CN107370188A - A kind of power system Multiobjective Scheduling method of meter and wind power output - Google Patents
A kind of power system Multiobjective Scheduling method of meter and wind power output Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses a kind of meter and the power system Multiobjective Scheduling method of wind power output, the height of high lower bound appraisal procedure output forecast interval based on neutral net, lower bound, wind power scene is produced in forecast interval sequence to describe the randomness of wind power, risk indicator is desired for cutting load rejection penalty, fallen into a trap and total fuel cost in object function simultaneously, pollutant discharge amount, risk cost and spinning reserve cost, build the model of power system optimal dispatch containing wind-powered electricity generation, solution is optimized to the multiple target object function of structure based on flower pollination algorithm and differential evolution algorithm, dispatching method after being optimized.The present invention is desired for risk indicator with cutting load rejection penalty, fallen into a trap and spinning reserve cost in object function simultaneously, build the model of power system optimal dispatch containing wind-powered electricity generation, and the model of structure is in addition to considering the conventional operation constraint of unit, reliability constraint is also added into, scheduling decision is taken into account environment, economy, security.
Description
Technical field
The present invention relates to a kind of meter and the power system Multiobjective Scheduling method of wind power output.
Background technology
Economic Dispatch problem refers to meeting the condition such as the system capacity equilibrium of supply and demand and unit output constraint
Down so that the minimum scheme of fired power generating unit coal consumption amount, the scheduling problem after wind-electricity integration must must consider the uncertain of wind power
Property, because wind speed has randomness and intermittent feature, will be transported when wind-powered electricity generation permeability reaches certain proportion to power system
Row adds risk, and the generation of traditional wind power scene is general in two steps：Directly assume that wind power obeys certain distribution, with
Machine analog sampling generates scene；It is assumed that distribution does not meet the distribution of wind power objective reality, larger error be present.Based on wind
Electrical power interval prediction obtains its experience accumulation distribution function, and then produces scene, therefore more objectivity, traditional interval prediction
Also there is certain limitation, it is necessary to be improved on this basis.
Multiple target Economic Dispatch Problem containing wind power plant considers the coal-fired production of safety problem, fired power generating unit that wind-electricity integration is brought
The factor such as raw environmental pollution and spinning reserve cost, this is a high dimensional nonlinear multi-objective problem, and processing multiple target is excellent
Typically be added during change problem using weight or penalty function method, multi-objective problem be converted into single-objective problem, but weight and penalize because
Sub- value does not have selection gist, and therefore, it is difficult to obtain optimal solution.
The content of the invention
The present invention is in order to solve the above problems, it is proposed that a kind of to count and the power system Multiobjective Scheduling side of wind power output
Method, the present invention be desired for risk indicator with cutting load rejection penalty, while is fallen into a trap and spinning reserve cost in object function, structure
Build the model of power system optimal dispatch containing wind-powered electricity generation.The model is also added into reliable in addition to considering the conventional operation constraint of unit
Property constraint, scheduling decision is taken into account environment, economy, security.Wind is described using a kind of new and effective scene generation technique
The randomness of electrical power.A kind of more mesh with time-varying blurring selection mechanism are proposed based on flower pollination algorithm and differential evolution algorithm
Mark optimized algorithm.
To achieve these goals, the present invention adopts the following technical scheme that：
A kind of power system Multiobjective Scheduling method of meter and wind power output, the high lower bound appraisal procedure based on neutral net
The high and low limit of forecast interval is exported, produces wind power scene in forecast interval sequence to describe the randomness of wind power, with
Cutting load rejection penalty is desired for risk indicator, while is fallen into a trap and total fuel cost, pollutant discharge amount, risk in object function
Cost and spinning reserve cost, the model of power system optimal dispatch containing wind-powered electricity generation is built, calculated based on flower pollination algorithm and differential evolution
Method optimizes solution to the multiple target object function of structure, the dispatching method after being optimized.
Further, the generating process of forecast interval sequence is that the high lower bound appraisal procedure based on neutral net directly exports
The high and low limit of forecast interval, determine that neutral net inputs by correlation analysis；Pass through Mutation Particle Swarm Optimizer optimization neural network
Structure and imparting weight.
