CN103077429A - Capacity-optimizing method of isolated micro-electrical network containing wind-solar electricity-generating and electric-automobile electricity-transforming station - Google Patents

Abstract

The invention discloses a capacity-optimizing method of an isolated micro-electrical network containing a wind-solar electricity-generating and electric-automobile electricity-transforming station in the technical field for the optimization of an electric-power system. The invention adopts the technical scheme that the method comprises the following steps of: firstly selecting a reasonable system structure, establishing the model of each part in an integrated system, then establishing an objective function by using the minimum total cost as an object, determining corresponding constraint conditions, and finally solving through an intelligent optimization algorithm to obtain the optimal capacity configuration of the system. In an optimized capacity-calculating method disclosed by the invention, the electricity-consuming requirement of an electric automobile and the possessed energy-storing capability of an electricity-transforming mode are considered, and the constraint conditions of a charging-discharging machine and a power battery are added; iterative solution is carried out through the intelligent optimization algorithm, the thought is clear and rigorous, the method is reasonable and reliable, and the problem of optimized capacity configuration of the isolated micro-electrical network containing the wind-solar supplementary electricity-generating and electric-automobile electricity-transforming station can be effectively solved.

Description

The isolated little net capacity optimization method that contains wind light generation and electric automobile charging station

Technical field

The invention belongs to the Optimal Technology of Power Systems field, relate in particular to a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station.

Background technology

Wind-photovoltaic complementary power supply system is a kind of important application form of regenerative resource power supply system, has obtained at present broad research and Demonstration Application.Although wind, light primary energy are in the time and seasonal natural complementarity is arranged, owing to have time variation, intermittence and be difficult to the inherent characteristics such as prediction, still need accumulator system cooperation guarantee power supply reliability.On the other hand, Development of EV generally is asserted the important channel that ensures energy security and make the transition low-carbon economy by countries in the world, yet according to present achievement in research, if electric automobile is adopted fully the charge mode of access electrical network, by the current primary energy structure of China's electric system, the carbon emission of electric automobile is lower unlike fuel-engined vehicle.

According to current development, the energy structure that truly change electrical network is very difficult, realize the zero-emission of electric automobile, realizes the integrated application of electric automobile and regenerative resource by little electrical network mode, is the most direct mode.(such as the island power supply) if growth requirement and the wind-photovoltaic complementary power supply system of electric automobile can be combined, can remedy deficiency separately under specific application scenarios.On the one hand, the electrokinetic cell of electric automobile can be used as energy storage, improves the power supply reliability of wind-light complementary system; On the other hand, can improve again the clean energy resource utilization factor of electric automobile, effectively reduce the carbon emission amount.

The outer achievement in research of Present Domestic has been proved the feasibility of the method substantially from technology and economic aspect.But under the prerequisite of considering charging demand, load level and wind, light generating capacity, how this type systematic being planned, determined rational capacity ratio, is present open question still.

Summary of the invention

In the problem of considering not have under charging demand, load level and the prerequisites such as wind, light generating capacity the rational capacity proportioning, the present invention proposes a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station at present isolated little electrical network.

A kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station is characterized in that described method comprises step:

Step 1: adopt honourable modeling method to set up the output model of blower fan and photovoltaic cell; And calculate the blower fan generated energy according to the output model of blower fan; Calculate the generated energy of photovoltaic cell according to the output model of photovoltaic cell;

Step 2: set up electrokinetic cell electric weight exchange model and electric automobile and change electric demand model; Obtain the electric weight that power battery pack is stored constantly at t according to electrokinetic cell electric weight exchange model; Change electric demand model according to electric automobile and obtain constantly swap out total electric weight of electrokinetic cell of charging station of t;

Step 3: obtain the reliability model of system, i.e. system's expected loss of energy according to the result who obtains in step 1 and the step 2; And according to being worth in the year of step system expected loss of energy computing system electric quantity loss cost;

Step 4: the year value of calculating respectively blower fan, photovoltaic generating system, electrokinetic cell and charge-discharge machine cost; And the year value of system's electric quantity loss cost of obtaining of integrating step 3 is set up the objective function of system;

Step 5: determine to comprise electrokinetic cell electric weight, each component count, the constraint condition at electrokinetic cell quantity, charge-discharge machine power and the photovoltaic cell angle of inclination of the electricity service of changing can be provided;

Step 6: the aims of systems function in the simultaneous step 4 and the constraint condition in the step 5, the capacity of obtaining is distributed equation rationally; Select a kind of intelligent optimization algorithm, and determine the concrete steps found the solution; The result that the capacity of obtaining is distributed rationally;

Step 7: according to the result that capacity is distributed rationally, carry out capacity and distribute rationally.

