CN105337315B - A kind of scene stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization collocation method - Google Patents

Abstract

The invention discloses a kind of scene stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization collocation method.Based on wind-driven generator, photovoltaic array output mathematical model and accumulator cell charging and discharging characteristic and life cycle mathematical model, many Performance Evaluating Indexes such as year value investment cost, energy surplus multiplying power and load dead electricity rate such as consider, design higher-dimension multiple target Extremal optimization method, as solver, is realized scene and is stored complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization configuration.Can achieve that scene stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization allocative effect using the present invention, there is the advantages below that traditional single object optimization method and traditional Multipurpose Optimal Method do not possess：The configuration scheme providing for micro-capacitance sensor design planning person is more reasonable, configuration scheme investment in the case of meeting identical power supply reliability index is less, optimization method is implemented simple, adjust without complex cost function weight coefficient, parameters optimization that need not be complicated is adjusted, and optimization efficiency is higher.

Description

A kind of scene stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization collocation method

Technical field

The present invention relates to a kind of new forms of energy micro-capacitance sensor planning and designing and field of energy management intelligent optimization and decision method, special It is not related to a kind of scene and store complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization collocation method.

Background technology

The proposition of " microgrid (Microgrid) " concept is the milestone of distributed power system development, and intelligent micro-grid is not Carry out the new type of organization of intelligent distribution network, be that the large-scale application of regenerative resource distributed generation technology provides possibility.Wind Light stores the important way that complementary independent micro-grid system is realized as autonomous power supply system, be effectively solving mountain area remote districts with The regional power supply difficult problem such as coastal island provides a kind of feasible program, therefore receives domestic and international academia in recent years and engineering should Extensive concern and research and probe with boundary.It is micro-capacitance sensor rule that distributed power source and power electronic equipment type selecting, capacity are distributed rationally Draw one of the major issue that must take into of design phase.Because wind-power electricity generation and photovoltaic generation have randomness, load needs in addition The multiformity asked and complexity, micro-capacitance sensor capacity is distributed rationally and in abundant analysis micro-grid system installation place environmental condition and should be born On the basis of lotus demand characteristic, consider the power characteristic of equipment, maintenance cost and control method are installed, and finally realize micro- Network system is safe and reliable, economic and environment-friendly.Therefore, micro-capacitance sensor lectotype selection and capacity optimization allocation are also micro-capacitance sensor planning One of difficult problem of design field.At present, domestic and international academia and engineering circles are typically by micro-capacitance sensor lectotype selection and capacity optimization Configure the many factors that must take into and be converted into a weighted target function according to importance, then calculated using genetic algorithm, particle The single object optimization algorithm such as method is optimized solution.But these existing methods all generally existings be difficult to accurately to set weight coefficient, Adjust complexity, allocation plan of algorithm parameter is difficult to instruct the defects such as engineering practice.Although existing part research staff adopts NSGA- The Multipurpose Optimal Methods such as II attempt to solve micro-capacitance sensor lectotype selection and capacity optimization allocation, but many mesh such as NSGA-II Mark Optimization Algorithm flow process and algorithm parameter are adjusted all extremely complex, and computational efficiency is relatively low, is not easy to concrete engineering and implements. In state natural sciences fund (51207112), Zhejiang Province's public good planning item (2014C31074,2014C31093), Zhejiang Province Natural Science Fund In The Light (LY16F030011, LZ16E050002, LQ14F030006, LQ14F030007) and the Zhejiang Province new talent talent Under the support of planning item (2014R424014), it is excellent that a kind of open scene of the present invention stores complementary independent micro-capacitance sensor higher-dimension multiple target Change collocation method, there is the advantages below not available for traditional single object optimization method and traditional Multipurpose Optimal Method：For micro- The configuration scheme that electrical reticulation design designer provides is more reasonable, excellent in the case of meeting identical power supply reliability index Change allocation plan investment less, optimization method is implemented simple, adjusts without complex cost function weight coefficient, need not be complicated excellent Change parameter tuning, and optimization efficiency is higher.

