CN117674263A - Distribution network photovoltaic site selection and volume determination and bearing capacity evaluation method considering photovoltaic absorption rate - Google Patents
Distribution network photovoltaic site selection and volume determination and bearing capacity evaluation method considering photovoltaic absorption rate Download PDFInfo
- Publication number
- CN117674263A CN117674263A CN202311720577.5A CN202311720577A CN117674263A CN 117674263 A CN117674263 A CN 117674263A CN 202311720577 A CN202311720577 A CN 202311720577A CN 117674263 A CN117674263 A CN 117674263A
- Authority
- CN
- China
- Prior art keywords
- photovoltaic
- distribution network
- capacity
- model
- power distribution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 93
- 238000010521 absorption reaction Methods 0.000 title claims abstract description 13
- 238000011156 evaluation Methods 0.000 title claims description 29
- 238000004513 sizing Methods 0.000 claims abstract description 34
- 238000000034 method Methods 0.000 claims abstract description 21
- 239000002245 particle Substances 0.000 claims description 86
- 238000013210 evaluation model Methods 0.000 claims description 13
- 230000029087 digestion Effects 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 8
- 230000006978 adaptation Effects 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 230000035699 permeability Effects 0.000 claims description 2
- 230000003068 static effect Effects 0.000 claims description 2
- 238000005457 optimization Methods 0.000 abstract description 7
- 238000010276 construction Methods 0.000 abstract description 5
- 239000000243 solution Substances 0.000 description 30
- 238000005516 engineering process Methods 0.000 description 5
- 239000011159 matrix material Substances 0.000 description 5
- 239000013598 vector Substances 0.000 description 5
- 230000007547 defect Effects 0.000 description 4
- 238000013178 mathematical model Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000010219 correlation analysis Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 2
- 238000004220 aggregation Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a distribution network photovoltaic site selection capacity determination and bearing capacity assessment method considering photovoltaic absorption rate, and belongs to the technical field of distribution network optimization. According to the invention, the multi-target photovoltaic locating and sizing model of the distributed photovoltaic access power distribution network is constructed, and the voltage drop and line loss of a power distribution network line are reduced to the minimum on the premise that the photovoltaic consumption is increased as much as possible by the multi-target photovoltaic locating and sizing model; solving the multi-target photovoltaic addressing and sizing model to obtain the pareto optimal solution of the model; and comprehensively evaluating the photovoltaic bearing capacity of the power distribution network based on the solutions of the multi-target photovoltaic locating and sizing model. The distributed photovoltaic power distribution network system based on the grid-connected power distribution network realizes the large-scale consumption of distributed photovoltaic energy sources through the site selection and volume determination model, and evaluates the photovoltaic bearing capacity of the power distribution network in the future so as to provide guidance for planning and construction of the distributed photovoltaic power distribution network.
Description
Technical Field
The invention belongs to the technical field of distribution network optimization, and particularly relates to a distribution network photovoltaic site selection capacity determination and bearing capacity evaluation method considering photovoltaic absorption rate.
Background
In recent years, new energy has been rapidly developed. The distributed photovoltaic is used as an effective utilization form of the solar energy at the load side, and under the promotion of related excitation policies, the distributed photovoltaic in China is increased at a high speed, and the distributed photovoltaic has the characteristics of wide points and multiple sides and local high-density grid connection. The distributed photovoltaic output is closely related to the local solar energy real-time irradiation intensity, and has obvious intermittence and fluctuation. After the large-scale distributed photovoltaic is accessed into a power grid, the characteristics of the traditional power distribution network are changed, the power distribution network is changed from a passive network to an active network, and the power flow of the power distribution network is changed from one-way to two-way.
The transformation of the power grid characteristics caused by the distributed photovoltaic brings a plurality of adverse effects to the voltage, the electric energy quality, the relay protection, the planning, the scheduling operation and the like of the power distribution network, and seriously threatens the safe and stable operation of the power distribution network. In addition, the distributed photovoltaic is usually located at the end of the power distribution network, the access voltage level is low, the information access rate is not high, the observability is poor, and the security risk caused by unordered access of the distributed photovoltaic is often unpredictable for a power grid dispatching department. The coordinated development of distributed photovoltaic, load and power grid is difficult to ensure in the prior art, and the capacity margin of the distributed photovoltaic which can be accessed in the future by each node must be estimated based on the stable operation boundary and the actual operation state of the power distribution network, so that guidance is provided for planning and construction of the distributed photovoltaic and the power distribution network.
