CN103187784A - Method and device for optimizing photovoltaic charging station integrated system - Google Patents

Abstract

The invention belongs to the technical field of a smart power grid and discloses a method and device for optimizing a photovoltaic charging station integrated system. The method concretely comprises the steps of: firstly, determining a target function used for configuring hardware on the photovoltaic charging station integrated system, wherein the target function at least includes performance parameters for representing the system cost and system power; determining preset constraint conditions of the performance parameters, obtaining basic data of the photovoltaic charging station integrated system; then calculating the target function according to the basic data and the constraint condition, and obtaining at least two groups of configuration results; finally, choosing the target configuration result according to the preset service demand in the at least two groups of configuration results; and carrying out hardware configuration on the photovoltaic charging station integrated system according to the hardware configuration scheme corresponding to the target configuration result. Thus, corresponding optimal hardware configuration schemes of the photovoltaic charging station integrated system in different areas under different photovoltaic utilization rates can be obtained.

Description

A kind of method and device of optimizing photovoltaic charged station integrated system

Technical field

The invention belongs to the intelligent grid technical field, particularly a kind of method and device of optimizing photovoltaic charged station integrated system.

Background technology

Along with the broad development of electric automobile in countries in the world, the planning of charging infrastructure and more concerns that Construction Problems has obtained the Chinese government.At present, the primary energy of China's electric power system generating side is still based on coal (accounting for 75%～80%), electric automobile directly inserts grid charging by charging infrastructure, the actual indirect carbon emission amount that produces is lower unlike traditional fuel-engined vehicle, and is difficult to alleviate the dependence to traditional fossil fuel.In this case, realize low-carbon (LC) truly, have dual mode: the one, greatly develop renewable energy system, improve electrical network to the ability of dissolving of regenerative resource, increase the utilance of regenerative resource in the electrical network; The 2nd, directly set up electric automobile and discharge and recharge the related of facility and renewable energy system, realize the on-site elimination utilization of regenerative resource by little electrical network.From current development, the primary energy structure of adjusting electrical network is very difficult, and adopts little electrical network mode to realize electric automobile to the integrated utilization of renewable energy power generation, will become the most direct mode.

At present, the complementary benefit that photovoltaic generation and energy-storage system and electric automobile electrical changing station are integrated, under the certain prerequisite of cost of investment, the economic benefit that this integration mode brings is higher than utilizes photovoltaic generation to come the pumped storage mode, and, the integration mode of photovoltaic generation and charging electric vehicle can not bring extra power transmission and distribution pressure to the down town, and electrokinetic cell " energy storage device " as an alternative can effectively be alleviated the intermittence fluctuation of illumination power output, realizes the effect of doulbe-sides' victory.The schematic diagram of photovoltaic charged station integrated system is consulted shown in Figure 1, mainly comprise four parts: photovoltaic cell group, energy-storage battery group, network system and photovoltaic charged station parking stall system, and the photovoltaic cell group is made up of the solar panel series and parallel, the photovoltaic cell group absorbs solar energy and sends direct current, inserting charging system through DC/DC unsteady flow module, is the main power supply of charging electric vehicle in the station; The energy-storage battery group plays energy and stores and regulating action in system, namely when the energy output of photovoltaic cell is superfluous, store unnecessary electric energy; When the energy output of photovoltaic cell is not enough, by energy storage (or with exchanging distribution) to charging electric vehicle.

Wherein, photovoltaic cell group, energy-storage battery group and network system and photovoltaic charged station parking stall system all comprise many group DC/DC unsteady flow modules, wherein, DC/DC unsteady flow module, current transforming unit as photovoltaic cell group, energy-storage battery group and charging system for electric automobile, photovoltaic cell group and charging system for electric automobile use the DC/DC module of energy one-way flow, and the energy-storage battery group is used the DC/DC module of energy two-way flow.Network system comprises AC/DC unsteady flow module, and AC/DC unsteady flow module is as the linkage unit of AC distribution net and photovoltaic cell group.According to charging needs in the station, the alternating current that power distribution network is imported is converted to direct current access charging system.

But, prior art is for the integrated complementary benefit of the generating of photovoltaic cell group and energy-storage battery group and electric automobile electrical changing station, only set forth the advantage of this integration mode from the economic benefit of the network configuration of integration mode, integrated application, these three aspects of operational effect of integrated utilization, fail to solve the optimization allocation of each component units in the charging station that contains the photovoltaic cell group.

In order to take all factors into consideration economic benefit and environmental benefit, in the planning of photovoltaic charged station integrated system, to reduce as much as possible on the one hand and build and operating cost; On the other hand, the energy output that will improve the photovoltaic cell group as far as possible in the charging electric vehicle energy shared ratio (because the charging electric vehicle energy comes from network system on the one hand, come from the photovoltaic cell group on the one hand, if the charging electric vehicle energy comes from the less of network system, then come from the more of photovoltaic cell group), this is a multi-objective optimization question that waits to solve, but, at present a kind of photovoltaic charged station integrated system of definite zones of different is not proposed as yet under different photovoltaic utilances, the method of the hardware configuration of corresponding optimum, zones of different can't be determined best this regional allocation plan that is fit to.

Summary of the invention

The embodiment of the invention provides a kind of method of optimizing photovoltaic charged station integrated system, in order to realize determining that the photovoltaic charged station integrated system of zones of different is under different photovoltaic utilances, the method of the hardware configuration of corresponding optimum, and the best allocation plan in zone separately that is fit to can be determined according to default user demand in each zone.