Content of the invention

Present invention aims to the deficiencies in the prior art, a kind of scene is provided to store complementary independent micro-capacitance sensor higher-dimension many Objective optimization collocation method.

The purpose of the present invention is achieved through the following technical solutions：It is many that a kind of scene stores complementary independent micro-capacitance sensor higher-dimension Objective optimization collocation method, the method comprises the following steps：

(1) read the scene with 1 hour as step-length and store the annual meteorological data that complementary independent micro-grid system implements area (including wind speed, intensity of illumination, ambient temperature etc.), scene store the complementary each component parameter information of independent micro-grid system and load number According to (including hourly average DC load and hourly average AC load etc.)；Produce reference point, using NBI (Normal- Boundary intersection) method H reference point of generation, Population Size NP=H is determined according to the reference point number of generation If (H is even number), NP=H+1 (if H is odd number)；

(2) initialize, generate one at random and be uniformly distributed the initial population P={ P that Population Size is NP_i, i=1,2 ..., NP }, wherein i-th individual P_i=(N_WGi,N_PVi,N_BATi), N_WGiFor wind-driven generator, number of units, N are installed_PViFor photovoltaic battery module Block number, N are installed_BATiFor secondary battery unit group number；

(3) to each of population P individuality P_i, i=1,2 ..., NP, carry out non-uniform mutation, multiple objective function assessment The multiple-objection optimizations such as calculating, non-dominated ranking operate, and specifically include following sub-step：

(3.1). to P_i={ P_i(j), j=1,2,3 } in each constituent element execute many non-uniform mutation (Multi-non- one by one Uniform mutation, MNUM), keep other constituent elements constant simultaneously, obtain new individual P_ij, j=1,2,3；

Wherein r, r₁It is the random number producing in the range of [0,1], t represents current iteration number of times, L (j) represents j-th optimization The lower limit of variable, U (j) represents the upper limit of j-th optimized variable, and b is the MNUM coefficient of variation, I_maxThe maximum setting for user changes Generation number；

(3.2) calculate P_ijCorresponding multiple objective function value, including etc. year value investment cost ACS (P_ij), energy surplus multiplying power B_exc(P_ij) and load dead electricity rate L_PSP(P_ij)；

(3.3) to current 3 son individuals P_ijCarry out non-dominated ranking, thus obtaining its dominated Sorting number r_ij∈[0,2],j =1,2,3, the individual record that dominated Sorting number is 0 is P_i0, and by P_i0And corresponding multiple objective function value achieves；

(4) by P_i0As new population at individual, thus producing new population P_N={ P_i0, i=1,2 ..., NP }；

(5) unconditionally accept P=P_N；

(6) repeat step 3-5 is until meeting the maximum iteration time of user's setting；

(7) output Pareto optimal solution and corresponding ACS, energy surplus multiplying power B_exc, load dead electricity rate L_PSPEvaluation refers to Scale value, provides the user scene and stores complementary independent micro-capacitance sensor configuration scheme.

Further, in step (3.2), higher-dimension multiple objective function can set according to the actual requirements, has certain motility And different effect of optimizations can be reached, the year such as general selecting system is worth investment cost ACS, energy surplus multiplying power B_exc, load short of electricity Probability L_PSPAs object function, that is,：

L_maxFor independent micro-grid system patient maximum load short of electricity probit, B_maxFor patient ceiling capacity mistake Surplus multiplier value.A the years such as () system are worth investment cost ACS calculating process as follows：

ACS (X)=(C₁₁N_WG+C₁₂N_PV+C₁₃N_BAT)+(C₂₁N_WG+C₂₂N_PV+C₂₃N_BAT)+C₃N_BAT(16)

In formula：X is optimized variable set, X=(N_WG,N_PV,N_BAT)；C₁₁、C₁₂、C₁₃It is respectively blower fan, photovoltaic and accumulator Each assembly installation cost Average Annual Cost；C₂₁、C₂₂、C₂₃It is respectively blower fan, photovoltaic becomes with accumulator each unit year operation maintenance This；C₃For the average annual replacement cost of accumulator.

