CN108596438B - Dynamic environment economic dispatching method of multi-target wildflower algorithm - Google Patents
Dynamic environment economic dispatching method of multi-target wildflower algorithm Download PDFInfo
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Abstract
The invention relates to a dynamic environment economic dispatching method of a multi-target wildflower algorithm, which comprises the following steps: s1, establishing a dynamic environment economic dispatching model of the power system by taking minimum fuel cost and minimum pollution emission as objective functions and considering equality constraint and inequality constraint; s2, initializing population and algorithm iteration data by combining a dynamic environment economic dispatching model, and calculating the particle fitness; s3, executing a constraint domination strategy to select a non-inferior solution set; s4, executing a wild flower algorithm to update the population, and specifically comprising the following steps: 1) executing normal diffusion and phototactic evolution mechanism; 2) executing a paired breeding mechanism; 3) performing a chromosomal mutation mechanism; updating a non-inferior solution set; s5, termination condition: if the number of the non-inferior solutions reaches a preset value, executing a density mechanism to upgrade and maintain the non-inferior solutions; otherwise, go to S4; and S6, executing fuzzy decision to select an optimal compromise solution.
Description
Technical Field
the invention relates to the field of economic dispatching of power systems, in particular to a dynamic environment economic dispatching method of a multi-target wildflower algorithm.
background
with the increasingly prominent environmental pollution problem, each thermal power plant pursues the power generation benefit and considers the emission reduction of the waste gas more, and makes a pollution gas emission limit regulation. Power scheduling is also shifted more from traditional single-target economic scheduling to scheduling that is both environmentally and economically compatible.
the dynamic environment economic dispatching of the power system has extremely important significance for continuous, safe and reliable operation of the power system and reduction of environmental pollution. As a multi-objective optimization problem of the power system, the uncertain dynamic multi-objective optimization scheduling under the condition of considering the randomness influence factors is more in line with the actual requirements and has higher operation difficulty.
With the intensive research on the power system scheduling method, various methods for solving the multi-target dynamic environment economic scheduling appear, such as: the method comprises a weight coefficient method, a price penalty factor method, a fuzzy satisfaction degree method, an NSGA-II algorithm and the like, and the methods are complex, have more parameters needing to be set, have low efficiency and are inconvenient to operate.
therefore, developing a method which can adapt to the economic scheduling problem of the multi-target dynamic environment and is efficient, fast and simple is always a technical problem.
Disclosure of Invention
The invention aims to provide a dynamic environment economic dispatching method which can optimize an environment emission function and a fuel cost function simultaneously and has strong global convergence capability.
in order to realize the purpose, the technical scheme is as follows:
a dynamic environment economic dispatching method of a multi-target wildflower algorithm comprises the following steps:
S1, establishing a dynamic environment economic dispatching model of the power system by taking minimum fuel cost and minimum pollution emission as objective functions and considering equality constraint and inequality constraint;
S2, initializing population and algorithm iteration data by combining a dynamic environment economic dispatching model, and calculating the particle fitness;
s3, executing a constraint domination strategy to select a non-inferior solution set;
S4, executing a wild flower algorithm to update the population, and specifically comprising the following steps:
1) executing normal diffusion and phototactic evolution mechanism;
2) executing a paired breeding mechanism;
3) performing a chromosomal mutation mechanism;
updating a non-inferior solution set;
S5, termination condition: if the number of the non-inferior solutions reaches a preset value, executing a density mechanism to upgrade and maintain the non-inferior solutions; otherwise, go to S4;
And S6, executing fuzzy decision to select an optimal compromise solution.
Preferably, in the step S1, the power system dynamic environment economic dispatch model includes an objective function and constraint conditions, the objective function is fuel cost and pollutant emission, and the constraint conditions include equality constraints and inequality constraints;
The specific form of the objective function with minimum fuel cost is as follows:
Wherein n is the total number of the generator sets; piThe active output of the ith generator is obtained; a isi、bi、ciA fuel cost factor for the ith generator;
Considering the valve point effect, the fuel cost function can be expressed as:
The specific form of the objective function with the minimum pollutant emission is as follows:
wherein alpha isi、βi、γiThe pollutant gas emission coefficient of the ith generator;
The equation is constrained to:
1) and power balance constraint:
wherein, PDFor total load demand, PLis transmission network loss;
The system transmission loss can be expressed by the following formula:
wherein, Bij、Boi、BooThe network loss coefficient of the generator;
2) The inequality constraints are:
And (3) unit operation constraint:
Wherein, Piminis the lower active power output limit, P, of unit iimaxand the active output upper limit of the unit i.