The output model of described blower fan and blower fan generated energy are respectively:

The output characteristics equation P of the output model of described blower fan _W(v) with piecewise function its actual output characteristic curve is carried out match,

$P_W\left(v\right)=\left\{\begin{array}{cc}0& (v<v_c)\\ a_1\&CenterDot;v^2+b_1\&CenterDot;v+c_1& (v_c\&le;v\&le;v_1)\\ a_2\&CenterDot;v^2+b_2\&CenterDot;v+c_2& (v_1\&le;v\&le;v_r)\\ P_r& (v_r\&le;v\&le;v_f)\\ 0& (v>v_f)\end{array}\right.---\left(1\right)$

Wherein, v represents the wind speed of fan shaft At The Height; v _c, v _r, v _fRepresent respectively startup wind speed, the wind rating of blower fan and cut off wind speed; v _c≤ v ₁≤ v _rP _rThe rated power of expression blower fan; a ₁, b ₁, c ₁, a ₂, b ₂, c ₂Be corresponding fitting coefficient;

So be carved into the computing formula of the t moment blower fan generated energy during t-1 be: E _W(t)=P _W(t) * Δ t

Wherein, P _WBe carved into t generated output constantly when (t) representing blower fan t-1; Δ t=1.

The output model of described photovoltaic cell and photovoltaic cell capable of generating power amount are:

The output model of photovoltaic cell:

At the output P that calculates photovoltaic cell _PV(t) time, consider radiation, temperature and photovoltaic cell angle of inclination to the impact of power stage, its computing method are as follows:

The computing method of angle of incidence of sunlight i are:

cosi=(sinφcosθ-cosφsinθcosγ)sinδ+

(cosφcosθ+sinφsinθcosγ)cosδcosw+sinθsinγcosδsinw (2)

Wherein, δ is the declination angle; W is solar hour angle; θ is the pitch angle on plane; γ is the position angle on plane, and clinoplane is zero during towards the due south, during towards east for negative, during towards the west for just.

The general data that obtains from the weather station is total solar radiation amount H on the surface level and the amount of scatter radiation H on the surface level _dSo, the straight radiant quantity H of surface level _bCan try to achieve by formula (3):

H _b=H-H _d (3)

And that will use when calculating the photovoltaic cell capable of generating power amount is total solar radiation amount H on the dip plane _θ:

H _θ=H _bθ+H _dθ+H _rθ (4)

Wherein, H _{B θ}Represent the direct solar radiation amount on the dip plane; H _{D θ}Represent the sky radiation amount on the dip plane; H _{R θ}Represent the ground return radiant quantity on the dip plane.Being calculated as follows of they:

$H_{\mathrm{b\&theta;}}=H_b\frac{\cos i}{\cos {\mathrm{\&theta;}}_z}---\left(5\right)$

$H_{\mathrm{d\&theta;}}=H_d\&CenterDot;[\frac{H_b}{H_0}\frac{\cos i}{\cos {\mathrm{\&theta;}}_z}+(1-\frac{H_b}{H_0})\left(\frac{1+\cos \mathrm{\&theta;}}{2}\right)]---\left(6\right)$

$H_{\mathrm{r\&theta;}}=H\&CenterDot;\left(\frac{1-\cos \mathrm{\&theta;}}{2}\right)\&CenterDot;\mathrm{\&rho;}---\left(7\right)$

Wherein, θ _zBe solar zenith angle; H ₀Represent the solar radiation quantity of atmospheric envelope outside level; ρ represents the reflectivity on atural object surface, generally gets 0.2 in engineering calculation, has snow-clad ground to get 0.7.

The best operating point electric current of PV assembly and voltage utilize formula (8)-(14) to calculate under the condition arbitrarily:

$I_{\mathrm{PV}}=I_{\mathrm{sc}}\&CenterDot;\{1-C_1[\exp \left(\frac{V_{\mathrm{PV}}-\mathrm{\&Delta;V}}{C_2\&CenterDot;V_{\mathrm{oc}}}\right)-1\left]\right\}+\mathrm{\&Delta;I}---\left(8\right)$

C ₁=(1-I _mp/I _sc)·exp[-V _mp/(C ₂·V _oc)] (9)

$C_2=\frac{V_{\mathrm{mp}}/V_{\mathrm{oc}}-1}{\mathrm{In}(1-I_{\mathrm{mp}}/I_{\mathrm{sc}})}---\left(10\right)$

$V_{\mathrm{PV}}=V_{\mathrm{mp}}\&CenterDot;[1+0.0539\&CenterDot;1g\left(\frac{H_{\mathrm{\&theta;}}}{H_t}\right)]+{\mathrm{\&beta;}}_o\&CenterDot;\mathrm{\&Delta;T}---\left(11\right)$

ΔV=V _PV-V _mp (12)

ΔT=T _A+0.02·H _θ-T _t (13)

