CN104242335A - Wind and light storage generating unit capacity optimal configuration method based on rated capacity - Google Patents

Abstract

The invention discloses a wind and light storage generating unit capacity optimal configuration method based on the rated capacity. The method comprises the following steps that firstly, a model is established according to the distribution situation of the local wind resource and the local light resource; secondly, a storage battery device is controlled according to the principle about utilizing the renewable energy sources to the maximum degree and performing constant output, and a coordinated operation strategy of the system is formulated; thirdly, a target function is set to show the expenditure in the life cycle of a generating unit and, and the expenditure is set to be minimal; fourthly, the constraint condition of capacity optimal configuration is determined; fifthly, a fuzzy logic control method is utilized for dynamically adjusting an energy converting model of an energy-storing storage battery so that the energy converting model can achieve rapid convergence; sixthly, based on the target function and the constraint condition, the target function of the generating unit and capacity proportion optimal values of all parts are resolved according to an improved iteration and self-adaptation genetic mixed algorithm. According to the method, a power grid can conveniently assess the generating capacity of the generating unit, and the power grid can easily formulate the dispatching plan and improve the accepting degree for the renewable energy sources.

Description

A kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity

Technical field

The present invention relates to a kind of wind-light storage generator unit capacity configuration optimizing method, relate to a kind of scale wind-light storage generator unit capacity configuration optimizing method more specifically.

Background technology

Wind energy and solar energy are all green clean energy resourcies, have a wide range of applications.At present, the existing some countries in the whole world begin one's study wind energy, solar energy, batteries to store energy cogeneration key technical problem.

In wind-solar-storage joint generate output is distributed rationally, emerge some achievements in research, mainly can be divided into single object optimization method and Multipurpose Optimal Method.Single object optimization method is mainly constraints with power supply reliability, and system investments cost minimization is target.And Multipurpose Optimal Method is main target of optimization mainly with system investments expense and power supply reliability, in addition also the environmental factors such as toxic emission can be considered.As mentioned above, at present to the research of wind-solar-storage joint generate output collocation method, essence is all the appropriate honourable capacity ratio of configuration, scene is combined exert oneself to approach load curve as far as possible, thus reduces discharge and recharge number of times and the depth of discharge of energy storage.In this case, capacity configuration result has larger association with overall power producing characteristics with load variations trend, is applied to scale wind-photovoltaic-storage hybrid grid-connected power generation and will be subject to great restriction.And scale generator unit is incorporated into the power networks in process, electrical network need be regarded as the power plant that a rated capacity is a certain value, and formulates corresponding scheduler task with this, and current electrical network but cannot be accomplished.

Summary of the invention

Goal of the invention: the object of the invention is for the deficiencies in the prior art, provides a kind of and Life Cycle Cost is down to the minimized wind-light storage generator unit capacity configuration optimizing method based on rated capacity.

Technical scheme: a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity of the present invention, carry out as follows:

Step1: according to the wind of locality, the distribution situation of light resources, sets up the model of exerting oneself of Wind turbines and photovoltaic module, and the power conversion model of energy storage battery;

Step2: the principle based on maximum renewable energy utilization and constant output controls accumulator plant, formulates system coordination operation reserve;

Step3: design object function is that generator unit life cycle management is through expense C _lCCminimum: in formula: K is the engineering life-span time limit; R is discount rate; C _iN(k) and C _oUTk () is respectively cost and the income in generator unit kth year;

Step4: determine the constraints that capacity is distributed rationally, power-balance constraints, the energy is divided into lack constraints and wind light mutual complementing constraints, wherein, power-balance constraints is that the overall networking power of any time wind-light storage generator unit must expect networking power P with scheduling _rbe consistent; Energy disappearance constraints is that generator unit should utilize regenerative resource to greatest extent, reduces energy waste, energy miss rate is limited in certain limit; Scene benefit property constraints, for utilizing wind light mutual complementing characteristic, makes overall system export held stationary, and reduces accumulator cell charging and discharging number of times and depth of discharge;

Step5: the power conversion model adopting Neungmatcha fuzzy logic control methodology dynamic adjustments energy storage battery, improves from initialization of population, selection, intersection, variation, makes its Fast Convergent;

Step6: according to target function and constraints, adopts the iteration of improvement and Adaptive Genetic hybrid algorithm to solve generator unit FLC-NPC target function, and Wind turbines, photovoltaic array and accumulator plant each several part capacity ratio optimal value.

