CN104242335B - A kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity - Google Patents

Abstract

The present invention discloses a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity, carries out as follows: (1), according to local wind, the distribution situation of light resources, sets up model；(2) accumulator plant is controlled by principle based on maximum renewable energy utilization and constant output, formulates system coordination operation reserve；(3) design object function is that generator unit life cycle management is minimum through expense；(4) constraints that capacity is distributed rationally is determined；(5) the energy transformation model using fuzzy logic control methodology dynamically to regulate energy storage battery makes its Fast Convergent；(6) according to object function and constraints, the iteration improved and Adaptive Genetic hybrid algorithm is used to solve generator unit object function, and each several part capacity ratio optimal value.The present invention facilitates the electrical network assessment to generator unit generating capacity, beneficially electrical network and formulates operation plan and improve the receiving degree to regenerative resource.

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, more particularly relate to a kind of scale wind-light storage generator unit capacity configuration optimizing method.

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.

Distribute aspect rationally at wind-solar-storage joint generating capacity, emerged some achievements in research, can be divided mainly into single object optimization method and Multipurpose Optimal Method.Single object optimization method is mainly with power supply reliability as constraints, and system investments cost minimization is target.And Multipurpose Optimal Method is many with system investments expense and power supply reliability as main target of optimization, in addition further accounts for the environmental factors such as waste gas discharge.As it has been described above, at present research to wind-solar-storage joint generating capacity collocation method, essence is all the honourable capacity ratio that configuration is appropriate, makes scene combine to exert oneself and 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 and overall power producing characteristics have bigger association with load variations trend, and being applied to scale wind-photovoltaic-storage hybrid grid-connected power generation will be extremely limited.And during scale generator unit is incorporated into the power networks, electrical network need to be considered as the power plant that 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: present invention aim at for the deficiencies in the prior art, it is provided that a kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity being down to Life Cycle Cost minimize.

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

Step1: according to local wind, the distribution situation of light resources, set up the model of exerting oneself of Wind turbines and photovoltaic module, and the energy transformation model of energy storage battery；

Step2: accumulator plant is controlled by principle based on maximum renewable energy utilization and constant output, 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, it is divided into power-balance constraints, energy disappearance constraints and wind light mutual complementing constraints, wherein, power-balance constraints is that any time wind-light storage generator unit entirety networking power must be with scheduling expection networking power P_rKeep 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 system totally export held stationary, and reduces accumulator cell charging and discharging number of times and depth of discharge；

Step5: use Neungmatcha fuzzy logic control methodology dynamically to regulate the energy transformation model of energy storage battery, from initialization of population, select, intersect, making a variation improves so that it is Fast Convergent；

Step6: according to object function and constraints, uses the iteration improved and Adaptive Genetic hybrid algorithm to solve generator unit FLC-NPC object 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 that t Wind turbines is exerted oneself, P_ratFor unit rated power, v_min、v_max、v_ratIt is 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_pvArea (the m of solar irradiation radiation is received for photovoltaic panel²), G (t) light radiation numerical value (W/m²), 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 the impact of photovoltaic module energy conversion efficiency is by environment temperature:η in formula_rFor the reference energy conversion efficiency of test under photovoltaic module normal temperature, β is the temperature coefficient that affects on 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, can work with environment temperature one and cause photovoltaic module temperature to change, and its expression formula is as follows:In formula, T is the environment temperature of surrounding, T_ratThe rated temperature that photovoltaic module runs.

Further, the energy transformation model of the energy storage battery described in step Step1 is: system charge model Soc (t)=Soc (t-1) (1-σ)+P_c(t)Δtη_c/E_max, system discharge modelIn formula Soc (t) be terminate the t time period after battery dump energy；σ is battery self-discharge rate per hour；P_cAnd P_dIt is respectively charge power and the discharge power of battery t time period；Δ t is t time period length；η_cAnd η_dIt is respectively battery charge efficiency and discharging efficiency；E_maxFor battery heap(ed) capacity.

Further, the method formulating system coordination operation reserve in step Step2 is: combines when scene and exerts oneself 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 battery stopping provides meritorious and exports:

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

Combine when scene and exert oneself more than dispatching requirement value P_refTime, energy more than needed is stored to battery by system, until battery reaches fullcharging electricity condition Soc_max, now battery stopping 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) 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 as target, utilizes generator unit optimum capacity configuration under genetic algorithm for solving rated capacity；The present invention is under set coordinated operation strategy, with the minimum target of FLC-NPC, consider the index such as power supply reliability, energy utilization rate, minimize generator unit engineering whole life cycle cost of investment, using iteration/Adaptive Genetic hybrid algorithm to solve generator unit each several part equipment optimum capacity ratio, the algorithm comparing Hocaoglu and Khatib proposition has faster convergence rate and search efficiency；Wind-solar-storage joint electricity generation system is connected to the grid by the present invention as the generator unit with rated capacity, consider that renewable energy utilization rate and wind light mutual complementing maximize to configure, but guarantee that generator unit stably exports, also facilitate the electrical network assessment to generator unit generating capacity, beneficially electrical network formulate operation plan and improve the receiving degree to 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 embodiment 1 generator unit power curve whole year figure 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 generated energy contrast block diagram.