$\mathrm{\&Delta;I}={\mathrm{\&alpha;}}_0\&CenterDot;\left(\frac{H_{\mathrm{\&theta;}}}{H_t}\right)\&CenterDot;\mathrm{\&Delta;T}+(\frac{H_{\mathrm{\&theta;}}}{H_t}-1)\&CenterDot;I_{\mathrm{sc}}---\left(14\right)$

Wherein, I _PVRepresent the best operating point electric current of photovoltaic cell under any condition; V _PVRepresent the best effort point voltage of photovoltaic cell under any condition; I _SCRepresent the short-circuit current of photovoltaic cell; V _OCRepresent the open-circuit voltage of photovoltaic cell; I _MpRepresent the maximum power point electric current of photovoltaic cell; V _MpRepresent the maximum power point voltage of photovoltaic cell; H _θRepresent the total solar radiation amount on the photovoltaic panel; H _TRepresent etalon optical power, get 1000W/m ²T _ARepresent environment temperature; T _tRepresent standard temperature, get 25 ℃.

The power that the PV square formation is per hour exported is:

P _PV=N _PVP·V _PVS·V _PV·I _PV·F _C·F _O (15)

Wherein, N _PVSThe serial number of expression PV assembly; N _PVPThe number in parallel of expression PV assembly; F _CAnd F _ORepresent respectively to be obtained by engineering practice by the factor that connects the introducing of loss and unknown losses;

The generated energy that is carved into the t moment photovoltaic cell during t-1 is: W _PV(t)=P _PV(t) * Δ t;

Wherein, P _PVBe carved into the constantly generated energy of photovoltaic cell of t during (t) for t-1; Δ t=1.

Electrokinetic cell electric weight exchange model is:

The electrokinetic cell electric weight exchange model of power mode is changed in employing: total electric weight of system's medium power electric battery is constantly to change, when the general power of wind light generation during greater than the load electric power, power battery pack is in charged state, and simultaneously some is full of that electric quantity consumption electrokinetic cell totally exchanges mutually in the electrokinetic cell of electricity and the electric automobile; When the general power of wind light generation during less than the load electric power, power battery pack is in discharge condition, needs that also a part is full of the electrokinetic cell of electricity and the electrokinetic cell in the electric automobile exchanges mutually.T constantly power battery pack its electric weight when charging and discharging uses respectively formula (16) and formula (17) to calculate:

E _b(t)=E _b(t-1)(1-σ)+[E _W(t)+E _PV(t)-E _L(t)/η _nv]·η _batt1-E _q(t) (16)

E _b(t)=E _b(t-1)(1-σ)-[E _L(t)/η _nv-E _W(t)-E _PV(t)]/η _batt2-E _q(t) (17)

Wherein, E _b(t) and E _b(t-1) represent that respectively power battery pack at t constantly and the electric weight constantly stored of t-1; η _Batt1, η _Batt2, η _InvThe efficient that represents respectively power battery charging, discharge and inverter energy conversion; E _LBe carved into the size of constantly load of t power consumption when (t) representing t-1; E _W(t) and E _PVBe carved into the generated energy of t moment blower fan and photovoltaic when (t) representing t-1 respectively; σ represents electrokinetic cell self-discharge rate hourly; E _q(t) expression t swap out total electric weight of electrokinetic cell of charging station constantly.

Electric automobile changes electric demand model: comprise the electric demand model of changing of Household electric motor car and electric bus.

For Household electric motor car, daily travel is approximately lognormal distribution, and its probability density function is:

$f_D\left(x\right)=\frac{1}{x{\mathrm{\&sigma;}}_D\sqrt{2\mathrm{\&pi;}}}\exp [-\frac{{(1\mathrm{nx}-{\mathrm{\&mu;}}_D)}^2}{2{\mathrm{\&mu;}}_D^2}]---\left(18\right)$

Wherein, σ _D=3.20, μ _D=0.88; X is operating range.The expectation that can obtain thus day operating range is:

$E\left(x\right)={\&Integral;}_0^{\&infin;}x{f}_D\left(x\right)\mathrm{dx}---\left(19\right)$

With reference to the technical merit of present electric automobile, suppose that per hundred km power consumption are fixed as 15kWh, there is N in this zone _PriElectric household automobile, then the electric weight demand of every day is E _Pri(t)=0.15N _PriE (x).Home vehicle one day change electric probability of demand distribute can think to the charging probability of demand distribute similar, and Normal Distribution N (13,6).