A kind of method of optimizing photovoltaic charged station integrated system comprises:

Definite target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration, described target function comprises the performance parameter of characterization system cost and system power at least;

Determine the constraints of default described performance parameter, and the basic data of obtaining described photovoltaic charged station integrated system, this basic data is used to indicate the value of described performance parameter;

Calculate described target function according to described basic data and described constraints, obtain at least two assembly and put the result;

Put among the result in described at least two assembly, select the target configuration result according to default user demand, and according to described target configuration as a result the corresponding hardware allocation plan described photovoltaic charged station integrated system is carried out hardware configuration.

A kind of device of optimizing photovoltaic charged station integrated system comprises:

First determining unit is used for definite target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration, and described target function comprises the performance parameter of characterization system cost and system power at least;

Second determining unit, for the constraints of determining default described performance parameter, and the basic data of obtaining described photovoltaic charged station integrated system, this basic data is used to indicate the value of described performance parameter;

Computing unit is used for calculating described target function according to described basic data and described constraints, obtains at least two assembly and puts the result;

Dispensing unit is used for putting the result in described at least two assembly, selects the target configuration result according to default user demand, and according to described target configuration as a result the corresponding hardware allocation plan described photovoltaic charged station integrated system is carried out hardware configuration.

In the embodiment of the invention, the definite target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration of elder generation, target function comprises the performance parameter of characterization system cost and system power at least, determine the constraints of default performance parameter again, and obtain the basic data of photovoltaic charged station integrated system, wherein, this basic data is used to indicate the value of performance parameter, then, according to basic data and constraints calculating target function, obtain at least two assembly and put the result, at last, put among the result at least two assembly, select the target configuration result according to default user demand, and according to target configuration as a result the corresponding hardware allocation plan photovoltaic charged station integrated system is carried out hardware configuration, like this, just can obtain the photovoltaic charged station integrated system of zones of different under different photovoltaic utilances, the hardware configuration scheme of corresponding optimum, and the best allocation plan in zone separately that is fit to can be determined according to default user demand in each zone.

Description of drawings

Fig. 1 is the schematic diagram of photovoltaic charged station integrated system in the prior art;

Fig. 2 is the flow chart of optimizing the integrated system configuration of photovoltaic charged station in the embodiment of the invention;

Fig. 3 is the schematic flow sheet of NSGA-II calculating target function in the embodiment of the invention;

Fig. 4 is the schematic diagram of result of calculation in the embodiment of the invention;

Fig. 5 A is the detail flowchart of optimizing the integrated system configuration of photovoltaic charged station in the embodiment of the invention;

Fig. 5 B is the photometric data schematic diagram in 1 year in a-quadrant in the embodiment of the invention;

Fig. 5 C is the charge power demand of the electric automobile of average every day in the a-quadrant 1 year in the embodiment of the invention;

Fig. 6 is the illustrative view of functional configuration of inking device in the embodiment of the invention.

Embodiment

For the photovoltaic charged station integrated system of determining zones of different under different photovoltaic utilances, the method of the hardware configuration of corresponding optimum, and, zones of different can be determined the best allocation plan in zone separately that is fit to according to default user demand, in the embodiment of the invention, the definite target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration of elder generation, target function comprises the performance parameter of characterization system cost and system power at least, determine the constraints of default performance parameter again, and obtain the basic data of photovoltaic charged station integrated system, wherein, this basic data is used to indicate the value of performance parameter, then, according to basic data and constraints calculating target function, obtain at least two assembly and put the result, at last, put among the result at least two assembly, select the target configuration result according to default user demand, and according to target configuration as a result the corresponding hardware allocation plan photovoltaic charged station integrated system is carried out hardware configuration, like this, just can obtain the photovoltaic charged station integrated system of zones of different under different photovoltaic utilances, the hardware configuration scheme of corresponding optimum, and zones of different can be determined the best allocation plan in zone separately that is fit to according to default user demand.

Below in conjunction with accompanying drawing the preferred embodiment of the present invention is elaborated.

In embodiments of the present invention, concrete implementing procedure is:

Consult shown in Figure 2ly, in the embodiment of the invention, the detailed process of optimizing the configuration of photovoltaic charged station integrated system is as follows:

Step 200: definite target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration, target function comprises the performance parameter of characterization system cost and system power at least.

In the embodiment of the invention, structure based on photovoltaic charged station integrated system shown in Figure 1, the target function that foundation is carried out the required use of hardware configuration to photovoltaic charged station integrated system, under the situation of the charging demand of the electric automobile that satisfies predetermined number, the purpose of setting up target function has two aspects: (1) makes gross investment, the operating cost minimum of photovoltaic charged station integrated system; (2) make the REUR(Renewable Energy Utilization Ratio of photovoltaic charged station integrated system, the renewable energy utilization rate) maximum, therefore, the target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration calculates based on minimum cost function and maximum renewable energy utilization rate function at least, wherein, cost function adopts the performance parameter of characterization system cost of each component units correspondence of photovoltaic charged station integrated system to calculate at least, the REUR function adopts the performance parameter of characterization system power to calculate at least, and the performance parameter of characterization system power comprises the performance parameter that characterizes charging electric vehicle power at least and characterizes electric automobile from the performance parameter of electrical network power absorbed.

