CN104600728B - Optimizing method of mixed energy accumulation capacity configuration for stabilization wind power fluctuation - Google Patents

Optimizing method of mixed energy accumulation capacity configuration for stabilization wind power fluctuation Download PDF

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CN104600728B
CN104600728B CN201410837838.6A CN201410837838A CN104600728B CN 104600728 B CN104600728 B CN 104600728B CN 201410837838 A CN201410837838 A CN 201410837838A CN 104600728 B CN104600728 B CN 104600728B
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energy storage
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CN104600728A (en
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李昌陵
何琳
贾政豪
孙梦洁
杨佩源
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Economic and Technological Research Institute of State Grid Xinjiang Electric Power Co Ltd
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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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses an optimizing method of mixed energy accumulation capacity configuration for stabilization wind power fluctuation; the method comprises the following steps: generating a coordination control strategy, using a fuzzy control theory for generating a correction adjustment coefficient Kc with SOE1(t-1) and Delta SOE1(t) based on a distribution reference power of a state of energy SOE1(t-1) constraint empirical mode decomposition of power type energy accumulation under a time domain, distributing the actual output power of the mixed energy accumulation; establishing an economy model using the mixed energy accumulation cost as a target function and constraint conditions of the model; using genetic algorithm with chaotic disturbance and quantum computation for optimizing the configuration scheme of the mixed energy accumulation system based on the established economy model and the constraint conditions of the model. The disclosed optimizing method of mixed energy accumulation capacity configuration for stabilization wind power fluctuation is able to achieve the protection to the power type energy accumulation by the coordination control; and the method has the advantages of high in optimization precision, fast in optimization process calculation, and wide in application range.

Description

A kind of hybrid energy-storing capacity configuration optimization method for stabilizing wind power fluctuation
Technical field
The present invention relates to technical field of wind power generation, in particular it relates to a kind of hybrid energy-storing for stabilizing wind power fluctuation Capacity configuration optimization method.
Background technology
Wind-power electricity generation is widely used due to efficient, cleaning advantage, but the larger randomness of wind-force wind speed The safety and stability being incorporated into the power networks with fluctuation affects wind-power electricity generation, causes wind power station electric energy serious waste.According to existing Wind-electricity integration standard GB/T 19963-2011《Wind energy turbine set accesses power system technology regulation》, the change of wind-power electricity generation active power Limit Δ PlimitReference table 1：
The active power of table 1 changes limit value recommendation tables
Mixed energy storage system due to the Time-spatial diversion of electric energy can be realized, so as to be considered as stabilize wind power fluctuation, The effective means that electrical network receives wind-powered electricity generation ability is improved, mixed energy storage system includes energy type energy-storage system and power-type energy storage system System.Energy type energy-storage system (such as battery) response speed is slow, cycle life is low, it is difficult to regulate and control the fluctuation point of wind-power electricity generation power high frequency Amount；Power-type energy-storage system (such as ultracapacitor) energy density is low, it is difficult to regulate and control long time scale wind-power electricity generation power swing.
Mixed energy storage system (energy-storage system that power-type-energy type energy-accumulating medium is constituted), is integrated with cycle-index height, work( The advantages of rate density is high big with capacity, solves to a certain extent exclusive use power-type or energy type energy-storage system receives energy The problem of the factor such as density and service life restriction.However, for power-type and the energy of the mixed energy storage system of energy type composition Amount distribution principle becomes the limiting factor that wind-powered electricity generation fluctuation is stabilized using mixed energy storage system.
Energy-storage system cost costly, and restricted lifetime, how distributive mixing energy-storage system exerts oneself to stabilize wind-powered electricity generation Active power fluctuation simultaneously configures the effective mixed energy storage system volume solutions of economical rationality based on control is coordinated, and becomes a class and needs badly The problem of solution.
At present, the mode for mixed energy storage system power distribution has a lot, such as Application No. CN201310091057.2 Chinese patent " a kind of mixed energy storage system stabilizes the control method of fitful power power swing " in time domain, although utilize The power output of empirical mode decomposition and mixed energy storage system discharge and recharge priority effectively distributive mixing energy-storage system, but do not have The protection of charge and discharge is crossed to the restriction of its power output ability and to power-type energy storage in view of energy storage energy state, with certain Limitation.
A kind of Chinese patent " power distribution method of mixed energy storage system " of application number CN201310675017.2 considers Hybrid energy-storing feature, based on frequency-domain analysis, using wave filter hybrid energy-storing power output is separated, and is changed using energy storage charge state Become filter constant T, but the filtering based on frequency domain is decomposed when energy storage realtime power distributes, and there is time delay phenomenon and detached storage Energy power output cannot also embody its detailed information and energy storage feature in time domain.
" the hybrid energy-storing power station for stabilizing wind power fluctuation holds the Chinese patent of application number CN201410062240.4 Amount determination method " sets up hybrid energy-storing charging and recharging model to reduce accumulator cell charging and discharging number of times as target, and with hybrid energy-storing electricity Capacity of standing is optimum for object function, and by particle cluster algorithm the optimum capacity in hybrid energy-storing power station is sought, the patent consider with It is that principle protects the coordination control strategy of battery to reduce battery charging and discharging, and establishes old middle operation cost of energy storage economy, but The factor of energy storage cycle life cost is not accounted for, and using classical particle cluster algorithm optimizing may be caused slowly, to be easily absorbed in The problems such as local optimum.
During the present invention is realized, inventor has found that at least there is coordination control in prior art does not store up to power-type Can be protected, optimization process precision is low, calculating speed is slow and the shortcomings of little fitness.
The content of the invention
It is an object of the present invention to be directed to the problems referred to above, a kind of hybrid energy-storing capacity for stabilizing wind power fluctuation is proposed Method for optimizing configuration, to realize coordinating protection and low optimization accuracy height, searching process calculating speed of the control to power-type energy storage The big advantage of the fast and scope of application.
For achieving the above object, the technical solution used in the present invention is：A kind of hybrid energy-storing for stabilizing wind power fluctuation Capacity configuration optimization method, including：
A, formulation coordination control strategy, according to the energy state SOE of power-type energy storage under time domain1(t-1) experience is constrained The assigned references power of mode decomposition, using fuzzy control theory with SOE1And Δ SOE (t-1)1T () formulates correction adjustment factor Kc, the real output of distributive mixing energy storage；
B, based on the allocation result to hybrid energy-storing real output, set up with hybrid energy-storing cost as object function The constraints of economy model and model；
The constraints of c, the economic model according to foundation and model, is calculated using band chaotic disturbance and quantum calculation heredity Method, optimizes the allocation plan of mixed energy storage system.
Further, step a, specifically includes：
Step 1：According to active power data P of wind energy turbine set typical case's dayW(t), sampling time T, common NtotalIndividual complete information Data point under collection, to meet wind-electricity integration requirement as principle, obtains hybrid energy-storing and stabilizes wind-powered electricity generation target using moving average method Power PH(t) and grid-connected power PG(t)；
Step 2：According to the energy state SOE of power-type energy storage under time domain1(t-1) distribution of empirical mode decomposition is constrained Reference power, fuzzy control theory is with SOE1And Δ SOE (t-1)1T () formulates fuzzy rule and obtains output calibration regulation for input COEFFICIENT K c, the real output of distributive mixing energy storage completes hybrid energy-storing and coordinates control.
Further, in step 1, the acquisition hybrid energy-storing stabilizes wind-powered electricity generation target power PHThe operation of (t), further Including：
According to the grid-connected requirement of active power for wind power, moving average method parameter N value is set, and separate wind power PWT (), obtains To PH(t)。
Further, step b, specifically includes：
Step 3：The economy model for setting up mixed energy storage system is object function, while according to energy storage technology demand, association Adjust control constraints condition and stabilize surge requirements and set up constraint；Mixed energy storage system is power-type and energy type energy storage, step 3 In carry out the power distribution of mixed energy storage system, that is, coordinate control.