Disclosure of Invention
In view of the above, the invention provides a distribution network photovoltaic locating, sizing and bearing capacity evaluation method considering the photovoltaic absorption rate, which aims to evaluate the future accessible distributed photovoltaic capacity margin of each node on the basis of guaranteeing the coordinated development of distributed photovoltaic, load and power grid, thereby providing guidance for planning and construction of the distributed photovoltaic and power distribution network.
In order to achieve the above object, the present invention provides the following technical solutions:
a distribution network photovoltaic locating, sizing and bearing capacity evaluation method considering photovoltaic absorption rate comprises the following steps:
constructing a multi-target photovoltaic locating and sizing model of a distributed photovoltaic access power distribution network, wherein the multi-target photovoltaic locating and sizing model reduces voltage drop and line loss of a power distribution network line to the minimum on the premise of increasing photovoltaic consumption as much as possible;
solving the multi-target photovoltaic addressing and sizing model to obtain the pareto optimal solution of the model;
and comprehensively evaluating the photovoltaic bearing capacity of the power distribution network based on the solutions of the multi-target photovoltaic locating and sizing model.
Further, the objective function of the multi-objective photovoltaic addressing and sizing model specifically comprises:
first objective function to minimize line active losses generated in the distribution network:
wherein f 1 N is the number of nodes of the power grid, i and j are node numbers, G is a two-objective function ij For the conductance of lines i-j, U i 、U j For the voltage amplitude of nodes i, j, cos θ ij Cosine values of the phase angle differences of the voltages of the nodes i and j;
a second objective function that minimizes the absolute value of the difference between each load node voltage and the standard voltage:
wherein f 2 As a second objective function, U N Is as the voltage standardA value;
third objective function that maximizes the ratio of photovoltaic capacity to total photovoltaic production:
wherein f 3 The third objective function is the photovoltaic capacity of node i, which is the maximum photovoltaic capacity of node i.
Further, constraint conditions of the multi-target photovoltaic addressing and sizing model comprise load flow amplitude constraint, voltage amplitude constraint and photovoltaic capacity constraint, and the constraint conditions are as follows:
P ij ＝G ij U i 2 -G ij U i U j cosθ ij -B ij U i U j sinθ ij
V i min ≤V i ≤V i max
0≤P i DG ≤P i DG,max
wherein p is ij For active power flow of line i-j, sin theta ij Cosine value of phase angle difference of voltage of node i and voltage of node j, Q ij Reactive power flow of line i-j, B ij For susceptance of line i-j, cos θ ij The cosine value of the phase angle difference of the voltage at node i and j,for the conductance of lines i-j, V i min And V i max Respectively the minimum and maximum voltage of node i, V i The voltage minimum and maximum at node i.
Furthermore, in solving the multi-target photovoltaic addressing and volume-fixing model, a niche particle swarm algorithm is adopted as a solving algorithm.
Further, the multi-target photovoltaic addressing and volume-fixing model is solved by adopting a niche particle swarm algorithm, and the method specifically comprises the following steps:
inputting power system parameters;
initializing the position and the speed of the particle swarm;
initializing the niche seeds, calculating the distance between the seeds and the particles, and bringing the particles into the nearest niche range;
comparing the current particle with the individual optimal particle according to the dominant relationship, selecting the particle with high dominant degree as a new pbest, and randomly selecting if the particles are not dominant; adjusting particle adaptation values according to crowding degrees of different niches, and updating global optimal particles gbest through the adaptation values;
updating the position and the speed of the particles according to the pbest and the gbest;
combining the particle groups before and after updating into a mixed particle group, performing non-dominant sorting on the mixed particle group, and calculating crowding distances of different dominant layers;
screening the particles ranked at the top from the mixed particle swarm according to the non-dominant ranking order and the crowding degree to form a new particle swarm;
and (5) continuously iterating the calculation until the stopping condition is reached, and ending the solution to obtain the corresponding pareto optimal solution.
Furthermore, the niche crowding distance is added into the adaptive value, and the mathematical expression of the adaptive value is as follows:
F p ＝(N-S f1,p ) 2 +(N-S f2,p ) 2 +(N-S f3,p ) 2 +(N-S f4,p ) 2
wherein F 'is' p For final fitness, F p X is the original fitness p 、X q Is the position value of the particles p and q, d share To share distance, beta isShape parameters.