preferably, in step S2, in combination with the dynamic environment economic dispatch model, the initializing population and the algorithm iteration data specifically include:
Setting the maximum value of the population size and the initial total population NmaxN, controlling the variable quantity D, wherein D is a dimension and is an active output value of each generator, and the maximum particle number MaxIter of the non-inferior solution set; randomly initializing a population X in a recovery space of a D-dimensional problem, wherein the ith individual is Xi＝[Xi1，Xi2，...XiD]N, i 1, 2.. No. N; lower and upper limits X of the initialization variable valuesmin、Xmaxminimum and maximum of the number of offspring generated by the parent particleLarge value Smin、SmaxInitial and final values of the standard deviation σinit、σfinalA nutrient enrichment radius R;
the initialization process of the population X is as follows: chaos variable generation by chaos Logistic equation
xi+1＝λ·xi·(1-xi)
In the formula, xi∈[0，1]，xiAnd the number of the control parameters is not equal to 0.25, 0.5 and 0.75, lambda is a control parameter and takes a value of 0-4, and when the chaotic state is completely realized, the lambda is 4. Take any initial point x0Can obtain [0, 1 ]]point set x of traversal abovei，i＝1、2......N；
the obtained chaos variable xiConversion to initial population
Xi＝Xmin+α(Xmax-Xmin)(1-xi)xi
Wherein α ═ 4 is a chaotic attractor.
preferably, in step S3, the executing of the constraint governing policy to select the non-inferior solution set specifically includes:
For two particles a, b in the feasible region, if fi(a)≤fi(b)，And f isi(a)＜fi(b)，When the particle a is said to dominate the particle b, that is,Wherein m is the target number; at this time, the particle a is a non-inferior solution or a Pareto solution, and a solution set formed by all the non-inferior solutions is a non-inferior solution set or a Pareto solution set; the upper limit of the number of particles in the non-inferior solution is MaxIter.
preferably, in step S4, the updating population by executing the wildflower algorithm specifically includes:
In the step 1), the mechanism for executing normal diffusion and phototactic evolution is specifically as follows:
in a normal diffusion propagation mechanism, the WFO algorithm defines the quality of an individual according to the individual fitness of wild flowers, and then determines the number of descendants which can propagate the wild flowers, specifically:
In the formula, Qithe number of offspring that the wild flower individual i can produce; fi、Fmax、Fminrespectively is the fitness value of the wild flower individual i, and the maximum and minimum fitness values in the current population; smin、SmaxRespectively is the minimum value and the maximum value of the number of offspring generated by the parent particle; round is a rounding function;
according to the obtained number of the offspring, carrying out random diffusion on the space around the parent particles by the WFO algorithm through Gaussian distribution to generate offspring individuals; the following formula:
In the formula, Itermaxand Iter is the maximum iteration number and the current iteration number respectively; sigmainit、σfinal、σiterRespectively an initial value, a final value and a current value of the standard deviation; n is a nonlinear harmonic factor, and the general value n is 3;
according to the diffusion value, one progeny particle from which the parent wild flower particle can be obtained is:
Xi+1＝Xi+σiter
In the formula, Xi+1is namely Xia daughter particle of (a), the particle being added to the population as part of the population;
In the phototactic evolution mechanism, progeny particles can breed towards areas with abundant nutrition in the population, and are influenced by other factors without breeding towards the phototactic areas with a certain probability, and a normal diffusion mechanism is used for replacing the breeding;
defining globally optimal particles X in a populationgbestIs a rich culture region with a radius of R and a rich culture region as a centerthe inner particles may or may not be attracted; setting a threshold value p to define the probability of the child particle phototropism evolution, setting a random number l, if l is less than p, then the child phototropism breeding, otherwise, breeding according to normal diffusion, specifically as follows:
when in usethen, breeding offspring particles according to a normal diffusion mechanism;
when in useIn time, there are:
wherein r is [0, 1 ]]a random number within;Is the probability value of the d-dimension variable of the particle i at the k-th iteration; r is the radius of the eutrophic radius, and the value of R is related to specific problems;
