CN110322123A - A kind of Multipurpose Optimal Method and system of Cascade Reservoirs combined dispatching - Google Patents
A kind of Multipurpose Optimal Method and system of Cascade Reservoirs combined dispatching Download PDFInfo
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Abstract
The invention discloses the Multipurpose Optimal Methods and system of a kind of Cascade Reservoirs combined dispatching, it include: with generated energy maximum and ecological holographic amount minimum building Multiobjective Scheduling model, using water level as individual, it is random to generate the initial population comprising m individual, in an iterative process, the maximum value and minimum value of each sub-goal are updated by comparing the fitness value of the corresponding sub-goal of individual each in front and back twice iteration population；The stress survey that each sub-goal is calculated using the maximum value and minimum value of each sub-goal, using each sub-goal stress survey by the Multiobjective Scheduling model conversion of Cascade Reservoirs be single-goal function, utilize single-goal function more new individual extreme value and global extremum；When the number of iterations reaches maximum number of iterations, by the corresponding individual of the global extremum obtained at this time as global optimum's individual, noninferior solution is selected from global optimum's individual, obtains the scheduling scheme of Cascade Reservoirs.Regulation goal of the present invention is abundant, model solution standard is high-efficient.
Description
Technical field
The invention belongs to high efficient utilization of water resources and Optimal Scheduling of Multi-reservoir System technical field, more particularly, to a kind of ladder
The Multipurpose Optimal Method and system of grade multi-reservoir combined dispatching.
Background technique
China's hydropower high speed development in recent years, more and more power stations are continuously developed utilization, especially with
The Wujiang River, the Lancang River, Dadu River, Hongsuihe River of southwest etc. are that the especially big Basin Hydropower base of representative is gone into operation operation successively,
While improving development and utilization level of the mankind to water resource, also to the natural landscape in place basin, regional climate, ecological environment
Cause different degrees of influence.Existing Cascade Reservoirs scheduling theory is only highlighted water resource and is imitated using bring society
Benefit and economic benefit, such as flood control, power generation, shipping, it is raw to have ignored basin caused by change of the dam safety evaluation to natural runoff
The change of state environment.Therefore, the Cascade Reservoirs Multiobjective Optimal Operation based on ecological dispatching has become the important of current research
Project, especially to China's Ecological Civilization Construction and ensuring that urban river ecosystem health is of great significance.In fact, both at home and abroad
There is the research much about multi-reservoir ecological dispatching, but focus mostly in the single ecological dispatching target of consideration, this mode is not enough to
Therefore the cascade operation service requirement of reaction under the new situation needs building Cascade Reservoirs multiple target joint optimal operation model
And realize Efficient Solution, to give full play to coordinative role of the reservoir in terms of society, economy, environment.
It can be seen that the prior art there is technical issues that regulation goal is single, model solution is quasi-.
Summary of the invention
Aiming at the above defects or improvement requirements of the prior art, the present invention provides a kind of Cascade Reservoirs combined dispatchings
Thus Multipurpose Optimal Method and system solve the prior art there are regulation goals single, the quasi- low efficiency of model solution technology
Problem.
To achieve the above object, according to one aspect of the present invention, a kind of the more of Cascade Reservoirs combined dispatching are provided
Purpose optimal method in turn includes the following steps:
(1) target is up to the generated energy of Cascade Reservoirs and establishes first object function, with the ecology of Cascade Reservoirs
The minimum target of water deficit establishes the second objective function, using first object function and the second objective function as two sub-goal structures
Build the Multiobjective Scheduling model of Cascade Reservoirs；
(2) the target weight vector for calculating the Multiobjective Scheduling model of Cascade Reservoirs, it is random raw using water level as individual
It is in an iterative process, corresponding by comparing each individual in front and back twice iteration population at the initial population comprising m individual
The fitness value of sub-goal updates the maximum value and minimum value of each sub-goal；
(3) stress survey that each sub-goal is calculated using the maximum value and minimum value of each sub-goal, utilizes each sub-goal
Stress survey combining target weight vectors by the Multiobjective Scheduling model conversion of Cascade Reservoirs be single-goal function, utilize
Single-goal function more new individual extreme value and global extremum；
(4) mutation operation is carried out to individual extreme value, obtains new individual extreme value, constructs new grain using new individual extreme value
Subclass carries out next iteration using new particle assembly Population Regeneration, will when the number of iterations reaches maximum number of iterations
The corresponding individual of the global extremum obtained at this time is selected noninferior solution from global optimum's individual, is obtained as global optimum's individual
The scheduling scheme of Cascade Reservoirs.