Further, in updating the position and speed of the particles according to the pbest and the gbest, a nonlinear updating mode of an inertia factor and an acceleration factor is adopted to ensure that the algorithm jumps out of a local optimal solution, and the mathematical expression is specifically as follows:
v new ＝w·v+c 1 ·rand·(pbest-X)+c 2 ·rand·(gbest-X)
wherein v is new And v is the particle speed of the next iteration and the current iteration respectively, w is the inertia factor, c 1 And c 2 As acceleration factor, rand is random number, X is particle position value, w min And w max Respectively minimum and maximum values of particle position values, c min And c max Respectively minimum and maximum values of the accelerated particles, T and T max The current iteration number and the maximum iteration number are respectively.
Further, the comprehensive evaluation of the photovoltaic bearing capacity of the power distribution network specifically comprises the following steps:
constructing an evaluation model of the photovoltaic bearing capacity, and determining a target layer, a criterion layer and an index layer of the evaluation model;
determining the relevancy ranking of the index layers according to a gray relevancy analysis method;
determining the weight of each index according to an analytic hierarchy process;
and (5) evaluating the photovoltaic bearing capacity of the power distribution network by adopting a paste comprehensive evaluation method.
Further, in the evaluation model, the target layer is an evaluation index of the photovoltaic bearing capacity of the power distribution network, the criterion layer comprises electric energy quality, power supply capacity and distributed photovoltaic grid-connected characteristics, and the index layer comprises voltage deviation and voltage fluctuation indexes corresponding to the electric energy quality, main transformer maximum load rate and access system line maximum load rate indexes corresponding to the power supply capacity, distributed photovoltaic static permeability, distributed photovoltaic output fluctuation, distributed photovoltaic natural absorption rate and distributed photovoltaic output and load normalization form matching rate corresponding to the distributed photovoltaic grid-connected characteristics.
Further, in the process of evaluating the photovoltaic bearing capacity of the power distribution network by adopting the paste comprehensive evaluation method, the evaluation score is calculated by adopting the following calculation formula:
wherein G is k To evaluate the result value, H ik Indicating the applicability of index i in evaluating k, w i I=1, 2, for the weight of index i, n,indicating that the sum of the two is compared with 1, and the minimum value is taken.
In summary, the invention provides a distribution network photovoltaic locating and sizing and bearing capacity evaluation method considering photovoltaic absorption rate, which is characterized in that a multi-target photovoltaic locating and sizing model of a distributed photovoltaic access power distribution network is constructed, and the voltage drop and line loss of the power distribution network line are reduced to the minimum on the premise that the photovoltaic absorption is increased as much as possible by the multi-target photovoltaic locating and sizing model; solving the multi-target photovoltaic addressing and sizing model to obtain the pareto optimal solution of the model; and comprehensively evaluating the photovoltaic bearing capacity of the power distribution network based on the solutions of the multi-target photovoltaic locating and sizing model. The distributed photovoltaic power distribution network system based on the grid-connected power distribution network realizes the large-scale consumption of distributed photovoltaic energy sources through the site selection and volume determination model, and evaluates the photovoltaic bearing capacity of the power distribution network in the future so as to provide guidance for planning and construction of the distributed photovoltaic power distribution network.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for evaluating capacity and carrying capacity of photovoltaic site selection of a distribution network according to an embodiment of the present invention;
FIG. 2 is a particle swarm flow chart incorporating a niche particle swarm algorithm and an update technique for nonlinear inertia/acceleration factors;
FIG. 3 is a structural framework of a photovoltaic load-bearing capacity evaluation model of a power distribution network;
FIG. 4 is an algorithm flow of a photovoltaic load-bearing capacity evaluation model of a power distribution network;
fig. 5 is a schematic diagram of a power grid before and after a distributed power supply is connected.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions of 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 apparent that the embodiments described below are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present embodiment provides a method for evaluating capacity and carrying capacity of a distribution network by photovoltaic site selection, which includes the following steps:
step one: constructing a multi-target photovoltaic locating and sizing model of a distributed photovoltaic access power distribution network, wherein the multi-target photovoltaic locating and sizing model reduces voltage drop and line loss of a power distribution network line to the minimum on the premise of increasing photovoltaic consumption as much as possible;
step two: solving the multi-target photovoltaic addressing and volume-fixing model by using a niche particle swarm algorithm, wherein the algorithm flow is shown in figure 2, and a niche technology and a nonlinear inertia/acceleration factor updating technology are added into a traditional particle swarm to finally obtain the pareto optimal solution of the model;
step three: based on the solution of the multi-target photovoltaic locating and sizing model, the photovoltaic bearing capacity of the power distribution network is comprehensively evaluated, and the structural framework and algorithm flow of the evaluation model are shown in figures 3 and 4.
