CN110635504A - Power distribution network distributed photovoltaic absorption capacity evaluation method based on improved pollen algorithm - Google Patents
Power distribution network distributed photovoltaic absorption capacity evaluation method based on improved pollen algorithm Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention provides a method for evaluating distributed photovoltaic absorption capacity of a power distribution network, which comprises the following steps: I. and constructing a power distribution network distributed photovoltaic absorption capacity evaluation model. II. And solving the model by adopting an improved pollen algorithm, an original pollen algorithm, a genetic algorithm and a Gaussian pollen algorithm, wherein the obtained result is the digestion capability of the distributed photovoltaic of the power distribution network. By comparing a plurality of optimization results, the effectiveness and the correctness of the model and the algorithm built by the method are verified; further calculation results show that the photovoltaic inverter can effectively improve the distributed photovoltaic absorption capacity by the phase-in operation. The method can ensure that the photovoltaic absorption capacity of the power distribution network is evaluated on the premise of safe and economic operation of the power distribution network. The method provides scientific guidance suggestions and technical support for grid-connected planning of the distributed photovoltaic system.
Description
Technical Field
The invention relates to the field of distributed photovoltaic planning of a power distribution network, in particular to a method for evaluating distributed photovoltaic absorption capacity in the power distribution network.
Background
Photovoltaic power generation is widely developed and utilized under the background of continuous consumption of fossil energy and increasingly worsened global environment. However, the output of the distributed photovoltaic power supply has intermittency and volatility, and a large amount of the distributed photovoltaic power supply is connected into the power distribution network, so that the original network topology structure and trend distribution are changed, and the voltage control, relay protection, electric energy quality, reliability and the like of the power distribution network are influenced to different degrees. Therefore, in order to reduce adverse effects caused by photovoltaic grid connection, it is necessary to evaluate the distributed photovoltaic consumption capacity of the power distribution network in advance, and scientific guidance suggestions are provided for reasonable planning of the power distribution network.
Disclosure of Invention
In view of this, the embodiment of the invention provides a method for evaluating distributed photovoltaic absorption capacity of a power distribution network, which can solve the problem that the maximum capacity of a distributed photovoltaic power supply allowed to be accessed to the power distribution network cannot be simply, conveniently and quickly calculated by using the conventional calculation method.
In order to achieve the purpose, the invention adopts the following technical scheme:
the method for evaluating the distributed photovoltaic absorption capacity of the power distribution network based on the improved pollen algorithm comprises the following steps:
step 1: to be provided with
Constructing a power distribution network distributed photovoltaic absorption capacity evaluation model by taking node power balance constraint, node voltage deviation constraint, node voltage fluctuation constraint, line thermal stability constraint, short circuit capacity constraint and photovoltaic power generation capacity constraint as constraint conditions as a target function;
wherein: n is a radical ofPVTotal number of nodes, P, for the photovoltaic access in the systemPV，mIs the photovoltaic access capacity of the mth node, { NBIs the set of all nodes in the system, Pij、QijFor the values of the active and reactive power flowing on branch ij, UiIs the voltage value of node i, RijIs the line resistance value between nodes i and j;
the node power balance constraint means that after the distributed photovoltaic power supply is connected, the active power and the reactive power of the node meet a power equation to ensure that the power flow can be converged;
node voltage deviation constraint and node voltage fluctuation constraint refer to node voltage deviation and voltage fluctuation caused by grid connection of a distributed photovoltaic power supply, and are obtained based on a system load flow calculation result after grid connection of the distributed photovoltaic power supply;
line thermal stability constraint is the power distribution network load flow calculation result after the distributed photovoltaic power supply is connected to the grid;
the short-circuit capacity constraint is the short-circuit capacity of a system after the distributed photovoltaic grid connection and is obtained based on a grid structure and parameters of a power distribution network;
the capacity constraint of photovoltaic power generation is obtained based on a load flow calculation result of the power distribution network after photovoltaic grid connection;
step 2: the basic pollen algorithm is improved, so that the algorithm has good ergodicity and convergence rapidity.