Further, detailed process includes：
(1) whole wind power historical data is divided into training set, test set two parts, and all normalized, training set
For adjusting neutral net weight, test set is used for assessing the performance of training gained forecast model；
(2) input data vector that correlation analysis determines neutral net is carried out, sets neutral net as with two hidden-layer
The feedforward neural network of dual output, with two hidden-layer neuron number n1×n2For variable, it is assumed that neural network structure alternatively integrates as B,
Maximum alternative construction J；Maximum iteration is set as K, sets the parameter of particle cluster algorithm；
(3) particle position and speed are initialized, with PiNeutral net weight is represented, i represents particle individual；
(4) particle rapidity and position are updated, carries out particle variations operation, is sample structure PI using training set, using CWC to select
Excellent index is assessed PI；
(5) more new individual and global optimum position；
(6) if meeting stopping criterion for iteration, PI is built by sample of test setj, Calculation Estimation index, preserve the overall situation most
Excellent position and evaluation index, otherwise, iterations go to step (4) after adding one；
(7) if reaching maximum alternative construction number, it is optimal god to take global optimum position corresponding to evaluation index maximum
Through network structure weight；Otherwise, alternative construction number adds one, goes to step (3).
Further, generate scene, using estimate the process of wind power experience accumulation distribution function as：
1st, forecast interval sequence is produced using high lower bound appraisal procedure, confidence level range and increment, common property life n is set
Forecast interval；
2nd, assume that prediction error is symmetrical, each forecast interval can uniquely be divided into two quantiles and represent, confidence level
Two quantiles expressions of (α/2) % and (1- α/2) % are divided into for (1- α) % forecast interval；
3rd, in each prediction time i, two point { (α on wind power experience accumulation distribution function curve are obtained
2) %, ECDFi[(α/2) %] }, { (1- α/2) %, ECDFi[(1- α/2) %] }, n forecast interval common property gives birth to 2n point, curve
Two end points (0,0), (1,1), this 2 points represent that the probability of wind power perunit values less than 0 and 1 are respectively 0 and 1, each pre-
Survey moment i and there are 2n+2 discrete point；
4th, using Hermite interpolation method three times is segmented, wind power experience accumulation distribution is produced by 2n+2 point fitting
Function each point, the wind power probability distribution of each prediction time is represented respectively；
5th, scene is produced using Latin Hypercube Sampling, random number between a 0-1 is produced to each scene, by wind-powered electricity generation work(
Rate experience accumulation distribution function each point produces wind power scene value.
Multi-objective Model based on scene includes object function and constraints, in this randomness Optimal Operation Model,
Preferable optimization aim is that each object function all reaches minimum, including 4 parts：Total fuel cost, pollutant discharge amount, risk cost
With spinning reserve cost.
Further, it is expected to represent system operation risk index with cutting load rejection penalty.
Further, the constraints of multi-objective Model includes power-balance constraint, unit output constraint, the constraint of climbing rate
Constrained with spinning reserve.
Further, carry out general domain search using flower pollination algorithm and obtain Pareto optimal solution sets.
Specifically include：
(i) initiation parameter, including flower population number and select probability；
(ii) each result appraisal index is calculated, and obtains current optimal solution and optimal evaluation index；
(iii) if select probability is more than setting value, solution is updated according to globally optimal solution and step-length；
(iv) if select probability is less than setting value, solution is updated according to identical floristic different flowers；
(v) evaluation index corresponding to the new explanation being calculated, if the evaluation index of new explanation is more excellent, with new explanation and its evaluation
Desired value replaces current solution and Evaluation: Current desired value respectively, otherwise retains current solution and Evaluation: Current desired value；
(vi) if evaluation index value is more excellent than global optimum corresponding to new explanation, renewal globally optimal solution and the overall situation are most
The figure of merit；
(vii) judge termination condition, if satisfied, quitting a program and exporting optimal solution and optimal evaluation index value, otherwise, turn
Step (iii).
Further, differential evolution algorithm is improved, calculates any one feasible solution its Fuzzy Selection index, will
Multiple target function values of feasible solution are integrated into an index, the index are replaced to the object function of differential evolution algorithm, with this
Optimization method is set to have time-varying blurring selective.
Further, the computational methods of time-varying blurring selection mechanism index include：By membership function, corresponding each target
Function is that any feasible solution assigns membership values, calculates each feasible solution grade sequence index under each scene of day part, and with this
Preferentially standard of the grade sequence index as multi-objective optimization question feasible solution.
