CN110070292A - Microgrid economic load dispatching method based on cross and variation whale optimization algorithm - Google Patents
Microgrid economic load dispatching method based on cross and variation whale optimization algorithm Download PDFInfo
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Abstract
The invention discloses a kind of microgrid economic load dispatching methods based on cross and variation whale optimization algorithm, it is characterized in that, the following steps are included: keeping the equality constraint of balance to be converted to unconstrained economic goal function the micro-grid system power moment, unconstrained economic goal function is brought into based on carrying out that optimal value is calculated in cross and variation whale optimization algorithm.The microgrid economic load dispatching method based on whale optimization algorithm that the present invention provides a kind of, has the advantages that the algorithm structure is simple, should be readily appreciated that, fast convergence rate, solving precision is high, can each micro- source and energy storage power output in reasonable arrangement microgrid, the cost of microgrid optimization operation is effectively reduced.With the shortening of microgrid economical operation scheduling time scale, the requirement to algorithm solving speed and precision is also harsher.Former algorithm and other intelligent optimization algorithms, which are compared, the present invention is based on improved whale optimization algorithm all has more advantage.
Description
Technical field
The present invention relates to a kind of microgrid economic load dispatching methods based on cross and variation whale optimization algorithm, and it is negative to belong to micro-capacitance sensor
Lotus dispatching technique field.
Background technique
With the continuous increase of power grid scale, the disadvantage of conventional electric power network also increasingly becomes aobvious: overlength distance charging belt comes
Power loss；Power load is continuously increased, and traditional power supply system is difficult to realize the security reliability of power supply；Bulk power grid exists
Natural calamity is not effective to ensure that the stable operation of important load under invading and harassing, lead to huge economic loss etc..Distribution hair
Electricity can be that remote area power supplies realize the self-power supply of this area, avoid long distance power transmission using the local existing energy
Loss problem.But distributed generation resource has uncontrollable characteristic, such as wind turbine power generation, photovoltaic power generation etc. works as these uncontrollable electricity
Source is accessed in power distribution network alone as distributed generation resource, so that distribution network voltage adjustment becomes complicated, is unfavorable for bulk power system
Stability.And complicated and diversified distributed generation technology is unfavorable for realizing the comprehensive utilization of the energy, is easy to network system
Stability and safety bring hidden danger.Therefore it can be realized micro- electricity of large-scale distributed generator unit flexible access bulk power grid
Net comes into being.
Micro-capacitance sensor be one can self-contr ol, the small-sized electric system of self-protection and self-management, when its with it is big
When power grid is grid-connected, the load or generator unit of bulk power grid can be regarded as.With permeability of the distributed energy in power grid
It gradually increases, high fluctuation, unstability, intermittent feature, severely impacts power quality, or even can threaten whole
The safety of a network system.As an effective solution scheme in energy-optimised configuration, micro-capacitance sensor is by the advanced fortune of science
The decision system of row control method and intelligence can be realized the energy economy, environmental protection, safe, reliable, efficient benefit in power grid
With.As an important component of smart grid, micro-capacitance sensor can effectively improve the utilization rate of resource, realize energy
It distributes rationally.The energy management of microgrid minimizes system loss and operating cost under the premise of ensureing homeostasis operation,
Maximally utilize renewable resource.Flexible and efficient, the microgrid energy Operation Mode Optimization of safety economy makes in micro-capacitance sensor
Method decision, the technical requirements such as energy management are more intelligent.
Summary of the invention
The object of the present invention is to provide a kind of microgrid energy optimizing operation methods.