And step 3: respectively adopting an improved pollen algorithm, an original pollen algorithm, a genetic algorithm and a Gaussian pollen algorithm to solve the distributed photovoltaic absorption capacity evaluation model, and optimizing to obtain a result, namely the distributed photovoltaic absorption capacity of the power distribution network; and comparing the photovoltaic absorption capacities obtained by optimizing several algorithms.
As a further description, in step 1, the evaluation model of distributed photovoltaic absorption capacity of the power distribution network is as follows:
an objective function:
in the formula, NPVTotal number of nodes, P, for the photovoltaic access in the systemPV，mIs the photovoltaic access capacity of the mth node, { NBIs the set of all nodes in the system, Pij、QijFor the values of the active and reactive power flowing on branch ij, UiIs the voltage value of node i, RijIs the line resistance value between nodes i and j;
constraint conditions are as follows:
node power balance constraint:
in the formula, Pi、Pi+1And Qi、Qi+1Respectively the active power and the reactive power flowing through the node i and the node i + 1; pPVi、PLiRespectively the active output and the load power of the distributed photovoltaic power supply at the node i; qLiIs the load reactive power of node i; ri、Ri+1And Xi、Xi+1Line resistance and reactance values between the node i-1 and the node i and between the node i and the node i +1 respectively; u shapei-1、Ui+1The voltage values of the node i-1 and the node i +1 are shown respectively.
Node voltage deviation constraint:
UN(1-ε1)≤Ui≤UN(1+ε2)
in the formula of UNIs the nominal voltage of the system; epsilon1、ε2The allowable voltage deviation ratio specified by the national standard.
And node voltage fluctuation constraint:
dk％≤dmax％
in the formula (d)k% is a node voltage fluctuation value caused after distributed photovoltaic access; dmax% is the maximum voltage fluctuation value specified by the national standard.
And (3) line thermal stability constraint:
Sl，i≤Sl，imaxi＝1，2，…，Nl
in the formula, NlIs the total number of lines; sl，i、Sl，imaxThe upper limits of the power of the ith branch and the allowable power of the line are respectively.
Short circuit capacity constraint:
considering that the short-circuit capacity and the short-circuit current are in a constant multiple relation, a constraint form of the short-circuit current is adopted:
Isc≤Isc max
in the formula IscIs the effective value of the fault point line current; i issc maxThe maximum short-circuit current value is specified by national standard.
Photovoltaic power generation capacity constraint:
0≤SPV，i≤SPV，max
in the formula, SPl′，iIs the photovoltaic power generation capacity of the ith node; sPV，maxIs the upper limit of the photovoltaic access capacity of the ith node.
As a further description, the improvement of the pollen algorithm in step 2 is as follows:
considering that most of the current pollen algorithms adopt a random mode to initialize the pollen position, the mode is difficult to ensure that the pollen position has better ergodicity. The chaotic sequence has better randomness, ergodicity and regularity, and the chaotic sequence generated by cubic mapping has better uniformity than Logistic mapping. The invention adopts cubic mapping to initialize pollen particles, namely:
in the formula, maxd、mindRespectively representing the upper and lower bounds of the d-th dimension of the search space, yi(d) Is the d-dimension, x, of the ith particle generated using the preceding equationidI.e. the coordinates of the ith particle in the d-dimension of the search space.
Pollen algorithm in the iterative optimization process, the pollen (gamete) population lacks a mutation mechanism. When the obtained optimal solution does not change in the continuous Q iterations or the variation mu is small, the algorithm can be considered to be trapped in the local optimal solution. At the moment, the optimal solution obtained by the pollen algorithm is used as an initial point of the genetic algorithm, the population is subjected to operations such as selection, crossing, mutation and the like, a group of new solutions is obtained, and the whole population is updated. Diversity of the population is increased by introducing a variation strategy, and optimizing capability of the algorithm is improved.
