CN111898309A - Photovoltaic intelligent edge terminal optimized layout method - Google Patents

Photovoltaic intelligent edge terminal optimized layout method Download PDF

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CN111898309A
CN111898309A CN202010724508.1A CN202010724508A CN111898309A CN 111898309 A CN111898309 A CN 111898309A CN 202010724508 A CN202010724508 A CN 202010724508A CN 111898309 A CN111898309 A CN 111898309A
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王文天
姜小涛
方磊
牛睿
张明
嵇文路
葛磊蛟
张玮亚
张继东
周科峰
秦羽飞
刘嘉恒
朱红勤
胡君
傅乔清
朱宇超
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Tianjin University
Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The optimal layout method of the photovoltaic intelligent edge terminals aims at optimizing the economic efficiency, and optimizes the number and the positions of the photovoltaic intelligent edge terminals in an area range and a topological structure connected with a distributed photovoltaic station. The invention constructs a photovoltaic intelligent edge terminal optimization layout model, solves the problem of difficult photovoltaic intelligent edge terminal optimization layout caused by the scattered disorder and complex wiring of distributed photovoltaic power stations connected to a power distribution network, also provides an improved particle swarm algorithm, realizes the inertia weight self-adaptation, overcomes the defects of the existing inertia weight self-adaptation method, introduces the birth operation of the young wolf in the suburb optimization algorithm, adds new genes for the particle swarm by utilizing the variation in the birth of the young wolf of the suburb, improves the population diversity of the particle swarm algorithm, avoids the premature convergence of the population caused by simply using the self extreme value and the global extreme value to guide the particle flight, and has higher solving precision.

Description

Photovoltaic intelligent edge terminal optimized layout method
Technical Field
The invention belongs to the technical field of electric power, relates to a distributed photovoltaic data acquisition technology, and provides an optimal layout method for a photovoltaic intelligent edge terminal.
Technical Field
Photovoltaic intelligence edge terminal is a neotype photovoltaic data acquisition equipment, not only can realize the automation of photovoltaic data acquisition, and is intelligent, can carry out the preliminary treatment to the photovoltaic data of gathering moreover, alleviates the pressure of high in the clouds treater, effectively guarantees photovoltaic station safety and stability and operates. Regarding the optimal layout problem of the photovoltaic intelligent edge terminal, from the viewpoint of the operational reliability of the power station, the optimal configuration mode is a one-station-one-desk configuration mode, however, the photovoltaic intelligent edge terminal is very expensive, and the implementation difficulty of the configuration mode in the practical engineering is high. Distributed photovoltaic stations in the current area have the characteristics of dispersion disorder, complex wiring and the like of data acquisition requirements, how to reasonably optimize the layout of the photovoltaic intelligent edge terminals and realize the data acquisition of one photovoltaic intelligent edge terminal to a plurality of distributed photovoltaic stations is significant and very necessary.
The photovoltaic intelligent edge terminal products are not available in relevant documents for optimizing layout at home and abroad shortly after being made available, but the optimization layout principle and the model solution method are similar to those of the intelligent distribution network edge terminal optimization layout method. In recent years, the following researches are made by domestic and foreign scholars on the configuration scheme of similar devices related to a power grid: some scholars establish a multi-target PMU optimal configuration model aiming at the optimal configuration problem of a synchronous Phasor Measurement Unit (PMU), and solve the multi-target PMU optimal configuration model by using an improved self-adaptive multi-target binary differential evolution algorithm. And the two-layer optimization model of the generalized energy storage configuration is provided by some scholars aiming at the problem of the generalized energy storage optimization configuration of the power distribution network containing the high-permeability renewable energy sources, the outer layer adopts a genetic algorithm to search the generalized energy storage configuration scheme, the inner layer obtains the optimal operation strategy of the generalized energy storage according to a dynamic programming algorithm, and the generalized energy storage capacity optimization configuration is carried out on the power distribution network containing the renewable energy sources with different permeabilities and controllable loads through the inner-layer and outer-layer alternate optimization. However, the characteristics of multiple and wide distributed photovoltaic stations and disordered dispersion need to be considered, and the research cannot be directly applied to the optimal layout occasion of the photovoltaic power generation system. Therefore, a new layout optimization model needs to be proposed for how the distributed photovoltaic station sets the photovoltaic intelligent edge terminal.
The solution method of the optimization model can be roughly divided into an enumeration method and an intelligent algorithm. Enumeration ensures global optimality of the final solution by enumerating and evaluating all feasible solutions, but is easy to encounter dimensionality disasters and has no universality. Although the intelligent algorithm does not necessarily find out the global optimal solution, the intelligent algorithm does not need to enumerate all feasible solutions, and has great advantage in the aspect of solving speed. For the distributed photovoltaic station aimed by the invention, due to the characteristics of dispersion disorder, complex wiring, similar data acquisition requirements and the like, a suitable intelligent algorithm is found to solve an optimization model and improve the solving precision, and the research is still needed.
Disclosure of Invention
The invention aims to solve the problems that: the distributed photovoltaic stations have the characteristics of disorder dispersion, complex wiring and the like of data acquisition requirements, one station is configured with a photovoltaic intelligent edge terminal with high cost and low cost performance, a proper scheme needs to be provided, the photovoltaic intelligent edge terminal is reasonably optimized and distributed, and data acquisition of the photovoltaic intelligent edge terminal to the distributed photovoltaic stations is realized.
