CN105337315B - A high-dimensional and multi-objective optimal configuration method for wind-storage-storage complementary independent microgrids - Google Patents

Abstract

The invention discloses a high-dimensional and multi-objective optimal configuration method for a wind-solar-storage-storage complementary independent microgrid. Based on the mathematical model of output power of wind turbines and photovoltaic arrays and the mathematical model of battery charge and discharge characteristics and life cycle, comprehensively considering multi-performance evaluation indicators such as annual value investment costs, energy excess ratio and load loss rate, etc., the design of high-dimensional multi- The objective extremum optimization method is used as a solver to realize the high-dimensional and multi-objective optimal configuration of the wind-storage-storage complementary independent microgrid. The invention can realize the high-dimensional multi-objective optimal configuration effect of wind-storage-storage complementary independent micro-grid, and has the following advantages that traditional single-objective optimization methods and traditional multi-objective optimization methods do not have: the optimal configuration scheme provided for micro-grid design planners is more efficient Reasonable, the optimization configuration scheme under the condition of meeting the same power supply reliability index has less investment, the optimization method is simple to implement, does not require complex objective function weight coefficient setting, and does not require complex optimization parameter setting, and the optimization efficiency is higher.

Description

A high-dimensional and multi-objective optimal configuration method for wind-storage-storage complementary independent microgrids

technical field

The invention relates to an intelligent optimization and decision-making method in the field of new energy micro-grid planning and design and energy management, and in particular to a high-dimensional and multi-objective optimal configuration method for a wind-solar-storage-storage complementary independent micro-grid.

Background technique

The concept of "Microgrid" is a milestone in the development of distributed power systems. Smart microgrids are a new organizational form of smart distribution networks in the future, and provide the possibility for large-scale applications of renewable energy distributed power generation technologies. As an important way to realize the independent power supply system, the wind-storage-storage complementary independent micro-grid system provides a feasible solution for effectively solving the power supply problems in remote mountainous areas and coastal islands. Extensive attention and research exploration. Distributed power supply and power electronic equipment selection and capacity optimization configuration are one of the important issues that must be considered in the planning and design stage of microgrid. Due to the randomness of wind power and photovoltaic power generation, coupled with the diversity and complexity of load demand, the optimal allocation of microgrid capacity should be based on a full analysis of the environmental conditions and load demand characteristics of the microgrid system installation site, comprehensively considering the power characteristics of the equipment, Installation and maintenance costs and control methods, and finally realize the safety, reliability, economical and environmental protection of the microgrid system. Therefore, the problem of microgrid equipment selection and capacity optimization configuration is also one of the difficult problems in the field of microgrid planning and design. At present, the academic and engineering circles at home and abroad usually convert the various factors that must be considered in the selection of microgrid equipment and the optimal configuration of capacity into a weighted objective function according to the importance, and then use single-objective optimization algorithms such as genetic algorithm and particle algorithm to carry out optimization solution. However, these existing methods generally have defects such as difficulty in setting weight coefficients accurately, complex algorithm parameter setting, and configuration schemes that are difficult to guide engineering practice. Although some researchers have used NSGA-II and other multi-objective optimization methods to try to solve the problem of microgrid equipment selection and capacity optimization, the design process and algorithm parameter setting of NSGA-II and other multi-objective optimization algorithms are very complicated, and the calculation efficiency is very high. Low, not convenient for specific engineering implementation. With the support of the National Natural Science Foundation of China (51207112), the Zhejiang Provincial Public Welfare Program (2014C31074, 2014C31093), the Zhejiang Provincial Natural Science Foundation of China (LY16F030011, LZ16E050002, LQ14F030006, LQ14F030007) and the Zhejiang Provincial Young Talents Program (2014R424014), this The invention discloses a high-dimensional multi-objective optimal configuration method for wind-storage-storage complementary independent microgrids, which has the following advantages that traditional single-objective optimization methods and traditional multi-objective optimization methods do not possess: the optimal configuration scheme provided for microgrid design planners is more Reasonable, the optimization configuration scheme under the condition of meeting the same power supply reliability index has less investment, the optimization method is simple to implement, does not require complex objective function weight coefficient setting, and does not require complex optimization parameter setting, and the optimization efficiency is higher.