The computational methods of time-varying blurring selection mechanism index include calculating Fuzzy Selection index by bell membership function.
Further, the process for solving Multiobjective Scheduling is：Definition meets the solution of four indexs to greatest extent, and determines
It is expected, optimal solution is searched in the neighborhood of solution for meeting four indexs to greatest extent；
Using iterations as foundation, whole improved differential evolution algorithm iteration process is divided into some, bell is subordinate to
The width of function constantly narrows with the progress of iteration, and this is multistep time variation, is stepped up search precision, and each bell is subordinate to
Function has different centers and width, and the different bell membership function calculating Fuzzy Selection of the solution imparting concentrated is solved to Pareto and is referred to
Scale value.
Compared with prior art, beneficial effects of the present invention are：
(1) present invention is desired for risk indicator with cutting load rejection penalty, while is fallen into a trap in object function and rotation is standby
With cost, the model of power system optimal dispatch containing wind-powered electricity generation, and the operation constraint that the model built is conventional except considering unit are built
Outside, reliability constraint is also added into, scheduling decision is taken into account environment, economy, security.
(2) when the present invention solves the optimal solution of multiple objective function, weight and penalty factor value have selection gist, can obtain
To optimal solution.
Brief description of the drawings
The Figure of description for forming the part of the application is used for providing further understanding of the present application, and the application's shows
Meaning property embodiment and its illustrate be used for explain the application, do not form the improper restriction to the application.
Fig. 1 corresponds to iterations 1-100 bell membership function figures；
Fig. 2 HFPA-TVFSM integration algorithm flow charts；
Fig. 3 fuel costs-disposal of pollutants Pareto forward positions comparison diagram；
Fig. 4 fuel costs-stand-by cost Pareto forward positions comparison diagram；
Fig. 5 disposals of pollutants-risk indicator Pareto forward positions comparison diagram；
Tri- kinds of algorithms of Fig. 6 restrain comparison diagram on the optimizing of fuel cost.
Embodiment：
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
It is noted that described further below is all exemplary, it is intended to provides further instruction to the application.It is unless another
Indicate, all technologies used herein and scientific terminology are with usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative
It is also intended to include plural form, additionally, it should be understood that, when in this manual using term "comprising" and/or " bag
Include " when, it indicates existing characteristics, step, operation, device, component and/or combinations thereof.
In the present invention, term as " on ", " under ", "left", "right", "front", "rear", " vertical ", " level ", " side ",
The orientation or position relationship of instructions such as " bottoms " are based on orientation shown in the drawings or position relationship, only to facilitate describing this hair
Bright each part or component structure relation and the relative determined, not refer in particular to either component or element in the present invention, it is impossible to understand
For limitation of the present invention.
In the present invention, term such as " affixed ", " connected ", " connection " should be interpreted broadly, and expression can be fixedly connected,
Can also be integrally connected or be detachably connected；Can be joined directly together, can also be indirectly connected by intermediary.For
The related scientific research of this area or technical staff, the concrete meaning of above-mentioned term in the present invention can be determined as the case may be,
It is not considered as limiting the invention.
As background technology is introduced, in the prior art in the presence of processing multi-objective optimization question when typically use weight phase
Add or penalty function method, multi-objective problem is converted into single-objective problem, but weight and penalty factor value do not have selection gist, therefore
It is difficult to the deficiency for obtaining optimal solution, in order to solve technical problem as above, present applicant proposes a kind of meter and the electricity of wind power output
Force system multiple target economic load dispatching method.Risk indicator is desired for cutting load rejection penalty, at the same fall into a trap in object function and
Spinning reserve cost, build power system optimal dispatch containing wind-powered electricity generation model.The model is except considering the conventional operation of unit about
Outside beam, reliability constraint is also added into, scheduling decision is taken into account environment, economy, security.Using a kind of new and effective scene
Generation technique describes the randomness of wind power.It is proposed that one kind has time-varying mould based on flower pollination algorithm and differential evolution algorithm
Paste the multi-objective optimization algorithm of selection mechanism.
