CN103646295B

CN103646295B - Electric automobile charging and conversion electric network integration dispatching method based on service station universal model

Info

Publication number: CN103646295B
Application number: CN201310616187.3A
Authority: CN
Inventors: 高赐威; 陆婷婷
Original assignee: Southeast University
Current assignee: Southeast University
Priority date: 2013-11-28
Filing date: 2013-11-28
Publication date: 2016-07-06
Anticipated expiration: 2033-11-28
Also published as: CN103646295A

Abstract

The invention discloses an integrated scheduling method for electric vehicle charging and swapping networks based on a general model of service stations, and establishes general models of three types of service stations in the charging and swapping network to characterize the behavior of service stations (charging, battery swapping, etc.) , battery deployment) and state (the number of full battery packs and the number of empty battery packs); according to the initial battery replacement demand in each sampling period of the service station, the actual replacement of the service station is obtained by using the queuing theory simulation The number of charging devices in the service station, the average arrival rate of users, and the impact of the upper limit of the waiting team on the service station’s battery replacement capacity are fully taken into account; an integrated scheduling model for the actual charging and replacement network operation is established, and the charging of the battery pack is optimized. Solutions, deployment solutions, and logistics solutions minimize the operating costs of the network.

Description

An integrated scheduling method for electric vehicle charging and swapping networks based on a general model of service stations

技术领域technical field

本发明属于电动汽车充电领域，具体涉及“集中充电、统一配送”和充换电两种模式下电动汽车充换电网络的建模和电池调度方法。The invention belongs to the field of electric vehicle charging, and in particular relates to a modeling and battery dispatching method of an electric vehicle charging and swapping network under two modes of "centralized charging and unified distribution" and charging and swapping.

背景技术Background technique

基于电池租赁的换电模式在降低用户初始购车成本、延长电池寿命、加快充电时间等方面表现出明显的优势，成为当前电动汽车发展具有竞争力的商业模式之一，具体包含“集中充电、统一配送”和充换电两种模式。“集中充电、统一配送”模式的主要能源供给方式为集中型充电站与配送站；充换电模式的主要能源供给方式为充电站。充换电网络中，集中型充电站承担大规模的电池充电功能，充满的电池将被配送至具有小规模充电能力和换电功能的充换电站以及仅具备换电池功能的配送站，从而实现对用户的电池供应。充换电网络的实际运行过程中，涉及到有关电池充电、电池调配、物流配送等诸多环节，而且各个环节紧密相关。为了满足用户的换电需求,保障电网安全、稳定、经济运行,科学合理的充换电网络一体化运行调度起着至关重要的作用。The battery replacement model based on battery leasing has shown obvious advantages in reducing the user's initial car purchase cost, extending battery life, and speeding up charging time. It has become one of the current competitive business models for the development of electric vehicles, specifically including "centralized charging, unified Delivery" and charging and swapping modes. The main energy supply mode of "centralized charging and unified distribution" mode is centralized charging station and distribution station; the main energy supply mode of charging and swapping mode is charging station. In the charging and swapping network, centralized charging stations undertake large-scale battery charging functions, and fully charged batteries will be delivered to charging and swapping stations with small-scale charging capabilities and battery swapping functions, as well as distribution stations that only have battery swapping functions, so as to realize battery supply to the user. The actual operation of the charging and swapping network involves many links related to battery charging, battery deployment, logistics and distribution, and all links are closely related. In order to meet the needs of users for battery replacement and ensure the safety, stability, and economic operation of the power grid, scientific and reasonable integrated operation scheduling of charging and battery replacement networks plays a vital role.

目前学术界还鲜有关于换电模式下充换电网络运行调度的研究，仅有部分文献针对其中的充电优化进行了讨论。总体而言，现有研究主要将充电站作为主体，对其充电负荷和控制策略的研究完全没有全面考虑配送策略、配送时间等因素的影响。忽视了电池调配对维持电池供需的时空平衡以及物流调配对电池调配的实际支撑作用。At present, there are few studies in the academic circle on the operation scheduling of the charging and swapping network in the swapping mode, and only some literatures discuss the charging optimization. In general, the existing research mainly focuses on charging stations, and the research on charging load and control strategies does not fully consider the influence of distribution strategies, delivery time and other factors. The actual supporting role of battery deployment to maintain the space-time balance of battery supply and demand and logistics deployment to battery deployment is ignored.

发明内容Contents of the invention

本发明的目的在于为了克服现有技术的不足，提供一种基于服务站通用模型的电动汽车充换电网络一体化调度方法，通过对三种类型服务站的通用建模，精细化表征服务站的各种行为与状态之间相互影响与制约的关系；通过电池充电、调配和物流配送的一体化调度模型，实现换电模式下充电方案、电池调配方案和物流配送方案的最优化求解，在满足用户换电需求的前提下尽可能降低网络运营成本，为充换电网络的运行奠定理论基础。The purpose of the present invention is to overcome the deficiencies of the prior art and provide an integrated dispatching method for electric vehicle charging and swapping networks based on the general model of service stations. Through the general modeling of three types of service stations, the service stations can be finely characterized The mutual influence and restriction relationship between various behaviors and states of the battery; through the integrated scheduling model of battery charging, deployment and logistics distribution, the optimal solution of the charging scheme, battery deployment scheme and logistics distribution scheme under the battery replacement mode is realized. Under the premise of meeting the user's battery replacement needs, the network operation cost should be reduced as much as possible, and the theoretical foundation for the operation of the charging and battery replacement network should be laid.