The line loss and node voltage of the power distribution network are affected by the access of the photovoltaic energy, the line loss of a power distribution network system is possibly increased, the voltage drop is possibly increased, and even serious consequences such as unbalance of the power grid are possibly caused, so that the influence of the access of the photovoltaic energy on the power grid needs to be analyzed, and a theoretical basis is provided for the follow-up photovoltaic constant volume and location problem.
A schematic diagram of the photovoltaic access to the distribution network is shown in fig. 5. Wherein P is S 、Q S Representing the active injection power and the reactive injection power of the distribution substation respectively, P DG 、Q DG Respectively representing active power and reactive power of photovoltaic access, P D 、Q D Representing active and reactive loads, respectively, U S 、U D Node voltage amplitudes at a distribution substation end and a load end are respectively, R+jX represents line impedance, and w% represents the ratio of the distance from a DG access point to the distribution substation to the whole length of a line.
By applying a voltage drop formula and a superposition principle, the variation of voltage drops before and after photovoltaic access can be expressed as:
before access:
after the access:
influence on voltage drop before and after access:
the amount of change in line loss before and after photovoltaic access can be expressed as:
before access:
after the access:
effects on line loss before and after access:
according to formulas (1) and (2), assuming constant power distribution injected power, the line loss variation and the voltage drop variation before and after photovoltaic access mainly depend on the capacity and the position of the distributed power supply. Therefore, by reasonably selecting the position and the capacity of the photovoltaic energy access, the circuit loss and the voltage drop in the power distribution network can be improved, and meanwhile, the problem of the absorption of the photovoltaic energy is solved.
According to the distribution network photovoltaic locating and sizing and bearing capacity evaluation method considering the photovoltaic digestion rate, a multi-target photovoltaic locating and sizing model of a distributed photovoltaic access power distribution network is constructed, and voltage drop and line loss of a power distribution network line are reduced to the minimum on the premise that the photovoltaic digestion is increased as much as possible; solving the multi-target photovoltaic addressing and sizing model to obtain the pareto optimal solution of the model; and comprehensively evaluating the photovoltaic bearing capacity of the power distribution network based on the solutions of the multi-target photovoltaic locating and sizing model. The distributed photovoltaic power distribution network system based on the grid-connected power distribution network realizes the large-scale consumption of distributed photovoltaic energy sources through the site selection and volume determination model, and evaluates the photovoltaic bearing capacity of the power distribution network in the future so as to provide guidance for planning and construction of the distributed photovoltaic power distribution network.
In one embodiment of the invention, the current power distribution network lacks an evaluation model of the photovoltaic bearing capacity, the traditional evaluation methods have the defects of the current power distribution network, and some documents propose the combination of different evaluation methods so as to avoid the defects of over-subjectivity, susceptibility to data accidental influence and the like. In order to comprehensively and objectively evaluate the photovoltaic capacity of the power distribution network, the embodiment synthesizes a hierarchical analysis method, a gray correlation analysis method and a fuzzy comprehensive evaluation method, provides a power distribution network photovoltaic bearing capacity evaluation model, overcomes the defect that part of evaluation algorithms are too subjective, and has the following algorithm flow:
(1) Build target layer, criteria layer, and index layer
According to the characteristics of the distributed photovoltaic access distribution network, a target layer, a criterion layer and an index layer of an evaluation model are constructed, as shown in fig. 3.
(2) Determining the relevance ranking of the index layer according to a gray relevance analysis method
Collecting reference data and sample data, and performing normalization processing to obtain reference vectors M respectively 0 And sample vector M i By comparing different samples with the reference vector, constructing an association coefficient matrix, wherein the association coefficient of the ith index in the sample j is as follows:
wherein m represents the number of samples and n represents the number of indexes.
Thus, the degree of association of the index i can be expressed as:
(3) Determining the weight of each index according to the analytic hierarchy process
The traditional analytic hierarchy process has the defect that the judgment matrix is too subjective, and the obtained result is not convincing. In order to overcome the problem caused by subjectivity, the evaluation model introduces the relevance of a gray correlation analysis method, compares the importance of different indexes according to w values obtained by the gray correlation analysis method, and constructs an n multiplied by n judgment matrix A according to a 9-point scale method, namely any integer value and relevant reciprocal value in the range [1,9 ].
