阅读说明：本技术 电动汽车有序充电控制方法、系统、设备及可读存储介质 (Electric vehicle ordered charging control method, system, equipment and readable storage medium ) 是由 张爱民 韩植 王珊 于 2021-07-27 设计创作，主要内容包括：本发明公开了电动汽车有序充电控制方法、系统、设备及可读存储介质,获取台区内每个待充电电动汽车对应的停车时段和预期充电电量,以及台区负荷和台区负荷变化曲线；根据每个待充电电动汽车对应的预期充电电量确定该待充电电动汽车达到预期充电电量所需的充电时长；根据每个待充电电动汽车的停车时段、每个待充电电动汽车达到预期充电电量所需的充电时长、台区负荷和台区负荷变化曲线,利用电动汽车有序充电控制模型制定每个待充电电动汽车对应的充电时段分配计划。本发明能有效考虑电网安全和电动汽车用户参与有序充电的决策行为特性及其相互影响,以满足用户充电需求并提高电网性能、减小配电网络负荷峰谷差,保障配电网络安全和稳定地运行。(The invention discloses an electric automobile ordered charging control method, a system, equipment and a readable storage medium, which are used for acquiring a parking time period and expected charging electric quantity corresponding to each electric automobile to be charged in a platform area, and a platform area load change curve; determining the charging time required by the electric automobile to be charged to reach the expected charging electric quantity according to the expected charging electric quantity corresponding to each electric automobile to be charged; and according to the parking period of each electric automobile to be charged, the charging time required for each electric automobile to be charged to reach the expected charging electric quantity, the distribution area load and the distribution area load change curve, making a charging period distribution plan corresponding to each electric automobile to be charged by using the electric automobile ordered charging control model. The invention can effectively consider the power grid safety and the decision behavior characteristics of the electric vehicle users participating in the ordered charging and the mutual influence thereof, so as to meet the charging requirements of the users, improve the performance of the power grid, reduce the load peak-valley difference of the power distribution network and ensure the safe and stable operation of the power distribution network.)

1. An orderly charging control method for an electric automobile is characterized by comprising the following steps:

acquiring a parking time interval and expected charging electric quantity corresponding to each electric automobile to be charged in a platform area, and a platform area load change curve;

determining the charging time required by the electric automobile to be charged to reach the expected charging electric quantity according to the expected charging electric quantity corresponding to each electric automobile to be charged;

and according to the parking period of each electric automobile to be charged, the charging time required for each electric automobile to be charged to reach the expected charging electric quantity, the distribution area load and the distribution area load change curve, making a charging period distribution plan corresponding to each electric automobile to be charged by using the electric automobile ordered charging control model.

2. The method for controlling orderly charging of electric vehicles according to claim 1, wherein according to the parking period of each electric vehicle to be charged, the charging duration required for each electric vehicle to be charged to reach the expected charging capacity, the distribution plan of the charging period corresponding to each electric vehicle to be charged is formulated by using the ordered charging control model of the electric vehicle, specifically:

dividing each hour into a plurality of time periods according to an equal interval dividing mode, wherein the time periods are used as a minimum charging time unit;

determining the number of charging time units required by each electric vehicle to be charged according to the charging time required by each electric vehicle to be charged to reach the expected charging electric quantity;

randomly distributing the number of the charging time units required by each electric vehicle to be charged to the corresponding parking time period to obtain an initial charging time unit distribution plan corresponding to each electric vehicle to be charged;

and obtaining a charging time period distribution plan corresponding to each electric vehicle to be charged by utilizing the electric vehicle ordered charging control model according to the distribution plan of the platform area load, the platform area load change curve and the initial charging time unit corresponding to each electric vehicle to be charged.

