CN103187784B - A kind of method and device optimizing photovoltaic charge station integrated system - Google Patents

Abstract

The invention belongs to intelligent power grid technology field, disclose a kind of method and the device of optimizing photovoltaic charge station integrated system, be specially: first determine to carry out to photovoltaic charge station integrated system the target function that uses needed for hardware configuration, target function at least comprises the performance parameter of characterization system cost and system power, determine the constraints of the performance parameter preset again, and obtain the basic data of photovoltaic charge station integrated system, then, according to basic data and constraints calculating target function, obtain at least two group configuration results, finally, in at least two group configuration results, target configuration result is selected in user demand according to presetting, and the hardware configuration scheme corresponding according to target configuration result carries out hardware configuration to photovoltaic charge station integrated system, like this, just can obtain the photovoltaic charge station integrated system of zones of different under different photovoltaic utilance, the hardware configuration scheme of corresponding optimum.

Description

Method and device for optimizing photovoltaic charging station integrated system

Technical Field

The invention belongs to the technical field of smart power grids, and particularly relates to a method and a device for optimizing a photovoltaic charging station integrated system.

Background

With the wide development of electric vehicles in various countries of the world, the planning and construction problems of charging infrastructures have received more attention from governments of China. At present, the primary energy at the power generation side of the power system in China is mainly coal (about 75-80%), electric vehicles are directly connected to a power grid for charging through charging infrastructure, the actually generated indirect carbon emission is not lower than that of traditional fuel oil vehicles, and the dependence on traditional fossil fuels is difficult to reduce. In this case, there are two ways to realize low carbon in the true sense: the renewable energy power generation system is vigorously developed, the consumption capacity of a power grid on renewable energy is improved, and the utilization rate of the renewable energy in the power grid is increased; and secondly, the association between the electric vehicle charging and discharging facility and the renewable energy power generation system is directly established, and the on-site consumption and utilization of renewable energy are realized through the micro-grid. From the current development situation, it is very difficult to adjust the primary energy structure of the power grid, and the realization of the integrated utilization of the electric vehicle on the renewable energy power generation by adopting the micro-grid mode will become the most direct mode.

At present, the photovoltaic power generation and energy storage system and the electric automobile power exchange station are integrated to achieve complementary benefits, on the premise that the investment cost is certain, the economic benefits brought by the integration mode are higher than those brought by a photovoltaic power generation and water pumping energy storage mode, in addition, the integration mode of photovoltaic power generation and electric automobile charging cannot bring extra power transmission and distribution pressure to city centers, and the power battery as a substituted energy storage device can effectively relieve intermittent fluctuation of illumination output power, so that the win-win effect is achieved. Referring to fig. 1, a schematic diagram of a photovoltaic charging station integrated system mainly includes four parts: the photovoltaic battery pack absorbs solar energy and generates direct current, and the direct current is connected to a charging system through a DC/DC converter module and is a main power supply for charging electric automobiles in the station; the energy storage battery pack plays a role in energy storage and regulation in the system, namely when the generated energy of the photovoltaic cell is excessive, the redundant electric energy is stored; when the power generation amount of the photovoltaic cell is insufficient, the electric automobile is charged by the stored energy (or the electric automobile is charged together with the alternating current distribution network).

The photovoltaic battery pack, the energy storage battery pack, the power grid system and the photovoltaic charging station parking space system respectively comprise a plurality of groups of DC/DC converter modules, wherein the DC/DC converter modules are used as converter units of the photovoltaic battery pack, the energy storage battery pack and the electric vehicle charging system, the photovoltaic battery pack and the electric vehicle charging system use DC/DC modules with energy flowing in a one-way mode, and the energy storage battery pack uses DC/DC modules with energy flowing in a two-way mode. The power grid system comprises an AC/DC conversion module which is used as a connecting unit of the AC power distribution network and the photovoltaic battery pack. And converting the alternating current input by the power distribution network into direct current to access the charging system according to the charging requirement in the station.

However, in the prior art, for complementary benefits of integration of the power generation of the photovoltaic battery pack and the energy storage battery pack with the electric vehicle charging station, the advantages of the integration mode are only explained from three aspects of a network structure of the integration mode, economic benefits of the integration application and operation effects of the integration utilization, and the problem of optimal configuration of each component unit in the charging station containing the photovoltaic battery pack cannot be solved.

In order to comprehensively consider economic benefits and environmental benefits, in the planning of the integrated system of the photovoltaic charging station, on one hand, the construction and operation costs are reduced as much as possible; on the other hand, the multi-objective optimization problem that needs to be solved is to increase the proportion of the power generation amount of the photovoltaic battery pack in the charging energy of the electric vehicle as much as possible (since the charging energy of the electric vehicle comes from the grid system on the one hand and the photovoltaic battery pack on the other hand, and if the charging energy of the electric vehicle comes from the grid system less, the charging energy comes from the photovoltaic battery pack more), but a method for determining the corresponding optimal hardware configuration of the photovoltaic charging station integrated system in different areas under different photovoltaic utilization rates is not proposed at present, and the optimal configuration scheme suitable for the areas cannot be determined in different areas.