The weight vector W and the maximum feature root lambda can be solved by solving the following equation max ：
AW＝λ max W (5)
Using maximum characteristic root lambda max And (3) carrying out consistency verification of the solution, if the solution is satisfied, W is a weight value satisfying consistency, otherwise, adjusting a judgment matrix and recalculating.
(4) Evaluation of photovoltaic bearing capacity of power distribution network by fuzzy comprehensive evaluation method
And according to the fuzzy mathematical model, dividing the photovoltaic bearing capacity of the power distribution network into three grades of excellent grade, good grade and poor grade. Establishing an n multiplied by 3 fuzzy relation matrix H, wherein H ik Indicated as the applicability of index i in evaluating k. The model selects a fuzzy operator M (-) with wider applicability,) Wherein->Indicating that the sum of the two is compared with 1, and the minimum value is taken. The evaluation score was calculated as follows:
the maximum value of the G vector is located, and the maximum value is an evaluation grade output by the mathematical model, namely one of the excellent, good and poor.
In one embodiment of the invention, the access of photovoltaic energy affects the line loss and voltage drop of the distribution network, and a large number of photovoltaic accesses affect the operation stability of the distribution network. Therefore, the problem of locating and sizing of photovoltaic energy is studied, a multi-target mathematical model of the distributed photovoltaic access power distribution network is established, and voltage drop and line loss of a power distribution network line are reduced to the minimum on the premise that photovoltaic consumption is increased as much as possible.
The existing photovoltaic addressing and volume-fixing model is mostly a single-target problem, the problem is difficult to optimize in an omnibearing manner, and meanwhile, different optimization items cannot be ordered accurately in importance, so that the invention establishes a multi-target-based photovoltaic addressing and volume-fixing model and solves the problem through a multi-target particle swarm algorithm. However, the optimal solution of the multi-objective optimization problem is often presented in pareto solution form, and is no longer the only optimal solution. Therefore, how to avoid the excessive aggregation of the optimal solution is a key problem to be solved by the pareto optimal solution. Therefore, the invention adds the niche technology in the traditional multi-target particle swarm algorithm.
(1) Objective function
The model takes three angles of line loss, voltage deviation and photovoltaic natural absorption rate as target values of an addressing and volume-fixing model, and finds the pareto optimal solution under a multi-target mathematical model.
The objective function 1 minimizes the line active loss generated in the distribution network, and the expression is as follows:
wherein N is the number of nodes of the power grid, i and j are the node numbers, G ij For the conductance of lines i-j, U i 、U j For the voltage amplitude of nodes i, j, cos θ ij Cosine values of the phase angle differences of the voltages of the nodes i and j;
the objective function 2 minimizes the absolute value of the difference between the voltage of each load node and the standard voltage as follows:
in U N Is a voltage standard value;
objective function 3 maximizes the ratio of photovoltaic capacity to total photovoltaic production expressed as follows:
wherein P is i DG For the photovoltaic capacity of node i, P i DG，max Is the maximum photovoltaic capacity of node i.
(2) Constraint conditions
The model uses three parts of tidal current amplitude constraint, voltage amplitude constraint and photovoltaic capacity constraint, and the expression is as follows:
P ij ＝G ij U i 2 -G ij U i U j cosθ ij -B ij U i U j sinθ ij
V i min ≤V i ≤V i max
0≤P i DG ≤P i DG,max (10)
wherein p is ij For active power flow of line i-j, sin theta ij Cosine value of phase angle difference of voltage of node i and voltage of node j, Q ij Reactive power flow of line i-j, B ij For susceptance of line i-j, cos θ ij The cosine value of the phase angle difference of the voltage at node i and j,for the conductance of lines i-j, V i min And V i max Respectively the minimum and maximum voltage of node i, V i The voltage minimum and maximum at node i.
In one embodiment of the invention, a niche particle swarm algorithm is employed as the solution algorithm.
The particle swarm optimization algorithm is firstly proposed by Kennedy doctor and Eberhart doctor, the feasible solution is compared with particles moving in space, and the effect of solving the optimal solution is finally achieved by continuously updating the movement position and movement speed of the particles, so that the particle swarm optimization algorithm has fast convergence speed and global optimizing capability for solving the large-scale optimization problem.
The niche technology is derived from the concept of a niche in nature, and refers to the fact that similar species exist in a certain range, all species compete for living resources and space, and punishment is conducted on individuals with similar distances in the same niche so as to ensure species diversity in the niche. After the niche technology is added into the particle swarm algorithm, the crowding distance and the sharing degree are introduced into the fitness, the particle fitness value is adjusted according to the crowding degree of the niche, the diversity in the population is increased, the algorithm is prevented from converging in an optimal solution, and the solved pareto optimal solution is prevented from being not representative.