3. The ordered charging control method of the electric vehicle as claimed in claim 2, wherein the ordered charging control model of the electric vehicle comprises a calculation model and an optimization model, and the calculation model is as follows:

wherein the content of the first and second substances,

F₂＝min[max(P_lk')-min(P_lk')]

P_lk+P_k<P_T

in the formula, F is a total objective function of the calculation model; f₁The power grid of the transformer area contains the load fluctuation variance of the charging load of the electric automobile; f₂The power grid of the transformer area contains the peak-valley difference of the charging load curve of the electric automobile; f₃Charging cost for the electric vehicle when the electric vehicle participates in dispatching; f₁ ⁰Load fluctuation variance of the charging load of the electric vehicle is not contained in the power grid of the transformer area, and the load fluctuation variance is obtained through load prediction before the day; p_TThe rated power of the transformer; f₃ ⁰Charging cost when the electric automobile does not participate in dispatching; alpha is alpha₁Weight coefficient, alpha, for the load fluctuation variance of the grid₂Weight coefficient, alpha, for the peak-to-valley difference of the grid load curve₃Cost weighting factor for charging an electric vehicle, and α₁+α₂+α₃＝1；P_lkLoading the power grid of the transformer area in the kth time period without the charging load of the electric automobile; p_kCharging power for the charging station for the kth time period; p_avThe daily average load of a power grid of a transformer area without the charging load of the electric automobile is obtained; max (P)_lk') is a platform area power grid load peak value containing the charging load of the electric automobile;min(P_lk') is a platform district electric network load valley value containing the electric automobile charging load; x is the number of_iThe charging pile is in the working state of the ith minimum charging time unit, wherein '1' represents working, and '0' represents non-working; q_iThe power grid price of the ith minimum charging time unit; p_cCharging power for the charging pile; Δ t is the time interval size of the minimum charging time unit; t is the total elapsed time of the current vehicle charge; c_n,endThe expected electric quantity at the end of charging of the nth electric automobile; c_n,sartThe electric quantity when the nth electric automobile starts to be charged; c_n,maxThe maximum allowable electric quantity of the nth electric automobile;

the optimization model is as follows:

v_id＝ω*v_id+c₁*rand()*(p_id-x_id)+c₂*rand()*(p_ig-x_id)

in the formula, v_idThe velocity vector of the ith dimension of the group d in the particle swarm optimization is obtained; omega is an inertia weight coefficient in the particle swarm algorithm; c. C₁The method comprises the following steps of (1) obtaining a cognitive learning factor in a particle swarm algorithm; c. C₂The social learning factor in the particle swarm algorithm is adopted; p is a radical of_idThe optimal position of the ith dimension of the group of the d group is obtained; x is the number of_idAs the current group d populationThe particle position in the ith dimension; p is a radical of_igThe particle position of the ith dimension of the currently calculated optimal solution; s (v)_id) Represents the position x_idTaking the probability of 1; f (n) is the probability that the ith population is selected; fp_dThe optimal fitness of the d group in the artificial bee colony algorithm is adopted; x is the number of_id' is the calculated position of the new particle;the step length is in the range of [ -1,1 [ ]]；p_gAnd the optimal position of the population particles.

4. The ordered charging control method for the electric vehicles according to claim 1, wherein the method for acquiring the parking time interval and the expected charging electric quantity corresponding to each electric vehicle to be charged in the platform area comprises the following steps:

and receiving the parking time interval and the expected charging electric quantity corresponding to the electric automobile to be charged sent by the user.

5. The ordered charging control method for the electric automobile according to claim 1, wherein the method for acquiring the platform area load and the platform area load change curve comprises the following steps:

and reading the corresponding platform area load and the platform area load change curve in the database.

6. The ordered charging control method for the electric vehicles according to claim 1, wherein the charging time period required for the electric vehicle to be charged to reach the expected charging capacity is determined according to the expected charging capacity corresponding to each electric vehicle to be charged, and specifically comprises:

and dividing the expected charging electric quantity corresponding to each electric automobile to be charged by the charging power of the charging pile to obtain the charging time required by each electric automobile to be charged to reach the expected charging electric quantity.

7. The utility model provides an orderly charge control system of electric automobile which characterized in that includes:

the acquisition module is used for acquiring a parking time period and expected charging electric quantity corresponding to each electric vehicle to be charged in the platform area, and a platform area load change curve;

the charging time length determining module is used for determining the charging time length required by the electric automobile to be charged to reach the expected charging electric quantity according to the expected charging electric quantity corresponding to each electric automobile to be charged;

and the charging period distribution plan making module is used for making a charging period distribution plan corresponding to each electric vehicle to be charged by utilizing the electric vehicle ordered charging control model according to the parking period of each electric vehicle to be charged, the charging time required for each electric vehicle to be charged to reach the expected charging electric quantity, the platform area load and the platform area load change curve.