When the parent propagation reaches the preset number of offspring, the population size is larger than Nmaxwhen in use, the wild flowers of the parents and the offspring are removed from the first N in the sequence of the fitness from high to lowmaxthe individuals of the individuals serve as parents of the next generation, and then enter a pairing propagation mechanism;
In the substep 2), the execution of the pairing propagation mechanism is specifically as follows:
(1) randomly and repeatedly pairing all individuals in the population;
(2) If particles X (i) and X (j) are paired, then the propagation formula for X (i) is:
X′(i，d)＝r1·X(i，d)+(1-r1)·X(j，d)
The propagation formula for X (j) is:
X′(j，d)＝r1·X(j，d)+(1-r2)·X(i，d)
Wherein D ∈ (1, D); r is1，r2Is [0, 1 ]]Uniformly distributed random numbers thereon; x (i, d) and X (j, d) are the d-th dimensions of particles i and j, respectively; x '(i, d) and X' (j, d) are respectively new filial generations obtained after pairing propagation;
If X '(i) is better than its parent X (i), X (i) ← X' (i); otherwise, keeping the value of the original parent particle X (i) unchanged;
(3) Repeating the step (1) and the step (2) for N/2 times;
In the substep 3), the mechanism for carrying out the chromosomal mutation is specifically:
(1) and (3) carrying out normalization processing on each dimension of the population individuals, wherein the formula is as follows:
Wherein i belongs to (1, N), j belongs to (1, D); xj minand Xj maxThe upper limit and the lower limit of the j-th dimension control variable respectively; k is the current algebra;
(2) selecting a parent individual particle Xion which the mutation mechanism is performed, the formula is as follows
Y←Xi，1
Xi，d←Xi，d+1
Xi，D←Y
Wherein i is 1, 2 … Nmax；Xi，dAnd Xi，d+1Are respectively particles XiD and D + 1D, D ═ 1, 2 … D;
(3) to XiPerforming inverse normalization to obtain an optimized solution, wherein the formula is as follows:
X′(i，j)＝X(i，j)·(Xj max-Xj min)+Xj min
wherein X' (i, j) is a new progeny obtained after mutation; xj minand Xj maxThe upper limit and the lower limit of the j-th dimension control variable respectively;
If X '(i, j) is better than its parent X (i, j), X (i, j) ← X' (i, j); otherwise, keeping the value of the original parent particle X (i, j) unchanged;
after the population is updated, step S4 is executed to add the newly screened non-inferior solutions to the non-inferior solution set, and the non-inferior solution set is updated.
preferably, in the step S5, the performing of upgrading and maintaining the non-inferior solution by the intensity mechanism specifically includes:
The density mechanism is as follows:
wherein O is the neighborhood center; f. ofi(A)，fi(B)，fi(C) and fi(O) the ith objective function for particle A, B, C and domain center O, respectively;
The MaxIter individuals with high concentration are reserved.
Preferably, in the step S6, the step of performing fuzzy decision to select an optimal compromise solution specifically includes:
and evaluating the satisfaction degree of each decision variable by using a membership function, wherein for each non-inferior solution k in the Pareto frontier, the membership function is expressed as:
in the formula (I), the compound is shown in the specification,respectively the maximum and minimum values of the ith objective function;
obviously, when FDMi(Pk) 0 is completely unsatisfactory; when FDMi(Pk) 1 is completely satisfactory; after normalization, the satisfaction of each non-inferior solution k was evaluated as follows:
The non-inferior solution is the solution with the maximum satisfaction, namely the optimal compromise solution of the environmental economic scheduling problem.
Compared with the prior art, the invention has the beneficial effects that:
(1) the dynamic environment economic dispatching method provided by the invention is efficient and reliable, simple to operate, less in initial parameters, strong in robustness and ergodicity and high in operation efficiency;
(2) the chaos initialization used by the dynamic environment economic dispatching method provided by the invention ensures that the initial total group has strong ergodicity and contains more information;
(3) The normal diffusion evolution mechanism has strong adaptive capacity and good robustness, and the particles can stably evolve towards the optimal solution by combining the phototactic evolution mechanism, so that the aim is strong and the convergence speed is high;
(4) the pairing propagation mechanism fully utilizes the information between individuals, promotes the individuals to exchange and update the information, and combines a chromosome mutation mechanism, so that the total group can jump out of local optimum in time, the later convergence speed is accelerated, a potential optimum solution is searched, and the searching performance of the algorithm is greatly improved.