Blower fan, photovoltaic and accumulator each assembly installation cost Average Annual Cost are related to the assembly life-span cycle time limit, its pass It is that expression formula is：

C_1i=C_Pi.CRF_i(h,Y_proj) (17)

In formula：C_PFor installation cost；Y_projFor the assembly life-span time limit；CRF is recovery of the capital coefficient (capital Recovery factor, CRF), its expression formula is：

Wherein, h is discount rate.

Each assembly operation and maintenance cost C of 1 year_2iN () is calculated as follows：

C_2i(n)=C_2i(1).(1+f)ⁿ(19)

Wherein C_2i(1) it is the operating cost of the 1st year, f represents annual inflation.

In the project time limit, if system component reaches its end-of-life time limit, need assembly to be carried out reset to replace, group The replacement expense of part is calculated as follows：

C₃=C_r.SFE(h,Y_r) (20)

In formula：C_rFor the replacement cost；Y_rReset the life-span for assembly；SFE is the compensation fund factor, is counted according to formula (21) Calculate：

(b) energy surplus multiplying power B_excThe energy wasting by (being usually arranged as 1 year) within the specific period being considered be The ratio of system load aggregate demand energy, is specifically calculated as follows：

In formula：P_e(t) system excess power；P_l(t) system total load power；T is power supply total time hop count, usually T= 8760；Δ t is simulation step length, usually Δ t=1；

P_l(t)=P_D(t)+P_A(t)/e_n(23)

Wherein, P_DT () represents total DC load, P_AT () represents total AC load, e_nRepresent inverter efficiency.

C () is to load short of electricity probability L_PSPAs reliability evaluation index, represent system short of electricity time and total power-on time Ratio, is calculated as follows：

In formula：S_lossT () is system short of electricity marker character, its value is that 1 expression system short of electricity (is provided that in t system General power be less than system load demand), its value can meet all workload demands for 0 expression system.、

The invention has the beneficial effects as follows：With wind-driven generator, photovoltaic array output mathematical model and accumulator charge and discharge Based on electrical characteristics and life cycle mathematical model, consider and wait year value investment cost (to include equipment investment expense, operation is tieed up Shield expense and equipment replacement expense etc.), energy surplus multiplying power and load dead electricity rate (micro-capacitance sensor whole year operation reliability index) etc. Many Performance Evaluating Indexes, design higher-dimension multiple target Extremal optimization method, as solver, is realized scene and is stored complementary independent micro-capacitance sensor Higher-dimension multiple-objection optimization configures.The present invention can achieve that scene stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization allocative effect, tool There is the advantages below not available for traditional single object optimization method and traditional Multipurpose Optimal Method：For micro-capacitance sensor design planning person The configuration scheme providing is more reasonable, the configuration scheme investment in the case of meeting identical power supply reliability index Less, optimization method is implemented simply, to adjust without complex cost function weight coefficient, and parameters optimization that need not be complicated is adjusted, and Optimization efficiency is higher.

Brief description

Fig. 1 is independent micro-grid system structure chart；

Fig. 2 is that scene stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization collocation method schematic diagram；

Fig. 3 is that scene stores complementary independent micro-capacitance sensor multi-object evaluation model computational methods flow chart.

Specific embodiment

The present invention is further described below in conjunction with the accompanying drawings, and the purpose of the present invention and effect will be apparent from.

Fig. 1 is independent micro-grid system structure chart, including dual-feed asynchronous wind power generator, photovoltaic array, lead-acid accumulator, Dc bus, AC/DC changer, DC/DC changer, DC/AC changer, DC load equipment and AC load equipment etc..

Fig. 2 is that a kind of scene proposed by the present invention stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization collocation method total figure. Store as a example complementary independent micro-grid system optimization design engineering by Wenzhou City somewhere 150kW scene, using proposed by the present invention Light stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization collocation method and is designed implementing.