The technical scheme of the invention is as follows: a photovoltaic intelligent edge terminal optimal layout method is used for optimizing the photovoltaic intelligent edge terminal layout by taking the optimal economical efficiency as a target, and optimizing the number and the positions of the photovoltaic intelligent edge terminals in an area range and the three aspects of a topological structure connected with a distributed photovoltaic station, and comprises the following steps:
step 1, establishing a photovoltaic intelligent edge terminal optimization layout model, setting an optimized objective function according to economic indexes, and determining constraint conditions of photovoltaic intelligent edge terminal layout, wherein the constraint conditions comprise communication connection constraint, layout fund constraint, data capacity constraint, communication distance constraint and interface quantity constraint of the photovoltaic intelligent edge terminal;
step 2, setting the number of the distributed photovoltaic intelligent edge terminals as N and the number of the distributed photovoltaic stations needing to be configured with the terminals as M, and setting the minimum number of the photovoltaic intelligent edge terminals in the distribution as Nmin and the maximum number of the photovoltaic intelligent edge terminals in the distribution as Nmax (M) according to the model constraint condition, wherein the Nmax state indicates that all the distributed photovoltaic stations are configured with one photovoltaic intelligent edge terminal; n is equal to Nmin in the initial state, the cost C at the moment is calculated as the current lowest cost Cmin, and the optimal layout number Nbest of the photovoltaic intelligent edge terminal is equal to Nmax in the initial state;
step 3, solving the position of the optimal photovoltaic intelligent edge terminal and the connection mode of the photovoltaic intelligent edge terminal and each distributed photovoltaic station in the number of the photovoltaic intelligent edge terminals by a particle swarm algorithm on the basis of the number N of the photovoltaic intelligent edge terminals;
step 4, recording the cost C corresponding to the optimal particles when the number of the photovoltaic intelligent edge terminals is N;
step 5, comparing the result of the step 4 with the lowest cost Cmin of the step 2, if the cost C is lower than the global lowest cost Cmin at the moment, updating the global lowest cost Cmin by using the cost C, and updating the global optimal quantity Nbest by using the current quantity N;
and 6, if N is M-1, exiting the algorithm, and outputting the particles corresponding to the global optimal number Nbest, namely the optimal scheme, otherwise, N is N +1, and returning to the step 3.
The invention provides an optimal layout method of photovoltaic intelligent edge terminals, which considers the characteristics of scattered disorder, complex wiring and the like of distributed photovoltaic stations in a region, provides an optimal layout model of the photovoltaic intelligent edge terminals with optimal economy as a target, and optimizes the layout of the number and the positions of the photovoltaic intelligent edge terminals in the region, the topological structure connected with the distributed photovoltaic stations and the like.
The model has huge solution space and a plurality of optimal solutions, the problem is difficult to solve by the conventional integer optimization method, and the particle swarm algorithm is adopted for solving. The particle swarm optimization is a very popular meta-heuristic intelligent optimization algorithm, some scholars optimize the particle swarm optimization from the aspects of particle speed, iteration times, parameter optimization and the like, and some scholars study several inertia weight self-adaptive modes of the particle swarm optimization to find that the convex function subtraction method and the random inertia weight method can achieve better effects. The evolution of a population does not follow the characteristics of first-fast and then-slow or first-slow and then-fast, and the random inertia weight method just reflects the characteristics. However, for a multi-peak function, if a random inertia weight method is used, the obtained result is likely to be that the inertia weight ω is too small and is only locally optimal, or the inertia weight ω is too large and cannot achieve sufficient precision. In order to integrate the advantages of the two inertial weight self-adaptive methods, the invention provides an inertial weight self-adaptive method combining a convex function subtraction method and a random inertial weight method, so as to make up for the defects of the two methods. In addition, the particle swarm optimization guides the particles to fly by utilizing the self-optimization and the global optimization together, the convergence speed is high, but the particle swarm is easy to fall into the local optimization.
The invention provides a photovoltaic intelligent edge terminal optimal layout method under the condition of multiple regional distributed photovoltaic stations, which comprises the steps of calculating constraints such as data acquisition quantity, communication interface number and communication distance between a photovoltaic intelligent edge terminal and a distributed photovoltaic station according to the economical efficiency of a photovoltaic intelligent edge terminal configuration scheme, constructing a photovoltaic intelligent edge terminal optimal layout model, and solving the problem of difficult optimal layout of the photovoltaic intelligent edge terminal caused by the scattered disorder and complex wiring of the distributed photovoltaic stations connected to a power distribution network. The inertia weight self-adaptive method combining the convex function subtraction method and the random inertia weight method is adopted to make up the defects of the two inertia weight self-adaptive methods, the birth operation of the young wolf in the suburb optimization algorithm is introduced, new genes are added to the particle swarm by the variation in the birth of the young wolf of the suburb, the problem of low solving precision of the traditional particle swarm algorithm is solved, and the solving precision is higher.
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FIG. 1 is a flow chart of the present invention for optimizing layout of a photovoltaic intelligent edge terminal.
FIG. 2 is a solution result of the photovoltaic intelligent edge terminal optimization layout model according to the present invention.
Detailed Description
The invention provides an optimal layout model of a photovoltaic intelligent edge terminal, which aims at optimizing the number and the position of the photovoltaic intelligent edge terminals in an area range and optimizing the layout of a topological structure connected with distributed photovoltaic stations and the like, in order to realize the aim of integrating multi-station data by one photovoltaic intelligent edge terminal, combine engineering practice, consider the characteristics of scattered disorder, complex wiring and the like of the distributed photovoltaic stations in the area, and provide the optimal layout model of the photovoltaic intelligent edge terminal with optimal economy.
