阅读说明：本技术 一种广域分布的电动汽车充电方法及系统 (Wide-area-distribution electric vehicle charging method and system ) 是由 史双龙 严喆 李帅华 邢宇恒 江正涛 于 2018-07-20 设计创作，主要内容包括：一种广域分布的大规模电动汽车充电策略及系统,包括：基于各次级控制中心,将电动汽车的电池信息和充电时间带入次级控制中心优化模型中计算；将计算结果和新能源发电的预测出力带入两阶段优化模型中计算,得出各次级控制中心的负荷指导曲线；基于负荷指导曲线制定次级控制中心下每个电动汽车的充电负荷跟随指导曲线；电动汽车根据充电负荷跟随指导曲线进行充电。本发明的电动汽车有序充电负荷跟随的策略都可以嵌入分层控制策略中,通过分层控制策略,能够解决广域分布的大规模电动汽车充电时与清洁能源发电的写协同控制问题,使得电动汽车充电行为能够更好地与间歇性新能源发电相配合,从而为社会.电网.电动汽车运行商创造经济效益和社会效益。(A wide-area distributed large-scale electric vehicle charging strategy and system comprises: based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a secondary control center optimization model for calculation; substituting the calculation result and the predicted output of the new energy power generation into a two-stage optimization model for calculation to obtain a load guidance curve of each secondary control center; formulating a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve; and the electric automobile is charged according to the charging load following the guide curve. The strategy of the electric automobile ordered charging load following can be embedded into a layered control strategy, and the write cooperative control problem of wide-area distributed large-scale electric automobile charging and clean energy power generation can be solved through the layered control strategy, so that the charging behavior of the electric automobile can be better matched with intermittent new energy power generation, and economic benefits and social benefits are created for society, power grids and electric automobile operators.)

1. A wide-area distributed electric vehicle charging method is characterized by comprising the following steps:

based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation;

substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain a load guidance curve of each secondary control center;

the secondary control center formulates a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center;

the electric automobile is charged along the guide curve according to the charging load;

the battery of the electric automobile is as follows: a lithium ion battery.

2. The method of charging an electric vehicle according to claim 1,

the battery information of the electric vehicle includes: power, capacity and battery state of charge, SOC;

the charging time includes: and charging start-stop time.

3. The electric vehicle charging method of claim 1, wherein the two-stage optimization model comprises:

and a first stage objective function taking the peak clipping and valley filling of the total load as an objective.

And the first-stage objective function is corresponding to the constraint condition.

Second stage objective function and method aimed at stabilizing desired total load curve fluctuations at each secondary control center

And the constraint condition corresponding to the objective function of the second stage.

4. The method of charging an electric vehicle of claim 3, wherein the first stage objective function is calculated as follows:

in the formula (f)₁A first stage objective function taking total load peak clipping and valley filling as targets; g_i,tGuiding the load of the electric automobile at the moment t of the ith secondary control center control area; d_i,tControlling the total conventional load of the area t moment for the ith secondary control center; r_i,tThe new energy output at the moment t of the ith secondary control center control area is shown as omega, the set of the secondary control centers is shown as tau, and tau is excellentThe aging period.

5. The method of claim 4, wherein the constraints associated with the first-stage objective function include:

R_i,t≤G_i,t+D_i,t

in the formula (I), the compound is shown in the specification,

And setting an upper limit of the electricity abandoning proportion of the new energy.

6. The method of charging an electric vehicle according to claim 4, wherein the second stage objective function is calculated as follows:

in the formula (f)₂The load value is directed for time t.

7. The method according to claim 6, wherein the constraints associated with the second stage objective function include:

in the formula (I), the compound is shown in the specification,

8. The electric vehicle charging method according to claim 3, wherein the step of bringing the calculation results of the optimization models of all the secondary control centers and the predicted output of the new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of the secondary control centers comprises the steps of:

substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a two-stage optimization model, and solving the first-stage objective function to obtain a minimum value;

setting a weight coefficient;

and substituting the minimum value of the first-stage objective function and the weight coefficient into the second-stage objective function to solve to obtain a charging load guidance curve and new energy output of each secondary control center:

wherein the weighting factor is greater than 1.

9. The electric vehicle charging method of claim 1, wherein the secondary control center optimization model comprises:

and the secondary control objective function and the corresponding secondary control constraint condition take the minimum Euclidean distance between the total charging load curve and the load guide curve of the electric automobile as a target.