For electric bus, suppose that bus first time of departure is 6:00, be 22:00-23:00 the last time of departure, be 7:00-9:00 and 16:30-18:30 peak period, then can change electricity once at 13:00-16:00; For guaranteeing that bus does not change electricity when the second day morning peak, after operation on the same day finishes, at the interchangeable electricity of 22:00-23:00 once.Suppose that the per hundred km power consumption of electric bus are fixed as 110kWh, if electric bus quantity is N _Pub, then one day the electric aggregate demand of changing is 1.1 * 200 * N _Pub=220N _PubThe operation of considering bus is in sequence, can adopt even distribution in the time period changing electricity, and then the demand model of electric bus is:

The electric weight that changes according to home vehicle and electric bus obtains swap out total electric weight of electrokinetic cell of electrical changing station and is:

E _q(t)＝E _pub(t)+E _pri(t)； (21)

Wherein, E _q(t) be t swap out total electric weight of electrokinetic cell of electrical changing station constantly; E _Pub(t) be electric bus t electric weight demand constantly; E _Pri(t) be home vehicle t electric weight demand constantly, according to the electric weight demand of home vehicle every day with change electric probability of demand and distribute and obtain.

System reliability model is: consider the economy of system, adopt expected loss of energy (EENS) to describe.Year is divided into 8760 equal time periods by the hour, the demand of supposing wind speed, light intensity, load and electric automobile within each time period is constant, obtain wind, luminous power curve of output and the load curve in 1 year by actual measurement or simulation, obtain to change electric demand curve by the electric demand model of changing of electric automobile, then carry out Calculation of Reliability following (its value was 0 when the etching system electric weight was more than or equal to the demand electric weight when t):

$\mathrm{EENS}=\underset{t=1}{\overset{8760}{\mathrm{\&Sigma;}}}(E_L\left(t\right)+E_q\left(t\right)-E_{\mathrm{PV}}\left(t\right)-E_W\left(t\right)-(E_b(t-1)-E_{b\min }))---\left(22\right)$

Wherein, E _BminThe minimum of expression electrokinetic cell allows capacity; E _LBe carved into the size of constantly load of t power consumption when (t) representing t-1; E _q(t) expression t swap out total electric weight of electrokinetic cell of charging station constantly; E _W(t) and E _PVBe carved into the generated energy of t moment blower fan and photovoltaic when (t) representing t-1 respectively; E _b(t-1) represent the electric weight that power battery pack is stored constantly at t-1; E _BminMinimum permission capacity for electrokinetic cell.

The computing formula of year value of described system electric quantity loss cost is:

C _r=coe*EENS (23)

Wherein, coe represents to compensate coefficient; EENS is system's expected loss of energy.

The year value of described blower fan, photovoltaic generating system, electrokinetic cell and charge-discharge machine cost, computing method are respectively:

The year value of blower fan cost is:

$C_w=N_w\&CenterDot;(C_i\&CenterDot;\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(i\right))---\left(24\right)$

Wherein, N _wQuantity for blower fan; C _iUnit price for blower fan; M is the period of depreciation of equipment; r ₀Be rate of discount; U (i) safeguards and operating cost in the year of blower fan; I represents blower fan;

The year value of photovoltaic generating system cost is:

$C_{\mathrm{pv}}=N_{\mathrm{pv}}\&CenterDot;(C_j\&CenterDot;\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(j\right))---\left(25\right)$

Wherein, N _PvQuantity for photovoltaic cell; C _jUnit price for photovoltaic cell; M is the period of depreciation of equipment; r ₀Be rate of discount; U (j) safeguards and operating cost in the year of photovoltaic generating system; J represents photovoltaic generating system;

The year value of electrokinetic cell cost is:

$C_b=N_b\&CenterDot;(C_k\&CenterDot;\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(k\right))---\left(26\right)$

Wherein, N _bQuantity for electrokinetic cell; C _kUnit price for electrokinetic cell; M is the period of depreciation of equipment; r ₀Be rate of discount; U (k) safeguards and operating cost in the year of electrokinetic cell; K represents electrokinetic cell;

The year value of charge-discharge machine cost is:

$C_c=N_c\&CenterDot;(C_l\&CenterDot;\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(l\right))---\left(27\right)$

Wherein, N _cQuantity for charge-discharge machine; C _lUnit price for charge-discharge machine; M is the period of depreciation of equipment; r ₀Be rate of discount; U (l) safeguards and operating cost in the year of charge-discharge machine; L represents charge-discharge machine.

The objective function of system is: optimization aim is in the situation that satisfies charging and workload demand, and the integrated cost minimums such as reliability are invested, moved and take into account to system's primary equipment, and its objective function is:

min C _t=min(C _w+C _pv+C _b+C _c+C _r) (28)

Wherein, C _tBe the system synthesis basis; C _w, C _Pv, C _b, C _c, C _rThe year value that represents respectively blower fan, photovoltaic generating system, electrokinetic cell, charge-discharge machine and system's electric quantity loss cost.

The Constraint condition of electrokinetic cell is:

E _bmin≤E _b(t)≤E _bmax (29)

Wherein, E _BmaxBe the maximum permission capacity of electrokinetic cell, the rated capacity of generally getting electrokinetic cell; E _BminMinimum permission capacity for electrokinetic cell is determined by maximum depth of discharge.