In the embodiment of the invention, the formula that calculates the cost function of photovoltaic charged station integrated system has multiple, for example:

C _Σ=(C _PV+ C _B+ C _DC1+ C _DC2+ C _DC3+ C _AD+ C _G) (formula one); Perhaps,

C _Σ=(C _PV+ C _B+ C _G) (formula two); Perhaps,

C _Σ=(C _PV+ C _B+ C _DC1+ C _DC2+ C _DC3+ C _AD) (formula three); Perhaps,

C _Σ=(C _DC1+ C _DC2+ C _DC3+ C _AD) (formula four),

Wherein, C _ΣTotal cost for photovoltaic charged station integrated system; C _PVBe the photovoltaic cell total cost that comprises in the integrated system of photovoltaic charged station, specifically calculate shown in formula five; C _BBe the energy-storage battery total cost that comprises in the integrated system of photovoltaic charged station, specifically calculate shown in formula six; C _DC1For the photovoltaic unsteady flow module assembly basis that comprises in the integrated system of photovoltaic charged station, specifically calculate shown in formula seven; C _DC2Be the charging module total cost that comprises in the integrated system of photovoltaic charged station, specifically calculate shown in formula eight; C _DC3For the energy storage unsteady flow module assembly basis that comprises in the integrated system of photovoltaic charged station, specifically calculate shown in formula nine; C _ADFor the grid-connected converter module assembly basis that comprises in the integrated system of photovoltaic charged station, specifically calculate shown in formula ten; C _GFor purchasing total electricity charge of electricity in the network system that from the integrated system of photovoltaic charged station, comprises, specifically calculate shown in formula 11, shown in formula five-formula 11 is following:

$C_{\mathrm{PV}}=N_{\mathrm{PV}}{C}_a\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m}+u\left(a\right)$ (formula five)

$C_B=N_B{C}_b(\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+d)+u\left(b\right)$ (formula six)

${\mathrm{<figure-callout\; id="1"\; label="C\; DC"\; filenames="HDA00002871536100031.png,HDA00002871536100041.png"\; state="\{\{state\}\}">C</figure-callout>}}_{\mathrm{DC}}=N_{\mathrm{DC}1}{C}_c\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(c\right)$ (formula seven)

${\mathrm{<figure-callout\; id="2"\; label="C\; DC"\; filenames="HDA00002871536100061.png"\; state="\{\{state\}\}">C</figure-callout>}}_{\mathrm{DC}}=N_{\mathrm{DC}2}{C}_d\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(d\right)$ (formula eight)

$C_{\mathrm{DC}3}=N_{\mathrm{DC}3}{C}_e\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(e\right)$ (formula nine)

$C_{\mathrm{AD}}=N_{\mathrm{AD}}{C}_f\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(f\right)$ (formula ten)

$C_G=C_G{\&Integral;}_0^{8760}{P}_G\left(t\right)\mathrm{dt}$ (formula 11)

In the formula, N _PV, N _B, N _DC1, N _DC2, N _DC3, N _ADBe respectively the quantity of photovoltaic cell, energy-storage battery, photovoltaic unsteady flow module, charging module, energy storage unsteady flow module, grid-connected converter module; C _a, C _b, C _c, C _d, C _e, C _f, C _gBe respectively the unit price of the electric energy in photovoltaic cell, energy-storage battery, photovoltaic unsteady flow module, charging module, grid-connected converter module, the purchase network system; U (a), u (b), u (c), u (d), u (e), u (f) are respectively maintenance and the operating cost of photovoltaic cell, energy-storage battery, photovoltaic unsteady flow module, charging module, energy storage unsteady flow module, grid-connected converter module; M is the default time limit (namely representing the service life that this photovoltaic charged station integrated system is default) of photovoltaic charged station integrated system; r ₀For setting up the discount rate of photovoltaic charged station integrated system; D represents in the integrated system of photovoltaic charged station that the energy of annual energy-storage battery quantity correspondence of eliminating accounts for the ratio of the gross energy that energy-storage battery provides.

In like manner, the mode of calculating the REUR function of photovoltaic charged station integrated system also has multiple, and for example, the REUR function is:

$\mathrm{REUR}=\frac{{\&Integral;}_0^{8760}(P_{\mathrm{EV}}\left(t\right)-P_G\left(t\right))\mathrm{dt}}{{\&Integral;}_0^{8760}{P}_{\mathrm{PV}}\left(t\right)\mathrm{dt}}\&times;100\%$ (formula 12); Perhaps,

$\mathrm{RPUR}=\frac{\underset{i=1}{\overset{8760}{\mathrm{\&Sigma;}}}[P_{\mathrm{EV}}\left(i\right)-P_G\left(i\right)]/8760}{N_{\mathrm{PV}}\&CenterDot;P_{\mathrm{PVN}}}$ (formula 13); Perhaps,

$\mathrm{REUR}=\frac{{\&Integral;}_0^{8760}(P_{\mathrm{EV}}\left(t\right)-P_G\left(t\right))\mathrm{dt}}{{\&Integral;}_0^{8760}{P}_{\mathrm{PV}}\left(t\right)\mathrm{dt}}\&times;100\%$ (formula 14)

Wherein, P _EV(t) be t charge power, the P of electric automobile constantly _G(t) be that t moment electric automobile is from electrical network power absorbed, P _PVNRated capacity, P for single group photovoltaic cell _PV(t) be the t generated output of photovoltaic cell constantly.

