CN116142014A

CN116142014A - Intelligent charging method, device, equipment and medium for electric automobile

Info

Publication number: CN116142014A
Application number: CN202310159449.1A
Authority: CN
Inventors: 顾映彬; 吴超杭; 王晓丰; 黄培锋; 王涓; 陈觉非; 许映春; 方怡苑; 邱灿树; 陈陆野; 唐力则; 张瀚; 黄树强; 刘塽; 洪少凌; 翁佳; 郑锦泉; 陈柏良
Original assignee: Guangdong Power Grid Co Ltd; Chaozhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Current assignee: Guangdong Power Grid Co Ltd; Chaozhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date: 2023-02-23
Filing date: 2023-02-23
Publication date: 2023-05-23

Abstract

The invention discloses an intelligent charging method, device, equipment and medium for an electric automobile, wherein the method comprises the following steps: acquiring power data of a target power distribution network and power data of a charging pile which is being charged; extracting the charging power of the charging electric automobile in the charging pile from the electric power data of the charging pile under charging, and determining an objective function according to the charging power of the charging electric automobile; determining constraint conditions according to the power data of the target power distribution network; wherein the constraints include at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile; and determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition based on the particle swarm algorithm.

Description

Intelligent charging method, device, equipment and medium for electric automobile

Technical Field

The invention relates to the technical field of electric automobile charging, in particular to an intelligent electric automobile charging method, device, equipment and medium.

Background

The rapid development of electric transportation technology has led to the entry of a large number of electric vehicles and corresponding charging facilities into existing power distribution systems. The electrified traffic system is an important component of the next generation energy grid framework.

However, the unordered charging of a large number of electric vehicles gives a great impact to the local power grid. The unordered charging of the electric automobile can push the electricity consumption peak of the local power grid, so that the safety of the operation of the local power grid is affected, and the cost of the electric automobile in the aspects of infrastructure and energy sources is increased.

Disclosure of Invention

The invention provides an intelligent charging method, device, equipment and medium for an electric automobile, which are used for improving the running completeness of a local power grid and reducing the capital cost and the running cost.

According to an aspect of the present invention, there is provided an intelligent charging method for an electric vehicle, including:

acquiring power data of a target power distribution network and power data of a charging pile which is being charged;

extracting the charging power of the charging electric automobile in the charging pile from the electric power data of the charging pile under charging, and determining an objective function according to the charging power of the charging electric automobile;

determining constraint conditions according to the power data of the target power distribution network; wherein the constraints include at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile;

And determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition based on the particle swarm algorithm.

According to another aspect of the present invention, there is provided an intelligent charging apparatus for an electric vehicle, comprising:

the power data acquisition module is used for acquiring power data of a target power distribution network and power data of a charging pile which is being charged;

the objective function determining module is used for extracting the charging power of the charging electric automobile in the charging pile from the electric power data of the charging pile being charged, and determining an objective function according to the charging power of the charging electric automobile;

the constraint condition determining module is used for determining constraint conditions according to the power data of the target power distribution network; wherein the constraints include at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile;

and the output power determining module is used for determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition based on the particle swarm algorithm.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of intelligent charging of an electric vehicle according to any one of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the method for intelligent charging of an electric vehicle according to any one of the embodiments of the present invention.

According to the technical scheme, the power data of the target power distribution network and the power data of the charging pile which is being charged are obtained; extracting the charging power of the charging electric automobile in the charging pile from the electric power data of the charging pile under charging, and determining an objective function according to the charging power of the charging electric automobile; determining constraint conditions according to the power data of the target power distribution network; wherein the constraints include at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile; and determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition based on the particle swarm algorithm. According to the technical scheme, the optimal output power of the charging piles which are being charged is determined based on the particle swarm algorithm, the objective function and the constraint condition, so that each charging pile which is being charged in the current power grid charges each charging electric automobile in the charging pile with the optimal output power, the charging requirement of each charging electric automobile in the charging pile is met, the real-time distribution of the optimal charging power of each charging electric automobile is realized, the peak of the power consumption requirement of the local power grid is further reduced, and the running completeness of the local power grid is improved; meanwhile, the additional arrangement of the charging infrastructure of the electric automobile and the waste of the output electric quantity of each charging pile which is being charged are avoided, and then the capital cost and the operation cost are reduced.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1A is a schematic structural diagram of an intelligent charging system for an electric vehicle according to an embodiment of the present invention;

fig. 1B is a flowchart of an intelligent charging method for an electric vehicle according to a first embodiment of the present invention;

fig. 2 is a flowchart of an intelligent charging method for an electric vehicle according to a second embodiment of the present invention;

fig. 3 is a flowchart of an intelligent charging method for an electric vehicle according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of an intelligent charging device for an electric vehicle according to a fourth embodiment of the present invention;

Fig. 5 is a schematic structural diagram of an electronic device for implementing the intelligent charging method of the electric automobile according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "object," "first," and "second," and the like in the description and claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In addition, it should be noted that, in the technical scheme of the invention, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the electric power data of the target power distribution network and the electric power data of the charging pile being charged are in accordance with the regulations of the related laws and regulations, and the public welfare is not violated.