Contents of the invention

The purpose of the present invention is to provide a high-dimensional and multi-objective optimal configuration method for a wind-solar-storage-storage complementary independent microgrid to address the deficiencies in the prior art.

The object of the present invention is achieved through the following technical solutions: a high-dimensional and multi-objective optimal configuration method for a wind-storage-storage complementary independent microgrid, the method comprising the following steps:

(1) Read the annual meteorological data (including wind speed, light intensity, ambient temperature, etc.), the parameter information and load of each component of the wind-solar-storage hybrid independent micro-grid system with a step size of 1 hour Data (including hourly average DC load and hourly average AC load, etc.); generate reference points, use the NBI (Normal-boundary intersection) method to generate H reference points, and determine the population size NP=H according to the number of generated reference points (if H is an even number), NP=H+1 (if H is an odd number);

(2) Initialization. Randomly generate an initial population P={P _i ,i=1,2,…,NP} with a uniformly distributed population size of NP, where the i-th individual P _i =(N _WGi ,N _PVi ,N _BATi ), N _WGi is the number of installed wind turbines, N _PVi is the number of photovoltaic cell modules installed, N _BATi is the number of battery unit groups;

(3) For each individual P _i ,i=1,2,...,NP in the population P, multi-objective optimization operations such as non-uniform mutation, multi-objective function evaluation calculation, non-dominated sorting, etc. are performed, specifically including the following sub-steps:

(3.1). Perform multi-non-uniform mutation (MNUM) on each component in P _i ={P _i (j),j=1,2,3} one by one, while keeping other components unchanged, get a new individual P _ij , j=1,2,3;

Where r and r ₁ are random numbers generated within the range of [0,1], t represents the current iteration number, L(j) represents the lower limit of the jth optimization variable, U(j) represents the upper limit of the jth optimization variable, b is the coefficient of variation of MNUM, and I _max is the maximum number of iterations set by the user;

(3.2) Calculate the multi-objective function value corresponding to P _ij , including the equivalent annual investment cost ACS(P _ij ), excess energy ratio B _exc (P _ij ) and load loss rate L _PSP (P _ij );

(3.3) Perform non-dominated sorting on the current three sub-individuals P _ij to obtain their dominant sorting number r _ij ∈ [0,2], j=1,2,3, and record the individual whose dominant sorting number is 0 as P _i0 , and archive P _i0 and the corresponding multi-objective function values;

(4) Take P _i0 as a new population individual, thereby generating a new population P _N ={P _i0 , i=1,2,...,NP};

(5) Accept P=P _N unconditionally;

(6) Repeat steps 3-5 until the maximum number of iterations set by the user is met;

(7) Output the Pareto optimal solution and the corresponding ACS, energy excess rate B _exc , and load power failure rate L _PSP evaluation index values to provide users with an optimal configuration plan for wind-storage-storage complementary independent microgrids.

Furthermore, the high-dimensional multi-objective function in step (3.2) can be set according to actual needs, which has certain flexibility and can achieve different optimization effects. Generally, the annual investment cost of the system such as ACS, energy excess multiplier B _exc , load deficiency The electrical probability L _PSP is used as the objective function, namely:

L _max is the maximum load power loss probability value that the independent microgrid system can tolerate, and B _max is the maximum energy surplus rate that can be tolerated. (a) The calculation process of ACS equivalent annual value investment fee is as follows:

ACS(X)＝(C ₁₁ N _WG +C ₁₂ N _PV +C ₁₃ N _BAT )+(C ₂₁ N _WG +C ₂₂ N _PV +C ₂₃ N _BAT )+C ₃ N _BAT (16)

In the formula: X is the set of optimization variables, X=(N _WG , N _PV , N _BAT ); C ₁₁ , C ₁₂ , and C ₁₃ are the annual average installation costs of wind turbines, photovoltaics, and batteries; C ₂₁ , C ₂₂ , C ₂₃ are the annual operation and maintenance costs of each unit of wind turbine, photovoltaic and battery respectively; C ₃ is the average annual replacement cost of the battery.

