A method for analyzing the capacity of an electric vehicle storage and swap station

Abstract

The present invention discloses a method for analyzing the capacity of an electric vehicle storage-swap station, the method comprising: establishing an electric vehicle storage-swap station model according to the energy exchange between the electric vehicle battery, the energy storage service battery and the power grid; obtaining the driving data of the electric vehicle, obtaining the time distribution of the daily driving mileage according to the driving data, obtaining a mathematical model of the change of the remaining power of the electric vehicle over time according to the time distribution of the daily driving mileage, and obtaining the battery replacement demand of several electric vehicles according to the mathematical model and the remaining driving mileage; according to the battery replacement demand of several electric vehicles, using the opportunity constraint condition to establish a two-level planning model of the capacity of the storage-swap station, solving the two-level planning model by the particle swarm algorithm, and obtaining the optimal planning scheme of the electric vehicle storage-swap station. The present invention provides a new idea for the construction of the storage-swap station, and the proposed capacity planning scheme can improve the voltage level of the distribution network and improve the revenue of the storage-swap station.

Description

Technical Field

[0001] The invention relates to the field of urban distribution network planning, and more specifically, to a method for analyzing the capacity of an electric vehicle storage-swap integrated station. Background Art

[0002] Under the general background of "carbon peak and carbon neutrality", the scale of access to the power grid for renewable energy and electric vehicles with uncertainty continues to expand, posing huge challenges to power grid planning and operation. Among them, energy storage technology is an important means to meet this challenge. It is used to reduce peaks and fill valleys, smooth fluctuations, and realize the peak-shaving, frequency regulation, voltage regulation, black start power supply, and backup power supply functions of the power grid. In recent years, with the rise of the electric vehicle battery replacement model, integrated storage and replacement stations that have both electric vehicle battery replacement functions and energy storage power station functions have received high attention. The integrated electric vehicle storage and replacement station has the peak-shaving, frequency regulation, and black start functions of traditional energy storage power stations. It can also reduce the adverse effects of electric vehicle loads on the power grid and provide further power supply support. It is of great significance to the development of electric vehicles and the safe and stable operation of distribution networks.

[0003] At present, the research on the planning of electric vehicle charging and swapping stations and energy storage stations mainly focuses on the single planning or collaborative planning of charging and swapping stations and energy storage stations as independent entities. A small number of literatures have studied the planning of charging and storage, and charging and swapping integrated power stations. There is a lack of research on the operation mode of integrated stations and the adverse factors brought about by the reduction of distribution network voltage level caused by the increase of uncertain loads.

[0004] Therefore, how to propose a new capacity analysis method for integrated storage and exchange stations is an urgent problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide a method for analyzing the capacity of an electric vehicle storage and swapping integrated station, so as to solve the problem in the prior art of lack of an integrated station operation mode and a reduction in the voltage level of the distribution network caused by an increase in uncertain loads.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] A method for analyzing the capacity of an electric vehicle storage and swapping station comprises the following steps:

[0008] Establish an electric vehicle storage and exchange integrated station model based on the energy exchange between electric vehicle battery replacement, energy storage service battery and power grid;

[0009] Acquire driving data of electric vehicles, obtain time distribution of daily driving mileage based on the driving data, obtain a mathematical model of how the remaining power of electric vehicles changes over time based on the time distribution of daily driving mileage, and obtain battery replacement requirements of several electric vehicles based on the mathematical model and the remaining driving mileage;

[0010] According to the battery replacement needs of several electric vehicles, a two-level programming model for the capacity of the storage-and-replacement station is established using chance constraints. The particle swarm algorithm is used to solve the two-level programming model and obtain the optimal planning scheme for the electric vehicle storage-and-replacement station.

