,t The loss power of the distribution line l in the power flow analysis time period t is a random variable and can be obtained through probability power flow calculation; e (-) is an operator that expects a random variable. The grid loss cost is one of important components of the operation cost of the power distribution system, the charging load and the photovoltaic output have random characteristics, therefore, the grid loss electric quantity in a typical day of the planning of the power distribution system is also a random variable, and under the background, the expectation of the grid loss electric quantity in the typical day of the planning of the power distribution system is the minimum by the optimization target of the random collaborative planning model of the photovoltaic power station and the charging network of the electric vehicle.
The S20 further includes determination constraints including an opportunity constraint representing a total number of construction of the charging station, an opportunity constraint representing a total number of construction of the photovoltaic power station, an opportunity constraint representing a total construction capacity of the charging station, and an opportunity constraint representing a total construction capacity of the photovoltaic power station, which are obtained by equations (2) to (5), respectively, the opportunity constraints including an opportunity constraint representing a node voltage shift and an opportunity constraint representing a line flow overrun, which are obtained by equations (6) to (7), respectively,
wherein M is ch Building a total number for the charging station; n is a radical of ch A total number of candidate addresses for the charging station; i is a candidate address index; x is a radical of a fluorine atom i The method is characterized in that a 0-1 variable for representing whether charging stations are built at the candidate address i or not is a 0-1 optimization variable in a stochastic collaborative planning model of a photovoltaic power station and electric vehicle charging network, a 1 is taken to represent that charging stations are built at the candidate address i, a 0 is taken to represent that charging stations are not built at the candidate address i, i =1,2,3, ·, N ch ；
Wherein M is pv The total number of the photovoltaic power station is built; n is a radical of hydrogen pv The total number of the candidate addresses of the photovoltaic power station is; j is a candidate address index; y is j The method is characterized in that a 0-1 variable for representing whether a charging station is built at a candidate address j is a 0-1 optimized variable in a stochastic collaborative planning model of the photovoltaic power station and the electric vehicle charging network, a 1 is taken to represent that the photovoltaic power station is built at the candidate address j, a 0 is taken to represent that the photovoltaic power station is not built at the candidate address j, j =1,2,3, ·, N pv ；
Wherein z is i For the charging station construction capacity of the candidate address i, dividing the electric vehicle charging station to be constructed into Q ev Class, photovoltaic power station and electricIn the random collaborative planning model of the automobile charging network, charging stations to be built are divided into Q according to different capacities ev Class, that is, z i With Q ev Different values of z i The method comprises the following steps of (1) obtaining discrete optimization variables in a photovoltaic power station and electric vehicle charging network combined stochastic programming model; c ch The total construction capacity of the charging station is determined by the number of electric vehicles in the area to be planned, the construction cost of the charging station, the total amount of investment planned for the construction of the charging station and other factors;
wherein, W j Building capacity for the photovoltaic power station with the candidate address j, and dividing the photovoltaic power station to be built into Q photovoltaic power stations according to different capacities in the random collaborative planning model of the photovoltaic power station and the electric vehicle charging network pv Class, that is, w j Having Q pv Different values of W j The method comprises the following steps of (1) obtaining a discrete optimization variable in a photovoltaic power station and electric vehicle charging network combined random planning model; c pv The total construction capacity of the photovoltaic power station is determined by the factors such as the construction cost of the photovoltaic power station, the total amount of investment planned for the construction of the photovoltaic power station and the like.
Wherein, P r {. Denotes the probability of a random event occurring in parentheses; k is a power distribution node index; omega bus Indexing a set for a power distribution node; u shape k The voltage of the node k is a random variable, and the probability distribution characteristic is given by a probability load flow analysis result; u shape N Rated voltage for the distribution system; alpha% is the maximum voltage allowed deviation percentage of the node; beta is a 1 Is the voltage out-of-limit confidence;
P r {I l ＞I l，max }≤β 2 l∈Ω br (7)
wherein, I l Is the load current in the distribution line l, is a random variable,the probability distribution characteristics are given by the probability power flow analysis result; I.C. A l,max The maximum allowable current of the distribution line l; beta is a 2 And the confidence of the power flow out-of-limit.