Being further defined to of technical solution of the present invention, the model of exerting oneself of the Wind turbines described in step Step1 is:

$P_{\mathrm{wd}}\left(t\right)=\left\{\begin{array}{cc}0& v\left(t\right)<v_{\min }\mathrm{or\; v}\left(t\right)>v_{\max }\\ P_{\mathrm{rat}}& v_{\mathrm{rat}}\&le;v\left(t\right)\&le;v_{\max }\\ P_{\mathrm{rat}}\frac{v{\left(t\right)}^2-v_{\min }^2}{v_{\mathrm{rated}}^2-v_{\min }^2}& v_{\min }\&le;v\left(t\right)<v_{\mathrm{rat}}\end{array}\right.,$ P in formula _wdt () is exerted oneself for t Wind turbines, P _ratfor unit rated power, v _min, v _max, v _ratbe respectively the minimum threshold wind velocity of running of wind generating set, excision wind speed, minimum rated wind speed.

Further, the model of exerting oneself of the photovoltaic module described in step Step1 is: P _pv(t)=η _invη _pv(t) G (t) S _pv, S in formula _pvfor photovoltaic panel receives the area (m of solar irradiation radiation ²), G (t) light radiation numerical value (W/m ²), η _pvt () is photovoltaic module energy conversion efficiency, η _invfor inverter conversion efficiency; Wherein, the energy conversion efficiency of photovoltaic module is relevant with the temperature of environment, and ambient temperature on the impact of photovoltaic module energy conversion efficiency is: η in formula _rfor the reference energy conversion efficiency of testing under photovoltaic module normal temperature, β is the influence coefficient of temperature to energy conversion efficiency, T _ct () is the temperature value of t photovoltaic module, for photovoltaic module normative reference temperature value; Photovoltaic module absorbs solar radiation, and can work with ambient temperature one and cause photovoltaic module temperature to change, its expression formula is as follows: in formula, T is the ambient temperature of surrounding, T _ratthe rated temperature that photovoltaic module runs.

Further, the power conversion model of the energy storage battery described in step Step1 is: system charge model Soc (t)=Soc (t-1) (1-σ)+P _c(t) Δ t η _c/ E _max, system discharge model in formula Soc (t) be terminate the t time period after storage battery dump energy; σ is storage battery self-discharge rate per hour; P _cand P _dbe respectively charge power and the discharge power of storage battery t time period; Δ t is t time period length; η _cand η _dbe respectively charge in batteries efficiency and discharging efficiency; E _maxfor storage battery heap(ed) capacity.

Further, the method formulating system coordination operation reserve in step Step2 is: combine to exert oneself when scene and be less than dispatching requirement value P _reftime, power difference is supplemented by battery discharging, until all accumulator plants all reach maximum depth of discharge Soc _min, now storage battery stops providing meritorious output:

$\left\{\begin{array}{c}{P}_d\left(t\right)=P_{\mathrm{ref}}-[P_{\mathrm{wd}}\left(t\right)+P_{\mathrm{pv}}\left(t\right)]\\ \mathrm{Soc}\left(t\right)>{\mathrm{Soc}}_{\min }\end{array}\right.$

Combine to exert oneself when scene and be greater than dispatching requirement value P _reftime, system by stored energy more than needed in storage battery, until storage battery reaches fullcharging electricity condition Soc _max, now storage battery stops charging energy-storing:

$\left\{\begin{array}{c}{P}_c\left(t\right)=[P_{\mathrm{wd}}\left(t\right)+P_{\mathrm{pv}}\left(t\right)]-P_{\mathrm{ref}}\\ \mathrm{Soc}\left(t\right)<{\mathrm{Soc}}_{\max }\end{array}\right..$

Beneficial effect: (1) the present invention proposes a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity, method, to minimize Life Cycle Cost for target, utilizes the optimum capacity configuration of generator unit under genetic algorithm for solving rated capacity; The present invention is under set coordinated operation strategy, minimum for target with FLC-NPC, consider the index such as power supply reliability, energy utilization rate, minimize the whole life cycle cost of investment of generator unit engineering, adopt iteration/Adaptive Genetic hybrid algorithm to solve the optimum capacity ratio of generator unit each several part equipment, the algorithm comparing Hocaoglu and Khatib proposition has convergence rate and search efficiency faster; Wind-solar-storage joint electricity generation system is connected to the grid as the generator unit with rated capacity by the present invention, consider that renewable energy utilization rate and wind light mutual complementing maximize to be configured, but guarantee that generator unit is stable to export, also facilitate the assessment of electrical network to generator unit generating capacity, be conducive to the receiving degree that electrical network is formulated operation plan and improved regenerative resource.