[0011] The present invention constructs an integrated storage-and-swap station model based on the energy exchange between the electric vehicle's battery replacement battery, the energy storage service battery and the power grid, and combines the energy flow relationship among the battery replacement service battery, the energy storage service battery and the power grid to realize the integrated storage-and-swap station operation model for electric vehicles to replace energy. The battery replacement demand of several electric vehicles is obtained according to the driving data of the electric vehicle, and a two-level planning model is established based on the battery replacement demand and the adoption of opportunity constraints. The optimal planning scheme obtained by solving the model through the particle swarm algorithm can avoid the reduction of the voltage level of the distribution network caused by the increase of uncertain load caused by the uncertain battery replacement demand when implementing the optimal planning scheme, thereby improving the voltage level of the distribution network, avoiding the increase of power loss of the distribution network due to the reduction of the voltage level of the distribution network and endangering the stability of the power grid operation, resulting in the voltage collapse of the distribution network.

[0012] Furthermore, establishing an electric vehicle storage-swap integrated model includes the following steps:

[0013] Determine the capacity of the energy storage service battery, the number of replacement batteries for electric vehicles, and the power of converters I and II;

[0014] Determine whether the electric vehicle's battery swap meets the battery swap requirements. If so, energy is exchanged between the electric vehicle's battery swap, the energy storage service battery and the power grid.

[0015] Furthermore, the energy exchange between electric vehicle replacement batteries, energy storage service batteries and the power grid is specifically implemented as follows:

[0016] The storage and exchange station provides battery exchange services to electric vehicles, provides fully charged batteries, and collects low-charged batteries for recharging;

[0017] The depleted batteries in the storage-swap station are charged from the grid through converter I according to the charging strategy;

[0018] The energy storage service battery in the integrated storage and swap station transmits electricity to the grid through converter I according to the discharge strategy;

[0019] The energy storage service battery in the integrated storage and swap station charges the battery swap service battery through converter I;

[0020] The redundant battery swap service battery transmits power to the grid through the converter II auxiliary energy storage service battery.

[0021] Furthermore, the charging and discharging strategies of the energy storage service battery and the battery swap service battery are as follows: when exchanging energy with the grid, converters I and II are charged at low electricity prices and discharged at high electricity prices according to the time-of-use electricity price;

[0022] When the energy storage service battery exchanges energy with the battery swap service battery, during the period of normal time-of-use electricity prices, the energy storage service battery discharges to the battery swap service battery.

[0023] Furthermore, obtaining the battery replacement demand of electric vehicles is specifically implemented as follows:

[0024] According to the travel start time, travel end time, daily mileage and mileage distribution per unit time of the electric vehicle, the calculation formula for the remaining power of the electric vehicle over time is: In the formula, E i,t represents the remaining power of the i-th vehicle at time t, E i，0 represents the initial charge of the i-th vehicle, d j represents the mileage of the i-th electric vehicle in the j-th time period, and e represents the power consumption per unit mileage of the electric vehicle;

[0025] According to the remaining power and remaining mileage of the electric vehicle, the calculation formula for the battery replacement demand of the i-th electric vehicle is: In the formula, x i，t represents the battery replacement demand of the i-th electric vehicle at time t, x i，t =1 indicates that there is a demand for battery replacement, x i，t =0 means there is no demand for battery replacement, E i,t represents the remaining power of the i-th electric car at time t, E min,i represents the minimum remaining power of the i-th electric vehicle, E i,t,r represents the power required for the remaining driving distance of the i-th electric car at time t, E T Indicates the remaining power of the electric vehicle when replacing the battery;

[0026] The Monte Carlo simulation method is used to repeatedly calculate the charging load of a single electric vehicle N times. The convergence condition is the number of electric vehicles. The calculation formula for the battery replacement demand of N electric vehicles is obtained as follows: In the formula, X t represents the total battery replacement demand at time t, N EV Represents the total number of electric vehicles.

[0027] Furthermore, an inner and outer double-layer planning model of the storage-swap station capacity is established with the minimization of the average annual net cost as the outer objective function, the minimization of the daily voltage variance as the inner objective function and the constraint conditions.