8. The ordered charging control system for the electric vehicle according to claim 7, wherein the charging period distribution planning module comprises:

the time period dividing module is used for dividing each hour into a plurality of time periods according to an equal interval dividing mode, and the time periods are used as a minimum charging time unit;

the charging time unit number determining module is used for determining the number of the charging time units required by each electric vehicle to be charged according to the charging time required by each electric vehicle to be charged to reach the expected charging electric quantity;

the system comprises an initial charging time unit distribution plan module, a charging time unit distribution plan module and a charging time unit distribution module, wherein the initial charging time unit distribution plan module is used for randomly distributing the number of charging time units required by each electric vehicle to be charged to a corresponding parking time period to obtain an initial charging time unit distribution plan corresponding to each electric vehicle to be charged;

and the charging period distribution plan module is used for obtaining a charging period distribution plan corresponding to each electric vehicle to be charged by utilizing the electric vehicle ordered charging control model according to the distribution plan of the platform area load, the platform area load change curve and the initial charging time unit distribution plan corresponding to each electric vehicle to be charged.

9. An apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of controlling orderly charging of electric vehicles according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored, and the computer program is executed by a processor to implement the steps of the orderly charging control method for electric vehicles according to any one of claims 1 to 6.

Technical Field

The invention belongs to the technical field of electric vehicle charging, and particularly relates to an electric vehicle ordered charging control method, system, equipment and a readable storage medium.

Background

With the popularization of green and low-carbon economy, more and more people can select electric automobiles, but large-scale electric automobiles are like small capacitors or power supplies and can continuously exchange energy with a power grid. At present, the charging mode of the electric automobile basically adopts a plug-and-play charging mode, and has randomness and similarity in time and space, the influence on a power grid is not considered, and the condition of 'adding peak on peak' can be generated, which inevitably increases the burden of a power distribution network.

In the related technology, the adverse effect of electric vehicle charging on a power distribution network is mostly improved, an optimization objective function is not established for a user side, and the user has no initiative. Most of the electric vehicles are researched by aiming at single charging user individuals, certain relation between the electric vehicles and a power distribution network is not considered, a plurality of electric vehicle centralized charging scenes can appear in a single distribution area along with popularization of the electric vehicles, and the effect is difficult to achieve by an existing ordered charging control strategy. Therefore, how to carry out ordered charging control on the electric automobile has important practical significance for guaranteeing electric energy supply and power grid operation safety of the electric automobile, improving the utilization rate of power grid equipment and bringing benefits to users.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides an electric vehicle ordered charging control method, a system, equipment and a readable storage medium, which can effectively consider the decision behavior characteristics and mutual influence of the power grid safety and the electric vehicle user participating in ordered charging so as to meet the charging requirements of the user, improve the power grid performance, reduce the peak-valley difference of the load of a power distribution network and ensure the safe and stable operation of the power distribution network.

In order to solve the technical problems, the invention is realized by the following technical scheme:

an orderly charging control method for an electric automobile comprises the following steps:

acquiring a parking time interval and expected charging electric quantity corresponding to each electric automobile to be charged in a platform area, and a platform area load change curve;

determining the charging time required by the electric automobile to be charged to reach the expected charging electric quantity according to the expected charging electric quantity corresponding to each electric automobile to be charged;

and according to the parking period of each electric automobile to be charged, the charging time required for each electric automobile to be charged to reach the expected charging electric quantity, the distribution area load and the distribution area load change curve, making a charging period distribution plan corresponding to each electric automobile to be charged by using the electric automobile ordered charging control model.