In a kind of typical embodiment of the application, as shown in Fig. 2 the present invention comprises the following steps：
Step 1: wind power scene generates；
High lower bound appraisal procedure (lower upper bound based on neutral net (neural network, NN)
Estimation, LUBE) the high and low limit of forecast interval is directly exported, assume without making any data distribution；By correlation analysis
Determine that neutral net inputs；By Mutation Particle Swarm Optimizer (PSO) Optimal Neural Network Architectures and weight, whole process is sketched such as
Under：
1st, whole wind power historical data is divided into training set Dtrain, test set DtestTwo parts, and all normalize.Instruction
Practice collection to be used for adjusting NN weights, test set is used for assessing the performance of training gained forecast model.
2nd, carry out correlation analysis and determine NN input data vectors；NN is set herein as the feedforward with two hidden-layer dual output
Neutral net, with two hidden-layer neuron number n1×n2For variable, it is assumed that NN structures alternatively integrate as B, individual comprising alternative construction J altogether；
Maximum iteration is set as K；Set PSO algorithm relevant parameters.
3rd, particle position P is initializediAnd speed Vi, with PiNN weights are represented, i represents particle individual.
4th, particle rapidity, position are updated；Carry out particle variations operation.
5th, with DtrainPI is built for sample, PI is assessed by preferentially index of CWC.
6th, more new individual, global optimum position PbestAnd Gbest。
If the 7, meeting stopping criterion for iteration, with DtestPI is built for samplej, Calculation Estimation index CWCj, preserve Gbest,j
And CWCj, i.e. optimal weights and evaluation index of the alternative NN structures of jth kind；Otherwise, k=k+1,4 are gone to step.
If the 8, reaching maximum alternative construction number J, i.e., all alternative NN structures have all trained, have tested, assess and finish, then take
CWCjG corresponding to maximumbest,jFor optimal N N structure ratios；Otherwise, j=j+1,3 are gone to step.
Above interval prediction method produces forecast interval sequence, for estimating wind power experience accumulation distribution function
(empirical cumulative distribution function, ECDF), its step is as follows：
1st, forecast interval sequence, confidence level 5%-95%, increment 5%, common property life 19 are produced using LUBE methods
Individual forecast interval.
2nd, assume that prediction error is symmetrical, each forecast interval can uniquely be divided into two quantiles and represent.Confidence level
Two quantiles expressions of (α/2) % and (1- α/2) % are divided into for (1- α) % forecast interval.
3rd, in each prediction time i, ECDF is obtainedi, two points { (α/2) %, ECDF on the curves of i=1 ... 24i[(α/
2) %] }, { (1- α/2) %, ECDFi[(1- α/2) %] }, therefore 19 forecast interval common properties give birth to 38 points.Two ends of curve
Point (0,0), (1,1), this 2 points represent that probability of the wind power perunit value less than 0 and 1 is respectively 0 and 1.Each prediction time i
It there are 40 discrete points.
4th, using Hermite interpolation method three times is segmented, ECDF are produced by 40 points fittingsi, wind-powered electricity generation a few days ago used herein
Power prediction temporal resolution is one hour, therefore common property gives birth to 24 ECDF curves, represents the wind-powered electricity generation of 24 prediction times respectively
Power probability is distributed.
5th, scene is produced using Latin Hypercube Sampling.To random number between one 0-1 of each scene generation, by ECDFi
Produce wind power scene value Pi w, i=1 ... 24.
Step 2: multi-objective Model of the structure based on scene；
Multi-objective Model based on scene includes object function and constraints, in this randomness Optimal Operation Model,
Preferable optimization aim is that each object function all reaches minimum, including 4 parts：Total fuel cost, pollutant discharge amount, risk into
Originally, spinning reserve cost；
Count and the conventional power unit fuel cost Expectation-based Representation for Concepts of valve point effect is：
PG,s=[P1,sP2,s…Pt,s…PNT,s], Pt,s=[P1,t,sP2,t,s…Pug,t,s…PNG,t,s]T (2)
In formula, πsRepresent scene s probability；Pug,t,sRepresent that conventional generator ug contributes under t period scenes s；NS is represented
Scene number；NT represents hop count during total activation；NG represents conventional generator number；aug、bug、cugRepresent fuel cost coefficient；
dug、eugRepresent valve point effect coefficient correlation；Represent conventional power unit ug minimum loads.
Dusty gas total emission volumn it is expected to be represented by：
In formula, αug、βug、γug、ξug、λugTo give dusty gas emission factor.