[0028] Furthermore, the average annual net cost is the difference between the average annual investment cost and the average annual income. The annual investment cost includes the initial investment cost, operation and maintenance costs, battery life degradation costs and the cost of purchasing electricity from the superior power grid; the average annual income includes the time-of-use electricity price income of the integrated storage and swap station, the battery swap service income and subsidy costs.

[0029] Furthermore, the inner objective function voltage daily variance and expression is: Where J represents the total number of system nodes, u j，t represents the voltage of node j in the tth time period, represents the average voltage of node j in one day, and T represents the number of time intervals divided in a single day.

[0030] Furthermore, the constraints include electric vehicle battery replacement demand constraints, battery charging and discharging power constraints, battery charging and discharging state constraints, and battery charge state constraints.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present invention constructs an integrated storage-and-swap station model based on the energy exchange between electric vehicle battery replacement batteries, energy storage service batteries and power grids, and combines the energy flow relationship among battery replacement service batteries, energy storage service batteries and power grids to achieve reliable operation of the integrated storage-and-swap station for electric vehicles to perform energy replacement.

[0033] 2. The present invention obtains the battery replacement demand of several electric vehicles according to the driving data of the electric vehicles, establishes a two-level planning model based on the battery replacement demand and the adoption of opportunity constraints, and obtains the optimal planning scheme by solving the model through the particle swarm algorithm. When implementing the optimal planning scheme, it can avoid the decrease in the voltage level of the distribution network caused by the increase in uncertain load due to the uncertain battery replacement demand, thereby improving the voltage level of the distribution network, avoiding the increase in the power loss of the distribution network due to the decrease in the voltage level of the distribution network and endangering the stability of the power grid operation, resulting in a voltage collapse accident in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0035] Figure 1 A flow chart of a planning method provided by an embodiment of the present invention;

[0036] Figure 2 An IEEE 33-node network structure diagram is provided for an embodiment;

[0037] Figure 3 A time-of-use electricity price map is provided for an embodiment;

[0038] Figure 4 An embodiment provides a time distribution diagram of electric vehicle battery replacement demand;

[0039] Figure 5 A distribution diagram of the number of battery replacement demands for electric vehicles is provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0041] It should be noted that when a component is referred to as being "fixed to" or "disposed on" another component, it can be directly on the other component or indirectly on the other component. When a component is referred to as being "connected to" another component, it can be directly or indirectly connected to the other component.

[0042] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0043] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0044] Embodiment 1

[0045] like Figure 1 As shown, this embodiment 1 provides a method for analyzing the capacity of an electric vehicle storage and replacement integrated station, comprising the following steps:

[0046] Establish an electric vehicle storage and exchange integrated station model based on the energy exchange between electric vehicle battery replacement, energy storage service battery and power grid;

[0047] Acquire driving data of electric vehicles, obtain time distribution of daily driving mileage based on the driving data, obtain a mathematical model of how the remaining power of electric vehicles changes over time based on the time distribution of daily driving mileage, and obtain battery replacement requirements of several electric vehicles based on the mathematical model and the remaining driving mileage;

[0048] According to the battery replacement needs of several electric vehicles, a two-level programming model for the capacity of the storage-and-replacement station is established using chance constraints. The particle swarm algorithm is used to solve the two-level programming model and obtain the optimal planning scheme for the electric vehicle storage-and-replacement station.

[0049] Specifically, a storage-swap station model is constructed based on the energy exchange between electric vehicle battery replacement, energy storage service battery and power grid. Combined with the energy flow relationship among battery replacement service battery, energy storage service battery and power grid, the storage-swap station operation model for electric vehicle energy replacement is realized. Figure 1 As shown in the figure, the battery replacement demand of several electric vehicles is obtained based on Monte Carlo simulation, which is equivalent to the battery replacement demand of electric vehicles with uncertainty. A two-level planning model is established based on the battery replacement demand and the use of opportunity constraints. The optimal planning scheme obtained by solving the model through the particle swarm algorithm can avoid the decrease in the voltage level of the distribution network caused by the increase in uncertain load due to the uncertain battery replacement demand, thereby improving the voltage level of the distribution network, avoiding the increase in the power loss of the distribution network due to the decrease in the voltage level of the distribution network and endangering the stability of the power grid operation, resulting in the voltage collapse of the distribution network.