For easy understanding, first, a brief description will be given of a structure in an intelligent charging system for an electric vehicle according to an embodiment of the present invention, referring to fig. 1A, the intelligent charging system for an electric vehicle includes an information system 10, a physical system 20, a sensor system 30, a charging system 40, a charging electric vehicle driver 50, and a service provider 60;

the information system 10 includes an electric vehicle intelligent charging module 11, a data storage module 12, a communication interface 13, and a mobile phone APP (i.e., mobile phone application program) 14, which is configured to collect and store power data of a target power distribution network and power data of charging piles being charged, and calculate charging power of each charging electric vehicle; the electric automobile intelligent charging module 11 is used for storing an electric automobile intelligent charging method; the communication interface 13 receives the sensor data collected by the sensor system 30 and transmits the sensor data to the data storage module 12; in addition, the communication interface 13 can also transmit a charging power signal generated by the intelligent charging method of the electric automobile to corresponding electric automobile charging equipment (such as a charging pile); the data storage module 12 includes 1 relational database and 1 time series database; wherein the relational database is used for storing information such as site configuration, driver configuration files, charging session parameters and the like; the time sequence database is used for specially storing measured values such as voltage, current and power readings obtained from the power grid sensor; the mobile phone APP14 is configured to collect relevant data of the electric vehicle from the charging electric vehicle driver 50, and store the data;

The physical system 20 comprises a grid facility 21, an electric car 22, other loads 23 and local power generation equipment 24; the physical system 20 transmits power from the target distribution network to electric vehicles and other loads via the charging system 40; the power grid facility 21 comprises a power grid topology of a target power distribution network and a target power distribution network model; the local power generation equipment is used for connecting local power generation equipment such as wind power generation equipment and photovoltaic power generation equipment into a target power distribution network;

the sensor system 30 is used to measure power, current and voltage within the local grid;

the charging system 40 is composed of an electric vehicle charging device and an electric vehicle battery management system, and is configured to execute a charging power signal sent by the information system 10 to control the charging power of the charging electric vehicle;

the charging electric car driver 50 is configured to provide the information system 10 with data related to the charging electric car, and determine a charging start time of the electric car; the charging electric automobile driver 50 enters the corresponding mobile phone APP14 by scanning a two-dimensional code on electric automobile charging equipment (such as a charging pile), then the mobile phone APP14 is provided with the preset departure time and the required energy of the charging electric automobile, and the mobile phone APP14 can charge charging fees paid by the charging electric automobile driver 50 through a website;

The service provider 60 is configured to modify the weight coefficient of each sub-objective function in the objective function on line according to the load condition of the local power grid and the traffic condition at the current moment.

Example 1

Fig. 1B is a flowchart of an intelligent charging method for an electric vehicle according to an embodiment of the present invention, and fig. 1A is a schematic structural diagram of an intelligent charging system for an electric vehicle according to an embodiment of the present invention, where the embodiment is applicable to a situation that a local power supply network and an electric vehicle are relatively random, the method may be performed by an intelligent charging device for an electric vehicle, and the device may be implemented in a hardware and/or software manner, and may be configured in an electronic device, where the electronic device may be one of a desktop computer and a notebook computer.

As shown in fig. 1B, the method includes:

and S101, acquiring the power data of the target power distribution network and the power data of the charging pile being charged.

The target power distribution network may refer to a power distribution network where the charging pile that is being charged is located. The power data of the target power distribution network may include the total number of nodes in the target power distribution network, the power data of the nodes, an admittance matrix of the target power distribution network, a maximum optimization step size, a single step optimization time interval, charging income per kilowatt, a maximum apparent power of a line in the target power distribution network, a rated reverse power of a power distribution station in the target power distribution network, a rated scheduling power of the power distribution station in the target power distribution network, and the like. The power data of the node comprises power data such as power generation power of the node, load active power of the node, load reactive power of the node, charging power of the node, maximum voltage of the node, minimum voltage of the node, maximum value of total charging power of the node, minimum value of total charging power of the node, maximum value of total charging energy of the node, minimum value of total charging energy of the node and the like. It should be noted that each line in the target power distribution network includes a start node and a termination node. Accordingly, (i, j) may be used to represent a line in the target distribution network, where i and j are positive integers, i represents a start node in the line, and j represents a termination node in the line. The electric power data of the charging piles being charged may include electric power data such as the total number of charging piles being charged, the total number of electric vehicles being charged in the charging piles being charged, and the charging power of the electric vehicles being charged in the charging piles being charged.

Specifically, from the data storage module 12 of the information system 10 in the intelligent charging system for electric vehicles shown in fig. 1A, the power data of the target power distribution network and the power data of the charging pile being charged are obtained.

S102, extracting the charging power of the charging electric automobile in the charging pile from the electric power data of the charging pile under charging, and determining an objective function according to the charging power of the charging electric automobile.

The objective function may be a function of a relationship between each electric power data required for designing intelligent charging of the electric vehicle.

Specifically, based on a preset rule, the charging power of the charging electric vehicle in the charging pile is extracted from the electric power data of the charging pile being charged, and an objective function is determined according to the charging power of the charging electric vehicle. The preset rule can be preset according to actual needs.

Illustratively, according to the license plate numbers of the electric vehicles charged in the charging piles being charged, the charging power of the electric vehicles corresponding to each license plate number is extracted from the electric power data of the charging piles being charged, and the objective function is determined according to the charging power of the electric vehicles corresponding to each license plate number.