本发明所述的一种基于服务站通用模型的电动汽车充换电网络一体化调度方法，包括以下步骤：An integrated scheduling method for electric vehicle charging and swapping networks based on a general model of a service station according to the present invention comprises the following steps:

1）建立充换电网络中三种类型服务站：集中型充电站、充换电站和配送站的通用模型，以表征服务站的状态（包括满电池组数和空电池组数）与行为（包括换电、充电、电池调配）之间的关系：1) Establish a general model of three types of service stations in the charging and swapping network: centralized charging stations, charging and swapping stations, and distribution stations to characterize the status of service stations (including the number of full and empty batteries) and behavior ( Including the relationship between battery replacement, charging, and battery deployment):

${Q Q}_{i i,, t t}^{f f} = = \{\begin{matrix} {Q Q}_{i i,, t t - - 11}^{f f} + + {Q Q}_{i i,, t t - - [[t t__chg chg / / T T]]}^{c c} - - {Q Q}_{i i,, t t - - 11}^{h h} - - {Q Q}_{i i,, t t - - 11}^{d d},, & {Q Q}_{i i,, t t - - 11}^{d d} &GreaterEqual; &Greater Equal; 00 \\ {Q Q}_{i i,, t t - - 11}^{f f} + + {Q Q}_{i i,, t t - - [[t t__chg chg / / T T]]}^{c c} - - {Q Q}_{i i,, t t - - 11}^{h h} + + \underset{j j}{Σ Σ} {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} / / T T]]}^{d d},, & {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} / / T T]]}^{d d} < < 00 \end{matrix} - - - - - - ((11))$

${Q Q}_{i i,, t t}^{e e} = = \{\begin{matrix} {Q Q}_{i i,, t t - - 11}^{e e} + + {Q Q}_{i i,, t t - - 11}^{h h} - - {Q Q}_{i i,, t t - - 11}^{c c} + + \underset{j j}{Σ Σ} {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} * * 22 / / T T]]}^{d d},, & {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} * * 22 / / T T]]}^{d d} &GreaterEqual; &Greater Equal; 00 \\ {Q Q}_{i i,, t t - - 11}^{e e} + + {Q Q}_{i i,, t t - - 11}^{h h} - - {Q Q}_{i i,, t t - - 11}^{c c} - - \underset{j j}{Σ Σ} {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} / / T T]]}^{d d},, & {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} / / T T]]}^{d d} < < 00 \end{matrix} - - - - - - ((22))$

$\{\begin{matrix} {Q Q}_{i i,, t t}^{h h} \leq \leq {Q Q}_{i i,, t t}^{f f} \\ {Q Q}_{i i,, t t}^{c c} \leq \leq {Q Q}_{i i,, t t}^{e e} \\ {Q Q}_{i i,, t t}^{d d} \leq \leq {Q Q}_{i i,, t t}^{f f} - - {Q Q}_{i i,, t t}^{h h},, if if {Q Q}_{i i,, t t}^{d d} > > 00 \end{matrix} - - - - - - ((33))$

式中：为t时刻服务站i的满电池组数量；为t时刻服务站i的空电池组数量，t＝1表示初始时刻；为t时刻服务站i为用户换电的满电池组数量；为t时刻服务站i开始充电的空电池组数量；为t时刻服务站i开始调配的满电池组数量，表示t时刻服务站i调出电池组，表示t时刻有满电池组从其他服务站向服务站i调出。t_chg为电池组充电所需时间(min)；T为采样周期（min）；dis_ij为服务站i到服务站j的电池组调配最少单程用时(min)；[]表示向上取整。In the formula: is the number of full battery packs of service station i at time t; is the number of empty battery packs at service station i at time t, and t=1 represents the initial time; is the number of full battery packs that service station i replaces for users at time t; is the number of empty battery packs that service station i starts charging at time t; is the number of full battery packs deployed by service station i at time t, Indicates that service station i calls out the battery pack at time t, Indicates that a full battery pack is transferred from other service stations to service station i at time t. t_chg is the time required for battery pack charging (min); T is the sampling period (min); dis _ij is the minimum one-way time (min) for deploying the battery pack from service station i to service station j; [] indicates rounding up.

2）依据排队论的相关理论，电动汽车换电服务系统可看成一个排队系统，系统的顾客是电动汽车，服务台是换电设备，服务台提供的服务是更换电池。根据排队论原理获得服务站的实际换电能力，对原始换电需求曲线进行修正：2) According to the relevant theories of queuing theory, the electric vehicle battery replacement service system can be regarded as a queuing system. The customers of the system are electric vehicles, the service desk is the battery replacement equipment, and the service provided by the service desk is battery replacement. According to the principle of queuing theory, the actual power exchange capacity of the service station is obtained, and the original power exchange demand curve is corrected:

${Q Q}_{i i,, t t}^{r r} = = min min (({Q Q}_{i i,, t t}^{R R},, {N N}_{i i,, t t})) - - - - - - ((44))$

式中：N_i,t第t个采样周期内服务站i的换电能力；为t时刻服务站i的原始换电需求；为修正后t时刻服务站i的换电需求。In the formula: N _i,t is the power exchange capacity of service station i in the tth sampling period; is the original power exchange demand of service station i at time t; is the power exchange demand of service station i at time t after correction.

3）根据网络交通图，利用dijkstra算法求解最短用时路径；3) According to the network traffic map, use the Dijkstra algorithm to solve the shortest time-consuming path;

4）计算一个运营日内充换电网络的物流费用：4) Calculate the logistics cost of charging and swapping network within one operation day:

${F f}_{d d} = = \underset{t t}{Σ Σ} \underset{i i}{Σ Σ} \underset{j j}{Σ Σ} ceil the ceil ((abs abs (({Q Q}_{ij ij,, t t}^{d d})) / / vc vc)) * * {dis dis}_{ij ij} * * {c c}_{ev ev} / / 120120 - - - - - - ((55))$

式中：F_d为物流费用；为t时刻服务站i配送给服务站j的满电池组数，如果满电池组从服务站j配送至服务站i，则为负；vc为每辆车能够装载的电池组数，单位为块；c_ev为单个物流车辆每小时的运费，单位为元/小时。In the formula: F _d is logistics cost; is the number of full battery packs delivered from service station i to service station j at time t, if the full battery pack is delivered from service station j to service station i, then is negative; vc is the number of battery packs that each vehicle can load, and the unit is block; c _ev is the hourly freight of a single logistics vehicle, and the unit is yuan/hour.

5）计算一个运营日内充换电网络的充电费用：5) Calculate the charging cost of the charging and swapping network within one operating day:

${F f}_{c c} = = \underset{i i}{Σ Σ} {Σ Σ}_{time time = = 11}^{4848} {Σ Σ}_{t t = = time time}^{time time + + [[t t__chg chg / / 3030]] - - 11} {Q Q}_{i i,, t t}^{c c} * * {p p}_{ack ack} * * T T * * {p p}_{t t} - - - - - - ((66))$

式中：F_c为充电费用；p_ack为电池组的充电功率，单位为KW；p_t为t时刻的电价，单位为元。In the formula: F _c is the charging cost; pack is the charging power of the battery pack, the unit is KW; p _t is the electricity price at time _t , the unit is yuan.