Constraints in the photovoltaic power station and electric vehicle charging network stochastic collaborative planning model are respectively shown in formulas (2) to (7), wherein the formulas (2) to (5) are deterministic constraints. Considering the random characteristic of the operation condition of the power distribution system, the node voltage deviation constraint and the line power flow out-of-limit constraint given by the formulas (6) to (7) are opportunity constraints.
As shown in fig. 2, the step S30 includes the following steps: s301, setting genetic algorithm parameters, wherein the genetic algorithm parameters comprise a population size N for optimizing a photovoltaic power station construction scheme pop1 Population scale N for optimizing electric vehicle charging station construction scheme pop2 Cross over ratio P c The rate of variation P m And maximum evolutionary algebra G of co-evolution max 。
S302 initializing population Ψ ev Of (3) chromosomes, using an integer coding scheme for Ψ pairs of populations ev Wherein Ψ encodes ev The method is a population for the optimization of the charging network construction scheme. According to the characteristics of the photovoltaic power station and electric vehicle charging network random collaborative planning model, an integer coding scheme is adopted for the group psi ev The chromosome(s) in (a) is (are) encoded. Each chromosome is composed of N ch The ith code bit represents the charging station construction condition of the ith candidate address (i =1,2,3, N · ch ) When the value is 0, the charging station is not built at the candidate address i, and the value is Q, the charging station of the Q-th class is built at the candidate address i (Q =1,2,3, ·, Q) ev ) Corresponding construction capacity of C ev,q . To satisfy the equality constraints given by equation (2), in a chromosome, there is and only is M ch The code bits take on integers other than 0. Thus, the population Ψ was initialized as follows ev Chromosome (b): firstly, assigning all code bits of a chromosome to be 0; then, randomly selecting M ch Code bits, change the value from "0" to no more than Q ev Is a random integer of (a).
S303 initialChemical population psi pv Of (3) chromosomes, using an integer coding scheme for Ψ pairs of populations pv Wherein Ψ encodes pv The method is used for optimizing the photovoltaic power station construction scheme. According to the characteristics of the photovoltaic power station and electric vehicle charging network random collaborative planning model, an integer coding scheme is adopted to carry out psi on the population pv The chromosome in (a) encodes. Each chromosome is composed of N pv The jth code bit characterizes the photovoltaic power station construction condition of the jth candidate address (j =1,2,3, ·, N) pv ) The value is 0, which means that the photovoltaic power station is not built at the candidate address j, the value is m, which means that the mth type photovoltaic power station (m =1,2,3, · ·, Q) is built at the candidate address j pv ) Corresponding construction capacity of C pv,m . To satisfy the equality constraints given by equation (3), there is and only M in the chromosome pv The code bits take on integers other than 0. Thus, the population Ψ was initialized as follows pv Chromosome (b): firstly, assigning all code bits of the chromosome to be 0; then, randomly selecting M pv A code bit changes the value from '0' to not more than Q pv Is a random integer of (a).
S304 from the population Ψ ev With Ψ pv One chromosome is randomly selected in the method, and an initial ecosystem is constructed.
S305 evolution algebraic index g is initialized to 0, i.e. let g =0.
S306, starting to carry out the g generation evolution when g = g +1, and carrying out the group psi ev Chromosome index m and population Ψ pv The chromosome index n in (1) is initialized to 1, i.e. let m =1, n =1.
S307 pairs of populations psi ev The M-th chromosome in (c) is decoded to determine M ch Construction position, construction capacity and total construction capacity C of individual electric vehicle charging station t-ev Decoding the chromosome representing the construction scheme of the photovoltaic power station in the ecosystem to determine M ev The construction position, construction capacity and total construction capacity C of each photovoltaic power station t-pv (ii) a Calculating the probability load flow of the power distribution system by adopting a scene probability method, and determining the expected F of the network loss electric quantity in a planning typical day loss Amplitude of voltage at each nodeCalculating the population psi according to the formulas (8) - (12) according to the probability distribution characteristics of each line power flow ev Fitness of the m-th chromosome of (1) V fit-ev,m ；
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)
Wherein, F max For a predetermined, relatively large positive number to ensure non-negative chromosome fitness, an operator
Show to get
The larger number in the middle is treated by a penalty function method according to the constraints eta given by the formulas (4) to (7) 1 、η 2 、η 3 And η 4 Is a penalty factor; v p1 、V p2 、V p3 And V p4 The degrees of violation of the constraints given by equations (4) to (7) are expressed and calculated by equations (9) to (12), respectively.