As a further description, step 3 uses an improved pollen algorithm to solve the evaluation model, which is:
initializing algorithm parameters including pollen population N, conversion probability P, maximum iteration number N _ iter, population scale sizepop of genetic algorithm, and cross probability PcrossProbability of mutation PmutationThe iteration times Maxgen and the initial iteration times t and threshold parameters Q and mu trapping local optimization. And simultaneously, determining upper and lower limit values of branch current, short-circuit current, node voltage deviation and voltage fluctuation according to power grid parameters.
An initial population is formed. Each flower corresponds to a scheme of photovoltaic access, and the dimension of each flower is the number of the photovoltaic grid-connected points. And initializing the pollen population according to the chaotic effect of the cubic mapping. Meanwhile, according to the load flow calculation, an objective function with a penalty term is formed and solved, an objective function value of each pollen is obtained through calculation, and the position of the optimal pollen is obtained through comparison.
And (5) iterating the population. The number of iterations increases by 1. And generating a random number, updating the position of the pollen according to the random number and the pollen conversion probability, and performing border crossing treatment.
The global optimum is updated. And calculating the objective function value of the flower after population updating, if the objective function value of the flower after population updating is superior to the objective function value of the current flower, replacing the current flower and the current objective function value with the new flower and the corresponding objective function value, or not updating.
Judging whether the local optimum is trapped or not, and if so, continuing to perform; otherwise, judging the termination condition.
And carrying out mutation and other operations by using a genetic algorithm. And taking the solution generated in the previous step as an initial point of a genetic algorithm, carrying out selection, crossing and mutation operations on the population, and solving a group of new solutions. If the new solution is superior to the current optimal solution, replacing the current optimal solution with the new solution, and updating the population; otherwise, no update is performed.
Judging whether an end condition is met, if so, stopping iteration and outputting an optimal solution; otherwise, a random number is regenerated and the operation after the random number is continued.
Drawings
FIG. 1 is a diagram: an equivalent circuit diagram of a distributed photovoltaic grid connection.
FIG. 2 is a diagram of: improved pollen algorithm flow chart.
FIG. 3 is a diagram of: IEEE33 node system architecture diagram.
FIG. 4 is a diagram of: and (4) a convergence curve graph of different optimization algorithm algorithms in the optimizing process.
FIG. 5 is a diagram: and (3) adopting a photovoltaic absorption capacity optimization diagram of each node of an IEEE33 node test system.
Detailed Description
The technical solution of the present invention is further explained in detail by the accompanying drawings.
Step 1: and calculating voltage deviation and voltage fluctuation after the distributed photovoltaic grid connection.
Fig. 1 is an equivalent circuit diagram of a distributed photovoltaic power grid connection. According to fig. 1, the voltage deviation at the grid-connected point k of the distributed photovoltaic power supply can be obtained as follows:
in the formula, Δ Uk% is voltage deviation at a grid-connected point; pPV、PL、QPV、QLThe active power and the reactive power of the distributed photovoltaic power supply and the node load are respectively; z is the equivalent impedance of the grid-connected point system side; u shapeNIs the rated voltage of the system side.
Voltage fluctuations refer to rapid changes in the effective value of the grid voltage. The voltage fluctuation of the grid-connected point caused by the output fluctuation of the distributed photovoltaic power supply can be represented as follows:
in the formula (d)k% is voltage fluctuation at the photovoltaic grid-connected point k; lambda [ alpha ]pThe proportion of the instantaneous power change amplitude of the distributed photovoltaic power supply in the rated output power of the distributed photovoltaic power supply caused by the influence of factors such as illumination, temperature and the like is disclosed.
Step 2: and constructing a distributed photovoltaic absorption capacity evaluation model.