The constraint condition of blower fan, electrokinetic cell, photovoltaic cell and charge-discharge machine quantity:

0≤N _pv≤N _pvmax

0≤N _w≤N _wmax (30)

0≤N _b≤N _bmax

0≤N _c≤N _cmax

Wherein, N _Pvmax, N _Wmax, N _Bmax, N _CmaxBe respectively the number of the photovoltaic that satisfies user charging and workload demand, blower fan, electrokinetic cell, charge-discharge machine.

T constantly can be electric automobile the electrokinetic cell quantity constraint condition of changing the electricity service is provided:

N _ba(t)≥N _b(t)t∈[1,T] (31)

Wherein, N _Ba(t) be that t can be the electrokinetic cell quantity that electric automobile changes electricity constantly; N _b(t) be the electrokinetic cell quantity that t moment electric automobile need to be changed; T=8760 represents the hourage in 1 year.Suppose that power battery charging needs 4 hours, then N _Ba(t) but through type (32) draws and N _b(t) can change electric probability of demand distribution by electric automobile calculates with electric automobile quantity.

$N_{\mathrm{ba}}\left(t\right)=\left\{\begin{array}{cc}0& t\&le;0\\ N_b-N_b(t-1)-N_b(t-2)-N_b(t-3)& t\&Element;[1,T]\end{array}\right.---\left(32\right)$

Wherein, N _b(t-1), N _b(t-2) and N _b(t-3) be respectively the electrokinetic cell quantity that the t-1 moment, the t-2 moment and t-3 moment electric automobile need to be changed.

Charge-discharge machine power constraint condition:

P(t)≤P _c≤P _cmaxt∈[1,T] (33)

Wherein, P (t) for charge and discharge electric weight according to system and obtain any time charge-discharge machine power; P _CmaxBe the charge-discharge machine peak power, the power when namely maximum available is all emitted by charge-discharge machine in a period of time in the electric battery, its computing method are seen respectively formula (33)-(34):

$P_{c\max }=\frac{E_{b\max }-E_{b\min }}{\mathrm{\&Delta;t}}---\left(35\right)$

Wherein, Δ t=1.If P (t) is greater than upper limit P _Cmax, P (t)=P then _Cmax

The constraint at photovoltaic cell angle of inclination: 0＜θ＜90 °.

Described intelligent optimization algorithm comprises differential evolution algorithm, genetic algorithm or particle cluster algorithm.

The capacity optimized calculation method that the present invention proposes, consider electric automobile electricity consumption demand and changed the energy storage capacity that power mode possesses, comprehensive minimum as target take the not enough loss of system's cost of investment, operating cost and electric weight cost, and considered the constraint condition of honourable system, charge-discharge machine and electrokinetic cell, use the intelligent optimization algorithm iterative, clear thinking is rigorous, method is rationally reliable, can effectively solve the isolated little net capacity optimization allocation that contains wind light mutual complementing power generation and electric automobile charging station.

Description of drawings

Fig. 1 is the process flow diagram that contains isolated little net capacity Optimal Configuration Method of wind light mutual complementing power generation and electric automobile charging station;

Fig. 2 is the isolated little configuration of power network that contains wind light mutual complementing power generation and electric automobile charging station;

Fig. 3 is 1 year 8760 hours air speed data;

Fig. 4 is surface level sun scattered quantum and total radiation daily variation diagram in each in season;

Fig. 5 is 1 year 8760 hours load data;

Fig. 6 is that electric automobile per hour changes electric demand figure in one day;

Fig. 7 is the evolution iterative process that Differential Evolution Algorithm for Solving integrated system capacity is distributed rationally.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that following explanation only is exemplary, rather than in order to limit the scope of the invention and to use.

Fig. 1 is the process flow diagram that contains isolated little net capacity Optimal Configuration Method of wind light mutual complementing power generation and electric automobile charging station, and the basic step of finding the solution the optimum capacity configuration of integrated system is:

Step 1: isolated little electric network composition of selecting reasonably to contain wind light mutual complementing power generation and electric automobile charging station.Fig. 2 is the isolated little configuration of power network that contains wind light mutual complementing power generation and electric automobile charging station, adopts the mode of direct current microgrid, comprises wind light mutual complementing power generation module, energy-storage module, load output module and control module; Wherein energy-storage module is comprised of electrokinetic cell, namely satisfies the demand of travelling, and does again energy storage and uses; Control module links together various current transformers and supervising device by computer network, realize the reasonable distribution of power and electric weight.

Step 2: the data such as wind speed, illumination, load and main equipment parameters of collecting a year;

Fig. 3 is 1 year 8760 hours air speed data.

Fig. 4 is surface level sun scattered quantum and total radiation daily variation diagram in each in season.

Fig. 5 is 1 year 8760 hours load data.

Table 1 is the correlation parameter of major equipment in the system.