S308, judging whether the calculation of the group psi is finished ev Fitness of all chromosomes in the population, i.e. determining whether the chromosome index m is equal to the population size N pop2 If m is<N pop2 If m = m +1, and go to step S307 to continue calculating the population Ψ ev Of the next chromosomeFitness; otherwise, continue to step S309;
s309 from the population Ψ ev Selecting the most elegant chromosome, replacing the chromosome which represents the construction scheme of the charging network in the ecological system, and updating the ecological system;
s310 pairs of populations Ψ pv The nth chromosome in (1) is decoded to determine M ev Construction position, construction capacity and total construction capacity C of individual photovoltaic power station t-pv Decoding chromosomes representing charging network construction schemes in the ecosystem, determining M ch Construction position, construction capacity and total construction capacity C of electric vehicle charging station t-ev (ii) a On the basis, a scene probability method is adopted to calculate the probability load flow of the power distribution system, and the expected grid loss capacity F in a planning typical day is determined loss The voltage amplitude of each node and the probability distribution characteristic of each line load flow, and calculating the population psi according to the formula (13) pv Fitness V of the nth chromosome in (1) fit-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 calculation of the population Ψ is completed pv Fitness of all chromosomes in the population, i.e. determining whether the chromosome index N is equal to the population size N pop1 If n is<N pop1 If n = n +1, and go to step S310, continue to calculate the population Ψ pv Fitness of the next chromosome; otherwise, the process continues to step S312.
S312, based on fitness, selecting Ψ from the population pv Selecting the most elegant chromosome, replacing the chromosome representing the construction scheme of the photovoltaic power station in the ecosystem, and updating the ecosystem;
s313, determining whether the maximum evolution algebra is reached, i.e., determining whether the evolution algebra index G is equal to the maximum evolution algebra G max . If G = G max If G = G max Then, go on to step S314; otherwise, based on fitness, the population psi is respectively matched ev To Ψ pv Performing a replication process,Performing cross and variation operation, updating the two populations, and skipping to the step S306;
in order to improve the solving efficiency, the method is respectively designed for the population psi according to the characteristics of the photovoltaic power station and the electric vehicle charging network random collaborative planning model ev And the population Ψ pv The crossover operator and the mutation operator of (2),
to the population Ψ ev In other words, to ensure that the crossed chromosomes satisfy the equation constraint given by equation (2), the crossing operation is performed as follows, as shown in fig. 3:
s41 deriving Ψ from the population ev Two chromosomes are randomly selected as chromosomes to be crossed. S42, repeatedly and randomly generating cross bit N to be selected can1 Wherein 1 is<N can1 <N ch Until a feasible cross position N is found cr1 . And S43 with a crossover probability P c Swapping the Nth of two chromosomes to be crossed cr1 And (5) completing the cross operation of the code strings after the code bits.
For population psi pv In other words, to ensure that the crossed chromosomes satisfy the equation constraint given by equation (3), the crossing operation is performed as follows, as shown in fig. 4:
s51 deriving from the population Ψ pv Two chromosomes are randomly selected as chromosomes to be crossed. S52, repeatedly and randomly generating cross bit N to be selected can2 Wherein 1 is<N can2 <N pv Until a feasible cross position N is found cr2 . And S53 with a crossover probability P c Swapping the Nth of two chromosomes to be crossed cr2 And (5) completing the cross operation of the code strings after the code bits.
As shown in FIG. 5, population Ψ ev The mutation operator comprises the following steps:
s61 from the population Ψ ev One chromosome is randomly selected as a chromosome to be mutated. S62, randomly generating two code bits N to be varied mu1 And N mu2 Wherein 1 is<N mu1 <N ch ，1<N mu2 <N ch And ensuring that one of the two code bits to be varied is '0' and the other is a non-0 integer. And S63 mutation probability P m Treating simultaneously the ectopic N mu1 And N mu2 Performing mutation operation, namely changing the position to be varied with the value of 0 to be not more than Q ev The random integer of not 0, the mutation position to be mutated which is not 0 is changed into 0, and the mutation operation is finished.