Accompanying drawing explanation

Fig. 1 is the flow chart of a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity of the present invention;

Fig. 2 is the annual power curve figure of embodiment 1 generator unit under allocation optimum;

Fig. 3 is embodiment 1 local power curve figure under allocation optimum;

Fig. 4 is that embodiment 1 is at allocation optimum leeward, light, storage moon energy output contrast block diagram.

Embodiment

Below by accompanying drawing, technical solution of the present invention is described in detail, but protection scope of the present invention is not limited to described embodiment.

Embodiment 1: a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity, carry out as follows:

Step1: according to the wind of locality, the distribution situation of light resources, sets up the model of exerting oneself of Wind turbines and photovoltaic module, and the power conversion model of energy storage battery.

Blower fan is exerted oneself model:

Wind turbines power producing characteristics can adopt following formula to represent:

$P_{\mathrm{wd}}\left(t\right)=\left\{\begin{array}{cc}0& v\left(t\right)<v_{\min }\mathrm{or\; v}\left(t\right)>v_{\max }\\ P_{\mathrm{rat}}& v_{\mathrm{rat}}\&le;v\left(t\right)\&le;v_{\max }\\ P_{\mathrm{rat}}\frac{v{\left(t\right)}^2-v_{\min }^2}{v_{\mathrm{rated}}^2-v_{\min }^2}& v_{\min }\&le;v\left(t\right)<v_{\mathrm{rat}}\end{array}\right.---\left(11\right)$

P in formula _wdt () is exerted oneself for t Wind turbines, P _ratfor unit rated power, v _min, v _max, v _ratbe respectively the minimum threshold wind velocity of running of wind generating set, excision wind speed, minimum rated wind speed.

Photovoltaic module is exerted oneself model

Photovoltaic module model of exerting oneself can be determined by the factor such as solar radiation, ambient temperature, and formula is as follows:

P _pv(t)＝η _invη _pv(t)G(t)S _pv (12)

S in formula _pvfor photovoltaic panel receives the area (m of solar irradiation radiation ²), G (t) light radiation numerical value (W/m ²), η _pvt () is photovoltaic module energy conversion efficiency, η _invfor inverter conversion efficiency, the energy conversion efficiency of photovoltaic module is relevant with the temperature of environment, ambient temperature on the impact of photovoltaic module energy conversion efficiency as shown in the formula:

${\mathrm{\&eta;}}_{\mathrm{pv}}\left(t\right)={\mathrm{\&eta;}}_r[1-\mathrm{\&beta;}(T_C\left(t\right)-T_{C_r})]---\left(13\right)$

η in formula _rfor the reference energy conversion efficiency of testing under photovoltaic module normal temperature, β is the influence coefficient of temperature to energy conversion efficiency, T _ct () is the temperature value of t photovoltaic module, for photovoltaic module normative reference temperature value.Photovoltaic module absorbs solar radiation, and can work with ambient temperature one and cause photovoltaic module temperature to change, its expression formula is as follows:

$T_C\left(t\right)-T=\frac{T_{\mathrm{rat}}}{800}G\left(t\right)---\left(14\right)$

In formula, T is the ambient temperature of surrounding, T _ratthe rated temperature that photovoltaic module runs.

