CN112418734A - A kind of electric vehicle charging load distribution method - Google Patents

A kind of electric vehicle charging load distribution method Download PDF

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CN112418734A
CN112418734A CN202011468781.9A CN202011468781A CN112418734A CN 112418734 A CN112418734 A CN 112418734A CN 202011468781 A CN202011468781 A CN 202011468781A CN 112418734 A CN112418734 A CN 112418734A
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林志贤
田启东
何蓝图
李腾飞
于兆一
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Abstract

本发明公开一种电动汽车充电负荷分配方法，根据非柔性负荷用电特征以及电动汽车通勤规律，制定电动汽车总体有序充电计划；进而，综合考虑每个用户出行习惯、充电偏好以及里程焦虑度等信息，将充电负荷在每辆电动汽车间公平合理分配。本发明在完成充电要求的同时，可以减少电动汽车充电进程开端次数，维持电池使用寿命；在尊重电动汽车用户自主响应的情况下，将每个时段充电负荷分配策略与电动汽车固有参数和用电特性紧密联系，可以对电动汽车有序充电负荷进行合理调控。

Figure 202011468781

The invention discloses a charging load distribution method for electric vehicles. According to the power consumption characteristics of non-flexible loads and the commuting law of electric vehicles, an overall orderly charging plan for electric vehicles is formulated; and further, each user's travel habits, charging preferences and mileage anxiety are comprehensively considered. and other information, the charging load will be fairly and reasonably distributed among each electric vehicle. While fulfilling the charging requirements, the invention can reduce the number of times of starting the charging process of the electric vehicle and maintain the service life of the battery; under the condition of respecting the autonomous response of the user of the electric vehicle, the charging load distribution strategy of each period is combined with the inherent parameters of the electric vehicle and the power consumption. The characteristics are closely related, and the orderly charging load of electric vehicles can be reasonably regulated.