As shown in FIG. 6, the population Ψ pv The mutation operator comprises the following steps:
s71 from the population Ψ pv Randomly selecting a chromosome as a chromosome to be mutated. S72, randomly generating two code bits N to be varied mu3 And N mu4 In which 1 is<N mu3 <N pv ，1<N mu4 <N pv And ensuring that one of the values of the two code bits to be varied is '0' and the other is a non-0 integer. And S73 mutation probability P m Treating simultaneously the ectopic N mu3 And N mu4 Performing mutation operation, namely changing the position to be mutated with the value of 0 to be not more than Q pv The random integer of not 0, the bit to be mutated which takes the value of not 0 is mutated into 0, and the mutation operation is completed. S314, decoding two chromosomes respectively representing the electric vehicle charging network construction scheme and the photovoltaic power station construction scheme in the ecological system, outputting the two chromosomes as the optimal solution of the photovoltaic power station and electric vehicle charging network random collaborative planning model, and ending the algorithm process.
The above description is only an exemplary embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes that are transformed by the content of the present specification and the attached drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (5)
1. A photovoltaic power station and electric vehicle charging network planning method based on co-evolution is characterized by comprising the following steps:
s10, planning boundary conditions are given, wherein the planning boundary conditions comprise: the method comprises the following steps that topological parameters of a power distribution system, loads in a planned typical day, a charging load and photovoltaic output probability scene set in the planned typical day, the total number of candidate addresses of charging stations, the total number of construction and total construction capacity of the charging stations, the construction type and corresponding construction capacity of the charging stations, the total number of candidate addresses of the photovoltaic stations, the total number and total construction capacity of the photovoltaic stations, the construction type and corresponding construction capacity of the photovoltaic stations, the maximum allowable deviation percentage of node voltage, and the confidence coefficient of node voltage overrun and tidal current overrun are calculated;
s20, establishing a photovoltaic power station and electric vehicle charging network random collaborative planning model by using the planning boundary conditions; and
the step S20 includes:
the optimization goal of the photovoltaic power station and electric vehicle charging network stochastic collaborative planning model is to reduce the network loss electric quantity within a typical day of power distribution system planning, as shown in formula (1),
wherein, F loss Planning the expected grid loss capacity in a typical day for the power distribution system; t is a tide analysis time interval index, T f The number of time periods for power flow analysis in a typical day; l is distribution line index; omega br Indexing a set for a distribution line; delta P loss,l,t The loss power of the distribution line l in the power flow analysis time period t is a random variable; e (-) is an operator to expect a random variable;
the S20 further includes determination constraints including an opportunity constraint representing a total number of construction of the charging station, an opportunity constraint representing a total number of construction of the photovoltaic power station, an opportunity constraint representing a total construction capacity of the charging station, and an opportunity constraint representing a total construction capacity of the photovoltaic power station, which are obtained by equations (2) to (5), respectively, the opportunity constraints including an opportunity constraint representing a node voltage shift and an opportunity constraint representing a line flow overrun, which are obtained by equations (6) to (7), respectively,
wherein M is ch Building a total number for the charging stations; n is a radical of ch A total number of candidate addresses for the charging station; i is a candidate address index; x is the number of i The variable is a 0-1 variable for representing whether charging stations are built at the candidate address i or not, the variable is a 0-1 optimization variable in a stochastic collaborative planning model of the photovoltaic power station and the electric vehicle charging network, a '1' is taken to represent that the charging stations are built at the candidate address i, a '0' is taken to represent that the charging stations are not built at the candidate address i, i =1,2,3, ·, N ch ；
Wherein M is pv The total number of photovoltaic power station construction is calculated; n is a radical of pv The total number of the candidate addresses of the photovoltaic power station is; j is a candidate address index; y is j The method is characterized in that a 0-1 variable for representing whether a charging station is built at a candidate address j is a 0-1 optimized variable in a stochastic collaborative planning model of the photovoltaic power station and the electric vehicle charging network, a 1 is taken to represent that the photovoltaic power station is built at the candidate address j, a 0 is taken to represent that the photovoltaic power station is not built at the candidate address j, j =1,2,3, ·, N pv ；