The flow of the niche particle swarm algorithm adopted by the invention is as follows:
(a) Input power system parameters (including total photovoltaic, line impedance, etc.);
(b) Initializing the position and speed of a particle swarm;
(c) Initializing the niche seeds, calculating the distance between the seeds and the particles, and bringing the particles into the nearest niche range;
(d) Comparing the current particle with the individual optimal particle according to the dominant relationship, selecting the particle with high dominant degree as a new pbest, and randomly selecting if the particles are not dominant; and adjusting the particle adaptation value according to the crowding degree of different niches, and updating the global optimal particle gbest through the adaptation value. In order to avoid the excessive aggregation of particles, the invention adds a niche crowding distance in the original fitness, and the mathematical expression is as follows:
wherein F is p For final fitness, F p X is the original fitness p 、X q Is the position value of the particles p and q, d share To share distance, β is a shape parameter.
(e) According to the positions and speeds of the particles updated by the pbest and the gbest, adding a nonlinear update mode of an inertia factor and an acceleration factor in the update process to ensure that the algorithm has enough capacity to jump out of a local optimal solution in the early stage, wherein the mathematical expression is as follows:
wherein v is new And v is the particle speed of the next iteration and the current iteration respectively, w is the inertia factor, c 1 And c 2 As acceleration factor, rand is random number, X is particle levelSetting the value, w min And w max Respectively minimum and maximum values of particle position values, c min And c max Respectively minimum and maximum values of the accelerated particles, T and T max The current iteration number and the maximum iteration number are respectively. Meanwhile, judging whether the updated particles are out of limit, and if so, generating non-out-of-limit particle substitution;
(f) Combining the particle groups before and after updating into a mixed particle group, performing non-dominant sorting on the mixed particle group, and calculating crowding distances of different dominant layers;
(g) Screening the particles with the higher rank from the mixed particle swarm according to the non-dominant ranking order and the crowding degree to form a new particle swarm;
(h) The iteration times t=t+1, and judging whether T is larger than T max If so, ending the algorithm, otherwise, entering the step (c).
Compared with the prior art, the technical scheme of the invention has the following advantages:
1. the locating and sizing model realizes the large-scale absorption of the distribution network to the distributed photovoltaic energy sources;
2. the site selection and volume determination model fully considers the change conditions of line loss and voltage drop when the photovoltaic is consumed, and reduces the harm of the change to the power distribution network;
3. the comprehensive evaluation model of the photovoltaic bearing capacity of the power distribution network provides a comprehensive, comprehensive and digital photovoltaic digestion strategy evaluation method, so that the analysis and selection of the photovoltaic digestion strategy are more dependent.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The distribution network photovoltaic site selection capacity determination and bearing capacity evaluation method considering the photovoltaic absorption rate is characterized by comprising the following steps of:
constructing a multi-target photovoltaic locating and sizing model of a distributed photovoltaic access power distribution network, wherein the multi-target photovoltaic locating and sizing model reduces voltage drop and line loss of a power distribution network line to the minimum on the premise of increasing photovoltaic consumption as much as possible;
solving the multi-target photovoltaic addressing and volume-fixing model to obtain the pareto optimal solution of the model;
and comprehensively evaluating the photovoltaic bearing capacity of the power distribution network based on the solution of the multi-target photovoltaic locating and sizing model.
2. The method for evaluating the capacity and the carrying capacity of the photovoltaic network according to claim 1, wherein the objective function of the multi-objective photovoltaic capacity-selecting model comprises the following specific steps:
first objective function to minimize line active losses generated in the distribution network:
wherein N is the number of nodes of the power grid, i and j are the node numbers, G ij For the conductance of lines i-j, U i 、U j For the voltage amplitude of nodes i, j, cos θ ij Cosine values of the phase angle differences of the voltages of the nodes i and j;
a second objective function that minimizes the absolute value of the difference between each load node voltage and the standard voltage:
in U N Is a voltage standard value;
third objective function that maximizes the ratio of photovoltaic capacity to total photovoltaic production:
wherein P is i DG For the photovoltaic capacity of node i, P i DG,max Is the maximum photovoltaic capacity of node i.