Further, the step 3, further includes：
Step 3-1：Power-type energy storage SOE1Energy state is defined as high electric energy area, low electric energy area and mesozone, works as SOE1 At high electric energy area, it has larger discharge capability, and charging ability is very poor；At low electric energy area, it has larger filling Electric energy power and poor discharge capability；There are good charging and discharging capabilities in zone line；
Step 3-2：If the rated power of power-type energy storage is P1, capacity be Q1, the sampling time is T, during to t and t The value at top n moment is carved, empirical mode decomposition is carried out and is obtained n intrinsic mode functions component Aj(tr) and remainder B (tr), wherein trRepresent the t-N moment of t, t-1 ...；
1)PH(t)>When 0, take and meet Ci(t) ＜ min (Q1*(1-SOE1(t-1),P1) maximum i values, and make power-type store up Can power Pc1(t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
2)PH(t)<When 0, take and meet Ci(t) ＞ max (Q1*SOE1(t-1),-P1) maximum i values, and make power-type energy storage Power Pc1(t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
3) P is worked asHDuring (t)=0, Pc1(t)=Pb1(t)=0；
Step 3-3：Subregion is carried out to the energy state of energy-storage system, energy storage very high energies area H is definedi, high-energy area HMi, Mesozone Mi, low energy area LMi, extremely low energy range Li, it is assumed that energy storage SOEiWorking range is [xil,xiu], rated power and capacity For Pi、Qi, wherein i=1,2 represent respectively power-type energy-storage system and energy type energy-storage system；
Step 3-4：According to power-type energy storage t energy state SOE1(t) and Pc1T () is sampling time T's Estimated energy variation delta SOE1T (), to prevent power-type energy storage overshoot from putting as First Principles, and considers to reduce energy type energy storage Discharge and recharge changes number of times, sets up input quantity SOE1(t)、ΔSOE1(t) and output quantity KcFuzzy word set and degree of membership letter Number, and fuzzy reasoning table is set up, obtain correction coefficient KcFuzzy word, de-fuzzy completes correction coefficient KcCalculating；
It is P that step 3-5 makes power-type energy storage real outputc(t)=Kc*Pc1(t), then energy type energy storage reality output Power：
1)PH(t)>When 0, Pb(t)=min ((1-SOE2(t))*Q2, PH(t)-Pc(t))；
2)PH(t)<When 0, Pb(t)=- min (SOE2(t)*Q2, Pc(t)-PH(t))；
3)PHDuring (t)=0, Kc=0；
Then according to energy storage real output, update t+1 moment hybrid energy-storings energy state, and into it is next when Carve.
Further, step c, specifically includes：
Step 4：Based on step 1,2,3, using with chaotic disturbance quantum genetic algorithm optimization aim, obtain mixing storage Can optimum capacity allocation plan；Hybrid energy-storing typical case's day i.e. cost of a cycle is set up, with the minimum object function of its cost； And based on the characteristics of coordination control strategy, type selecting energy storage and active power fluctuation stabilize requirement set up constraint, constitute configuration The object module of mixed energy storage system capacity preferred plan.
Further, the step 4, further includes：
Step 4-1：Consider that the cost of energy-storage system in a typical day, i.e. energy storage cost are divided into purchase cost, recovery and are full of Profit and operation cost；
Step 4-1-1 purchase cost and recovery profit are relevant with energy storage rated power and capacity, only calculate its specific work Price differential under rate and capacity is usedRepresent wherein 1 represent power-type energy storage, 2 represent energy type energy storage,Represent power Unit price difference and θ represent that capacity unit price is poor；
Step 4-1-2 converts typical day internal strength according to the respective situation of actually exerting oneself of hybrid energy-storing using rain flow method Rate type and the conversion discharge and recharge frequency n of energy type energy storage1And n2；
Step 4-1-3 operation cost is relevant with the extent of deterioration of energy storage, and relevant with energy storage cycle charge-discharge degree, typical case Discharge and recharge number of times of energy storage can then set up the economy of hybrid energy-storing cost in typical day divided by its respective cycle life in it Model is object functionWherein power-type cycle life N1, energy type N2；
Step 4-2：Comprising 4 variables it is power-type energy storage power P in object function1And capacity Q1, energy is to energy storage work( Rate P2And capacity Q2；
Step 4-2-1 defines region constraint：Consider that the physical significance of variable can not possibly be infinitely great, therefore set its domain of definition, Pi =[ai,bi] and Qi=[li,ui]；
Step 4-2-2 energy storage technology is constrained, it is considered to the rated power and capacity relationship of power-type energy storage and energy type energy storage, Then Qi/Pi≤Ti, wherein TiRepresent energy storage maximum discharge and recharge time under nominal power；
Step 4-2-3 coordinates control constraints：Energy storage energy subregion guarantees MiThe presence in region, then high-energy area lower limit is necessary Higher than the low energy area upper limit, i.e. (1-xiu)*Qi-Pi* T ＞ xil*Qi+Pi*T；
Step 4-2-4 stabilizes constraint of demand：What consideration hybrid energy-storing fluctuated to active power for wind power stabilizes situation, and foundation refers to 1) active power for wind power relative fluctuation compares I to mark1,2) wind-powered electricity generation is remaining most Great fluctuation process amount I2：3) energy type energy storage protective rate I3：Meter Calculate in Pc(t) ≡ 0, power-type cycle charge-discharge frequency n after typical day energy type energy storage conversion3,
Further, step c, specifically also includes：
Step 5：Accelerate Optimization Progress and lifting population diversity by introducing quantum double-strand coding, and utilize quantum rotation The process of selection, intersection and variation in the heredity of the operation simulations such as door, not gate；And when judging optimal solution again, introduce chaos and disturb It is dynamic to increase the possibility that optimized algorithm jumps out locally optimal solution, improve its energy for finding global optimum.
Further, the step 5, further includes：
Step 5-1：Initialization population, produces NqThe population of individuality composition, and arrange parameter, such as genetic iteration number of times g =1, maximum iteration time maxgen, chromosome code length are variable number Mq, corner initial value θ0, mutation probability Pq, in chaotic disturbance Logistic equation parameter μ；
Step 5-2：In 1st generation, the genotype of every chromosome is transformed into into phenotype, and calculates its fitness function I.e. object function, records contemporary optimal value gyWith optimum individual gx, make global optimum Gy=gyAnd corresponding Chromosome Gx=gx；
Step 5-3：To the quantum bit on each chromosome in population, with GxQuantum bit is target, is revolved using quantum Revolving door updates chromosome；
Step 5-4：To the quantum bit on the chromosome in population, with mutation probability PqThe quantum bit of variation is selected, and should Mutation operator is completed with quantum non-gate；
Step 5-5：In g generations, the genotype of every chromosome is transformed into into phenotype, and calculates its fitness function I.e. object function, records contemporary optimal value gyWith optimum individual gx, and with g for front optimal value GyRelatively, if gy<=Gy, then more New GyFor gy, while updating the corresponding Chromosome G of optimal valuexFor gx；
Step 5-6：Calculate the optimal solution rate of change I in g generationsG=(Gy(g-1)-Gy(g))/Gy(g-1) * 100%, if γ is to introduce chaotic disturbance condition, if rate of change IGStep 5-7 is then entered higher than γ, otherwise into step 5-8；
Step 5-7：θ corresponding to current globally optimal solution Gxxj, j=1：Mq, produce chaos with chebyshev mappings and become Amount δxj, disturb near optimal solution Gx, and calculation perturbation after stain colour solid gxAnd fitness function gyIf, gy≤Gy, then update global Optimum variable Gy=gyAnd optimal solution Gx=gx, otherwise do not change, into step 5-9；
Step 5-8：Assume disturbance frequency function K (g), take Mini=Gy(1：G) i-th value and i=1:G, and in i=1 Under, take MiniAnd and GyBetween with Tent mapping produce dynamic disturbances scope, in dynamic range, with chebyshev mapping carry out Chaotic disturbance, if finding less than GyOptimal solution, make its replace Gy, corresponding optimal solution replacement Gx；If without less than Gy, then i=is made I+1, until i=K (g) stops, and into step 5-9；
Step 5-9：G=g+1 is made, if g<Maxgen, then return to step 5-3；Otherwise stop being calculated optimal value GyWith And corresponding genotype Gx, the phenotype of variable is calculated, configure the mixed energy storage system capacity of optimization.