6）以分时电价为背景，以网络运营费用最小化为目标，建立实际电动汽车充换电网络运营一体化调度模型，从而得出一体化调度的充电和电池调配策略：6) With the time-of-use electricity price as the background and the goal of minimizing network operating costs, an integrated scheduling model for the actual charging and swapping network operation of electric vehicles is established, and the charging and battery allocation strategy for integrated scheduling is obtained:

minF＝F_c+F_d(7) _minF ＝Fc+ _Fd (7)

$s the s . . t t . . {Q Q}_{i i,, t t}^{h h} = = min min (({Q Q}_{i i,, t t}^{f f},, {Q Q}_{i i,, t t}^{r r})),, &ForAll; &ForAll; i i,, t t - - - - - - ((88))$

${Q Q}_{i i,, t t}^{h h} = = {Q Q}_{i i,, t t}^{r r},, &ForAll; &ForAll; i i,, t t - - - - - - ((99))$

${Q Q}_{i i,, t t}^{c c} \leq \leq min min (({Q Q}_{i i,, t t}^{e e},, {P P}_{i i} * * 10001000 / / {p p}_{ack ack})),, &ForAll; &ForAll; i i,, t t - - - - - - ((1010))$

$\underset{j j}{Σ Σ} ceil ceil (({Q Q}_{ij ij,, t t}^{d d} / / vc vc)) \leq \leq {nc nc}_{i i},, if if {Q Q}_{ij ij,, t t}^{d d} > > 00,, &ForAll; &ForAll; i i,, t t - - - - - - ((1111))$

${Q Q}_{i i,, t t}^{d d} \leq \leq {Q Q}_{i i,, t t}^{f f} - - {Q Q}_{i i,, t t}^{h h},, if if {Q Q}_{i i,, t t}^{d d} > > 00,, {Q Q}_{i i,, t t}^{d d} = = \underset{j j}{Σ Σ} {Q Q}_{ij ij,, t t}^{d d},, &ForAll; &ForAll; i i,, t t - - - - - - ((1212))$

${Q Q}_{ij ij,, t t}^{d d} + + {Q Q}_{ji the ji,, t t}^{d d} = = 00 - - - - - - ((1313))$

$\underset{i i}{Σ Σ} {Q Q}_{i i,, t t}^{d d} = = 00,, &ForAll; &ForAll; t t - - - - - - ((1414))$

式中：P_i为服务站i的容量，单位为MW；nc_i为站点i的物流车辆数。In the formula: P _i is the capacity of service station i, in MW; nc _i is the number of logistics vehicles at station i.

所述步骤1建立的服务站通用模型揭示了服务站的状态与行为之间相互影响相互制约的关系：The general model of the service station established in step 1 reveals the mutual influence and mutual restriction relationship between the state and behavior of the service station:

1）服务站的行为影响其状态变化1) The behavior of the service station affects its state change

即时行为，如换电立即对服务站的状态产生影响，而过程行为具有后效性，即过程行为会影响服务站其后某个时刻的状态:①充电行为发生时会影响服务站的空电池组数，但要再经过一个充电时长，服务站的满电池组数才会发生变化；②电池调配开始时，只影响调出满电池组的服务站，调配到达时接收满电池组的服务站满电池组和空电池组数量都发生变化，等到物流车辆回到调出满电池组的服务站时，其空电池组数也发生变化。Immediate behavior, such as changing the battery immediately affects the state of the service station, while the process behavior has aftereffect, that is, the process behavior will affect the state of the service station at a certain moment later: ① When the charging behavior occurs, it will affect the empty battery of the service station The number of groups, but the number of full battery groups at the service station will change after another charging time; ②When the battery allocation starts, only the service station that has transferred out the full battery group will be affected, and the service station that receives the full battery group will be affected when the deployment arrives The number of full battery packs and empty battery packs both changes, and when the logistics vehicle returns to the service station where the full battery pack was transferred, the number of empty battery packs also changes.

2）服务站的状态约束其行为2) The state of the service station constrains its behavior

换电和电池调配受满电池组数的约束，充电受空电池组数的约束。Battery replacement and battery allocation are limited by the number of full battery packs, and charging is limited by the number of empty battery packs.

所述步骤2中系统服务能力的计算过程计及了系统服务能力受到服务站的换电设备数量、用户的平均到达率、可接受的等待对长、换电时间等因素的限制，具体包括以下步骤：The calculation process of the system service capability in step 2 takes into account that the system service capability is limited by factors such as the number of power replacement equipment at the service station, the average arrival rate of users, the acceptable waiting time, and power replacement time, and specifically includes the following step:

1）建立电动汽车换电服务系统的M/D/C排队模型,M/D/C表示顾客到达时间间隔服从负指数分布、服务时间为固定值和系统中有C个服务台的模型。1) Establish the M/D/C queuing model of the electric vehicle battery replacement service system. M/D/C means that the customer arrival time interval obeys a negative exponential distribution, the service time is a fixed value, and there are C service desks in the system.

2）根据服务站在各采样周期的初始换电需求，计算其平均其到达率和用户到达时间间隔：2) Calculate the average arrival rate and user arrival time interval according to the service station's initial power replacement demand in each sampling period:

${λ λ}_{i i,, t t} = = \frac{{Q Q}_{i i,, t t}^{R R}}{T T} - - - - - - ((1515))$

${P P}_{i i,, t t} ((T T \leq \leq t t)) = = 11 - - {e e}^{- - {λ λ}_{i i,, t t} t t} - - - - - - ((1616))$

式中:λ_i,t为第t个采样周期内服务站i的平均到达率；为第t个采样周期内服务站i的初始换电需求。In the formula: λi _,t is the average arrival rate of service station i in the tth sampling period; is the initial battery replacement demand of service station i in the tth sampling period.