The invention adopts the following technical scheme:
step 1, establishing a photovoltaic intelligent edge terminal optimization layout model, setting an optimized objective function according to economic indexes, and determining constraint conditions of photovoltaic intelligent edge terminal layout, wherein the constraint conditions comprise communication connection constraint, layout fund constraint, data capacity constraint, communication distance constraint and interface quantity constraint of the photovoltaic intelligent edge terminal;
step 2, setting the number of the distributed photovoltaic intelligent edge terminals as N and the number of the distributed photovoltaic stations needing to be configured with the terminals as M, and setting the minimum number of the photovoltaic intelligent edge terminals in the distribution as Nmin and the maximum number of the photovoltaic intelligent edge terminals in the distribution as Nmax (M) according to the model constraint condition, wherein the Nmax state indicates that all the distributed photovoltaic stations are configured with one photovoltaic intelligent edge terminal; n is equal to Nmin in the initial state, the cost C at the moment is calculated as the current lowest cost Cmin, and the optimal layout number Nbest of the photovoltaic intelligent edge terminal is equal to Nmax in the initial state;
step 3, solving the position of the optimal photovoltaic intelligent edge terminal and the connection mode of the photovoltaic intelligent edge terminal and each distributed photovoltaic station in the number of the photovoltaic intelligent edge terminals by a particle swarm algorithm on the basis of the number N of the photovoltaic intelligent edge terminals;
step 4, recording the cost C corresponding to the optimal particles when the number of the photovoltaic intelligent edge terminals is N;
step 5, comparing the result of the step 4 with the lowest cost Cmin of the step 2, if the cost C is lower than the global lowest cost Cmin at the moment, updating the global lowest cost Cmin by using the cost C, and updating the global optimal quantity Nbest by using the current quantity N;
and 6, if N is M-1, exiting the algorithm, and outputting the particles corresponding to the global optimal number Nbest, namely the optimal scheme, otherwise, N is N +1, and returning to the step 3.
The invention is described in further detail below with reference to the accompanying drawings:
as shown in fig. 1, the method for optimizing the layout of the photovoltaic intelligent edge terminal includes the following steps.
Step 1, establishing a photovoltaic intelligent edge terminal optimization layout model; the objective function of the model is as follows:
(1) equal annual value investment cost
The equal-year-number investment cost of the photovoltaic intelligent edge terminal is as follows:
CI＝CD(1+ρ)A(r,n) (1)
CD＝PTN (2)
Figure BDA0002601187240000041
in the formula, CIEqual annual value investment cost; cDThe cost for purchasing a batch of photovoltaic intelligent edge terminals; n is the number of photovoltaic intelligent edge terminals configured in the region; pTThe price of a single photovoltaic intelligent edge terminal; a (r, n) is a factor for measuring the economy of the photovoltaic intelligent edge terminal; r is the discount rate; n is the service life of the photovoltaic intelligent edge terminal; rho is the proportion of the maintenance cost and the operation cost of the photovoltaic intelligent edge terminal to the cost for purchasing the photovoltaic intelligent edge terminal.
(2) Equal annual value communication cost
In the scene of the invention, the configuration of the photovoltaic intelligent edge terminals is not completely in the distributed photovoltaic station, and some photovoltaic intelligent edge terminals are outside the distributed photovoltaic station, so that the erection cost of the communication cable needs to be calculated, as shown in formulas (4) to (7), data of all devices in the photovoltaic station are collected by the photovoltaic intelligent edge terminals, and data of the same devices are integrated and uploaded to the data photovoltaic intelligent edge terminals.
CC＝CG(1+)A(r,m) (4)
Figure BDA0002601187240000051
Figure BDA0002601187240000052
Figure BDA0002601187240000053
In the formula, CCEqual annual value communication costs; cGConstruction costs for communication cables; the maintenance cost of the communication cable is in proportion to the construction cost; m is the service life of the communication cable, A (r, m) is a factor for measuring the economy of the communication cable; m is the number of distributed photovoltaic stations in the region; a. theijIs a variable from 0 to 1; if the communication connection is established between the photovoltaic intelligent edge terminal i in the area and the distributed photovoltaic station j in the area, the communication connection is 1, otherwise, the communication connection is 0; l isijRepresenting the communication distance between the photovoltaic intelligent edge terminal i and the distributed photovoltaic station j; (x)i,yi) The coordinates of the photovoltaic intelligent edge terminal in the area are obtained; (x)j,yj) Coordinates of the distributed photovoltaic stations in the area. Lambda is the erection cost of erecting all communication cables in unit distance between one distributed photovoltaic station and a photovoltaic intelligent edge terminal; d is the number of equipment types needing data acquisition in the distributed photovoltaic station; y is the number of communication cable types;
Figure BDA0002601187240000054
the variable is 0-1, if all the devices with the type of s in the station adopt communication cables with the type of z to establish communication connection with the photovoltaic intelligent edge terminal, the variable is 1, otherwise, the variable is 0; alpha is alphazRepresenting the erection cost per unit distance of the z-type communication cable.
In summary, the objective function is:
minC＝CC+CI(8)。
the constraint conditions of the photovoltaic intelligent edge terminal optimization layout model are as follows:
(1) communication connection constraints
A batch of distributed photovoltaic stations are distributed in the area range, and in order to ensure smooth collection of photovoltaic data, the distributed photovoltaic stations have to erect a communication cable with any photovoltaic intelligent edge terminal in the area to establish communication connection:
Figure BDA0002601187240000055
Figure BDA0002601187240000056
(2) initial gross capital constraints
Due to the limitation of total investment funds, the cost of purchasing the intelligent edge terminal and the operation and maintenance cost of the photovoltaic intelligent edge terminal cannot exceed the total investment funds in the initial stage.