Because target function calculates based on minimum cost function and maximum REUR function at least, therefore, according to foregoing as can be known, the computing formula of target function also has multiple, for example,

$\left\{\begin{array}{c}\min C_{\mathrm{\&Sigma;}}=\min (C_{\mathrm{PV}}+C_B+{\mathrm{<figure-callout\; id="1"\; label="C\; DC"\; filenames="HDA00002871536100031.png,HDA00002871536100041.png"\; state="\{\{state\}\}">C</figure-callout>}}_{\mathrm{DC}}+{\mathrm{<figure-callout\; id="2"\; label="C\; DC"\; filenames="HDA00002871536100061.png"\; state="\{\{state\}\}">C</figure-callout>}}_{\mathrm{DC}}+C_{\mathrm{DC}3}+C_{\mathrm{AD}}+C_G)\\ \max \mathrm{REUR}=\max \frac{{\&Integral;}_0^{8760}(P_{\mathrm{EV}}\left(t\right)-P_G\left(t\right))\mathrm{dt}}{{\&Integral;}_0^{8760}{P}_{\mathrm{EV}}\left(t\right)\mathrm{dt}}\&times;100\%\end{array}\right.$ (formula 15); Perhaps,

$\left\{\begin{array}{c}\min C_{\mathrm{\&Sigma;}}=\min (C_{\mathrm{PV}}+C_B+{\mathrm{<figure-callout\; id="1"\; label="C\; DC"\; filenames="HDA00002871536100031.png,HDA00002871536100041.png"\; state="\{\{state\}\}">C</figure-callout>}}_{\mathrm{DC}}+{\mathrm{<figure-callout\; id="2"\; label="D\; DC"\; filenames="HDA00002871536100061.png"\; state="\{\{state\}\}">D</figure-callout>}}_{\mathrm{DC}}+D_{\mathrm{DC}3}+C_{\mathrm{AD}}+C_G)\\ \mathrm{RPUR}=\frac{\underset{i=1}{\overset{8760}{\mathrm{\&Sigma;}}}[P_{\mathrm{EV}}\left(i\right)-P_G\left(i\right)]/8760}{N_{\mathrm{PV}}\&CenterDot;P_{\mathrm{PVN}}}\end{array}\right.$ (formula 16); Perhaps

$\left\{\begin{array}{c}\min C_{\mathrm{\&Sigma;}}=\min (C_{\mathrm{PV}}+C_B+{\mathrm{<figure-callout\; id="1"\; label="C\; DC"\; filenames="HDA00002871536100031.png,HDA00002871536100041.png"\; state="\{\{state\}\}">C</figure-callout>}}_{\mathrm{DC}}+{\mathrm{<figure-callout\; id="2"\; label="C\; DC"\; filenames="HDA00002871536100061.png"\; state="\{\{state\}\}">C</figure-callout>}}_{\mathrm{DC}}+C_{\mathrm{DC}3}+C_{\mathrm{AD}}+C_G)\\ \mathrm{REUR}=\frac{{\&Integral;}_0^{8760}(P_{\mathrm{EV}}\left(t\right)-P_G\left(t\right))\mathrm{dt}}{{\&Integral;}_0^{8760}{P}_{\mathrm{PV}}\left(t\right)\mathrm{dt}}\&times;100\%\end{array}\right.,$ (formula 17)

Step 210: determine the constraints of default performance parameter, and the basic data of obtaining photovoltaic charged station integrated system, this basic data is used to indicate the value of performance parameter.

In the embodiment of the invention, the constraints of performance parameter comprises the content of following two aspects at least: be the quantity span of corresponding each component units of performance parameter of characterization system cost on the one hand, be the equilibrium condition that performance parameter satisfied of characterization system power on the one hand, wherein, the quantity span that is used for corresponding each component units of performance parameter of characterization system cost has multiple, preferable, shown in formula 18:

$\left\{\begin{array}{c}0<N_{\mathrm{PV}}\&le;N_{\mathrm{PV}.\max }\\ 0\&le;N_B\&le;N_{B.\max }\\ 0\&le;N_{\mathrm{DC}3}\&le;N_{\mathrm{DC}3.\max }\end{array}\right.$ (formula 18)

Wherein, N _PV.max, N _B.max, N _DC3.maxBe respectively the maximum quantity of photovoltaic cell, energy-storage battery, energy storage unsteady flow module, determined by actual conditions, the maximum quantity of photovoltaic cell is mainly retrained by floor space, energy-storage battery and associated DC/DC module number need arrange according to the charging demand, to reduce to obtain the search volume in the result of calculation process.

Because concerning a certain town, the charge power demand distribute and year sunshine rule be certain, therefore, the inclination angle theta of photovoltaic cell also has certain influence to the cost of photovoltaic charged station integrated system, in the embodiment of the invention, the span of θ is shown in formula 19:

0≤θ＜90 ° (formula 19)

In like manner, the equilibrium condition that performance parameter satisfied of characterization system power also has multiple, and is preferable, and the equilibrium condition that performance parameter satisfied of characterization system power is shown in formula 20 and formula 21:

P _PV(t) η _DC1=P _EV(t)/η _DC2+ P _B(t)/η _DC3(formula 20);

P _EV(t)/η _DC2=P _PV(t) η _DC1+ P _B(t) η _DC3+ P _G(t) η _AD(formula 21)

Wherein, when formula 20 is in charged state for energy-storage battery, the performance parameter of characterization system power, when formula 21 is in discharge condition for energy-storage battery, the performance parameter of characterization system power, and P _B(t) be t charge power, the η of energy-storage battery group constantly _DC1Operating efficiency, η for photovoltaic unsteady flow module _DC2Operating efficiency, η for charging module _DC3Operating efficiency, η for energy storage unsteady flow module _ADOperating efficiency for the grid-connected converter module.