The method includes the steps of firstly extracting the total number of the electric vehicles charged in the charging pile from the electric power data of the charging pile under charging, then extracting the charging power of the electric vehicles charged in the charging pile under charging from the electric power data of the charging pile under charging by adopting a traversing method according to the total number of the electric vehicles charged in the charging pile, and determining an objective function according to the extracted charging power of the electric vehicles charged in the charging pile.

S103, determining constraint conditions according to the power data of the target power distribution network; wherein the constraints include at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile.

The constraint conditions may refer to various constraint conditions that need to be satisfied to realize intelligent charging of the electric automobile. The minimum charge requirement sub-constraint required by the electric vehicle may refer to the minimum charge requirement required by the electric vehicle to leave when the preset departure time arrives. The preset departure time is preset by the electric vehicle driver 50.

Specifically, each sub-constraint condition in the constraint conditions is determined according to at least one piece of power data in the power data of the target power distribution network.

Optionally, determining a node power balance sub constraint condition and a line power capacity limit sub constraint condition in the constraint conditions according to the power data of the target power distribution network;

The constraint conditions of the node power balance sub are as follows:

in the method, in the process of the invention,

for the voltage of node i at time t, +.>

Is Y _ij Transpose of Y _ij For the admittance matrix of the target distribution network,

is->

Transpose of->

For the voltage of node j at time t, +.>

For the generator power of node i at time t, +.>

Load active power of node i at time t, < >>

For the charging power of node i at time t, +.>

The load reactive power of the node i at the moment t is N, the total number of nodes in the target power distribution network, and t is a single stepOptimization time, T, is the maximum optimization step size, Γ represents the set of T, i.e. Γ= {1,2, …, T };

the line power capacity limit sub-constraint conditions are:

in the method, in the process of the invention,

apparent power for line (i, j) at time t, < >>

For the maximum apparent power of line (i, j), E is the set of lines in the target distribution network, i.e. +.>

The above t is a positive integer, and is a single-step optimization time. Under the condition that the node i is connected with only one charging pile which is being charged, the charging power of the node i at the moment t is as follows:

under the condition that the node i is connected with at least two charging piles which are being charged, the charging power of the node i at the moment t is as follows: />

Where H is the set of charging posts that are charging.

Admittance matrix Y of target distribution network _ij The method comprises the following steps:

wherein k represents a kth node in the target power distribution network, y _i0 Representing the equivalent admittance of the shunt compensator at node i, y _ij Representing the line (i, j) E model, i.e. y _ij ＝(R _ij +jwL _ij ) ^-1 ，R _ij Representing the resistance of line (i, j), L _ij Representing the inductance of line (i, j), w represents the power system frequency.

Optionally, extracting the maximum voltage of the node and the minimum voltage of the node from the power data of the target power distribution network, and further determining node voltage sub-constraint conditions in the constraint conditions according to the maximum voltage of the node and the minimum voltage of the node; the node voltage sub constraint conditions are as follows:

wherein,,

for the ith (i=1, 2, …, N) node voltage at time t, +.>

Is the minimum voltage of the ith (i=1, 2, …, N) node, +.>

For the maximum voltage of the ith (i=1, 2, …, N) node, N is the total number of nodes in the target power distribution network, T is a single step optimization time, Γ represents a set of T, i.e. Γ= {1,2, …, T }, and T is the maximum optimization step size.

Optionally, extracting rated reverse power and rated dispatching power of the power distribution station from the power data of the target power distribution network, and further determining a capacity sub constraint condition of the distribution transformer in the constraint condition according to the rated reverse power and the rated dispatching power of the power distribution station; the capacity sub constraint conditions of the distribution transformer are as follows:

Wherein,,

for rated reverse power of distribution station in target distribution network, u _a (T) is the charging power of the a-th charging electric automobile at the moment T, v is the set of the charging electric automobiles, T is the single-step optimization time, Γ represents the set of T, namely Γ= {1,2, …, T }, T is the maximum optimization step length, and }, and @ is the maximum optimization step length>

And rated power is scheduled for the power distribution station in the target power distribution network.

Optionally, extracting a maximum value of the node total charging power and a minimum value of the node total charging power from the power data of the target power distribution network, and further determining a charging power sub-constraint condition in the constraint condition according to the maximum value of the node total charging power and the minimum value of the node total charging power; the charging power sub constraint conditions are as follows:

wherein,,

minimum value of total charging power of node i at time t,/, for>

The charging power of node i at time t,

for the maximum value of the total charging power of the node i at the moment T, H is the set of charging piles which are being charged in the target power distribution network, N is the total number of nodes in the target power distribution network, T is the single-step optimization time, Γ represents the set of T, namely Γ= {1,2, …, T }, and T is the maximum optimization step length.

Optionally, extracting a single-step optimization time interval, a maximum value of node charging total energy and a minimum value of node charging total energy from the power data of the target power distribution network, and further determining a charging energy quantum constraint condition in the constraint condition according to the single-step optimization time interval, the maximum value of node charging total energy and the minimum value of node charging total energy; the charging energy quantum constraint conditions are as follows:

Wherein,,

charging the ith node with the minimum value of total energy,/->

Charging the ith node with the maximum value of the total energy,/-)>

For the charging power of the node i at the moment T, Δt is a single-step optimization time interval, N is the total number of nodes in the target power distribution network, H is a set of charging piles being charged in the target power distribution network, T is a single-step optimization time, Γ represents a set of T, i.e., Γ= {1,2, …, T }, and T is a maximum optimization step length.