(5) the introduction of the density mechanism improves the quality of non-inferior solutions and the operation speed of the algorithm, so that the leading edges of the non-inferior solutions are more uniform and have strong diversity.
drawings
FIG. 1 is a schematic flow diagram of a method.
fig. 2 is a schematic diagram of an optimal non-inferior solution set of the eco-economical scheduling problem obtained by the method of the present invention.
Detailed Description
the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
the invention is further illustrated below with reference to the figures and examples.
example 1
the dynamic environment economic dispatching of a 10-machine electric power system is realized, a system model considers valve point effect, network loss, climbing constraint and the like, the dispatching cycle is 24 hours and is divided into 24 time periods, and each time period is 1 hour long.
fig. 1 is a flowchart of a dynamic environment economic dispatching method of a multi-target wildflower algorithm in a 10-unit actual power system example, which includes the following steps:
Step 1, combining with a 10-unit actual electric power system, taking minimum fuel cost and minimum pollution emission as objective functions, considering valve point effect, network loss and climbing constraint, and establishing an electric power system economic dispatching model, wherein the specific form is as follows:
the specific form of the objective function with minimum fuel cost is as follows:
Wherein n is the total number of the generator sets; piThe active output of the ith generator is obtained; a isi、bi、ciIs the fuel cost factor of the ith generator.
Considering the valve point effect, the fuel cost function can be expressed as:
the specific form of the objective function with the minimum pollutant emission is as follows:
Wherein alpha isi、βi、γithe pollutant gas emission coefficient of the ith generator;
1) the equation is constrained to:
and power balance constraint:
wherein, PDFor total load demand, PLIs the transmission network loss.
The system transmission loss can be expressed by the following formula:
Wherein, Bij、Boi、BooIs the network loss coefficient of the generator.
2) the inequality constraints are:
And (3) unit operation constraint:
Pi min≤Pi≤Pi max
wherein, PiminIs the lower active power output limit, P, of unit iimaxAnd the active output upper limit of the unit i.
And 2, calculating population fitness by combining system model initialization and algorithm data.
setting the maximum value of the population size and the initial total population Nmax100 and N80, the control variable number D is 10 (i.e. dimension, which is the active output value of each generator), and the maximum number of particles of the non-inferior solution set maxter is 100; randomly initializing a population X in a recovery space of a D-dimensional problem, wherein the ith individual is Xi＝[Xi1，Xi2，...XiD]n, i 1, 2.. No. N; lower and upper limits X of the initialization variable valuesmin、Xmaxminimum and maximum values S of the number of offspring generated by parent particlemin＝2、SmaxInitial and final values of standard deviation σ ═ 5init＝1、σfinal0.0001, 0.05
the initialization process of the population X is as follows: chaos variable generation by chaos Logistic equation
xi+1＝λ·xi·(1-xi)
In the formula, xi∈[0，1]，xiAnd the number of the control parameters is not equal to 0.25, 0.5 and 0.75, lambda is a control parameter and takes a value of 0-4, and when the chaotic state is completely realized, the lambda is 4. Take any initial point x0Can obtain [0, 1 ]]Point set x of traversal abovei，i＝1、2......N。
will obtain chaosVariable xiConversion to initial population
Xi＝Xmin+α(Xmax-Xmin)(1-xi)xi
wherein α ═ 4 is a chaotic attractor.
And 3, executing a constraint domination strategy to select a primary non-inferior solution set.
For two particles a, b in the feasible region, if fi(a)≤fi(b)，And f isi(a)＜fi(b)，when the number of particles a is not bad, the particles a are stored in a non-bad solution set.
And 4, executing a wildflower algorithm to update the population.
In the step 4-1, the mechanism for executing normal diffusion and phototactic evolution is specifically as follows:
In a normal diffusion propagation mechanism, the WFO algorithm defines the quality of an individual according to the individual fitness of wild flowers, and then determines the number of descendants which can propagate the wild flowers, specifically:
In the formula, QiThe number of offspring that the wild flower individual i can produce; fi、Fmax、Fminrespectively is the fitness value of the wild flower individual i, and the maximum and minimum fitness values in the current population; smin、SmaxRespectively is the minimum value and the maximum value of the number of offspring generated by the parent particle; round is a rounding function.