It is expected that (expected energy not served, EENS) represents system operation risk with cutting load rejection penalty
Index, it is represented by：
In formula, VOLL (value of lost load, VOLL) represents system cutting load penalty coefficient (for unit MWh
The expense of interruptible load)；EENSt,sRepresent that the cutting load under t period scenes s it is expected；σnet_dThe standard of error is predicted for net load
Variance, USRt,sRepresent that the system positive rotation under t period scenes s is standby.
Spinning reserve cost：
In formula, DSRt,sRepresent that the system under t period scenes s bears spinning reserve；kU、kDPositive and negative spinning reserve is represented respectively
Cost coefficient.
Constraints includes power-balance constraint, unit output constraint, the constraint of climbing rate, spinning reserve constraint；
Power-balance constraint：
In formula, PWt,sRepresent output of the wind power plant under t period scenes s；PDtRepresent t period system loadings.
Unit output constrains：
In formula,Unit ug maximum, minimum load limitation is represented respectively.
Climbing rate constrains：
Pug,t,s-Pug,t-1,s≤RUug·T60, Pug,t-1,s-Pug,t,s≤RDug·T60 (9)
In formula, RUug、RDugUnit ug upper and lower climbing rate is represented respectively；T60Represent an operation period, i.e. 60min.
Spinning reserve constrains：
In formula, T10Represent 10min.
System reliability constrains：
EENSt,s≤EENSmax (12)
In formula, EENSmaxFor the system cutting load upper limit, represented with power system capacity percentage.
Obtained Step 3: carrying out general domain search by flower pollination algorithm (flower pollination algorithm, FPA)
Pareto optimal solution sets；
Flower pollination algorithm realizes that step is as follows：
1st, initiation parameter, including flower population number n, select probability ρ；
2nd, each result appraisal index is calculated, and obtains current optimal solution and optimal evaluation index；
If the 3, select probability ρ ＞ rand, updated by formula (13)；
In formula,The i-th ter+1 generations, the solution in the i-th ter generations, g are represented respectively*It is globally optimal solution, L is step-length,
Lay dimension distribution is obeyed, its calculating process is as follows：
In formula, μ, υ Normal Distributions,And have：
In formula, β=3/2, Γ (β) are the gamma functions of standard.
If the 4, select probability ρ ＜ rand, updated by formula (16)；
In formula, η is that equally distributed random number is obeyed on [0,1],It is identical floristic do not suit
Piece.
5th, evaluation index corresponding to the new explanation that calculation procedure 3 or 4 obtains, if the evaluation index of new explanation is more excellent, with newly
Solution and its evaluation index value replace current solution and Evaluation: Current desired value respectively, otherwise retain current solution and Evaluation: Current index
Value；
If the 6, evaluation index value corresponding to new explanation is more excellent than global optimum, globally optimal solution and global optimum are updated
Value；
7th, judge termination condition, if satisfied, quitting a program and exporting optimal solution and optimal evaluation index value, otherwise, turn step
Rapid 3.
Step 4: improved differential evolution algorithm (DE)；
DE selection operation is improved, makes whole integration algorithm have time-varying blurring selective.
Conventional DE selection operation is as follows：
In formula,Represent the progeny population of generation, that is, follow-on father vector；Represent parent present age population；Represent experiment progeny population；F () represents object function.
Typically using weight addition or penalty function method when handling multi-objective optimization question, but weight and penalty factor value do not have
Selection gist, therefore, it is difficult to obtain optimal solution.The present invention proposes to calculate any one feasible solution its Fuzzy Selection index
(fuzzy selection index, FSI), multiple target function values of feasible solution are integrated into an index by FSI, by FSI
() replaces the f () in (17) formula, then selection operation is expressed as：
Step 5: each index of time-varying blurring selection mechanism calculates；
By membership function, corresponding each object function assigns membership values (member value, MV) for any feasible solution,
Calculating process is as follows：
In formula, MVi t,s(fm) represent that i-th of feasible solution corresponds to the membership values of m-th of object function under period t scene s；Represent m-th of object function minimum value；Represent m-th of object function maximum.
I-th of feasible solution grade sequence index expression under t period s scenes is：
The preferentially standard of multi-objective optimization question feasible solution is used as using RI.
Fuzzy Selection index is calculated by bell membership function：
Represent i-th of feasible solution Fuzzy Selection index under t periods s scene c classification；K is function order；
acC fuzzy classifications center is represented, Fuzzy Selection index is 1 herein；bcRepresent c fuzzy classification width.