3. The distribution network photovoltaic locating and sizing and carrying capacity evaluation method considering the photovoltaic digestion rate according to claim 2, wherein the constraint conditions of the multi-target photovoltaic locating and sizing model comprise a tide amplitude constraint, a voltage amplitude constraint and a photovoltaic capacity constraint, and are specifically as follows:
P ij ＝G ij U i 2 -G ij U i U j cosθ ij -B ij U i U j sinθ ij
V i min ≤V i ≤V i max
0≤P i DG ≤P i DG,max
wherein p is ij For active power flow of line i-j, sin theta ij Cosine value of phase angle difference of voltage of node i and voltage of node j, Q ij Reactive power flow of line i-j, B ij For susceptance of line i-j, cos θ ij The cosine value of the phase angle difference of the voltage at node i and j,for the conductance of lines i-j, V i min And V i max Respectively the minimum and maximum voltage of node i, V i The voltage minimum and maximum at node i.
4. The distribution network photovoltaic locating and sizing and carrying capacity assessment method considering the photovoltaic digestion rate according to claim 1, wherein a niche particle swarm algorithm is adopted as a solving algorithm in solving the multi-target photovoltaic locating and sizing model.
5. The method for evaluating the capacity and the bearing capacity of the photovoltaic site selection of the distribution network according to claim 4, which is characterized by solving the multi-target photovoltaic site selection and the capacity determination model by adopting a niche particle swarm algorithm, and specifically comprises the following steps:
inputting power system parameters;
initializing the position and the speed of the particle swarm;
initializing the niche seeds, calculating the distance between the seeds and the particles, and bringing the particles into the nearest niche range;
comparing the current particle with the individual optimal particle according to the dominant relationship, selecting the particle with high dominant degree as a new pbest, and randomly selecting if the particles are not dominant; adjusting particle adaptation values according to crowding degrees of different niches, and updating global optimal particles gbest through the adaptation values;
updating the position and the speed of the particles according to the pbest and the gbest;
combining the particle groups before and after updating into a mixed particle group, performing non-dominant sorting on the mixed particle group, and calculating crowding distances of different dominant layers;
screening the particles ranked at the top from the mixed particle swarm according to the non-dominant ranking order and the crowding degree to form a new particle swarm;
and (5) continuously iterating the calculation until the stopping condition is reached, and ending the solution to obtain the corresponding pareto optimal solution.
6. The method for evaluating capacity and carrying capacity of a distribution network according to claim 5, wherein the congestion distance is used as the adaptation value, and the adaptation value is combined with the congestion distance, and the mathematical expression is as follows:
wherein F 'is' p For final fitness, F p X is the original fitness p 、X q Is the position value of the particles p and q, d share To share distance, β is a shape parameter.
7. The method for evaluating capacity and carrying capacity of photovoltaic site selection of a distribution network according to claim 5, wherein in the position and speed of updating particles according to pbest and gbest, a nonlinear updating mode of an inertia factor and an acceleration factor is adopted to ensure that an algorithm jumps out of a local optimal solution, and the mathematical expression is as follows:
v new ＝w·v+c 1 ·rand·(pbest-X)+c 2 ·rand·(gbest-X)
wherein v is new And v is the particle speed of the next iteration and the current iteration respectively, w is the inertia factor, c 1 And c 2 As acceleration factor, rand is randomNumber of machines, X is particle position value, w min And w max Respectively minimum and maximum values of particle position values, c min And c max Respectively minimum and maximum values of the accelerated particles, T and T max The current iteration number and the maximum iteration number are respectively.
8. The distribution network photovoltaic locating, sizing and bearing capacity evaluation method considering the photovoltaic digestion rate according to claim 1 is characterized by comprehensively evaluating the photovoltaic bearing capacity of a power distribution network, and specifically comprising the following steps:
constructing an evaluation model of the photovoltaic bearing capacity, and determining a target layer, a criterion layer and an index layer of the evaluation model;
determining the relevancy ranking of the index layers according to a gray relevancy analysis method;
determining the weight of each index according to an analytic hierarchy process;
and (5) evaluating the photovoltaic bearing capacity of the power distribution network by adopting a paste comprehensive evaluation method.
9. The grid-connected photovoltaic site-selection capacity-determining and load-carrying capacity assessment method considering the photovoltaic digestion rate according to claim 8, wherein in the assessment model, the target layer is a grid-connected photovoltaic load-carrying capacity assessment index, the criterion layer comprises power quality, power supply capacity and distributed photovoltaic grid-connected characteristics, and the index layer comprises voltage deviation and voltage fluctuation indexes corresponding to the power quality, main transformer maximum load rate and access system line maximum load rate indexes corresponding to the power supply capacity, and distributed photovoltaic static permeability, distributed photovoltaic output fluctuation, distributed photovoltaic natural digestion rate and distributed photovoltaic output and load normalization morphology matching rate corresponding to the distributed photovoltaic grid-connected characteristics.