[0050] In a further embodiment of the present application, establishing an electric vehicle storage-swap integrated model includes the following steps:

[0051] Determine the capacity of the energy storage service battery, the number of replacement batteries for electric vehicles, and the power of converters I and II;

[0052] Determine whether the electric vehicle's battery swap meets the battery swap requirements. If so, energy is exchanged between the electric vehicle's battery swap, the energy storage service battery and the power grid.

[0053] Specifically, realizing power storage and battery replacement of electric vehicle batteries requires inverters, energy storage service batteries and battery replacement service batteries. Therefore, it is necessary to determine the capacity of the energy storage service battery and its power of the grid energy interaction inverter I, the number of battery replacement service batteries and its power of the grid energy interaction inverter II.

[0054] In a further embodiment of the present application, the energy exchange between the electric vehicle replacement battery, the energy storage service battery and the power grid is specifically implemented as follows:

[0055] The storage and exchange station provides battery exchange services to electric vehicles, provides fully charged batteries, and collects low-charged batteries for recharging;

[0056] The depleted batteries in the storage-swap station are charged from the grid through converter I according to the charging strategy;

[0057] The energy storage service battery in the integrated storage and swap station transmits electricity to the grid through converter I according to the discharge strategy;

[0058] The energy storage service battery in the integrated storage and swap station charges the battery swap service battery through converter I;

[0059] The redundant battery swap service battery transmits power to the grid through the converter II auxiliary energy storage service battery.

[0060] Specifically, converter I has two uses: energy storage service battery interacts with the grid, and energy storage service battery interacts with battery swap service battery; converter II has two uses: energy swap service battery interacts with the grid, and energy storage service battery interacts with battery swap service battery. Therefore, five directions of energy flow of the integrated storage and swap station are proposed to meet the demand for power swap. The energy storage service battery in the integrated storage and swap station transmits power to the grid through converter I according to the discharge strategy, and can transmit power to the grid to support the grid. Since the energy storage service battery is always discharging at the same time, the battery swap service battery will only transmit power to the grid to support the grid if there is surplus.

[0061] In a further embodiment of the present application, the charging and discharging strategies of the energy storage service battery and the battery replacement service battery are respectively: when exchanging energy with the power grid, converters I and II are charged at low electricity prices and discharged at high electricity prices according to the time-of-use electricity price;

[0062] When the energy storage service battery exchanges energy with the battery swap service battery, during the period of normal time-of-use electricity prices, the energy storage service battery discharges to the battery swap service battery.

[0063] Specifically, when exchanging energy with the power grid, when electricity prices are low, the battery swap service battery can be charged. Because electricity prices are cheap at this time and electricity consumption is at a low point, the impact of the power grid charging the battery is not significant. When electricity prices are high, it means that electricity consumption is at a peak and the power grid is under a heavy load, so power is transmitted to the power grid to support its operation.

[0064] When exchanging energy with the energy storage service battery and the battery swap service battery, during the period of normal TOU electricity prices, discharge is carried out from the energy storage service battery to the battery swap service battery. Based on the amount of energy stored in the energy storage service battery when the electricity price is low, when an electric vehicle needs to replace the battery during the TOU electricity price stage, the electricity from the battery charged at the low electricity price will be transmitted to the battery swap service battery. In this way, the grid revenue will increase, which can improve the economic benefits of the distribution network.