The hybrid energy-storing capacity configuration optimization method for stabilizing wind power fluctuation of various embodiments of the present invention, due to including： Coordination control strategy is formulated, according to the energy state SOE of power-type energy storage under time domain1(t-1) empirical mode decomposition is constrained Assigned references power, using fuzzy control theory with SOE1And Δ SOE (t-1)1T () formulates correction adjustment factor Kc, distributive mixing The real output of energy storage；The constraints of economy model and model of the foundation with hybrid energy-storing cost as object function； According to the constraints of the economic model and model set up, using band chaotic disturbance and quantum calculation genetic algorithm, optimization mixing The allocation plan of energy-storage system；Can accurately and rapidly be completed for the purpose of economy optimum by Revised genetic algorithum Mixed energy storage system capacity configuration；Coordinate control in such that it is able to overcome prior art not protect power-type energy storage, it is excellent Change that Process Precision is low, slow calculating speed and the shortcomings of little fitness, realizing coordinating control to the protection of power-type energy storage and Low optimization accuracy is high, the advantage that searching process calculating speed is fast and the scope of application is big.
Other features and advantages of the present invention will be illustrated in the following description, also, the partly change from specification Obtain it is clear that or being understood by implementing the present invention.
Below by drawings and Examples, technical scheme is described in further detail.
Description of the drawings
Accompanying drawing is used for providing a further understanding of the present invention, and constitutes a part for specification, the reality with the present invention Applying example is used to explain the present invention together, is not construed as limiting the invention.In the accompanying drawings：
Fig. 1 is the hybrid energy-storing capacity configuration optimized algorithm frame diagram that wind power fluctuation is stabilized in the present invention；
Fig. 2 is coordination control strategy figure for mixed energy storage system active power distribution in the present invention；
Fig. 3 is energy storage energy state block plan in the present invention；
Fig. 4 is fuzzy control membership function figure in the present invention；
Fig. 5 is energy storage cycle life conversion flow chart in the present invention；
Fig. 6 is chaos quantum genetic algorithm optimizing hybrid energy-storing capacity flow chart in the present invention.
Specific embodiment
The preferred embodiments of the present invention are illustrated below in conjunction with accompanying drawing, it will be appreciated that preferred reality described herein Apply example and be merely to illustrate and explain the present invention, be not intended to limit the present invention.
According to embodiments of the present invention, as shown in figs 1 to 6, there is provided a kind of hybrid energy-storing for stabilizing wind power fluctuation holds Amount method for optimizing configuration, the present invention relates to a kind of capacity collocation method of mixed energy storage system, and in particular to one kind stabilizes interval The hybrid energy-storing coordination control strategy and capacity collocation method of formula power fluctuation.
The present invention establishes a kind of hybrid energy-storing capacity optimization method for stabilizing active power for wind power fluctuation.Hybrid energy-storing system System includes power-type energy-storage system and energy type energy-storage system.The present invention includes altogether 3 parts：Part I is to formulate to coordinate control System strategy, according to the energy state SOE of power-type energy storage under time domain1(t-1) the assigned references work(of empirical mode decomposition is constrained Rate, and charge and discharge was also easy to produce based on power-type energy storage, the characteristics of be unfavorable for service life, using fuzzy control theory with SOE1 And Δ SOE (t-1)1T () formulates correction adjustment factor Kc, the real output of distributive mixing energy storage；Part II is to set up excellent Change model, that is, set up the economy model with hybrid energy-storing cost as object function, and according to the technical of mixed energy storage system Requirement, coordination control strategy hypothesis of wind-powered electricity generation fluctuation etc., can be stabilized and set up model constraint；3rd is used according to economic model Allocation plan with chaotic disturbance and quantum calculation genetic algorithm optimization mixed energy storage system.Compared to the prior art, the present invention There is provided a kind of capacity Optimization method of mixed energy storage system for stabilizing wind power, there is provided rationally, effectively mix storage Energy traffic signal coordination, and the mixing storage for the purpose of economy optimum is accurately and rapidly completed by Revised genetic algorithum Can power system capacity allocation plan.
Technical scheme, sets up with energy storage cost minimization as target, and energy-storage property, coordination are controlled, stabilize fluctuation Require etc. for constraint Optimized model；And one kind is set up under time-domain analysis, introduce the mixing of correction using fuzzy control theory Energy storage control method for coordinating；Based on model and coordinated control mode, propose that a kind of quantum calculation genetic algorithm of chaotic disturbance is excellent Change model, draw optimal hybrid energy-storing allocation plan：
Step 1：According to active power data P of wind energy turbine set typical case's dayW(t), sampling time T, common NtotalIndividual complete information Data point under collection, to meet wind-electricity integration requirement as principle, obtains hybrid energy-storing and stabilizes wind-powered electricity generation target using moving average method Power PH(t) and grid-connected power PG(t)；
Energy storage target power P in step 1HObtain, according to the grid-connected requirement of active power for wind power, set moving average method parameter N values, and separate wind power PWT (), obtains PH(t)；
Step 2：According to the energy state SOE of power-type energy storage under time domain1(t-1) distribution of empirical mode decomposition is constrained Reference power, fuzzy control theory is with SOE1And Δ SOE (t-1)1T () formulates fuzzy rule and obtains output calibration regulation for input COEFFICIENT K c, the real output of distributive mixing energy storage completes hybrid energy-storing and coordinates control；
Step 3：The economy model for setting up mixed energy storage system is object function, while according to energy storage technology demand, association Adjust control constraints condition and stabilize surge requirements and set up constraint；Mixed energy storage system is power-type and energy type energy storage, step 3 In carry out the power distribution of mixed energy storage system, that is, coordinate control；
Step 3-1：Power-type energy storage SOE1Energy state is defined as high electric energy area, low electric energy area and mesozone, works as SOE1 At high electric energy area, it has larger discharge capability, and charging ability is very poor；At low electric energy area, it has larger filling Electric energy power and poor discharge capability；There are good charging and discharging capabilities in zone line；
Step 3-2：If the rated power of power-type energy storage is P1, capacity be Q1, the sampling time is T, during to t and t The value at top n moment is carved, empirical mode decomposition is carried out and is obtained n intrinsic mode functions component Aj(tr) and remainder B (tr), wherein trRepresent the t-N moment of t, t-1 ...；
1)PH(t)>When 0, take and meet Ci(t) ＜ min (Q1*(1-SOE1(t-1),P1) maximum i values, and make power-type store up Can power Pc1(t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
2)PH(t)<When 0, take and meet Ci(t) ＞ max (Q1*SOE1(t-1),-P1) maximum i values, and make power-type energy storage Power Pc1(t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
3) P is worked asHDuring (t)=0, Pc1(t)=Pb1(t)=0；
Step 3-3：Subregion is carried out to the energy state of energy-storage system, energy storage very high energies area H is definedi, high-energy area HMi, Mesozone Mi, low energy area LMi, extremely low energy range Li, it is assumed that energy storage SOEiWorking range is [xil,xiu], rated power and capacity For Pi、Qi, wherein i=1,2 represent respectively power-type energy-storage system and energy type energy-storage system；
Step 3-4：According to power-type energy storage t energy state SOE1(t) and Pc1T () is sampling time T's Estimated energy variation delta SOE1T (), to prevent power-type energy storage overshoot from putting as First Principles, and considers to reduce energy type energy storage Discharge and recharge changes number of times, sets up input quantity SOE1(t)、ΔSOE1(t) and output quantity KcFuzzy word set and degree of membership letter Number, and fuzzy reasoning table is set up, obtain correction coefficient KcFuzzy word, de-fuzzy completes correction coefficient KcCalculating.