The correlation parameter of major equipment in table 1 system

Step 3: adopt typical honourable modeling method to set up the output model of scene;

Step 4: set up electrokinetic cell electric weight exchange model, electric automobile changes electric demand model and system reliability model;

Fig. 6 is that electric automobile per hour changes electric demand figure in one day, changes electric demand model by home vehicle and electric bus and obtains, and wherein home vehicle is 500,10 of electric bus.

Step 5: take the investment of system primary equipment, move and take into account the integrated cost minimum such as reliability is set up system as target objective function;

Step 6: determine to comprise power battery charged amount, each component count, can provide and change electric electrokinetic cell quantity of serving and the constraint condition of charge-discharge machine power;

Step 7: the concrete steps of selecting a kind of intelligent optimization algorithm and determining to find the solution.Select differential evolution algorithm, and with matlab software the capacity Optimal Allocation Model is programmed, concrete solution procedure is:

1) algorithm parameter setting.Population quantity N is set _p=30, termination of iterations number of times C=100, mutagenic factor F=0.5, hybridization factor C _R=0.4.

2) initialization of population.In the variation range of decision variable, generate at random N _pIndividual solution, wherein the upper and lower limit of decision variable is determined according to actual conditions: the upper limit of blower fan, photovoltaic cell, electrokinetic cell, charge-discharge machine quantity is respectively 10,2000,500,300, and lower limit all gets zero.

3) make a variation and interlace operation, generate progeny population.

4) progeny population substitution constraint equation is checked that what do not satisfy condition processes by following formula:

$x_{i,j}=\left\{\begin{array}{cc}{x}_j^{\max }& \mathrm{if}(x_{i,j}>x_j^{\max })\\ x_j^{\min }& \mathrm{if}(x_{i,j}<x_j^{\min })\end{array}\right.$

Wherein,

With

Be respectively the upper and lower bound of decision variable j.

5) calculate the adaptive value of parent population and progeny population, namely system synthesis this, then select operation, keep the little individuality of adaptive value, and record current optimized individual and corresponding adaptive value.

6) repeat 3) ~ 5) until satisfy the termination of iterations condition.

Step 8: carry out the calculating that capacity is distributed rationally.Fig. 7 is the evolution iterative process that Differential Evolution Algorithm for Solving integrated system capacity is distributed rationally, substantially reaches optimal value in the time of can seeing for 30 generation, and optimal speed is very fast.The capacity of trying to achieve is distributed rationally and be the results are shown in Table 2.

Table 2 capacity is distributed the result rationally

Required parameter	Parameter value	Rated capacity
			Optimum incline angle	22.92°	/
The blower fan number	6	9MW

The photovoltaic cell number	1218	3.9MW
			The electrokinetic cell number	186	4.46MWh
The charge-discharge machine number of modules	90	0.9MW
			Minimum cost	1.7036×10 7Unit	/

The capacity optimized calculation method that the present invention proposes, consider electric automobile electricity consumption demand and changed the energy storage capacity that power mode possesses, comprehensive minimum as target take the not enough loss of system's cost of investment, operating cost and electric weight cost, and considered the constraint condition of honourable system, charge-discharge machine and electrokinetic cell, use the intelligent optimization algorithm iterative, clear thinking is rigorous, method is rationally reliable, can effectively solve the isolated little net capacity optimization allocation that contains wind light mutual complementing power generation and electric automobile charging station.

The above; only for the better embodiment of the present invention, but protection scope of the present invention is not limited to this, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.

Claims (10)

1. isolated little net capacity optimization method that contains wind light generation and electric automobile charging station is characterized in that described method comprises step:

Step 1: adopt honourable modeling method to set up the output model of blower fan and photovoltaic cell; And calculate the blower fan generated energy according to the output model of blower fan; Calculate the generated energy of photovoltaic cell according to the output model of photovoltaic cell;

Step 2: set up electrokinetic cell electric weight exchange model and electric automobile and change electric demand model; Obtain the electric weight that power battery pack is stored constantly at t according to electrokinetic cell electric weight exchange model; Change electric demand model according to electric automobile and obtain constantly swap out total electric weight of electrokinetic cell of charging station of t;

Step 3: obtain the reliability model of system, i.e. system's expected loss of energy according to the result who obtains in step 1 and the step 2; And according to being worth in the year of step system expected loss of energy computing system electric quantity loss cost;

Step 4: the year value of calculating respectively blower fan, photovoltaic generating system, electrokinetic cell and charge-discharge machine cost; And the year value of system's electric quantity loss cost of obtaining of integrating step 3 is set up the objective function of system;

Step 5: determine to comprise electrokinetic cell electric weight, each component count, the constraint condition at electrokinetic cell quantity, charge-discharge machine power and the photovoltaic cell angle of inclination of the electricity service of changing can be provided;

Step 6: the aims of systems function in the simultaneous step 4 and the constraint condition in the step 5, the capacity of obtaining is distributed equation rationally; Select a kind of intelligent optimization algorithm, and determine the concrete steps found the solution; The result that the capacity of obtaining is distributed rationally;

Step 7: according to the result that capacity is distributed rationally, carry out capacity and distribute rationally.