Figure 202011468781

Description

Charging load distribution method for electric automobile
Technical Field
The invention relates to the technical field of intelligent control of electric automobiles, in particular to a charging load distribution method of an electric automobile.
Background
The generation of energy crisis and the development of each item of technique of electric automobile have promoted electric automobile's extensive popularization, and nowadays, each country increases the dynamics and carries out policy support to electric automobile, can foresee, will have a large amount of electric automobile to insert the electric wire netting in the future. After the large-scale electric automobile is connected to a power grid, economic benefit problems caused by the electric automobile and the influence of the electric automobile on the planning operation of a power system cannot be ignored.
If the charging behavior of the electric automobile user is not guided and controlled, the disordered charging of the electric automobile can cause a result of 'peak-to-peak' on the original load of the power grid, the safe and stable operation of the power grid is influenced, and the adverse effect on the economic benefit is also generated. Therefore, it is necessary to master the usage rule of the electric vehicle and control the charging load in the electric vehicle cluster.
However, the ordered charging of the large-scale electric vehicles is a group strategy formed by aggregating the charging behaviors of each electric vehicle individual, and when the overall charging strategy of the electric vehicles is prepared, how to extend and distribute the group strategy to each individual needs to be further researched, and the fairness and the rationality of the charging strategy are embodied by fully considering the self condition and the will of the individual.
At present, the research on orderly charging of electric automobiles at home and abroad mainly focuses on automobile centralized charging control, and the research on how to decompose the charging strategy result of the regional power grid level to each electric automobile involves less research. Therefore, how to reasonably distribute the ordered charging control requirements of the large-scale electric vehicles on the basis of fully considering the individual charging requirements of each electric vehicle and the wishes of users is an urgent problem to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing an electric vehicle charging load distribution method so as to reasonably distribute the ordered charging control requirements of large-scale electric vehicles.
In order to solve the technical problem, the invention provides a method for distributing charging load of an electric vehicle, which comprises the following steps:
step S1, dividing one day into a plurality of time periods at set intervals according to the input original data of the distributed electric vehicle charging load, and obtaining the historical charging power of each time period;
step S2, determining the charging priority of the electric automobile according to the collected electric automobile data and the set data input by the user;
step S3, when the power grid has no dispatching requirement in the current time period, all electric vehicles are connected to the power grid for charging, otherwise, the step S4 is carried out;
step S4, traversing the information of each electric vehicle user, if the user does not want to accept the dispatching, immediately starting charging when the electric vehicle accesses the system, otherwise, turning to step S5;
step S5, if the battery capacity of the electric automobile reaches the total battery capacity in the current time period, the electric automobile does not participate in the optimization process, otherwise, the step S6 is carried out;
step S6, if the current time interval is judged to be the charging time of the electric automobile, immediately charging, otherwise, entering step S7;
step S7, if there is no charging arrangement needing to be distributed, then go to the next period, otherwise go to step S8;
step S8, a mathematical model of the charging load distribution is established based on the remaining electric vehicles, and the result is output.
Further, the step S1 is to divide one day into 96 time periods at intervals of 15 minutes.
Further, in step S2, the collected electric vehicle data includes driving mileage and historical driving mileage of the electric vehicle in the current state of charge; the setting data input by the user comprises whether the user is willing to participate in the dispatching plan or not, the expected residence time of the electric automobile, the expected electric quantity of the electric automobile after being charged and the planned trip on the next day.
Further, the step S6 is specifically: if tm,n<TjThen immediately charging, wherein tm,nM-th detection time node, T, representing the n-th vehiclejIndicating that the charging time is necessary, otherwise, the process proceeds to step S7.
Further, the mathematical model in step S8 takes the minimized number of times of charging the electric vehicle as an objective function, and satisfies the charging load arrangement in each time interval as a constraint condition, which is specifically expressed as:
Figure BDA0002835417370000021
the constraint conditions are as follows:
Figure BDA0002835417370000022
wherein n isjTotal number of electric vehicles, x, that can be scheduled for this periodn,jFor the charging decision of the nth electric vehicle in the present period, xn,j1, indicates that the vehicle is charging, x n,j0 indicates that the vehicle is not charged, xn,j-1For the charging decision of the nth electric vehicle in the previous period, Pn,jCharging power of the nth vehicle in this period, Pref,jCharging power, omega, schedulable for this periodnThe charging priority of the nth electric automobile.