An objective function:
according to the operation characteristics of the power system, the branch tide flow direction and size in the power distribution network can be influenced after the distributed photovoltaic power supply is connected, so that the line loss is changed, and the economic operation of the power distribution network is not facilitated due to the large network loss. Based on this, the invention takes the difference between the total access amount and the network loss of the distributed photovoltaic power supply as the effective photovoltaic absorption capacity, and the expression is as follows:
in the formula, NPVTotal number of nodes, P, for the photovoltaic access in the systemPV，mIs the photovoltaic access capacity of the mth node, { NBIs the set of all nodes in the system, Pij、QijFor the values of the active and reactive power flowing on branch ij, UiIs the voltage value of node i, RijIs the line resistance value between nodes i and j.
Constraint conditions are as follows:
node power balance constraint:
in the formula, Pi、Pi+1And Qi、Qi+1Respectively the active power and the reactive power flowing through the node i and the node i + 1; pPVi、PLiRespectively the active output and the load power of the distributed photovoltaic power supply at the node i; qLiIs the load reactive power of node i; ri、Ri+1And Xi、Xi-1Line resistance and reactance values between the node i-1 and the node i and between the node i and the node i +1 respectively; u shapei-1、Ui+1The voltage values of the node i-1 and the node i +1 are shown respectively.
Node voltage deviation constraint:
UN(1-ε1)≤Ui≤UN(1+ε2) (5)
in the formula of UNIs the nominal voltage of the system; epsilon1、ε2The allowable voltage deviation ratio specified by the national standard.
And node voltage fluctuation constraint:
dk％≤dmax％ (6)
in the formula (d)k% is a node voltage fluctuation value caused after distributed photovoltaic access; dmax% is the maximum voltage fluctuation value specified by the national standard.
And (3) line thermal stability constraint:
Sl，i≤Sl，imaxi＝1，2，…，Nl (7)
in the formula, NlIs the total number of lines; sl，i、Sl，imaxThe upper limits of the power of the ith branch and the allowable power of the line are respectively.
Short circuit capacity constraint:
considering that the short-circuit capacity and the short-circuit current are in a constant multiple relation, a constraint form of the short-circuit current is adopted:
Isc≤Isc max (8)
in the formula IscIs the effective value of the fault point line current; i issc maxThe maximum short-circuit current value is specified by national standard.
Photovoltaic power generation capacity constraint:
0≤SPV，i≤SPV，max (9)
in the formula, SPV，iIs the photovoltaic power generation capacity of the ith node; sPV，maxIs the upper limit of the photovoltaic access capacity of the ith node.
And step 3: the basic pollen algorithm is improved, so that the algorithm has good ergodicity and convergence rapidity.
The pollen algorithm is a novel meta-heuristic type group intelligence optimization algorithm proposed in 2012 by the scholar Yang bridge scholar Yang in England. The pollen algorithm simulates two pollination processes of biological allopollen (cross-pollination) and non-biological self-pollination (self-pollination) of a flowering plant in the nature, and realizes the solution of an optimization problem. The algorithm assumes that each plant blooms only one flower, that each flower produces only one pollen gamete, and that each flower serves as a solution to the optimization problem. The algorithm controls the transition between cross pollination and self-pollen by the probability p.
The cross pollination of the organism is a global pollination process, the movement of the pollinated organism follows Levy flight, and therefore the organism is regarded as a global search, and the formula is as follows:
wherein the content of the first and second substances,is the t-th iteration of pollen i; g*Is the current global optimal position; γ is a scaling factor for one control step; l (lambda) is a parameter related to pollination strength and is a main step length of Levy flight, and the calculation formula is shown as the following formula:
wherein, the value of lambda is 1.5; Γ (λ) is a standard gamma function; s is the step size of the move.
The abiotic self-pollen is a local pollination process, and is therefore considered as a local search process, and the location update formula can be expressed as follows:
in the formula (12), the reaction mixture is,
and
is pollen from different flowers of the same species; ε is in [0, 1 ]]Uniformly distributed in the middle.