It is P that step 3-5 makes power-type energy storage real outputc(t)=Kc*Pc1(t), then energy type energy storage reality output Power：
1)PH(t)>When 0, Pb(t)=min ((1-SOE2(t))*Q2, PH(t)-Pc(t))；
2)PH(t)<When 0, Pb(t)=- min (SOE2(t)*Q2, Pc(t)-PH(t))；
3)PHDuring (t)=0, Kc=0；
Then according to energy storage real output, update t+1 moment hybrid energy-storings energy state, and into it is next when Carve；
Step 4：Based on step 1,2,3, using with chaotic disturbance quantum genetic algorithm optimization aim, obtain mixing storage Can optimum capacity allocation plan；The cost of hybrid energy-storing typical case day (a cycle) is set up, with the minimum object function of its cost； And based on the characteristics of coordination control strategy, type selecting energy storage and active power fluctuation stabilize requirement set up constraint, constitute configuration The object module of mixed energy storage system capacity preferred plan；
Step 4-1：Consider that the cost of energy-storage system in a typical day, i.e. energy storage cost are divided into purchase cost, recovery and are full of Profit and operation cost；
Step 4-1-1 purchase cost and recovery profit are relevant with energy storage rated power and capacity, only calculate its specific work Price differential under rate and capacity is usedRepresent wherein 1 represent power-type energy storage, 2 represent energy type energy storage,Represent power Unit price difference and θ represent that capacity unit price is poor；
Step 4-1-2 converts typical day internal strength according to the respective situation of actually exerting oneself of hybrid energy-storing using rain flow method Rate type and the conversion discharge and recharge frequency n of energy type energy storage1And n2；
Step 4-1-3 operation cost is relevant with the extent of deterioration of energy storage, and relevant with energy storage cycle charge-discharge degree, typical case Discharge and recharge number of times of energy storage can then set up the economy of hybrid energy-storing cost in typical day divided by its respective cycle life in it Model is object functionWherein power-type cycle life N1, energy type N2；
Step 4-2：Comprising 4 variables it is power-type energy storage power P in object function1And capacity Q1, energy is to energy storage work( Rate P2And capacity Q2；
Step 4-2-1 defines region constraint：Consider that the physical significance of variable can not possibly be infinitely great, therefore set its domain of definition, Pi =[ai,bi] and Qi=[li,ui]；
Step 4-2-2 energy storage technology is constrained, it is considered to the rated power and capacity relationship of power-type energy storage and energy type energy storage, Then Qi/Pi≤Ti, wherein TiRepresent energy storage maximum discharge and recharge time under nominal power；
Step 4-2-3 coordinates control constraints：Energy storage energy subregion guarantees MiThe presence in region, then high-energy area lower limit is necessary Higher than the low energy area upper limit, i.e. (1-xiu)*Qi-Pi* T ＞ xil*Qi+Pi*T；
Step 4-2-4 stabilizes constraint of demand：What consideration hybrid energy-storing fluctuated to active power for wind power stabilizes situation, and foundation refers to 1) active power for wind power relative fluctuation compares I to mark1,2) wind-powered electricity generation Remaining maximum fluctuation amount I2：3) energy type storage Can protective rate I3：Calculate in Pc(t) ≡ 0, power-type cycle charge-discharge frequency n after typical day energy type energy storage conversion3,
Step 5：Optimized algorithm selects more ripe genetic algorithm, to make up classical genetic algorithm in population diversity It is low, easily sink into the slow shortcoming of globally optimal solution, Optimization Progress.Accelerate Optimization Progress and lifting kind by introducing quantum double-strand coding Group's diversity, and using the selection in the heredity of the operation simulations such as Quantum rotating gate, not gate, the process intersected and make a variation；And again When judging optimal solution, introducing chaotic disturbance increases the possibility that optimized algorithm jumps out locally optimal solution, improves it and finds global optimum Energy；
Step 5-1：Initialization population, produces NqThe population of individuality composition, and arrange parameter, such as genetic iteration number of times g =1, maximum iteration time maxgen, chromosome code length are variable number Mq, corner initial value θ0, mutation probability Pq, in chaotic disturbance Logistic equation parameter μ etc.；
Step 5-2：In 1st generation, the genotype of every chromosome is transformed into into phenotype, and calculates its fitness function I.e. object function, records contemporary optimal value gyWith optimum individual gx, make global optimum Gy=gyAnd corresponding Chromosome Gx=gx；
Step 5-3：To the quantum bit on each chromosome in population, with GxQuantum bit is target, is revolved using quantum Revolving door updates chromosome；
Step 5-4：To the quantum bit on the chromosome in population, with mutation probability PqThe quantum bit of variation is selected, and should Mutation operator is completed with quantum non-gate；
Step 5-5：In g generations, the genotype of every chromosome is transformed into into phenotype, and calculates its fitness function I.e. object function, records contemporary optimal value gyWith optimum individual gx, and with g for front optimal value GyRelatively, if gy<=Gy, then more New GyFor gy, while updating the corresponding Chromosome G of optimal valuexFor gx；
Step 5-6：Calculate the optimal solution rate of change I in g generationsG=(Gy(g-1)-Gy(g))/Gy(g-1) * 100%, if γ is to introduce chaotic disturbance condition, if rate of change IGStep 5-7 is then entered higher than γ, otherwise into step 5-8；
Step 5-7：θ corresponding to current globally optimal solution Gxxj, j=1：Mq, produce chaos with chebyshev mappings and become Amount δxj, disturb near optimal solution Gx, and calculation perturbation after stain colour solid gxAnd fitness function gyIf, gy≤Gy, then update global Optimum variable Gy=gyAnd optimal solution Gx=gx, otherwise do not change, into step 5-9；
Step 5-8：Assume disturbance frequency function K (g), take Mini=Gy(1：G) i-th value and i=1:G, and in i=1 Under, take MiniAnd and GyBetween with Tent mapping produce dynamic disturbances scope, in dynamic range, with chebyshev mapping carry out Chaotic disturbance, if finding less than GyOptimal solution, make its replace Gy, corresponding optimal solution replacement Gx；If without less than Gy, then i=is made I+1, until i=K (g) stops, and into step 5-9；
Step 5-9：G=g+1 is made, if g<Maxgen, then return to step 5-3；Otherwise stop being calculated optimal value GyWith And corresponding genotype Gx, the phenotype of variable is calculated, configure the mixed energy storage system capacity of optimization.
Technical scheme, in consideration hybrid energy-storing initial cost, cost recovery and operation cost, and considers it On the basis of service life affects, the economic model for setting up hybrid energy-storing more will accurately analyze the most beutiful face of hybrid energy-storing Amount；When mixed energy storage system distributes, the impact of energy-storage system time-domain information and energy storage energy state is taken into account, can be more real Border, the power output for being reliably separated mixed energy storage system；On optimizing algorithm, more ripe hereditary intelligent algorithm is selected, And consider that its shortcoming introduces quantum information and chaotic disturbance theory and will improve searching process, obtain more rationally reliable optimum Hybrid energy-storing capacity configuration scheme.
Specifically, wind power economy and technical needs, the technology of the present invention are stabilized in order to meet configuration energy storage Scheme, there is provided a kind of hybrid energy-storing capacity configuration optimized algorithm for stabilizing wind power fluctuation, mixed energy storage system includes work( Rate type energy-storage system and energy type energy-storage system, the hybrid energy-storing capacity configuration optimization method for stabilizing wind power fluctuation includes Following step：
Step 1：According to active power data P of wind energy turbine set typical case's dayW(t), sampling time T, common NtotalIndividual complete information Data point under collection, is required using moving average method and wind-electricity integration, sets moving average method parameter N value, and separates wind-powered electricity generation work( Rate PWT (), obtains hybrid energy-storing and stabilizes wind-powered electricity generation target power PH(t) and grid-connected power PG(t)；
Step 2：Hybrid energy-storing to obtaining stabilizes wind-powered electricity generation target power PHT (), carries out meeting mixed energy storage system state Power distribution, that is, coordinate control；
Step 2-1：Power-type energy storage SOE1Energy state is defined as high electric energy area, low electric energy area and mesozone, works as SOE1 At high electric energy area, it has larger discharge capability, and charging ability is very poor；At low electric energy area, it has larger filling Electric energy power and poor discharge capability；There are good charging and discharging capabilities in zone line；
Step 2-2：If the rated power of power-type energy storage is P1, capacity be Q1, the sampling time is T, during to t and t The value at top n moment is carved, empirical mode decomposition is carried out and is obtained n intrinsic mode functions component Aj(tr) and remainder B (tr), wherein trRepresent the t-N moment of t, t-1 ...；
1)PH(t)>When 0, take and meet Ci(t) ＜ min (Qc*(1-SOE1(t-1),P1) maximum i values, and make power-type store up Can power Pc1(t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
2)PH(t)<When 0, take and meet Ci(t) ＞ max (Q1*SOE1(t-1),-P1) maximum i values, and make power-type energy storage Power Pc1(t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
3)PHDuring (t)=0, Pc1(t)=Pb1(t)=0.