In order to achieve the above object, cross and variation whale optimization algorithm is based on the technical solution of the present invention is to provide a kind of
Microgrid economic load dispatching method, which comprises the following steps:
Microgrid economic optimization operational objective function with constraint condition is converted into unconstrained warp using penalty function method
Help optimization object function, and unconstrained economic goal function is brought into based on calculate in cross and variation whale optimization algorithm
Optimal value out, be based on cross and variation whale optimization algorithm the following steps are included:
Step 1, the parameter that cross and variation whale optimization algorithm is set；
Step 2, according to opposition learning strategy initialization population individual；
Step 3, the fitness value for calculating each individual, set 1 for the number of iterations t, record current optimum individual and position
It sets；
Step 4 judges whether the number of iterations t reaches preset threshold value Tmax, if so, 14 are entered step, if it is not,
Then enter step 5；
Step 5 calculates convergence factorUpdate coefficient vector
In formula, λ indicates non-linear convergence weight,
In formula,Indicate the random number between [0,1]；
Step 6 takes random number p；
Step 7 judges p whether less than 0.5, if so, 8 are entered step, if it is not, then entering step 9；
Step 8, judgementWhether less than 1, if it is not, 10 are then entered step, if so, entering step 11；
Step 9 according to the following formula behind more new individual position, enters step 12:
In formula,Indicate updated a body position；Indicate current individual positionWith target prey positionDistance；B indicates to limit logarithmic spiral
The constant of form；L is the random number between [- 1,1]；
Step 10 according to the following formula behind more new individual position, enters step 12:
In formula,It indicates to randomly select individual position vector；
Step 11 according to the following formula behind more new individual position, enters step 12:
Step 12 carries out cross and variation to each individual, the fitness value after calculating variation, if better than the adaptation before variation
Angle value carries out individual location updating, otherwise keeps original position constant；
Step 13 records current optimum individual position and corresponding adaptive optimal control angle value, updates the number of iterations t, t=t+1,
Return step 4；
Step 14, output optimum individual position and adaptive optimal control angle value.
Preferably, the step 2 the following steps are included:
Step 201, setting population scale N；
Step 202 generates individual X using random devicei, Xi=li+rand(0,1)·(ui-li), in formula, liFor variable
Lower limit, uiFor the upper limit of variable；
Step 203, the individual X that step 202 is generatediOpposition operation is carried out, OX is denoted asi, then have: OXi=li+ui-Xi；
Step 204 carries out OXiWith XiComparison two-by-two, leave more excellent individual.
The microgrid economic load dispatching method based on whale optimization algorithm that the present invention provides a kind of, has the advantages that the calculation
Method structure is simple, it can be readily appreciated that fast convergence rate, solving precision is high, can each micro- source and energy storage power output in reasonable arrangement microgrid,
The cost of microgrid optimization operation is effectively reduced.With the shortening of microgrid economical operation scheduling time scale, to algorithm solving speed
And the requirement of precision is also harsher.Former algorithm is compared the present invention is based on improved whale optimization algorithm and other intelligent optimizations are calculated
Method all has more advantage.
Detailed description of the invention
Flow chart based on cross and variation whale optimization algorithm of the invention.
Specific embodiment
Present invention will be further explained below with reference to specific examples.It should be understood that these embodiments are merely to illustrate the present invention
Rather than it limits the scope of the invention.In addition, it should also be understood that, after reading the content taught by the present invention, those skilled in the art
Member can make various changes or modifications the present invention, and such equivalent forms equally fall within the application the appended claims and limited
Range.
A kind of microgrid economic load dispatching method based on cross and variation whale optimization algorithm provided by the invention is based on following skill
Art:
1. micro-grid system optimal operation model
1.1 microgrid economic optimization operational objectives:
Economic goal mainly includes the operating cost of each distributed generation resource and the maintenance cost in each micro- source.Because of photovoltaic,
Wind-powered electricity generation is all clean energy resource, and substantially without operating cost, therefore the micro- source operating cost of micro-capacitance sensor is mainly miniature gas turbine, bavin
The fuel cost of oil machine consumption, specific formula is as follows:
Fuel cost CfuelCalculation formula is as follows:
In formula: ep,iFor the market price of fuel i；Pi(t) Δ t is the output energy of i generator Δ t period.
Maintenance cost CmtFormula are as follows:
In formula: Kmt,iFor the operation and maintenance coefficient of equipment i；Pi,tFor the place capacity of the maintenance needed for t moment of equipment i
Size.