Further, target weight vector are as follows:
{λ1, λ2)={ 1-pr, pr)
Pr=(a+1)/(l+1)
A=0,1,2 ... l-1
Wherein, λ1For the weighted value of first object function, λ2For the weighted value of the second objective function, l is the total of noninferior solution
Number, pr are bi-distribution probability, and a is probability parameter.
Further, single-goal function are as follows:
Wherein, rJ, iFor the stress survey of i-th of individual j-th of sub-goal, λjFor the corresponding weight of j-th of sub-goal
Value,For in single-goal functionCorresponding fitness value,For kth i-th body position of generation.
Further, stress survey in step (3) are as follows:
When the fitness value of the sub-goal of Multiobjective Scheduling model is more bigger more excellent,When
When the fitness value of the sub-goal of Multiobjective Scheduling model is smaller more excellent,Wherein,
ForThe fitness value of j-th corresponding of sub-goal,For the minimum value of j-th of sub-goal,It is j-th
The maximum value of sub-goal.
Further, the specific implementation of single-goal function more new individual extreme value and global extremum is utilized in step (3)
Are as follows:
In formula,Indicate kth time iteration individual i desired positions experienced, i.e., individual extreme value；GBkIndicate kth time repeatedly
For the desired positions of all Individual Experiences, i.e. global extremum；For -1 iteration individual i desired positions experienced of kth,It indicates in single-goal functionCorresponding fitness value.
Further, step (4) includes the following steps:
(41) mutation operation is carried out to individual extreme value, obtains new individual extreme value；
(42) the current location U of new individual extreme value more new individual is utilizedg, when the condition for meeting mixed search strategy starting
When, from current location Ug(D+1) individual of moving out at random constitutes external archive collection SgAnd enter step (43), D is preset population
Dimension；Otherwise, step (46) are directly entered；
(43) to SgIn individual successively using mapping, shrink, amplification carry out secondary optimization；
(44) judge whether to meet mapping termination condition, shrink termination condition, amplify any one in termination condition, it is full
It is sufficient then go to (45)；Otherwise (43) are gone to；
(45) new external archive collection is formedThis (D+1) individual is moved back into current location U simultaneouslygTo constitute new grain
Subclass
(46) next iteration is carried out using new particle assembly Population Regeneration, when the number of iterations reachesWhen, it will at this time
The corresponding individual of obtained global extremum selects noninferior solution from global optimum's individual, obtains step as global optimum's individual
The scheduling scheme of multi-reservoir.
Further, the specific implementation of noninferior solution is selected in step (46) from global optimum's individual are as follows:
The corresponding first object function of global optimum's individual is calculated using all global optimum's individuals that step (46) obtains
Fitness value and corresponding second objective function of global optimum's individual fitness value, by global optimum's individual corresponding first
The fitness value of objective function as abscissa, using the fitness value of corresponding second objective function of global optimum's individual as indulging
Coordinate draws the forward position figure of noninferior solution, selects noninferior solution from the forward position figure of noninferior solution.