2. a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station according to claim 1, it is characterized in that the computing formula of the generated energy of the output model of the output model of blower fan, blower fan generated energy, photovoltaic cell and photovoltaic cell is in the described step 1:

The blower fan output model is:

$P_W\left(v\right)=\left\{\begin{array}{cc}0& (v<v_c)\\ a_1\&CenterDot;v^2+b_1\&CenterDot;v+c_1& (v_c\&le;v\&le;v_1)\\ a_2\&CenterDot;v^2+b_2\&CenterDot;v+c_2& (v_1\&le;v\&le;v_r)\\ P_r& (v_r\&le;v\&le;v_f)\\ 0& (v>v_f)\end{array}\right.$

Wherein, v represents the wind speed of fan shaft At The Height; v _c, v _r, v _fRepresent respectively startup wind speed, the wind rating of blower fan and cut off wind speed; v _c≤ v ₁≤ v _rP _rThe rated power of expression blower fan; a ₁, b ₁, c ₁, a ₂, b ₂, c ₂Be corresponding fitting coefficient;

The computing formula that is carved into the t moment blower fan generated energy during t-1 is: E _W(t)=P _W(t) * Δ t;

Wherein, P _WBe carved into t generated output constantly when (t) representing blower fan t-1; Δ t=1;

The output model of photovoltaic cell is:

P _PV=N _PVP·N _PVS·V _PV·I _PV·F _C·F _O

Wherein, N _PVSThe serial number of expression PV assembly; N _PVPThe number in parallel of expression PV assembly; F _CAnd F _oRepresent respectively by the factor that connects the introducing of loss and unknown losses; I _PVRepresent the best operating point electric current of photovoltaic cell under any condition; V _PVRepresent the best effort point voltage of photovoltaic cell under any condition; P _PVThe power of per hour exporting for the PV square formation;

The computing formula that is carved into the generated energy of the t moment photovoltaic cell during t-1 is: E _PV(t)=P _PV(t) * Δ t;

Wherein, P _PVBe carved into the constantly generated energy of photovoltaic cell of t during (t) for t-1; Δ t=1.

3. a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station according to claim 1 is characterized in that, described step 2 medium power battery electric quantity exchange model is:

T constantly power battery pack at the electric weight of charging is:

E _b(t)=E _b(t-1)(1-σ)+[E _W(t)+E _PV(t)-E _L(t)/η _inv]·η _batt1-E _q(t)

T is the electric weight of power battery pack when discharge constantly:

E _b(t)=E _b(t-1)(1-σ)-[E _L(t)/η _inv-E _W(t)-E _PV(t)]/η _batt2-E _q(t)

Wherein, E _b(t) and E _b(t-1) represent that respectively power battery pack at t constantly and the electric weight constantly stored of t-1; η _Batt1, η _Batt2, η _InvThe efficient that represents respectively power battery charging, discharge and inverter energy conversion; E _LBe carved into the size of constantly load of t power consumption when (t) representing t-1; E _W(t) and W _PVBe carved into the generated energy of t moment blower fan and photovoltaic when (t) representing t-1 respectively; σ represents electrokinetic cell self-discharge rate hourly; E _q(t) expression t swap out total electric weight of electrokinetic cell of charging station constantly.

4. a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station according to claim 1 is characterized in that, electric automobile changes electric demand model and is in the described step 2:

Electric automobile comprises Household electric motor car and electric bus:

It is similar to the distribution of charging probability of demand that Household electric motor car changes electric probability of demand distribution, and Normal Distribution N (13,6); The electric weight demand of Household electric motor car every day is E _Pri(t)=0.15N _PriE (x); Wherein,

Expectation value for the day operating range; N _PriQuantity for this regional electric household automobile; f _D(x) be the probability density function of Household electric motor car; X is the day operating range of Household electric motor car;

The electric demand model that changes of electric bus is:

Wherein, N _PubBe electric bus quantity.

5. a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station according to claim 4 is characterized in that, t constantly swap out total electric weight of electrokinetic cell of charging station is:

E _q(t)＝E _pub(t)+E _pri(t)；

Wherein, E _q(t) be t swap out total electric weight of electrokinetic cell of electrical changing station constantly; E _Pub(t) be electric bus t electric weight demand constantly; E _Pri(t) be home vehicle t electric weight demand constantly, according to the electric weight demand of home vehicle every day with change electric probability of demand and distribute and obtain.

