CN111985598A - Configuration method of distributed power supply - Google Patents
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Abstract
The invention provides a configuration method of a distributed power supply, which comprises the steps of calculating an objective function value of a pre-established DG-containing power distribution network optimization configuration model; when the objective function value reaches a constraint condition, outputting a corresponding configuration mode; when the target function value does not reach the constraint condition, carrying out iterative operation by adopting a firefly algorithm, when the iteration times reach a set threshold value, adding adaptive genetic iterative operation, calculating an optimal solution Gtest, and carrying out constraint condition judgment on the obtained optimal solution Gtest; when the adaptive genetic iterative operation exceeds an iterative threshold value, adding improved Gaussian disturbance and calculating an optimal solution xbestAnd for the obtained optimal solution xbestAnd judging constraint conditions. Compared with the AGA algorithm, the algorithm adopted by the invention has higher convergence speed, can quickly find out the optimal convergence point, and has fine convergenceThe degree is high.
Description
Technical Field
The invention relates to the technical field of distributed power supply configuration, in particular to a configuration method of a distributed power supply.
Background
Due to many superior features of Distributed Generation (DG), many scholars in various countries around the world have developed research on locating and sizing DG.
In the existing research, an improved particle swarm algorithm is provided for site selection and volume fixing, but the specific access capacity range is not described, how to generate an initial population is not described in detail, and the site selection problem is not described in detail; the firefly algorithm is also used for carrying out site selection and volume determination on DG, but the algorithm is not good in convergence effect and is easy to be limited to a local optimal solution; in addition, research is carried out on optimal configuration of DGs by adopting an improved adaptive genetic algorithm, the convergence rate of the algorithm is improved, but the stability of the algorithm is still not good.
Disclosure of Invention
The invention provides a configuration method of a distributed power supply, which is used for solving the problem that the existing distributed power supply configuration algorithm is incomplete.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a configuration method of a distributed power supply, which comprises the following steps:
calculating a target function value of a pre-established DG-containing power distribution network optimization configuration model;
when the objective function value reaches a constraint condition, outputting a corresponding configuration mode;
when the target function value does not reach the constraint condition, carrying out iterative operation by adopting a firefly algorithm, when the iteration times reach a set threshold value, adding adaptive genetic iterative operation, calculating an optimal solution Gtest, and carrying out constraint condition judgment on the obtained optimal solution Gtest;
when the adaptive genetic iterative operation exceeds an iterative threshold value, adding improved Gaussian disturbance and calculating an optimal solution xbestAnd for the obtained optimal solution xbestAnd judging constraint conditions.
Further, adding voltage constraint and current constraint to the objective function, and obtaining a normalized objective function through a penalty function form as follows:
in the formula, n is the number of branches, Pi、QiInput power, U, flowing into node i for the ith branchiIs the voltage value of node i, RTiLine resistance of the i-th branch, fu(Ui) And fI(Ii) Representing the voltage constraint and the current constraint, respectively.
Further, the voltage constraint satisfies:
in the formula, KuIs a penalty factor, U, for node voltages exceeding a limit valueiminAnd UimaxRespectively, a lower limit value and an upper limit value of the node voltage.
Further, the current constraint satisfies:
in the formula, KIIs a penalty factor, I, for a line current exceeding a limit valueimaxThe maximum allowable current through branch i.
Further, the constraint condition further includes a node admission power constraint, specifically:
0≤PDGi≤PDGmaxi
in the formula, PDGmaxiIs the maximum capacity of node i allowed to access DG.
Further, the specific process of adding the adaptive genetic iterative operation and calculating the optimal solution is as follows:
reserving one copy of the current population P1 as P2, disturbing the sequence of individuals in the current population P1, randomly selecting P% of individuals, and performing adaptive crossover and variation operation to obtain the optimal solution Gbest;
if the optimal solution Gbest is better than the optimal solution in the population P2, replacing the poorer individuals in the population P2 with the P% individuals to form a new population P' 2, and repeatedly adopting a firefly algorithm to carry out iterative operation;
if the optimal solution does not satisfy the optimal solution better than the optimal solution of the population P2, repeating the self-adaptive intersection and mutation operation until a better solution is obtained.
Further, the adaptive crossover and mutation operation includes an adaptive crossover operator and a mutation operator, and the adaptive crossover operator is calculated as:
wherein f' represents the larger individual participating in the crossover, favgIs the fitness average of all individuals of the current population, fmaxThe fitness of all individuals of the current population is the maximum value, and A is a regulating factor;
the calculation of the mutation operator is:
wherein f' is the fitness value of the individual to be varied in the population of the current generation, favgIs the fitness average of all individuals of the current population, fmaxThe fitness of all individuals in the current population is the maximum value, and B is a regulating factor.