Disclosure of Invention

The embodiment of the invention provides a method for optimizing a photovoltaic charging station integrated system, which is used for determining the corresponding optimal hardware configuration of the photovoltaic charging station integrated system in different areas under different photovoltaic utilization rates, and each area can determine the optimal configuration scheme suitable for each area according to the preset use requirement.

A method of optimizing a photovoltaic charging station integration system, comprising:

determining an objective function required to be used for hardware configuration of a photovoltaic charging station integrated system, wherein the objective function at least comprises performance parameters representing system cost and system power;

determining a preset constraint condition of the performance parameter, and acquiring basic data of the photovoltaic charging station integrated system, wherein the basic data is used for indicating the value of the performance parameter;

calculating the objective function according to the basic data and the constraint conditions to obtain at least two groups of configuration results;

and selecting a target configuration result according to a preset use requirement from the at least two groups of configuration results, and performing hardware configuration on the photovoltaic charging station integrated system according to a hardware configuration scheme corresponding to the target configuration result.

An apparatus to optimize a photovoltaic charging station integrated system, comprising:

the system comprises a first determination unit, a second determination unit and a control unit, wherein the first determination unit is used for determining an objective function required to be used for hardware configuration of the photovoltaic charging station integrated system, and the objective function at least comprises performance parameters representing system cost and system power;

the second determining unit is used for determining a preset constraint condition of the performance parameter and acquiring basic data of the photovoltaic charging station integrated system, wherein the basic data is used for indicating a value of the performance parameter;

the calculation unit is used for calculating the objective function according to the basic data and the constraint conditions to obtain at least two groups of configuration results;

and the configuration unit is used for selecting a target configuration result according to a preset use requirement in the at least two groups of configuration results, and performing hardware configuration on the photovoltaic charging station integrated system according to a hardware configuration scheme corresponding to the target configuration result.

In the embodiment of the invention, an objective function required to be used for hardware configuration of a photovoltaic charging station integrated system is determined, the objective function at least comprises performance parameters representing system cost and system power, a preset constraint condition of the performance parameters is determined, basic data of the photovoltaic charging station integrated system is obtained, the basic data is used for indicating the value of the performance parameters, the objective function is calculated according to the basic data and the constraint condition to obtain at least two groups of configuration results, finally, the target configuration result is selected according to a preset use requirement in the at least two groups of configuration results, and the hardware configuration is carried out on the photovoltaic charging station integrated system according to a hardware configuration scheme corresponding to the target configuration result, so that the optimal hardware configuration scheme corresponding to the photovoltaic charging station integrated systems in different areas under different photovoltaic utilization rates can be obtained, and each region can determine the optimal configuration scheme suitable for the respective region according to the preset use requirement.

Drawings

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

FIG. 2 is a flow chart of optimizing the configuration of a photovoltaic charging station integrated system in an embodiment of the present invention;

FIG. 3 is a schematic flow chart of NSGA-II calculating the objective function according to the embodiment of the present invention;

FIG. 4 is a diagram illustrating the calculation results according to an embodiment of the present invention;

FIG. 5A is a detailed flow chart of optimizing the configuration of a photovoltaic charging station integrated system in an embodiment of the present invention;

FIG. 5B is a graph illustrating lighting data for area A for one year in accordance with an embodiment of the present invention;

FIG. 5C is a graph illustrating the average daily charging power requirement of the electric vehicle in region A during a year, in accordance with an embodiment of the present invention;

fig. 6 is a functional structure diagram of a configuration apparatus according to an embodiment of the present invention.

Detailed Description

In order to determine the corresponding optimal hardware configuration method of the photovoltaic charging station integrated system in different areas under different photovoltaic utilization rates, and the different areas can determine the optimal configuration scheme suitable for the respective areas according to the preset use requirements, in the embodiment of the invention, an objective function required for hardware configuration of the photovoltaic charging station integrated system is determined, the objective function at least comprises performance parameters representing system cost and system power, then the constraint conditions of the preset performance parameters are determined, and the basic data of the photovoltaic charging station integrated system is obtained, wherein the basic data is used for indicating the value of the performance parameters, then the objective function is calculated according to the basic data and the constraint conditions to obtain at least two groups of configuration results, and finally, in at least two groups of configuration results, the target configuration result is selected according to the preset use requirements, and carrying out hardware configuration on the photovoltaic charging station integrated system according to the hardware configuration scheme corresponding to the target configuration result, so that the corresponding optimal hardware configuration scheme of the photovoltaic charging station integrated system in different areas under different photovoltaic utilization rates can be obtained, and the optimal configuration scheme suitable for each area can be determined in the different areas according to preset use requirements.

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

In the embodiment of the invention, the specific implementation flow is as follows:

referring to fig. 2, in the embodiment of the present invention, a detailed process for optimizing the configuration of the photovoltaic charging station integrated system is as follows:

step 200: and determining an objective function required to be used for hardware configuration of the photovoltaic charging station integrated system, wherein the objective function at least comprises performance parameters representing system cost and system power.