Energy storage battery model

For scale wind-light storage generator unit, accumulator plant can be set up separately factory building and leave concentratedly, temperature constant in holding chamber, and therefore without the need to considering the impact of temperature on accumulator cell charging and discharging efficiency, its model of exerting oneself is as follows:

System is charged:

Soc(t)＝Soc(t-1)(1-σ)+P _c(t)Δtη _c/E _max (15)

System discharge:

$\mathrm{Soc}\left(t\right)=\mathrm{Soc}(t-1)(1-\mathrm{\&sigma;})-\frac{P_d\left(t\right)\mathrm{\&Delta;t}}{E_{\max }{\mathrm{\&eta;}}_d}---\left(16\right)$

In formula Soc (t) be terminate the t time period after storage battery dump energy; σ is storage battery self-discharge rate per hour; P _cand P _dbe respectively charge power and the discharge power of storage battery t time period; Δ t is t time period length; η _cand η _dbe respectively charge in batteries efficiency and discharging efficiency; E _maxfor storage battery heap(ed) capacity.

Step2: the principle based on maximum renewable energy utilization and constant output controls accumulator plant, formulates system coordination operation reserve.

Operation reserve:

System controls accumulator plant based on the principle of maximum renewable energy utilization and constant output, and its basic ideas are: combine to exert oneself when scene and be less than dispatching requirement value P _reftime, power difference is supplemented by battery discharging, until all accumulator plants all reach maximum depth of discharge Soc _min, now storage battery stops providing meritorious output:

$\left\{\begin{array}{c}{P}_d\left(t\right)=P_{\mathrm{ref}}-[P_{\mathrm{wd}}\left(t\right)+P_{\mathrm{pv}}\left(t\right)]\\ \mathrm{Soc}\left(t\right)>{\mathrm{Soc}}_{\min }\end{array}\right.---\left(17\right)$

Combine to exert oneself when scene and be greater than dispatching requirement value P _reftime, system by stored energy more than needed in storage battery, until storage battery reaches fullcharging electricity condition Soc _max, now storage battery stops charging energy-storing:

$\left\{\begin{array}{c}{P}_c\left(t\right)=[P_{\mathrm{wd}}\left(t\right)+P_{\mathrm{pv}}\left(t\right)]-P_{\mathrm{ref}}\\ \mathrm{Soc}\left(t\right)<{\mathrm{Soc}}_{\max }\end{array}\right.---\left(18\right).$

Step3: design object function is that generator unit life cycle management is through expense C _lCCminimum: in formula: K is the engineering life-span time limit; R is discount rate; C _iN(k) and C _oUTk () is respectively cost and the income in generator unit kth year.

Step4: determine the constraints that capacity is distributed rationally, is divided into power-balance constraints, the energy to lack constraints and wind light mutual complementing constraints.

Power-balance constraints is that the overall networking power of any time wind-light storage generator unit must expect networking power P with scheduling _rbe consistent.

P _r＝P _pv(t)+P _wd(t)+P _bat(t) (24)

P in formula _pv(t), P _wd(t), P _batt () is respectively t photovoltaic module power output valve, Wind turbines power stage value, energy storage device power stage value.

Energy disappearance constraints is that generator unit should utilize regenerative resource to greatest extent, reduces energy waste, energy miss rate is limited in certain limit.

$P_{\mathrm{LPSP}}=\frac{E_{\mathrm{LFS}}}{E}---\left(25\right)$

P in formula _lPSPand E _lFSbe respectively energy disappearance amount and dispatching requirement total amount, E is the maximum energy miss rate of reference of generator unit.

Scene benefit property constraints, for utilizing wind light mutual complementing characteristic, makes overall system export held stationary, and reduces accumulator cell charging and discharging number of times and depth of discharge.

$D_{\mathrm{wp}}=\frac{1}{P_r}\sqrt{\frac{1}{T}{\mathrm{\&Sigma;}}_{t=1}^T(P_{\mathrm{wp}}\left(t\right)-P_r)}:---\left(26\right)$

D in formula _wpfor scene combines the fluctuation ratio of exerting oneself and exerting oneself relative to scheduling expection, P _wpcombine for t scene and exert oneself, λ is the reference maximum fluctuation rate of wind light mutual complementing.

Step5: the power conversion model adopting Neungmatcha fuzzy logic control methodology dynamic adjustments energy storage battery, improves from initialization of population, selection, intersection, variation, makes its Fast Convergent.

Concrete improvement is as follows:

5a) individual UVR exposure

Adopt binary coding representation optimized variable Wind turbines number, photovoltaic module number, storage battery number.Can by constraints determination each several part binary coding figure place, then cascade forms a length L completed _wqbbinary coding.