For the micro-capacitance sensor grid-connected with bulk power grid, operating cost CexcIt also needs to include the energetic interaction with bulk power grid
The expense of generation, formula are as follows:
In above-mentioned formula: Pexc,tThe interaction power of t moment between micro-capacitance sensor and power grid, when the value is greater than zero, micro- electricity
Net is to bulk power grid power purchase, c at this timegrid,tFor the power purchase price at corresponding moment, conversely, micro-capacitance sensor is to bulk power grid sale of electricity, cgrid,tFor
The sale of electricity price at corresponding moment；T is optimizing cycle；N is the number of distributed generation resource；
Therefore, total economic cost C of micro-capacitance sensortcIt can be described as:
Ctc=Cfuel+Cmt+Cexc (1-4)
The constraint condition of 1.2 micro-capacitance sensor stable operations
Inequality constraints: (that classmate of front does not pay attention to very much, has changed part formula herein)
1) distributed generation resource power output power constraint:
Pmin,i< PDG,i< Pmax,i (1-5)
In formula, Pmin,iAnd Pmax,iRespectively i-th of distributed generation resource power output PDG,iBound；
2) interconnection constrains:
In formula,WithRespectively microgrid and bulk power grid bound P that cross-power is submitted in interconnectionexc,tWhen for t
Carve the interaction power of microgrid and bulk power grid on interconnection；
3) energy storage charge and discharge constrain:
In order to ensure the service life of battery, generally all avoids battery from being completely full of and put, SOCGenerally all constrain in one
Determine in range, such as following formula:
SoCmin< SoC (t) < SoCmax
Pbat,min< Pbat(t) < Pbat,max (1-7)
In formula: SoCminFor battery minimum state-of-charge；SoCmaxFor battery maximum state-of-charge；PbatIt (t) is energy-storage battery
The charge-discharge electric power of t moment；Pbat,minFor the minimum charge-discharge electric power of energy-storage battery；Pbat,maxFor the maximum charge and discharge of energy-storage battery
Electrical power.
When battery charging, the battery capacity residue at t+1 moment be can be described as:
When electric power storage tank discharge: the battery capacity residue at t+1 moment can be described as:
In above formula: Pdiss,Pch(t) be respectively energy-storage battery electric discharge and charge power；W is the capacity of energy-storage battery.
In above formula: Pdiss,Pch(t) be respectively energy-storage battery electric discharge and charge power；W is the capacity of energy-storage battery.
4) it climbing power constraint: also needs to consider its Climing constant for miniature gas turbine and fuel oil motor.
In formula:For controllable electric power i maximum climbing rate of descent；For the maximum climbing climbing of controllable electric power i；
PDGi(t) be t moment distributed generation resource i active power output.
Equality constraint: the system power moment keeps balance, and when micro-grid connection operation, mathematic(al) representation can be described
Are as follows:
Pexc,t+Pbatt,t+Ppv,t+Pwind,t+PDG,t=PLoad,t (1-12)
In formula: Pbatt,tFor the charge-discharge electric power of t moment energy storage, when being greater than zero, energy storage is in discharge condition, otherwise is in
Charged state；Ppv,tFor the power of t moment photovoltaic power generation；Pwind,tFor the power of t moment wind turbine power generation；PDG,tBeing for t moment can
Control the power of power supply power generation；PLoad,tFor the load of t moment system.
2 whale optimization algorithms:
Whale optimization algorithm (Whale Optimization Algorithm, WOA) was by MIRJALILI S in 2016
A kind of novel meta-heuristic algorithm proposed.Algorithm simulation humpback unique group's predation, whale is from bottom to top
The position of itself is updated to the encirclement range of prey by manufacture bubble Stepwize Shrink along helical form.The behavior is referred to as bubble
Net is looked for food method.The algorithm is always divided into: surrounding prey, the attack of bubble net and the three phases that quarter a prey.