Step 2-3：Energy storage energy subregion, is divided into energy storage very high energies area Hi, high-energy area HMi, mesozone Mi, low energy Area LMi, extremely low energy range Li, it is assumed that energy storage SOEiWorking range is [xil,xiu], rated power and capacity are Pi、Qi, wherein i= 1,2 represent power-type and energy type energy storage：
Step 2-4：According to power-type energy storage t energy state SOE1And Pc1The energy of (t) in sampling time T Variation delta SOE1, to prevent power-type energy storage overshoot from putting as First Principles, and consider that reducing energy type energy storage discharge and recharge changes Number of times, energy state SOE1Universe of fuzzy sets, word set；Estimated energy state change amount Δ SOE1T the fuzzy domain of () selects word Collection；Correction coefficient KcUniverse of fuzzy sets, selects word set.Its membership function and regulation correction coefficient are set up using above fuzzy set KcFuzzy control rule, obtain correction coefficient KcFuzzy Representation, by its de-fuzzy and calculates power-type energy storage reality output work( Rate is Pc(t)=Kc*Pc1(t).Then, energy type energy storage real output P is calculatedb(t)：
1)PH(t)>When 0, Pb(t)=min ((1-SOE2(t))*Q2, PH(t)-Pc(t))；
2)PH(t)<When 0, Pb(t)=- min (SOE2(t)*Q2, Pc(t)-PH(t))；
3)PHDuring (t)=0, Kc=0；
Finally, the energy state of t+1 moment hybrid energy-storings is updated according to energy storage real output, (t+1) moment is entered Hybrid system power distribution calculate.
Step 3：The cost of hybrid energy-storing typical case day (a cycle) is set up, with the minimum object function of its cost；And base The characteristics of coordination control strategy, type selecting energy storage and active power fluctuation stabilize requirement set up constraint, constitute configuration mixing The object module of energy storage system capacity preferred plan；
Step 3-1：Consider that the cost of energy-storage system in a typical day, i.e. energy storage cost are divided into purchase cost, recovery and are full of Profit and operation cost；
Step 3-1-1 purchase cost and recovery profit are relevant with energy storage rated power and capacity, only calculate its specific work Price differential under rate and capacity is usedRepresent wherein 1 represent power-type energy storage, 2 represent energy type energy storage,Represent power Univalent difference and θ represent capacity unit price difference；
Step 3-1-2 calculates typical day internal strength according to the respective situation of actually exerting oneself of hybrid energy-storing using rain flow method Rate type and the conversion discharge and recharge frequency n of energy type energy storage1And n2；
Step 3-1-2 operation cost is relevant with the extent of deterioration of energy storage, and relevant with energy storage cycle charge-discharge degree, typical case Discharge and recharge number of times of energy storage can then set up the economy model i.e. target letter of energy storage cost divided by its respective cycle life in it NumberWherein power-type cycle life N1, energy type N2；
Step 3-2：Comprising 4 variables it is power-type energy storage power P in object function1And capacity Q1, energy is to energy storage work( Rate P2And capacity Q1；
Step 3-2-1 defines region constraint：Consider that the physical significance of variable can not possibly be infinitely great, therefore set its domain of definition, Pi =[ai,bi] and Qi=[li,ui]；
Step 3-2-2 energy storage technology is constrained, it is considered to the rated power and capacity relationship of power-type energy storage and energy type energy storage, Then Qi/Pi≤Ti, wherein TiRepresent energy storage maximum discharge and recharge time under nominal power；
Step 3-2-3 coordinates control constraints：Energy storage energy subregion guarantees MiThe presence in region, then high-energy area lower limit be higher than The low energy area upper limit, i.e. (1-xiu)*Qi-Pi* T ＞ xil*Qi+Pi*T；
Step 3-2-4 stabilizes constraint of demand：What consideration hybrid energy-storing fluctuated to active power for wind power stabilizes situation, and foundation refers to 1) active power for wind power relative fluctuation compares I to mark1,2) wind-powered electricity generation is remaining most Great fluctuation process amount I2：3) energy type energy storage protective rate I3：Calculate In Pc(t) ≡ 0, power-type cycle charge-discharge frequency n after typical day energy type energy storage conversion3,
Step 4：Population diversity is low in make up classical genetic algorithm, it is slow easily to sink into globally optimal solution, Optimization Progress Shortcoming.Accelerate Optimization Progress and lifting population diversity by introducing quantum double-strand coding, and using Quantum rotating gate, not gate etc. The process of selection, intersection and variation in operation simulation heredity；And when judging optimal solution again, introducing chaotic disturbance increases optimization Algorithm jumps out the possibility of locally optimal solution, improves its energy for finding global optimum；
Step 4-1：Initialization population, produces NqThe population of individuality composition, and arrange parameter, such as genetic iteration number of times g =1, maximum iteration time maxgen, chromosome code length are variable number Mq, corner initial value θ0, mutation probability Pq, in chaotic disturbance Logistic equation parameter μ etc.；
Step 4-2：In 1st generation, the genotype of every chromosome is transformed into into phenotype, and calculates its fitness function I.e. object function, records contemporary optimal value gyWith optimum individual gx, make global optimum Gy=gyAnd corresponding Chromosome Gx=gx；
Step 4-3：To the quantum bit on each chromosome in population, with GxQuantum bit is target, determines rotational angle thetaij Change direction and size, i=1：Nq, j=1：Mq, using Quantum rotating gate chromosome, wherein θ are updatedijCorner size by setting Step delta θ put determines that its direction is by GxAnd each quantum bit probability amplitude [α of current chromosomex,βx] with [α, β] constitute square Battle arrayDetermine.When A ≠ 0, direction is-sgn (A)；As A=0, direction is any；
Step 4-4：To the quantum bit on the chromosome in population, with mutation probability PqThe quantum bit of variation is selected, and should Mutation operator is completed with quantum non-gate；
Step 4-5：In g generations, the genotype of every chromosome is transformed into into phenotype, and calculates its fitness function I.e. object function, records contemporary optimal value gyWith optimum individual gx, and with g for front optimal value GyRelatively, if gy<=Gy, then more New GyFor gy, while updating the corresponding Chromosome G of optimal valuexFor gx；
Step 4-6：Calculate the optimal solution rate of change I in g generationsG=(Gy(g-1)-Gy(g))/Gy(g-1) * 100%, if γ is to introduce chaotic disturbance condition, if rate of change IGStep 4-7 is then entered higher than γ, otherwise into step 4-8；
Step 4-7：θ corresponding to current globally optimal solution Gxxj, j=1：Mq, produce chaos with chebyshev mappings and become Amount δxjIf, range of disturbance [- Δ δxj,Δδxj], then using θ 'xj=θxj*Δ'δxjChange variable parameter, and calculation perturbation after stain Colour solid gxAnd fitness function gyIf, gy≤Gy, then global optimum variable G is updatedy=gyAnd optimal solution Gx=gx, otherwise do not change, Into step 4-9；
Step 4-8：Assume disturbance frequency function K (g), take Mini=Gy(1：G) i-th value and i=1:G, and in i=1 Under, take MiniAnd and GyBetween with Tent mapping produce dynamic disturbances scope, with chebyshev mapping carry out chaotic disturbance, if looking for To less than GyOptimal solution, make its replace Gy, corresponding optimal solution replacement Gx；If without less than Gy, then i=i+1 is made, until i=K G () stops, and into step 4-9；
Step 4-9：G=g+1 is made, if g<Maxgen, then return to step 4-3；Otherwise stop being calculated optimal value GyWith And corresponding genotype Gx, the phenotype of variable is calculated, configure the mixed energy storage system capacity of optimization.