In the integrated system running of photovoltaic charged station, the energy-storage battery group is used for realizing energy adjustment, when namely sufficient at sunshine, can store the unnecessary generating electric energy of unnecessary photovoltaic cell; When inadequate at sunshine, then exporting to electric automobile charges, therefore, total electric weight of the energy-storage battery group in the integrated system of photovoltaic charged station is constantly to change, but total electric weight of energy-storage battery group also changes within the specific limits, that is to say, total electric weight of energy-storage battery group also has certain span, and span is specifically shown in formula 22:

E _{B min}≤ E _B(t)≤E _{B max}(formula 22)

Wherein, E _{B max}For total maximum permission capacity of energy-storage battery group, preferable, be the total rated capacity of energy-storage battery group; E _{B min}Be total minimum permission capacity of energy-storage battery group, by total maximum depth of discharge decision of energy-storage battery group.

In the embodiment of the invention, also need to obtain the basic data of photovoltaic charged station integrated system, this basic data is used to indicate the value of performance parameter, and wherein, basic data comprises following content at least:

1) the illumination statistics in the integrated system region, photovoltaic charged station 1 year; 2) the charge capacity demand of integrated system region, photovoltaic charged station average every day in above-mentioned 1 year; 3) the standard configuration parameter of each component units of photovoltaic charged station integrated system is as unit price, life-span, efficient and rated capacity etc.

Step 220: according to basic data and constraints calculating target function, obtain at least two assembly and put the result.

In the embodiment of the invention, can adopt multiple algorithm according to basic data and constraints calculating target function, preferable, adopt the multi-objective optimization algorithm calculating target function, wherein, multi-objective optimization algorithm comprises NSGA-II(fast and elitist multi-objective genetic algorithm at least, quick non-domination ordering genetic algorithm), multiple target differential evolution algorithm and multi-target particle group algorithm, based on the flow process of NSGA-II calculating target function as shown in Figure 3.

In the embodiment of the invention, according to basic data and constraints calculating target function, can be obtained up to few two assembly and put the result, the schematic diagram of configuration result as shown in Figure 4, wherein, each result characterizes with REUR value and the corresponding value at cost of this REUR value, each REUR value has the value at cost of a correspondence, the corresponding value at cost of any one REUR value is represented, under this REUR value, build the needed minimum cost of photovoltaic charged station integrated system, this value at cost is the optimal solution corresponding with this REUR value.

Step 230: put among the result at least two assembly, select the target configuration result according to default user demand, and according to target configuration as a result the corresponding hardware allocation plan photovoltaic charged station integrated system is carried out hardware configuration.

In order to understand the embodiment of the invention better, below provide concrete application scenarios, at the process of optimizing the integrated system configuration of photovoltaic charged station, make that (consulting shown in Fig. 5 A) being described in further detail:

So that (north latitude 36o41 ') sets up photovoltaic charged station integrated system B and be optimized and be configured to example in the A area, the illumination statistics in 1 year in a-quadrant is shown in Fig. 5 B, and the charge capacity demand of a-quadrant every day is shown in Fig. 5 C.

Step 500: determine that B carries out the target function of the required use of hardware configuration, target function comprises the performance parameter of characterization system cost and system power at least.

Among this embodiment, target function is:

$\left\{\begin{array}{c}\min C_{\mathrm{\&Sigma;}}=\min (C_{\mathrm{PV}}+C_B+{\mathrm{<figure-callout\; id="1"\; label="C\; DC"\; filenames="HDA00002871536100031.png,HDA00002871536100041.png"\; state="\{\{state\}\}">C</figure-callout>}}_{\mathrm{DC}}+{\mathrm{<figure-callout\; id="2"\; label="C\; DC"\; filenames="HDA00002871536100061.png"\; state="\{\{state\}\}">C</figure-callout>}}_{\mathrm{DC}}+C_{\mathrm{DC}3}+C_{\mathrm{AD}}+C_G)\\ \max \mathrm{REUR}=\max \frac{{\&Integral;}_0^{8760}(P_{\mathrm{EV}}\left(t\right)-P_G\left(t\right))\mathrm{dt}}{{\&Integral;}_0^{8760}{P}_{\mathrm{EV}}\left(t\right)\mathrm{dt}}\&times;100\%\end{array}\right.$

Step 510: the constraints of determining default performance parameter.

Among this embodiment, constraints is:

$\left\{\begin{array}{c}0<N_{\mathrm{PV}}\&le;2400\\ 0\&le;N_B\&le;1400\\ 0\&le;N_{\mathrm{DC}3}\&le;25\end{array}\right.$

P _PV(t) * 0.97=P _EV(t)/0.97+P _B(t)/0.98; With

P _EV(t)/0.97=P _PV(t)*0.97+P _B(t)*0.98+P _G(t)*0.97

Step 520: obtain the basic data of B, basic data is used to indicate the value of performance parameter.

Among this embodiment, basic data mainly comprises: the illumination statistics in 1 year in a-quadrant: shown in Fig. 5 B, and the charge capacity demand of average every day in the a-quadrant 1 year: shown in Fig. 5 C; The standard configuration parameter of each component units of B as the efficient of photovoltaic cell is: 80%, and the efficient of photovoltaic unsteady flow module is: 97%, the efficient of charging electric vehicle module is: 97%, the efficient of energy storage unsteady flow module is: 98%, and the efficient of grid-connected converter module is: 97%, etc.

Step 530: adopt the NSGA-II calculating target function according to basic data and constraints, obtain at least two assembly and put the result.

Among this embodiment, based on the flow process of NSGA-II calculating target function as shown in Figure 3, the schematic diagram of configuration result as shown in Figure 4.

Step 540: put among the result at least two assembly, pick out the target configuration result according to default user demand.

For example, a-quadrant target REUR is 15%, and the acceptable tip heigh that draws that passes budgets of 15% correspondence is 1,500,000, and in the configuration result that obtains, the needed minimum cost of B of setting up of 15% correspondence is 1,250,000, therefore, the target configuration result who picks out is: REUR is 15%, and setting up the required cost of B is 1,250,000.