In the embodiment of the present invention, based on the structure of the photovoltaic charging station integrated system shown in fig. 1, an objective function required to be used for hardware configuration of the photovoltaic charging station integrated system is established, and under the condition that the charging requirements of a preset number of electric vehicles are met, the objective function is established in two aspects: (1) the total investment and the operation cost of the photovoltaic charging station integrated system are minimized; (2) the method includes the steps that the REUR (Renewable Energy Utilization Ratio) of the photovoltaic charging station integrated system is maximized, therefore, an objective function needed for hardware configuration of the photovoltaic charging station integrated system is calculated at least based on a minimum cost function and a maximum Renewable Energy Utilization function, wherein the cost function is calculated at least by using performance parameters representing system cost corresponding to each component unit of the photovoltaic charging station integrated system, the REUR function is calculated at least by using performance parameters representing system power, and the performance parameters representing system power at least comprise performance parameters representing electric vehicle charging power and performance parameters representing power absorbed by an electric vehicle from a power grid.

In the embodiment of the present invention, there are various formulas for calculating the cost function of the photovoltaic charging station integrated system, for example:

C_Σ＝(C_PV+C_B+C_DC1+C_DC2+C_DC3+C_AD+C_G) (formula one); or,

C_Σ＝(C_PV+C_B+C_G) (formula two); or,

C_Σ＝(C_PV+C_B+C_DC1+C_DC2+C_DC3+C_AD) (formula three); or,

C_Σ＝(C_DC1+C_DC2+C_DC3+C_AD) (formula four) of the reaction solution,

wherein, C_ΣThe total cost of integrating the system for the photovoltaic charging station; c_PVCalculating the total cost of the photovoltaic cells in the photovoltaic charging station integrated system specifically as shown in a formula V; c_BThe total cost of the energy storage battery in the photovoltaic charging station integrated system is calculated specifically as shown in a formula six; c_DC1Calculating the total cost of the photovoltaic converter modules in the photovoltaic charging station integrated system specifically as shown in a formula seven; c_DC2Calculating the total cost of the charging modules in the photovoltaic charging station integrated system specifically as shown in a formula eight; c_DC3Specifically calculating the total cost of the energy storage and current conversion module in the photovoltaic charging station integrated system according to a formula nine; c_ADSpecifically calculating the total cost of the grid-connected converter module in the photovoltaic charging station integrated system according to a formula ten; c_GFor the total electricity charge purchased from the power grid system included in the photovoltaic charging station integrated system, the specific calculation is as shown in formula eleven, and formula five-formula eleven is as follows:

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

$C_{\mathrm{DC}1}=N_{\mathrm{DC}1}{C}_c\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(c\right)$ (formula seven)

$C_{\mathrm{DC}2}=N_{\mathrm{DC}2}{C}_d\frac{r_0{(1+r_0)}^m}{{(1+r_0)}^m-1}+u\left(d\right)$ (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)

<math> <mrow> <msub> <mi>C</mi> <mi>G</mi> </msub> <mo>=</mo> <msub> <mi>C</mi> <mi>g</mi> </msub> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </math> (formula eleven)

In the formula, N_PV、N_B、N_DC1、N_DC2、N_DC3、N_ADThe number of the photovoltaic cells, the energy storage cells, the photovoltaic converter modules, the charging modules, the energy storage converter modules and the grid-connected converter modules is respectively; c_a、C_b、C_c、C_d、C_e、C_f、C_gThe unit prices of the photovoltaic cell, the energy storage cell, the photovoltaic converter module, the charging module, the grid-connected converter module and the electric energy purchased in the power grid system are respectively; u (a), u (b), u (c), u (d), u (e) and u (f) are respectively the maintenance and operation costs of a photovoltaic cell, an energy storage cell, a photovoltaic conversion module, a charging module, an energy storage conversion module and a grid-connected conversion module; m is a preset age of the photovoltaic charging station integrated system (i.e. represents a preset service life of the photovoltaic charging station integrated system); r is₀The method comprises the steps of establishing a current pasting rate of a photovoltaic charging station integrated system; d represents the number of the energy storage batteries eliminated every year in the photovoltaic charging station integrated systemThe amount of energy required is proportional to the total energy provided by the energy storage battery.

Similarly, there are various ways to calculate the resur function of the photovoltaic charging station integrated system, for example, the resur function is:

<math> <mrow> <mi>REUR</mi> <mo>=</mo> <mfrac> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </mfrac> <mo>&times;</mo> <mn>100</mn> <mo>%</mo> </mrow> </math> (formula twelve); or,

<math> <mrow> <mi>RPUR</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>8760</mn> </munderover> <mo>[</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>]</mo> <mo>/</mo> <mn>8760</mn> </mrow> <mrow> <msub> <mi>N</mi> <mi>PV</mi> </msub> <mo>&CenterDot;</mo> <msub> <mi>P</mi> <mi>PVN</mi> </msub> </mrow> </mfrac> </mrow> </math> (formula thirteen); or,

<math> <mrow> <mi>REUR</mi> <mo>=</mo> <mfrac> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>PV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </mfrac> </mrow> </math> (formula fourteen)

Wherein, P_EV(t) charging Power of electric vehicle at time t, P_G(t) power P absorbed by the electric vehicle from the grid at time t_PVNRated capacity, P, for a single group of photovoltaic cells_PVAnd (t) is the generated power of the photovoltaic cell at the time t.