Further, according to the parking period of each electric vehicle to be charged, the charging time required for each electric vehicle to be charged to reach the expected charging capacity, the distribution point load and the distribution point load change curve, the electric vehicle ordered charging control model is used for making a charging period distribution plan corresponding to each electric vehicle to be charged, and the method specifically comprises the following steps:

dividing each hour into a plurality of time periods according to an equal interval dividing mode, wherein the time periods are used as a minimum charging time unit;

determining the number of charging time units required by each electric vehicle to be charged according to the charging time required by each electric vehicle to be charged to reach the expected charging electric quantity;

randomly distributing the number of the charging time units required by each electric vehicle to be charged to the corresponding parking time period to obtain an initial charging time unit distribution plan corresponding to each electric vehicle to be charged;

and obtaining a charging time period distribution plan corresponding to each electric vehicle to be charged by utilizing the electric vehicle ordered charging control model according to the distribution plan of the platform area load, the platform area load change curve and the initial charging time unit corresponding to each electric vehicle to be charged.

Further, the electric automobile ordered charging control model comprises a calculation model and an optimization model, wherein the calculation model comprises the following steps:

wherein the content of the first and second substances,

F₂＝min[max(P_lk')-min(P_lk')]

P_lk+P_k<P_T

in the formula, F is a total objective function of the calculation model; f₁The power grid of the transformer area contains the load fluctuation variance of the charging load of the electric automobile; f₂The power grid of the transformer area contains the peak-valley difference of the charging load curve of the electric automobile; f₃Charging cost for the electric vehicle when the electric vehicle participates in dispatching; f₁ ⁰Load fluctuation variance of the charging load of the electric vehicle is not contained in the power grid of the transformer area, and the load fluctuation variance is obtained through load prediction before the day; p_TThe rated power of the transformer; f₃ ⁰Charging cost when the electric automobile does not participate in dispatching; alpha is alpha₁Weight coefficient, alpha, for the load fluctuation variance of the grid₂Weight coefficient, alpha, for the peak-to-valley difference of the grid load curve₃Cost weighting factor for charging an electric vehicle, and α₁+α₂+α₃＝1；P_lkLoading the power grid of the transformer area in the kth time period without the charging load of the electric automobile; p_kCharging power for the charging station for the kth time period; p_avThe daily average load of a power grid of a transformer area without the charging load of the electric automobile is obtained; max (P)_lk') is a platform area power grid load peak value containing the charging load of the electric automobile; min (P)_lk') is a platform district electric network load valley value containing the electric automobile charging load; x is the number of_iThe charging pile is in the working state of the ith minimum charging time unit, wherein '1' represents working, and '0' represents non-working; q_iThe power grid price of the ith minimum charging time unit; p_cCharging power for the charging pile; Δ t is the time interval size of the minimum charging time unit; t is the total elapsed time of the current vehicle charge; c_n,endThe expected electric quantity at the end of charging of the nth electric automobile; c_n,sartThe electric quantity when the nth electric automobile starts to be charged; c_n,maxThe maximum allowable electric quantity of the nth electric automobile;

the optimization model is as follows:

v_id＝ω*v_id+c₁*rand()*(p_id-x_id)+c₂*rand()*(p_ig-x_id)

in the formula, v_idThe velocity vector of the ith dimension of the group d in the particle swarm optimization is obtained; omega is an inertia weight coefficient in the particle swarm algorithm; c. C₁The method comprises the following steps of (1) obtaining a cognitive learning factor in a particle swarm algorithm; c. C₂The social learning factor in the particle swarm algorithm is adopted; p is a radical of_idThe optimal position of the ith dimension of the group of the d group is obtained; x is the number of_idThe position of the ith dimension of the current group population is obtained; p is a radical of_igThe particle position of the ith dimension of the currently calculated optimal solution; s (v)_id) Represents the position x_idTaking the probability of 1; f (n) is the probability that the ith population is selected; fp_dThe optimal fitness of the d group in the artificial bee colony algorithm is adopted; x is the number of_id' is the calculated position of the new particle;the step length is in the range of [ -1,1 [ ]]；p_gAnd the optimal position of the population particles.

Further, the method for acquiring the parking time interval and the expected charging electric quantity corresponding to each electric vehicle to be charged in the platform area comprises the following steps:

and receiving the parking time interval and the expected charging electric quantity corresponding to the electric automobile to be charged sent by the user.

Further, the method for acquiring the platform load and the platform load change curve comprises the following steps:

and reading the corresponding platform area load and the platform area load change curve in the database.