And according to the obtained number of the offspring, carrying out random diffusion on the space around the parent particles by the WFO algorithm through Gaussian distribution to generate the offspring individuals. The following formula:
In the formula, ItermaxAnd Iter is the maximum iteration number and the current iteration number respectively; sigmainit、σfinal、σiterrespectively an initial value, a final value and a current value of the standard deviation; n is a nonlinear harmonic factor, and the value n is generally 3.
According to the diffusion value, one progeny particle from which the parent wild flower particle can be obtained is:
Xi+1＝Xi+σiter
In the formula, Xi+1Is namely XiThe particle is added to the population as part of the population.
in the phototactic evolution mechanism, the progeny particles will propagate toward the nutrient-rich areas of the population, and will not propagate toward the nutrient-rich areas but will be replaced by the normal diffusion mechanism due to the influence of other factors with a certain probability.
defining globally optimal particles X in a populationgbestthe particle having a radius of R is attracted or not attracted to the enriched region around the enriched region. Setting a threshold value p to define the probability of the child particle phototropism evolution, setting a random number l, if l is less than p, then the child phototropism breeding, otherwise, breeding according to normal diffusion, specifically as follows:
when in useThen, breeding offspring particles according to a normal diffusion mechanism;
When in useIn time, there are:
Wherein r is [0, 1 ]]A random number within;For dimension d of particle i at the k iterationA probability value of the variable; r is the radius of the eutrophic radius, and the value of R is related to specific problems.
When the parent propagation reaches the preset number of offspring, the population size is larger than NmaxWhen in use, the wild flowers of the parents and the offspring are removed from the first N in the sequence of the fitness from high to lowmaxIndividuals of individuals serve as parents of the next generation and then enter a pairing propagation mechanism.
In the substep 4-2, the execution of the pairing propagation mechanism is specifically:
(1) randomly and repeatedly pairing all individuals in the population;
(2) If particles X (i) and X (j) are paired, then the propagation formula for X (i) is:
X′(i，d)＝r1·X(i，d)+(1-r1)·X(j，d)
the propagation formula for X (j) is:
X′(j，d)＝r1·X(j，d)+(1-r2)·X(i，d)
wherein D ∈ (1, D); r is1，r2is [0, 1 ]]Uniformly distributed random numbers thereon; x (i, d) and X (j, d) are the d-th dimensions of particles i and j, respectively; x '(i, d) and X' (j, d) are respectively new filial generations obtained after pairing propagation;
if X '(i) is better than its parent X (i), X (i) ← X' (i); otherwise, the value of the original parent particle X (i) is kept unchanged.
(3) Repeating the step (1) and the step (2) for N/2 times;
In the substep 4-3, the mechanism for carrying out the chromosomal mutation is specifically:
(1) And (3) carrying out normalization processing on each dimension of the population individuals, wherein the formula is as follows:
wherein i belongs to (1, N), j belongs to (1, D); xj minAnd Xj maxThe upper limit and the lower limit of the j-th dimension control variable respectively; k is the current algebra;
(2) selecting a parent individual particle Xion which the mutation mechanism is performed, the formula is as follows
Y←Xi，1
Xi，d←Xi，d+1
Xi，D←Y
wherein i is 1, 2 … Nmax；Xi，dAnd Xi，d+1are respectively particles XiD and D +1, D1, 2 … D:
(3) to Xiperforming inverse normalization to obtain an optimized solution, wherein the formula is as follows:
X′(i，j)＝X(i，j)·(Xj max-Xj min)+Xj min
Wherein X' (i, j) is a new progeny obtained after mutation; xj minAnd Xj maxthe upper limit and the lower limit of the j-th dimension control variable respectively;
If X '(i, j) is better than its parent X (i, j), then X (i, j) ← X' (i, j); otherwise, keeping the value of the original parent particle X (i, j) unchanged;
In step S6, the implementation of the fuzzy decision to select the optimal compromise solution specifically includes:
And evaluating the satisfaction degree of each decision variable by using a membership function, wherein for each non-inferior solution k in the Pareto frontier, the membership function is expressed as:
In the formula (I), the compound is shown in the specification,respectively the maximum and minimum values of the ith objective function;
obviously, when FDMi(Pk) 0 is completely unsatisfactory; when FDMi(Pk) 1 is completely satisfactory; after normalization, the satisfaction of each non-inferior solution k was evaluated as follows:
The non-inferior solution is the solution with the maximum satisfaction, namely the optimal compromise solution of the environmental economic scheduling problem.