Further, the adding of the improved gaussian disturbance, calculating an optimal solution, and performing constraint condition judgment on the obtained optimal solution specifically includes:
adding the optimal solution of the population P2 into improved Gaussian disturbance, and calculating the optimal solution xbestIf xbestIf the optimal solution is better, replacing the optimal solution of the population P2, and then making a new population P' 2 to judge whether the optimal solution meets the constraint condition; if not, the original population P2 is reserved, and the constraint condition is judged.
Further, the optimal solution xbestIs calculated as:
xbest＝xbest·(1+m·N(0，1))
in the formula, N (0,1) is a standard normal distribution, t is the current iteration number, and Maxgen is the maximum iteration number set by the algorithm.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
compared with an AGA (adaptive genetic algorithm) algorithm, the convergence rate of the algorithm adopted by the invention is higher, and the optimal convergence point can be quickly found. The two operations ensure the convergence rate of the firefly algorithm and improve the convergence precision at the same time.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of the algorithm solving method of the invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
As shown in fig. 1, the configuration method of a distributed power supply of the present invention includes the following steps:
step 1, calculating a target function value of a pre-established DG-containing power distribution network optimization configuration model;
step 2, outputting a corresponding configuration mode when the objective function value reaches a constraint condition;
step 4, when the adaptive genetic iterative operation exceeds an iterative threshold value, adding improved Gaussian disturbance, and calculating an optimal solution xbestAnd for the obtained optimal solution xbestAnd judging constraint conditions.
Step 1, an objective function is established to solve the problem of access capacity and position distribution after a power distribution network is added into a DG. The target function ensures that the loss of the whole network frame line is minimum:
in which n denotes n branches, Pi、QiInput power, U, flowing into node i for the ith branchiIs the voltage value of node i, RTiIs the line resistance of the ith branch.
The constraints include node admission power constraints, node voltage constraints, and line current constraints.
Node admission power constraint:
0≤PDGi≤PDGmaxi (2)
PDGmaxiis the maximum capacity of node i allowed to access DG.
Node voltage constraint:
in this embodiment, the voltage constraint is processed by a penalty function to become an inequality constraint condition, and the formula is as follows:
in the formula, KuIs a penalty coefficient for the node voltage exceeding the limit value, generally taking a larger value, Uimin，UimaxThe node voltage lower limit value and the node voltage upper limit value.
And (3) line current constraint:
and processing the current constraint to change the current constraint into an inequality constraint condition, wherein the formula is as follows:
in the formula, KIIs a penalty coefficient for the line current exceeding the limit value, generally taking a larger value, IimaxThe maximum allowable current through branch i.
And finally, uniformly normalizing the voltage constraint and the current constraint into an objective function through a penalty function form, wherein the obtained normalized objective function is as follows:
as shown in fig. 2, when calculating the objective function value based on the established objective function and the constraint condition, the steps S1-S10 are performed as follows:
s1, inputting a distribution network parameter matrix (including node numbers, loads and line impedance); inputting a voltage grade and a rated power reference value; inputting an objective function and a constraint condition; and setting various parameters of the firefly algorithm, the maximum iteration times, the iteration times of the self-adaptive genetic mechanism, influence factor parameters of crossover and mutation operators and the like.
And S2, determining the maximum access capacity of the distributed power supply of each node by using a sensitivity analysis method, and then generating an initial population, namely an initial solution, by using cubic mapping.
S3, the light-emission luminance of the initial population, i.e., the objective function value, is calculated.
Steps S1-S3 complete the objective function value calculation process of step 1 above. The luminance is calculated based on a firefly algorithm, which is introduced as follows:
the firefly algorithm is a theory proposed according to the mutual attraction of fireflies through luminous brightness, and mainly comprises two parameters of luminous brightness and attraction degree. N fireflies in each firefly, the dimension of the position of each firefly is M, and the initial position of the ith firefly can be set as xi＝(xi1,xi2,xi3,...,xiM)。
Luminance of the ith firefly:
in the formula IoiRepresenting the maximum brightness of the location of the ith firefly, i.e.The value of the objective function itself.
Relative luminance of firefly i:
where γ represents the light intensity absorption factor and can be set as a constant, rijIs xiAnd xjThe term is used to indicate that the luminance of the glowworm changes with the distance.
Attraction of the ith firefly:
in the formula, beta0The coefficient of maximum attraction is generally set to a constant value of 1.