Further, the determining, according to the expected charging electric quantity corresponding to each electric vehicle to be charged, a charging time length required for the electric vehicle to be charged to reach the expected charging electric quantity includes:

and dividing the expected charging electric quantity corresponding to each electric automobile to be charged by the charging power of the charging pile to obtain the charging time required by each electric automobile to be charged to reach the expected charging electric quantity.

An orderly charging control system of an electric vehicle, comprising:

the acquisition module is used for acquiring a parking time period and expected charging electric quantity corresponding to each electric vehicle to be charged in the platform area, and a platform area load change curve;

the charging time length determining module is used for determining the charging time length required by the electric automobile to be charged to reach the expected charging electric quantity according to the expected charging electric quantity corresponding to each electric automobile to be charged;

and the charging period distribution plan making module is used for making a charging period distribution plan corresponding to each electric vehicle to be charged by utilizing the electric vehicle ordered charging control model according to the parking period of each electric vehicle to be charged, the charging time required for each electric vehicle to be charged to reach the expected charging electric quantity, the platform area load and the platform area load change curve.

Further, the charging period distribution planning module includes:

the time period dividing module is used for dividing each hour into a plurality of time periods according to an equal interval dividing mode, and the time periods are used as a minimum charging time unit;

the charging time unit number determining module is used for determining the number of the charging time units required by each electric vehicle to be charged according to the charging time required by each electric vehicle to be charged to reach the expected charging electric quantity;

the system comprises an initial charging time unit distribution plan module, a charging time unit distribution plan module and a charging time unit distribution module, wherein the initial charging time unit distribution plan module is used for randomly distributing the number of charging time units required by each electric vehicle to be charged to a corresponding parking time period to obtain an initial charging time unit distribution plan corresponding to each electric vehicle to be charged;

and the charging period distribution plan module is used for obtaining a charging period distribution plan corresponding to each electric vehicle to be charged by utilizing the electric vehicle ordered charging control model according to the distribution plan of the platform area load, the platform area load change curve and the initial charging time unit distribution plan corresponding to each electric vehicle to be charged.

An apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method for ordered charging control of an electric vehicle when executing the computer program.

A computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of an electric vehicle ordered charging control method.

Compared with the prior art, the invention has at least the following beneficial effects: the invention comprehensively considers the requirements of a user side and a power grid side, takes the load fluctuation variance of the power grid side, the peak-valley difference of a load curve and the charging cost of the electric automobile during scheduling as objective functions, and can obtain the optimal charging period distribution plan corresponding to each electric automobile to be charged by utilizing the ordered charging control model of the electric automobile. The adverse effect of charging of the electric automobile on a power grid can be reduced to the maximum extent, and the personal benefits of users are maintained. The optimization model part in the ordered charging control model adopts a hybrid optimization algorithm of the particle swarm and the artificial bee colony, has higher calculation speed compared with a simple particle swarm optimization algorithm, a genetic algorithm and other models, and can greatly reduce the calculation time when calculating the optimal charging period distribution plan of each electric vehicle to be charged.

In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a flow chart of an orderly charging control method for an electric vehicle according to an embodiment of the present invention;

FIG. 2 is a flow chart of an ordered charge control model algorithm in an embodiment of the present invention;

FIG. 3 is a flow chart of the orderly charging of an electric vehicle in an embodiment of the present invention;

FIG. 4 is a graph of power load before and after ordered charging control in an industrial area with only the grid side as an objective function in an embodiment of the present invention;

fig. 5 is a graph of power load before and after the orderly charging control of the industrial area with the grid side and the user side as objective functions in the embodiment of the present invention.

FIG. 6 is a graph comparing the convergence performance of the PSO algorithm and the hybrid algorithm;

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, the method for controlling orderly charging of an electric vehicle according to the present invention specifically includes the following steps:

step 1: acquiring a parking time interval and expected charging electric quantity corresponding to each electric automobile to be charged in a platform area, and a platform area load change curve;

specifically, the method for acquiring the parking time interval and the expected charging electric quantity corresponding to each electric vehicle to be charged in the platform area comprises the following steps:

and receiving the parking time interval and the expected charging electric quantity corresponding to the electric automobile to be charged sent by the user.

The method for acquiring the load of the transformer area and the load change curve of the transformer area comprises the following steps:

and reading the corresponding platform area load and the platform area load change curve in the database.