1) prey is surrounded
Humpback is that priori is unknown when identifying prey, to prey position, it is therefore assumed that target prey position is
Optimal or close to optimal whale individual position in current population.Other whale individuals are close to target prey, position
More new formula can be described as:
In formula:Indicate the position of prey, dimension M；Indicate the position of current whale；t
For current iteration number；For coefficient vector, it is defined as follows:
In formula:As the number of iterations is from 2 linear decreases to 0；The random number being expressed as between [0,1].
It can be said that
In formula: t is the number of iterations, TmaxFor maximum number of iterations.
2) bubble net is attacked
The attack of bubble net is by two mechanism: shrinking encirclement mechanism, spiral update mechanism.Shrinking encirclement mechanism is exactly to reduce
In 2-3 formula
Spiral updates position: calculating whale individual at a distance from target prey, the position for then updating oneself goes predation to hunt
Object, mathematic(al) representation can be described as:
In formula:Indicate i-th whale individual at a distance from target prey；B is to limit logarithmic spiral
The constant of form；L is the random number between [- 1,1].But whale is also reducing when carrying out the attack of bubble net
The ring of encirclement, therefore in order to realize this synchronization, select identical probability to carry out shrinking encirclement mechanism and spiral update position.Mathematical modulo
Formula can be described as:
In formula: p is the random number between [01].
3) it quarters a prey
In fact, when whale quarters a prey, it can be according to the position random search between other individuals, use and encirclement
Similar vector in prey formulaCoefficient goes the simulation behavior, herein | A | >=1, and so that whale is far from reference to whale, (target is hunted
Object), to find a more preferably prey.Mathematical model indicates are as follows:
In formula:Expression randomly selects the position vector of whale.
2.2 improved whale optimization algorithms
Whale optimization algorithm is easily trapped into local optimum as remaining group of algorithms, Premature convergence, this hair occurs
The bright limitation for the algorithm introduces 4 improvement strategies, is respectively: utilizing the initialization kind of improved opposition learning strategy
Group's individual, changes original linear convergence factor, introduces adaptive weight value updating and addition cross and variation mechanism.Temporarily life
Entitled cross and variation whale optimization algorithm (Cross Mutation based improved Whale Optimization
Algorithm,CM-WOA)
2.2.1 oppose learning strategy
The position of initial population has embodied the diversity of population, and good population diversity is conducive to improve the solution of algorithm
Precision accelerates convergence speed of the algorithm.And traditional group algorithm generally goes to the position of initial population using random method, in this way
It cannot be guaranteed that the diversity of group, and the useful information in object space region cannot be utilized well.Oppose learning strategy
(Opposition-based Learning, OBL) is learnt generally by a kind of new machine that Tizhoosh was proposed in 2005
It reads, which is that there are antagonistic relations to inspire between by entity, is answered in many Swarm Intelligence Algorithms at present
With.This strategy is used for reference in the design thus, and has done variation slightly, the initialization for carrying out a body position to whale population is grasped
Make, steps are as follows:
1) population scale N is set.
2) individual X is generated using random devicei, method are as follows: Xi=li+rand(0,1)·(ui-li)；liFor under variable
Limit uiFor the upper limit of variable.
3) the individual i generated to step 2) carries out opposition operation, is denoted as OXi, method are as follows: OXi=li+ui-Xi。
4) original method is to merge { X (N) ∪ OX (N) } to obtain 2N individual, therefrom chooses the best N number of whale of fitness value
Individual is used as initial population, and since algorithm complexity is too high, the design briefly carries out XiWith OXiIt compares two-by-two, leaves more excellent
Body, remains the function of former algorithm to a certain extent, and time complexity is relatively low.