10. The method of charging an electric vehicle of claim 9, wherein the secondary control objective function is as follows:

in the formula, P_tIs the total charging load of the electric automobile at the time t, G_tThe component of the load guidance curve at time t that the main control center is required to follow is given.

11. The method of charging an electric vehicle of claim 10, wherein the secondary control constraints comprise:

in the formula (I), the compound is shown in the specification,

wherein, the

P _t＝0

wherein is the chargeable period of the jth electric vehicle, and τ_j＝[τ_begin,j,τ_end,j]；

the above-mentioned

in the formula (I), the compound is shown in the specification,

12. The method for charging the electric vehicle according to claim 11, wherein the step of bringing the battery information and the charging time of the electric vehicle into a pre-constructed optimization model of the secondary control center for calculation based on each secondary control center comprises the following steps:

the solution is carried out by using a direct solution method, a distributed algorithm or a probability transfer matrix method.

13. The electric vehicle charging method according to claim 1, wherein the secondary control center formulates a charging load following guidance curve for each electric vehicle under the secondary control center based on the load guidance curve of the secondary control center, and the method comprises the following steps: and setting the charging power of each electric automobile at each moment in the time range of the load guidance curve.

14. The method of charging an electric vehicle according to claim 13, wherein the electric vehicle follows a guideline curve for charging according to the charging load, comprising: the electric automobile adjusts the charging power according to the charging time and the charging power set at the charging time.

15. The method of charging an electric vehicle according to any one of claims 1 to 14, wherein the positive electrode of the lithium ion battery comprises a current collector, an active material layer and a polymer conductive layer, which are sequentially disposed;

the active material layer includes a first active material layer, a second active material layer, and a third active material layer; the first active material layer is made of lithium cobaltate particles, a binder and a thickening agent, the second active material layer is made of lithium cobaltate particles, lithium nickel cobalt manganese oxide particles, a binder and a thickening agent, and the third active material layer is made of lithium nickel cobalt manganese oxide particles, a binder and a thickening agent;

the polymer conducting layer is made of conducting polymer, inorganic filler and binder, and the conducting polymer is selected from polyaniline, polythiophene or polypyrrole.

16. The method for charging an electric vehicle according to claim 15, wherein the inorganic filler is selected from titanium dioxide, zirconium dioxide or silicon dioxide.

17. The method of charging an electric vehicle according to claim 15, wherein the binder of the first active material layer, the second active material layer, the third active material layer and the polymer conductive layer is PVDF.

18. A wide-area distributed large-scale electric vehicle charging system, comprising: the secondary control center module, the main control center module and the electric vehicle charging calculation module;

the secondary control center computing module is configured to: based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation, and uploading the calculation result to a control center calculation module; the system is also used for formulating a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center and issuing the curve to the corresponding electric automobile;

the control center calculation module: substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of all the secondary control centers, and sending the load guidance curves of the secondary control centers to corresponding secondary control center modules;

electric automobile calculation module that charges: and the other electric vehicle is used for charging according to the charging load following the guide curve.

Technical Field

The invention relates to the fields of electric vehicle charging, new energy consumption, computer technology and the like, in particular to a method and a system for charging electric vehicles in wide area distribution.

Background

The charging requirements of the electric automobile have certain controllability and certain randomness. In addition, the clean energy power generation mainly based on wind power generation and photovoltaic power generation is limited by natural conditions, and the output of the clean energy power generation is random and intermittent. The method solves the problems of complex structure, large impact on a power grid and a battery and the like of the conventional high-power wired charging equipment by considering the charging requirement of the electric automobile and the uncertainty of the generated output of the clean energy, realizes the ordered charging control suitable for the cooperation of the large-scale electric automobile and the clean energy generation, can realize the cleanness of the electric automobile under the cooperative charging strategy, and is one of the difficulties of the current research in the industry.

Aiming at the aspect of a clean energy collaborative charging strategy such as electric vehicles, wind energy, solar energy and the like, an evaluation index is formulated based on an electric vehicle and power grid interaction platform framework, and the effect of the electric vehicle on absorbing new energy fluctuation under different interaction intentions is analyzed. For example, some research researches develop a collaborative optimization scheduling model of EV and distributed energy under different time scales, and verify that the charging load is scheduled under the model, so that the equivalent load of a power grid is stabilized by using the charging load. Although the research comprehensively considers the combined operation optimization of the electric automobile and the distributed power supply and the energy storage system, the research is basically carried out from the angle of regulation and control of the electric automobile, and the influence on the power demand, the load characteristic and the gasoline consumption cannot be well solved.