Step 550: according to target configuration as a result corresponding hardware allocation plan B carry out hardware configuration.

Based on technique scheme, consult shown in Figure 6ly, in the embodiment of the invention, optimize device and comprise first determining unit 60, second determining unit 61, computing unit 62 and dispensing unit 63, wherein,

First determining unit 60 is used for definite target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration, and target function comprises the performance parameter of characterization system cost and system power at least;

Second determining unit 61, for the constraints of determining default performance parameter, and the basic data of obtaining photovoltaic charged station integrated system, this basic data is used to indicate the value of performance parameter;

Computing unit 62 is used for according to basic data and constraints calculating target function, obtains at least two assembly and puts the result;

Dispensing unit 63 is used for putting the result at least two assembly, selects the target configuration result according to default user demand, and according to target configuration as a result the corresponding hardware allocation plan photovoltaic charged station integrated system is carried out hardware configuration.

In the embodiment of the invention, the target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration that first determining unit 60 is determined, at least calculate based on minimum cost function and maximum renewable energy utilization rate function, wherein, cost function adopts the performance parameter of characterization system cost of each component units correspondence of photovoltaic charged station integrated system to calculate at least, and renewable energy utilization rate function adopts the performance parameter of characterization system power to calculate at least.

In the embodiment of the invention, the constraints of the default performance parameter that second determining unit 61 is determined, comprise: determine the quantity span of corresponding each component units of performance parameter of characterization system cost, and the equilibrium condition that performance parameter satisfied of definite characterization system power.

Preferable, computing unit 62 specifically is used for, and according to basic data and constraints, adopts the multi-objective optimization algorithm calculating target function, and wherein, multi-objective optimization algorithm comprises at least: NSGA-II; Perhaps, multiple target differential evolution algorithm; Perhaps, multi-target particle group algorithm.

In sum, in the embodiment of the invention, the definite target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration of elder generation, target function comprises the performance parameter of characterization system cost and system power at least, determine the constraints of default performance parameter again, and obtain the basic data of photovoltaic charged station integrated system, wherein, this basic data is used to indicate the value of performance parameter, then, according to basic data and constraints calculating target function, obtain at least two assembly and put the result, last, put among the result at least two assembly, select the target configuration result according to default user demand, and according to target configuration as a result the corresponding hardware allocation plan photovoltaic charged station integrated system is carried out hardware configuration, like this, just can obtain the photovoltaic charged station integrated system of zones of different under different photovoltaic utilances, the hardware configuration scheme of corresponding optimum, and zones of different can be determined the best allocation plan in zone separately that is fit to according to default user demand.

Those skilled in the art should understand that embodiments of the invention can be provided as method, system or computer program.Therefore, the present invention can adopt complete hardware embodiment, complete software embodiment or in conjunction with the form of the embodiment of software and hardware aspect.And the present invention can adopt the form of the computer program of implementing in one or more computer-usable storage medium (including but not limited to magnetic disc store, CD-ROM, optical memory etc.) that wherein include computer usable program code.

The present invention is that reference is described according to flow chart and/or the block diagram of method, equipment (system) and the computer program of the embodiment of the invention.Should understand can be by the flow process in each flow process in computer program instructions realization flow figure and/or the block diagram and/or square frame and flow chart and/or the block diagram and/or the combination of square frame.Can provide these computer program instructions to the processor of all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing device to produce a machine, make the instruction of carrying out by the processor of computer or other programmable data processing device produce to be used for the device of the function that is implemented in flow process of flow chart or a plurality of flow process and/or a square frame of block diagram or a plurality of square frames.

These computer program instructions also can be stored in energy vectoring computer or the computer-readable memory of other programmable data processing device with ad hoc fashion work, make the instruction that is stored in this computer-readable memory produce the manufacture that comprises command device, this command device is implemented in the function in flow process of flow chart or a plurality of flow process and/or square frame of block diagram or a plurality of square frame.

These computer program instructions also can be loaded on computer or other programmable data processing device, make to carry out the sequence of operations step to produce computer implemented place at computer or other programmable devices

Reason, thus be provided for being implemented in the step of the function in flow process of flow chart or a plurality of flow process and/or square frame of block diagram or a plurality of square frame in the instruction that computer or other programmable devices are carried out.

Although described the preferred embodiments of the present invention, in a single day those skilled in the art get the basic creative concept of cicada, then can make other change and modification to these embodiment.So claims are intended to all changes and the modification that are interpreted as comprising preferred embodiment and fall into the scope of the invention.

Obviously, those skilled in the art can carry out various changes and modification to the embodiment of the invention and not break away from the spirit and scope of the embodiment of the invention.Like this, if these of the embodiment of the invention are revised and modification belongs within the scope of claim of the present invention and equivalent technologies thereof, then the present invention also is intended to comprise these changes and modification interior.

Claims (16)

1. a method of optimizing photovoltaic charged station integrated system is characterized in that, comprising:

Definite target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration, described target function comprises the performance parameter of characterization system cost and system power at least;

Determine the constraints of default described performance parameter, and the basic data of obtaining described photovoltaic charged station integrated system, this basic data is used to indicate the value of described performance parameter;

Calculate described target function according to described basic data and described constraints, obtain at least two assembly and put the result;

Put among the result in described at least two assembly, select the target configuration result according to default user demand, and according to described target configuration as a result the corresponding hardware allocation plan described photovoltaic charged station integrated system is carried out hardware configuration.