0≤C≤CK(11)
CK＝CD+CG(12)
In the formula, CKRepresenting the total investment funds in the initial stages of the project.
(3) Photovoltaic intelligent edge terminal data capacity constraints
Because photovoltaic power generation needs sufficient illumination, so distributed photovoltaic station is worked daytime, when illumination is not enough, just can upload the photovoltaic data of gathering to high in the clouds server, and photovoltaic intelligence edge terminal and distributed photovoltaic station work at same time quantum. Therefore, the total photovoltaic data collection amount of each photovoltaic intelligent edge terminal cannot exceed the maximum total photovoltaic data collection amount of the photovoltaic intelligent edge terminals.
Figure BDA0002601187240000061
Figure BDA0002601187240000062
Figure BDA0002601187240000063
In the formula, QmaxThe maximum data total amount which can be acquired by the photovoltaic intelligent edge terminal every day; qjThe total data volume of the devices in the jth distributed photovoltaic station in the region per day; beta is the data quantity of a single measuring point of a data acquisition terminal, psRepresenting the acquisition frequency, n, of the device ssRepresenting the number of devices s, e, within a distributed photovoltaic plantsCounting the number of single measurement points of the equipment s; t isonRepresents the time of day of work; t issRepresenting the acquisition cycle of the device s.
(4) Communication distance constraint
In order to ensure the data acquisition quality and the data acquisition efficiency of the photovoltaic intelligent edge terminal, the communication distance between the photovoltaic intelligent edge terminal in the region and the distributed photovoltaic stations in the region must be limited within a certain range.
AijLij≤Rmax(16)
In the formula, RmaxAnd the limit communication distance between the photovoltaic intelligent edge terminal and the distributed photovoltaic station is represented.
(5) Photovoltaic intelligent edge terminal interface quantity constraint
The photovoltaic intelligent edge terminal can support various communication modes, and collects the data with devices such as inverters of distributed photovoltaic stations in regions, power station field environment monitors, components and the like in different modes, but because the number of interfaces of the photovoltaic intelligent edge terminal is limited, the maximum connection number of the photovoltaic intelligent edge terminal connected with the distributed photovoltaic station devices cannot exceed the maximum connection number of the photovoltaic intelligent edge terminal in a certain communication mode.
Figure BDA0002601187240000064
In the formula, gammajs hThe variable is 0-1, if the s equipment in the distributed photovoltaic station j is in communication connection with the photovoltaic intelligent acquisition i terminal in the communication mode l, the variable is 1, otherwise, the variable is 0; n isjsThe number of s devices in the distributed photovoltaic station j is shown; j. the design is a squarehmaxThe maximum connection number of the photovoltaic intelligent edge terminal in the h communication mode is obtained.
Step 2, setting the number of the distributed photovoltaic intelligent edge terminals as N and the number of the distributed photovoltaic stations needing to be configured with the terminals as M, and setting the minimum number of the photovoltaic intelligent edge terminals in the distribution as Nmin and the maximum number of the photovoltaic intelligent edge terminals in the distribution as Nmax (M) according to the model constraint condition, wherein the Nmax state indicates that all the distributed photovoltaic stations are configured with one photovoltaic intelligent edge terminal; n is equal to Nmin in the initial state, the cost C at the moment is calculated as the current lowest cost Cmin, and the optimal layout number Nbest of the photovoltaic intelligent edge terminal is equal to Nmax in the initial state;
step 3, solving the model by using a particle swarm algorithm on the basis of the number N of the photovoltaic intelligent edge terminals in the step 2, and solving the positions of the photovoltaic intelligent edge terminals and the connection modes of the photovoltaic intelligent edge terminals and each distributed photovoltaic station under the number; the conventional particle swarm algorithm is as follows.
A Particle Swarm Optimization (POS) algorithm belongs to one of evolutionary algorithms, starts from random solutions, searches for an optimal solution through iteration, evaluates the quality of the solution through fitness, is more suitable for solving real numbers compared with a genetic algorithm, and is not easy to damage because of no intersection and variation operation. The conventional particle swarm algorithm specifically processes as follows.
(1) Initializing the position X of each particle of the populationl＝(xl1,xl2,…xlQ) And velocity Vl＝(vl1,vl2,…vlQ) Q in the subscript represents the dimension of the search space;
(2) calculating a fitness value F [ l ] of each particle;
(3) for each particle, its fitness value F [ l ] is used]And individual extremum plBy comparison, if F [ l ]]＞plThen use F [ l ]]Substitution of pl；
(4) For each particle, its fitness value F [ l ] is used]And a global extremum pgBy comparison, if F [ l ]]＞pgThen use F [ l ]]Substitution of pg；
(5) Velocity V of renewed particlelAnd position Xl(ii) a The particle state is updated in the particle swarm optimization as follows:
Figure BDA0002601187240000071
in the formula (I), the compound is shown in the specification,
Figure BDA0002601187240000072
the position and velocity, respectively, of the ith particle in the kth iteration;
Figure BDA0002601187240000073
the position and velocity of the ith particle in the (k + 1) th iteration, respectively; w represents an inertial weight; c. C1,c2A learning factor that is a particle; r is1,r2Is [0,1 ]]Random numbers uniformly distributed therein; p is a radical oflIs the individual extremum of the l particle; p is a radical ofgIs a global extremum;
(6) and if the iteration times reach the maximum, exiting the algorithm and outputting an optimal result.