FIG. 2 is a diagram of an optimal non-inferior solution set of the environmental economic scheduling problem obtained by the method of the present invention, and it can be seen that the solution set is good in both density and diversity. Table 1 shows the optimal fuel cost and the minimum pollution emission value obtained by the method provided by the invention, and Table 2 shows the scheduling scheme obtained by the method provided by the invention.
TABLE 1
| Method of producing a composite material | cost (S/10)6) | discharge (kg/10)5) | loss of network (MW) |
| Multi-target wildflower algorithm | 2.498 | 1.362 | 1263.32 |
TABLE 2
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (2)
1. A dynamic environment economic dispatching method of a multi-target wildflower algorithm is characterized by comprising the following steps:
s1, establishing a dynamic environment economic dispatching model of the power system by taking minimum fuel cost and minimum pollution emission as objective functions and considering equality constraint and inequality constraint;
S2, initializing population and algorithm iteration data by combining a dynamic environment economic dispatching model, and calculating the particle fitness;
S3, executing a constraint domination strategy to select a non-inferior solution set;
S4, executing a wild flower algorithm to update the population, and specifically comprising the following steps:
1) Executing normal diffusion and phototactic evolution mechanism;
2) Executing a paired breeding mechanism;
3) Performing a chromosomal mutation mechanism;
updating a non-inferior solution set;
S5, termination condition: if the number of the non-inferior solutions reaches a preset value, executing a density mechanism to upgrade and maintain the non-inferior solutions; otherwise, go to S4;
s6, executing fuzzy decision to select an optimal compromise solution;
in the step S1, the power system dynamic environment economic dispatch model includes an objective function and constraint conditions, the objective function includes fuel cost and pollutant emission, and the constraint conditions include equality constraint and inequality constraint;
the specific form of the objective function with minimum fuel cost is as follows:
Wherein n is the total number of the generator sets; piThe active output of the ith generator is obtained; a isi、bi、cia fuel cost factor for the ith generator;
considering the valve point effect, the fuel cost function can be expressed as:
The specific form of the objective function with the minimum pollutant emission is as follows:
wherein alpha isi、βi、γiThe pollutant gas emission coefficient of the ith generator;
the equation is constrained to:
1) And power balance constraint:
Wherein, PDFor total load demand, PLIs transmission network loss;
The system transmission loss can be expressed by the following formula:
Wherein, Bij、Boi、BooThe network loss coefficient of the generator;
2) The inequality constraints are:
And (3) unit operation constraint:
Pimin≤Pi≤Pimax
Wherein, Piminis the lower active power output limit, P, of unit iimaxis active for unit ian upper limit of output;
In step S2, the initializing population and the algorithm iteration data by combining the dynamic environment economic dispatch model are specifically:
setting the maximum value of the population size and the initial total population Nmaxn, controlling the variable quantity D, wherein D is a dimension and is an active output value of each generator, and the maximum particle number MaxIter of the non-inferior solution set; randomly initializing a population X in a recovery space of a D-dimensional problem, wherein the ith individual is Xi＝[Xi1，Xi2，...XiD]N, i 1, 2.. No. N; lower and upper limits X of the initialization variable valuesmin、Xmaxminimum and maximum values S of the number of offspring generated by parent particlemin、SmaxInitial and final values of the standard deviation σinit、σfinalA nutrient enrichment radius R;
the initialization process of the population X is as follows: chaos variable generation by chaos Logistic equation
xi+1＝λ·xi·(1-xi)
in the formula, xi∈[o，1]，xinot equal to 0.25, 0.5 and 0.75, wherein lambda is a control parameter and takes a value of 0-4, and when the chaotic state is completely realized, the lambda is 4; take any initial point x0Can obtain [0, 1 ]]Point set x of traversal abovei，i＝1、2......N；
The obtained chaos variable xiConversion to initial population
Xi＝Xmin+α(Xmax-Xmin)(1-xi)xi
wherein α ═ 4 is a chaotic attractor.
2. the method for dynamic environmental economic dispatch of a multi-target wildflower algorithm as claimed in claim 1, wherein: in step S5, the step of executing the intensity mechanism to upgrade and maintain the non-inferior solution specifically includes:
the density mechanism is as follows:
Wherein O is the neighborhood center; f. ofi(A)，fi(B)，fi(C) And fi(O) the ith objective function for particle A, B, C and domain center O, respectively; the MaxIter individuals with high concentration are reserved.
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