Step 6: HFPA-TVFSM solves Multiobjective Scheduling；
Fuel cost, pollutant emission, risk indicator, stand-by cost are conflicting 4 targets, there are apparently no are made
Four solutions being optimal simultaneously, i.e. RI=1.Therefore BCS is defined as the solution for meeting four indexs to greatest extent, and it is expected RI
Value searches for optimal solution typically between 0.5~0.9 in BCS neighborhoods.acMake RI values between being set in 0.5~0.9 in this scope
Interior solution obtains higher FSI values.
Using iterations as foundation, whole DE algorithm iteration processes of improving are divided into 4 parts (setting greatest iteration time herein
Number is 500), the width of bell membership function constantly narrows with the progress of iteration, and this is 4 step time variations, is stepped up searching for
Precision.
4 step time-varying, 5 class Fuzzy Selection mechanism can increase the diversity of Pareto optimal solution sets, and enhancing algorithm is local in BCS
Search capability, and then it is quickly found out optimal solution.There is each bell membership function different centers and width (5 classes are divided into, with c=
1st, 2,3,4,5 represent respectively), therefore the solution concentrated is solved to Pareto and assigns different FSI values.ac、bc, K setting values be shown in Table 1, it is right
Answer wherein K=6, bc=0.6 figure is as shown in Figure 1.Fuzzy classification is by iterative random number randiterDetermine, rule is as follows：
HFPA-TVFSM algorithm flows are as shown in Fig. 2 step is as follows：
Step1：Unit parameter is inputted, predicted load, wind power scene prediction value, sets the total iterations of FPA, DE
Respectively K, J, population at individual sum is L.
Step2：Initialize population N0, population at individual present age Pareto optimal solution sets M0, global Pareto optimal solution sets G, entirely
Office extreme value g*.Population at individualIt is NG × NT matrix, such as formula (2), wherein each element Pug,t,sMeet machine
Group units limits, i.e. (8) formula, are initialized as the following formula：
λ is equally distributed random number on [0,1].
Step3：Calculate each target function value；By population NkContemporary Pareto is updated by prevailing conditions formula (24), (25)
Optimal solution set Mk；By MkGlobal Pareto optimal solution sets G is updated, determines global extremum g*。
Conditional (24), (25) are described as follows：
Pareto (Pareto) is dominant, and and if only if by A ＜ Β (A be dominant B)：
In formula, NobjRepresent the number of object function.
Pareto optimal solutions, A are Pareto optimal solutions, and and if only if：
Step4：Determined by select probability ρ, Experimental population E is produced by formula (13) or (16)k, and pass through formula (24), (25)
Selection, renewal Nk。
Step5：Check Population Regeneration NkWhether middle individual meets each constraints, the individual to being unsatisfactory for constraints
Corrected.
Step6：K=k+1, into step3, such as reach the general domain search process of maximum iteration K, FPA and terminate, output is complete
Office Pareto optimal solution set G, into the improvement accurate searching process of DE algorithms.
Step7：Assign DE algorithms initial father population F Pareto collection G0。
Step8：By FjEnter row variation, crossover operation produces Experimental population Sj, and calculate F by formula (19), (20)j、Sj's
MV, RI value.
Step9：Fuzzy classification is randomly choosed by formula (22), and calculates Fj、SjFSI indexs, by formula (18) selection, renewal
Fj, and update global Pareto optimal solution sets G by prevailing conditions.
Step10：J=j+1, into Step8, such as reach maximum iteration J, export global Pareto optimal solution sets G,
And optimal solution is determined by RI indexs.
The present invention carries model and method for solving and emulated in 4 machine set systems with an integrated wind plant.Should
Wind power plant rated power is 110MW.For convenience of analyzing Optimized Operation result and its influence factor, the system research cycle sets
It is set to 12h, each period is 1h.History wind power data come from Weihai in Shandong province's wind power plant, and method production is introduced using Section 1
Raw 1000 scenes, then cut down technology using scene and scene quantity is reduced to 10.kU、kDValue is all 20 $/(MW
H), EENSmax1%, VOLL is taken to take 100 $/MW.