New position of the ith firefly after moving to the jth firefly:
where α is a step coefficient, taking a value between [0,1], and can be set as a constant, and rand represents a random factor, which is uniformly distributed over [0,1 ].
In step S4, it is determined whether the calculated objective function value satisfies the constraint condition, and if yes, the operation is ended, and step S10 is executed to output the optimal node position and capacity configuration, the optimized network loss, and the voltage value of each node, that is, the configuration mode of the distributed power supply is obtained. If the constraint condition is not satisfied, step S5 is executed to calculate the relative brightness and the attraction degree, change the position of the firefly, and perform iterative computation. A threshold N is set. S6, when the firefly algorithm is not iterated to N times or multiple times of N times, reserving a part of the current population P1, setting the reserved population as P2, randomly disordering and sequentially arranging the current population P1, randomly selecting P% of individuals in the current population P1, performing self-adaptive crossing and mutation processes, finding out the optimal solution Gbest in the current population P1, and performing adaptive crossing and mutation processes on the optimal solution Gbest in the current population P1 and the optimal solution in the P2Comparing, if the population is better, replacing the poorer individuals in the reserved population with P% of individuals according to a certain proportion q to form a sub-population P' 2, and continuing to perform the firefly algorithm; and setting the iteration times M of the adaptive genetic mechanism, judging whether the iteration of the adaptive genetic mechanism exceeds M times or not by S7, and if not, continuing the adaptive crossing and mutation process until a more optimal solution is found out by S8. If the optimal solution is not obtained after M times, executing S9, adding the optimal solution of the reservation population P2 into improved Gaussian disturbance, and solving the optimal solution xbestJudging the optimal solution xbestWhether the optimal solution is better than the optimal solution of the population P2 or not, if so, replacing the original solution and converting the optimal solution x into the optimal solutionbestAnd (5) judging the constraint condition, if not, keeping the optimal solution of the original population P2, and judging the constraint condition.
In the operation process, a firefly algorithm, an improved adaptive genetic algorithm and a Gaussian algorithm are respectively introduced.
The firefly algorithm is simple in principle, few in set parameters, good in local optimization capability and more widely concerned, but the firefly algorithm has the defect that the search capability is poor, and when the algorithm is optimized to be close to a local solution, the algorithm can oscillate at the local solution due to the fact that the attraction degree is increased, and the firefly algorithm cannot jump out. The crossover operation in Genetic Algorithm (GA) enables the population to be spread in the global scope, the diversity operation increases the diversity of the population, and the two operations enable the Algorithm to be better optimized in the global space. Therefore, the embodiment proposes that the crossing and mutation operations in the GA algorithm are added into the firefly algorithm, the advantages of the two operations are complementary, and the overall performance of the algorithm is improved. Meanwhile, in order to improve the optimizing performance of crossing and mutation operations, the embodiment provides a novel self-adaptive crossing and mutation operator, the maximum and minimum crossing and mutation probability values do not need to be set, the operator can be automatically changed according to the change of the fitness, the population diversity is ensured, and a certain convergence speed is also ensured. The formula of the adaptive crossover and mutation operator is as follows:
wherein f' represents the larger individual participating in the crossover, favgIs the fitness average of all individuals of the current population, fmaxThe fitness of all individuals in the current population is the maximum value, A is an adjusting factor, and the value of 1.2 is more suitable after multiple simulation tests.
Wherein f' is the fitness value of the individual to be varied in the population of the current generation, favgIs the fitness average of all individuals of the current population, fmaxThe fitness of all individuals in the current population is the maximum value, B is a regulating factor, and the value of 10 is more suitable after multiple simulation tests.
In order to avoid the situation that the iteration frequency of the adaptive genetic mechanism is too small, and a global optimal solution cannot be found, when the adaptive genetic mechanism iterates to M times and cannot find a solution better than the current population, an improved Gaussian disturbance is added to the current population optimal solution, and when the iteration frequency is increased, the added disturbance is smaller. Let the optimal solution be xbestThe formula is as follows:
xbest＝xbest·(1+m·N(0，1)) (12)
in the formula, N (0,1) is a standard normal distribution, t is the current iteration number, and Maxgen is the maximum iteration number set by the algorithm.