The invention further provides an improvement on the particle swarm algorithm, and the calculation precision is improved, specifically as follows:
some documents research several inertia weight self-adaptive modes of a particle swarm algorithm, and find that a good effect can be obtained by adopting a convex function subtraction method and a random inertia weight method. The literature indicates that the evolution of a population does not follow the characteristics of fast first to slow or slow first to fast, and the random inertia weight method just reflects the characteristics. However, for a multi-peak function, if a random inertia weight method is used, the obtained result is likely to be that the inertia weight w is too small and is only locally optimal, or the inertia weight w is too large and cannot achieve sufficient precision. In order to integrate the advantages of the two inertial weight adaptive methods, an inertial weight adaptive method combining a convex function subtraction method and a random inertia weight method is provided to make up for the defects of the two methods. The specific inertial weight change method is as follows:
w＝wmin+(1-k/kmax)2wmax+rpcp(19)
wherein wmin is the set minimum inertial weight, wmax is the set maximum inertial weight, k is the current iteration number, kmax is the maximum iteration number, rpIs [ -1,1 [ ]]Uniformly distributed random numbers in between, cpRepresenting the magnitude of the weight change.
The particle swarm optimization guides the particle flight by utilizing self-optimization and global optimization together, the convergence speed is high, but the particle swarm is easy to fall into local optimization, the birth operation of the young wolf in the suburb optimization algorithm is introduced, new genes are added to the particle swarm by utilizing the variation in the birth of the young wolf of the suburb, the population diversity of the particle swarm optimization is improved, the premature convergence of the population caused by simply using self extremum and global extremum to guide the particle flight is avoided, and the particle birth and death steps are as follows:
(1) after the particle speed and position are updated, randomly dividing the particles into f groups, wherein each group comprises c (c is more than 2) particles, respectively carrying out birth and death operations of new particles on the f small groups by taking the group as a unit, setting the age of the initial particles to be 0, and adding 1 to the age of all the particles after the birth and death operations of all the particles each time;
(2) in a group of particles, two particles cr are randomly selected1,cr2As parents of new particles and selects two random dimensions q1And q is2；
(3) The new particle pup and new particle velocity pupv of the set are generated as follows
Figure BDA0002601187240000081
Wherein pup is a new particle of the group, pupv is the velocity of the new set of particles, and the subscript q denotes the current dimension, cr1,cr2Parents of randomly chosen new particles within the group,
Figure BDA0002601187240000082
and
Figure BDA0002601187240000083
respectively represent particles cr1,cr2The decision variables in the q-dimension are,
Figure BDA0002601187240000084
and
Figure BDA0002601187240000085
respectively represent particles cr1,cr2A variable value of the velocity of (a) in the q dimension; r isqIs [0,1 ]]Random numbers, R, distributed uniformly withinqIs the variation value randomly generated in the q-dimension decision variable range and range of the particle; vqIs the variance value of the particle velocity randomly generated in the velocity amplitude limit of the q-th dimension; psAnd PaThe scattering probability and the correlation probability, respectively, which determine the inherited and mutated states of the particle, are calculated as follows:
Ps＝1/U (21)
Pa＝(1-Ps)/2 (22)
where U represents the dimension of the solution space.
(4) After the new particles are born, calculating and comparing fitness values of the particles in the group with the new particles, and when the fitness of only one particle in the group is worse than that of the new particle, the particle is dead, the new particle is alive, and the age of the new particle is set as 0; when the fitness of a plurality of particles in the group is worse than that of new particles, the particle with the highest age dies, if the particles have the same age, the particle with the worst fitness dies, and the new particles survive, and the age of the new particles is set as 0; when all particles in the group had better fitness than the new particle, the new particle died.
Step 4, recording the cost corresponding to the optimal particles when the number of the photovoltaic intelligent edge terminals is N;
and 5, comparing with the current lowest cost, as shown in a formula (23), if the cost C obtained by the current method is lower than the global lowest cost Cmin at the moment, replacing the global optimal cost with the current cost, and replacing the global optimal quantity Nbest with the current quantity N.
Figure BDA0002601187240000091
And 6, if N is M-1, exiting the algorithm, and outputting the particles corresponding to the global optimal number Nbest, namely the optimal scheme, otherwise, N is N +1, and returning to the step 3.
In order to verify the feasibility of the invention, design examples are used for verification, the number of devices in a coordinate photovoltaic station of a selected distributed photovoltaic station is shown in table 1, inverters, electric energy meters and environment monitors are connected with a photovoltaic intelligent edge terminal through RS485 interfaces, a data collector and an electric energy quality wave recorder are connected with the photovoltaic intelligent edge terminal through RJ45 Ethernet, and the collection period and the number of single measuring points of each device are shown in table 2.