Disclosure of Invention

In order to solve the technical problems in the prior art, the invention provides a wide-area distributed large-scale electric vehicle charging strategy and system.

The technical scheme provided by the invention is as follows:

based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation;

substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain a load guidance curve of each secondary control center;

the secondary control center formulates a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center;

the electric automobile is charged along the guide curve according to the charging load;

the battery of the electric automobile is as follows: a lithium ion battery with high storage performance.

Wherein the battery information of the electric vehicle includes: power, capacity and battery state of charge, SOC;

the charging time includes: and charging start-stop time.

Preferably, the two-stage optimization model includes:

and a first stage objective function taking the peak clipping and valley filling of the total load as an objective.

And the first-stage objective function is corresponding to the constraint condition.

Second stage objective function and method aimed at stabilizing desired total load curve fluctuations at each secondary control center

And the constraint condition corresponding to the objective function of the second stage.

Wherein the first stage objective function is calculated as follows:

in the formula (f)₁A first stage objective function taking total load peak clipping and valley filling as targets; g_i,tGuiding the load of the electric automobile at the moment t of the ith secondary control center control area; d_i,tControlling the total conventional load of the area t moment for the ith secondary control center; r_i,tAnd the new energy output at the moment t of the ith secondary control center control area is shown, wherein omega is the set of the secondary control centers, and tau is the optimization time interval.

Specifically, the constraint conditions corresponding to the first-stage objective function include:

R_i,t≤G_i,t+D_i,t

in the formula (I), the compound is shown in the specification,

P _i,trespectively setting the upper limit and the lower limit of the total charging power of the electric automobile at the moment t of the ith secondary control center;

E _i,trespectively setting the upper limit and the lower limit of the total charging energy of the electric automobile at the moment t of the ith secondary control center; Δ t is the time interval;

R _i,trespectively corresponding upper and lower limits of new energy output at the moment t of the ith secondary control center; and lambda is the set upper limit of the power abandoning proportion of the new energy.

Preferably, the calculation formula of the second stage objective function is as follows:

in the formula (f)₂The load value is directed for time t.

The corresponding constraint conditions of the second stage objective function comprise:

in the formula (I), the compound is shown in the specification,in order to be the weight coefficient,

is the minimum of the first stage objective function.

Preferably, the step of substituting the calculation results of the optimization models of all the secondary control centers and the predicted output of the new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of all the secondary control centers includes:

substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a two-stage optimization model, and solving the first-stage objective function to obtain a minimum value;

setting a weight coefficient;

and substituting the minimum value of the first-stage objective function and the weight coefficient into the second-stage objective function to solve to obtain a charging load guidance curve and new energy output of each secondary control center:

wherein the weighting factor is greater than 1.

Wherein the secondary control center optimization model comprises:

and the secondary control objective function and the corresponding secondary control constraint condition take the minimum Euclidean distance between the total charging load curve and the load guide curve of the electric automobile as a target.

Specifically, the secondary control objective function is as follows:

in the formula, P_tIs the total charging load of the electric automobile at the time t, G_tThe component of the load guidance curve at time t that the main control center is required to follow is given.

Specifically, the secondary control constraints include:

in the formula (I), the compound is shown in the specification,

P _tthe upper limit and the lower limit of the total charging power of the electric automobile at the moment t; e_tFor the total energy that the electric vehicle has been charged at time t, E _tthe upper limit and the lower limit of the total charging energy of the electric automobile at the moment t;

wherein, the

P_tSatisfies the following conditions:

P_t＝0

wherein is the chargeable period of the jth electric vehicle, and τ_j＝[τ_begin,j,τ_end,j]；

R_jThe sum of rated charging power of the electric automobile at the time t is contained in all chargeable periods; p_limitIs the total power upper limit;

the above-mentioned

E _tSatisfies the following conditions:

in the formula (I), the compound is shown in the specification,

the energy required by the jth electric automobile.

Preferably, the bringing the battery information and the charging time of the electric vehicle into a pre-constructed secondary control center optimization model for calculation based on each secondary control center includes:

the solution is carried out by using a direct solution method, a distributed algorithm or a probability transfer matrix method.