It is another aspect of this invention to provide that providing a kind of Multi objective optimization system of Cascade Reservoirs combined dispatching, wrap
It includes:
Model construction module is up to target for the generated energy with Cascade Reservoirs and establishes first object function, with ladder
The minimum target of ecological holographic amount of grade multi-reservoir establishes the second objective function, and first object function and the second objective function are made
The Multiobjective Scheduling model of Cascade Reservoirs is constructed for two sub-goals；
Fitness value comparison module, the target weight vector of the Multiobjective Scheduling model for calculating Cascade Reservoirs will
Water level is random to generate comprising m individual initial population as individual, in an iterative process, by comparing front and back iteration twice
The fitness value of the corresponding sub-goal of each individual updates the maximum value and minimum value of each sub-goal in population；
Single-goal function conversion module, for calculating the opposite of each sub-goal using the maximum value and minimum value of each sub-goal
Subordinate degree is turned the Multiobjective Scheduling model of Cascade Reservoirs using the stress survey combining target weight vectors of each sub-goal
It is changed to single-goal function, utilizes single-goal function more new individual extreme value and global extremum；
Noninferior solution chooses module, for carrying out mutation operation to individual extreme value, obtains new individual extreme value, utilizes new
Body extreme value constructs new particle assembly, next iteration is carried out using new particle assembly Population Regeneration, when the number of iterations reaches
When maximum number of iterations, by the corresponding individual of the global extremum obtained at this time as global optimum's individual, from global optimum's individual
In select noninferior solution, obtain the scheduling scheme of Cascade Reservoirs.
In general, through the invention it is contemplated above technical scheme is compared with the prior art, can obtain down and show
Beneficial effect:
(1) Multiobjective Scheduling model of the invention contains the generated energy maximum and ecological holographic amount minimum of Cascade Reservoirs
The two targets overcome the prior art technical problem single there are regulation goal, while utilizing stress survey by more mesh
Mark scheduling model is converted to single-goal function, thus improves the quasi- efficiency of model solution.
(2) target weight vector is arranged using bi-distribution enabling legislation in the present invention, with the multi objective fuzzy of relative defects
Model for Multi-Objective Optimization is converted to single object optimization model by preferred method, then is solved with improved quanta particle swarm optimization, compared with
Good multi-objective optimization question between having handled different target enriches the side for solving Cascade Reservoirs multi-objective scheduling optimization problem
Method.
Detailed description of the invention
Fig. 1 is a kind of process of the Multipurpose Optimal Method of Cascade Reservoirs combined dispatching provided in an embodiment of the present invention
Figure；
Fig. 2 (a) is the scheduling result of Multipurpose Optimal Method under the low flow year that the embodiment of the present invention 1 provides；
Fig. 2 (b) is the scheduling result for the hemiplegia Multipurpose Optimal Method during the lunar New Year that the embodiment of the present invention 1 provides；
Fig. 2 (c) is the scheduling result for the withered Multipurpose Optimal Method during the lunar New Year of spy that the embodiment of the present invention 1 provides；
Fig. 3 (a) is the water deficit comparative result figure that the embodiment of the present invention 1 provides；
Fig. 3 (b) is the generated energy comparative result figure that the embodiment of the present invention 1 provides.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
Not constituting a conflict with each other can be combined with each other.
A kind of Multipurpose Optimal Method of Cascade Reservoirs combined dispatching, comprising:
(1) target is up to the generated energy of Cascade Reservoirs and establishes first object function, with the ecology of Cascade Reservoirs
The minimum target of water deficit establishes the second objective function, using first object function and the second objective function as two sub-goal structures
The Multiobjective Scheduling model of Cascade Reservoirs is built, setting population scale is m, population dimension is D, maximum number of iterations isIt is non-
The scale l (i.e. the sum of noninferior solution) of inferior solution collection；
Wherein, target is up to the generated energy of Cascade Reservoirs and establishes first object function:
The second objective function is established with the minimum target of ecological holographic amount of Cascade Reservoirs:
In formula: E1And E2Respectively Cascade Reservoirs annual electricity generating capacity (unit: kWh) and total ecological holographic amount (unit:
m3)；N is the number in power station in Cascade Reservoirs；N is the serial number in power station；T is dispatching cycle；T is the serial number of period；Δ t is every
The hourage (unit: h) of a period；PN, tWith Δ ON, tN-th of power station is respectively represented in the power output (unit: kW) of t-th of period
With ecological holographic amount (unit: m3), ON, tWithRespectively represent n-th of power station t-th of period letdown flow (unit:
m3/ s) and Water Requirement (unit: m3/s)。
The Multiobjective Scheduling model solution of Cascade Reservoirs needs the constraint condition met:
Water balance constraint equation: VN, t+1=VN, t+(qN, t-QN, t-SN, t) Δ t, in formula, VN, t+1、VN, tRespectively n-th
Power station is in (t+1) and the pondage (m of t period3)；qN, t、QN, t、SN, tStorage stream of respectively n-th of the power station in the t period
Measure (m3/ s), power generation reference flow (m3/ s), abandon water flow (m3/s)；Δ t is Period Length.