Optionally, determining a sub-constraint condition of the expected charging amount in the constraint conditions according to the power data of the target power distribution network; the desired charge amount sub-constraint condition is:

/>

wherein b is the b-th charging pile connected with the node i and is charging, D _H,i For the total charge amount of node i,

the charging amount of the b-th charging pile connected with the node i at the moment T is a single-step optimization time interval, delta T is the total number of nodes in the target power distribution network, H is the set of charging piles in the target power distribution network, T is single-step optimization time, gamma represents the set of T, namely gamma= {1,2, …, T } and T is the maximum optimization step length.

Optionally, determining a minimum charging requirement sub-constraint condition required by the electric automobile in the constraint conditions according to the electric power data of the target power distribution network; the minimum charging requirement sub-constraint conditions required by the electric automobile are as follows:

Wherein,,

for the electric vehicle to leave the minimum charging requirement required at the end of the maximum optimization step T, i.e. when the preset departure time has arrived, the minimum charging requirement required for the electric vehicle to leave +.>

And S104, determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition based on the particle swarm algorithm.

The optimal output power may be the minimum output power that meets the charging requirements of each charging electric vehicle in the charging pile being charged.

Specifically, under the condition that the constraint condition is met, calculating the optimal output power of the charging pile being charged based on a particle swarm algorithm and an objective function.

According to the technical scheme, the power data of the target power distribution network and the power data of the charging pile which is being charged are obtained; extracting the charging power of the charging electric automobile in the charging pile from the electric power data of the charging pile under charging, and determining an objective function according to the charging power of the charging electric automobile; determining constraint conditions according to the power data of the target power distribution network; wherein the constraints include at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile; and determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition based on a particle swarm algorithm. According to the technical scheme, the optimal output power of the charging piles which are being charged is determined based on the particle swarm algorithm, the objective function and the constraint condition, so that each charging pile which is being charged in the current power grid charges each charging electric automobile in the charging pile with the optimal output power, the charging requirement of each charging electric automobile in the charging pile is met, the real-time distribution of the optimal charging power of each charging electric automobile is realized, the peak of the power consumption requirement of the local power grid is further reduced, and the running completeness of the local power grid is improved; meanwhile, the additional arrangement of the charging infrastructure of the electric automobile and the waste of the output electric quantity of each charging pile which is being charged are avoided, and then the capital cost and the operation cost are reduced.

Example two

Fig. 2 is a flowchart of an intelligent charging method for an electric vehicle according to a second embodiment of the present invention, where the "determining an objective function according to a charging power of a charging electric vehicle" is further optimized as follows on the basis of the foregoing embodiment: the objective function comprises at least one of a quick charge objective function, a profit maximization objective function, a demand cost minimization objective function, a load fluctuation minimization objective function, a capacity fairness allocation objective function and the like; correspondingly, determining an objective function according to the charging power of the charging electric automobile comprises: determining a sub-objective function in the objective function and a weight coefficient corresponding to the sub-objective function; and determining an objective function according to the charging power of the charged electric automobile and the weight coefficient corresponding to the sub-objective function, thereby providing an alternative implementation scheme. In the embodiments of the present invention, parts not described in detail may be referred to for related expressions of other embodiments. As shown in fig. 2, the method includes:

and S201, acquiring the power data of the target power distribution network and the power data of the charging pile being charged.

And S202, extracting the charging power of the electric automobile charged in the charging pile from the power data of the charging pile being charged.

S203, determining a weight coefficient corresponding to the sub-objective function in the objective function.

The objective functions comprise at least one of a quick charge objective function, a profit maximization objective function, a demand cost minimization objective function, a load fluctuation minimization objective function, a capacity fairness allocation objective function and the like. The weight coefficient is used for representing the importance degree of the sub-objective function in the objective function.

By way of example, the sub-objective functions in the objective function are preset according to actual requirements, and the number of the sub-objective functions in the objective function can be further determined; and randomly setting the weight coefficient corresponding to each sub-objective function.

Optionally, the determining the weight coefficient corresponding to the sub-objective function in the objective function may be: and determining a weight coefficient corresponding to the neutron target function in the target function according to the load condition of the target power distribution network and the traffic condition at the current moment.

The load conditions may include, among others, heavy, medium and light conditions. Traffic conditions may include congestion, moderate, and rare conditions. Correspondingly, the weight coefficients corresponding to the neutron target function in the target function can be divided into three categories of large, medium and small.

Specifically, the load condition of a target power distribution network and the traffic condition at the current moment are used as inputs of fuzzy logic reasoning, and fuzzy rules in the fuzzy logic reasoning are determined by combining expert experience and a data analysis method; and further analyzing and obtaining the weight coefficient corresponding to the sub-objective function in the objective function by using the fuzzy rule in the fuzzy logic inference.

S204, determining an objective function according to the charging power of the charging electric automobile and the weight coefficient corresponding to the sub-objective function.