Since the objective function is calculated based on at least the minimum cost function and the maximum REUR function, it can be understood from the above that the calculation formula of the objective function is also various, for example,

<math> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mi>min</mi> <msub> <mi>C</mi> <mi>&Sigma;</mi> </msub> <mo>=</mo> <mi>min</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>PV</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>B</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>2</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>3</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>AD</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>G</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mi>max</mi> <mi>REUR</mi> <mo>=</mo> <mi>max</mi> <mfrac> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </mfrac> <mo>&times;</mo> <mn>100</mn> <mo>%</mo> </mtd> </mtr> </mtable> </mfenced> </math> (formula fifteen); or,

<math> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mi>min</mi> <msub> <mi>C</mi> <mi>&Sigma;</mi> </msub> <mo>=</mo> <mi>min</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>PV</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>B</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>2</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>3</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>AD</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>G</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mi>RPUR</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>8760</mn> </munderover> <mo>[</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>]</mo> <mo>/</mo> <mn>8760</mn> </mrow> <mrow> <msub> <mi>N</mi> <mi>PV</mi> </msub> <mo>&CenterDot;</mo> <msub> <mi>P</mi> <mi>PVN</mi> </msub> </mrow> </mfrac> </mtd> </mtr> </mtable> </mfenced> </math> (formula sixteen); or

<math> <mrow> <mfenced open='{' close=','> <mtable> <mtr> <mtd> <mi>min</mi> <msub> <mi>C</mi> <mi>&Sigma;</mi> </msub> <mo>=</mo> <mi>min</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>PV</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>B</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>2</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>3</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>AD</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>G</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mi>RPUR</mi> <mo>=</mo> <mfrac> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>PV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </mfrac> <mo>&times;</mo> <mn>100</mn> <mo>%</mo> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow> </math> (formula seventeen)

Step 210: the method comprises the steps of determining constraint conditions of preset performance parameters, and obtaining basic data of the photovoltaic charging station integrated system, wherein the basic data are used for indicating values of the performance parameters.

In the embodiment of the present invention, the constraint conditions of the performance parameters at least include the following two aspects: on one hand, the number value range of each component unit corresponding to the performance parameter representing the system cost, and on the other hand, the balance condition met by the performance parameter representing the system power_，The number value range of each component unit corresponding to the performance parameter for representing the system cost is various, preferably, as shown in formula eighteen:

<math> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>N</mi> <mi>PV</mi> </msub> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>PV</mi> <mo>.</mo> <mi>max</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> <mo>&le;</mo> <msub> <mi>N</mi> <mi>B</mi> </msub> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>B</mi> <mo>.</mo> <mi>max</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>DC</mi> <mn>3</mn> </mrow> </msub> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>DC</mi> <mn>3</mn> <mo>.</mo> <mi>max</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> </math> (eighteen formula)

Wherein N is_PV.max、N_B.max、N_DC3.maxThe maximum number of the photovoltaic cells, the energy storage cells and the energy storage and conversion modules is determined by actual conditions, the maximum number of the photovoltaic cells is mainly constrained by the occupied area, and the number of the energy storage cells and the number of the related DC/DC modules need to be set according to charging requirements so as to reduce the search space in the process of obtaining the calculation result.

Because the charging power demand distribution and the annual sunshine law are constant for a certain city, the inclination angle theta of the photovoltaic cell has a certain influence on the cost of the integrated system of the photovoltaic charging station, and the value range of theta in the embodiment of the invention is shown as a formula nineteenth:

theta is more than or equal to 0 and less than 90 degrees (nineteen formula)

Similarly, the performance parameters representing the system power may satisfy a plurality of balance conditions, and preferably, the balance conditions satisfied by the performance parameters representing the system power are as shown in formula twenty and formula twenty-one:

P_PV(t)η_DC1＝P_EV(t)/η_DC2+P_B(t)/η_DC3(equation twenty);

P_EV(t)/η_DC2＝P_PV(t)η_DC1+P_B(t)η_DC3+P_G(t)η_AD(formula twenty-one)

Wherein, the formula twenty is a performance parameter representing the system power when the energy storage battery is in a charging state, the formula twenty-one is a performance parameter representing the system power when the energy storage battery is in a discharging state, and P is_B(t) is the charging and discharging power, eta of the energy storage battery pack at the time t_DC1For the working efficiency, eta, of the photovoltaic converter module_DC2Working efficiency, eta, for charging modules_DC3Working efficiency, eta, for energy-storing converter modules_ADThe working efficiency of the grid-connected converter module is improved.

In the operation process of the photovoltaic charging station integrated system, the energy storage battery pack is used for realizing energy regulation, namely when sunlight is sufficient, redundant power generation electric energy of redundant photovoltaic cells can be stored; when sunshine is insufficient, then export and charge for electric automobile, consequently, the total electric quantity of the energy storage group battery among the photovoltaic charging station integrated system is constantly changing, however, the total electric quantity of energy storage group battery also changes in certain extent, that is to say, the total electric quantity of energy storage group battery also has certain value range, and the value range specifically as shown in formula twenty two:

E_{B min}≤E_B(t)≤E_{B max}(formula twenty-two)

Wherein E is_{B max}The total maximum allowable capacity of the energy storage battery pack, preferably the total rated capacity of the energy storage battery pack; e_{B min}The total minimum allowable capacity of the energy storage battery pack is determined by the total maximum discharge depth of the energy storage battery pack.