Described scene stores complementary independent micro-capacitance sensor higher-dimension multiple target extremal optimization collocation method, comprises the following steps：

(1) read the Wenzhou City industrial occupancy year meteorological data with 1 hour as step-length and (include wind speed, intensity of illumination, ring Border temperature etc.), scene store the complementary each component parameter information of independent micro-grid system (system construction drawing is as shown in Figure 1) and Wenzhou City Certain industrial occupancy load data load data (includes hourly average DC load and hourly average AC load etc.)；Produce reference Point, produces H reference point using NBI (Normal-boundary intersection) method, according to the reference point producing Number determines Population Size NP=H in higher-dimension multiple target extremal optimization collocation method (if H is even number), and NP=H+1 is (if H is strange Number)；

(2) initialize, generate one at random and be uniformly distributed the initial population P={ P that Population Size is NP_i, i=1,2 ..., NP }, wherein i-th individual P_i=(N_WGi,N_PVi,N_BATi), N_WGiFor wind-driven generator, number of units, N are installed_PViFor photovoltaic battery module Block number, N are installed_BATiFor secondary battery unit group number；

(3) to each of population P individuality P_i, i=1,2 ..., NP, carry out non-uniform mutation, multiple objective function assessment The multiple-objection optimizations such as calculating, non-dominated ranking operate, and specifically include following sub-step：

(3.1) to P_i={ P_i(j), j=1,2,3 } in each constituent element execute many non-uniform mutation (Multi-non- one by one Uniform mutation, MNUM), keep other constituent elements constant simultaneously, obtain new individual P_ij, j=1,2,3；

Wherein r, r₁It is the random number producing in the range of [0,1], t represents current iteration number of times, L (j) represents j-th optimization The lower limit of variable, U (j) represents the upper limit of j-th optimized variable, and b is the MNUM coefficient of variation, I_maxThe maximum setting for user changes Generation number；

(3.2) calculate P_ijCorresponding multiple objective function value, including etc. year value investment cost ACS (P_ij), energy surplus multiplying power B_exc(P_ij) and load dead electricity rate L_PSP(P_ij), concrete calculating process stores complementary independent micro-capacitance sensor Multi-target evaluation mould referring to scene Step described by type computational methods；

(3.3) to current 3 son individuals P_ijCarry out non-dominated ranking, thus obtaining its dominated Sorting number r_ij∈[0,2],j =1,2,3, the individual record that dominated Sorting number is 0 is P_i0, and by P_i0And corresponding multiple objective function value achieves；

(4) by P_i0As new population at individual, thus producing new population P_N={ P_i0, i=1,2 ..., NP }；

(5) unconditionally accept P=P_N；

(6) repeat step 3-5 is until meeting the maximum iteration time of user's setting；

(7) output Pareto optimal solution and corresponding ACS, energy surplus multiplying power B_exc, load dead electricity rate L_PSPEvaluation refers to Scale value, provides the user scene and stores complementary independent micro-capacitance sensor configuration scheme.

Fig. 3 gives the complementary independent micro-capacitance sensor multi-object evaluation model computational methods of scene storage in step (3.2) and specifically flows Cheng Tu, higher-dimension multiple objective function can set according to the actual requirements, have certain motility and can reach different effect of optimizations, Year value investment cost ACS such as general selecting system, energy surplus multiplying power B_exc, load short of electricity probability L_PSPAs object function, that is,：

L_maxFor independent micro-grid system patient maximum load short of electricity probit, B_maxFor patient ceiling capacity mistake Surplus multiplier value.A the years such as () system are worth investment cost ACS calculating process as follows：

ACS (X)=(C₁₁N_WG+C₁₂N_PV+C₁₃N_BAT)+(C₂₁N_WG+C₂₂N_PV+C₂₃N_BAT)+C₃N_BAT(28)

In formula：X is optimized variable set, X=(N_WG,N_PV,N_BAT)；C₁₁、C₁₂、C₁₃It is respectively blower fan, photovoltaic and accumulator Each assembly installation cost Average Annual Cost；C₂₁、C₂₂、C₂₃It is respectively blower fan, photovoltaic becomes with accumulator each unit year operation maintenance This；C₃For the average annual replacement cost of accumulator.