Wherein z is i Dividing the electric vehicle charging station to be built into Q for the charging station building capacity of the candidate address i ev Class; c ch Total construction capacity for the charging station;
wherein, W j For the photovoltaic power station construction capacity of the candidate address j, for the photovoltaic power station, the photovoltaic power station to be constructed is divided into Q pv Class; c pv The total construction capacity of the photovoltaic power station;
wherein, P r {. Denotes the probability of a random event occurring in parentheses; k is the distribution node index; omega bus Indexing a set for a power distribution node; u shape k The voltage of the node k is a random variable, and the probability distribution characteristic is given by a probability load flow analysis result; u shape N Rated voltage for the distribution system; alpha% is the maximum voltage allowed deviation percentage of the node; beta is a 1 Is the voltage out-of-limit confidence;
P r {I l >I l,max }≤β 2 l∈Ω br (7)
wherein, I l The load current in the distribution line l is a random variable, and the probability distribution characteristic is given by a probability load flow analysis result; i is l,max The maximum allowable current of the distribution line l; beta is a 2 The confidence coefficient of the power flow out-of-limit;
s30, designing a chromosome coding strategy and corresponding intersection and variation operators which are 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 giving an optimal planning scheme of the photovoltaic power station and electric vehicle charging network;
the step S30 includes the steps of:
s301, setting genetic algorithm parameters, wherein the genetic algorithm parameters comprise a population size N for optimizing a photovoltaic power station construction scheme pop1 Population scale N for optimizing electric vehicle charging station construction scheme pop2 Cross over ratio P c The rate of variation P m And maximum evolutionary algebra G of co-evolution max ；
S302 initializing population Ψ ev Of (3) chromosomes, using an integer coding scheme for Ψ pairs of populations ev Wherein Ψ encodes ev A population optimized for a charging network construction scheme;
s303 initializing the population Ψ pv Chromosome of (2), using integer coding scheme to pair populations psi pv Wherein Ψ encodes pv For lightA population optimized by a photovoltaic power station construction scheme;
s304 from the population Ψ ev To Ψ pv Randomly selecting a chromosome in the system, and constructing an initial ecosystem;
s305, initializing an evolutionary algebra index g to 0, that is, letting g =0;
s306, letting g = g +1, starting to carry out the evolution of the g-th generation, and carrying out the population psi ev Chromosome index m and population Ψ pv The chromosome index n in (a) is initialized to 1, i.e. let m =1, n =1;
s307 Pair population Ψ ev The M-th chromosome in (c) is decoded to determine M ch Construction position, construction capacity and total construction capacity C of individual electric vehicle charging station t-ev Decoding the chromosome representing the construction scheme of the photovoltaic power station in the ecosystem to determine M ev The construction position, construction capacity and total construction capacity C of each photovoltaic power station t-pv (ii) a Calculating the probability load flow of the power distribution system by adopting a scene probability method, and determining the expected F of the network loss electric quantity in a planning typical day loss Calculating the population psi according to the formulas (8) - (12) based on the probability distribution characteristics of the voltage amplitude of each node and the power flow of each line ev Fitness of the mth chromosome in (a);
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)
wherein, F max Is a predetermined positive number to ensure a chromosomeFitness non-negative operator
Show to get
The larger number in the middle is treated by a penalty function method according to the constraints eta given by the formulas (4) to (7) 1 、η 2 、η 3 And η 4 A penalty factor; v p1 、V p2 、V p3 And V p4 Respectively expressing the violation degrees of the constraints given by formulas (4) to (7);
s308, judging whether the calculation of the group psi is finished ev Fitness of all chromosomes in the population, i.e. determining whether the chromosome index m is equal to the population size N pop2 If m is<N pop2 If m = m +1, and go to step S307 to continue calculating the population Ψ ev Fitness of the next chromosome; otherwise, continue to step S309;
s309 from the population Ψ ev Selecting the most elegant chromosome, replacing the chromosome which represents the construction scheme of the charging network in the ecological system, and updating the ecological system;