5b) the generation of initial population

Guarantee the diversity of initial population, be conducive to the whole solution space of algorithm search, avoid Premature Convergence.(x as the tolerance of distribution individual in population, and requires the Hamming distances H (x in population between all individualities to adopt the absolute Hamming distances H between Different Individual herein _i, x _j) (i, j=1,2, ∈ ..., L, i ≠ j).The population scale that now individual lengths is is distance D needs the complexity according to problem and determines, and generally remains between 30 to 200 as far as possible.

5c) selection strategy

Operate the optimum individual that may destroy in current population due to randomnesss such as selection, intersection, variations and adverse effect is caused to operational efficiency and convergence.Elite's retention mechanism will be adopted herein, good for fitness individuality is remained into population of future generation, and parent population and progeny population be competed jointly, sort from big to small by fitness, front P as far as possible _cHthe excellent individual of ratio is directly copied to the next generation.

5d) self-adaptive cross operation operator

Traditional genetic algorithm arranges constant intersection, mutation probability value, and therefore the quality of crossover and mutation parameter probability valuing will affect algorithm search efficiency greatly.For this reason, adopt the fuzzy logic control methodology dynamic adjustments genetic operator of Neungmatcha herein, make crossover probability P _cand mutation probability P _mcan adjust along with the change of the average fitness of the relative population of the fitness of individuality, method of adjustment is as follows: $\left\{\begin{array}{c}\mathrm{\&Delta;}{\mathrm{fit}}_{\mathrm{avg}}\left(t\right)=\frac{1}{\mathrm{pop}}{\mathrm{\&Sigma;}}_{k=1}^{\mathrm{pop}}{\mathrm{fit}}_k\left(t\right)-\frac{1}{\mathrm{offsize}}{\mathrm{\&Sigma;}}_{k=1}^{\mathrm{off}}{\mathrm{fit}}_k\left(t\right)\\ \mathrm{\&Delta;c}\left(t\right)=\mathrm{\&alpha;}\&times;Z(i,j),\mathrm{\&Delta;m}\left(t\right)=\mathrm{\&beta;}\&times;Z(i,j)\\ P_C\left(t\right)=\mathrm{\&Delta;c}\left(t\right)+P_C(t-1),P_M\left(t\right)=\mathrm{\&Delta;m}\left(t\right)+P_M(t-1)\end{array}\right.---\left(27\right)$

Δ fit in formula _avgt () is the average fitness of t for population, Δ c and Δ m is the side-play amount of cross and variation, pop and off is respectively parent population quantity and progeny population quantity, k is particle numbering, α and β is the peak excursion scope of crossover and mutation probability each time, Z (i, j) is fuzzy control rule, by Δ fit _avg(t) and Δ fit _avg(t-1) determine.

Step6: according to target function and constraints, adopts the iteration of improvement and Adaptive Genetic hybrid algorithm to solve generator unit FLC-NPC target function, and Wind turbines, photovoltaic array and accumulator plant each several part capacity ratio optimal value.

Method for solving:

Iteration/Adaptive Genetic hybrid algorithm is when solving generator unit each several part equipment optimum capacity ratio, and the algorithm comparing Hocaoglu and Khatib proposition has convergence rate and search efficiency faster.Concrete solution procedure is as follows:

Algorithm is divided into two stages to carry out:

Stage 1: to exert oneself etc. based on data by specification of equipment parameter, meteorological condition, scheduling expection, what flow process (1) was set up exert oneself, and model is equipment self constraints, formula (24)-(26) are overall performance constraints, adopt iterative method to calculate all feasible solutions meeting system performance index, be met the set of feasible solution PSS of system performance index.

Concrete solution procedure is:

6a) obtain meteorological condition, specification of equipment parameter, system expection such as to exert oneself at the data.Optimized variable span is set to reduce iterative algorithm cycle-index.Wherein Wind turbines and photovoltaic module numerical lower limits are 1, and the upper limit is by planning that place retrains, and P _wdn _wd+ P _pvn _pv> P _rstorage battery numerical lower limits is 1, and the upper limit is P _r/ P _bat.

6b) given ω and λ value, each combined capacity is calculated, be met the feasible solution of constraints, and statistics often organizes feasible solution system performance index value as running of wind generating set hourage, gross generation, accumulator cell charging and discharging number of times, energy waste rate etc.