Specifically, counting the number of the neutron target functions in the target function, and recording the total number of the neutron target functions in the target function as m, wherein m is a positive integer; the weight coefficient corresponding to the i (i=1, 2, …, m) th sub-objective function is denoted as alpha _i The ith (i=1, 2, …, m) sub-objective function in the objective function is noted as J _i And (u), recording the charging power of the charged electric automobile as u, and determining the objective function by the following formula:

optionally, determining at least one of a fast charge sub-objective function and a capacity fair allocation sub-objective function in the objective function according to the charging power of the charging electric automobile; determining the net load of a target power distribution network according to the charging power of the charging electric automobile, the load power of other loads and the output power of other power generation equipment; the other loads are other power consumption equipment except the charging electric automobile in the charging pile which is being charged; the other power generation equipment is power generation equipment except the local power generation equipment in the target power distribution network; and determining at least one of a profit maximization sub-objective function, a demand cost minimization sub-objective function and a load fluctuation minimization sub-objective function in the objective function by adopting the net load of the target power distribution network.

The other loads are other power consumption devices, such as a circuit breaker, in the charging pile being charged, except for the charging electric automobile. The local power generation device may include a local generator and a new energy power generation device (e.g., photovoltaic power generation, wind power generation, etc.). Accordingly, the other power generation devices are power generation devices except the local power generation device in the target power distribution network.

And determining at least one of a quick charge sub-objective function and a capacity fair allocation sub-objective function in the objective function according to the charging power of the charging electric automobile. Illustratively, according to the charging power of the charged electric vehicle, the fast charge sub-objective function in the objective function is determined by the following formula:

wherein J is ₁ (u) denotes a fast charge sub-objective function, T denotes a single step optimization time, T denotes a maximum optimization step, Γ denotes a set of T, i.e. Γ= {1,2, …, T }, u _a And (t) is the charging power of the a-th charging electric automobile at the t moment, and v is the set of the charging electric automobiles.

Illustratively, according to the charging power of the charging electric vehicle, the capacity fair allocation sub-objective function in the objective function is determined by the following formula:

wherein J is ₂ (u) represents a capacity fairness allocation sub-objective function, u _a And (T) is the charging power of the a-th charging electric automobile at the moment T, v is the set of the charging electric automobiles, T represents the single-step optimization time, T represents the maximum optimization step length, and Γ represents the set of T, namely Γ= {1,2, …, T }.

And determining the net load of the target power distribution network according to the charging power of the charging electric automobile, the load power of other loads and the output power of other power generation equipment. Specifically, the net load of the target distribution network may be determined by the following formula:

N(t)＝∑ _a∈v u _a (t)+L(t)-G(t)；

wherein N (t) is the net load of the target power distribution network, u _a And (t) is the charging power of the a-th charging electric automobile at the t moment, v is the set of the charging electric automobiles, t represents single-step optimization time, L (t) is the load power of other loads at the t moment, and G (t) is the output power of other power generation equipment at the t moment.

Further, at least one of a profit maximization sub-objective function, a demand cost minimization sub-objective function and a load fluctuation minimization sub-objective function in the objective function is determined by adopting the obtained net load of the target power distribution network.

By way of example, with the net load of the target power distribution network obtained as described above, the profit maximization sub-objective function in the objective function is determined by the following formula:

Wherein J is ₃ (u) is a profit maximization sub-objective function, pi is charge income per kilowatt, u _a (T) is the charging power of the a-th charging electric automobile at the moment T, v is the set of the charging electric automobiles, K (T) is the cost of energy per kilowatt changing along with T, N (T) is the net load of the target power distribution network, T is the single-step optimization time, T is the maximum optimization step length, and Γ represents the set of T, namely Γ= {1,2, …, T }.

Illustratively, with the resulting net load of the target distribution network, the minimum demand charge sub-objective function of the objective function is determined by the following formula:

wherein J is ₄ (u) is a minimum demand cost sub-objective function,

for proxy cost, N (t) is the net load of the target distribution network, q ₀ For the peak value of the required power up to now during charging, q' is the predicted value of the optimal peak value, T is the single step optimization time, T is the maximum optimization step size, Γ represents the set of T, i.e. Γ= {1,2, …, T }.

Illustratively, with the net load of the target power distribution network obtained as described above, the minimized load fluctuation sub-objective function of the objective function is determined by the following formula:

J ₅ (u)＝-∑ _t∈Γ N(t) ² ；

wherein J is ₅ (u) is a load fluctuation minimization sub-objective function, N (T) is the net load of the target power distribution network, T is the single step optimization time, and T is the maximum optimization step Long Γ represents a set of T, i.e. Γ= {1,2, …, T }.

S205, determining constraint conditions according to the power data of the target power distribution network; wherein the constraints include at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile.

S206, based on a particle swarm algorithm, determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition.

The technical scheme of the embodiment of the invention provides a plurality of selectable sub-objective functions, and the number of the sub-objective functions in the objective functions can be further determined by determining the sub-objective functions in the objective functions; the method for determining the objective function is provided by determining the objective function through the charging power of the charging electric automobile and the weight coefficient corresponding to the objective function in the determined objective function, and the needed objective function can be selected according to actual needs, so that various objective functions can be determined according to the selected objective function, the operation is simple, the applicability of the objective function is improved, the flexibility of the objective function determination is improved, and the requirements of users can be better met.

Example III

Fig. 3 is a flowchart of an intelligent charging method for an electric vehicle according to a third embodiment of the present invention, where on the basis of the foregoing embodiment, an alternative implementation is provided for further optimizing "determining an optimal output power of a charging pile being charged according to the objective function and the constraint condition based on a particle swarm algorithm". In the embodiments of the present invention, parts not described in detail may be referred to for related expressions of other embodiments. As shown in fig. 3, the method includes:

and S301, acquiring the power data of the target power distribution network and the power data of the charging pile being charged.