10. The evaluation method for photovoltaic site selection, volume setting and bearing capacity of a distribution network according to claim 8, wherein in the evaluation of the photovoltaic bearing capacity of the distribution network by adopting a paste comprehensive evaluation method, an evaluation score is calculated by adopting the following calculation formula:
wherein G is k To evaluate the result value, H ik Indicating the applicability of index i in evaluating k, w i I=1, 2, for the weight of index i, n,indicating that the sum of the two is compared with 1, and the minimum value is taken.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311720577.5A CN117674263A (en) | 2023-12-14 | 2023-12-14 | Distribution network photovoltaic site selection and volume determination and bearing capacity evaluation method considering photovoltaic absorption rate |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311720577.5A CN117674263A (en) | 2023-12-14 | 2023-12-14 | Distribution network photovoltaic site selection and volume determination and bearing capacity evaluation method considering photovoltaic absorption rate |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117674263A true CN117674263A (en) | 2024-03-08 |
Family
ID=90080718
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311720577.5A Withdrawn CN117674263A (en) | 2023-12-14 | 2023-12-14 | Distribution network photovoltaic site selection and volume determination and bearing capacity evaluation method considering photovoltaic absorption rate |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117674263A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117911197A (en) * | 2024-03-20 | 2024-04-19 | 国网江西省电力有限公司电力科学研究院 | Photovoltaic addressing and volume-fixing method and system based on improved multi-target particle swarm algorithm |
-
2023
- 2023-12-14 CN CN202311720577.5A patent/CN117674263A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117911197A (en) * | 2024-03-20 | 2024-04-19 | 国网江西省电力有限公司电力科学研究院 | Photovoltaic addressing and volume-fixing method and system based on improved multi-target particle swarm algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110795692A (en) | Active power distribution network operation state evaluation method | |
| CN104408562B (en) | A kind of photovoltaic system generating efficiency comprehensive estimation method based on BP neural network | |
| CN110689240A (en) | Fuzzy comprehensive evaluation method for economic operation of power distribution network | |
| CN103679544A (en) | Comprehensive assessment method for running of intelligent power distribution network | |
| CN110111024A (en) | Scientific and technological achievement market valuation method based on AHP model of fuzzy synthetic evaluation | |
| CN117674263A (en) | Distribution network photovoltaic site selection and volume determination and bearing capacity evaluation method considering photovoltaic absorption rate | |
| CN113300380B (en) | Load curve segmentation-based power distribution network reactive power optimization compensation method | |
| CN108876114A (en) | Consider the power grid efficiency benefit appraisal procedure of new energy access | |
| CN109003010A (en) | The evaluation method of power grid project investment benefit | |
| CN112990627B (en) | Power quality evaluation method | |
| Yin et al. | Equilibrium stability of asymmetric evolutionary games of multi-agent systems with multiple groups in open electricity market | |
| CN109389272A (en) | A kind of comprehensive estimation method and system for voltage coordination control strategy effect | |
| CN114611842B (en) | Whole-county roof distributed photovoltaic power prediction method | |
| CN111680469A (en) | Medium-low voltage distribution network line loss limit value calculation method | |
| CN108009684A (en) | A kind of micro-grid connection state energy management method comprising short-term load forecasting | |
| Um et al. | Comprehensive evaluation indexes framework and evaluation method for substation site selection | |
| CN115860552A (en) | Rural energy Internet comprehensive benefit evaluation method | |
| Yang et al. | Power quality assessment based on combinatorial weight method of variance maximizing | |
| CN111340370A (en) | Model-based power distribution network gridding evaluation management and control method | |
| Liu et al. | Research and Application of Grid-based Integrated Evaluation and Management of Distribution Grid | |
| Liu et al. | Accurate investment evaluation model of power grid based on Improved Fuzzy Neural Inference | |
| Shi et al. | Study on stability feature extraction of power system using deep learning | |
| Wei et al. | weighting power quality evaluation based on cloud model and anti-entropy | |
| Wang et al. | Dynamic neural network based genetic algorithm optimizing for short term load forecasting | |
| Zhang et al. | Research on Evaluation Method of Application Maturity of BIM Technology in Transmission and Distribution Engineering Based on Fuzzy Theory |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20240308 |
|
| WW01 | Invention patent application withdrawn after publication |