Detailed description of the invention

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, is carried out as follows:

Step1: according to local wind, the distribution situation of light resources, set up the model of exerting oneself of Wind turbines and photovoltaic module, and the energy transformation model of energy storage battery.

Blower fan is exerted oneself model:

Wind turbines power producing characteristics can use equation below 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 that t Wind turbines is exerted oneself, P_ratFor unit rated power, v_min、v_max、v_ratIt is 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, environment temperature, and formula is as follows:

P_pv(t)=η_invη_pv(t)G(t)S_pv (12)

S in formula_pvArea (the m of solar irradiation radiation is received for photovoltaic panel²), G (t) light radiation numerical value (W/m²), 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, the environment temperature impact such as following formula on photovoltaic module energy conversion efficiency:

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

η in formula_rFor the reference energy conversion efficiency of test under photovoltaic module normal temperature, β is the temperature coefficient that affects on 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, can work with environment temperature one and cause photovoltaic module temperature to change, and 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 environment 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 individually be set up factory building and leave concentratedly, keeps indoor temperature constant, and therefore without considering the temperature impact 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 battery dump energy；σ is battery self-discharge rate per hour；P_cAnd P_dIt is respectively charge power and the discharge power of battery t time period；Δ t is t time period length；η_cAnd η_dIt is respectively battery charge efficiency and discharging efficiency；E_maxFor battery heap(ed) capacity.

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

Operation reserve:

Accumulator plant is controlled by system principle based on maximum renewable energy utilization and constant output, and its basic ideas are: combine when scene and exert oneself 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 battery stopping provides meritorious and exports:

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

Combine when scene and exert oneself more than dispatching requirement value P_refTime, energy more than needed is stored to battery by system, until battery reaches fullcharging electricity condition Soc_max, now battery stopping 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, energy disappearance constraints and wind light mutual complementing constraints.

Power-balance constraints is that any time wind-light storage generator unit entirety networking power must be with scheduling expection networking power P_rKeep 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 output valve, energy storage device power output valve.

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_LFSBeing 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 system totally 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_wpThe stability bandwidth exerted oneself relative to scheduling expection of exerting oneself, P is combined for scene_wpCombining for t scene and exert oneself, λ is the reference maximum fluctuation rate of wind light mutual complementing.

Step5: use Neungmatcha fuzzy logic control methodology dynamically to regulate the energy transformation model of energy storage battery, from initialization of population, select, intersect, making a variation improves so that it is Fast Convergent.

Concrete improvement is as follows:

5a) individual UVR exposure

Use binary coding representation optimized variable Wind turbines number, photovoltaic module number, battery number.Can be determined each several part binary coding figure place by constraints, then cascade forms length L completed_wqbBinary coding.

5b) the generation of initial population

Guarantee the whole solution space of the diversity of initial population, beneficially algorithm search, it is to avoid Premature Convergence.Absolute Hamming distances H between Different Individual is used (x as the tolerance of distribution individual in population, and to require in population the Hamming distances H (x between all individualities herein_i, x_j) (i, j=1,2, ∈ ..., L, i ≠ j).The population scale that now individual lengths is isDepending on distance D needs the complexity according to problem, it is maintained between 30 to 200 the most as far as possible.

5c) selection strategy

Owing to selecting, intersecting, the operation of the randomness such as variation may destroy the optimum individual in current population and adversely affect operational efficiency and convergence.Elite retention mechanism will be used herein, fitness preferably individuality will be remained into population of future generation as far as possible, and parent population and progeny population will be competed jointly, sort from big to small by fitness, front P_CHThe excellent individual of ratio is directly copied to the next generation.

5d) self-adaptive cross operation operator

Traditional genetic algorithm is to arrange constant intersection, mutation probability value, therefore intersects and the quality of mutation probability value is by strong influence algorithm search efficiency.To this end, use the fuzzy logic control methodology of Neungmatcha dynamically to regulate genetic operator herein, make crossover probability P_CAnd mutation probability P_MCan be adjusted relative to the change of the average fitness of population along with individual fitness, 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 t average fitness 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 to intersect each time and the peak excursion scope of mutation probability, (i, j) is fuzzy control rule to Z, by Δ fit_avg(t) and Δ fit_avg(t-1) determine.