[0065] In a further embodiment of the present application, obtaining the battery replacement demand of the electric vehicle is specifically implemented as follows:

[0066] According to the travel start time, travel end time, daily mileage and mileage distribution per unit time of the electric vehicle, the calculation formula for the remaining power of the electric vehicle over time is: In the formula, Ei，t represents the remaining power of the i-th vehicle at time t, E i，0 represents the initial charge of the i-th vehicle, d j represents the mileage of the i-th electric vehicle in the j-th time period, and e represents the power consumption per unit mileage of the electric vehicle;

[0067] According to the remaining power and remaining mileage of the electric vehicle, the calculation formula for the battery replacement demand of the i-th electric vehicle is: In the formula, x i，t represents the battery replacement demand of the i-th electric vehicle at time t, x i，t =1 indicates that there is a demand for battery replacement, x i，t =0 means there is no demand for battery replacement, E i,t represents the remaining power of the i-th electric car at time t, E min,i represents the minimum remaining power of the i-th electric vehicle, E i,t,r represents the power required for the remaining driving distance of the i-th electric car at time t, E T Indicates the remaining power of the electric vehicle when replacing the battery;

[0068] The Monte Carlo simulation method is used to repeatedly calculate the charging load of a single electric vehicle N times. The convergence condition is the number of electric vehicles. The calculation formula for the battery replacement demand of N electric vehicles is obtained as follows: (i=1,2,...,N EV ), where X t represents the total battery replacement demand at time t, N EV Represents the total number of electric vehicles.

[0069] Specifically, a total battery replacement demand is derived based on the explanation of the following technical solution.

[0070] Determine the number of electric vehicles N, and randomly generate the electric vehicle travel start time T according to the travel start time probability density function s :

[0071]

[0072] In the above formula, Respectively represent the mean and standard deviation of the user's travel start time. Then, according to the travel end time probability density function, the electric vehicle travel end time T is randomly generated. e :

[0073]

[0074] In the above formula, Respectively represent the mean and standard deviation of the user's travel end time. Then, according to the daily mileage probability density function, the daily mileage d of electric vehicles is randomly generated:

[0075]

[0076] In the above formula, μ d , σ d They represent the mean and standard deviation of the user’s daily mileage.

[0077] According to the historical driving data of electric vehicles, the daily mileage d and the travel start time T of electric vehicles are obtained. s and the travel end time T e ; According to the mileage distribution per unit time, it is judged whether the electric vehicle meets the battery replacement demand after each period of time, and the battery replacement demand x of the i-th electric vehicle at time t is randomly obtained by Monte Carlo simulation i,t , and repeatedly calculate the number of electric vehicles N times, the convergence condition is the number of electric vehicles:

[0078]

[0079]

[0080] In the formula, x i，t represents the battery replacement demand of the i-th electric vehicle at time t, x i,t =1 indicates that there is a demand for battery replacement, x i，t =0 means there is no demand for battery replacement; E i,t represents the remaining power of the i-th electric car at time t; E min,i represents the minimum remaining power of the i-th electric vehicle; E i,t,r E represents the amount of electricity required for the remaining driving distance of the i-th electric car at time t; T Indicates the remaining power of the electric vehicle when replacing the battery; X t represents the total battery replacement demand at time t; N EV Represents the number of electric vehicles.

[0081] In a further embodiment of the present application, an inner and outer double-layer planning model of the storage-swap station capacity is established with the minimum annual average net cost as the outer objective function, the minimum daily voltage variance as the inner objective function, and constraints.

[0082] Specifically, an inner and outer double-layer planning model established by the average annual net cost, daily voltage variance and constraints is solved by the particle swarm algorithm. Since the daily voltage variance and this objective function are added, the resulting planning scheme can avoid the problem of lower voltage level in the distribution network caused by the increase in uncertain load due to uncertain battery replacement demand. In addition, by adding the average annual net cost objective function, the resulting planning scheme can also improve the economic benefits of the power grid to a certain extent.

[0083] In a further embodiment of the present application, the average annual net cost is the difference between the average annual investment cost and the average annual income, the annual investment cost includes the initial investment cost, operation and maintenance costs, battery life degradation costs and the cost of purchasing electricity from the superior power grid; the average annual income includes the time-of-use electricity price income of the integrated storage and swap station, the battery swap service income and subsidy costs.