The annual average cost of installation cost of each component of wind turbine, photovoltaic and storage battery is related to the life cycle of the component, and the relationship expression is:

C _1i ＝C _Pi .CRF _i (h,Y _proj ) (17)

In the formula: C _P is the installation cost; Y _proj is the life span of the component; CRF is the capital recovery factor (CRF), and its expression is:

Among them, h is the discount rate.

The operation and maintenance cost C _2i (n) of each component in the nth year is calculated as follows:

C _2i(n) = C _2i (1).(1+f) ⁿ (19)

Among them, C _2i (1) is the operating cost in the first year, and f represents the annual inflation rate.

During the project period, if the system components reach their end-of-life years, they need to be replaced and replaced. The replacement cost of the components is calculated as follows:

C ₃ ＝C _r .SFE(h,Y _r ) (20)

In the formula: C _r is the replacement cost; Y _r is the replacement life of the component; SFE is the compensation fund factor, which is calculated according to formula (21):

(b) The excess energy ratio B _exc is the ratio of the wasted energy to the total energy demanded by the system load in the specific period considered (usually set to 1 year), and the specific calculation is as follows:

In the formula: P _e (t) excess power of the system; P _l (t) total load power of the system; T is the total time period of power supply, usually T=8760; Δt is the simulation step size, usually Δt=1;

P _l (t) = P _D (t) + P _A (t) / e _n (23)

Among them, P _D (t) represents the total DC load, PA ₍ t) represents the total AC load, and e _n represents the efficiency of the inverter.

(c) Taking the load power-shortage probability L _PSP as the reliability evaluation index, it represents the ratio of the system power-shortage time to the total power supply time, which is calculated as follows:

In the formula: S _loss (t) is the system power shortage marker, and its value of 1 means that the system is short of power (that is, the total power that the system can provide at time t is less than the system load demand), and its value of 0 means that the system can meet all loads need. ,

The beneficial effects of the present invention are: based on the wind power generator, the mathematical model of the output power of the photovoltaic array and the mathematical model of the charging and discharging characteristics of the storage battery and the life cycle mathematical model, comprehensively consider the annual investment cost (including equipment investment cost, operation and maintenance cost and equipment weight) Costs, etc.), excess energy ratio and load loss rate (microgrid annual operation reliability index) and other multi-performance evaluation indicators, design a high-dimensional multi-objective extreme value optimization method as a solver, to achieve high Dimensional multi-objective optimization configuration. The invention can realize the effect of high-dimensional multi-objective optimal configuration of wind-storage-storage complementary independent micro-grid, and has the following advantages that traditional single-objective optimization methods and traditional multi-objective optimization methods do not possess: the optimal configuration scheme provided for micro-grid design planners is more efficient Reasonable, the optimization configuration scheme under the condition of meeting the same power supply reliability index has less investment, the optimization method is simple to implement, does not require complex objective function weight coefficient setting, and does not require complex optimization parameter setting, and the optimization efficiency is higher.

Description of drawings

Figure 1 is a structural diagram of an independent microgrid system;

Figure 2 is a schematic diagram of a high-dimensional and multi-objective optimal configuration method for a wind-storage-storage hybrid independent microgrid;

Fig. 3 is a flow chart of the calculation method for the multi-objective evaluation model of the wind-storage-storage hybrid independent microgrid.

detailed description

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

Figure 1 is a structural diagram of an independent microgrid system, including doubly-fed asynchronous wind generators, photovoltaic arrays, lead-acid batteries, DC buses, AC/DC converters, DC/DC converters, DC/AC converters, DC load equipment and AC load equipment, etc.

Fig. 2 is a general diagram of a high-dimensional multi-objective optimal configuration method for a wind-solar-storage-storage complementary independent microgrid proposed by the present invention. Taking the optimal design project of a 150kW solar-storage-storage complementary independent micro-grid system in a certain area of Wenzhou City as an example, the high-dimensional and multi-objective optimal configuration method for the solar-storage-storage complementary independent micro-grid proposed by the present invention is used for design and implementation.