2.2.2 nonlinear change convergence factor:
It is well known that the global ability explored and locally developed is most important for the algorithm based on population iteration, it is higher
Global exploring ability mean that algorithm is not easy to fall into local optimum, the purpose locally developed is mainly based upon group and has letter
Breath scans for neighborhoods certain in solution room, enables to algorithm fast convergence.By formula 2-2, we be can analyze
Out, original whale algorithm relies primarily on vector coefficient in the encirclement prey stageReduction realize, and can by formula 2-3
To find out, vector coefficientReduction relies primarily on convergence factor againBiggish convergence factor can increase global search rank
Section, is conducive to the exploring ability for improving algorithm, and the local search ability of algorithm can be improved in lesser convergence factor.Original algorithm
In, convergence factorLinear decrease, this method are unfavorable for the fast convergence of algorithm.And nonlinear adjustment strategy can not change
Become in original algorithm and under the premise of convergence factor variation tendency, retain the global exploration and local development ability of algorithm, accelerates to calculate
The convergence of method.The design uses a kind of non-linear convergence factor and raising differential evolution algorithm is gone to jump out locally optimal solution in the later period
Ability nonlinear adjustment carried out to convergence factor in conjunction with whale optimization algorithm, the nonlinear factor algorithm initial stage possess compared with
The ability of searching optimum of algorithm can be improved in big value, obvious in mid-term decrease speed trend, promptly declines convergence factor
The value smaller to one, in the later period, convergence factor variation is relatively slow, is conducive to local exploitation.Specific formula is as follows:
In formula: t is the number of iterations；TmaxFor maximum number of iterations；
2.2.3 adaptive weight value updating
As convergence factor, biggish inertial factor is conducive to global exploration, and office can be improved in lesser inertial factor
Portion's development ability.In order to further increase the superiority of algorithm, the design adds adaptively in original whale optimization algorithm
Weight, specific formula is as follows:
In formula: λ is identical with formula 2-11 intermediate value, reduces as the number of iterations increases.
2.2.4 cross and variation
In the later period of algorithm, whale all tends to be gathered in around more excellent individual, this results in the diversity of population by
It destroys, is easy algorithm is caused to fall into local optimum.For this problem, the design introduces variation thought.
In order to which further such that algorithm possesses preferably local development ability, after above-mentioned location updating, the design is introduced
Cauchy function mechanism.Since Cauchy function operator can generate biggish variation step-length, individual can be guided to jump out local optimum,
Institute's ability is searched with the superior overall situation, but Cauchy function is unfavorable for the part exploitation of algorithm, causes algorithmic statement slower.And it is high
This mutation operator has stronger local development ability, therefore the design combines both mutation operators to carry out more optimal location
Newly, Variation mechanism are as follows: if the position after variation is more excellent, carries out the replacement of optimal location, otherwise do not do position replacement.Intersect
Variation can be described as:
In formula: k is the random number of [0,1].Improved algorithm flow chart sees reference Fig. 1.
2.3 experimental results and analysis
It in order to verify the superiority of this algorithm, is solved using 10 benchmark test functions, and optimizes with former whale and calculate
Method compares.Emulation experiment of the present invention is based on Windows10 (64) operating system, processor are as follows: Intel (R) Core
(TM) i5-8250U CPU@1.60Hz 1.80GHz, running memory 8G, programming use MATLAB R2016A software.
The present invention carrys out the superiority of verification algorithm in 30 dimension search spaces, and each independent operating of algorithm 30 times is sought average
Value and standard deviation.It the results are shown in Table 1.
Table 1CM-WOA and other algorithm reference functions comparison (Dim=30)
The solution of 3 optimization problems and model
For the optimization problem with constraint condition, method for solving can be roughly divided into deterministic algorithm and based on random
Two kinds of algorithm of property.Deterministic algorithm mainly has method of Lagrange multipliers, sequential quadratic programming, gradient method etc..In Practical Project
In, optimization aim is often non-convex, non-linear, non-differentiability and discrete；Additionally, due to the presence of constraint condition, decision variable
Feasible search space it is often disconnected.Therefore, this kind of algorithm is relatively difficult when seeking result, and the knot sought
Fruit is often locally optimal solution.In recent years, it is widely used in objective optimization as the randomness algorithm of representative using evolution algorithm to solve.
Evolution algorithm is scanned for based on all individuals in group, is gradually searched for compared to deterministic algorithm from a point, the former has
Search efficiency is fast, is easy the characteristics of obtaining global optimum.For constrained optimization problem, present invention design is carried out using penalty function method
Processing.