3）matlab仿真模拟一定规模系统的实际运行情况，获得各采样周期内服务站实际服务的电动汽车数，计算服务率η：3) Matlab simulates the actual operation of a system of a certain scale, obtains the number of electric vehicles actually served by the service station in each sampling period, and calculates the service rate η:

4）计算服务站实际换电能力为：4) Calculate the actual power exchange capacity of the service station as:

N_i,t＝λ_i,t*T*η_i,t(18)N _i,t =λ _i,t *T*η _i,t (18)

所述步骤6的约束条件为：The constraints of step 6 are:

1）服务站为用户提供的换电量等于其满电池组数和用户换电需求的较小值；1) The replacement power provided by the service station to the user is equal to the smaller value of the number of full battery packs and the user's replacement demand;

2）系统运营满足基本的换电需求；2) The system operation meets the basic needs of power exchange;

3）各站点的充电电池组数小于该时刻的空电池组并且总的充电功率小于站点的容量；3) The number of rechargeable battery packs at each station is less than the empty battery pack at that moment and the total charging power is less than the capacity of the station;

4）每次调配所需的物流车辆数小于站点的实际拥有车辆数；4) The number of logistics vehicles required for each deployment is less than the actual number of vehicles owned by the site;

5）各站点每次调出的电池组总数不大于该时刻的满电池组总数；5) The total number of battery packs called out at each station is not greater than the total number of full battery packs at that moment;

6）单次调配满电池组在两个服务站之间的流动是单向的；6) The flow of a single-deployed full battery pack between two service stations is one-way;

7）各次调配，网络内总的调出电池组数与总的配进电池组数相等。7) For each deployment, the total number of battery packs that are transferred out of the network is equal to the total number of battery packs that are deployed.

采用本发明的技术方案，可实现如下有益效果：本发明针对电动汽车充换电网络物流配送和充电环节的调度运行进行了基础研究，形成充换电网络一体化调度的基本理论，为充换电网络的运行提供了科学理论支持，充分发挥了其运行效率：（1）建立充换电网络中集中型充电站、充换电站和配送站这三种类型服务站的通用模型，作为一个模块引入一体化调度模型中，在编程时无需考虑不同类型服务站的区别，同时，通用模型充分考虑了充电和电池调度行为对服务站状态影响的后效性，使模型更为精细化；（2）引入换电需求修正曲线，从服务站的实际服务能力出发，充分计及服务站的换电设备数量、用户的平均到达率、可接受的等待对长、换电时间等现实因素对换电能力限制，使服务站的换电行为更具现实意义；（3）建立充换电网络的一体化调度模型，对网络的充电方案、电池调配方案和物流配送方案进行整体优化，从而减少网络的运营费用，提高系统经济性，同时模型基于分时电价建立，分时电价对电网负荷峰谷的反映又能有效引导网络充电方案平抑电网负荷，起到削峰填谷的作用。By adopting the technical solution of the present invention, the following beneficial effects can be achieved: the present invention conducts basic research on the scheduling operation of the logistics distribution and charging link of the electric vehicle charging and swapping network, and forms the basic theory of integrated scheduling of the charging and swapping network, which provides a basis for charging and swapping The operation of the electricity network provides scientific and theoretical support and gives full play to its operating efficiency: (1) Establish a general model of three types of service stations in the charging and swapping network: centralized charging station, charging and swapping station, and distribution station, as a module Introduced into the integrated scheduling model, there is no need to consider the difference between different types of service stations when programming. At the same time, the general model fully considers the aftereffect of charging and battery scheduling behaviors on the status of service stations, making the model more refined; (2 ) introduces the power exchange demand correction curve, starting from the actual service capacity of the service station, fully taking into account the number of battery exchange equipment at the service station, the average arrival rate of users, the acceptable waiting time, and battery exchange time. Capacity constraints make the battery swapping behavior of service stations more realistic; (3) Establish an integrated dispatching model for the charging and swapping network, and optimize the charging scheme, battery deployment scheme, and logistics distribution scheme of the network as a whole, thereby reducing network traffic. Operating costs improve system economy. At the same time, the model is established based on time-of-use electricity price. The reflection of time-of-use electricity price on the peak and valley of grid load can effectively guide the network charging scheme to stabilize the grid load and play the role of peak load reduction and valley filling.

附图说明Description of drawings

图1为本发明方法的总流程图；Fig. 1 is the general flowchart of the inventive method;

图2为测试算例交通网络；Figure 2 is the traffic network of the test example;

图3为次日原始换电需求预测曲线；Figure 3 is the forecast curve of the original power exchange demand for the next day;

图4为电动汽车换电服务系统的M/D/C排队模型；Figure 4 is the M/D/C queuing model of the electric vehicle battery exchange service system;

图5为matlab仿真排队系统运行结果；Fig. 5 is the running result of matlab simulation queuing system;

图6为充换电站2修正前后的换电需求曲线；Fig. 6 is the power swap demand curve before and after the correction of the charging swap station 2;

图7为充换电站初始满电池组数不同时的一体化调度结果；Figure 7 shows the integrated scheduling results when the number of initial full battery packs in the charging and swapping station is different;

图8为充换电站初始空电池组数不同时的一体化调度结果。Figure 8 shows the integrated scheduling results when the number of initial empty battery packs in the charging and swapping station is different.

具体实施方式detailed description

下面对本发明技术方案进行详细说明，但是本发明的保护范围不局限于所述实施例。The technical solutions of the present invention will be described in detail below, but the protection scope of the present invention is not limited to the embodiments.

本实施例为一种基于服务站通用模型的电动汽车充换电网络一体化调度方法，如图1所示，实施例的参数如下：This embodiment is an integrated scheduling method for electric vehicle charging and swapping networks based on a general model of a service station, as shown in FIG. 1 , and the parameters of the embodiment are as follows:

1）交通信息如图2所示，图中C代表集中型充电站，G代表充换电站。图中标注的数字为节点编号，用来表征站点的位置信息。服务站编号与地区交通分布图的对应关系见表1，不同路段的配送所需时间见表2。1) The traffic information is shown in Figure 2. C in the figure represents a centralized charging station, and G represents a charging station. The number marked in the figure is the node number, which is used to represent the location information of the station. See Table 1 for the corresponding relationship between the service station number and the regional traffic distribution map, and Table 2 for the delivery time of different road sections.