Pondage constraint: Respectively n-th of power station is in t period pondage
Lower limit, the upper limit, m3。
Generating flow constraint: It generates electricity reference to be respectively n-th of power station in the t period
The lower limit of flow, the upper limit, m3/s。
The constraint of reservoir letdown flow: Respectively outbound of the power station n in the t period
Flux lower limit, the upper limit, m3/s。
Power station units limits: Respectively n-th of power station t period contribute lower limit, on
Limit, kw.
(2) target of the Multiobjective Scheduling model of Cascade Reservoirs is calculated using the multiple target enabling legislation based on bi-distribution
Weight vectors { λ1, λ2}={ 1-pr, pr }, pr=(a+1)/(l+1) enables a=0.
(3) k=1 is enabled, under pondage constraint, using water level as individual, is wrapped using the method generated at random
Initial population containing m individualThe recording individual extreme value in initial populationEach sub-goal optimal locationWith it is each
The worst position of sub-goal
In formula,Indicate kth i-th body position of generation, i=1,2,3 ... m；J represents sub-goal number, j=1, and 2；K is
The number of iterations；R is the random number of [0,1] section distribution； XThe bound of respectively individual value.
(4) fitness value of the corresponding sub-goal of individual each in Means of Penalty Function Methods calculating population is utilizedIt calculates
It is as follows:
Wherein,It indicatesThe fitness value of j-th corresponding of sub-goal,ForCorresponding
The fitness value of one objective function,ForThe fitness value of the second corresponding objective function,ForIt is right
The destruction item answered, G are constraint condition number, g=1,2 ..., G；PN, tFor n-th of power station period t power output；ΔON, tIt is n-th
Ecological holographic flow of a power station in period t；ON, tWithLetdown flow and ecology of respectively n-th of the power station in period t
Water requirement；Δ t is scheduling slot hourage；XXK, gIt indicatesThe corresponding value of g-th of constraint condition in gained scheduling process；θg
For the destruction penalty coefficient of g-th of constraint condition；Respectively XXK, qUpper and lower limit.
(5) maximum value by each sub-goal in current location is updatedWith minimum valueCorresponding expression formula is such as
Under:
In formulaRespectively indicate j-th sub-goal kth generation individual i optimal location experienced, worst position
It sets；Respectively j-th of sub-goal kth -1 generation individual i optimal location experienced, worst position；RespectivelyFitness value； RespectivelyFitness value.
(6) use the multi objective fuzzy preferred method of relative defects by the Multiobjective Scheduling model conversion of Cascade Reservoirs
For single-goal function, then single-goal function calculates as follows:
Wherein, rJ, iFor the stress survey of i-th of individual j-th of sub-goal；When the sub-goal of Multiobjective Scheduling model
When fitness value is more bigger more excellent,When the fitness value of the sub-goal of Multiobjective Scheduling model is
When smaller more excellent,λjFor the corresponding weighted value of j-th of sub-goal,For single-goal function
InCorresponding fitness value.
(7) more new individual extreme value and global extremum, more new-standard cement are as follows:
In formula,Indicate kth generation individual i desired positions experienced, i.e., individual extreme value；GBkIndicate kth for all
The desired positions of body experience, i.e. global extremum；For kth -1 generation individual i desired positions experienced,Indicate single
In objective functionCorresponding fitness value.
(8) mutation operation is carried out to individual extreme value, corresponding expression formula is as follows:
In formula:Indicate the new individual extreme value generated after mutation operation；For [0,1] section distribution random number,
Ind1 and Ind2 respectively indicates the integer selected from set { 1,2 ..., m } at random, and has Ind1 ≠ Ind2；Respectively in single-goal functionCorresponding fitness value.