In the embodiment of the present invention, basic data of the photovoltaic charging station integrated system needs to be acquired, where the basic data is used to indicate values of the performance parameters, and the basic data at least includes the following contents:

1) the method comprises the following steps of counting illumination data of an area where a photovoltaic charging station integrated system is located within one year; 2) the charging electric quantity demand of the area where the photovoltaic charging station integrated system is located is averaged every day in the year; 3) and the standard parameters of each component unit of the photovoltaic charging station integrated system, such as unit price, service life, efficiency, rated capacity and the like.

Step 220: and calculating an objective function according to the basic data and the constraint conditions to obtain at least two groups of configuration results.

In the embodiment of the present invention, a plurality of algorithms may be adopted to calculate the objective function according to the basic data and the constraint conditions, preferably, a multi-objective optimization algorithm is adopted to calculate the objective function, wherein the multi-objective optimization algorithm at least includes NSGA-II (fast and inertia multi-objective genetic algorithm, fast non-dominated sorting genetic algorithm), a multi-objective differential evolution algorithm and a multi-objective particle swarm algorithm, and a flow of calculating the objective function based on NSGA-II is shown in fig. 3.

In the embodiment of the present invention, at least two sets of configuration results may be obtained by calculating an objective function according to basic data and constraint conditions, and a schematic diagram of the configuration results is shown in fig. 4, where each result is represented by a REUR value and a cost value corresponding to the REUR value, each REUR value has a corresponding cost value, and the cost value corresponding to any one of the REUR values represents a minimum cost required for building the photovoltaic charging station integrated system at the REUR value, and the cost value is an optimal solution corresponding to the REUR value.

Step 230: and selecting a target configuration result according to a preset use requirement from the at least two groups of configuration results, and performing hardware configuration on the photovoltaic charging station integrated system according to a hardware configuration scheme corresponding to the target configuration result.

For better understanding of the embodiment of the present invention, the following specific application scenarios are given, and further detailed description is made for a process of optimizing the configuration of the photovoltaic charging station integrated system (see fig. 5A):

for an example of an optimal configuration of building the photovoltaic charging station integrated system B in the area a (36 ° north latitude 41'), the lighting statistics of the area a for one year is shown in fig. 5B, and the daily charging power demand of the area a is shown in fig. 5C.

Step 500: and B, determining an objective function required by hardware configuration, wherein the objective function at least comprises performance parameters representing system cost and system power.

In this embodiment, the objective function is:

<math> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mi>min</mi> <msub> <mi>C</mi> <mi>&Sigma;</mi> </msub> <mo>=</mo> <mi>min</mi> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>PV</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>B</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>1</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>2</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mi>DC</mi> <mn>3</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>AD</mi> </msub> <mo>+</mo> <msub> <mi>C</mi> <mi>G</mi> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mi>max</mi> <mi>REUR</mi> <mo>=</mo> <mi>max</mi> <mfrac> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </mfrac> <mo>&times;</mo> <mn>100</mn> <mo>%</mo> </mtd> </mtr> </mtable> </mfenced> </math>

step 510: and determining the constraint conditions of the preset performance parameters.

In this embodiment, the constraint conditions are:

<math> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>N</mi> <mi>PV</mi> </msub> <mo>&le;</mo> <mn>2400</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> <mo>&le;</mo> <msub> <mi>N</mi> <mi>B</mi> </msub> <mo>&le;</mo> <mn>1400</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>DC</mi> <mn>3</mn> </mrow> </msub> <mo>&le;</mo> <mn>25</mn> </mtd> </mtr> </mtable> </mfenced> </math>

P_PV(t)*0.97＝P_EV(t)/0.97+P_B(t)/0.98; and

P_EV(t)/0.97＝P_PV(t)*0.97+P_B(t)*0.98+P_G(t)*0.97

step 520: and acquiring basic data of the B, wherein the basic data is used for indicating the value of the performance parameter.

In this embodiment, the basic data mainly includes: illumination statistics for area a for one year: as shown in fig. 5B, the average daily charge capacity requirement in the a region for one year: as shown in fig. 5C; b, the standard matching parameters of all the constituent units, such as the efficiency of the photovoltaic cell, are as follows: 80%, the efficiency of the photovoltaic converter module is: 97%, the efficiency of electric automobile charging module does: 97%, the efficiency of the energy storage converter module is: 98%, the efficiency of grid-connected converter module is: 97%, etc.

Step 530: and calculating an objective function by adopting NSGA-II according to the basic data and the constraint conditions to obtain at least two groups of configuration results.

In this embodiment, the process of calculating the objective function based on NSGA-II is shown in fig. 3, and a schematic diagram of the configuration result is shown in fig. 4.

Step 540: and selecting a target configuration result from at least two groups of configuration results according to a preset use requirement.

For example, the target resur of the a area is 15%, and the maximum acceptable cost obtained by budgeting is 150 ten thousand for 15%, and the minimum cost required for building B is 125 ten thousand for 15% of the obtained configuration results, so the selected target configuration result is: REUR is 15%, and the cost required to establish B is 125 ten thousand.

Step 550: and carrying out hardware configuration according to the hardware configuration scheme B corresponding to the target configuration result.