[0084] Specifically, in this embodiment, mathematical expressions of various objective functions are provided. The outer objective function is the difference between the average annual investment cost and the average annual return. The annual investment cost includes the initial investment cost S IN , Operation and maintenance fee S OM , Battery life decay cost S S And the cost of purchasing electricity from the upper power grid S P The average annual income includes the time-of-use electricity price income of the storage and exchange integrated station S G , Battery replacement service income S EV and subsidy costs S B ; Initial investment cost S IN Including the cost of batteries, energy conversion equipment and necessary auxiliary facilities purchased in the initial stage of construction, the mathematical expression is:

[0085]

[0086] In the formula, r is the discount rate, N is the life cycle of the storage and replacement station, C E，EV represents the unit capacity price of battery in the battery swapping service in the integrated storage and swapping station, C E，ES represents the unit capacity price of the energy storage service battery in the storage-swap station, C P It represents the unit power price of the energy conversion device and its auxiliary facilities in the integrated storage and exchange station, E BAT，EV It represents the total capacity of the power battery in the battery swapping station, E BAT，ES Represents the total capacity of the energy storage service battery in the storage-swap integrated station, P BAT Indicates the rated charging and discharging power of the storage-swap integrated station; the operation and maintenance cost S S Refers to the maintenance and management costs of the integrated storage and replacement station, which is related to the technical type and rated power of the integrated storage and replacement station. Its expression is:

[0087] S OM =C OM (P I +P II )

[0088] In the formula, C OM is the annual operation and maintenance cost per unit power; the battery life attenuation cost S S It refers to the loss of service life due to capacity attenuation and the cost of replacing the battery during battery operation. Its expression is:

[0089]

[0090] In the formula, S i represents the purchase cost of the i-th battery, I represents the total number of batteries, and life i represents the cycle life of the ith battery, c represents the average number of battery cycles in a single day, and the electricity purchase fee S P The expression is:

[0091]

[0092] In the formula, S P represents the cost of electricity purchased by the integrated storage and swap station from the superior power grid, Y represents the annual operating days of the integrated storage and swap station, I represents the number of batteries in the integrated storage and swap station, T represents the number of time intervals divided in a single day, represents the charging power of the i-th battery in the t-th time period on the y-th day, represents the electricity price in the tth time period, η cha represents charging efficiency; time-of-use electricity price income S G The expression is:

[0093]

[0094] In the formula, represents the discharge amount of the i-th battery in the storage-swap station during the t-th time period on the y-th day, Indicates the electricity price in the tth time period, η dis Indicates the battery discharge efficiency, represents the discharge power of the i-th battery in the t-th time period on the y-th day of the energy storage service; the battery replacement service income S EV Including the battery replacement electricity cost, battery replacement service fee and battery replacement battery rental fee, the expression is:

[0095]

[0096] In the formula, Indicates the total power of the electric vehicle replacement battery. Indicates the remaining battery power of the electric vehicle, D EV ，price represents the price of electric vehicle replacement electricity, D ser，price represents the price of battery replacement service, D rent represents the battery rental price; the subsidy cost expression is as follows:

[0097]

[0098] Where D ES，Bprice represents the unit capacity subsidy price of energy storage service, D EV，Bprice Represents the unit capacity subsidy price for battery swapping services.

[0099] In a further embodiment of the present application, the inner layer objective function voltage daily variance and expression is: Where J represents the total number of system nodes, u j，t represents the voltage of node j in the tth time period, represents the average voltage of node j in one day, and T represents the number of time intervals divided in a single day.

[0100] Specifically, the minimum sum of the daily voltage variances of the integrated storage-swap station is calculated to reduce the problem of voltage reduction in the distribution network caused by the increase in uncertain loads.

[0101] In a further embodiment of the present application, the constraints include electric vehicle battery replacement demand constraints, battery charging and discharging power constraints, battery charging and discharging state constraints, and battery charge state constraints.