Compared with the AGA algorithm, the convergence rate of the algorithm is higher, the optimal convergence point can be quickly found, and the reason is the first, the firefly algorithm is higher in local optimization capability and higher in optimization speed, and the second, in the running process of the algorithm, self-adaptive intersection and variation operation with certain iteration times is added, so that the algorithm can jump out of a local solution in time, meanwhile, Gaussian disturbance is added as assistance, and the influence of the Gaussian disturbance is smaller when the iteration is in the later stage, and the global optimal solution is found because the global optimal solution is close to the moment. The two operations ensure the convergence rate of the firefly algorithm and improve the convergence precision at the same time.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (9)
1. A method for configuring a distributed power supply, the method comprising the steps of:
calculating a target function value of a pre-established DG-containing power distribution network optimization configuration model;
when the objective function value reaches a constraint condition, outputting a corresponding configuration mode;
when the target function value does not reach the constraint condition, carrying out iterative operation by adopting a firefly algorithm, when the iteration times reach a set threshold value, adding adaptive genetic iterative operation, calculating an optimal solution Gtest, and carrying out constraint condition judgment on the obtained optimal solution Gtest;
when the adaptive genetic iterative operation exceeds an iterative threshold value, adding improved Gaussian disturbance and calculating an optimal solution xbestAnd for the obtained optimal solution xbestAnd judging constraint conditions.
2. The method of claim 1, wherein the voltage constraint and the current constraint are added to the objective function, and the normalized objective function is obtained by a penalty function form as follows:
in the formula, n is the number of branches, Pi、QiInput power, U, flowing into node i for the ith branchiBeing node iVoltage value, RTiLine resistance of the i-th branch, fu(Ui) And fI(Ii) Representing the voltage constraint and the current constraint, respectively.
3. The method of configuring a distributed power supply according to claim 2, wherein the voltage constraint satisfies:
in the formula, KuIs a penalty factor, U, for node voltages exceeding a limit valueiminAnd UimaxRespectively, a lower limit value and an upper limit value of the node voltage.
4. The method of configuring a distributed power supply according to claim 2, wherein the current constraint satisfies:
in the formula, KIIs a penalty factor, I, for a line current exceeding a limit valueimaxThe maximum allowable current through branch i.
5. The method according to claim 4, wherein the constraint further includes a node admission power constraint, specifically:
0≤PDGi≤PDGmaxi
in the formula, PDGmaxiIs the maximum capacity of node i allowed to access DG.
6. The method for configuring the distributed power supply according to claim 1, wherein the specific process of adding the adaptive genetic iterative operation and calculating the optimal solution is as follows:
reserving one copy of the current population P1 as P2, disturbing the sequence of individuals in the current population P1, randomly selecting P% of individuals, and performing adaptive crossover and variation operation to obtain the optimal solution Gbest;
if the optimal solution Gbest is better than the optimal solution in the population P2, replacing the poorer individuals in the population P2 with the P% individuals to form a new population P' 2, and repeatedly adopting a firefly algorithm to carry out iterative operation;
if the optimal solution does not satisfy the optimal solution better than the optimal solution of the population P2, repeating the self-adaptive intersection and mutation operation until a better solution is obtained.
7. The method for configuring distributed power supplies according to claim 6, wherein the adaptive crossover and mutation operation includes an adaptive crossover operator and a mutation operator, and the adaptive crossover operator is calculated by:
wherein f' represents the larger individual participating in the crossover, favgIs the fitness average of all individuals of the current population, fmaxThe fitness of all individuals of the current population is the maximum value, and A is a regulating factor;
the calculation of the mutation operator is:
wherein f' is the fitness value of the individual to be varied in the population of the current generation, favgIs the fitness average of all individuals of the current population, fmaxThe fitness of all individuals in the current population is the maximum value, and B is a regulating factor.
8. The method for configuring a distributed power supply according to claim 1, wherein the adding of the improved gaussian disturbance, the calculating of the optimal solution, and the determining of the constraint condition of the obtained optimal solution specifically comprise:
adding the optimal solution of the population P2 into improved Gaussian disturbance, and calculating the optimal solution xbestIf xbestIf the optimal solution is better, replacing the optimal solution of the population P2, and then making a new population P' 2 to judge whether the optimal solution meets the constraint condition; if not, the original population P2 is reserved, and the constraint condition is judged.
9. The method of configuring a distributed power supply according to claim 8, wherein the optimal solution x isbestIs calculated as:
xbest＝xbest·(1+m·N(0，1))
in the formula, N (0,1) is a standard normal distribution, t is the current iteration number, and Maxgen is the maximum iteration number set by the algorithm.
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| CN112365196A (en) * | 2020-12-03 | 2021-02-12 | 国网河北省电力有限公司信息通信分公司 | Spark-based power supply planning method for distributed improved differential evolution algorithm |
| CN113204417A (en) * | 2021-04-30 | 2021-08-03 | 武汉大学 | Multi-satellite multi-point target observation task planning method based on improved genetic and firefly combined algorithm |
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