Step 2: determining the charging time required by the electric automobile to be charged to reach the expected charging electric quantity according to the expected charging electric quantity corresponding to each electric automobile to be charged;

determining the charging time required by the electric vehicle to be charged to reach the expected charging electric quantity according to the expected charging electric quantity corresponding to each electric vehicle to be charged, specifically:

and dividing the expected charging electric quantity corresponding to each electric automobile to be charged by the charging power of the charging pile to obtain the charging time required by each electric automobile to be charged to reach the expected charging electric quantity.

And step 3: according to the parking period of each electric vehicle to be charged, the charging time required for each electric vehicle to be charged to reach the expected charging capacity, the distribution plan of the charging period corresponding to each electric vehicle to be charged is formulated by utilizing the ordered charging control model of the electric vehicle, and the distribution plan of the charging period is specifically as follows:

dividing each hour into a plurality of time periods according to an equal interval dividing mode, wherein the time periods are used as a minimum charging time unit;

determining the number of charging time units required by each electric vehicle to be charged according to the charging time required by each electric vehicle to be charged to reach the expected charging electric quantity;

randomly distributing the number of the charging time units required by each electric vehicle to be charged to the corresponding parking time period to obtain an initial charging time unit distribution plan corresponding to each electric vehicle to be charged;

and obtaining a charging time period distribution plan corresponding to each electric vehicle to be charged by utilizing the electric vehicle ordered charging control model according to the distribution plan of the platform area load, the platform area load change curve and the initial charging time unit corresponding to each electric vehicle to be charged.

The electric automobile ordered charging control model comprises a calculation model and an optimization model, wherein the calculation model comprises the following components:

wherein the content of the first and second substances,

F₂＝min[max(P_lk')-min(P_lk')]

P_lk+P_k<P_t

in the above formula, P_lk+P_k<P_tAndorderly charging control module for electric automobileConstraint of type P_lk+P_k<P_tIs the rated capacity constraint of the transformer in the transformer area,the electric quantity of the battery of the electric automobile is restricted;

in the formula, F is a total objective function of the calculation model; f₁The power grid of the transformer area contains the load fluctuation variance of the charging load of the electric automobile; f₂The power grid of the transformer area contains the peak-valley difference of the charging load curve of the electric automobile; f₃Charging cost for the electric vehicle when the electric vehicle participates in dispatching; f₁ ⁰Load fluctuation variance of the charging load of the electric vehicle is not contained in the power grid of the transformer area, and the load fluctuation variance is obtained through load prediction before the day; p_TThe rated power of the transformer; f₃ ⁰Charging cost when the electric automobile does not participate in dispatching; alpha is alpha₁Weight coefficient, alpha, for the load fluctuation variance of the grid₂Weight coefficient, alpha, for the peak-to-valley difference of the grid load curve₃Cost weighting factor for charging an electric vehicle, and α₁+α₂+α₃＝1；P_lkLoading the power grid of the transformer area in the kth time period without the charging load of the electric automobile; p_kCharging power for the charging station for the kth time period; p_avThe daily average load of a power grid of a transformer area without the charging load of the electric automobile is obtained; max (P)_lk') is a platform area power grid load peak value containing the charging load of the electric automobile; min (P)_lk') is a platform district electric network load valley value containing the electric automobile charging load; x is the number of_iThe charging pile is in the working state of the ith minimum charging time unit, wherein '1' represents working, and '0' represents non-working; q_iThe power grid price of the ith minimum charging time unit; p_cCharging power for the charging pile; Δ t is the time interval of the minimum charging time unit, and is fixed to 15 minutes in the embodiment; t is the total elapsed time of the current vehicle charge; c_n,endThe expected electric quantity at the end of charging of the nth electric automobile; c_n,sartThe electric quantity when the nth electric automobile starts to be charged; c_n,maxThe maximum allowable electric quantity of the nth electric automobile;

and the upper part of the calculation model calculates the total objective function of the calculation model according to the working state of the charging pile in each minimum charging time unit, so as to respectively obtain the self optimal fitness of each population and the optimal fitness of all the populations, wherein the fitness is the result of calculation on the total objective function. And then saving and updating the calculated result for the use of the lower part optimization model.