Initializing a chaotic sequence:
most of the current pollen algorithms adopt a random mode to initialize the position of pollen, and the mode is difficult to ensure that the position of the pollen has better ergodicity. In consideration of the fact that the chaotic sequence has good randomness, ergodicity and regularity, the method adopts the chaotic sequence to initialize the position of the pollen, and guarantees the search diversity of the population. The cubic mapping is selected in the text because the chaotic sequence generated by the cubic mapping has better uniformity than the Logistic mapping. The expression is as follows:
therein, maxd、mindRespectively representing the upper and lower bounds of the d-th dimension of the search space, yi(d) Is the d-dimension of the i-th particle produced by equation (13), then xidI.e. the coordinates of the ith particle in the d-dimension of the search space.
The pollen algorithm based on the genetic algorithm comprises the following steps:
according to the analysis, the flower (pollen) population is updated mainly according to the formula (10) or the formula (12), and pollen gametes tend to be normalized with the increase of iteration times, so that the pollen gametes are easy to fall into local optimum and cannot jump out. The algorithm is improved as follows by using the variation idea of the genetic algorithm.
In the iterative optimization process of the pollen algorithm, when the obtained optimal solution does not change in the continuous Q (4) iterations or the variation mu (0.0001) is small, the algorithm is considered to be trapped in the local optimal solution. At this time, the optimal solution obtained by the pollen is used as an initial point of a genetic algorithm, and the population is subjected to operations such as selection, crossing, mutation and the like, so that a new group of solutions is obtained and the whole population is updated. Diversity of the population is increased by introducing a variation strategy, and optimizing capability of the algorithm is improved.
And 4, step 4: and (4) solving and optimizing the photovoltaic absorption capacity evaluation model by using the improved pollen algorithm in the step (3), wherein the obtained result is the distributed photovoltaic absorption capacity of the power distribution network.
(1) Various parameters are initialized.
The algorithm parameters comprise that the number N of pollen populations is 25, the conversion probability p is 0.8, and the maximum iteration number N _ iter is 500; population size sizepop of genetic algorithm 25, cross probability Pcross0.6, probability of mutation Pmutation0.01 and 30 iterations Maxgen. The initial iteration number t is set to 0 and the sizes of Q and μ are set.
And determining upper and lower limit values of branch current, short-circuit current, node voltage deviation and voltage fluctuation according to the power grid parameters.
(2) An initial population is formed.
Each flower corresponds to a scheme of photovoltaic access, and the number of the flowers is n. And (4) setting the number of the photovoltaic grid-connected points as d, wherein the pollen in each flower is a vector with the dimension of d, and the value of the pollen is the photovoltaic access amount corresponding to each photovoltaic grid-connected point. And (3) initializing the positions of the pollen by using chaotic sequences of formulas (13) and (14) within the capacity range of photovoltaic grid connection permission of each node. And forming and solving an objective function with a penalty term according to the load flow calculation, calculating to obtain an objective function value of each pollen, and comparing to obtain the position of the optimal pollen.
(3) And (5) iterating the population.
The number of iterations t is increased by 1. Generating a random number rand, and if rand is greater than p (conversion probability), updating the global position according to a formula (10) and performing border crossing treatment; otherwise, updating the local position and performing border crossing processing according to the equation (12).
(4) The global optimum is updated.
And calculating the objective function value of the flower after population updating, if the objective function value of the flower after population updating is superior to the objective function value of the current flower, replacing the current flower and the current objective function value with the new flower and the corresponding objective function value, or not updating.
(5) Judging whether the local optimum is trapped or not, and if so, continuing to perform; otherwise go to step 7.
(6) And carrying out mutation and other operations by using a genetic algorithm.