Blower fan, photovoltaic and accumulator each assembly installation cost Average Annual Cost are related to the assembly life-span cycle time limit, its pass It is that expression formula is：

C_1i=C_Pi.CRF_i(h,Y_proj) (29)

In formula：C_PFor installation cost；Y_projFor the assembly life-span time limit；CRF is recovery of the capital coefficient (capital Recovery factor, CRF), its expression formula is：

Wherein, h is discount rate.

Each assembly operation and maintenance cost C of 1 year_2iN () is calculated as follows：

C_2i(n)=C_2i(1).(1+f)ⁿ(31)

Wherein C_2i(1) it is the operating cost of the 1st year, f represents annual inflation.

In the project time limit, if system component reaches its end-of-life time limit, need assembly to be carried out reset to replace, group The replacement expense of part is calculated as follows：

C₃=C_r.SFE(h,Y_r) (32)

In formula：C_rFor the replacement cost；Y_rReset the life-span for assembly；SFE is the compensation fund factor, is counted according to formula (33) Calculate：

(b) energy surplus multiplying power B_excThe energy wasting by (being usually arranged as 1 year) within the specific period being considered be The ratio of system load aggregate demand energy, is specifically calculated as follows：

In formula：P_e(t) system excess power；P_l(t) system total load power；T is power supply total time hop count, usually T= 8760；Δ t is simulation step length, usually Δ t=1；

P_l(t)=P_D(t)+P_A(t)/e_n(35)

Wherein, P_DT () represents total DC load, P_AT () represents total AC load, e_nRepresent inverter efficiency.

C () is to load short of electricity probability L_PSPAs reliability evaluation index, represent system short of electricity time and total power-on time Ratio, is calculated as follows：

In formula：S_lossT () is system short of electricity marker character, its value is that 1 expression system short of electricity (is provided that in t system General power be less than system load demand), its value can meet all workload demands for 0 expression system.

Involved dual-feed asynchronous wind power generator, photovoltaic array output mathematical model and lead in step 1 and step 3 Acid accumulator discharge and recharge is calculated as follows with life cycle mathematical model：

1) wind driven generator output power model

It is possible to the characteristic passed through between the output of wind power generating set and wind speed is bent after the distribution of known wind speed Line obtains the average output power of blower fan system：

In formula：P_rFor rated output power；The wind speed that v highly locates for wind turbine hub, v_rFor rated wind speed, v_inFor incision Wind speed；v_outFor the wind speed cutting out.Wind speed has the randomness of height, and here adopts two-parameter Weibull (Weibull) model Produce the air speed data obtaining simulation, its probability density function expression formula is：

In formula：2 parameters that k and c is distributed for Weibull, k is referred to as form parameter, k>0, c is scale parameter, c>1.

2) photovoltaic array output calculates model

Quantity is N_PVPhotovoltaic array output power model P_PVIt is calculated as follows：

Wherein, G, G_maxIt is respectively actual intensity of illumination within a certain period of time and maximum intensity of illumination；P_p(G) it is single-piece The output of photovoltaic module,For intensity of illumination probability density function, it is respectively calculated as follows：

Wherein, P_STCRepresent the full test power under standard test condition, G is actual intensity of illumination；K is power temperature Degree coefficient；T_aFor ambient temperature, T_NOCFor assembly operating temperature ratings.

Wherein, the form parameter that α, β are distributed for Beta.

3) battery model

State-of-charge (SOC), terminal voltage and life cycle are several important parameters of battery management, its computation model As follows：

Storage battery charge state SOC be reaction accumulator dump energy account for its total capacity ratio parameter, in front and back two when Between quarter, accumulator SOC is represented by following relational expression：

SOC (t+1)=SOC (t) (1- δ (t))+I_BAT(t).Δt.η(t)/C_BAT(43)