(9) using the current location U of the new individual extreme value more new individual generated after mutation operationg, mixed when meeting
When the condition of search strategy starting, from current location Ug(D+1) individual of moving out at random constitutes external archive collection SgAnd it enters step
(10)；Conversely, being directly entered step (13)；
The condition of mixed search strategy starting are as follows: δ >=Pa, wherein δ is the random number of [0,1] section random distribution,K indicates current iteration number,Indicate maximum number of iterations.
(10) to SgIn individual successively using mapping, shrink, amplification carry out secondary optimization；
(11) judge whether to meet mapping termination condition, shrink termination condition, amplify any one in termination condition, it is full
It is sufficient then go to (12)；Otherwise (10) are gone to；
(12) new external archive collection is formedThis (D+1) individual is moved back into current location U simultaneouslygTo constitute new grain
Subclass
(13) k=k+1 is enabled, judges whether to meet stop condition, i.e. the number of iterations(14) are gone to if meeting, instead
The new particle assembly of utilization(4) are gone to after Population Regeneration；
(14) stop calculating and exporting the individual of the global optimum under the weight and it is corresponding suitable in single-goal function
Answer angle value.
(15) a=a+1 is enabled, whether true judges a≤l-1, if satisfied, then stop calculating and entering step (16), conversely,
Go to (2).
(16) each sub-goal is judged, noninferior solution is selected from obtained global optimum's individual, to obtain non-bad
Disaggregation.
The corresponding first object function of global optimum's individual is calculated using all global optimum's individuals that step (16) obtains
Fitness value and corresponding second objective function of global optimum's individual fitness value, by global optimum's individual corresponding first
The fitness value of objective function as abscissa, using the fitness value of corresponding second objective function of global optimum's individual as indulging
Coordinate, drafting obtain the forward position figure of noninferior solution, select noninferior solution from the forward position figure of noninferior solution according to Pareto dominance relation, from
And obtain Noninferior Solution Set.
Scheduling scheme by the corresponding individual of Noninferior Solution Set as Cascade Reservoirs.
The specific implementation of mapping are as follows:
Determine SgIn maximum adaptation angle value f (Xhigh) corresponding to individual Xhigh, secondary big fitness value f (Xsec) corresponding
Individual Xsec, minimum fitness value f (Xlow) corresponding to individual Xlow, and calculate SgIn remove XhighThe average bit of outer all individuals
Set Xcenter, and byCalculate XhighMapping point Xr, α is mapping coefficient, is reflected if meeting
Penetrate termination condition f (Xlow)≤f(Xr)≤f(Xsec), then Xhigh=XrAnd step (12) are executed, if f (Xr) < f (Xlow), then into
Row amplification, if f (Xr) > f (Xsec), then it is shunk.
The specific implementation of amplification are as follows:
By Xe=Xcenter+β(Xr-Xcenter) to mapping point XrIt amplifies to obtain amplification point Xe, β is amplification coefficient, if full
Foot amplification termination condition f (Xe)≤f(Xlow), then enable Xhigh=XeAnd it executes step (12) and otherwise enables Xhigh=XrAnd execute step
Suddenly (12).
The specific implementation of contraction are as follows:
If f (Xr) > f (Xsec) and f (Xr)≤f(Xhigh), enable Xhigh=Xr, X is redefined according to mapping processr, then
By Xc=Xcenter+γ(Xhigh-Xcenter) carry out shrinkage operation obtain constriction point XcIf f (Xr) > f (Xhigh), then directly by Xc
=Xcenter+γ(Xhigh-Xcenter) carry out shrinkage operation obtain constriction point Xc, γ is constriction coefficient；Termination condition is shunk if meeting
f(Xc)≤f(Xhigh), then enable Xhigh=XcAnd execute step (12).
Embodiment 1
With the method for the present invention, according to Wujiang River multi-reservoir for different water situations in the ecological holographic that various combination is arranged
Amount passes through the multiple target tune for repeating to obtain the Multiobjective Scheduling model solution of Cascade Reservoirs under the weight of generated energy target
Journey is spent, and then obtains generated energy and ecological holographic amount, Fig. 2 (a) is the scheduling result of Multipurpose Optimal Method under the low flow year；Figure
2 (b) be the scheduling result of hemiplegia Multipurpose Optimal Method during the lunar New Year；Fig. 2 (c) is the scheduling knot of special withered Multipurpose Optimal Method during the lunar New Year
Fruit；As seen from the figure, the method for the present invention can obtain the scheduling scheme set being reasonably distributed in non-bad forward position, when generated energy is bigger
When, water deficit is also bigger, it was demonstrated that in Model for Multi-Objective Optimization result generated energy it is maximum between ecological holographic amount minimum the two
Interactive relation.