Inequality constraints: (formula needs and the correspondence after above-mentioned change)
By inequality Pmin,i< Pdp,i< Pmax,iRelease the inequality about g function:
gi,1=Pdp,i-Pmax,i gi,2=-Pdp,i+Pmin,i
And so on, inequality constraints function is released by following inequality.
Pmin,i< Pdp,i< Pmax,i
Inequality constraints function:
Equality constraint: the system power moment keeps balance, and when micro-grid connection operation, mathematic(al) representation can be described
Are as follows:
Pexc,t+Pbatt,t+Ppv,t+Pwind,t+PDG,t=PLoad,t
In formula: Pbatt,tFor the charge-discharge electric power of t moment energy storage, when being greater than zero, energy storage is in discharge condition, otherwise is in
Charged state；Ppv,tFor the power of t moment photovoltaic power generation；Pwind,tFor the power of t moment wind turbine power generation；PDG,tBeing for t moment can
Control the power of power supply power generation；PLoad,tFor the load of t moment system.
Inequality constraints is converted by equality constraint；
H (t)=| Pexc,t+Pbatt,t+Ppv,t+Pi,t-PLoad,t|-δ≤0 (3-2)
In formula: δ is the tolerance value of equality constraint, generally takes lesser positive number
Then individual violates constraint degree are as follows:
Therefore the unconstrained optimization problem after converting can be described as this similar function:
In formula: μl>=0 is penalty coefficient, and sufficiently large.
Microgrid economic load dispatching method provided by the invention based on cross and variation whale optimization algorithm, as shown in Figure 1, step
It is as follows:
It (1) is exactly that optimal economic problems are solved under its constraint condition for the objective function of the optimization of economic goal,
The economic cost of microgrid mainly includes the operating cost of each distributed generation resource and the maintenance cost in each micro- source.Because of photovoltaic, wind
Electricity is all clean energy resource, and substantially without operating cost, therefore the micro- source operating cost of micro-capacitance sensor is mainly miniature gas turbine, diesel oil
Machine consumption fuel cost, economic optimization problem be exactly each distributed generation resource operating cost and gas turbine and
The constraint conditions such as the limitation of the fuel cost of diesel engine.
(2) solution of economic problems model is first kept for the system power moment equality constraint of balance:
Pexc,t+Pbatt,t+Ppv,t+Pwind,t+PDG,t=PLoad,t
Be converted into inequality constraints condition h (t)=| Pexc,t+Pbatt,t+Ppv,t+Pi,t-PLoad,t|-δ≤0, δ are that constraint is held
Bear value.It can be carried out making constrained optimization problem be converted into nothing by the way that penalty term is added in primitive economy objective function in this way
The problem of constraint.Construction penalty term is generally based on the journey that individual violates constraint condition
DegreeTherefore the unconstrained optimization problem after converting can be described as this similar function:
(2) economic goal function unconstrained after conversion is brought into based on calculating in cross and variation whale optimization algorithm
Obtain optimal value.
Based on cross and variation whale optimization algorithm the following steps are included:
Step 1, the parameter that cross and variation whale optimization algorithm is set；
Step 2, according to opposition learning strategy initialization population individual, step 2 the following steps are included:
Step 201, setting population scale N；
Step 202 generates individual X using random devicei, Xi=li+rand(0,1)·(ui-li), in formula, liFor variable
Lower limit, uiFor the upper limit of variable；
Step 203, the individual X that step 202 is generatediOpposition operation is carried out, OX is denoted asi, then have: OXi=li+ui-Xi；
Step 204 carries out OXiWith XiComparison two-by-two, leave more excellent individual.