Step6: according to object function and constraints, uses the iteration improved and Adaptive Genetic hybrid algorithm to solve generator unit FLC-NPC object 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 faster convergence rate and search efficiency.Concrete solution procedure is as follows:

Algorithm is divided into two stages to carry out:

Stage 1: based on specification of equipment parameter, meteorological condition, scheduling expection such as exert oneself at the data, model is equipment self constraints that what flow process (1) was set up exert oneself, formula (24)-(26) are overall situation performance constraints, use 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 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_rBattery 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, it is met the feasible solution of constraints, and adds up often group feasible solution system performance index value such 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.Encode according to the coded system in flow process (5) often organizing feasible solution, population minimum Hamming distances D is set, obtain Population Size N, algorithm iteration number of times Iter, elite individuality ratio P 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) as object function, use self-adapted genetic algorithm to concentrate from PSS and solve optimum capacity configuration result.

Concrete solution procedure is:

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

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

In formula: λ₁And λ₂It is respectively energy waste rate and the penalty coefficient of 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) judging whether to meet algorithm end condition, if being unsatisfactory for, then going to (6b), if meeting, selecting G_iterThe individuality that middle fitness is the highest, output capacity allocative decision X '=[N '_pvN′_wdN_bat]^T, each several part expense, systematic entirety energy desired value etc..

Sample calculation analysis:

The present invention chooses area, Zhangbei County meteorological condition data of 2003 and is modeled and emulates, and exerts oneself as P if scheduling expection is constant_r=100MW, specification of equipment parameter, economic parameters and algorithm parameter such as table 1-3.The photovoltaic panel of the most some same types is connected to independent inverter cell by feeder line and forms photovoltaic cells; if dry cell cabinet is connected into a group string and forms energy-storage units access dc bus with other group connection in series-parallel again, be conducive to operations staff's control to scale generator unit by dividing 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 such as Fig. 3-4.

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

Analyze reason further by Fig. 3 generator unit local power curve: area, Zhangbei County wind resource all compare to enrich the whole year, and it is more uniform to be distributed ratio, make Wind turbines exert oneself with to dispatch anticipated demand matching higher；Illumination resource the most just has, poor with scheduling requirement matching, and especially after summer enters rainy season, whole day light application time shortens, and temperature raises and makes photovoltaic module energy conversion efficiency reduce, and photovoltaic is exerted oneself reduction.

The contrast of Fig. 4 generated energy is it can be seen that wind-power electricity generation occupies leading position in generator unit, and photovoltaic generation is relatively average for the whole year, and battery generated energy in summer is more.

Table 4

Optimized algorithm capacity configuration result (as shown in table 4) under conditions of expection is exerted oneself as 100MW that the present invention proposes.

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

Although as it has been described above, represented and described the present invention with reference to specific preferred embodiment, but it shall not be construed as the restriction to the present invention self.Under the spirit and scope of the present invention premise defined without departing from claims, can various changes can be made in the form and details to it.

Claims (4)

1. a wind-light storage generator unit capacity configuration optimizing method based on rated capacity, it is characterised in that by following step Suddenly carry out:

Step1: according to local wind, the distribution situation of light resources, set up the model of exerting oneself of Wind turbines and photovoltaic module, And the energy transformation model of energy storage battery；

Step2: accumulator plant is controlled by principle based on maximum renewable energy utilization and constant output, formulates system The coordinated operation strategy of system；

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 the one-tenth in generator unit kth year Basis and income；

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

Step5: use Neungmatcha fuzzy logic control methodology dynamically to regulate the energy transformation model of energy storage battery, From initialization of population, select, intersect, making a variation improves so that it is Fast Convergent；

Step6: according to object function and constraints, uses the iteration improved and Adaptive Genetic hybrid algorithm to solve generating Unit F LC-NPC object function, and Wind turbines, photovoltaic array and accumulator plant each several part capacity ratio optimal value；

The method formulating system coordination operation reserve in step Step2 is: combines when scene and exerts oneself 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 battery stopping provides meritorious and exports:

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

Combine when scene and exert oneself more than dispatching requirement value P_refTime, energy more than needed is stored to battery by system, until Battery reaches fullcharging electricity condition Soc_max, now battery stopping charging energy-storing:

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

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

P in formula_wdT () is that t Wind turbines is exerted oneself, P_ratFor unit rated power, v_min、v_max、v_ratIt is respectively the minimum threshold wind velocity of running of wind generating set, excision wind speed, Small rated wind speed.

A kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity the most 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_pvArea (the m of solar irradiation radiation is received for photovoltaic panel²), G (t) light radiation numerical value (W/m²), η_pv(t) For photovoltaic module energy conversion efficiency, η_invFor inverter conversion efficiency.

A kind of wind-light storage generator unit capacity configuration optimizing method based on rated capacity the most according to claim 1, It is characterized in that, the energy transformation model of the energy storage battery described in step Step1 is: system charge model Soc (t)=Soc (t-1) (1-σ)+P_c(t)Δtη_c/E_max, system discharge model In formula Soc (t) be terminate the t time period after battery dump energy；σ is battery self-discharge rate per hour；P_cAnd P_d It is respectively charge power and the discharge power of battery t time period；Δ t is t time period length；η_cAnd η_dIt is respectively and stores Battery charge efficiency and discharging efficiency；E_maxFor battery heap(ed) capacity.