Based on the above technical solution, referring to fig. 6, in an embodiment of the present invention, an optimization apparatus includes a first determining unit 60, a second determining unit 61, a calculating unit 62, and a configuring unit 63, wherein,

a first determining unit 60, configured to determine an objective function that is required to be used for hardware configuration of the photovoltaic charging station integrated system, where the objective function at least includes performance parameters representing system cost and system power;

the second determining unit 61 is configured to determine a constraint condition of a preset performance parameter, and acquire basic data of the photovoltaic charging station integrated system, where the basic data is used to indicate a value of the performance parameter;

a calculating unit 62, configured to calculate an objective function according to the basic data and the constraint conditions, and obtain at least two sets of configuration results;

and the configuration unit 63 is configured to select a target configuration result according to a preset use requirement from the at least two sets of configuration results, and perform hardware configuration on the photovoltaic charging station integrated system according to a hardware configuration scheme corresponding to the target configuration result.

In the embodiment of the present invention, the objective function determined by the first determining unit 60 and required to be used for hardware configuration of the photovoltaic charging station integrated system is calculated based on at least a minimum cost function and a maximum renewable energy utilization function, where the cost function is calculated by using at least performance parameters representing system costs corresponding to each component unit of the photovoltaic charging station integrated system, and the renewable energy utilization function is calculated by using at least performance parameters representing system power.

In this embodiment of the present invention, the constraint condition of the preset performance parameter determined by the second determining unit 61 includes: determining the number value range of each component unit corresponding to the performance parameter representing the system cost, and determining the balance condition met by the performance parameter representing the system power.

Preferably, the calculating unit 62 is specifically configured to calculate the objective function by using a multi-objective optimization algorithm according to the basic data and the constraint condition, where the multi-objective optimization algorithm at least includes: NSGA-II; or, a multi-objective differential evolution algorithm; or, a multi-target particle swarm algorithm.

To sum up, in the embodiments of the present invention, an objective function to be used for hardware configuration of a photovoltaic charging station integrated system is determined, where the objective function at least includes performance parameters representing system cost and system power, a constraint condition of a preset performance parameter is determined, and basic data of the photovoltaic charging station integrated system is obtained, where the basic data is used to indicate a value of the performance parameter, then the objective function is calculated according to the basic data and the constraint condition to obtain at least two sets of configuration results, and finally, a target configuration result is selected according to a preset use requirement from the at least two sets of configuration results, and the hardware configuration is performed on the photovoltaic charging station integrated system according to a hardware configuration scheme corresponding to the target configuration result, so that an optimal hardware configuration scheme corresponding to the photovoltaic charging station integrated systems in different areas under different photovoltaic utilization rates is obtained, and the optimal configuration scheme suitable for each area can be determined by different areas according to preset use requirements.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit or scope of the embodiments of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.

Claims (12)

1. A method of optimizing a photovoltaic charging station integration system, comprising:

determining an objective function required to be used for hardware configuration of a photovoltaic charging station integrated system, wherein the objective function at least comprises performance parameters representing system cost and system power; the method comprises the steps that an objective function required by hardware configuration of a photovoltaic charging station integrated system is calculated at least based on a minimum cost function and a maximum renewable energy utilization function, the cost function is calculated at least by adopting performance parameters representing system cost corresponding to each component unit of the photovoltaic charging station integrated system, and the renewable energy utilization function is calculated at least by adopting performance parameters representing system power;

determining a preset constraint condition of the performance parameter, and acquiring basic data of the photovoltaic charging station integrated system, wherein the basic data is used for indicating the value of the performance parameter; the determining of the preset constraint condition of the performance parameter includes: determining the number value range of each component unit corresponding to the performance parameter representing the system cost, and determining the balance condition met by the performance parameter representing the system power;

calculating the objective function according to the basic data and the constraint conditions to obtain at least two groups of configuration results;

and selecting a target configuration result according to a preset use requirement from the at least two groups of configuration results, and performing hardware configuration on the photovoltaic charging station integrated system according to a hardware configuration scheme corresponding to the target configuration result.

2. The method of claim 1, wherein the cost function is:

C_Σ＝(C_PV+C_B+C_DC1+C_DC2+C_DC3+C_AD+C_G) (ii) a Or,

C_Σ＝(C_PV+C_B+C_G) (ii) a Or,

C_Σ＝(C_PV+C_B+C_DC1+C_DC2+C_DC3+C_AD) (ii) a Or,

C_Σ＝(C_DC1+C_DC2+C_DC3+C_AD)；

the renewable energy utilization function is:

<math> <mrow> <mi>REUR</mi> <mo>=</mo> <mfrac> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </mfrac> <mo>&times;</mo> <mn>100</mn> <mo>%</mo> <mo>;</mo> </mrow> </math> or,

<math> <mrow> <mi>RPUR</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>8760</mn> </munderover> <mo>[</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>]</mo> <mo>/</mo> <mn>8760</mn> </mrow> <mrow> <msub> <mi>N</mi> <mi>PV</mi> </msub> <mo>&CenterDot;</mo> <msub> <mi>P</mi> <mi>PVN</mi> </msub> </mrow> </mfrac> <mo>;</mo> </mrow> </math> or,