For example, Fig. 1 shows the structured flowchart of the methods described in the present embodiment, the invention provides one kind stabilizes wind-powered electricity generation The mixed energy storage system capacity configuration optimization method of power swing, comprises the steps：
Step 1：According to active power data P of wind energy turbine set typical case's dayW(t), sampling time T, common NtotalIndividual complete information Data point under collection, is required using moving average method and wind-electricity integration, is obtained hybrid energy-storing and is stabilized wind-powered electricity generation target power PH(t) with And grid-connected power PG(t)；
Step 2：Wind-powered electricity generation target power P is stabilized according to hybrid energy-storing is obtainedHT () sets up mixed energy storage system and coordinates control plan Omit, i.e. power distribution method, as shown in Figure 2；
Step 2-1：If the rated power of power-type energy storage is P1, capacity be Q1, the sampling time is T, during to t and t The value at top n moment is carved, empirical mode decomposition is carried out and is obtained n intrinsic mode functions component Aj(tr) and remainder B (tr), wherein trRepresent the t-N moment of t, t-1 ...；
Power-type energy storage SOE1Energy state is defined as high electric energy area, low electric energy area and mesozone, works as SOE1In high electric energy Qu Shi, it has larger discharge capability, and charging ability is very poor；At low electric energy area, its there is larger charging ability with And poor discharge capability；There are good charging and discharging capabilities in zone line；
1)PH(t)>When 0, take and meet Ci(t) ＜ min (Q1*(1-SOE1(t-1),P1) maximum i values, and make power-type store up Can power Pc1(t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
2)PH(t)<When 0, take and meet Ci(t) ＞ max (Q1*SOE1(t-1),-P1) maximum i values, and make power-type energy storage Power Pc1(t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
3)PHDuring (t)=0, Pc1(t)=Pb1(t)=0；
Step 2-2：The electric energy partitioning scenario of refinement energy-storage system, as shown in Figure 3.Define energy storage very high energies area Hi, it is high Energy range HMi, mesozone Mi, low energy area LMi, extremely low energy range Li, it is assumed that energy storage SOEiWorking range is [xil,xiu], it is specified Power and capacity are Pi、Qi, wherein i=1,2 represent power-type energy-storage system and energy type energy-storage system, then：
Step 2-3：According to power-type energy storage t energy state SOE1(t) and Pc1T () is sampling time T's Estimated energy variation delta SOE1T (), to prevent power-type energy storage overshoot from putting as First Principles, and considers to reduce energy type energy storage Discharge and recharge changes number of times.
Setting energy state SOE1Universe of fuzzy sets be { 0,1,2,3,4,5,6,7 }, select word set for VL, L, M, H, VH}；Estimated energy state change amount Δ SOE1Fuzzy domain be { -4, -3, -2, -1,0,1,2,3,4 }, select word set be {NB,NS,Z,PS,PB}；Correction coefficient KcUniverse of fuzzy sets be { 0,1,2,3,4,5,6,7,8,9,10 }, select word set for VS, S,MS,M,MB,B,VB}.Correction coefficient K is set up using above fuzzy setcFuzzy control rule, as shown in table 2.For example：
1) when power-type energy storage under extremely low energy state (VL), if estimated energy variable quantity is for negative value and very big (NB) When, it should make KcVery little (VS), reduces power-type energy storage discharge capacity, gives energy type energy storage to exert oneself；
2) when power-type energy storage is under extremely low energy state (VL), if estimated energy variable quantity is on the occasion of value and very big (PB) When correction coefficient should be made very big (VB), increase power-type energy storage energy state and reduce energy type energy storage and exert oneself；
Other states are similar to, and concrete condition is given by table 2.
Correction coefficient K of table 2 c Fuzzy reasoning table
Correction coefficient K is obtained according to table 2cFuzzy Representation, by its de-fuzzy and calculates power-type energy storage reality output work( Rate is Pc(t)=Kc*Pc1(t).Then, energy type energy storage real output P is calculatedb(t)：
1)PH(t)>When 0, Pb(t)=min ((1-SOE2(t))*Q2, PH(t)-Pc(t))；
2)PH(t)<When 0, Pb(t)=- min (SOE2(t)*Q2, Pc(t)-PH(t))；
3)PHDuring (t)=0, Kc=0；
Finally, the energy state of t+1 moment hybrid energy-storings is updated according to energy storage real output, (t+1) moment is entered Hybrid system power distribution calculate.
Step 3：The cost of hybrid energy-storing typical case day (a cycle) is set up, with the minimum object function of its cost；And base The characteristics of coordination control strategy, type selecting energy storage and active power fluctuation stabilize requirement set up constraint, constitute configuration mixing The object module of energy storage system capacity preferred plan；
Step 3-1：Consider that the cost of energy-storage system in a typical day, i.e. energy storage cost are divided into purchase cost, recovery and are full of Profit and operation cost；
Step 3-1-1 purchase cost and recovery profit are relevant with energy storage rated power and capacity, only calculate its specific work Price differential under rate and capacity is usedRepresent wherein 1 represent power-type energy storage, 2 represent energy type energy storage,Represent power Unit price and θ represent capacity unit price；
Step 3-1-2 operation cost is relevant with the extent of deterioration of energy storage, and relevant with energy storage cycle charge-discharge degree, typical case Discharge and recharge number of times of energy storage can then set up the economy model i.e. target letter of energy storage cost divided by its respective cycle life in it NumberWherein power-type cycle life N1, energy type N2, work(after typical day power-type energy storage conversion Rate type cycle charge-discharge frequency n1, energy type n2；
Step 3-1-2 is as shown in figure 5, by taking power-type energy storage as an example, using rain flow method conversion discharge and recharge number of times is calculated n1, according to power-type energy storage real output Pc(t) and its initial energy state SOE10It is calculated power-type energy storage SOE1T () change curve, analyzes and counts a typical day internal power type energy storage with amplitude ν using rain flow methodiDischarge and recharge one The number of times λ in individual cyclei, circulation Ω is co-existed in, then Ω=Σ λiCalculate the full share discharge and recharge frequency n of its conversion1=Σ λi(νi/ 1), energy type energy storage similar process calculates n2。
Step 3-2：Comprising 4 variables it is power-type energy storage power P in object function1And capacity Q1, energy is to energy storage work( Rate P2And capacity Q1；
Step 3-2-1 defines region constraint：Consider that the physical significance of variable can not possibly be infinitely great, therefore set its domain of definition, Pi =[ai,bi] and Qi=[li,ui]；
Step 3-2-2 energy storage technology is constrained, it is considered to the rated power and capacity relationship of power-type energy storage and energy type energy storage, Then Qi/Pi≤Ti, wherein TiRepresent energy storage maximum discharge and recharge time under nominal power；
Step 3-2-3 coordinates control constraints：Energy storage energy subregion guarantees MiThe presence in region, then high-energy area lower limit be higher than The low energy area upper limit, i.e. (1-xiu)*Qi-Pi* T ＞ xil*Qi+Pi*T；
Step 3-2-4 stabilizes constraint of demand：What consideration hybrid energy-storing fluctuated to active power for wind power stabilizes situation, and foundation refers to 1) active power for wind power relative fluctuation compares I to mark1,2) the remaining maximum ripple of wind-powered electricity generation Momentum I2：3) energy type energy storage protective rate I3：Meter Calculate in Pc(t) ≡ 0, power-type cycle charge-discharge frequency n after typical day energy type energy storage conversion3,
Step 4：Population diversity is low in make up classical genetic algorithm, it is slow easily to sink into globally optimal solution, Optimization Progress Shortcoming.Accelerate Optimization Progress and lifting population diversity by introducing quantum double-strand coding, and using Quantum rotating gate, not gate etc. The process of selection, intersection and variation in operation simulation heredity；And when judging optimal solution again, introducing chaotic disturbance increases optimization Algorithm jumps out the possibility of locally optimal solution, improves its energy for finding global optimum.The quantum genetic algorithm flow process of chaotic disturbance As shown in Figure 6；
Step 4-1：Initialization population, produces NqThe population of individuality composition, and arrange parameter, such as genetic iteration number of times g =1, maximum iteration time maxgen, chromosome code length are variable number Mq, corner initial value θ0, mutation probability Pq, in chaotic disturbance Logistic equation parameter μ etc.；