And S302, extracting the charging power of the charging electric automobile in the charging pile from the electric power data of the charging pile under charging, and determining an objective function according to the charging power of the charging electric automobile.

S303, determining constraint conditions according to the power data of the target power distribution network; wherein the constraints include at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile.

S304, determining the initial position and initial speed of the particles in the particle swarm algorithm.

Wherein, the particle may refer to the charging pile being charged, the number of particles may refer to the total number of charging piles being charged, and the initial position and initial speed of the particles may be randomly set.

Illustratively, the initial position and initial velocity of the particles in the particle swarm algorithm are randomly determined using a random algorithm.

For example, the initial position and initial velocity of the particles in the particle swarm algorithm are preset according to actual needs.

It should be noted that, the particles in the particle swarm move in a preset search space according to a preset movement rule. The preset movement rule is as follows:

wherein,,

is i (i=1, 2, …, n _N ) Position of the particle in the kth iteration, n _N For the total number of particles, +.>

Is the ith particle in the (th)Speed during k iterations, +.>

For the optimal position of the ith particle during the kth iteration,/for example>

In order to optimize the position of the particle swarm in the kth iteration process, k is the iteration number, ω is the inertial weight, c ₁ C is a cognitive parameter ₂ Environmental parameter, r ₁ And r ₂ Is in [0,1 ]]Random numbers uniformly distributed within the range.

It should be noted that the dimension of the preset search space may be preset according to the actual requirement. Illustratively, the dimension of the preset search space is D, then the i (i=1, 2, …, n) _N ) The position of the particle in the kth iteration is

The speed of the ith particle during the kth iteration is +.>

The optimal position of the ith particle during the kth iteration is +.>

The optimal position of the particle swarm during the kth iteration is +.>

S305, determining the optimal position of the particle swarm in each iteration process according to the objective function, the constraint condition, the initial position and the initial speed of the particle swarm algorithm particles.

Wherein, the fitness is used for representing the good and bad degree of particle position. The smaller the value of the fitness, the better the position where the particle is located.

Specifically, for each iteration process, on the basis of determining the initial position and the initial speed of particles in the particle swarm, all the particles in the particle swarm meet constraint conditions; further, calculating the fitness of each particle in the particle swarm by using an objective function; and determining the optimal position of the particle swarm in each iteration process according to the adaptability of each particle in the particle swarm. The fitness of the particles in the particle swarm can be determined by the following formula:

wherein F (u, sigma) is the fitness of particles in the particle swarm, J (u) is an objective function, sigma is a positive penalty, beta is a constant, generally beta is more than or equal to 1, u is the charging power of the charging electric automobile, m is the number of constraint conditions, and h _i (u) is the i (i=1, 2, …, m) th constraint. In the case where u is a feasible point, max {0, h _i (u) } =0, otherwise max {0, h _i (u)}＝h _i (u)。

Further, in the current iteration process, aiming at the ith particle in the particle swarm, if the iteration frequency of the current iteration process is k, the fitness of the ith particle in the current iteration process is

The i-th particle has a fitness of +.>

At->

Under the condition of (1) updating the optimal position of the ith particle in the current iteration process, namely +.>

Updated to->

Otherwise, the optimal position of the ith particle in the current iteration process is not updated, namely the optimal position of the ith particle in the current iteration process is +.>

Still +.>

And then the fitness of the ith particle in the current iterative process (i.e.)>

) The fitness corresponding to the optimal position of the particle swarm during k-1 iterations (i.e.)>

) Comparing at +.>

In the case of (2), the optimal position of the particle swarm in the current iteration is updated, i.e. +.>

And S306, when the iteration process meets the iteration termination condition, taking the optimal position of the particle swarm in the iteration process as the optimal output power of the charging pile under charging.

Specifically, in each iteration process of the particle swarm algorithm, detecting whether the iteration process meets an iteration termination condition; and under the condition that the iteration process meets the iteration termination condition, taking the optimal position of the particle swarm in the iteration process as the optimal output power of the charging pile being charged, otherwise, continuing the next iteration of the particle swarm algorithm.

Optionally, the iteration termination condition includes at least one of the following:

when the iteration times corresponding to the iteration process are greater than or equal to the iteration times threshold, terminating the iteration of the particle swarm algorithm;

and when the change value of the fitness corresponding to the iterative process is smaller than or equal to the fitness threshold value, terminating the iteration of the particle swarm algorithm.

The iteration number threshold and the fitness threshold can be preset according to actual needs.

Exemplary, if the iteration number threshold is k _max The iteration number of the current iteration process is k; at k is greater than or equal to k _max In the case of (2), terminating the iteration of the particle swarm algorithm; and taking the optimal position of the particle swarm in the current iteration process as the optimal output power of the charging pile under charging.

Exemplary, if the fitness threshold is ε _min The iteration frequency of the current iteration process is k, and the change value of the adaptability corresponding to the current iteration process is

At B.ltoreq.epsilon _min In the case of (2), terminating the iteration of the particle swarm algorithm; and taking the optimal position of the particle swarm in the current iteration process as the optimal output power of the charging pile under charging.