Preferably, the secondary control center formulates a charging load following guidance curve of each electric vehicle under the secondary control center based on the load guidance curve of the secondary control center, and the method includes: and setting the charging power of each electric automobile at each moment in the time range of the load guidance curve.

Preferably, the electric vehicle is charged according to the charging load following a guidance curve, and includes: the electric automobile adjusts the charging power according to the charging time and the charging power set at the charging time.

Preferably, the positive electrode of the lithium ion battery comprises a current collector, an active material layer and a polymer conducting layer which are arranged in sequence;

the active material layer includes a first active material layer, a second active material layer, and a third active material layer; the first active material layer is made of lithium cobaltate particles, a binder and a thickener, the second active material layer is made of lithium cobaltate particles, lithium nickel cobalt manganese oxide particles, a binder and a thickener, and the third active material layer is made of lithium nickel cobalt manganese oxide particles, a binder and a thickener.

The polymer conducting layer is made of conducting polymer, inorganic filler and binder, and the conducting polymer is selected from polyaniline, polythiophene or polypyrrole.

The binders in the first active material layer, the second active material layer, the third active material layer and the polymer conducting layer are all made of PVDF.

The conductive polymer is polyaniline.

The inorganic filler is selected from titanium dioxide, zirconium dioxide or silicon dioxide, preferably silicon dioxide.

The invention also provides a large-scale electric vehicle charging strategy and system based on the same invention concept, which are distributed in a wide area, and the strategy comprises the following steps: the secondary control center module, the main control center module and the electric vehicle charging calculation module;

the secondary control center computing module is configured to: based on each secondary control center, bringing the battery information and the charging time of the electric automobile into a pre-constructed secondary control center optimization model for calculation, and uploading the calculation result to a control center calculation module; the system is also used for formulating a charging load following guidance curve of each electric automobile under the secondary control center based on the load guidance curve of the secondary control center and issuing the curve to the corresponding electric automobile;

the control center calculation module: substituting the calculation results of all secondary control center optimization models and the predicted output of new energy power generation into a pre-constructed two-stage optimization model for calculation to obtain the load guidance curves of all the secondary control centers, and sending the load guidance curves of the secondary control centers to corresponding secondary control center modules;

electric automobile calculation module that charges: and the electric automobile is charged according to the charging load following the guide curve.

Compared with the prior art, the invention has the beneficial effects that:

1. the electric automobile cooperative charging hierarchical control strategy has good expandability, and strategies for realizing the ordered charging load following of the electric automobile can be embedded into the hierarchical control strategy. By means of the hierarchical control strategy, the write cooperative control problem of wide-area distributed large-scale electric automobile charging and clean energy power generation can be well solved, so that the electric automobile charging behavior can be better matched with intermittent new energy power generation, and therefore, the society, the power grid and the electric automobile operator create economic benefits and social benefits.

2. By using the lithium ion battery with high storage performance, the polymer conducting layer is present, so that the side reaction between the active substance and the electrolyte is relieved, and the decomposition of the electrolyte is avoided; meanwhile, lithium cobaltate provides high energy density and rate capability, and the nickel cobalt lithium manganate is stable in performance and provides high cycle life performance.

Drawings

FIG. 1 is a three-level architecture of a layered control model according to the present invention;

FIG. 2 is a flow chart of a hierarchical control method of the present invention;

FIG. 3 illustrates the conventional load and predicted wind power output of the secondary control center of the present invention;

FIG. 4 is a diagram of a secondary control center normal load versus a desired load for the present invention;

FIG. 5 is a total conventional load curve versus a total desired load curve (three regions) for the present invention;

FIG. 6 is a predicted wind power output versus an expected wind power output of the present invention;

fig. 7 shows the normal load and the desired load of the secondary control center according to the present invention.

Detailed Description

The basic idea of hierarchical control is to divide a control object into different hierarchies, and each hierarchy carries out control activities relatively independently on the basis of obeying the overall goal. The idea of layered control is clear, the expansion is easy, and the method is suitable for the optimization control of large-scale electric vehicles. Most of the layered control assumes that the charging situations and modes of all electric vehicles are consistent, and actually, the charging situations of the electric vehicles governed by different control centers are different, and the ordered charging control mode is also suitable according to local conditions. For example, electric vehicles corresponding to the jurisdiction are mainly charged in a centralized charging/converting station, and centralized control is suitable for the charging; if the electric automobile corresponding to the district is mainly charged in the widely distributed and sparse charging pile, distributed control is suitable to be adopted.

For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.