2. the method for claim 1, it is characterized in that, the described target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration calculates based on minimum cost function and maximum renewable energy utilization rate function at least, wherein, described cost function adopts the performance parameter of characterization system cost of each component units correspondence of described photovoltaic charged station integrated system to calculate at least, and described renewable energy utilization rate function adopts the performance parameter of characterization system power to calculate at least.

3. method as claimed in claim 2 is characterized in that, described cost function is:

C _Σ=(C _PV+ C _B+ C _DC1+ C _DC2+ C _DC3+ C _AD+ C _G); Perhaps,

C _Σ=(C _PV+ C _B+ C _G); Perhaps,

C _Σ=(C _PV+ C _B+ C _DC1+ C _DC2+ C _DC3+ C _AD); Perhaps,

C _Σ＝(C _DC1+C _DC2+C _DC3+C _AD)；

Described renewable energy utilization rate function is:

$\mathrm{REUR}=\frac{{\&Integral;}_0^{8760}(P_{\mathrm{EV}}\left(t\right)-P_G\left(t\right))\mathrm{dt}}{{\&Integral;}_0^{8760}{P}_{\mathrm{EV}}\left(t\right)\mathrm{dt}}\&times;100\%;$ Perhaps,

$\mathrm{RPUR}=\frac{\underset{i=1}{\overset{8760}{\mathrm{\&Sigma;}}}[P_{\mathrm{EV}}\left(i\right)-P_G\left(i\right)]/8760}{N_{\mathrm{PV}}\&CenterDot;P_{\mathrm{PVN}}};$ Perhaps,

$\mathrm{REUR}=\frac{{\&Integral;}_0^{8760}(P_{\mathrm{EV}}\left(t\right)-P_G\left(t\right))\mathrm{dt}}{{\&Integral;}_0^{8760}{P}_{\mathrm{PV}}\left(t\right)\mathrm{dt}}\&times;100\%,$

Wherein, C _ΣTotal cost, C for described photovoltaic charged station integrated system _PVBe photovoltaic cell total cost, the C that comprises in the integrated system of described photovoltaic charged station _BBe energy-storage battery total cost, the C that comprises in the integrated system of described photovoltaic charged station _DC1Be photovoltaic unsteady flow module assembly basis, the C that comprises in the integrated system of described photovoltaic charged station _DC2Be charging module total cost, the C that comprises in the integrated system of described photovoltaic charged station _DC3Be energy storage unsteady flow module assembly basis, the C that comprises in the integrated system of described photovoltaic charged station _ADBe grid-connected converter module assembly basis, the C that comprises in the integrated system of described photovoltaic charged station _GThe total electricity charge, the REUR that purchase electricity for the electrical network that comprises in the integrated system of described photovoltaic charged station are renewable energy utilization rate, P _EV(t) be t charging electric vehicle power, P constantly _G(t) be that t moment electric automobile is from electrical network power absorbed, N _PVQuantity, P for the photovoltaic cell in the photovoltaic cell group _PVNRated capacity, P for single group photovoltaic cell _PV(t) be the t generated output of photovoltaic cell constantly.

4. method as claimed in claim 2 is characterized in that, determines the constraints of default described performance parameter, comprising:

Determine the quantity span of corresponding each component units of performance parameter of characterization system cost, and the equilibrium condition that performance parameter satisfied of definite characterization system power.

5. method as claimed in claim 4 is characterized in that, the quantity span of corresponding each component units of performance parameter of described characterization system cost is:

$\left\{\begin{array}{c}0<N_{\mathrm{PV}}\&le;N_{\mathrm{PV}.\max }\\ 0\&le;N_B\&le;N_{B.\max }\\ 0\&le;N_{\mathrm{DC}3}\&le;N_{\mathrm{DC}3.\max }\end{array}\right.$

Wherein, N _BQuantity, N for the energy-storage battery in the energy-storage battery group _DC3Be energy storage unsteady flow module number, N _PV.maxMaximum quantity, N for the photovoltaic cell in the photovoltaic cell group _B.maxMaximum quantity, N for the energy-storage battery in the energy-storage battery group _DC3.maxMaximum quantity for energy storage unsteady flow module.

6. method as claimed in claim 4 is characterized in that, the equilibrium condition that performance parameter satisfied of described characterization system power is:

P _PV(t) η _DC1=P _EV(t)/η _DC2+ P _B(t)/η _DC3With

P _EV(t)/η _DC2=P _PV(t)η _DC1+P _B(t)η _DC3+P _G(t)η _AD，

Wherein, P _B(t) for t constantly the energy-storage battery group discharge and recharge power, η _DC1Operating efficiency, η for photovoltaic unsteady flow module _DC2Operating efficiency, η for charging module _DC3Operating efficiency, η for energy storage unsteady flow module _ADOperating efficiency for the grid-connected converter module.

7. the method for claim 1 is characterized in that, calculates described target function according to described basic data and described constraints, comprising:

According to described basic data and described constraints, adopt multi-objective optimization algorithm to calculate described target function.

8. method as claimed in claim 7 is characterized in that, adopts multi-objective optimization algorithm to calculate described target function, comprising:

Adopt quick non-domination ordering genetic algorithm NSGA-II to calculate described target function; Perhaps,

Adopt multiple target differential evolution algorithm to calculate described target function; Perhaps,

Adopt multi-target particle group algorithm to calculate described target function.