According to the technical scheme provided by the embodiment of the invention, based on the particle swarm algorithm, the optimal position of the particle swarm is searched through iteration according to the objective function and the constraint condition, namely, the optimal output power of the charging pile which is being charged is searched, so that the real-time distribution of the optimal charging power of the charging electric automobile in the charging pile which is being charged is realized, the impact of the simultaneous charging of the large-scale electric automobile on a local power grid is reduced, the health of the electric automobile and the charging pile is ensured, and the running completeness of the local power grid is improved; and meanwhile, under the condition that the iteration termination condition is met, the iteration of the particle swarm algorithm is terminated, and the particle swarm algorithm is prevented from falling into infinite loop.

Example IV

Fig. 4 is a schematic structural diagram of an intelligent charging device for an electric vehicle, which is provided in a fourth embodiment of the present invention, and the embodiment is applicable to situations with larger randomness in power supply of a local power grid and charging of the electric vehicle. As shown in fig. 4, the apparatus includes:

the power data acquisition module 401 is configured to acquire power data of a target power distribution network and power data of a charging pile being charged;

the objective function determining module 402 is configured to extract charging power of the charging electric vehicle in the charging pile from power data of the charging pile being charged, and determine an objective function according to the charging power of the charging electric vehicle;

a constraint condition determining module 403, configured to determine a constraint condition according to the power data of the target power distribution network; wherein the constraints include at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile;

The output power determining module 404 is configured to determine, based on a particle swarm algorithm, an optimal output power of the charging pile being charged according to an objective function and a constraint condition.

According to the technical scheme, through the power data acquisition module, the power data of the target power distribution network and the power data of the charging pile which is being charged are acquired; determining an objective function by an objective function determining module; determining a constraint condition by a constraint condition determining module; and determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition based on a particle swarm algorithm through an output power determining module. According to the technical scheme, the optimal output power of the charging piles which are being charged is determined based on the particle swarm algorithm, the objective function and the constraint condition, so that each charging pile which is being charged in the current power grid charges each charging electric automobile in the charging pile with the optimal output power, the charging requirement of each charging electric automobile in the charging pile is met, the real-time distribution of the optimal charging power of each charging electric automobile is realized, the peak of the power consumption requirement of the local power grid is further reduced, and the running completeness of the local power grid is improved; meanwhile, the additional arrangement of the charging infrastructure of the electric automobile and the waste of the output electric quantity of each charging pile which is being charged are avoided, and then the capital cost and the operation cost are reduced.

Optionally, the objective function includes at least one of a quick charge objective function, a profit maximization objective function, a demand cost minimization objective function, a load fluctuation minimization objective function, a capacity fairness allocation objective function, and the like;

accordingly, the objective function determining module 402 includes:

the first determining unit is used for determining the sub-objective function in the objective function and the weight coefficient corresponding to the sub-objective function;

and the second determining unit is used for determining the objective function according to the charging power of the charging electric automobile and the weight coefficient corresponding to the sub-objective function.

Optionally, the first determining unit is specifically configured to:

and determining a weight coefficient corresponding to the neutron target function in the target function according to the load condition of the target power distribution network and the traffic condition at the current moment.

Optionally, the second determining unit is specifically configured to:

determining at least one of a quick charge sub-objective function and a capacity fair allocation sub-objective function in the objective function according to the charging power of the charging electric automobile; determining the net load of a target power distribution network according to the charging power of the charging electric automobile, the load power of other loads and the output power of other power generation equipment; the other loads are other power consumption equipment except the charging electric automobile in the charging pile which is being charged; the other power generation equipment is power generation equipment except the local power generation equipment in the target power distribution network; and determining at least one of a profit maximization sub-objective function, a demand cost minimization sub-objective function and a load fluctuation minimization sub-objective function in the objective function by adopting the net load of the target power distribution network.

Optionally, the constraint condition determining module 403 includes:

the sub constraint condition determining unit is used for determining a node power balance sub constraint condition and a line power capacity limit sub constraint condition in the constraint conditions according to the power data of the target power distribution network;

the constraint conditions of the node power balance sub are as follows:

in the method, in the process of the invention,

for the voltage of node i at time t, +.>

is->

Transpose of->

For the voltage of node j at time t, +.>

For the generator power of node i at time t, +.>

Load active power of node i at time t, < >>

For the charging power of node i at time t, +.>

The load reactive power of the node i at the moment T is N, the total number of nodes in the target power distribution network is N, T is single-step optimization time, and T is maximum optimalThe step size, Γ, represents the set of T, Γ= {1,2, …, T };

the line power capacity limit sub-constraint conditions are:

in the method, in the process of the invention,

apparent power for line (i, j) at time t, < >>

/>

Optionally, the output power determining module 404 includes:

the position and speed determining unit is used for determining the initial position and initial speed of the particles in the particle swarm algorithm;

The optimal value determining unit is used for determining the optimal position of the particle swarm in each iteration process according to the objective function, the constraint condition, the initial position and the initial speed of the particle swarm algorithm particles;

and the output power determining unit is used for taking the optimal position of the particle swarm in the iteration process as the optimal output power of the charging pile under charging when the iteration process meets the iteration termination condition.

when the iteration times corresponding to the iteration process are greater than or equal to the iteration times threshold value, terminating the iteration of the particle swarm algorithm;

The intelligent charging device for the electric automobile provided by the embodiment of the invention can execute the intelligent charging method for the electric automobile provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the intelligent charging method for each electric automobile.