Table 1 gives the detailed results of the half-way house obtained by present invention under the conditions of low flow year water, it can be seen that each electricity
Water level of standing changes between level of dead water and ordinary water level, and lower station is run as far as possible in high water head to reduce step water
Consumption increases generated energy, embodies the intension of step compensative dispatching, while power station average output is respectively less than installed capacity, this sufficiently says
The reasonability and feasibility of acquired results of the present invention is illustrated.
Table 1
Fig. 3 (a) is water deficit comparative result figure；Fig. 3 (b) is generated energy comparative result figure, as seen from the figure, Multiobjective Scheduling
The scheduling result of model in each month basically between two single goal models, particularly with 6,7, August part, multiple target
Model significantly alleviates the situation for not considering the generated energy maximum model serious water shortage of downstream ecology water, further verifies
Multi-objective Model result has found preferable equalization point between generated energy and ecological holographic amount, to coordinate therebetween
Relationship, i.e., as far as possible improve system generated energy while, meet the needs of downstream ecology water.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should all include
Within protection scope of the present invention.
Claims (8)
1. a kind of Multipurpose Optimal Method of Cascade Reservoirs combined dispatching, which is characterized in that in turn include the following steps:
(1) target is up to the generated energy of Cascade Reservoirs and establishes first object function, with the ecological holographic of Cascade Reservoirs
It measures minimum target and establishes the second objective function, using first object function and the second objective function as two sub-goal building ladders
The Multiobjective Scheduling model of grade multi-reservoir；
(2) the target weight vector for calculating the Multiobjective Scheduling model of Cascade Reservoirs, it is random to generate packet using water level as individual
Initial population containing m individual, in an iterative process, by comparing the corresponding specific item of individual each in front and back twice iteration population
Target fitness value updates the maximum value and minimum value of each sub-goal；
(3) stress survey that each sub-goal is calculated using the maximum value and minimum value of each sub-goal, utilizes the phase of each sub-goal
To subordinate degree combining target weight vectors by the Multiobjective Scheduling model conversion of Cascade Reservoirs be single-goal function, utilize monocular
Scalar functions more new individual extreme value and global extremum；
(4) mutation operation is carried out to individual extreme value, obtains new individual extreme value, new particle collection is constructed using new individual extreme value
It closes, carrying out next iteration using new particle assembly Population Regeneration will at this time when the number of iterations reaches maximum number of iterations
The corresponding individual of obtained global extremum selects noninferior solution from global optimum's individual, obtains step as global optimum's individual
The scheduling scheme of multi-reservoir.
2. a kind of Multipurpose Optimal Method of Cascade Reservoirs combined dispatching as described in claim 1, which is characterized in that described
Target weight vector are as follows:
{λ1, λ2}={ 1-pr, pr }
Pr=(a+1)/(l+1)
A=0,1,2 ... l-1
Wherein, λ1For the weighted value of first object function, λ2For the weighted value of the second objective function, l is the sum of noninferior solution, pr
For bi-distribution probability, a is probability parameter.
3. a kind of Multipurpose Optimal Method of Cascade Reservoirs combined dispatching as claimed in claim 1 or 2, which is characterized in that
The single-goal function are as follows:
Wherein, rJ, iFor the stress survey of i-th of individual j-th of sub-goal, λjFor the corresponding weighted value of j-th of sub-goal,For in single-goal functionCorresponding fitness value,For kth i-th body position of generation.
4. a kind of Multipurpose Optimal Method of Cascade Reservoirs combined dispatching as claimed in claim 3, which is characterized in that described
Stress survey in step (3) are as follows:
When the fitness value of the sub-goal of Multiobjective Scheduling model is more bigger more excellent,When more mesh
When the fitness value of the sub-goal of mark scheduling model is smaller more excellent,Wherein,For
The fitness value of j-th corresponding of sub-goal,For the minimum value of j-th of sub-goal,For j-th of sub-goal
Maximum value.