The high-dimensional and multi-objective extreme value optimal configuration method for independent micro-grid with wind-storage-storage hybrid includes the following steps:

(1) Read the annual meteorological data (including wind speed, light intensity, ambient temperature, etc.) of an industrial area in Wenzhou City with a step size of 1 hour, and the parameter information of each component of the independent microgrid system for wind-solar-storage-storage hybrid system (the system structure diagram is shown in Figure 1 shown) and load data of an industrial area in Wenzhou City (including hourly average DC load and hourly average AC load, etc.); to generate reference points, use the NBI (Normal-boundary intersection) method to generate H reference points, according to the generated reference points The number of points determines the population size NP=H (if H is an even number) and NP=H+1 (if H is an odd number) in the high-dimensional multi-objective extreme value optimal configuration method;

(2) Initialization. Randomly generate an initial population P={P _i ,i=1,2,…,NP} with a uniformly distributed population size of NP, where the i-th individual P _i =(N _WGi ,N _PVi ,N _BATi ), N _WGi is the number of installed wind turbines, N _PVi is the number of photovoltaic cell modules installed, and N _BATi is the number of battery units;

(3) For each individual P _i ,i=1,2,...,NP in the population P, multi-objective optimization operations such as non-uniform mutation, multi-objective function evaluation calculation, non-dominated sorting, etc. are performed, specifically including the following sub-steps:

(3.1) Perform multi-non-uniform mutation (MNUM) on each component in P _i ={P _i (j),j=1,2,3} one by one, while keeping other components Change to get a new individual P _ij , j=1,2,3;

Where r and r ₁ are random numbers generated within the range of [0,1], t represents the current iteration number, L(j) represents the lower limit of the jth optimization variable, U(j) represents the upper limit of the jth optimization variable, b is the coefficient of variation of MNUM, and I _max is the maximum number of iterations set by the user;

(3.2) Calculate the multi-objective function value corresponding to P _ij , including the equivalent annual investment cost ACS(P _ij ), excess energy ratio B _exc (P _ij ) and load loss rate L _PSP (P _ij ). For the specific calculation process, see The steps described in the calculation method of the multi-objective evaluation model of the wind-storage-storage hybrid independent microgrid;

(3.3) Perform non-dominated sorting on the current three sub-individuals P _ij to obtain their dominant sorting number r _ij ∈ [0,2], j=1,2,3, and record the individual whose dominant sorting number is 0 as P _i0 , and archive P _i0 and the corresponding multi-objective function values;

(4) Take P _i0 as a new population individual, thereby generating a new population P _N ={P _i0 , i=1,2,...,NP};

(5) Accept P=P _N unconditionally;

(6) Repeat steps 3-5 until the maximum number of iterations set by the user is met;

(7) Output the Pareto optimal solution and the corresponding ACS, energy excess rate B _exc , and load power failure rate L _PSP evaluation index values to provide users with an optimal configuration plan for wind-storage-storage complementary independent microgrids.

Figure 3 shows the specific flow chart of the multi-objective evaluation model calculation method for wind-storage-storage hybrid independent microgrid in step (3.2). The high-dimensional multi-objective function can be set according to actual needs, which has certain flexibility and can achieve different optimization effects , generally select the annual value investment cost ACS of the system, the energy excess rate B _exc , and the load power shortage probability L _PSP as the objective function, namely:

L _max is the maximum load power loss probability value that the independent microgrid system can tolerate, and B _max is the maximum energy surplus rate that can be tolerated. (a) The calculation process of ACS equivalent annual value investment fee is as follows:

ACS(X)＝(C ₁₁ N _WG +C ₁₂ N _PV +C ₁₃ N _BAT )+(C ₂₁ N _WG +C ₂₂ N _PV +C ₂₃ N _BAT )+C ₃ N _BAT (28)

In the formula: X is the set of optimization variables, X=(N _WG , N _PV , N _BAT ); C ₁₁ , C ₁₂ , and C ₁₃ are the annual average installation costs of wind turbines, photovoltaics, and batteries; C ₂₁ , C ₂₂ , C ₂₃ are the annual operation and maintenance costs of each unit of wind turbine, photovoltaic and battery respectively; C ₃ is the average annual replacement cost of the battery.