Further, the charging priority ω of the nth electric vehiclenThe determination method comprises the following steps:
if the user sets the next day's mileage, ω isnComprises the following steps:
Figure BDA0002835417370000031
otherwise:
Figure BDA0002835417370000032
wherein L isn,tThe next day mileage, L, set for the usern,cIndicating the range that the nth electric vehicle can travel, L, in view of the battery state of the periodn,hAnd representing the historical trip mileage of the nth electric automobile.
The embodiment of the invention has the beneficial effects that: according to the method, on the premise of mileage anxiety of electric vehicle users, the charging frequency of each electric vehicle battery is minimum, the weighting coefficient of electric vehicle charging is determined by utilizing the historical trip mileage and the real-time battery state of the electric vehicles, the charging arrangement of the electric vehicle group is decomposed to each electric vehicle, and the reasonability and fairness of a large-scale electric vehicle ordered charging strategy are reflected; the charging requirement is completed, meanwhile, the charging start-stop times of the electric automobile can be reduced, and the service life of the battery is maintained; under the condition of respecting the autonomous response of the electric automobile user, the charging load distribution strategy in each time period is closely linked with the intrinsic parameters and the power utilization characteristics of the electric automobile, and the ordered charging load of the electric automobile can be reasonably regulated and controlled.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for distributing charging loads of an electric vehicle according to an embodiment of the present invention.
Fig. 2 is a curve diagram of the total load and the inflexible load of the power grid during the disordered charging and the ordered charging.
Fig. 3 is a schematic diagram of the number of times of charging the electric vehicle in consideration of the number of times of charging.
Fig. 4 is a schematic diagram of the number of cars charged in different time periods in two optimized charging modes.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1, an embodiment of the invention provides a method for distributing charging loads of an electric vehicle, including:
step S1, dividing one day into a plurality of time periods at set intervals according to the input original data of the distributed electric vehicle charging load, and obtaining the historical charging power of each time period;
step S2, determining the charging priority of the electric automobile according to the collected electric automobile data and the set data input by the user;
step S3, when the power grid has no dispatching requirement in the current time period, all electric vehicles are connected to the power grid for charging, otherwise, the step S4 is carried out;
step S4, traversing the information of each electric vehicle user, if the user does not want to accept the dispatching, immediately starting charging when the electric vehicle accesses the system, otherwise, turning to step S5;
step S5, if the battery capacity of the electric automobile reaches the total battery capacity in the current time period, the electric automobile does not participate in the optimization process, otherwise, the step S6 is carried out;
step S6, if the current time interval is judged to be the charging time of the electric automobile, immediately charging, otherwise, entering step S7;
step S7, if there is no charging arrangement needing to be distributed, then go to the next period, otherwise go to step S8;
step S8, a mathematical model of the charging load distribution is established based on the remaining electric vehicles, and the result is output.
Specifically, step S1 divides one day into 96 time periods specifically at 15 minute intervals.
In step S2, the collected electric vehicle data includes driving mileage and historical driving mileage of the electric vehicle under the current state of charge; the setting data input by the user comprises whether the user is willing to participate in the dispatching plan or not, the expected residence time of the electric automobile, the expected electric quantity of the electric automobile after being charged and the planned trip on the next day.
Step S6 is specifically executed if tm,n<TjThen immediately charging, wherein tm,nM-th detection time node, T, representing the n-th vehiclejIndicating that the charging time is necessary, otherwise, the process proceeds to step S7.
In step S8, the mathematical model takes the minimum number of times of charging the electric vehicle as an objective function, and satisfies the charging load arrangement in each time interval as a constraint condition, which is specifically expressed as:
Figure BDA0002835417370000041
the constraint conditions are as follows:
Figure BDA0002835417370000051
wherein n isjTotal number of electric vehicles, x, that can be scheduled for this periodn,jIs xn,j1, indicates that the vehicle is charging, x n,j0 indicates that the vehicle is not charged, xn,j-1For the charging decision of the nth electric vehicle in the previous period, Pn,jCharging power of the nth vehicle in this period, Pref,jCharging power, omega, schedulable for this periodnThe charging priority of the nth electric automobile.
Charging priority omega of nth electric automobilenThe determination method comprises the following steps:
if the user sets the next day's mileage, ω isnComprises the following steps:
Figure BDA0002835417370000052
otherwise:
Figure BDA0002835417370000053
wherein L isn,tThe next day mileage, L, set for the usern,cIndicating the range that the nth electric vehicle can travel, L, in view of the battery state of the periodn,hAnd representing the historical trip mileage of the nth electric automobile.