Step 4-2：In 1st generation, by variable composition of vector in optimization aim, I is mapped to4That is the solution space of unit length On, the quantum coding for being then expressed as double-strand in solution space constitutes chromosome,Wherein 1～4 represents that 4 variables are the specified of power-type and energy type energy storage Power and capacity, i=1,2 ..., Nq, the genotype of every chromosome is transformed into phenotype, and calculates its fitness function i.e. mesh Scalar functions, are ranked up to fitness function value, contemporary optimal value g of recordyWith optimum individual gx, make global optimum Gy=gyWith And corresponding Chromosome Gx=gx；
Step 4-3：To the quantum bit on each chromosome in populationWhereinRepresent g in The quantum bit of jth position in i-th individuality, with GxQuantum bit is target, determines rotational angle thetagijChange direction and size, i=1：Nq, j =1：Mq, using Quantum rotating gate chromosome, wherein θ are updatedgijCorner size determined by step delta θ for arranging, its direction by GxAnd each quantum bit probability amplitude [α of current chromosomex,βx] with [α, β] constitute matrixDetermine.When A ≠ 0, Direction is-sgn (A)；As A=0, direction is any；
Step 4-4：To the quantum bit on the chromosome in population, with mutation probability PqThe quantum bit of variation is selected, and should Mutation operator is completed with quantum non-gate；
Step 4-5：In g generations, the genotype of every chromosome is transformed into into phenotype, and calculates its fitness function I.e. object function, records contemporary optimal value gyWith optimum individual gx, and with g for front optimal value GyRelatively, if gy<=Gy, then more New GyFor gy, while updating the corresponding Chromosome G of optimal valuexFor gx；
Step 4-6：Calculate the optimal solution rate of change I in g generationsG=(Gy(g-1)-Gy(g))/Gy(g-1) * 100%, if γ is to introduce chaotic disturbance condition, if rate of change IGStep 4-7 is then entered higher than γ, otherwise into step 4-8；
Step 4-7：θ corresponding to current globally optimal solution Gxxj, j=1：Mq, produce chaos with chebyshev mappings and become Amount δxjIf, range of disturbance [- Δ δxj,Δδxj], then using θ 'xj=θxj*Δ'δxjChange variable parameter, and calculation perturbation after stain Colour solid gxAnd fitness function gyIf, gy≤Gy, then global optimum variable G is updatedy=gyAnd optimal solution Gx=gx, otherwise do not change, Into step 4-9；
Step 4-8：Assume disturbance frequency function K (g), take Mini=Gy(1：G) i-th value and i=1:G, and in i=1 Under, take MiniAnd and GyBetween with Tent mapping produce dynamic disturbances scope, with chebyshev mapping carry out chaotic disturbance, if looking for To less than GyOptimal solution, make its replace Gy, corresponding optimal solution replacement Gx；If without less than Gy, then i=i+1 is made, until i=K G () stops, and into step 4-9；
Step 4-9：G=g+1 is made, if g<Maxgen, then return to step 4-3；Otherwise stop being calculated optimal value GyWith And corresponding genotype Gx, the phenotype of variable is calculated, configure the mixed energy storage system capacity of optimization.
Compared with immediate prior art, the invention has the beneficial effects as follows：
(1) in technical solution of the present invention, using empirical mode decomposition and root based on the regulation and control of power-type energy storage energy state Mixed energy storage system preliminary power distribution condition is obtained according to energy state, which embodies energy state under time domain and energy storage is gone out The constraint of power, and take into full account protection energy type energy storage, the principle of pre-emptive power type energy storage discharge and recharge；
(2) under previous stage mixed energy storage system power distribution, according to energy storage energy state and the knots modification of energy state, Correction coefficient K of hybrid energy-storing power distribution rear stage is obtained using fuzzy theoryc, while also reduce power-type energy storage overcharging The possibility put, obtains the real output of mixed energy storage system；
(3) in technical solution of the present invention, it is contemplated that cost of the energy storage under a typical day, it is considered to up-front investment, reclaim with And operation cost, and tentatively consider impact of the energy storage cycle life to cost, under setting up a typical day (or a cycle) The minimum Optimized model of cost；
(4) in technical solution of the present invention, with it is economical as optimization object on the premise of, it is considered to energy storage feature constraint, coordinate Control constraints and requirement constraint is stabilized, and set up its restriction range of corresponding index characterization and ability；
(5) in technical solution of the present invention, it is considered to which model constraint causes the incoherence of feasible solution, is increased using chaotic disturbance Search capability, improves classical genetic algorithm to sink into the situation of local optimum, strengthens the ability that algorithm finds globally optimal solution；
(6) in the present invention program, quantum information is introduced in genetic algorithm, improves energy storage using quantum coded system and become The individual diversity in population of amount, and double-strand chromosome and solution space conversion are set up, reduce in genetic algorithm and frequently compile Decoding process, and increase the search speed of genetic algorithm.
Finally it should be noted that：The preferred embodiments of the present invention are the foregoing is only, the present invention is not limited to, Although being described in detail to the present invention with reference to the foregoing embodiments, for a person skilled in the art, it still may be used To modify to the technical scheme described in foregoing embodiments, or equivalent is carried out to which part technical characteristic. All any modification, equivalent substitution and improvements within the spirit and principles in the present invention, made etc., should be included in the present invention's Within protection domain.

Claims (7)

1. it is a kind of to stabilize the hybrid energy-storing capacity configuration optimization method that wind power fluctuates, it is characterised in that to include：
A, formulation coordination control strategy, according to the energy state SOE of power-type energy storage under time domain1(t-1) empirical modal point is constrained The assigned references power of solution, using fuzzy control theory with SOE1And Δ SOE (t-1)1T () formulates correction adjustment factor Kc, distribution The real output of hybrid energy-storing, Δ SOE1T () represents power-type energy storage in the work(for needing to export after empirical mode decomposition Energy variation of the rate in a sampling time T, SOE1(t-1) energy state of t-1 moment power-type energy storage is represented；
B, based on the allocation result to hybrid energy-storing real output, set up the economy with hybrid energy-storing cost as object function The constraints of property model and model；
The constraints of c, the economic model according to foundation and model, it is excellent using band chaotic disturbance and quantum calculation genetic algorithm Change the allocation plan of mixed energy storage system,
Step a, specifically includes：
Step 1：According to active power data P of wind energy turbine set typical case's dayW(t), sampling time T, common NtotalUnder individual complete information collection Data point, to meet wind-electricity integration requirement as principle, using moving average method obtain hybrid energy-storing stabilize wind-powered electricity generation target power PH(t) and grid-connected power PG(t)；
Step 2：According to the energy state SOE of power-type energy storage under time domain1(t-1) assigned references of empirical mode decomposition are constrained Power, fuzzy control theory is with SOE1And Δ SOE (t-1)1T () formulates fuzzy rule and obtains output calibration adjustment factor for input Kc, the real output of distributive mixing energy storage completes hybrid energy-storing and coordinates control；
In step 1, the acquisition hybrid energy-storing stabilizes wind-powered electricity generation target power PHT the operation of (), further includes：
According to the grid-connected requirement of active power for wind power, moving average method parameter N value is set, and separate wind power PWT (), obtains PH (t)。
2. according to claim 1 to stabilize the hybrid energy-storing capacity configuration optimization method that wind power fluctuates, its feature exists In step b is specifically included：
Step 3：The economy model for setting up mixed energy storage system is object function, while according to energy storage technology demand, coordinating control Constraints processed and stabilize surge requirements set up constraint；Mixed energy storage system is power-type and energy type energy storage, is entered in step 3 The power distribution of row mixed energy storage system, that is, coordinate control.