表1服务站编号与地区交通分布图对应关系Table 1 Correspondence between service station number and regional traffic distribution map

表2不同路段配送所需时间Table 2 Time required for delivery on different road sections

起点starting point 终点end 所需时间/minRequired time/min 起点starting point 终点end 所需时间/minRequired time/min 起点starting point 终点end 所需时间/minRequired time/min 11 22 1515 77 2525 1515 1515 21twenty one 1515 11 1414 1515 88 99 55 1616 1818 55 11 1616 3030 88 1717 1010 1717 21twenty one 1818 11 2020 2020 99 1010 1515 1717 23twenty three 1414 22 33 1515 99 1717 1010 1818 1919 1313 22 22twenty two 1515 1010 1111 1010 1818 21twenty one 1515 33 44 77 1010 1515 1010 1919 2020 1212 33 24twenty four 2020 1010 1717 2020 1919 21twenty one 1010 33 2525 3030 1111 1212 55 2020 22twenty two 1818 44 55 1515 1212 1313 77 21twenty one 22twenty two 2020 55 66 55 1212 1515 1212 21twenty one 23twenty three 1616 55 2525 1010 1313 1414 1010 21twenty one 24twenty four 2525 66 77 55 1313 1515 2020 22twenty two 24twenty four 1515 66 2525 1212 1313 1616 1515 23twenty three 24twenty four 1515 77 88 66 1414 1616 2020 23twenty three 2525 1010 77 1717 2020 1515 1616 1616 77 23twenty three 2525 1515 1717 1515

2）每日末时刻，服务站将次日原始换电需求预测曲线（如图3所示）、起始时刻备用满电池数量和空电池数量（如表3所示）发送至系统调度中心。同时，调度中心从电网调度部门获取次日电价信息，如表4所示：2) At the end of each day, the service station sends the original battery replacement demand forecast curve of the next day (as shown in Figure 3), the number of spare full batteries and the number of empty batteries at the beginning (as shown in Table 3) to the system dispatching center. At the same time, the dispatching center obtains the next day's electricity price information from the power grid dispatching department, as shown in Table 4:

表3网络内初始电池组配置情况Table 3 Initial configuration of battery packs in the network

CFCF CECE GFGF GEGE DFDF DEDE 00 600600 400400 600600 200200 00

表4峰、谷、平时段划分及各时段电价Table 4 Division of peak, valley, and normal periods and electricity prices in each period

3）配送车辆载重为0.9吨、单块电池组质量为30kg，则一辆车可装载电池组数为30块；每个集中型充电站配置物流车辆15辆，每个充换电站配置物流车辆5辆；一个完整的配送过程包括：①在电池组的调出站点装载充满电的电池组，需要5min；②把充满电的电池组运往电池组的配进站点；③在电池组的配进站点卸下满电池组，需要5min；④装载相应数量的更换下的电池组，需要5min；⑤把更换下的电池组运往配送发出站点；⑥把更换下的电池组放到充电架上,需要5min；3) The load of the distribution vehicle is 0.9 tons, and the mass of a single battery pack is 30kg, so one vehicle can carry 30 battery packs; each centralized charging station is equipped with 15 logistics vehicles, and each charging station is equipped with logistics vehicles 5 vehicles; a complete distribution process includes: ① Loading a fully charged battery pack at the battery pack transfer station takes 5 minutes; ② Transporting the fully charged battery pack to the battery pack distribution station; It takes 5 minutes to unload the full battery pack at the station; ④ Load the corresponding number of replaced battery packs, it takes 5 minutes; ⑤ Transport the replaced battery pack to the delivery station; 5min;

4）电池组的充电过程近似为恒功率特性，充电功率为2kW，充电所需时间为2.5h；集中型充电站可提供的充电功率为1MW，充换电站可提供充电功率为0.3MW。物流车辆的收费标准为50元/（时*辆）。4) The charging process of the battery pack is approximately constant power characteristic, the charging power is 2kW, and the charging time is 2.5h; the charging power provided by the centralized charging station is 1MW, and the charging power provided by the charging and swapping station is 0.3MW. The charging standard for logistics vehicles is 50 yuan/(hour * vehicle).

（1）建立服务站通用模型：(1) Establish a general model of the service station:

${Q Q}_{i i,, t t}^{f f} = = \{\begin{matrix} {Q Q}_{i i,, t t - - 11}^{f f} + + {Q Q}_{i i,, t t - - [[t t__chg chg / / T T]]}^{c c} - - {Q Q}_{i i,, t t - - 11}^{h h} - - {Q Q}_{i i,, t t - - 11}^{d d},, & {Q Q}_{i i,, t t - - 11}^{d d} &GreaterEqual; &Greater Equal; 00 \\ {Q Q}_{i i,, t t - - 11}^{f f} + + {Q Q}_{i i,, t t - - [[t t__chg chg / / T T]]}^{c c} - - {Q Q}_{i i,, t t - - 11}^{h h} + + \underset{j j}{Σ Σ} {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} / / T T]]}^{d d},, & {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} / / T T]]}^{d d} < < 00 \end{matrix}$

${Q Q}_{i i,, t t}^{e e} = = \{\begin{matrix} {Q Q}_{i i,, t t - - 11}^{e e} + + {Q Q}_{i i,, t t - - 11}^{h h} - - {Q Q}_{i i,, t t - - 11}^{c c} + + \underset{j j}{Σ Σ} {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} * * 22 / / T T]]}^{d d},, & {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} * * 22 / / T T]]}^{d d} &GreaterEqual; &Greater Equal; 00 \\ {Q Q}_{i i,, t t - - 11}^{e e} + + {Q Q}_{i i,, t t - - 11}^{h h} - - {Q Q}_{i i,, t t - - 11}^{c c} - - \underset{j j}{Σ Σ} {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} / / T T]]}^{d d},, & {Q Q}_{i i,, t t - - [[{dis dis}_{ij ij} / / T T]]}^{d d} < < 00 \end{matrix}$

$\{\begin{matrix} {Q Q}_{i i,, t t}^{h h} \leq \leq {Q Q}_{i i,, t t}^{f f} \\ {Q Q}_{i i,, t t}^{c c} \leq \leq {Q Q}_{i i,, t t}^{e e} \\ {Q Q}_{i i,, t t}^{d d} \leq \leq {Q Q}_{i i,, t t}^{f f} - - {Q Q}_{i i,, t t}^{h h},, if if {Q Q}_{i i,, t t}^{d d} > > 00 \end{matrix}$

（2）建立电动汽车换电服务系统的M/D/C排队模型，如图4所示。本实施例中服务站有6台换电设备，每辆车的服务时间为4min，图5为平均到达率为2.2辆/min，可接受等待队长为16辆时的matlab仿真系统运行结果。可以看到用户在采样周期内是陆续到达服务站的，且编号为34、36、43、49、53、54、55、56、57、58、59、60、61、62、63、64、65、66的用户未能获得换电服务。表5列出了充换电站1修正后的换电需求曲线，图6为充换电站2修正前后的换电需求曲线。(2) Establish the M/D/C queuing model of the electric vehicle battery exchange service system, as shown in Figure 4. In this embodiment, the service station has 6 battery replacement devices, and the service time of each vehicle is 4 minutes. Figure 5 shows the average arrival rate of 2.2 vehicles/min, and the matlab simulation system operation results when the waiting team is 16 vehicles is acceptable. It can be seen that users arrive at the service station one after another during the sampling period, and the numbers are 34, 36, 43, 49, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, Users of 65 and 66 failed to obtain battery replacement services. Table 5 lists the power exchange demand curve of charging and swap station 1 after correction, and Fig. 6 shows the power exchange demand curve of charging and swap station 2 before and after correction.