TABLE 1 coordinates and number of devices of distributed photovoltaic stations in the area
Figure BDA0002601187240000092
Figure BDA0002601187240000101
TABLE 2 acquisition cycle and number of points measured per photovoltaic plant
Type of device Acquisition period/s Single measuring pointNumber of
Inverter with a voltage regulator 5 20
Electric energy meter 900 10
Environment detector 60 10
Electric energy quality recorder 5 20
Component manager 60 1
Establish photovoltaic intelligence edge terminal list price PT1.5 ten thousand yuan per platform, the current rate r is 10 percent, the service life n of the equipment is 15 years, and the operation and maintenance cost accounts for PTThe ratio ρ of 0.2. Daily maximum data acquisition quantity Q of each photovoltaic intelligent edge terminalmax10000Mb, β 0.001171. The maximum number of RS485 interfaces of each photovoltaic intelligent edge terminal is 12, the maximum number of RJ45 Ethernet interfaces is 12, and the maximum allowable distance R for communication between the distributed photovoltaic stations and the photovoltaic intelligent edge terminalsmaxWas 4 km. The equipment which adopts the RS485 interface to be in communication connection with the photovoltaic intelligent edge terminal in the distributed photovoltaic station adopts an armored twisted-pair shielding cable to be in communication connection, the labor cost is taken into account, the cost for erecting the cable is 3 yuan/meter, the equipment which adopts the RJ45 Ethernet interface to be in communication connection with the photovoltaic intelligent edge terminal adopts an optical cable, the labor cost is taken into account, and the line is erectedThe cost of the cable is 4.2 yuan/m, the proportion of the operation and maintenance cost to the cost of constructing the communication cable is 0.35, and the service life is 10 years. Initial total investment capital CKIs 1.2 ten thousand yuan.
Under the method of the invention, the parameter setting of the improved particle swarm algorithm is as follows: the maximum iteration number is 50, the number of initialized particles is 200, the inertia weight is 0.5, and the learning factor c 11 is 2.5, learning factor c2Is 1.5, and the inertia weight random amplitude c is 0.1.
As shown in FIG. 2, the optimal number of the photovoltaic intelligent edge terminals obtained by the algorithm of the present invention is 3, where A, B, and C are the positions of the photovoltaic edge terminals, and their coordinates are A (-2.25, -8.66) B (1.54.1.62), and C (1.22, -1.89), respectively. The comparison between the method of the present invention and the conventional one-station one-desk configuration results is as follows:
TABLE 3 comparison of the configuration results of the inventive method with the conventional one-station-one-desk configuration results
Figure BDA0002601187240000102
From the above experimental results, the following conclusions can be drawn:
(1) the number of the photovoltaic intelligent edge terminals obtained by the method is 3, the equal-year-value cost is 1.343 ten thousand yuan, and if the number of the photovoltaic intelligent edge terminals obtained by the method is 10 according to the conventional one-station one-desk configuration method, the equal-year-value cost is 1.343 ten thousand yuan. The method of the invention can obviously reduce the quantity and the equal annual value investment cost.
(2) Although the method increases the equal-year-number communication cost, the total equal-year-number cost is greatly reduced, and the effectiveness and the feasibility of the method are verified.
In order to compare and verify the superiority of the algorithm, the improved method is compared with a particle swarm algorithm adopting an inertia weight convex function subtraction method and a random inertia weight method, wherein wmax and wmin of the random inertia weight method are respectively selected to be 0.4 and 0.9. The random inertia weight method has the parameter selection random range of [0.4,0.6 ]. The improved algorithm of the present invention has the parameter c of 0.1, wmax of 0.6 and wmin of 0.4. The performance of the algorithm is compared by adopting a mean value (mean) and a mean square error (std), wherein the algorithm 1 is a particle swarm algorithm adopting a convex function decreasing method, the algorithm 2 is a particle swarm algorithm adopting a random inertia weight method, and the algorithm 3 is an improved particle swarm algorithm. The three algorithms were run 80 times independently, and the results of the solution were as follows:
TABLE 4 comparison of three different algorithms
Figure BDA0002601187240000111
The experimental result shows that when the number of the photovoltaic intelligent edge terminals is 3, the improved particle swarm algorithm is superior to the algorithm 1 and the algorithm 2 in precision and stability. When the number of the photovoltaic intelligent edge terminals is 5, the number of the photovoltaic intelligent edge terminals is large, the problem complexity is reduced, and the advantage of the random inertia weight method is highlighted, so that the improved particle swarm algorithm is superior to the algorithm 1 in precision, but has little difference with the algorithm 2, but is superior to the algorithms 1 and 2 in stability. The improved particle swarm algorithm provided by the invention is proved to have certain superiority under specific conditions.

Claims (6)

1. A photovoltaic intelligent edge terminal optimization layout method is characterized in that optimal photovoltaic intelligent edge terminal optimization layout is carried out by taking economic optimization as a target, and three aspects of the number and the positions of photovoltaic intelligent edge terminals in an area range and a topological structure connected with a distributed photovoltaic station are optimized, and the method comprises the following steps:
step 1, establishing a photovoltaic intelligent edge terminal optimization layout model, setting an optimized objective function according to economic indexes, and determining constraint conditions of photovoltaic intelligent edge terminal layout, wherein the constraint conditions comprise communication connection constraint, layout fund constraint, data capacity constraint, communication distance constraint and interface quantity constraint of the photovoltaic intelligent edge terminal;
step 2, setting the number of the distributed photovoltaic intelligent edge terminals as N and the number of the distributed photovoltaic stations needing to be configured with the terminals as M, and setting the minimum number of the photovoltaic intelligent edge terminals in the distribution as Nmin and the maximum number of the photovoltaic intelligent edge terminals in the distribution as Nmax (M) according to the model constraint condition, wherein the Nmax state indicates that all the distributed photovoltaic stations are configured with one photovoltaic intelligent edge terminal; n is equal to Nmin in the initial state, the cost C at the moment is calculated as the current lowest cost Cmin, and the optimal layout number Nbest of the photovoltaic intelligent edge terminal is equal to Nmax in the initial state;
step 3, solving the position of the optimal photovoltaic intelligent edge terminal and the connection mode of the photovoltaic intelligent edge terminal and each distributed photovoltaic station in the number of the photovoltaic intelligent edge terminals by a particle swarm algorithm on the basis of the number N of the photovoltaic intelligent edge terminals;
step 4, recording the cost C corresponding to the optimal particles when the number of the photovoltaic intelligent edge terminals is N;
step 5, comparing the result of the step 4 with the lowest cost Cmin of the step 2, if the cost C is lower than the global lowest cost Cmin at the moment, updating the global lowest cost Cmin by using the cost C, and updating the global optimal quantity Nbest by using the current quantity N;
and 6, if N is M-1, exiting the algorithm, and outputting the particles corresponding to the global optimal number Nbest, namely the optimal scheme, otherwise, N is N +1, and returning to the step 3.