[0102] Specifically, this embodiment explains the expressions of various constraints, and the electric vehicle battery replacement demand constraint is:

[0103]

[0104] In the formula, represents the number of batteries in the battery swapping service at the storage-swap station at time t, I EV，BAT represents the total number of batteries in the battery swap service; P represents the probability of the event being established; 1-α represents the pre-set confidence level;

[0105] The battery charging and discharging power constraints are:

[0106]

[0107]

[0108] In the formula, represents the discharge power of the i-th battery in the t-th time period; represents the charging power of the i-th battery in the t-th time period; Indicates the maximum charging power; Indicates the maximum discharge power;

[0109] The charge and discharge state constraints are:

[0110]

[0111] In the formula, represents the discharge power of the ith battery in the tth time period, represents the charging power of the i-th battery in the t-th time period;

[0112] The battery state of charge constraint is:

[0113] soc min ≤soc i,t ≤soc max

[0114]

[0115] In the formula, soc i，t represents the state of charge of the ith battery in the tth time period, soc min 、soc max Respectively represent the upper and lower limits of the battery state of charge, E bat,rate Indicates the rated capacity of the battery.

[0116] Embodiment 2

[0117] In the second embodiment, the analysis method proposed in the first embodiment is verified by using a typical IEEE33 example model. The network structure of the example is as follows: Figure 2 As shown. Node 1 of the distribution network is a balancing node, the system head voltage is 12.66kV, and without considering uncertainty, the total system load is 3715kW+2300kvar, and the node voltage allowable range is 0.95-1.05pu. The load follows a normal distribution with a mean of the node load set value in the IEEE33 example and a standard deviation of 10% of the mean. The access point of the storage-swap integrated station is set to node 11, and the time-of-use electricity price is as follows Figure 3 shown.

[0118] Assume that the number of electric vehicles is 10,000 and the daily mileage d of electric vehicle users is subject to μ d =3,σ d =0.91 lognormal distribution, user travel start time T s obey Normal distribution, user travel end time T e obey The normal distribution of , the other parameter settings are shown in Table 1 below:

[0119] Table 1 Parameters of storage and exchange integrated station

[0120]

[0121]

[0122] According to the set model, the battery replacement demand of electric vehicles is obtained as follows: Figure 4 , Figure 5 As shown, Figure 4 and Figure 5 They are the normal distribution graph of the demand for electric vehicle battery replacement and the normal distribution graph of the number of electric vehicles that need to be replaced.

[0123] The optimal planning schemes obtained at different confidence levels are shown in Table 2 below. Confidence level refers to the degree to which a specific individual believes in the truth of a specific proposition, that is, probability is a measure of the rationality of personal beliefs. The confidence interpretation of probability shows that an event itself has no probability. The reason why an event is assigned a probability is only the belief evidence in the mind of the person who assigns the probability. Confidence level refers to the probability that the value of the population parameter falls within a certain range of the sample statistic value; and the confidence interval refers to the error range between the sample statistic value and the population parameter value at a certain confidence level. The larger the confidence interval, the higher the confidence level.

[0124] Table 2. Optimal planning schemes for storage and exchange stations at different confidence levels

[0125]

[0126]

[0127] As shown in Table 2, in the inner and outer double-layer planning model proposed in this application, the uncertainty caused by actual electric vehicle charging is simulated through the randomness of user behavior, which improves the accuracy of the planning model. Considering different confidence levels, different optimal planning schemes, net benefits, return on investment, and support capabilities for grid voltage will be obtained.