表5充换电站1修正后的换电曲线Table 5 Battery swap curve after charging and swapping station 1

时刻time 需求/块demand/block 时刻time 需求/块demand/block 时刻time 需求/块demand/block 时刻time 需求/块demand/block 0:300:30 66 6:306:30 11 12:3012:30 1212 18:3018:30 77 1:001:00 33 7:007:00 00 13:0013:00 55 19:0019:00 1212 1:301:30 11 7:307:30 66 13:3013:30 1616 19:3019:30 1212 2:002:00 66 8:008:00 44 14:0014:00 3232 20:0020:00 26 -->26 --> 2:302:30 33 8:308:30 33 14:3014:30 4444 20:3020:30 66 3:003:00 11 9:009:00 5151 15:0015:00 8080 21:0021:00 55 3:303:30 11 9:309:30 1414 15:3015:30 4040 21:3021:30 77 4:004:00 00 10:0010:00 1212 16:0016:00 1010 22:0022:00 1919 4:304:30 11 10:3010:30 77 16:3016:30 88 22:3022:30 44 5:005:00 22 11:0011:00 3838 17:0017:00 33 23:0023:00 1111 5:305:30 00 11:3011:30 55 17:3017:30 1919 23:3023:30 44 6:006:00 00 12:0012:00 44 18:0018:00 88 0:000:00 66

（3）根据网络交通图，利用dijkstra算法求解最短用时路径，表6列出了各个服务站之间的最短用时路径。(3) According to the network traffic graph, use the Dijkstra algorithm to solve the shortest time-consuming path. Table 6 lists the shortest time-consuming path between each service station.

表6各服务站之间的最短用时路径Table 6 The shortest time-consuming path between service stations

起始站starting station 目标站target station 最短用时/minMinimum time/min 最短用时路径shortest path 11 66 5757 1-2-3-4-5-61-2-3-4-5-6 11 1111 3737 1-14-13-12-111-14-13-12-11 11 1616 3030 1-161-16 11 1717 5757 1-14-13-12-15-171-14-13-12-15-17 11 23twenty three 5858 1-20-19-21-231-20-19-21-23 11 24twenty four 4545 1-2-22-241-2-22-24 66 1111 4141 6-7-8-9-10-116-7-8-9-10-11 66 1616 5050 6-7-8-17-15-166-7-8-17-15-16 66 1717 21twenty one 6-7-8-176-7-8-17 66 23twenty three 21twenty one 6-7-236-7-23 66 24twenty four 3636 6-7-23-246-7-23-24 1111 1616 2727 11-12-13-1611-12-13-16 1111 1717 3030 11-10-1711-10-17 1111 23twenty three 4444 11-10-17-2311-10-17-23 1111 24twenty four 5757 11-12-15-21-2411-12-15-21-24 1616 1717 2929 16-15-1716-15-17

1616 23twenty three 3636 16-18-21-2316-18-21-23 1616 24twenty four 4545 16-18-21-2416-18-21-24 1717 23twenty three 1414 17-2317-23 1717 24twenty four 2929 17-23-2417-23-24 23twenty three 24twenty four 1515 23-2423-24

（4）计算一个运营日内充换电网络的物流费用：(4) Calculate the logistics cost of charging and swapping network within one operation day:

${F f}_{d d} = = \underset{t t}{Σ Σ} \underset{i i}{Σ Σ} \underset{j j}{Σ Σ} ceil the ceil ((abs abs (({Q Q}_{ij ij,, t t}^{d d})) / / vc vc)) * * {dis dis}_{ij ij} * * {c c}_{ev ev} / / 120120$

式中：F_d为物流费用；为t时刻服务站i配送给服务站j的满电池组数，如果满电池组从服务站j配送至服务站i，则为负；vc为每辆车能够装载的电池组数，单位为块；c_ev为单个物流车辆每小时的运费，单位为元/小时；In the formula: F _d is logistics cost; is the number of full battery packs delivered from service station i to service station j at time t, if the full battery pack is delivered from service station j to service station i, then is negative; vc is the number of battery packs that each vehicle can load, and the unit is block; c _ev is the hourly freight of a single logistics vehicle, and the unit is yuan/hour;

（5）计算一个运营日内充换电网络的充电费用：(5) Calculate the charging cost of the charging and swapping network within one operating day:

${F f}_{c c} = = \underset{i i}{Σ Σ} {Σ Σ}_{time time = = 11}^{4848} {Σ Σ}_{t t = = time time}^{time time + + [[t t__chg chg / / 3030]] - - 11} {Q Q}_{i i,, t t}^{c c} * * {p p}_{ack ack} * * T T * * {p p}_{t t}$

式中：F_c为充电费用；p_ack为电池组的充电功率，单位为KW；p_t为t时刻的电价，单位为元；In the formula: F _c is the charging cost; pack is the charging power of the battery pack, the unit is KW; p _t is the electricity price at time _t , the unit is yuan;

（6）以分时电价为背景，以网络运营费用最小化为目标，建立实际电动汽车充换电网络运营一体化调度模型，从而得出一体化调度的充电和电池调配策略：(6) With the time-of-use electricity price as the background and the minimization of network operating costs as the goal, an integrated scheduling model for actual electric vehicle charging and swapping network operations is established, and the charging and battery deployment strategy for integrated scheduling is obtained:

minF＝F_c+F_d _minF ＝Fc+ _Fd

$s the s . . t t . . {Q Q}_{i i,, t t}^{h h} = = min min (({Q Q}_{i i,, t t}^{f f},, {Q Q}_{i i,, t t}^{r r})),, &ForAll; &ForAll; i i,, t t$

${Q Q}_{i i,, t t}^{h h} = = {Q Q}_{i i,, t t}^{r r},, &ForAll; &ForAll; i i,, t t$

${Q Q}_{i i,, t t}^{c c} \leq \leq min min (({Q Q}_{i i,, t t}^{e e},, {P P}_{i i} * * 10001000 / / {p p}_{ack ack})),, &ForAll; &ForAll; i i,, t t$