The multi-objective optimization algorithm that will be widely used at present simultaneously：Non-dominated sorted genetic algorithm, improve multi-target particle
Algorithm carries out model solution to group's algorithm as a comparison (maximum iteration is 500 times).Obtained by a certain scheduling slot, fuel into
Sheet-dusty gas discharge capacity, fuel cost-stand-by cost, dusty gas discharge capacity-local Pareto of risk indicator three
Forward position is to shown in such as Fig. 3,4,5.Obvious can be seen that carries the available more excellent Pareto forward positions of algorithm herein.
Three kinds of algorithms are as shown in Figure 6 on fuel cost, the contrast of its iterative convergent process in the first period.Can be with by Fig. 6
Find out that carry algorithm herein is respectively provided with clear superiority in terms of convergence rate and optimizing result, Fuzzy Selection mechanism promotes algorithm to exist
Fast Convergent near optimal compromise solution.
Table 1 is optimal scheduling result 12 period summations of corresponding four object functions of three kinds of algorithms, can by data in table
To find out, HFPA-TVFSM calculates gained scheduling strategy to be had closer to preferable optimum point in MOP solution spaces, each target function value
There is greater advantage.
1 three kinds of algorithm MOP Comparative results of table
The preferred embodiment of the application is the foregoing is only, is not limited to the application, for the skill of this area
For art personnel, the application can have various modifications and variations.It is all within spirit herein and principle, made any repair
Change, equivalent substitution, improvement etc., should be included within the protection domain of the application.
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, model not is protected to the present invention
The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need to pay various modifications or deformation that creative work can make still within protection scope of the present invention.
Claims (10)
1. a kind of power system Multiobjective Scheduling method of meter and wind power output, it is characterized in that：High lower bound based on neutral net
Appraisal procedure exports the high and low limit of forecast interval, produces wind power scene in forecast interval sequence to describe wind power
Randomness, risk indicator is desired for cutting load rejection penalty, while fallen into a trap and total fuel cost, pollutant row in object function
High-volume, risk cost and spinning reserve cost, the model of power system optimal dispatch containing wind-powered electricity generation is built, based on flower pollination algorithm and difference
Evolution algorithm is divided to optimize solution to the multiple target object function of structure, the dispatching method after being optimized.
2. the power system Multiobjective Scheduling method of a kind of meter as claimed in claim 1 and wind power output, it is characterized in that：Prediction
The generating process of sequence of intervals is the high and low limit that the high lower bound appraisal procedure based on neutral net directly exports forecast interval, by
Correlation analysis determines that neutral net inputs；By Mutation Particle Swarm Optimizer Optimal Neural Network Architectures and assign weight.
3. the power system Multiobjective Scheduling method of a kind of meter as claimed in claim 2 and wind power output, it is characterized in that：Specifically
Process includes：
(1) whole wind power historical data is divided into training set, test set two parts, and all normalized, training set is used for
Neutral net weight is adjusted, test set is used for assessing the performance of training gained forecast model；
(2) input data vector that correlation analysis determines neutral net is carried out, sets neutral net as with two hidden-layer lose-lose
The feedforward neural network gone out, with two hidden-layer neuron number n1×n2For variable, it is assumed that neural network structure alternatively integrates as B, maximum
Alternative construction J；Maximum iteration is set as K, sets the parameter of particle cluster algorithm；
(3) particle position and speed are initialized, with PiNeutral net weight is represented, i represents particle individual；
(4) particle rapidity and position are updated, carries out particle variations operation, is sample structure PI using training set, using CWC preferentially to refer to
Mark is assessed PI；
(5) more new individual and global optimum position；
(6) if meeting stopping criterion for iteration, PI is built by sample of test setj, Calculation Estimation index, preserve global optimum position
Put and evaluation index, otherwise, iterations goes to step (4) after adding one；
(7) if reaching maximum alternative construction number, it is optimal nerve net to take global optimum position corresponding to evaluation index maximum
Network structure ratio；Otherwise, alternative construction number adds one, goes to step (3).