Example five

Fig. 5 shows a schematic diagram of an electronic device 500 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 500 includes at least one processor 501, and a memory communicatively connected to the at least one processor 501, such as a Read Only Memory (ROM) 502, a Random Access Memory (RAM) 503, etc., where the memory stores computer programs executable by the at least one processor, and the processor 501 may perform various suitable actions and processes according to the computer programs stored in the Read Only Memory (ROM) 502 or the computer programs loaded from the storage unit 508 into the Random Access Memory (RAM) 503. In the RAM503, various programs and data required for the operation of the electronic device 500 may also be stored. The processor 501, ROM502, and RAM503 are connected to each other by a bus 505. An input/output (I/O) interface 505 is also connected to bus 505.

A number of components in electronic device 500 are connected to I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 501 performs the various methods and processes described above, such as the electric vehicle smart charge method.

In some embodiments, the electric vehicle smart charging method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 500 via the ROM502 and/or the communication unit 509. When the computer program is loaded into RAM503 and executed by processor 501, one or more steps of the electric vehicle smart charging method described above may be performed. Alternatively, in other embodiments, the processor 501 may be configured to perform the electric vehicle smart charging method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

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

determining constraint conditions according to the power data of the target power distribution network; wherein the constraint includes at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile;

And determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition based on a particle swarm algorithm.

2. The method of claim 1, wherein the objective function comprises at least one of a fast charge objective function, a profit maximization objective function, a demand cost minimization objective function, a load fluctuation minimization objective function, and a capacity fairness objective function;

correspondingly, the determining the objective function according to the charging power of the charging electric automobile comprises the following steps:

determining a sub-objective function in the objective function and a weight coefficient corresponding to the sub-objective function;

and determining an objective function according to the charging power of the charging electric automobile and the weight coefficient corresponding to the sub-objective function.

3. The method according to claim 2, wherein determining the weight coefficient corresponding to the sub-objective function in the objective function comprises:

4. The method according to claim 2, wherein the determining the objective function according to the charging power of the charging electric vehicle and the weight coefficient corresponding to the sub-objective function includes:

Determining at least one of a quick charge sub-objective function and a capacity fair allocation sub-objective function in an objective function according to the charging power of the charging electric automobile;

determining the net load of a target power distribution network according to the charging power of the charging electric automobile, the load power of other loads and the output power of other power generation equipment; the other loads are other power consumption equipment except for the charging electric automobile in the charging pile which is being charged; the other power generation equipment is power generation equipment except local power generation equipment in the target power distribution network;

and determining at least one of a profit maximization sub-objective function, a demand cost minimization sub-objective function and a load fluctuation minimization sub-objective function in the objective function by adopting the net load of the target power distribution network.

5. The method of claim 1, wherein determining constraints from the power data of the target distribution grid comprises:

determining node power balance sub constraint conditions and line power capacity limit sub constraint conditions in constraint conditions according to the power data of the target power distribution network;

the constraint conditions of the node power balance sub are as follows:

/>

In the method, in the process of the invention,

for the voltage of node i at time t, +.>

Is Y _ij Transpose of Y _ij Admittance matrix for target distribution network, +.>

Is that

Transpose of->

For the voltage of node j at time t, +.>

For the generator power of node i at time t, +.>

Load active power of node i at time t, < >>

For the charging power of node i at time t, +.>

For the load reactive power of the node i at the moment T, N is the total number of nodes in the target power distribution network, T is single-step optimization time, T is maximum optimization step length, and Γ represents a set of T, namely Γ= {1,2, …, T };

the line power capacity limit sub-constraint conditions are:

in the method, in the process of the invention,

apparent power for line (i, j) at time t, < >>

6. The method of claim 1, wherein the determining the optimal output power of the charging stake being charged based on the objective function and the constraint condition based on the particle swarm algorithm comprises:

determining the initial position and initial speed of particles in a particle swarm algorithm;

determining the optimal position of the particle swarm in each iteration process according to the objective function, the constraint condition, the initial position and the initial speed of the particle swarm algorithm particles;

And when the iteration process meets the iteration termination condition, taking the optimal position of the particle swarm in the iteration process as the optimal output power of the charging pile under charging.

7. The method of claim 6, wherein the iteration termination condition comprises at least one of:

8. An electric automobile intelligent charging device, characterized by comprising:

the objective function determining module is used for extracting the charging power of the charging electric automobile in the charging pile from the electric power data of the charging pile under charging and determining an objective function according to the charging power of the charging electric automobile;

the constraint condition determining module is used for determining constraint conditions according to the power data of the target power distribution network; wherein the constraint includes at least the following sub-constraints: node power balance sub-constraint conditions, line power capacity limit sub-constraint conditions, node voltage sub-constraint conditions, distribution transformer capacity sub-constraint conditions, charging power sub-constraint conditions, charging energy sub-constraint conditions, expected charging amount sub-constraint conditions and minimum charging requirement sub-constraint conditions required by the electric automobile;

And the output power determining module is used for determining the optimal output power of the charging pile being charged according to the objective function and the constraint condition based on a particle swarm algorithm.

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the electric vehicle smart charging method of any one of claims 1-7.

10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the electric vehicle smart charging method of any one of claims 1 to 7 when executed.