The annual average cost of installation cost of each component of wind turbine, photovoltaic and storage battery is related to the life cycle of the component, and the relationship expression is:

C _1i ＝C _Pi .CRF _i (h,Y _proj ) (29)

In the formula: C _P is the installation cost; Y _proj is the life span of the component; CRF is the capital recovery factor (CRF), and its expression is:

Among them, h is the discount rate.

The operation and maintenance cost C _2i (n) of each component in the nth year is calculated as follows:

C _2i(n) = C _2i (1).(1+f) ⁿ (31)

Among them, C _2i (1) is the operating cost in the first year, and f represents the annual inflation rate.

During the project period, if the system components reach their end-of-life years, they need to be replaced and replaced. The replacement cost of the components is calculated as follows:

C ₃ =C _r .SFE(h,Y _r ) (32)

In the formula: C _r is the replacement cost; Y _r is the replacement life of the component; SFE is the compensation fund factor, which is calculated according to formula (33):

(b) The excess energy ratio B _exc is the ratio of the wasted energy to the total energy demanded by the system load in the specific period considered (usually set to 1 year), and the specific calculation is as follows:

In the formula: P _e (t) excess power of the system; P _l (t) total load power of the system; T is the total time period of power supply, usually T=8760; Δt is the simulation step size, usually Δt=1;

P _l (t) = P _D (t) + P _A (t) / e _n (35)

Among them, P _D (t) represents the total DC load, PA ₍ t) represents the total AC load, and e _n represents the efficiency of the inverter.

(c) Taking the load power-shortage probability L _PSP as the reliability evaluation index, it represents the ratio of the system power-shortage time to the total power supply time, which is calculated as follows:

In the formula: S _loss (t) is the system power shortage marker, and its value of 1 means that the system is short of power (that is, the total power that the system can provide at time t is less than the system load demand), and its value of 0 means that the system can meet all loads need.

The mathematical model of doubly-fed asynchronous wind generator, photovoltaic array output power and lead-acid battery charging and discharging and life cycle mathematical model involved in step 1 and step 3 are calculated as follows:

1) Wind turbine output power model

When the distribution of wind speed is known, the average output power of the fan system can be obtained through the characteristic curve between the output power of the wind turbine and the wind speed:

In the formula: P _r is the rated output power; v is the wind speed at the hub height of the wind turbine, v _r is the rated wind speed, v _in is the cut-in wind speed; v _out is the cut-out wind speed. Wind speed has a high degree of randomness. Here, a two-parameter Weibull model is used to generate simulated wind speed data. The expression of its probability density function is:

In the formula: k and c are two parameters of Weibull distribution, k is called the shape parameter, k>0, c is the scale parameter, c>1.

2) Calculation model of photovoltaic array output power

The output power model P _PV of the photovoltaic array whose quantity is N _PV is calculated as follows:

Among them, G and G _max are the actual light intensity and the maximum light intensity in a certain period of time respectively; P _p (G) is the output power of a single photovoltaic module, is the light intensity probability density function, which is calculated as follows:

Among them, P _STC represents the maximum test power under standard test conditions, G is the actual light intensity; k is the power temperature coefficient; T _a is the ambient temperature, and T _NOC is the rated operating temperature of the module.

Among them, α and β are the shape parameters of the Beta distribution.