2. The method according to claim 1, wherein the objective function of the photovoltaic intelligent edge terminal optimized layout model in step 1 is as follows:
(1) equal annual value investment cost
The equal-year-number investment cost of the photovoltaic intelligent edge terminal is as follows:
CI＝CD(1+ρ)A(r,n) (1)
CD＝PTN (2)
Figure FDA0002601187230000011
in the formula, CIEqual annual value investment cost; cDThe cost for purchasing a batch of photovoltaic intelligent edge terminals; n is that regional configuration photovoltaic intelligence edge terminalThe number of the particles; pTThe price of a single photovoltaic intelligent edge terminal; a (r, n) is a factor for measuring the economy of the photovoltaic intelligent edge terminal; r is the discount rate; n is the service life of the photovoltaic intelligent edge terminal; rho is the proportion of the maintenance cost and the operation cost of the photovoltaic intelligent edge terminal to the expense of purchasing the photovoltaic intelligent edge terminal;
(2) equal annual value communication cost
Calculating the erection cost of the communication cable between the photovoltaic intelligent edge terminal and the distributed photovoltaic station:
CC＝CG(1+)A(r,m) (4)
Figure FDA0002601187230000021
Figure FDA0002601187230000022
Figure FDA0002601187230000023
in the formula, CCEqual annual value communication costs; cGConstruction costs for communication cables; the maintenance cost of the communication cable is in proportion to the construction cost; m is the service life of the communication cable, A (r, m) is a factor for measuring the economy of the communication cable; m is the number of distributed photovoltaic stations in the region; a. theijIs a variable of 0 to 1, if the communication connection is established between the photovoltaic intelligent edge terminal i in the region and the distributed photovoltaic station j in the region, Aij1, otherwise 0; l isijRepresenting the communication distance between the photovoltaic intelligent edge terminal i and the distributed photovoltaic station j; (x)i,yi) The coordinates of the photovoltaic intelligent edge terminal in the area are obtained; (x)j,yj) The method comprises the following steps that (1) lambda is a coordinate of a distributed photovoltaic station in an area, and lambda is the erection cost of erecting all communication cables in a unit distance between one distributed photovoltaic station and a photovoltaic intelligent edge terminal; d is the number of equipment types needing data acquisition in the distributed photovoltaic station; y is the number of communication cable types;
Figure FDA0002601187230000024
the variable is 0-1, if all the devices with the type of s in the station adopt communication cables with the type of z to establish communication connection with the photovoltaic intelligent edge terminal, the variable is 1, otherwise, the variable is 0; alpha is alphazRepresenting the erection cost of z-type communication cables in unit distance;
and (3) integrating the equal-year-number investment cost and the equal-year-number communication cost to obtain an objective function minC taking economic indexes as targets:
min C＝CC+CI(8)。
3. the photovoltaic intelligent edge terminal optimal layout method according to claim 2, wherein the constraint conditions in the step 1 are specifically as follows:
(1) communication connection constraints
The distributed photovoltaic station in the region scope has to erect a communication cable with any photovoltaic intelligent edge terminal in the region, and establishes communication connection:
Figure FDA0002601187230000025
Figure FDA0002601187230000031
(2) initial gross capital constraints
The cost of purchasing the intelligent edge terminal and the operation and maintenance cost of the photovoltaic intelligent edge terminal cannot exceed the total investment capital of the initial stage:
0≤C≤CK(11)
CK＝CD+CG(12)
in the formula, CKRepresenting the total investment fund of the initial stage of the project;
(3) photovoltaic intelligent edge terminal data capacity constraints
The total photovoltaic data collection amount of each photovoltaic intelligent edge terminal cannot exceed the maximum total photovoltaic data amount which can be collected by the photovoltaic intelligent edge terminals every day:
Figure FDA0002601187230000032
Figure FDA0002601187230000033
Figure FDA0002601187230000034
in the formula, QmaxThe maximum data total amount which can be acquired by the photovoltaic intelligent edge terminal every day; qjThe total data volume of the devices in the jth distributed photovoltaic station in the region per day; beta is the data quantity of a single measuring point of a data acquisition terminal, psRepresenting the acquisition frequency, n, of the device ssRepresenting the number of devices s, e, within a distributed photovoltaic plantsCounting the number of single measurement points of the equipment s; t isonRepresents the time of day of work; t issRepresents the acquisition cycle of the device s;
(4) communication distance constraint
In order to ensure the data acquisition quality and the data acquisition efficiency of the photovoltaic intelligent edge terminal, the communication distance between the photovoltaic intelligent edge terminal in the region and the distributed photovoltaic stations in the region must be limited within a certain range:
AijLij≤Rmax(16)
in the formula, RmaxThe method comprises the steps of representing the limit communication distance between a photovoltaic intelligent edge terminal and a distributed photovoltaic station;
(5) photovoltaic intelligent edge terminal interface quantity constraint
The number of interfaces of the photovoltaic intelligent edge terminal is limited, and the maximum connection number of the photovoltaic intelligent edge terminal in a certain communication mode cannot be exceeded when the photovoltaic intelligent edge terminal is connected with the distributed photovoltaic station equipment:
Figure FDA0002601187230000041
in the formula, gammajs hIs changed from 0 to 1The quantity is 1 if the s equipment in the distributed photovoltaic station j is in communication connection with the photovoltaic intelligent acquisition i terminal in the communication mode l, and is 0 if the s equipment in the distributed photovoltaic station j is not in communication connection with the photovoltaic intelligent acquisition i terminal in the communication mode l; n isjsThe number of s devices in the distributed photovoltaic station j is shown; j. the design is a squarehmaxThe maximum connection number of the photovoltaic intelligent edge terminal in the h communication mode is obtained.