3. according to claim 2 to stabilize the hybrid energy-storing capacity configuration optimization method that wind power fluctuates, its feature exists In the step 3 is further included：
Step 3-1：Power-type energy storage SOE1Energy state is defined as high electric energy area, low electric energy area and mesozone, works as SOE1In height During electric energy area, it has larger discharge capability, and charging ability is very poor；At low electric energy area, it has larger charging energy Power and poor discharge capability；There are good charging and discharging capabilities in zone line；
Step 3-2：If the rated power of power-type energy storage is P1, capacity be Q1, the sampling time is T, before t and t The value at N number of moment, carries out empirical mode decomposition and obtains n intrinsic mode functions component Aj(tr) and remainder B (tr), wherein trTable Show t, t-1 ... t-N moment；Ci (t) represents the sum of intrinsic mode function component A j (tr) for extracting；
1)PH(t)>When 0, take and meet Ci(t)<min(Q1*(1-SOE1(t-1),P1) maximum i values, and make power-type energy storage power Pc1(t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
2)PH(t)<When 0, take and meet Ci(t)>max(Q1*SOE1(t-1),-P1) maximum i values, and make power-type energy storage power Pc1 (t)=CiT (), energy type energy storage power is Pb1(t)=PH(t)-Pc1(t)；
3) P is worked asHDuring (t)=0, Pc1(t)=Pb1(t)=0；
Step 3-3：Subregion is carried out to the energy state of energy-storage system, energy storage very high energies area H is definedi, high-energy area HMi, it is middle Area Mi, low energy area LMi, extremely low energy range Li, it is assumed that energy storage SOEiWorking range is [xil,xiu], rated power and capacity are Pi、Qi, wherein i=1,2 represent respectively power-type energy-storage system and energy type energy-storage system；
Step 3-4：According to power-type energy storage t energy state SOE1(t) and Pc1The estimation of (t) in sampling time T Energy variation amount Δ SOE1T (), to prevent power-type energy storage overshoot from putting as First Principles, and considers to reduce energy type energy storage charge and discharge Electricity changes number of times, sets up input quantity SOE1(t)、ΔSOE1(t) and output quantity KcFuzzy word set and membership function, and Fuzzy reasoning table is set up, correction coefficient K is obtainedcFuzzy word, de-fuzzy completes correction coefficient KcCalculating；
It is P that step 3-5 makes power-type energy storage real outputc(t)=Kc*Pc1(t), then energy type energy storage real output：
1)PH(t)>When 0, Pb(t)=min ((1-SOE2(t))*Q2, PH(t)-Pc(t))；
2)PH(t)<When 0, Pb(t)=- min (SOE2(t)*Q2, Pc(t)-PH(t))；
3)PHDuring (t)=0, Kc=0；
Then according to energy storage real output, the energy state of t+1 moment hybrid energy-storings is updated, and enters subsequent time.
4. according to claim 1 to stabilize the hybrid energy-storing capacity configuration optimization method that wind power fluctuates, its feature exists In step c is specifically included：
Step 4：Based on step 1,2,3, using with chaotic disturbance quantum genetic algorithm optimization aim, obtain hybrid energy-storing most Good capacity configuration scheme；Hybrid energy-storing typical case's day i.e. cost of a cycle is set up, with the minimum object function of its cost；And base The characteristics of coordination control strategy, type selecting energy storage and active power fluctuation stabilize requirement set up constraint, constitute configuration mixing The object module of energy storage system capacity preferred plan.
5. according to claim 4 to stabilize the hybrid energy-storing capacity configuration optimization method that wind power fluctuates, its feature exists In the step 4 is further included：
Step 4-1：Consider the cost of energy-storage system in a typical day, i.e. energy storage cost be divided into purchase cost, reclaim profit and Operation cost；
Step 4-1-1 purchase cost and reclaim profit it is relevant with energy storage rated power and capacity, only calculate its unit power and Price differential under capacity, usesRepresent, wherein i=1 represent power-type energy storage, i=2 represent energy type energy storage,Represent Power unit price difference and θ represent that capacity unit price is poor；
Step 4-1-2 converts typical day internal power type according to the respective situation of actually exerting oneself of hybrid energy-storing using rain flow method With the conversion discharge and recharge frequency n of energy type energy storage1And n2；
Step 4-1-3 operation cost is relevant with the extent of deterioration of energy storage, and relevant with energy storage cycle charge-discharge degree, in typical day The discharge and recharge number of times of energy storage can then set up the economy model of hybrid energy-storing cost in typical day divided by its respective cycle life That is object functionWherein power-type cycle life N1, energy type cycle life is N2；
Step 4-2：Comprising 4 variables it is power-type energy storage power P in object function1And capacity Q1, energy is to energy storage power P2 And capacity Q2；
Step 4-2-1 defines region constraint：Consider that the physical significance of variable can not possibly be infinitely great, therefore set its domain of definition, Pi=[ai, bi] and Qi=[li,ui]；
Step 4-2-2 energy storage technology is constrained, it is considered to the rated power and capacity relationship of power-type energy storage and energy type energy storage, then Qi/ Pi≤Ti, wherein TiRepresent energy storage maximum discharge and recharge time under nominal power；
Step 4-2-3 coordinates control constraints：Energy storage energy subregion guarantees MiThe presence in region, then high-energy area lower limit necessarily be greater than The low energy area upper limit, i.e. (1-xiu)*Qi-Pi*T>xil*Qi+Pi*T；
Step 4-2-4 stabilizes constraint of demand：Consider that hybrid energy-storing stabilizes situation to what active power for wind power fluctuated, set up index 1) Active power for wind power relative fluctuation compares I1,2) Wind-powered electricity generation remnants maximum fluctuation amounts I2：3) Energy type energy storage protective rate I3：Calculate in Pc(t) ≡ 0, power-type cycle charge-discharge number of times after typical day energy type energy storage conversion n3,
6. according to claim 4 to stabilize the hybrid energy-storing capacity configuration optimization method that wind power fluctuates, its feature exists In step c specifically also includes：
Step 5：By introducing quantum double-strand coding and accelerating Optimization Progress and lift population diversity, and using Quantum rotating gate, The process of selection, intersection and variation in the heredity of not gate operation simulation；And when judging optimal solution again, introducing chaotic disturbance increases Optimized algorithm jumps out the possibility of locally optimal solution, improves its energy for finding global optimum.
7. according to claim 6 to stabilize the hybrid energy-storing capacity configuration optimization method that wind power fluctuates, its feature exists In the step 5 is further included：
Step 5-1：Initialization population, produces NqThe population of individuality composition, and arrange parameter, such as genetic iteration number of times g=1, most Big iterations maxgen, chromosome code length are variable number Mq, corner initial value θ0, mutation probability Pq, Logistic in chaotic disturbance Equation parameter μ；
Step 5-2：In 1st generation, the genotype of every chromosome is transformed into into phenotype, and calculates its fitness function i.e. mesh Scalar functions, contemporary optimal value g of recordyWith optimum individual gx, make global optimum Gy=gyAnd corresponding Chromosome Gx=gx；
Step 5-3：To the quantum bit on each chromosome in population, with GxQuantum bit is target, using Quantum rotating gate more New chromosome；
Step 5-4：To the quantum bit on the chromosome in population, with mutation probability PqThe quantum bit of variation is selected, and applies quantum Not gate completes mutation operator；
Step 5-5：In g generations, the genotype of every chromosome is transformed into into phenotype, and calculates its fitness function i.e. mesh Scalar functions, contemporary optimal value g of recordyWith optimum individual gx, and with g for front optimal value GyRelatively, if gy<=Gy, then G is updatedy For gy, while updating the corresponding Chromosome G of optimal valuexFor gx；
Step 5-6：Calculate the optimal solution rate of change I in g generationsG=(Gy(g-1)-Gy(g))/Gy(g-1) * 100%, if γ is to draw Enter chaotic disturbance condition, if rate of change IGStep 5-7 is then entered higher than γ, otherwise into step 5-8；
Step 5-7：θ corresponding to current globally optimal solution Gxxj, j=1：Mq, with chebyshev mappings Chaos Variable δ is producedxj, Disturb near optimal solution Gx, and calculation perturbation after stain colour solid gxAnd fitness function gyIf, gy≤Gy, then global optimum is updated Variable Gy=gyAnd optimal solution Gx=gx, otherwise do not change, into step 5-9；
Step 5-8：Assume disturbance frequency function K (g), take Mini=Gy(1：G) i-th value and i=1:G, and under i=1, Take MiniAnd and GyBetween with Tent mapping produce dynamic disturbances scope, in dynamic range, with chebyshev mapping mixed Ignorant disturbance, if finding less than GyOptimal solution, make its replace Gy, corresponding optimal solution replacement Gx；If without less than Gy, then i=i+ is made 1, until i=K (g) stops, and into step 5-9；
Step 5-9：G=g+1 is made, if g<Maxgen, then return to step 5-3；Otherwise stop being calculated optimal value GyAnd it is corresponding Genotype Gx, the phenotype of variable is calculated, configure the mixed energy storage system capacity of optimization.
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