$\underset{j j}{Σ Σ} ceil ceil (({Q Q}_{ij ij,, t t}^{d d} / / vc vc)) \leq \leq {nc nc}_{i i},, if if {Q Q}_{ij ij,, t t}^{d d} > > 00,, &ForAll; &ForAll; i i,, t t$

${Q Q}_{i i,, t t}^{d d} \leq \leq {Q Q}_{i i,, t t}^{f f} - - {Q Q}_{i i,, t t}^{h h},, if if {Q Q}_{i i,, t t}^{d d} > > 00,, {Q Q}_{i i,, t t}^{d d} = = \underset{j j}{Σ Σ} {Q Q}_{ij ij,, t t}^{d d},, &ForAll; &ForAll; i i,, t t$

${Q Q}_{ij ij,, t t}^{d d} + + {Q Q}_{ji the ji,, t t}^{d d} = = 00$

$\underset{i i}{Σ Σ} {Q Q}_{i i,, t t}^{d d} = = 00,, &ForAll; &ForAll; t t$

本实施例中，限定充电时间可选集合为{1:00,3:00,8:00,13:00,17:00,21:00},物流配送时间可选集合为{6:00,9:00,15:30,18:00,22:00}，求解得到最优充电方案见表7，最优电池组调配方案见表8。In this embodiment, the optional set of limited charging time is {1:00, 3:00, 8:00, 13:00, 17:00, 21:00}, and the optional set of logistics delivery time is {6:00, 9:00, 15:30, 18:00, 22:00}, the optimal charging scheme obtained by solving is shown in Table 7, and the optimal battery deployment scheme is shown in Table 8.

表7最优充电方案Table 7 Optimal charging scheme

表8最优电池调配方案Table 8 Optimal battery allocation scheme

出发站点departure point 到达站点arrive at the site 路径path 时间time 配送数量delivery quantity 1717 66 17-8-7-617-8-7-6 15:3015:30 308 -->308 --> 11 1111 1-14-13-12-111-14-13-12-11 6:006:00 9090 1717 1616 17-15-1617-15-16 6:006:00 6060 11 24twenty four 1-2-22-241-2-22-24 6:006:00 6060 11 23twenty three 1-20-19-21-231-20-19-21-23 6:006:00 2828 1717 23twenty three 17-2317-23 6:006:00 300300

最优方案的运行总费用为4818.9元,充电费用为3858.9元,物流费用为960元。缺电池块数为0,能够满足用户的所有换电需求。The total operating cost of the optimal scheme is 4818.9 yuan, the charging cost is 3858.9 yuan, and the logistics cost is 960 yuan. The number of missing battery blocks is 0, which can meet all the needs of users for battery replacement.

充换电站初始满电池组数不同时的一体化调度结果如图7，从图中可以看出，随着初始满电池组数的增加，充电费用和系统运行费用的总趋势都是减小的，物流费用先减小后保持不变。充换电站初始空电池组数不同时的一体化调度结果如图8，从图中可以看出，随着初始空电池组数的增加，充电费用、物流费用和系统运行费用的总趋势都是先减小后保持不变。The integrated scheduling results of charging and swapping stations with different initial full battery packs are shown in Figure 7. It can be seen from the figure that with the increase of the initial full battery packs, the general trend of charging costs and system operating costs is decreasing , the logistics cost first decreases and then remains the same. The integrated scheduling results of charging and swapping stations with different initial empty battery sets are shown in Fig. Decrease first and then remain unchanged.

如上，尽管参照特定的优选实施例已经表示和表述了本发明，但其不得解释为对本发明自身的限制。在不脱离所附权利要求定义的本发明的精神和范围前提下，可对其在形式上和细节上作出各种变化。As above, while the invention has been shown and described with reference to certain preferred embodiments, this should not be construed as limiting the invention itself. Various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. the electric automobile charging and conversion electric network integration dispatching method based on service station universal model, it is characterised in that: comprise the following steps:

1) universal model in three types service station in charging and conversion electric network is set up: the universal model of concentrated charging station, charging and conversion electric station and dispensing station, to characterize the relation between state and the behavior in service station, described state includes full set of cells number and empty set of cells number, described behavior includes changing electricity, charging, battery allotment, and the relation between described state and behavior is:

Q_{i, t}^{f} = \{\begin{matrix} Q_{i, t - 1}^{f} + Q_{i, t - [t_c h g / T]}^{c} - Q_{i, t - 1}^{h} - Q_{i, t - 1}^{d}, & Q_{i, t - 1}^{d} &GreaterEqual; 0 \\ Q_{i, t - 1}^{f} + Q_{i, t - [t_c h g / T]}^{c} - Q_{i, t - 1}^{h} + \underset{j}{Σ} Q_{i, t - [{dis}_{i j} / T]}^{d}, & Q_{i, t - [{dis}_{i j} / T]}^{d} < 0 \end{matrix} - - - (1)

Q_{i, t}^{e} = \{\begin{matrix} Q_{i, t - 1}^{e} + Q_{i, t - 1}^{h} - Q_{i, t - 1}^{c} + \underset{j}{Σ} Q_{i, t - [{dis}_{i j} * 2 / T]}^{d}, & Q_{i, t - [{dis}_{i j} * 2 / T]}^{d} &GreaterEqual; 0 \\ Q_{i, t - 1}^{e} + Q_{i, t - 1}^{h} - Q_{i, t - 1}^{c} - \underset{j}{Σ} Q_{i, t - [{dis}_{i j} / T]}^{d}, & Q_{i, t - [{dis}_{i j} / T]}^{d} < 0 \end{matrix} - - - (2)

\{\begin{matrix} Q_{i, t}^{h} \leq Q_{i, t}^{f} \\ Q_{i, t}^{c} \leq Q_{i, t}^{e} \\ Q_{i, t}^{d} \leq Q_{i, t}^{f} - Q_{i, t}^{h}, i f Q_{i, t}^{d} > 0 \end{matrix} - - - (3)

In formula:Full set of cells quantity for t service station i；For the empty set of cells quantity of t service station i, t=1 represents initial time；It is the full set of cells quantity that user changes electricity for t service station i；For the t service station i empty set of cells quantity started to charge up；The full set of cells quantity of allotment is started for t service station i,Represent that t service station i recalls set of cells,Represent that t has full set of cells to recall from other service stations to service station i；T_chg is set of cells charging required time, and unit is min；T is the sampling period, and unit is min；dis_ijSet of cells for service station i to service station j allocates the minimum one way used time, and unit is min；[] expression rounds up；