Step 3, the fitness value for calculating each individual, set 1 for the number of iterations t, record current optimum individual and position
It sets；
Step 4 judges whether the number of iterations t reaches preset threshold value Tmax, if so, 14 are entered step, if it is not,
Then enter step 5；
Step 5 calculates convergence factorUpdate coefficient vector
In formula, λ indicates nonlinear iteration weight,
In formula,Indicate the random number between [0,1]；
Step 6 takes random number p；
Step 7 judges p whether less than 0.5, if so, 8 are entered step, if it is not, then entering step 9；
Step 8, judgementWhether less than 1, if it is not, 10 are then entered step, if so, entering step 11；
Step 9 according to the following formula behind more new individual position, enters step 12:
In formula,Indicate updated a body position；Indicate current individual positionWith target prey positionDistance；B indicates to limit logarithmic spiral
The constant of form；L is the random number between [- 1,1]；
Step 10 according to the following formula behind more new individual position, enters step 12:
In formula,It indicates to randomly select individual position vector；Indicate step-length,
Step 11 according to the following formula behind more new individual position, enters step 12:
Step 12 carries out cross and variation to each individual, the fitness value after calculating variation, if better than the adaptation before variation
Angle value carries out individual location updating, otherwise keeps original position constant；
Step 13 records current optimum individual position and corresponding adaptive optimal control angle value, updates the number of iterations t, t=t+1,
Return step 4；
Step 14, output optimum individual position and adaptive optimal control angle value.
It is verified, the present invention by the improvement to whale optimization algorithm, proposition based on cross and variation whale optimization algorithm
The operation of more economical scheduling research can be preferably carried out to microgrid, and realize that process is relatively easy, have practical well
Value.
Claims (2)
1. a kind of microgrid economic load dispatching method based on cross and variation whale optimization algorithm, which comprises the following steps:
By the micro-grid system power moment keep balance equality constraint be converted to unconstrained economic goal function, by it is non-about
The economic goal function of beam is brought into based on carrying out that optimal value is calculated in cross and variation whale optimization algorithm, and cross and variation is based on
Whale optimization algorithm the following steps are included:
Step 1, the parameter that cross and variation whale optimization algorithm is set；
Step 2, according to opposition learning strategy initialization population individual；
Step 3, the fitness value for calculating each individual, set 1 for the number of iterations t, record current optimum individual and position；
Step 4 judges whether the number of iterations t reaches preset threshold value Tmax, if so, enter step 14, if it is not, then into
Enter step 5；
Step 5 calculates convergence factorUpdate coefficient vector
In formula, λ indicates nonlinear iteration weight, In formula,
Indicate the random number between [0,1]；
Step 6 takes random number p；
Step 7 judges p whether less than 0.5, if so, 8 are entered step, if it is not, then entering step 9；
Step 8, judgementWhether less than 1, if it is not, 10 are then entered step, if so, entering step 11；
Step 9 according to the following formula behind more new individual position, enters step 12:
In formula,Indicate updated a body position；
Indicate current individual positionWith target prey positionDistance；B indicates to limit the constant of logarithmic spiral form；L is
Random number between [- 1,1]；
Step 10 according to the following formula behind more new individual position, enters step 12:
In formula,It indicates to randomly select individual position vector；Indicate step-length,
Step 11 according to the following formula behind more new individual position, enters step 12:
Step 12 carries out cross and variation to each individual, the fitness value after calculating variation, if better than the fitness before variation
Value carries out individual location updating, otherwise keeps original position constant；
Step 13 records current optimum individual position and corresponding adaptive optimal control angle value, updates the number of iterations t, t=t+1, returns
Step 4；
Step 14, output optimum individual position and adaptive optimal control angle value.
2. a kind of microgrid economic load dispatching method based on cross and variation whale optimization algorithm as described in claim 1, feature
Be, the step 2 the following steps are included:
Step 201, setting population scale N；
Step 202 generates individual X using random devicei, Xi=li+rand(0,1)·(ui-li), in formula, liFor under variable
Limit, uiFor the upper limit of variable；
Step 203, the individual X that step 202 is generatediOpposition operation is carried out, OX is denoted asi, then have: OXi=li+ui-Xi；
Step 204 carries out OXiWith XiComparison two-by-two, leave more excellent individual.
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