Firstly, making an overall ordered charging plan of the electric automobile according to load characteristics and the service condition of the electric automobile; furthermore, the intention of each user and the mileage anxiety degree are comprehensively considered, and the charging load is fairly and reasonably arranged to each electric automobile.
The embodiment of the invention carries out charging load distribution on the electric automobile based on the user will and the travel rule, and comprises the steps of dividing one day into 96 time periods, namely, 15min, predicting 96-point conventional load data and the condition that the electric automobile is connected to a power grid on the day by adopting the existing method according to historical conventional data, carrying out online real-time optimization on the conventional load data and the condition that the electric automobile is connected to the power grid on the day by a power company, making an overall ordered charging plan of the electric automobile, and obtaining the ideal charging power of each time period. Collecting electric vehicle data and relevant settings input by a user, wherein the electric vehicle data comprises driving mileage and historical traveling mileage of the electric vehicle in the current charge state; the user inputs relevant settings including whether the user is willing to participate in the dispatching plan, the expected residence time of the electric vehicle, the expected final state of charge of the electric vehicle and the next day planned trip.
Electric vehicles used by users in a residential area are taken as objects, the residential area is assumed to have 780 residents, each resident has one vehicle, wherein the number of the electric vehicles is 100, and the permeability of the electric vehicles is 12.8%; in the peak period of electricity utilization, the average electricity utilization of residents of each household is 4kW, namely, the highest peak of the total load of the residents is 3120 kW; 10% of electric vehicle users do not want to accept electric vehicle charging scheduling, and about 5% of users who are willing to accept electric vehicle scheduling set the trip mileage of the next day; charging the electric automobile by adopting a conventional charging mode, wherein the charging power is kept unchanged in the charging process, the charging power is 7kW, and each electric automobile is charged once a day; a plurality of required electric vehicle charging data are randomly generated by using the monte carlo method, and specific data are shown in table 1.
TABLE 1 electric vehicle charging data settings
Figure BDA0002835417370000061
The charging arrangement of the power system level of the region is known, and the ordered charging load of the electric automobile is shown in fig. 2, so that the distribution method of the charging load of the electric automobile effectively reduces the peak-valley difference of the load curve of the power grid, and plays a good role in regulation and control. As can be seen from fig. 3, the number of electric vehicles charged under regulation is significantly reduced, and the EV battery life is protected while the regulation requirement of the power grid is satisfied. As can be seen from fig. 4, the proposed load distribution strategy utilizes the time flexibility of electric vehicle charging to shift the charging period from the night period with more other electric loads to the early period with less other electric loads, which is beneficial to peak clipping and valley filling of the power grid.
The charging load distribution mathematical model takes the minimum charging times of the electric vehicle as an objective function, and takes the charging load arrangement in each period as a constraint condition. Determining a weighting coefficient by utilizing the historical trip mileage and the real-time battery state of the electric automobile: preferentially charging the electric automobile with longer historical trip mileage, and referring to the setting of the user if the user sets the next-day trip mileage, wherein the longer the mileage set by the user is, the higher the charging priority is; the smaller the remaining capacity of the current battery is, the higher the charging priority is. And finally, outputting the charging times of all the electric automobiles participating in the scheduling under the condition of considering the charging times and comparing the number of the electric automobiles charged in different periods in the unordered and ordered charging modes.
As can be seen from the above description, the embodiments of the present invention have the following beneficial effects: according to the method, on the premise that the mileage anxiety of the electric automobile user (namely the current vehicle charge amount and the willingness of the user to participate in regulation) is taken as a target, the charging times of each electric automobile battery are minimum, the weighting coefficient of the electric automobile charging is determined by utilizing the historical trip mileage and the battery real-time state of the electric automobile, the charging arrangement of the electric automobile group is decomposed to each electric automobile, and the rationality and the fairness of the large-scale electric automobile ordered charging strategy are reflected. The charging requirement is completed, meanwhile, the charging start-stop times of the electric automobile can be reduced, and the service life of the battery is maintained; under the condition of respecting the autonomous response of the electric automobile user, the charging load distribution strategy in each time period is closely linked with the intrinsic parameters and the power utilization characteristics of the electric automobile, and the ordered charging load of the electric automobile can be reasonably regulated and controlled.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.