4. The photovoltaic intelligent edge terminal optimal layout method according to claim 1, wherein the particle swarm algorithm in the step 3 is as follows:
(1) initializing the position X of each particle of the populationl＝(xl1,xl2,…xlQ) And velocity Vl＝(vl1,vl2,…vlQ) Q represents the dimension of the search space;
(2) calculating a fitness value F [ l ] of each particle;
(3) for each particle, its fitness value F [ l ] is used]And individual extremum plBy comparison, if F [ l ]]＞plThen use F [ l ]]Substitution of pl；
(4) For each particle, its fitness value F [ l ] is used]And a global extremum pgBy comparison, if F [ l ]]＞pgThen use F [ l ]]Substitution of pg；
(5) Velocity V of renewed particlelAnd position Xl(ii) a The particle state is updated in the particle swarm optimization as follows:
Figure FDA0002601187230000042
in the formula (I), the compound is shown in the specification,
Figure FDA0002601187230000043
the position and velocity, respectively, of the ith particle in the kth iteration;
Figure FDA0002601187230000044
the position and velocity of the ith particle in the (k + 1) th iteration, respectively; w represents an inertial weight; c. C1,c2A learning factor that is a particle; r is1,r2Is [0,1 ]]Random numbers uniformly distributed therein; p is a radical oflIs the individual extremum of the l particle; p is a radical ofgIs a global extremum;
(6) and if the iteration times reach the maximum, exiting the algorithm and outputting an optimal result.
5. The photovoltaic intelligent edge terminal optimal layout method according to claim 4, wherein a particle swarm optimization is improved, and an inertial weight adaptive method combining convex function subtraction and random inertia weight method is provided for the inertial weight w, specifically as follows:
w＝wmin+(1-k/kmax)2wmax+rpcp(19)
wherein wmin is the set minimum inertial weight, wmax is the set maximum inertial weight, k is the current iteration number, kmax is the maximum iteration number, rpIs [ -1,1 [ ]]Uniformly distributed random numbers in between, cpA magnitude representing a weight change;
in the particle swarm optimization iteration, the birth operation of the young wolf in the suburb optimization algorithm is introduced, and the steps of particle birth and death are as follows:
(1) after the particle speed and the particle position are updated, randomly dividing the particles into f groups, wherein each group comprises c particles, c is more than 2, respectively carrying out birth and death operations of new particles on the f small groups by taking the group as a unit, setting the age of the initial particles to be 0, and adding 1 to the age of all the particles after the birth and death operations of all the particles each time;
(2) in a group of particles, two particles cr are randomly selected1,cr2As parents of new particles and selects two random dimensions q1And q is2；
(3) The new particle pup and new particle velocity pupv of the set are generated as follows
Figure FDA0002601187230000051
Where pup is the new particle for the set, pupv is the velocity of the new particle for the set, subscript q denotes the current dimension, cr1,cr2Parents of randomly chosen new particles within the group,
Figure FDA0002601187230000052
and
Figure FDA0002601187230000053
respectively represent particles cr1，cr2The decision variables in the q-dimension are,
Figure FDA0002601187230000054
and
Figure FDA0002601187230000055
respectively represent particles cr1,cr2A variable value of the velocity of (a) in the q dimension; r isqIs [0,1 ]]Random numbers, R, distributed uniformly withinqIs the variation value randomly generated in the q-dimension decision variable range and range of the particle; vqIs the variance value of the particle velocity randomly generated in the velocity amplitude limit of the q-th dimension; psAnd PaThe scattering probability and the correlation probability, respectively, which determine the inherited and mutated states of the particle, are calculated as follows:
Ps＝1/U (21)
Pa＝(1-Ps)/2 (22)
wherein U represents the dimension of the solution space;
(4) after the new particles are born, calculating and comparing fitness values of the particles in the group with the new particles, and when the fitness of only one particle in the group is worse than that of the new particle, the particle is dead, the new particle is alive, and the age of the new particle is set as 0; when the fitness of a plurality of particles in the group is worse than that of new particles, the particle with the highest age dies, if the particles have the same age, the particle with the worst fitness dies, and the new particles survive, and the age of the new particles is set as 0; when all particles in the group had better fitness than the new particle, the new particle died.
6. The optimal layout method for the photovoltaic intelligent edge terminals according to claim 1, wherein the global minimum cost and global optimal quantity updating method in the step 5 is as follows:
Figure FDA0002601187230000056
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