The optimization model is as follows:

v_id＝ω*v_id+c₁*rand()*(p_id-x_id)+c₂*rand()*(p_gd-x_id)

in the formula, v_idThe velocity vector of the ith dimension of the group d in the particle swarm optimization is obtained; omega is an inertia weight coefficient in the particle swarm algorithm; c. C₁The method comprises the following steps of (1) obtaining a cognitive learning factor in a particle swarm algorithm; c. C₂The social learning factor in the particle swarm algorithm is adopted; p is a radical of_idThe optimal position of the ith dimension of the group of the d group is obtained; x is the number of_idThe position of the ith dimension of the current group population is obtained; p is a radical of_igThe particle position of the ith dimension of the currently calculated optimal solution; s (v)_id) Represents the position x_idTaking the probability of 1; f (n) is the probability that the ith population is selected; fp_dThe optimal fitness of the d group in the artificial bee colony algorithm is adopted; x is the number of_id' is the calculated position of the new particle;the step length is in the range of [ -1,1 [ ]]；p_gAnd the optimal position of the population particles.

As shown in FIG. 2, the optimization model first passes through the particle swarm algorithm, and the position vector x is obtained by the particle swarm algorithm_idAnd velocity vector v_idInitializing, and entering an optimization model to start to calculate inertia weight, cognitive learning factors and social learning factors according to the optimal fitness of each population and the optimal fitness of all the populations obtained by the calculation model. The calculation formula adopts a discrete binary PSO algorithm. First, the particles are composed of binary codes, each binary code producing a respective velocity v_idI.e. the trend of the respective charging pile, the speed v_idIs related to a binary bit x_idThe probability of 1, namely the new working state of each charging pile is taken, so that the sigmoid function is adopted to map the speed to the interval [0,1]In the meantime. And then entering a calculation model to update the optimal fitness of each population and the optimal fitness of all the populations. And after the states of all the populations are updated, entering an artificial bee colony algorithm.

And calculating the next change trend of each charging pile according to the optimal fitness of each population and the optimal fitness of all the populations obtained by the calculation model, then obtaining the new state of each charging pile, entering the calculation model, and updating the optimal fitness of each population and the optimal fitness of all the populations. And after the states of all the populations are updated, entering an artificial bee colony algorithm.

And selecting by a roulette selection method according to the new state of each charging pile, the optimal fitness of each population and the optimal fitness of all the populations obtained by the particle swarm algorithm, randomly generating a number [0,1] during selection, selecting a population n with the first probability greater than the random number, calculating the new state of each charging pile, entering a calculation model, and updating the optimal fitness of each population and the optimal fitness of all the populations. And then entering an optimization model, and iterating until a proper result is obtained or the iteration times reach a preset value.

The obtained result is the charging period distribution plan made for each electric vehicle to be charged.

Table 1 shows 4 Benchmark standard functions selected for verifying the optimization performance and convergence speed of the hybrid algorithm, where the optimal values of all the functions are 0.

TABLE 1 Standard function

In the algorithm comparison experiment, the maximum iteration times are 3000 times, the population size is 60, the data dimension is 40, and the inertia weight w_maxIs 0.9, w_min0.4, cognitive learning factor c₁1.5, social learning factor c₂Is 2. The global optimal solution was calculated and averaged for each 30 calculations, with the results shown in table 2.

TABLE 2 comparison of the two algorithms on the standard function

Fig. 6 shows the convergence rates of the PSO algorithm and the hybrid algorithm, taking Griewank criteria as an example.

As can be seen from table 2 and fig. 6, the accuracy, i.e., the optimization performance, of the hybrid algorithm is higher than that of the PSO algorithm, and the convergence rate of the hybrid algorithm is also better than that of the PSO algorithm.

And because the ordered charging control model adopts the algorithm of the artificial bee colony, the algorithm has the greatest advantage that other solutions can be searched by giving up the current optimal solution, and the defect that the particle swarm falls into local optimization can be effectively avoided. Therefore, when the distribution plan of the charging time interval of the electric automobile is calculated, more situations can be considered, optimization can be performed, the safety of a power grid can be improved to the maximum extent, and the personal benefits of users can be guaranteed.