3) Battery model

State of charge (SOC), terminal voltage and life cycle are several important parameters of battery management, and their calculation models are as follows:

The SOC of the battery state of charge is a parameter that reflects the proportion of the remaining power of the battery to its total capacity. The SOC of the battery at two times before and after can be expressed as the following relationship:

SOC(t+1)＝SOC(t)(1-δ(t))+I _BAT (t).Δt.η(t)/C _BAT (43)

In the formula: I _BAT (t) is the charging and discharging current at time t, which is positive when charging and negative when discharging; P _BAT (t) is the charging and discharging power of the battery, which is positive when charging and negative when discharging; V _BAT (t) is the battery terminal voltage; δ is the natural discharge rate; Δt is the time interval between two moments before and after; C _BAT is the on-time capacity of the battery; Related, the calculation model is as follows:

The terminal voltage of the battery can be expressed by its open circuit voltage and the internal resistance voltage drop on its internal resistance due to the charge and discharge current, and the calculation formula is as follows:

V _BAT (t) = E _oc (t) + I _BAT (t) R _BAT (t) (46)

Eoc(t)＝VF+ _b.log (SOC(t)) (47)

R _BAT (t)＝R _electrode (t)+R _electrlyte (t) (48)

R _electrode (t)＝r ₁ +r ₂ .SOC(t) (49)

R _electrolyte (t)＝[r ₃ +r ₄ .SOC(t)] ^-1 (50)

In the formula: E _oc (t) is the open circuit voltage of the battery; I _BAT (t) is the battery current (a value greater than 0 indicates charging, and a value less than 0 indicates discharging); R _BAT (t) is the internal resistance of the battery, including electrolyte The resistance R _electrode (t) and the electrolyte resistance R _electrolyte (t) are two parts; b, r ₁ , r ₂ , r ₃ , r ₄ are empirical coefficients with different values in charging and discharging modes:

The battery life damage period T _BAT is calculated as follows:

n _S ＝T/Δt represents the total number of simulation periods, SOC _1,t and SOC _2,t are the SOC values at the beginning and end of the tth simulation period respectively, S _BAT,t represents the actual charge of the battery in the tth simulation period discharge state,

D _OD,t represents the battery discharge depth detected at the end of the t-th simulation period, D _OD,t =1−SOC _2,t .

The reset period Y _B of the battery is calculated as follows:

T _BR ＝min{T _BAT ,T _FL } (53)

Among them, T _FL represents the float life reference value provided by the storage battery manufacturer.

During the operation of the system, the battery is affected by the limited range of its state of charge (SOC _min ≤ SOC ≤ SOC _max ) and the technical limitations of the battery itself, and its maximum charge and discharge 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 the formula: SOC _max and SOC _min are the upper and lower limits of the battery state of charge respectively; C _BAT is the battery capacity; V _BAT (t) is the battery terminal voltage; Δt is the unit simulation time interval; P _cm (t), P _dm ( t) are the maximum chargeable current and maximum discharge current of the battery in t simulation periods respectively; I _cm , I _dm are the maximum charge current and maximum discharge current allowed by the battery respectively, and the maximum charge and discharge current per unit time is the rated safety of the battery 20% of capacity, i.e.,

I _cm =I _dm =0.2C _BAT /Δt (56)

Using the present invention to optimize the design of the 150kW wind-storage-storage complementary independent micro-grid system in Wenzhou area, the results show that: the present invention can realize the high-dimensional and multi-objective optimal configuration effect of the wind-storage-storage complementary independent micro-grid, under the condition of meeting the same power supply reliability index The optimal configuration scheme investment saves at least 20% more than traditional single-objective optimization methods (such as genetic algorithm, particle swarm algorithm) and traditional multi-objective optimization methods (such as NSGA-II algorithm), and the present invention only has MNUM variation coefficient and iteration optimization times The two parameters need to be adjusted, and there is no need to adjust the weight coefficient of the complex objective function. The optimization method does not require operations such as population crossover. The implementation is simpler, and the calculation time of the entire method is shorter.

To sum up, the present invention can realize the high-dimensional multi-objective optimal configuration effect of wind-storage-storage complementary independent microgrid, and has the following advantages that traditional single-objective optimization methods and traditional multi-objective optimization methods do not possess: provide microgrid design planners with The optimal configuration scheme is more reasonable, and the optimal configuration scheme under the condition of meeting the same power supply reliability index has less investment, the optimization method is simple to implement, no complex objective function weight coefficient adjustment is required, no complicated optimization parameter adjustment is required, and the optimization efficiency is higher. high.

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.