And (5) taking the solution generated in the step (4) as an initial point of a genetic algorithm, carrying out selection, crossing and mutation operations on the population, and solving a group of new solutions. If the new solution is superior to the current optimal solution, replacing the current optimal solution with the new solution, and updating the population; otherwise, no update is performed.
(7) Judging whether an end condition is met, if so, stopping iteration and outputting an optimal solution; otherwise, turning to the step (3).
And 5: and solving the distributed photovoltaic absorption capacity evaluation model by using an original pollen algorithm, a genetic algorithm and a Gaussian pollen algorithm, wherein the obtained result is the distributed photovoltaic absorption capacity of the power distribution network. And comparing the photovoltaic absorption capacity obtained by optimizing several algorithms.
Examples
The IEEE33 node system shown in fig. 3 is used as an embodiment of the present invention. In the system, a first node is a power supply point of a power distribution network system and is set as a balance node, and the voltage is 1.04 pu. The current limit value of the line is 250A, the photovoltaic capacity upper limit of each node is 1MW, and the voltage range of the nodes is 0.95-1.05 (pu). The voltage fluctuation allowable value was taken to be 3%. The maximum short-circuit current value is selected according to a specified value in '10 kV distribution project typical design technical guide rule' formulated by national grid company: the maximum three-phase short-circuit current cannot exceed 20 kA. It is assumed that all nodes in the test system have access to the photovoltaic power supply.
The photovoltaic absorption capacity is optimized through an original pollen algorithm, a Gaussian pollen algorithm, a genetic algorithm and the improved pollen algorithm provided by the method. In order to compare the optimizing ability of several algorithms, the iteration number of each algorithm is set to be 500, and the results are respectively averaged for 20 times. The convergence curve of the algorithm during the optimization process is shown in fig. 4.
Table 1 shows the distributed photovoltaic absorption capacity of an embodiment of the present invention
It can be known from the analysis of fig. 4 that the original pollen algorithm can be converged only after about 500 times of iteration, while the gaussian pollen algorithm and the genetic algorithm can be converged after about 400 times of iteration, but the convergence rate is far lower than that of the improved pollen algorithm provided by the invention. The algorithm provided by the invention can be quickly converged after about 50 iterations, and the optimization speed of the algorithm is obviously increased. For the optimization effect of the algorithm, the best results are obtained by improving the pollen algorithm, as can be seen by combining the data in table 1. The system network loss is respectively reduced by 19.0%, 8.6% and 6.9% compared with the network loss of the original pollen algorithm, the Gaussian pollen algorithm and the genetic algorithm; and the photovoltaic effective absorption capacity is respectively improved by 2.0%, 1.2% and 1.1%. Therefore, the optimization effect of the improved pollen algorithm is better, and the effectiveness of the algorithm and the applicability to the problem are proved.
Fig. 5 shows the actual photovoltaic installation capacity of each node when the distributed photovoltaic obtained by the improved pollen algorithm has the maximum absorption capacity. As can be seen from the above figure, the photovoltaic access at the node 19 is large, because the node 19 is located at the head end of the feeder line, and the excess power can be delivered to the next stage of load.
Under the constant power factor control mode, when the photovoltaic inverter is in different power factors, active power and reactive power generated by the photovoltaic inverter can influence the photovoltaic absorption capacity. Table 2 shows the comparison of the distributed photovoltaic absorption capacity of the test system when the photovoltaic inverter is set to different power factors.
Table 2 shows the photovoltaic absorption capacity of the inverter with different power factors according to an embodiment of the present invention
Analyzing the data in table 2 shows that when the photovoltaic inverter is in phase-in operation (the power factor in table 2 is less than 0), the inverter can absorb reactive power from the grid to effectively reduce the node voltage, and further improve the photovoltaic absorption capacity. When the inverter operates in a delayed phase (the power factor in the table 2 is greater than 0), the photovoltaic system transmits active power and reactive power to the power grid, the risk that the voltage of a grid-connected point is out of limit is increased, and the maximum access amount of the grid-connected point is reduced.