[0128] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing the capacity of an electric vehicle storage and replacement station, characterized in that: The following steps are involved: An electric vehicle integrated storage-swap station model is established based on the energy exchange between electric vehicle battery replacement, energy storage service battery and power grid; wherein, the establishment of the electric vehicle integrated storage-swap model includes the following steps: determining the capacity of the energy storage service battery, the number of electric vehicle battery replacement batteries and the power of converters I and II; judging whether the electric vehicle battery replacement battery meets the battery replacement demand, and if so, energy exchange is performed between the electric vehicle battery replacement battery, energy storage service battery and power grid; wherein, the energy exchange between the electric vehicle battery replacement battery, energy storage service battery and power grid is specifically implemented as follows: the integrated storage-swap station provides battery replacement services to electric vehicles, provides fully charged batteries, and recovers low-power batteries for charging; The depleted battery is charged from the grid through converter I according to the charging strategy; the energy storage service battery in the integrated storage and swap station transmits power to the grid through converter I according to the discharging strategy; the energy storage service battery in the integrated storage and swap station charges the battery swap service battery through converter I; the redundant battery swap service battery assists the energy storage service battery in transmitting power to the grid through converter II; wherein, the charging and discharging strategies of the energy storage service battery and the battery swap service battery are as follows: when exchanging energy with the grid, converters I and II charge at low electricity prices and discharge at high electricity prices according to the time-of-use electricity price; when the energy storage service battery exchanges energy with the battery swap service battery, the energy storage service battery discharges to the battery swap service battery during the normal time of the time-of-use electricity price; Acquire the driving data of electric vehicles, obtain the time distribution of daily mileage based on the driving data, obtain a mathematical model of the change of the remaining power of electric vehicles over time based on the time distribution of daily mileage, and obtain the battery replacement demand of several electric vehicles based on the mathematical model and the remaining mileage; wherein, obtaining the battery replacement demand of electric vehicles is specifically implemented as follows: according to the travel start time, travel end time, daily mileage and mileage distribution per unit time of the electric vehicle, the calculation formula for the change of the remaining power of the electric vehicle over time is calculated as follows: , where Indicates i Car t The remaining power at the moment, Indicates i The initial charge of the vehicle, Indicates i Electric cars in m Mileage traveled in a period of time, e Indicates the power consumption per unit mileage of electric vehicles. d represents the daily mileage of electric vehicles; according to the remaining power and remaining mileage of electric vehicles, the calculation formula for obtaining the battery replacement demand of the i-th electric vehicle is: , where Indicates i Electric vehicles t The need for battery replacement at all times, Indicates that there is a need for battery replacement. Indicates that there is no need for battery replacement. Indicates i Electric vehicles t The remaining power at the moment, Indicates i The minimum remaining power of an electric vehicle. Indicates the i Electric vehicles t The remaining distance required for driving Represents the remaining power of an electric vehicle when it is replaced; the Monte Carlo simulation method is used to repeatedly calculate the charging load of a single electric vehicle N times, and the convergence condition is the number of electric vehicles. The calculation formula for the replacement demand of N electric vehicles is obtained as follows: , where express t The total demand for battery replacement at any time, represents the total number of electric vehicles; According to the battery replacement demand of several electric vehicles, a two-level planning model of the storage-swap station capacity is established using opportunity constraints. The two-level planning model is solved by particle swarm algorithm to obtain the optimal planning scheme of the electric vehicle storage-swap station. Among them, the outer layer objective function is the minimum annual average net cost, the inner layer objective function is the minimum daily voltage variance, and the constraint conditions are used to establish the inner and outer two-level planning model of the storage-swap station capacity. Among them, the inner layer objective function voltage daily variance is expressed as ;in, J Indicates the total number of system nodes. Indicates t Nodes in a time period j The voltage, Indicates nodes within a day j The voltage mean value is , and T represents the number of time intervals divided in a single day.

2. The method for analyzing the capacity of an electric vehicle storage and replacement station according to claim 1, characterized in that: The average annual net cost is the difference between the average annual investment cost and the average annual income. The annual investment cost includes the initial investment cost, operation and maintenance fees, battery life degradation costs and the cost of purchasing electricity from the superior power grid; the average annual income includes the time-of-use electricity price income of the integrated storage and swap station, the battery swap service income and subsidy costs.

3. The method for analyzing the capacity of an electric vehicle storage and replacement station according to claim 1, characterized in that: The constraints include electric vehicle battery replacement demand constraints, battery charging and discharging power constraints, battery charging and discharging state constraints, and battery charge state constraints.

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