s310 pairs of populations Ψ pv The nth chromosome in (1) is decoded to determine M ev Construction position, construction capacity and total construction capacity C of individual photovoltaic power station t-pv Decoding chromosomes representing charging network construction schemes in the ecosystem, determining M ch Construction position, construction capacity and total construction capacity C of electric vehicle charging station t-ev (ii) a On the basis, a scene probability method is adopted to calculate the probability load flow of the power distribution system, and the expected grid loss capacity F in a planning typical day is determined loss The voltage amplitude of each node and the probability distribution characteristic of each line load flow, and calculating the population psi according to the formula (13) pv Fitness V of the nth chromosome in (1) fit-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 calculation of the population Ψ is completed pv Fitness of all chromosomes in the population, i.e. determining whether the chromosome index N is equal to the population size N pop1 If n is<N pop1 If n = n +1, and go to step S310, continue to calculate the population Ψ pv Fitness of the next chromosome; otherwise, continue to step S312;
s312, based on fitness, selecting Ψ from the population pv Selecting the most elegant chromosome, replacing the chromosome representing the construction scheme of the photovoltaic power station in the ecological system, and updating the ecological system;
s313, judging whether the maximum evolutionary algebra is reached, and if G = G max Then, go on to step S314; otherwise, respectively aiming at the group psi based on the fitness ev With Ψ pv Performing copying, crossing and mutation operations, updating the two populations, and skipping to the step S306;
s314, decoding two chromosomes respectively representing the electric vehicle charging network construction scheme and the photovoltaic power station construction scheme in the ecological system, outputting the two chromosomes as the optimal solution of the photovoltaic power station and electric vehicle charging network random collaborative planning model, and ending the algorithm flow.
2. The coevolution-based photovoltaic power plant and electric vehicle charging network planning method according to claim 1, characterized in that the population Ψ is ev In other words, to ensure that the crossed chromosomes satisfy the equation constraint given in equation (2), the crossing operation is performed as follows:
s41 from the population Ψ ev Randomly selecting two chromosomes as chromosomes to be crossed;
s42, repeatedly and randomly generating cross bit N to be selected can1 Wherein 1 is<N can1 <N ch Until a feasible cross position N is found cr1 (ii) a And
s43 with cross probability P c Crossover of the Nth of two chromosomes to be crossed cr1 And (5) completing the cross operation of the code strings after the code bits.
3. The coevolution-based photovoltaic power plant and electric vehicle charging network planning method according to claim 1, characterized in that the population Ψ is pv In other words, to ensure that the crossed chromosomes satisfy the equation constraint given in equation (3), the crossing operation is performed as follows:
s51 from the population Ψ pv Randomly selecting two chromosomes as chromosomes to be crossed;
s52, repeatedly and randomly generating cross bit N to be selected can2 Wherein 1 is<N can2 <N pv Until a feasible cross position N is found cr2 (ii) a And
s53 with cross probability P c Swapping the Nth of two chromosomes to be crossed cr2 And (5) completing the cross operation of the code strings after the code bits.
4. The coevolution-based photovoltaic power plant and electric vehicle charging network planning method according to claim 1, characterized in that the population Ψ ev The mutation operator comprises the following steps:
s61 from the population Ψ ev Randomly selecting a chromosome as a chromosome to be mutated;
s62, randomly generating two code bits N to be varied mu1 And N mu2 Wherein 1 is<N mu1 <N ch ，1<N mu2 <N ch Ensuring that one of the values of the two code bits to be varied is '0' and the other is a non-0 integer; and
s63 mutation probability P m Treating simultaneously ectopic N mu1 And N mu2 Performing mutation operation, namely changing the position to be mutated with the value of 0 to be not more than Q ev The random integer of not 0, the bit to be mutated which takes the value of not 0 is mutated into 0, and the mutation operation is completed.
5. The coevolution-based photovoltaic power plant and electric vehicle charging network planning method according to claim 1, characterized in that the population Ψ pv The mutation operator comprises the following steps:
s71 from the population Ψ pv ZhongrandSelecting a chromosome as a chromosome to be mutated;
s72, randomly generating two code bits N to be varied mu3 And N mu4 In which 1 is<N mu3 <N pv ，1<N mu4 <N pv Ensuring that one of the two code bits to be varied is '0' and the other is a non-0 integer; and
s73 mutation probability P m Treating simultaneously ectopic N mu3 And N mu4 Performing mutation operation, namely changing the position to be varied with the value of 0 to be not more than Q pv The random integer of not 0, the mutation position to be mutated which is not 0 is changed into 0, and the mutation operation is finished.
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