4. the power system Multiobjective Scheduling method of a kind of meter as claimed in claim 1 and wind power output, it is characterized in that：Generation
Scene, using estimate the process of wind power experience accumulation distribution function as：
(a) the high lower bound appraisal procedure of application produces forecast interval sequence, sets confidence level range and increment, it is individual pre- that common property gives birth to n
Survey section；
(b) assume that prediction error is symmetrical, each forecast interval can uniquely be divided into two quantiles and represent, confidence level is
(1- α) % forecast interval is divided into two quantiles of (α/2) % and (1- α/2) % and represented；
(c) in each prediction time i, obtain on wind power experience accumulation distribution function curve two points (α/2) %,
ECDFi[(α/2) %] }, { (1- α/2) %, ECDFi[(1- α/2) %] }, n forecast interval common property 2n point of life, the two of curve
Individual end points (0,0), (1,1), this 2 points represent that probability of the wind power perunit value less than 0 and 1 is respectively 0 and 1, when each predicting
Carve i and there are 2n+2 discrete point；
(d) using Hermite interpolation method three times is segmented, wind power experience accumulation distribution letter is produced by 2n+2 point fitting
Number each point, the wind power probability distribution of each prediction time is represented respectively；
(e) scene is produced using Latin Hypercube Sampling, random number between a 0-1 is produced to each scene, by wind power
Experience accumulation distribution function each point produces wind power scene value.
5. the power system Multiobjective Scheduling method of a kind of meter as claimed in claim 1 and wind power output, it is characterized in that：It is based on
The multi-objective Model of scene includes object function and constraints, in this randomness Optimal Operation Model, preferable optimization aim
It is that each object function all reaches minimum, including 4 parts：Total fuel cost, pollutant discharge amount, risk cost and spinning reserve into
This.
6. the power system Multiobjective Scheduling method of a kind of meter as claimed in claim 1 and wind power output, it is characterized in that：To cut
Load rejection penalty it is expected to represent system operation risk index；The constraints of multi-objective Model includes power-balance constraint, machine
Group units limits, the constraint of climbing rate and spinning reserve constraint.
7. the power system Multiobjective Scheduling method of a kind of meter as claimed in claim 1 and wind power output, it is characterized in that：Utilize
Flower pollination algorithm carries out general domain search and obtains Pareto optimal solution sets；
Specifically include：
(i) initiation parameter, including flower population number and select probability；
(ii) each result appraisal index is calculated, and obtains current optimal solution and optimal evaluation index；
(iii) if select probability is more than setting value, solution is updated according to globally optimal solution and step-length；
(iv) if select probability is less than setting value, solution is updated according to identical floristic different flowers；
(v) evaluation index corresponding to the new explanation being calculated, if the evaluation index of new explanation is more excellent, with new explanation and its evaluation index
Value replaces current solution and Evaluation: Current desired value respectively, otherwise retains current solution and Evaluation: Current desired value；
(vi) if evaluation index value is more excellent than global optimum corresponding to new explanation, globally optimal solution and global optimum are updated
Value；
(vii) judge termination condition, if satisfied, quitting a program and exporting optimal solution and optimal evaluation index value, otherwise, go to step
(iii)。
8. the power system Multiobjective Scheduling method of a kind of meter as claimed in claim 1 and wind power output, it is characterized in that：To difference
Divide evolution algorithm to be improved, its Fuzzy Selection index is calculated any one feasible solution, by multiple object functions of feasible solution
Value is integrated into an index, and the index is replaced to the object function of differential evolution algorithm, optimization method is had time-varying mould with this
Paste selectivity.
9. the power system Multiobjective Scheduling method of a kind of meter as claimed in claim 1 and wind power output, it is characterized in that：Time-varying
The computational methods of Fuzzy Selection mechanism indicator include：By membership function, corresponding each object function assigns for any feasible solution
Membership values, each feasible solution grade sequence index under each scene of day part is calculated, and more mesh are used as using these level sequence index
Mark the preferentially standard of optimization problem feasible solution；
The computational methods of time-varying blurring selection mechanism index include calculating Fuzzy Selection index by bell membership function.
10. the power system Multiobjective Scheduling method of a kind of meter as claimed in claim 1 and wind power output, it is characterized in that：Ask
Solution Multiobjective Scheduling process be：Definition meets the solution of four indexs to greatest extent, and determines it is expected, full to greatest extent
Search optimal solution in the neighborhood of the solution of four indexs of foot；
Using iterations as foundation, whole improved differential evolution algorithm iteration process is divided into some, bell membership function
Width constantly narrow with the progress of iteration, this is multistep time variation, is stepped up search precision, each bell membership function
With different centers and width, the solution concentrated is solved to Pareto and assigns different bell membership function calculating Fuzzy Selection indexs
Value.
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