5. a kind of Multipurpose Optimal Method of Cascade Reservoirs combined dispatching as claimed in claim 3, which is characterized in that described
The specific implementation of single-goal function more new individual extreme value and global extremum is utilized in step (3) are as follows:
In formula,Indicate kth time iteration individual i desired positions experienced, i.e., individual extreme value；GBkIndicate kth time iteration institute
There are the desired positions of Individual Experience, i.e. global extremum；For -1 iteration individual i desired positions experienced of kth,It indicates in single-goal functionCorresponding fitness value.
6. a kind of Multipurpose Optimal Method of Cascade Reservoirs combined dispatching as claimed in claim 1 or 2, which is characterized in that
The step (4) includes the following steps:
(41) mutation operation is carried out to individual extreme value, obtains new individual extreme value；
(42) the current location U of new individual extreme value more new individual is utilizedg, when meeting the condition of mixed search strategy starting, from
Current location Ug(D+1) individual of moving out at random constitutes external archive collection SgAnd enter step (43), D is preset population dimension；
Otherwise, step (46) are directly entered；
(43) to SgIn individual successively using mapping, shrink, amplification carry out secondary optimization；
(44) judge whether to meet mapping termination condition, shrink termination condition, amplify any one in termination condition, meet then
Go to (45)；Otherwise (43) are gone to；
(45) new external archive collection is formedThis (D+1) individual is moved back into current location U simultaneouslygTo constitute new particle collection
It closes
(46) next iteration is carried out using new particle assembly Population Regeneration, when the number of iterations reachesWhen, by what is obtained at this time
The corresponding individual of global extremum selects noninferior solution from global optimum's individual, obtains Cascade Reservoirs as global optimum's individual
Scheduling scheme.
7. a kind of Multipurpose Optimal Method of Cascade Reservoirs combined dispatching as claimed in claim 6, which is characterized in that described
The specific implementation of noninferior solution is selected in step (46) from global optimum's individual are as follows:
The suitable of the corresponding first object function of global optimum's individual is calculated using all global optimum's individuals that step (46) obtains
The fitness value for answering angle value and corresponding second objective function of global optimum's individual, by the corresponding first object of global optimum's individual
The fitness value of function is sat as abscissa, using the fitness value of corresponding second objective function of global optimum's individual as vertical
Mark, draws the forward position figure of noninferior solution, selects noninferior solution from the forward position figure of noninferior solution.
8. a kind of Multi objective optimization system of Cascade Reservoirs combined dispatching characterized by comprising
Model construction module is up to target for the generated energy with Cascade Reservoirs and establishes first object function, with step water
The minimum target of ecological holographic amount of library group establishes the second objective function, using first object function and the second objective function as two
The Multiobjective Scheduling model of a sub-goal building Cascade Reservoirs；
Fitness value comparison module, the target weight vector of the Multiobjective Scheduling model for calculating Cascade Reservoirs, by water level
It is random to generate comprising m individual initial population as individual, in an iterative process, by comparing front and back iteration population twice
In the fitness value of the corresponding sub-goal of each individual update the maximum value and minimum value of each sub-goal；
Single-goal function conversion module calculates the relatively optimal degree of each sub-goal for the maximum value and minimum value using each sub-goal
It spends, is by the Multiobjective Scheduling model conversion of Cascade Reservoirs using the stress survey combining target weight vectors of each sub-goal
Single-goal function utilizes single-goal function more new individual extreme value and global extremum；
Noninferior solution chooses module, for carrying out mutation operation to individual extreme value, obtains new individual extreme value, utilizes new individual pole
Value constructs new particle assembly, carries out next iteration using new particle assembly Population Regeneration, when the number of iterations reaches maximum
When the number of iterations, by the corresponding individual of the global extremum obtained at this time as global optimum's individual, selected from global optimum's individual
Noninferior solution out obtains the scheduling scheme of Cascade Reservoirs.
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