9. a device of optimizing photovoltaic charged station integrated system is characterized in that, comprising:

First determining unit is used for definite target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration, and described target function comprises the performance parameter of characterization system cost and system power at least;

Second determining unit, for the constraints of determining default described performance parameter, and the basic data of obtaining described photovoltaic charged station integrated system, this basic data is used to indicate the value of described performance parameter;

Computing unit is used for calculating described target function according to described basic data and described constraints, obtains at least two assembly and puts the result;

Dispensing unit is used for putting the result in described at least two assembly, selects the target configuration result according to default user demand, and according to described target configuration as a result the corresponding hardware allocation plan described photovoltaic charged station integrated system is carried out hardware configuration.

10. device as claimed in claim 9, it is characterized in that, the target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration that described first determining unit is determined, at least calculate based on minimum cost function and maximum renewable energy utilization rate function, wherein, described cost function adopts the performance parameter of characterization system cost of each component units correspondence of described photovoltaic charged station integrated system to calculate at least, and described renewable energy utilization rate function adopts the performance parameter of characterization system power to calculate at least.

11. device as claimed in claim 10 is characterized in that, the target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration that described first determining unit is determined calculate based on cost function be:

C _Σ=(C _PV+ C _B+ C _DC1+ C _DC2+ C _DC3+ C _AD+ C _G); Perhaps,

C _Σ=(C _PV+ C _B+ C _G); Perhaps,

C _Σ=(C _PV+ C _B+ C _DC1+ C _DC2+ C _DC3+ C _AD); Perhaps,

C _Σ＝(C _DC1+C _DC2+C _DC3+C _AD)；

The target function that photovoltaic charged station integrated system is carried out the required use of hardware configuration that described first determining unit is determined calculate based on renewable energy utilization rate function be:

Perhaps,

$\mathrm{RPUR}=\frac{\underset{i=1}{\overset{8760}{\mathrm{\&Sigma;}}}[P_{\mathrm{EV}}\left(i\right)-P_G\left(i\right)]/8760}{N_{\mathrm{PV}}\&CenterDot;P_{\mathrm{PVN}}};$ Perhaps,

$\mathrm{REUR}=\frac{{\&Integral;}_0^{8760}(P_{\mathrm{EV}}\left(t\right)-P_G\left(t\right))\mathrm{dt}}{{\&Integral;}_0^{8760}{P}_{\mathrm{PV}}\left(t\right)\mathrm{dt}}\&times;100\%,$

Wherein, C _ΣTotal cost, C for described photovoltaic charged station integrated system _PVBe photovoltaic cell total cost, the C that comprises in the integrated system of described photovoltaic charged station _BBe energy-storage battery total cost, the C that comprises in the integrated system of described photovoltaic charged station _DC1Be photovoltaic unsteady flow module assembly basis, the C that comprises in the integrated system of described photovoltaic charged station _DC2Be charging module total cost, the C that comprises in the integrated system of described photovoltaic charged station _DC3Be energy storage unsteady flow module assembly basis, the C that comprises in the integrated system of described photovoltaic charged station _ADThe grid-connected converter module assembly basis, the C that comprise in the integrated system of described photovoltaic charged station _GThe total electricity charge, the REUR that purchase electricity from the integrated system of described photovoltaic charged station for the electrical network that comprises are renewable energy utilization rate, P _EV(t) be t charging electric vehicle power, P constantly _G(t) be that t moment electric automobile is from electrical network power absorbed, N _PVQuantity, P for the photovoltaic cell in the photovoltaic cell group _PVNRated capacity, P for single group photovoltaic cell _PV(t) be the t generated output of photovoltaic cell constantly.

12. device as claimed in claim 10 is characterized in that, the constraints of the default described performance parameter that described second determining unit is determined comprises:

Determine the quantity span of corresponding each component units of performance parameter of characterization system cost, and the equilibrium condition that performance parameter satisfied of definite characterization system power.

13. device as claimed in claim 12 is characterized in that, the quantity span of corresponding each component units of performance parameter of the characterization system cost that described second determining unit is determined is:

$\left\{\begin{array}{c}0<N_{\mathrm{PV}}\&le;N_{\mathrm{PV}.\max }\\ 0\&le;N_B\&le;N_{B.\max }\\ 0\&le;N_{\mathrm{DC}3}\&le;N_{\mathrm{DC}3.\max }\end{array}\right.$

Wherein, N _BQuantity, N for the energy-storage battery in the energy-storage battery group _DC3Be energy storage unsteady flow module number, N _PV.maxMaximum quantity, N for the photovoltaic cell in the photovoltaic cell group _B.maxMaximum quantity, N for the energy-storage battery in the energy-storage battery group _DC3.maxMaximum quantity for energy storage unsteady flow module.

14. device as claimed in claim 12 is characterized in that, the equilibrium condition that performance parameter satisfied of the characterization system power that described second determining unit is determined is:

P _PV(t) η _DC1=P _EV(t)/η _DC2+ P _B(t)/η _DC3;Perhaps

P _EV(t)/η _DC2=P _PV(t)η _DC1+P _B(t)η _DC3+P _G(t)η _AD，

Wherein, P _B(t) for t constantly the energy-storage battery group discharge and recharge power, η _DC1Operating efficiency, η for photovoltaic unsteady flow module _DC2Operating efficiency, η for charging module _DC3Operating efficiency, η for energy storage unsteady flow module _ADOperating efficiency for the grid-connected converter module.

15. device as claimed in claim 9 is characterized in that, described computing unit specifically is used for:

According to described basic data and described constraints, adopt multi-objective optimization algorithm to calculate described target function.

16. device as claimed in claim 15 is characterized in that, described computing unit calculates the multi-objective optimization algorithm that described target function adopts, and comprising:

Quick non-domination ordering genetic algorithm NSGA-II; Perhaps,

Multiple target differential evolution algorithm; Perhaps,

Multi-target particle group algorithm.

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