As a specific embodiment of the present invention, an orderly charging control system for an electric vehicle includes:

the acquisition module is used for acquiring a parking time period and expected charging electric quantity corresponding to each electric vehicle to be charged in the platform area, and a platform area load change curve;

the charging time length determining module is used for determining the charging time length required by the electric automobile to be charged to reach the expected charging electric quantity according to the expected charging electric quantity corresponding to each electric automobile to be charged;

and the charging period distribution plan making module is used for making a charging period distribution plan corresponding to each electric vehicle to be charged by utilizing the electric vehicle ordered charging control model according to the parking period of each electric vehicle to be charged, the charging time required for each electric vehicle to be charged to reach the expected charging electric quantity, the platform area load and the platform area load change curve.

Specifically, the charging period distribution planning module includes:

the time period dividing module is used for dividing each hour into a plurality of time periods according to an equal interval dividing mode, and the time periods are used as a minimum charging time unit;

the charging time unit number determining module is used for determining the number of the charging time units required by each electric vehicle to be charged according to the charging time required by each electric vehicle to be charged to reach the expected charging electric quantity;

the system comprises an initial charging time unit distribution plan module, a charging time unit distribution plan module and a charging time unit distribution module, wherein the initial charging time unit distribution plan module is used for randomly distributing the number of charging time units required by each electric vehicle to be charged to a corresponding parking time period to obtain an initial charging time unit distribution plan corresponding to each electric vehicle to be charged;

and the charging period distribution plan module is used for obtaining a charging period distribution plan corresponding to each electric vehicle to be charged by utilizing the electric vehicle ordered charging control model according to the distribution plan of the platform area load, the platform area load change curve and the initial charging time unit distribution plan corresponding to each electric vehicle to be charged.

In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of the ordered charging control method of the electric automobile.

In a specific embodiment, the computer device is called an edge controller, the edge controller needs to carry peripheral communication modules including but not limited to an RS-485 interface, a UART serial port, a CAN port, and the like, besides a CPU main control chip, and a software platform used by the edge controller needs to be a linux operating system.

In another embodiment of the present invention, if the method is implemented in the form of a software functional unit and sold or used as an independent product, the method may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data.

The computer storage media may be any available media or data storage device that can be accessed by a computer, including but not limited to magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memories (NANDFLASH), Solid State Disks (SSDs)), etc.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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 specified 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.

The following provides a specific implementation case for performing ordered charging control by applying the ordered charging control method for the electric vehicle.

In the embodiment, the simulation is performed by taking the example that the electric vehicle in a certain industrial park participates in charging and discharging scheduling, and the charging time is 8: 00-19: 30. The electricity prices are shown in table 3:

TABLE 3 time of use electricity price in certain area

In this embodiment, the maximum number of iterations of the ordered charge control model is set to 3000, the population size is 60, and the data dimension d is 96, that is, the ordered charge control model is equally divided into 96 parts at intervals of 15 minutes for 24 hours a day. Inertia weight w_maxIs 0.9, w_min0.4, cognitive learning factor c₁1.5, social learning factor c₂Is 2. Weighting factor alpha of load fluctuation variance of power grid₁And the weight coefficient alpha of the peak-valley difference of the load curve of the power grid₂And the charging cost weight coefficient alpha of the electric automobile₃The value of (1) is (0.4, 0.3, 0.3).

As can be seen from fig. 4, compared with before optimization, the model has the effect of certain "peak shifting and valley filling" after half an hour after 8 points and after 17 points by changing the charging time of the user according to the requirement of improving the safety of the power grid, and can effectively improve the safety of the power grid.

As can be seen from fig. 5 and table 3, the model concentrates the charging time after 12 points compared to before optimization, and the main reason is to integrate the demand of the user side and transfer the charging time to the flat price segment of the charging price. The safety of part of the power grid is sacrificed, but the safety is still before the peak of electricity utilization at 17 points.

In general, the ordered charging control model can well integrate the power consumption requirements of a power grid side and a user side, and a relatively excellent ordered charging control method for the electric vehicle is worked out under the condition of comprehensively considering various factors, so that the safety of the power grid is met and the actual benefit is brought to the user.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.