<math> <mrow> <mi>REUR</mi> <mo>=</mo> <mfrac> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>PV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </mfrac> <mo>&times;</mo> <mn>100</mn> <mo>%</mo> <mo>,</mo> </mrow> </math>

wherein, C_ΣTotal cost, C, for the photovoltaic charging station integrated system_PVFor the total cost of the photovoltaic cells, C, included in the photovoltaic charging station integrated system_BFor the total cost, C, of the energy storage cells comprised in the photovoltaic charging station integrated system_DC1For the total cost of the photovoltaic converter modules, C, included in the photovoltaic charging station integrated system_DC2Total cost, C, for charging modules included in the photovoltaic charging station integrated system_DC3For the total cost, C, of the energy storage converter module included in the photovoltaic charging station integrated system_ADThe total cost and C of grid-connected converter modules included in the photovoltaic charging station integrated system_GTotal electricity charge, REUR, for electricity purchasing from the grid included in the photovoltaic charging station integrated system to renewable energy utilization, P_EV(t) charging power of electric vehicle at time t, P_G(t) power absorbed by the electric vehicle from the grid at time t, N_PVIs the number of photovoltaic cells, P, in a photovoltaic cell stack_PVNRated capacity, P, for a single group of photovoltaic cells_PVAnd (t) is the generated power of the photovoltaic cell at the time t.

3. The method of claim 1, wherein the number of the component units corresponding to the performance parameter representing the system cost is in a range of:

<math> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>N</mi> <mi>PV</mi> </msub> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>PV</mi> <mo>.</mo> <mi>max</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> <mo>&le;</mo> <msub> <mi>N</mi> <mi>B</mi> </msub> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>B</mi> <mo>.</mo> <mi>max</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>DC</mi> <mn>3</mn> </mrow> </msub> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>DC</mi> <mn>3</mn> <mo>.</mo> <mi>max</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> </math>

wherein N is_PVNumber of photovoltaic cells in a photovoltaic cell group, N_BFor the number of energy storage cells, N, in the energy storage battery pack_DC3Number of energy-storing current-transforming modules, N_PV.maxIs the maximum number of photovoltaic cells, N, in a photovoltaic cell group_B.maxFor the maximum number, N, of energy storage cells in an energy storage battery pack_DC3.maxIs the maximum number of energy storage conversion modules.

4. The method of claim 1, wherein the performance parameter characterizing system power satisfies a balance condition of:

P_PV(t)η_DC1＝P_EV(t)/η_DC2+P_B(t)/η_DC3(equation twenty); and

P_EV(t)/η_DC2＝P_PV(t)η_DC1+P_B(t)η_DC3+P_G(t)η_AD(the formula twenty-one),

the formula twenty is a performance parameter representing the system power when the energy storage battery is in a charging state, and the formula twenty-one is a performance parameter representing the system power when the energy storage battery is in a discharging state, P_PV(t) generated Power of photovoltaic cell at time t, P_EV(t) charging power of electric vehicle at time t, P_G(t) power P absorbed by the electric vehicle from the grid at time t_B(t) is the charging and discharging power, eta of the energy storage battery pack at the time t_DC1For the working efficiency, eta, of the photovoltaic converter module_DC2Working efficiency, eta, for charging modules_DC3Working efficiency, eta, for energy-storing converter modules_ADThe working efficiency of the grid-connected converter module is improved.

5. The method of claim 1, wherein computing the objective function based on the base data and the constraints comprises:

and calculating the objective function by adopting a multi-objective optimization algorithm according to the basic data and the constraint conditions.

6. The method of claim 5, wherein computing the objective function using a multi-objective optimization algorithm comprises:

calculating the objective function by adopting a fast non-dominated sorting genetic algorithm NSGA-II; or,

calculating the objective function by adopting a multi-objective differential evolution algorithm; or,

and calculating the objective function by adopting a multi-objective particle swarm algorithm.

7. An apparatus for optimizing a photovoltaic charging station integrated system, comprising:

the system comprises a first determination unit, a second determination unit and a control unit, wherein the first determination unit is used for determining an objective function required to be used for hardware configuration of the photovoltaic charging station integrated system, and the objective function at least comprises performance parameters representing system cost and system power; the method comprises the steps that a target function needed to be used for hardware configuration of a photovoltaic charging station integrated system is determined, calculation is carried out at least based on a minimum cost function and a maximum renewable energy utilization function, the cost function is calculated at least by adopting performance parameters representing system cost corresponding to all component units of the photovoltaic charging station integrated system, and the renewable energy utilization function is calculated at least by adopting performance parameters representing system power;

the second determining unit is used for determining a preset constraint condition of the performance parameter and acquiring basic data of the photovoltaic charging station integrated system, wherein the basic data is used for indicating a value of the performance parameter; the determined preset constraint conditions of the performance parameters comprise: determining the number value range of each component unit corresponding to the performance parameter representing the system cost, and determining the balance condition met by the performance parameter representing the system power;

the calculation unit is used for calculating the objective function according to the basic data and the constraint conditions to obtain at least two groups of configuration results;

and the configuration unit is used for selecting a target configuration result according to a preset use requirement in the at least two groups of configuration results, and performing hardware configuration on the photovoltaic charging station integrated system according to a hardware configuration scheme corresponding to the target configuration result.