6. a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station according to claim 1 is characterized in that the reliability model of system is in the described step 3:

$\mathrm{EENS}=\underset{t=1}{\overset{8760}{\mathrm{\&Sigma;}}}(E_L\left(t\right)+E_q\left(t\right)-E_{\mathrm{PV}}\left(t\right)-E_W\left(t\right)-(E_b(t-1)-E_{b\min }))$

Wherein, EENS is system's expected loss of energy; E _BminThe minimum of expression electrokinetic cell allows capacity; E _LBe carved into the size of constantly load of t power consumption when (t) representing t-1; E _q(t) expression t swap out total electric weight of electrokinetic cell of charging station constantly; E _W(t) and E _PVBe carved into the generated energy of t moment blower fan and photovoltaic when (t) representing t-1 respectively; E _b(t-1) represent the electric weight that power battery pack is stored constantly at t-1; E _BminMinimum permission capacity for electrokinetic cell;

The computing formula of year value of system's electric quantity loss cost is:

C _r=coe*EENS

Wherein, coe represents to compensate coefficient; EENS is system's expected loss of energy.

7. a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station according to claim 1 is characterized in that, the year value of described blower fan, photovoltaic generating system, electrokinetic cell and charge-discharge machine cost, and computing method are respectively:

The year value of blower fan cost is:

$C_w=N_w\&CenterDot;(C_i\&CenterDot;\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(i\right))$

Wherein, N _wQuantity for blower fan; C _iUnit price for blower fan; M is the period of depreciation of equipment; r ₀Be rate of discount; U (i) safeguards and operating cost in the year of blower fan;

The year value of photovoltaic generating system cost is:

$C_{\mathrm{pv}}=N_{\mathrm{pv}}\&CenterDot;(C_j\&CenterDot;\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(j\right))$

Wherein, N _PvQuantity for photovoltaic cell; C _jUnit price for photovoltaic cell; M is the period of depreciation of equipment; r ₀Be rate of discount; U (j) safeguards and operating cost in the year of photovoltaic generating system;

The year value of electrokinetic cell cost is:

$C_b=N_b\&CenterDot;(C_k\&CenterDot;\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(k\right))$

Wherein, N _bQuantity for electrokinetic cell; C _kUnit price for electrokinetic cell; M is the period of depreciation of equipment; r ₀Be rate of discount; U (k) safeguards and operating cost in the year of electrokinetic cell;

The year value of charge-discharge machine cost is:

$C_c=N_c\&CenterDot;(C_l\&CenterDot;\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(l\right))$

Wherein, N _cQuantity for charge-discharge machine; C _lUnit price for charge-discharge machine; M is the period of depreciation of equipment; r ₀Be rate of discount; U (l) safeguards and operating cost in the year of charge-discharge machine.

8. a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station according to claim 1 is characterized in that the objective function of system is in the described step 4:

min C _t=min(C _w+C _pv+C _b+C _c+C _r)

Wherein, C _tBe the system synthesis basis; C _w, C _Pv, C _b, C _c, C _rThe year value that represents respectively blower fan, photovoltaic generating system, electrokinetic cell, charge-discharge machine and system's electric quantity loss cost.

9. a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station according to claim 1, it is characterized in that, the electric weight of described electrokinetic cell, each component count, can provide the constraint condition at electrokinetic cell quantity, charge-discharge machine power and the photovoltaic cell angle of inclination of the electricity service of changing to be respectively:

The Constraint condition of electrokinetic cell is:

E _bmin≤E _b(t)≤E _bmax

Wherein, E _BmaxBe the maximum permission capacity of electrokinetic cell, the rated capacity of generally getting electrokinetic cell; E _BminMinimum permission capacity for electrokinetic cell;

The constraint condition of blower fan, electrokinetic cell, photovoltaic cell and charge-discharge machine quantity:

0≤N _pv≤N _pvmax

0≤N _w≤N _wmax

0≤N _b≤N _bmax

0≤N _c≤N _cmax

Wherein, N _Pvmax, N _Wmax, N _Bmax, N _CmaxBe respectively the number of the photovoltaic that satisfies user charging and workload demand, blower fan, electrokinetic cell, charge-discharge machine;

T constantly can be electric automobile the electrokinetic cell quantity constraint condition of changing the electricity service is provided:

N _ba(t)≥N _b(t)t∈[1,T]

Wherein, N _Ba(t) be that t can be the electrokinetic cell quantity that electric automobile changes electricity constantly; N _b(t) be the electrokinetic cell quantity that t moment electric automobile need to be changed; T=8760 represents the hourage in 1 year;

Charge-discharge machine power constraint condition:

P(t)≤P _c≤P _cmaxt∈[1,T]；

Wherein, P (t) for charge and discharge electric weight according to system and obtain any time charge-discharge machine power; P _CmaxBe the charge-discharge machine peak power;

The constraint at photovoltaic cell angle of inclination: 0＜θ＜90 °.

10. a kind of isolated little net capacity optimization method that contains wind light generation and electric automobile charging station according to claim 1 is characterized in that described intelligent optimization algorithm comprises differential evolution algorithm, genetic algorithm or particle cluster algorithm.

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