6c) obtain set of feasible solution and corresponding system performance index value, economic parameters.Encoding according to the coded system in flow process (5) to often organizing feasible solution, the minimum Hamming distances D of population being set, obtaining Population Size N, algorithm iteration number of times Iter, the individual ratio P of elite are set _cH, crossover probability P _cmutation probability P _mdeng initial value, obtain initial population G.

Stage 2: in PSS based on element and economic parameters, formula (19)-(23) are target function, adopt self-adapted genetic algorithm to concentrate from PSS and solve optimum capacity configuration result.

Concrete solution procedure is:

6d) calculate the economy of the capacity configuration scheme representated by individuality according to formula (19)-(23), consider the factor such as energy dissipation and wind light mutual complementing, calculate ideal adaptation degree according to formula (28):

$\mathrm{Fit}\left(x\right)=\frac{1}{C_{\mathrm{LCC}}\left(x\right)+{\mathrm{\&lambda;}}_1{R}_{\mathrm{LFSP}}+{\mathrm{\&lambda;}}_2{D}_{\mathrm{wp}}+1}---\left(28\right)$

In formula: λ ₁and λ ₂be respectively the penalty coefficient of energy waste rate and wind light mutual complementing.According to selection strategy described in flow process (5) and cross and variation policy update population, obtain the i-th generation population G _i.

6e) judge whether to meet algorithm end condition, if do not meet, then go to (6b), if meet, select G _iterthe individuality that middle fitness is the highest, output capacity allocative decision X '=[N ' _pvn ' _wdn _bat] ^t, each several part expense, entire system performance desired value etc.

Sample calculation analysis:

The present invention chooses area, Zhangbei County meteorological condition data of 2003 and carries out modeling and simulation, exerts oneself as P if scheduling expection is constant _r=100MW, specification of equipment parameter, economic parameters and algorithm parameter are as table 1-3.Wherein the photovoltaic panel of some identical types is connected to independently inverter cell by feeder line and forms photovoltaic cells; some battery racks are connected into a group string and organizes connection in series-parallel again with other and form energy-storage units and access DC bus, are conducive to the control of operations staff to scale generator unit by division photovoltaic cells and energy-storage units.The calculating parameter of this example is chosen as shown in table 1-3:

Table 1

Emulate based on capacity configuration optimizing method in this paper, obtain final optimum results as Fig. 3-4.

Fig. 2 gives annual scene associating power curve and the overall power curve of generator unit, can find out under the coordinated operation control strategy that system is set, and generator unit in annual any time constant output, can guarantee the stability that regenerative resource is incorporated into the power networks.It is Wind turbines 242MW that table 3 gives optimum capacity configuration scheme, photovoltaic array 81MW, stored energy capacitance 72MW, ratio is 0.61:0.21:0.18, compare the installed capacity of photovoltaic module and the installed capacity of accumulator plant, the installed capacity of Wind turbines is relatively high.

Analyze reason further by Fig. 3 generator unit local power curve: area, Zhangbei County wind resource is all abundanter for the whole year, and distribution is relatively even, make Wind turbines exert oneself with to dispatch anticipated demand matching stronger; Illumination resource only just has by day, poor with scheduling requirement matching, and especially after summer enters rainy season, whole day light application time shortens, and temperature raises and photovoltaic module energy conversion efficiency is reduced, and photovoltaic is exerted oneself reduction.

The contrast of Fig. 4 energy output can be found out, wind power generation is dominate in generator unit, and photovoltaic generation is relatively average for the whole year, and storage battery energy output in summer is more.

Table 4

The optimized algorithm that the present invention proposes is expecting capacity configuration result (as shown in table 4) under into the condition of 100MW of exerting oneself.

The above analysis, the algorithm the convergence speed that the present invention proposes is very fast, computational efficiency compare before achievement in research have certain advantage.

As mentioned above, although represented with reference to specific preferred embodiment and described the present invention, it shall not be construed as the restriction to the present invention self.Under the spirit and scope of the present invention prerequisite not departing from claims definition, various change can be made in the form and details to it.