8. The apparatus of claim 7, wherein the first determination unit determines the cost function based on which the objective function calculation needed to use for hardware configuration of the photovoltaic charging station integrated system is based on:

C_Σ＝(C_PV+C_B+C_DC1+C_DC2+C_DC3+C_AD+C_G) (ii) a Or,

C_Σ＝(C_PV+C_B+C_G) (ii) a Or,

C_Σ＝(C_PV+C_B+C_DC1+C_DC2+C_DC3+C_AD) (ii) a Or,

C_Σ＝(C_DC1+C_DC2+C_DC3+C_AD)；

the renewable energy utilization function based on which the objective function calculation required for hardware configuration of the photovoltaic charging station integrated system determined by the first determination unit is based on:

<math> <mrow> <mi>REUR</mi> <mo>=</mo> <mfrac> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </mfrac> <mo>&times;</mo> <mn>100</mn> <mo>%</mo> <mo>;</mo> </mrow> </math> or,

<math> <mrow> <mi>RPUR</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>8760</mn> </munderover> <mo>[</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>]</mo> <mo>/</mo> <mn>8760</mn> </mrow> <mrow> <msub> <mi>N</mi> <mi>PV</mi> </msub> <mo>&CenterDot;</mo> <msub> <mi>P</mi> <mi>PVN</mi> </msub> </mrow> </mfrac> <mo>;</mo> </mrow> </math> or,

<math> <mrow> <mi>REUR</mi> <mo>=</mo> <mfrac> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>EV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> <mrow> <msubsup> <mo>&Integral;</mo> <mn>0</mn> <mn>8760</mn> </msubsup> <msub> <mi>P</mi> <mi>PV</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mi>dt</mi> </mrow> </mfrac> <mo>&times;</mo> <mn>100</mn> <mo>%</mo> <mo>,</mo> </mrow> </math>

wherein, C_ΣTotal cost, C, for the photovoltaic charging station integrated system_PVFor the total cost of the photovoltaic cells, C, included in the photovoltaic charging station integrated system_BFor the total cost, C, of the energy storage cells comprised in the photovoltaic charging station integrated system_DC1For the total cost of the photovoltaic converter modules, C, included in the photovoltaic charging station integrated system_DC2Total cost, C, for charging modules included in the photovoltaic charging station integrated system_DC3For the total cost, C, of the energy storage converter module included in the photovoltaic charging station integrated system_ADThe total cost and C of grid-connected converter modules in the photovoltaic charging station integrated system_GTotal electricity charge for purchasing electricity from an electrical grid included in the photovoltaic charging station integrated system, REUR, renewable energy utilization, P_EV(t) charging power of electric vehicle at time t, P_G(t) power absorbed by the electric vehicle from the grid at time t, N_PVIs the number of photovoltaic cells, P, in a photovoltaic cell stack_PVNRated capacity, P, for a single group of photovoltaic cells_PVAnd (t) is the generated power of the photovoltaic cell at the time t.

9. The apparatus according to claim 7, wherein the number of each constituent unit corresponding to the performance parameter characterizing the system cost determined by the second determining unit ranges from:

<math> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <mn>0</mn> <mo>&lt;</mo> <msub> <mi>N</mi> <mi>PV</mi> </msub> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>PV</mi> <mo>.</mo> <mi>max</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> <mo>&le;</mo> <msub> <mi>N</mi> <mi>B</mi> </msub> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>B</mi> <mo>.</mo> <mi>max</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>DC</mi> <mn>3</mn> </mrow> </msub> <mo>&le;</mo> <msub> <mi>N</mi> <mrow> <mi>DC</mi> <mn>3</mn> <mo>.</mo> <mi>max</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> </math>

wherein N is_PVNumber of photovoltaic cells in a photovoltaic cell group, N_BFor the number of energy storage cells, N, in the energy storage battery pack_DC3Number of energy-storing current-transforming modules, N_PV.maxIs the maximum number of photovoltaic cells, N, in a photovoltaic cell group_B.maxFor the maximum number, N, of energy storage cells in an energy storage battery pack_DC3.maxIs the maximum number of energy storage conversion modules.

10. The apparatus of claim 7, wherein the performance parameter characterizing system power determined by the second determining unit satisfies a balance condition of:

P_PV(t)η_DC1＝P_EV(t)/η_DC2+P_B(t)/η_DC3(equation twenty); or

P_EV(t)/η_DC2＝P_PV(t)η_DC1+P_B(t)η_DC3+P_G(t)η_AD(equation twenty-one))，

The formula twenty is a performance parameter representing the system power when the energy storage battery is in a charging state, and the formula twenty-one is a performance parameter representing the system power when the energy storage battery is in a discharging state, P_PV(t) generated Power of photovoltaic cell at time t, P_EV(t) charging power of electric vehicle at time t, P_G(t) power P absorbed by the electric vehicle from the grid at time t_B(t) is the charging and discharging power, eta of the energy storage battery pack at the time t_DC1For the working efficiency, eta, of the photovoltaic converter module_DC2Working efficiency, eta, for charging modules_DC3Working efficiency, eta, for energy-storing converter modules_ADThe working efficiency of the grid-connected converter module is improved.

11. The apparatus as recited in claim 7, said computing unit to:

and calculating the objective function by adopting a multi-objective optimization algorithm according to the basic data and the constraint conditions.

12. The apparatus of claim 11, wherein the multi-objective optimization algorithm employed by the computation unit to compute the objective function comprises:

a fast non-dominated sorting genetic algorithm NSGA-II; or,

a multi-target differential evolution algorithm; or,

and (4) performing multi-target particle swarm algorithm.