2) according to the correlation theory of queueing theory, electric automobile changes electricity service system can regard a queuing system as, the client of system is electric automobile, information desk is battery replacement device, the service that information desk provides is to change battery, obtain the actual of service station according to queueing theory principle and change power, the original electricity demand curve that changes is modified:

Q_{i, t}^{r} = m i n (Q_{i, t}^{R}, N_{i, t}) - - - (4)

In formula: N_i,tIn the t sampling period, service station i's changes power；Original for t service station i changes electricity demand；For revise after t service station i change electricity demand；

3) according to network traffic figure, dijkstra algorithm is utilized to solve the shortest used time path；

4) logistics cost runing in a few days charging and conversion electric network is calculated:

F_{d} = \underset{t}{Σ} \underset{i}{Σ} \underset{j}{Σ} c e i l (a b s (Q_{i j, t}^{d}) / v c) * {dis}_{i j} * c_{e v} / 120 - - - (5)

In formula: F_dFor logistics cost；The full set of cells number of service station j it is distributed to, if full set of cells is distributed to service station i from service station j, then for t service station iIt is negative；Vc is the set of cells number that each car can load, and unit is block；c_evFor single logistics vehicles freight charges hourly, unit be unit/hour；

5) a charging expense runing in a few days charging and conversion electric network is calculated:

F_{c} = \underset{i}{Σ} Σ_{t i m e = 1}^{48} Σ_{t = t i m e}^{t i m e + [t_c h g / 30] - 1} Q_{i, t}^{c} * p_{a c k} * T * P (t) - - - (6)

In formula: F_cFor charging expense；p_ackFor the charge power of set of cells, unit is KW；The electricity price that P (t) is t, unit is unit；

6) with tou power price for background, with network operation cost minimization for target, actual electric automobile charging and conversion electric network operation integration scheduling model is set up, thus drawing charging and the battery allotment strategy of integration scheduling:

MinF=F_c+F_d(7)

\begin{matrix} s . t . & Q_{i, t}^{h} = m i n (Q_{i, t}^{f}, Q_{i, t}^{r}) & &ForAll; i, t \end{matrix} - - - (8)

\begin{matrix} Q_{i, t}^{h} = Q_{i, t}^{r} & &ForAll; i, t \end{matrix} - - - (9)

\begin{matrix} Q_{i, t}^{c} \leq m i n (Q_{i, t}^{e}, P_{i} * 1000 / p_{a c k}) & &ForAll; i, t \end{matrix} - - - (10)

\begin{matrix} \underset{j}{Σ} c e i l (Q_{i j, t}^{d} / v c) \leq {nc}_{i} & i f Q_{i j, t}^{d} > 0 & &ForAll; i, t \end{matrix} - - - (11)

\begin{matrix} Q_{i, t}^{d} \leq Q_{i, t}^{f} - Q_{i, t}^{h} & i f Q_{i, t}^{d} > 0, Q_{i, t}^{d} = \underset{j}{Σ} Q_{i j, t}^{d} & &ForAll; i, t \end{matrix} - - - (12)

Q_{i j, t}^{d} + Q_{j i, t}^{d} = 0 - - - (13)

\begin{matrix} \underset{i}{Σ} Q_{i, t}^{d} = 0 & &ForAll; t \end{matrix} - - - (14)

In formula: P_iFor the capacity of service station i, unit is MW；nc_iLogistics vehicles number for website i.

2. the electric automobile charging and conversion electric network integration dispatching method based on service station universal model according to claim 1, it is characterized in that, step 1) the service station universal model set up discloses the relation of the restriction mutually that influences each other between the state in service station and behavior:

1.1) its state of the behavioral implications in service station change

Instant behavior, immediately the state in service station is produced impact as changed electricity, and process state has aftereffect, namely process state can affect service station state sometime thereafter: 1. charging behavior can affect the empty set of cells number in service station when occurring, but will then through the duration that charges, the full set of cells number in service station just can change；2. when battery allotment starts, only impact recalls the service station of full set of cells, set of cells is packed in the service of reception completely set of cells when arriving of allocating and sky set of cells quantity all changes, and until logistics vehicles returns to the service station recalling full set of cells, its sky set of cells number also changes；

1.2) its behavior of the state constraint in service station

Changing electricity and the battery allotment constraint by full set of cells number, charging is by the constraint of empty set of cells number.

3. the electric automobile charging and conversion electric network integration dispatching method based on service station universal model according to claim 1, it is characterized in that, step 2) in the calculating process of system service ability taken into account system service ability and be subject to the battery replacement device quantity in service station, the average arrival rate of user, acceptable congestion lengths, change the restriction of electricity time factor, specifically include following steps:

2.1) setting up electric automobile and change the M/D/C queuing model of electricity service system, M/D/C represents that quantum condition entropy is obeyed at the client interval time of advent, service time is the model having C information desk in fixed value and system；

2.2) according to service station each sampling period initially change electricity demand, calculate its average arrival rate and the user interval time of advent:

λ_{i, t} = \frac{Q_{i, t}^{R}}{T} - - - (15)

P_{i, t} (T \leq t) = 1 - e^{- λ_{i, t} t} - - - (16)

In formula: λ_i,tIt it is the average arrival rate of service station i in the t sampling period；Be in the t sampling period service station i initially change electricity demand；

2.3) practical operation situation of matlab analogue simulation certain scale system, it is thus achieved that the electric automobile number of service station active service in each sampling period, calculates service rate η:

2.4) calculating the actual power that changes in service station is:

N_i,t=λ_i,t*T*η_i,t(18)。

4. the electric automobile charging and conversion electric network integration dispatching method based on service station universal model according to claim 1, it is characterised in that described step 6) constraints be:

6.1) what service station provided the user change, and electricity changes the smaller value of electricity demand equal to its full set of cells number and user；

6.2) system operation meets and basic changes electricity demand；

6.3) the rechargeable battery set number of each website is less than capacity less than website of the empty set of cells in this moment and total charge power；

6.4) the logistics vehicles number that allotment is required every time has vehicle number less than the actual of website；

6.5) the set of cells sum that each website recalls every time is not more than the full set of cells sum in this moment；

6.6) the full set of cells flowing between two service stations of single allotment is unidirectional；

6.7) each allotment, in network total recall set of cells number with total join into set of cells number equal.