In formula：I_BATT () is t charging and discharging currents, be that just electric discharge is to be negative during charging；P_BATT () is filling of accumulator Discharge power, is just charged as, it is negative for discharging；V_BATT () is accumulator voltage；δ is nature discharge rate；Δ t is two moment in front and back Time interval；C_BATFor accumulator capacity on time；η (t) is efficiency for charge-discharge, and during electric discharge, its value is 1, with SOC and filling during charging Electricity is current related, and computation model is as follows：

The terminal voltage of accumulator can be by its open-circuit voltage and the internal resistance voltage drop meter producing in its internal resistance because of charging and discharging currents Show, computing formula is as follows：

V_BAT(t)=E_oc(t)+I_BAT(t)R_BAT(t) (46)

E_oc(t)=VF+b.log (SOC (t)) (47)

R_BAT(t)=R_electrode(t)+R_electrlyte(t) (48)

R_electrode(t)=r₁+r₂.SOC(t) (49)

R_electrolyte(t)=[r₃+r₄.SOC(t)]^-1(50)

In formula:E_ocT () is the open-circuit voltage of accumulator；I_BAT(t) be battery current (its value be more than 0 represent charge, its Value represents electric discharge less than 0)；R_BATT () is accumulator internal resistance, including bath resistance R_electrode(t) and bath resistance R_electrolyte(t) two parts；b,r₁,r₂,r₃,r₄For empirical coefficient, its value has different values in both charge and discharge modes：

The life of storage battery damages phase T_BATIt is calculated as follows：

n_S=T/ Δ t represents the total fixed number of emulation, SOC_1,t、SOC_2,tIt is respectively t-th emulation period beginning and end When SOC numerical value, S_BAT,tRepresent the charging and discharging state of t-th emulation period accumulator reality,

D_OD,tRepresent the battery discharging depth detecting at the end of t-th emulation period, D_OD,t=1-SOC_2,t.

The reset cycle Y of accumulator_BIt is calculated as follows：

T_BR=min { T_BAT,T_FL} (53)

Wherein, T_FLRepresent the float life reference value that storage battery production producer provides.

In system operation, accumulator is limited scope (SOC by its state-of-charge_min≤SOC≤SOC_max) and electric power storage The impact of pond technical limitations itself, its maximum charge-discharge electric power is calculated as follows：

P_cm(t)=N_BAT.max{0,min{(SOC_max-SOC(t)).C_BAT/Δt,I_cm}.V_BAT(t)} (54)

P_dm(t)=N_BAT.max{0,min{(SOC(t)-SOC_min).C_BAT/Δt,I_dm}.V_BAT(t)} (55)

In formula：SOC_max,SOC_minIt is respectively the bound of storage battery charge state；C_BATFor accumulator capacity；V_BATT () is Accumulator voltage；Δ t is unit simulation time interval；P_cm(t),P_dmT () is respectively accumulator within t emulation period Maximum chargeable electric current and maximum discharge current；I_cm,I_dmIt is respectively maximum charging current and the maximum electric discharge electricity that accumulator allows Stream, in the unit interval, maximum charging and discharging currents are the 20% of the specified ampere-hour capacity of accumulator, i.e.

I_cm=I_dm=0.2C_BAT/Δt (56)

Complementary independent micro-grid system is stored to Wenzhou Area 150kW scene using the present invention and is optimized design, result table Bright：The present invention can achieve that scene stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization allocative effect, and meeting, identical power supply is reliable Property index in the case of configuration scheme output investment ratio tradition single object optimization method (as genetic algorithm, particle cluster algorithm) and Traditional Multipurpose Optimal Method (as NSGA-II algorithm) at least saves more than 20%, and the present invention only has the MNUM coefficient of variation Need to adjust with two parameters of iteration optimization number of times, adjust without complex cost function weight coefficient, optimization method is also without kind The operation links such as flock-mate fork, implement more simple, the calculating time of whole method is shorter.

In sum, can achieve that scene stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization allocative effect using the present invention, There is the advantages below not available for traditional single object optimization method and traditional Multipurpose Optimal Method：For micro-capacitance sensor design planning The configuration scheme that person provides is more reasonable, and the configuration scheme in the case of meeting identical power supply reliability index is thrown Money is less, and optimization method is implemented simply, to adjust without complex cost function weight coefficient, and parameters optimization that need not be complicated is adjusted, And optimization efficiency is higher.