Claims (6)

1.一种电动汽车充电负荷分配方法，其特征在于，包括：1. an electric vehicle charging load distribution method, is characterized in that, comprises: 步骤S1，根据输入的分配电动汽车充电负荷的原始数据，以设定间隔将一天划分为多个时间段，得到每个时段的历史充电功率；Step S1, according to the input original data of allocating the charging load of the electric vehicle, divide one day into a plurality of time periods at a set interval, and obtain the historical charging power of each period; 步骤S2，根据采集的电动汽车数据以及用户输入的设定数据确定电动汽车的充电优先级；Step S2, determining the charging priority of the electric vehicle according to the collected electric vehicle data and the setting data input by the user; 步骤S3当前时段电网无调度需求时，所有电动汽车接入电网充电，否则转入步骤S4；In step S3, when there is no scheduling demand in the power grid in the current period, all electric vehicles are connected to the power grid for charging, otherwise, go to step S4; 步骤S4，遍历每辆电动汽车用户的信息，如果用户不愿意接受调度，电动汽车接入系统时立即开始充电，否则转入步骤S5；Step S4, traverse the information of each electric vehicle user, if the user is unwilling to accept the scheduling, the electric vehicle starts charging immediately when it is connected to the system, otherwise, go to step S5; 步骤S5，如果电动汽车的当前时段电池容量已达到电池总容量，电动汽车不再参与优化流程，否则进行步骤S6；Step S5, if the current battery capacity of the electric vehicle has reached the total battery capacity, the electric vehicle will no longer participate in the optimization process, otherwise, go to step S6; 步骤S6，如果当前时段判断为电动汽车必须充电时间，则立即充电，否则进入步骤S7；Step S6, if the current time period determines that the electric vehicle must be charged, charge it immediately, otherwise go to step S7; 步骤S7，如果没有需要分配的充电安排，则进入下一时段，否则进入步骤S8；Step S7, if there is no charging arrangement that needs to be allocated, go to the next time period, otherwise go to Step S8; 步骤S8，基于剩余的电动汽车，建立充电负荷分配的数学模型，并输出结果。In step S8, a mathematical model of charging load distribution is established based on the remaining electric vehicles, and the result is output. 2.根据权利要求1所述的电动汽车充电负荷分配方法，其特征在于，所述步骤S1具体是以15分钟的间隔将一天划分为96个时间段。2 . The method for allocating electric vehicle charging load according to claim 1 , wherein, in the step S1 , a day is divided into 96 time periods at intervals of 15 minutes. 3 . 3.根据权利要求2所述的电动汽车充电负荷分配方法，其特征在于，所述步骤S2中，所述采集的电动汽车数据包括电动汽车当前荷电状态下所能行使的里程和历史出行里程；所述用户输入的设定数据包括是否愿意参加调度计划、电动汽车预期停留时间、期望达到的电动汽车充电后电量和第二天计划行程。3. The electric vehicle charging load distribution method according to claim 2, wherein in the step S2, the collected electric vehicle data includes the mileage and historical travel mileage that the electric vehicle can travel under the current state of charge of the electric vehicle ; The set data input by the user includes whether he is willing to participate in the dispatch plan, the expected stay time of the electric vehicle, the expected amount of electricity after the electric vehicle is charged, and the planned itinerary for the next day. 4.根据权利要求3所述的电动汽车充电负荷分配方法，其特征在于，所述步骤S6具体为：若tm,n<Tj则立即充电，其中，tm,n表示第n辆车的第m个检测时间节点，Tj表示必须充电时间，否则进入步骤S7。4. The electric vehicle charging load distribution method according to claim 3, wherein the step S6 is specifically: if t m,n <T j , charge immediately, wherein t m,n represents the nth vehicle The mth detection time node of , T j represents the time that must be charged, otherwise, go to step S7. 5.根据权利要求1所述的电动汽车充电负荷分配方法，其特征在于，所述步骤S8中数学模型以最小化电动汽车充电次数作为目标函数，以满足每个时段充电负荷安排为约束条件，具体表示为：5. The electric vehicle charging load distribution method according to claim 1, wherein the mathematical model in the step S8 takes minimizing the number of electric vehicle charging times as the objective function, so as to satisfy the charging load arrangement in each time period as a constraint condition, Specifically expressed as:
Figure FDA0002835417360000021
Figure FDA0002835417360000021
 约束条件为：The constraints are:
Figure FDA0002835417360000022
Figure FDA0002835417360000022
 其中，nj为本时段可以调度的电动汽车总数目，xn,j为本时段第n辆电动汽车的充电决策，xn,j＝1，表示该辆车在充电，xn,j＝0表示该辆车未充电，xn,j-1为上时段第n辆电动汽车的充电决策，Pn,j为本时段第n辆车的充电功率，Pref,j为本时段可调度的充电功率，ωn为第n辆电动汽车的充电优先级。Among them, n j is the total number of electric vehicles that can be dispatched in this period, x n,j is the charging decision of the nth electric vehicle in this period, x n, j =1, indicating that the vehicle is charging, x n,j = 0 means that the vehicle is not charged, x n,j-1 is the charging decision of the nth electric vehicle in the previous period, P n,j is the charging power of the nth vehicle in the period, and P ref,j is the scheduler for this period , ω n is the charging priority of the nth electric vehicle. 6.根据权利要求5所述的电动汽车充电负荷分配方法，其特征在于，第n辆电动汽车的充电优先级ωn的确定方法为：6. The electric vehicle charging load distribution method according to claim 5, wherein the method for determining the charging priority ω n of the nth electric vehicle is: 如果用户设置了第二天的行驶里程，则ωn为：If the user sets the mileage for the next day, then ω n is:
Figure FDA0002835417360000023
Figure FDA0002835417360000023
 否则：
Figure FDA0002835417360000024
otherwise:
Figure FDA0002835417360000024
 其中，Ln,t为用户设置的第二天行驶里程，Ln,c表示鉴于该时段的电池状态，第n辆电动汽车可以行使的里程，Ln,h表示第n辆电动汽车历史出行里程。Among them, L n,t is the mileage set by the user for the next day, L n,c represents the mileage that the nth electric vehicle can travel in view of the battery status in this period, and Ln,h represents the historical travel of the nth electric vehicle mileage.
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CN113327035A (en) * 2021-05-28 2021-08-31 兰州理工大学 Electric quantity distribution method and device, electronic equipment and storage medium
CN113715669A (en) * 2021-07-27 2021-11-30 西安交通大学 Electric vehicle ordered charging control method, system, equipment and readable storage medium
CN113910963A (en) * 2021-11-12 2022-01-11 集度科技有限公司 Electric vehicle ordered charging control method, device and system and storage medium

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CN105046371A (en) * 2015-08-19 2015-11-11 东南大学 Electric vehicle charge-discharge scheduling method based on demand side bidding
CN108944531A (en) * 2018-07-24 2018-12-07 河海大学常州校区 A kind of orderly charge control method of electric car

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CN105046371A (en) * 2015-08-19 2015-11-11 东南大学 Electric vehicle charge-discharge scheduling method based on demand side bidding
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CN113327035A (en) * 2021-05-28 2021-08-31 兰州理工大学 Electric quantity distribution method and device, electronic equipment and storage medium
CN113327035B (en) * 2021-05-28 2022-08-23 兰州理工大学 Electric quantity distribution method and device, electronic equipment and storage medium
CN113715669A (en) * 2021-07-27 2021-11-30 西安交通大学 Electric vehicle ordered charging control method, system, equipment and readable storage medium
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CN113910963A (en) * 2021-11-12 2022-01-11 集度科技有限公司 Electric vehicle ordered charging control method, device and system and storage medium

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Application publication date: 20210226