From the results, the improved pollen algorithm provided by the invention has better convergence speed and solving precision, and verifies the correctness of the established model and the effectiveness of the improved algorithm. Further computational analysis shows that the photovoltaic inverter enters the phase operation and can effectively improve the absorption capacity of the distributed photovoltaic.
Claims (4)
1. A method for evaluating distributed photovoltaic absorption capacity of a power distribution network is characterized by comprising the following steps: the method comprises the following steps:
I. to be provided with
As an objective function, node power balance constraint, node voltage deviation constraint, node voltage fluctuation constraint, line thermal stability constraint, short circuit capacity constraint and photovoltaic power generationCapacity constraint is a constraint condition, and a power distribution network distributed photovoltaic absorption capacity evaluation model is constructed; wherein:
NPVtotal number of nodes, P, for the photovoltaic access in the systemPV，mIs the photovoltaic access capacity of the mth node, { NBIs the set of all nodes in the system, Pij、QijFor the values of the active and reactive power flowing on branch ij, UiIs the voltage value of node i, RijIs the line resistance value between nodes i and j;
the node power balance constraint is that after the distributed photovoltaic power supply is connected, the active power and the reactive power of the node meet a power equation, and the convergence of the power flow is ensured;
the node voltage deviation constraint and the node voltage fluctuation constraint are node voltage deviation and voltage fluctuation caused by grid connection of the distributed photovoltaic power supply and are obtained based on a system load flow calculation result after grid connection of the distributed photovoltaic power supply;
line thermal stability constraint is the power distribution network load flow calculation result after the distributed photovoltaic power supply is connected to the grid;
the short-circuit capacity constraint is the short-circuit capacity of a system after the distributed photovoltaic grid connection and is obtained based on a grid structure and parameters of a power distribution network;
the capacity constraint of photovoltaic power generation is obtained based on a load flow calculation result of the power distribution network after photovoltaic grid connection;
II. The basic pollen algorithm is improved, so that the algorithm has good ergodicity and convergence rapidity.
III, solving the distributed photovoltaic absorption capacity evaluation model by respectively adopting an improved pollen algorithm, an original pollen algorithm, a genetic algorithm and a Gaussian pollen algorithm, wherein the optimized result is the distributed photovoltaic absorption capacity of the power distribution network; and comparing the photovoltaic absorption capacities obtained by optimizing several algorithms.
2. The method for evaluating the distributed photovoltaic absorption capacity of the power distribution network based on claim 1 is characterized in that: and I, constructing a power distribution network distributed photovoltaic absorption capacity evaluation model, and reasonably evaluating the absorption capacity of the distributed photovoltaic on the premise of ensuring safe and economic operation of the power distribution network.
3. The method for evaluating the distributed photovoltaic absorption capacity of the power distribution network based on claim 1 is characterized in that: and step II, improving the original pollen algorithm, so that the accuracy of the evaluation effect can be ensured on the convergence speed and the accuracy of the algorithm.
4. The method for evaluating the distributed photovoltaic absorption capacity of the power distribution network based on claim 1 is characterized in that: and step III, solving the distributed photovoltaic absorption capacity evaluation model by using an improved pollen algorithm, an original pollen algorithm, a genetic algorithm and a Gaussian pollen algorithm to obtain the distributed photovoltaic absorption capacity of the power distribution network, verifying the solving speed and precision of the improved pollen algorithm by comparing the calculation effects of several optimization algorithms, and ensuring the reasonability and correctness of the evaluation model.
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| CN114815953A (en) * | 2022-04-11 | 2022-07-29 | 青岛理工大学 | Photovoltaic global MPPT control system based on improved flower pollination optimization algorithm |
| CN114815953B (en) * | 2022-04-11 | 2023-11-21 | 青岛理工大学 | Photovoltaic global MPPT control system based on improved flower pollination optimization algorithm |
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