Claims (5)

1., based on a wind-light storage generator unit capacity configuration optimizing method for rated capacity, it is characterized in that, carry out as follows:

Step1: according to the wind of locality, the distribution situation of light resources, sets up the model of exerting oneself of Wind turbines and photovoltaic module, and the power conversion model of energy storage battery;

Step2: the principle based on maximum renewable energy utilization and constant output controls accumulator plant, formulates system coordination operation reserve;

Step3: design object function is that generator unit life cycle management is through expense C _lCCminimum: in formula: K is the engineering life-span time limit; R is discount rate; C _iN(k) and C _oUTk () is respectively cost and the income in generator unit kth year;

Step4: determine the constraints that capacity is distributed rationally, is divided into power-balance constraints, the energy to lack constraints and wind light mutual complementing constraints;

Step5: the power conversion model adopting Neungmatcha fuzzy logic control methodology dynamic adjustments energy storage battery, improves from initialization of population, selection, intersection, variation, makes its Fast Convergent;

Step6: according to target function and constraints, adopts the iteration of improvement and Adaptive Genetic hybrid algorithm to solve generator unit FLC-NPC target function, and Wind turbines, photovoltaic array and accumulator plant each several part capacity ratio optimal value.

2. a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity according to claim 1, is characterized in that, the model of exerting oneself of the Wind turbines described in step Step1 is:

$P_{\mathrm{wd}}\left(t\right)=\left\{\begin{array}{cc}0& v\left(t\right)<v_{\min }\mathrm{or\; v}\left(t\right)>v_{\max }\\ P_{\mathrm{rat}}& v_{\mathrm{rat}}\&le;v\left(t\right)\&le;v_{\max }\\ P_{\mathrm{rat}}\frac{v{\left(t\right)}^2-v_{\min }^2}{v_{\mathrm{rated}}^2-v_{\min }^2}& v_{\min }\&le;v\left(t\right)<v_{\mathrm{rat}}\end{array}\right.,$ P in formula _wdt () is exerted oneself for t Wind turbines, P _ratfor unit rated power, v _min, v _max, v _ratbe respectively the minimum threshold wind velocity of running of wind generating set, excision wind speed, minimum rated wind speed.

3. a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity according to claim 1, it is characterized in that, the model of exerting oneself of the photovoltaic module described in step Step1 is: P _pv(t)=η _invη _pv(t) G (t) S _pv, S in formula _pvfor photovoltaic panel receives the area (m of solar irradiation radiation ²), G (t) light radiation numerical value (W/m ²), η _pvt () is photovoltaic module energy conversion efficiency, η _invfor inverter conversion efficiency.

4. a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity according to claim 1, it is characterized in that, the power conversion model of the energy storage battery described in step Step1 is: system charge model Soc (t)=Soc (t-1) (1-σ)+P _c(t) Δ t η _c/ E _max, system discharge model in formula Soc (t) be terminate the t time period after storage battery dump energy; σ is storage battery self-discharge rate per hour; P _cand P _dbe respectively charge power and the discharge power of storage battery t time period; Δ t is t time period length; η _cand η _dbe respectively charge in batteries efficiency and discharging efficiency; E _maxfor storage battery heap(ed) capacity.

5. a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity according to claim 1, it is characterized in that, the method formulating system coordination operation reserve in step Step2 is: combine to exert oneself when scene and be less than dispatching requirement value P _reftime, power difference is supplemented by battery discharging, until all accumulator plants all reach maximum depth of discharge Soc _min, now storage battery stops providing meritorious output:

$\left\{\begin{array}{c}{P}_d\left(t\right)=P_{\mathrm{ref}}-[P_{\mathrm{wd}}\left(t\right)+P_{\mathrm{pv}}\left(t\right)]\\ \mathrm{Soc}\left(t\right)>{\mathrm{Soc}}_{\min }\end{array}\right.$

Combine to exert oneself when scene and be greater than dispatching requirement value P _reftime, system by stored energy more than needed in storage battery, until storage battery reaches fullcharging electricity condition Soc _max, now storage battery stops charging energy-storing:

$\left\{\begin{array}{c}{P}_c\left(t\right)=[P_{\mathrm{wd}}\left(t\right)+P_{\mathrm{pv}}\left(t\right)]-P_{\mathrm{ref}}\\ \mathrm{Soc}\left(t\right)<{\mathrm{Soc}}_{\max }\end{array}\right..$