Claims (2)

1. a kind of scene store complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization collocation method it is characterised in that the method include with Lower step：

(1) read the scene with 1 hour as step-length and store the annual meteorological data in complementary independent micro-grid system enforcement area, scene Store the complementary each component parameter information of independent micro-grid system and load data；Described year meteorological data to include wind speed, illumination strong Degree, ambient temperature；Described load data includes hourly average DC load and hourly average AC load；Produce reference point, adopt Produce H reference point with NBI (Normal-boundary intersection) method, determined according to the reference point number producing Population Size NP, if H is even number, NP=H；If H is odd number, NP=H+1；

(2) initialize, generate one at random and be uniformly distributed the initial population P={ P that Population Size is NP_i, i=1,2 ..., NP }, Wherein i-th individual P_i=(N_WGi,N_PVi,N_BATi), N_WGiFor i-th individual corresponding wind-driven generator, number of units, N are installed_PViFor I individual corresponding photovoltaic battery module installs block number, N_BATiFor i-th individual corresponding secondary battery unit group number；

(3) to each of population P individuality P_i, i=1,2 ..., NP, carry out multiple-objection optimization operation, described multiple-objection optimization Operation includes non-uniform mutation, multiple objective function assessment calculates, non-dominated ranking, specifically includes following sub-step：

(3.1) to P_i={ P_i(j), j=1,2,3 } in each constituent element execute many non-uniform mutations MNUM (Multi-non- one by one Uniform mutation), keep other constituent elements constant simultaneously, obtain new individual P_ij, j=1,2,3；

$P_j=\left\{\begin{array}{c}{P}_i\left(j\right)+\left(U\right(j)-P_i(j\left)\right).A\left(t\right),ifr<0.5\\ P_i\left(j\right)+\left(P_i\right(j)-L(j\left)\right).A\left(t\right),ifr\&GreaterEqual;0.5\end{array}\right.---\left(1\right)$

$A\left(t\right)={\&lsqb;r_1(1-\frac{t}{I_{\max }})\&rsqb;}^b---\left(2\right)$

Wherein r, r₁It is the random number producing in the range of [0,1], t represents current iteration number of times, and L (j) represents j-th optimized variable Lower limit, U (j) represent j-th optimized variable the upper limit, b be the MNUM coefficient of variation, I_maxThe greatest iteration time setting for user Number；

(3.2) calculate P_ijCorresponding multiple objective function value, including etc. year value investment cost ACS (P_ij), energy surplus multiplying power B_exc (P_ij) and load dead electricity rate L_PSP(P_ij)；

(3.3) to current 3 son individuals P_ijCarry out non-dominated ranking, thus obtaining its dominated Sorting number r_ij∈ [0,2], j=1, 2,3, the individual record that dominated Sorting number is 0 is P_i0, and by P_i0And corresponding multiple objective function value achieves；

(4) by P_i0As new population at individual, thus producing new population P_N={ P_i0, i=1,2 ..., NP }；

(5) unconditionally accept P=P_N；

(6) repeat step 3-5 is until meeting the maximum iteration time of user's setting；

(7) output Pareto optimal solution and corresponding ACS, energy surplus multiplying power B_exc, load dead electricity rate L_PSPEvaluation index value, Provide the user scene and store complementary independent micro-capacitance sensor configuration scheme.

2. a kind of scene according to claim 1 stores complementary independent micro-capacitance sensor higher-dimension multiple-objection optimization collocation method, and it is special Levy and be, the higher-dimension multiple objective function in described step (3.2) can set according to the actual requirements, has certain motility and energy Reach different effect of optimizations, the year such as general selecting system is worth investment cost ACS, energy surplus multiplying power B_exc, load short of electricity probability L_PSPAs object function, that is,：

Minimize(ACS,B_exc,L_PSP)

N_WGFor wind-driven generator, number of units, N are installed_PVFor photovoltaic battery module, block number, N are installed_BATFor secondary battery unit group number, L_maxFor Independent micro-grid system patient maximum load short of electricity probit, B_maxFor patient ceiling capacity surplus multiplier value, N table Show natural number set；

A the years such as () system are worth investment cost ACS calculating process as follows：

ACS (X)=(C₁₁N_WG+C₁₂N_PV+C₁₃N_BAT)+(C₂₁N_WG+C₂₂N_PV+C₂₃N_BAT)+C₃N_BAT(4)

In formula：X is optimized variable set, X=(N_WG,N_PV,N_BAT)；C₁₁、C₁₂、C₁₃It is respectively blower fan, photovoltaic and accumulator each group Part installation cost Average Annual Cost；C₂₁、C₂₂、C₂₃It is respectively blower fan, photovoltaic and accumulator each unit year operation expense；C₃ For the average annual replacement cost of accumulator；

Blower fan, photovoltaic and accumulator each assembly installation cost Average Annual Cost C_1iRelated to the assembly life-span cycle time limit, its relation Expression formula is：

C_1i=C_Pi.CRF_i(h,Y_proj), i=1,2,3 (5)

In formula：C_PiFor the installation cost of each assembly, i.e. C_P1、C_P2And C_P3Represent the installation of blower fan, photovoltaic and accumulator cell assembly respectively Cost；Y_projFor the assembly life-span time limit；CRF_iFor recovery of the capital coefficient (capital recovery factor, CRF), its expression Formula is：

${\mathrm{CRF}}_i(h,Y_{proj})=\frac{h.{(1+h)}^{Y_{proj}}}{{(1+h)}^{Y_{proj}}-1}---\left(6\right)$

Wherein, h is discount rate；

Each assembly operation and maintenance cost C of 1 year_2iN () is calculated as follows：

C_2i(n)=C_2i(1).(1+f)ⁿ(7)

Wherein C_2i(1) it is the operating cost of the 1st year, f represents annual inflation；

In the project time limit, if system component reaches its end-of-life time limit, need assembly to be carried out reset to replace, assembly Replacement expense is calculated as follows：

C₃=C_r.SFE(h,Y_r) (8)

In formula：C_rFor the replacement cost；Y_rReset the life-span for assembly；SFE is the compensation fund factor, is calculated according to formula (9)：

$SFF(h,Y_r)=\frac{h}{{(1+h)}^{Y_r}-1}---\left(9\right)$

(b) energy surplus multiplying power B_excThe energy wasting by (being usually arranged as 1 year) within the specific period being considered is born with system The ratio of lotus aggregate demand energy, is specifically calculated as follows：

$B_{exc}=\frac{\underset{t=1}{\overset{T}{\&Sigma;}}{P}_e\left(t\right).\mathrm{\&Delta;}t}{\underset{t=1}{\overset{T}{\&Sigma;}}{P}_l\left(t\right).\mathrm{\&Delta;}t}=\frac{\underset{t=1}{\overset{T}{\&Sigma;}}{P}_e\left(t\right)}{\underset{t=1}{\overset{T}{\&Sigma;}}{P}_l\left(t\right)}---\left(10\right)$

In formula：P_e(t) system excess power；P_l(t) system total load power；T is power supply total time hop count, usually T= 8760；Δ t is simulation calculation step-length, usually Δ t=1；

P_l(t)=P_D(t)+P_A(t)/e_n(11)

Wherein, P_DT () represents total DC load, P_AT () represents total AC load, e_nRepresent inverter efficiency；

C () is to load short of electricity probability L_PSPAs reliability evaluation index, represent the ratio of system short of electricity time and total power-on time Value, is calculated as follows：

$L_{PSP}=\frac{\underset{t=1}{\overset{T}{\&Sigma;}}{S}_{loss}\left(t\right)}{T}---\left(12\right)$

In formula：S_lossT () is system short of electricity marker character, its value is 1 expression system short of electricity, the total work being provided that in t system Rate is less than system load demand, and its value can meet all workload demands for 0 expression system.